@@ -27,7 +26,15 @@ knitr::opts_chunk$set(
[](https://doi.org/10.21105/joss.02027)
-# NOTE: This package is undergoing severe restructuring. A new version (with breaking changes) will be available on GitHub soon.
+### Breaking change (June 2023)
+
+The development verison of shapr (master branch on GitHub from June 2023) has been severely restructured, introducing a new syntax for explaining models, and thereby introducing a range of breaking changes. This essentially amounts to using a single function (`explain()`) instead of two functions (`shapr()` and `explain()`).
+The CRAN version of `shapr` (v0.2.2) still uses the old syntax.
+See the [NEWS](https://github.com/NorskRegnesentral/shapr/blob/master/NEWS.md) for details.
+The examples below uses the new syntax.
+[Here](https://github.com/NorskRegnesentral/shapr/blob/cranversion_0.2.2/README.md) is a version of this README with the syntax of the CRAN version (v0.2.2).
+
+## Introduction
The most common machine learning task is to train a model which is able to predict an unknown outcome (response variable) based on a set of known input variables/features.
When using such models for real life applications, it is often crucial to understand why a certain set of features lead to exactly that prediction.
@@ -43,11 +50,19 @@ The following methodology/features are currently implemented:
- Native support of explanation of predictions from models fitted with the following functions
`stats::glm`, `stats::lm`,`ranger::ranger`, `xgboost::xgboost`/`xgboost::xgb.train` and `mgcv::gam`.
-- Accounting for feature dependence assuming the features are Gaussian (@aas2019explaining).
-- Accounting for feature dependence with a Gaussian copula (Gaussian dependence structure, any marginal) (@aas2019explaining).
-- Accounting for feature dependence using the Mahalanobis distance based empirical (conditional) distribution approach of @aas2019explaining.
-- Accounting for feature dependence using conditional inference trees (@redelmeier2020explaining).
-- Combining any of the four methods.
+- Accounting for feature dependence
+ * assuming the features are Gaussian (`approach = 'gaussian'`, @aas2019explaining)
+ * with a Gaussian copula (`approach = 'copula'`, @aas2019explaining)
+ * using the Mahalanobis distance based empirical (conditional) distribution approach (`approach = 'empirical'`, @aas2019explaining)
+ * using conditional inference trees (`approach = 'ctree'`, @redelmeier2020explaining).
+ * using the endpoint match method for time series (`approach = 'timeseries'`, @jullum2021efficient)
+ * using the joint distribution approach for models with purely cateogrical data (`approach = 'categorical'`, @redelmeier2020explaining)
+ * assuming all features are independent (`approach = 'independence'`, mainly for benchmarking)
+- Combining any of the above methods.
+- Explain *forecasts* from time series models at different horizons with `explain_forecast()`
+- Batch computation to reduce memory consumption significantly
+- Parallelized computation using the [future](https://future.futureverse.org/) framework.
+- Progress bar showing computation progress, using the [`progressr`](https://progressr.futureverse.org/) package. Must be activated by the user.
- Optional use of the AICc criterion of @hurvich1998smoothing when optimizing the bandwidth parameter in the empirical (conditional) approach of @aas2019explaining.
- Functionality for visualizing the explanations.
- Support for models not supported natively.
@@ -58,27 +73,34 @@ Current methodological restrictions:
- The features must follow a continuous distribution
- Discrete features typically work just fine in practice although the theory breaks down
- Ordered/unordered categorical features are not supported
--->
Future releases will include:
-- Support for parallelization over explanations, Monte Carlo sampling and features subsets for non-parallelizable prediction functions.
- Computational improvement of the AICc optimization approach,
- Adaptive selection of method to account for the feature dependence.
+-->
+
+
+Note the prediction outcome must be numeric.
+All approaches except `approach = 'categorical'` works for numeric features, but unless the models are very gaussian-like, we recommend `approach = 'ctree'` or `approach = 'empirical'`, especially if there are discretely distributed features.
+When the models contains both numeric and categorical features, we recommend `approach = 'ctree'`.
+For models with a smaller number of categorical features (without many levels) and a decent training set, we recommend `approach = 'categorical'`.
+For (binary) classification based on time series models, we suggest using `approach = 'timeseries'`.
+To explain forecasts of time series models (at different horizons), we recommend using `explain_forecast()` instead of `explain()`.
+The former has a more suitable input syntax for explaining those kinds of forecasts.
+See the [vignette](https://norskregnesentral.github.io/shapr/articles/understanding_shapr.html) for details and further examples.
-Note that both the features and the prediction must be numeric. The approach is constructed for continuous features. Discrete features may also work just fine with the empirical (conditional) distribution approach.
Unlike SHAP and TreeSHAP, we decompose probability predictions directly to ease the interpretability, i.e. not via log odds transformations.
-The application programming interface (API) of `shapr` is inspired by @lime_api.
## Installation
-To install the current stable release from CRAN, use
+To install the current stable release from CRAN (note, using the old explanation syntax), use
```{r, eval = FALSE}
install.packages("shapr")
```
-To install the current development version, use
+To install the current development version (with the new explanation syntax), use
```{r, eval = FALSE}
remotes::install_github("NorskRegnesentral/shapr")
@@ -102,57 +124,60 @@ You can always check out the latest version of the vignette [here](https://norsk
## Example
`shapr` supports computation of Shapley values with any predictive model which takes a set of numeric features and produces a numeric outcome.
-The following example shows how a simple `xgboost` model is trained using the *Boston Housing Data*, and how `shapr` explains the individual predictions.
+The following example shows how a simple `xgboost` model is trained using the *airquality* dataset, and how `shapr` explains the individual predictions.
```{r basic_example, warning = FALSE}
library(xgboost)
library(shapr)
-data("Boston", package = "MASS")
+data("airquality")
+data <- data.table::as.data.table(airquality)
+data <- data[complete.cases(data), ]
-x_var <- c("lstat", "rm", "dis", "indus")
-y_var <- "medv"
+x_var <- c("Solar.R", "Wind", "Temp", "Month")
+y_var <- "Ozone"
-ind_x_test <- 1:6
-x_train <- as.matrix(Boston[-ind_x_test, x_var])
-y_train <- Boston[-ind_x_test, y_var]
-x_test <- as.matrix(Boston[ind_x_test, x_var])
+ind_x_explain <- 1:6
+x_train <- data[-ind_x_explain, ..x_var]
+y_train <- data[-ind_x_explain, get(y_var)]
+x_explain <- data[ind_x_explain, ..x_var]
# Looking at the dependence between the features
cor(x_train)
# Fitting a basic xgboost model to the training data
model <- xgboost(
- data = x_train,
+ data = as.matrix(x_train),
label = y_train,
nround = 20,
verbose = FALSE
)
-# Prepare the data for explanation
-explainer <- shapr(x_train, model)
-
# Specifying the phi_0, i.e. the expected prediction without any features
-p <- mean(y_train)
+p0 <- mean(y_train)
# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using
# the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default)
explanation <- explain(
- x_test,
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
approach = "empirical",
- explainer = explainer,
- prediction_zero = p
+ prediction_zero = p0
)
# Printing the Shapley values for the test data.
-# For more information about the interpretation of the values in the table, see ?shapr::explain.
-print(explanation$dt)
+# For more information about the interpretation of the values in the table, see ?shapr::explain.
+print(explanation$shapley_values)
# Finally we plot the resulting explanations
plot(explanation)
```
+See the [vignette](https://norskregnesentral.github.io/shapr/articles/understanding_shapr.html) for further examples.
+
+
## Contribution
All feedback and suggestions are very welcome. Details on how to contribute can be found
diff --git a/README.md b/README.md
index 43c610e7c..2cd194272 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,21 @@ MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.or
[](https://doi.org/10.21105/joss.02027)
-# NOTE: This package is undergoing severe restructuring. A new version (with breaking changes) will be available on GitHub soon.
+### Breaking change (June 2023)
+
+The development verison of shapr (master branch on GitHub from June
+2023) has been severely restructured, introducing a new syntax for
+explaining models, and thereby introducing a range of breaking changes.
+This essentially amounts to using a single function (`explain()`)
+instead of two functions (`shapr()` and `explain()`). The CRAN version
+of `shapr` (v0.2.2) still uses the old syntax. See the
+[NEWS](https://github.com/NorskRegnesentral/shapr/blob/master/NEWS.md)
+for details. The examples below uses the new syntax.
+[Here](https://github.com/NorskRegnesentral/shapr/blob/cranversion_0.2.2/README.md)
+is a version of this README with the syntax of the CRAN version
+(v0.2.2).
+
+## Introduction
The most common machine learning task is to train a model which is able
to predict an unknown outcome (response variable) based on a set of
@@ -42,7 +56,7 @@ consequently wrong interpretations of the predictions. Aas, Jullum, and
Løland (2021) extends and improves the Kernel SHAP method of Lundberg
and Lee (2017) to account for the dependence between the features,
resulting in significantly more accurate approximations to the Shapley
-values. [See the paper for details](https://www.sciencedirect.com/science/article/pii/S0004370221000539).
+values. [See the paper for details](https://arxiv.org/abs/1903.10464).
This package implements the methodology of Aas, Jullum, and Løland
(2021).
@@ -52,17 +66,32 @@ The following methodology/features are currently implemented:
- Native support of explanation of predictions from models fitted with
the following functions `stats::glm`, `stats::lm`,`ranger::ranger`,
`xgboost::xgboost`/`xgboost::xgb.train` and `mgcv::gam`.
-- Accounting for feature dependence assuming the features are Gaussian
- (Aas, Jullum, and Løland (2021)).
-- Accounting for feature dependence with a Gaussian copula (Gaussian
- dependence structure, any marginal) (Aas, Jullum, and Løland
- (2021)).
-- Accounting for feature dependence using the Mahalanobis distance
- based empirical (conditional) distribution approach of Aas, Jullum,
- and Løland (2021).
-- Accounting for feature dependence using conditional inference trees
- (Redelmeier, Jullum, and Aas (2020)).
-- Combining any of the four methods.
+- Accounting for feature dependence
+ - assuming the features are Gaussian (`approach = 'gaussian'`,
+ Aas, Jullum, and Løland (2021))
+ - with a Gaussian copula (`approach = 'copula'`, Aas, Jullum, and
+ Løland (2021))
+ - using the Mahalanobis distance based empirical (conditional)
+ distribution approach (`approach = 'empirical'`, Aas, Jullum,
+ and Løland (2021))
+ - using conditional inference trees (`approach = 'ctree'`,
+ Redelmeier, Jullum, and Aas (2020)).
+ - using the endpoint match method for time series
+ (`approach = 'timeseries'`, Jullum, Redelmeier, and Aas (2021))
+ - using the joint distribution approach for models with purely
+ cateogrical data (`approach = 'categorical'`, Redelmeier,
+ Jullum, and Aas (2020))
+ - assuming all features are independent
+ (`approach = 'independence'`, mainly for benchmarking)
+- Combining any of the above methods.
+- Explain *forecasts* from time series models at different horizons
+ with `explain_forecast()`
+- Batch computation to reduce memory consumption significantly
+- Parallelized computation using the
+ [future](https://future.futureverse.org/) framework.
+- Progress bar showing computation progress, using the
+ [`progressr`](https://progressr.futureverse.org/) package. Must be
+ activated by the user.
- Optional use of the AICc criterion of Hurvich, Simonoff, and
Tsai (1998) when optimizing the bandwidth parameter in the empirical
(conditional) approach of Aas, Jullum, and Løland (2021).
@@ -75,32 +104,43 @@ Current methodological restrictions:
- The features must follow a continuous distribution
- Discrete features typically work just fine in practice although the theory breaks down
- Ordered/unordered categorical features are not supported
--->
Future releases will include:
-- Support for parallelization over explanations, Monte Carlo sampling
- and features subsets for non-parallelizable prediction functions.
- Computational improvement of the AICc optimization approach,
- Adaptive selection of method to account for the feature dependence.
+-->
-Note that both the features and the prediction must be numeric. The
-approach is constructed for continuous features. Discrete features may
-also work just fine with the empirical (conditional) distribution
-approach. Unlike SHAP and TreeSHAP, we decompose probability predictions
-directly to ease the interpretability, i.e. not via log odds
-transformations. The application programming interface (API) of `shapr`
-is inspired by Pedersen and Benesty (2019).
+Note the prediction outcome must be numeric. All approaches except
+`approach = 'categorical'` works for numeric features, but unless the
+models are very gaussian-like, we recommend `approach = 'ctree'` or
+`approach = 'empirical'`, especially if there are discretely distributed
+features. When the models contains both numeric and categorical
+features, we recommend `approach = 'ctree'`. For models with a smaller
+number of categorical features (without many levels) and a decent
+training set, we recommend `approach = 'categorical'`. For (binary)
+classification based on time series models, we suggest using
+`approach = 'timeseries'`. To explain forecasts of time series models
+(at different horizons), we recommend using `explain_forecast()` instead
+of `explain()`. The former has a more suitable input syntax for
+explaining those kinds of forecasts. See the
+[vignette](https://norskregnesentral.github.io/shapr/articles/understanding_shapr.html)
+for details and further examples.
+
+Unlike SHAP and TreeSHAP, we decompose probability predictions directly
+to ease the interpretability, i.e. not via log odds transformations.
## Installation
-To install the current stable release from CRAN, use
+To install the current stable release from CRAN (note, using the old
+explanation syntax), use
``` r
install.packages("shapr")
```
-To install the current development version, use
+To install the current development version (with the new explanation
+syntax), use
``` r
remotes::install_github("NorskRegnesentral/shapr")
@@ -129,66 +169,68 @@ You can always check out the latest version of the vignette
which takes a set of numeric features and produces a numeric outcome.
The following example shows how a simple `xgboost` model is trained
-using the *Boston Housing Data*, and how `shapr` explains the individual
+using the *airquality* dataset, and how `shapr` explains the individual
predictions.
``` r
library(xgboost)
library(shapr)
-data("Boston", package = "MASS")
+data("airquality")
+data <- data.table::as.data.table(airquality)
+data <- data[complete.cases(data), ]
-x_var <- c("lstat", "rm", "dis", "indus")
-y_var <- "medv"
+x_var <- c("Solar.R", "Wind", "Temp", "Month")
+y_var <- "Ozone"
-ind_x_test <- 1:6
-x_train <- as.matrix(Boston[-ind_x_test, x_var])
-y_train <- Boston[-ind_x_test, y_var]
-x_test <- as.matrix(Boston[ind_x_test, x_var])
+ind_x_explain <- 1:6
+x_train <- data[-ind_x_explain, ..x_var]
+y_train <- data[-ind_x_explain, get(y_var)]
+x_explain <- data[ind_x_explain, ..x_var]
# Looking at the dependence between the features
cor(x_train)
-#> lstat rm dis indus
-#> lstat 1.0000000 -0.6108040 -0.4928126 0.5986263
-#> rm -0.6108040 1.0000000 0.1999130 -0.3870571
-#> dis -0.4928126 0.1999130 1.0000000 -0.7060903
-#> indus 0.5986263 -0.3870571 -0.7060903 1.0000000
+#> Solar.R Wind Temp Month
+#> Solar.R 1.0000000 -0.1243826 0.3333554 -0.0710397
+#> Wind -0.1243826 1.0000000 -0.5152133 -0.2013740
+#> Temp 0.3333554 -0.5152133 1.0000000 0.3400084
+#> Month -0.0710397 -0.2013740 0.3400084 1.0000000
+
# Fitting a basic xgboost model to the training data
model <- xgboost(
- data = x_train,
+ data = as.matrix(x_train),
label = y_train,
nround = 20,
verbose = FALSE
)
-# Prepare the data for explanation
-explainer <- shapr(x_train, model)
-#>
-#> Success with message:
-#> The specified model provides feature classes that are NA. The classes of data are taken as the truth.
# Specifying the phi_0, i.e. the expected prediction without any features
-p <- mean(y_train)
+p0 <- mean(y_train)
# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using
# the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default)
explanation <- explain(
- x_test,
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
approach = "empirical",
- explainer = explainer,
- prediction_zero = p
+ prediction_zero = p0
)
+#> Note: Feature classes extracted from the model contains NA.
+#> Assuming feature classes from the data are correct.
+#> Setting parameter 'n_batches' to 2 as a fair trade-off between memory consumption and computation time.
+#> Reducing 'n_batches' typically reduces the computation time at the cost of increased memory consumption.
# Printing the Shapley values for the test data.
# For more information about the interpretation of the values in the table, see ?shapr::explain.
-print(explanation$dt)
-#> none lstat rm dis indus
-#> 
+See the
+[vignette](https://norskregnesentral.github.io/shapr/articles/understanding_shapr.html)
+for further examples.
+
## Contribution
All feedback and suggestions are very welcome. Details on how to
@@ -230,6 +276,15 @@ Statistical Society: Series B (Statistical Methodology)* 60 (2): 271–93.
+
+
+Jullum, Martin, Annabelle Redelmeier, and Kjersti Aas. 2021. “Efficient
+and Simple Prediction Explanations with groupShapley: A Practical
+Perspective.” In *Proceedings of the 2nd Italian Workshop on Explainable
+Artificial Intelligence*, 28–43. CEUR Workshop Proceedings.
+
+
+
Lundberg, Scott M, Gabriel G Erion, and Su-In Lee. 2018. “Consistent
@@ -246,14 +301,6 @@ Processing Systems*, 4765–74.
-
-
-Pedersen, Thomas Lin, and Michaël Benesty. 2019. *Lime: Local
-Interpretable Model-Agnostic Explanations*.
-.
-
-
-
Redelmeier, Annabelle, Martin Jullum, and Kjersti Aas. 2020. “Explaining
diff --git a/bashscript.sh b/bashscript.sh
new file mode 100644
index 000000000..28c9c3219
--- /dev/null
+++ b/bashscript.sh
@@ -0,0 +1,6 @@
+while true
+ do
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -k -c pss -P 4.1.1 | tail -n 1)" | tee -a logfile2
+ sleep 2
+done
+#watch -t -n 10 "(date '+%Y-%m-%d, %H:%M:%S,' ; smem -t -k -c pss -P 4.1.1 | tail -n 1) | tee -a logfile"
diff --git a/inst/scripts/analyze_bash_test_data.R b/inst/scripts/analyze_bash_test_data.R
new file mode 100644
index 000000000..519801de3
--- /dev/null
+++ b/inst/scripts/analyze_bash_test_data.R
@@ -0,0 +1,120 @@
+
+
+library(data.table)
+### analysing bash data test
+
+dt_mem0 <- fread("inst/scripts/memory_test_2023_new2.csv")
+
+names(dt_mem0) <- c("date","time","mem_usage","rep","p","n_train","n_explain","n_batches","n_cores","approach","multicore_method","logfilename")
+
+#dt_mem0 <- dt_mem0[date>="2023-01-18"]
+
+
+dt_mem0[,max_mem_usage:=max(mem_usage),by=.(rep,p,n_train,n_explain,n_batches,n_cores,approach,multicore_method,logfilename)]
+dt_mem0[,n_batches_real:=pmin(2^p-2,n_batches)]
+
+dt_mem <- dt_mem0[mem_usage==max_mem_usage,.(date,time,mem_usage,rep,p,n_train,n_explain,n_batches_real,n_cores,approach,multicore_method)]
+
+dt_mem[,mem_usage_Mb:=mem_usage/1024]
+
+library(ggplot2)
+
+ggplot(dt_mem,aes(x=n_batches_real,y=mem_usage_Mb,col=as.factor(n_explain),linetype=as.factor(n_train)))+
+ geom_line()+
+ geom_point()+
+ facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+
+ scale_y_log10()+
+ scale_x_log10()+
+ ggtitle("Memory usage")
+
+ggplot(dt_mem[p<16& p>2& approach=="empirical"],aes(x=n_batches_real,y=mem_usage_Mb,col=as.factor(n_explain)))+
+ geom_line()+
+ geom_point()+
+ facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+
+ scale_y_log10()+
+ scale_x_log10()+
+ ggtitle("Memory usage for n_train=100")
+
+
+
+dt_mem0[p==8 & n_explain==100 & approach=="ctree"]
+dt_mem0[p==16 & n_explain==100 & approach=="ctree"]
+
+# Wierd and inconsistent results
+
+
+dt_time0 <- fread("inst/scripts/timing_test_2023_new2.csv")
+#names(dt_time0) <- c("p","n_train","n_explain","n_batches","n_cores","approach","time","sys_time_start_explain","sys_time_end_explain",
+# "secs_explain","rep","max_n","max_p","rho","sigma","mu_const","beta0","sigma_eps")
+
+#dt_time0 <- dt_time0[time>="2023-01-18"]
+
+
+dt_time0[,n_batches_real:=pmin(2^p-2,n_batches)]
+
+dt_time <- dt_time0[,.(time,secs_explain,timing_setup,timing_test_prediction, timing_setup_computation ,timing_compute_vS ,timing_postprocessing ,timing_shapley_computation, rep,p,n_train,n_explain,n_batches_real,approach,n_combinations)]
+
+dt_time[n_batches_real==1,secs_explain_singlebatch :=secs_explain]
+dt_time[,secs_explain_singlebatch:=mean(secs_explain_singlebatch,na.rm=T),by=.(p,n_train,n_explain,approach,n_combinations)]
+dt_time[,secs_explain_prop_singlebatch:=secs_explain/secs_explain_singlebatch]
+
+ggplot(dt_time[p<14],aes(x=n_batches_real,y=secs_explain,col=as.factor(n_explain),linetype=as.factor(n_train)))+
+ geom_line()+
+ geom_point()+
+ facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+
+ #scale_y_log10()+
+ scale_x_log10()+
+ ggtitle("Time usage")
+
+ggplot(dt_time[p<14],aes(x=n_batches_real,y=secs_explain_prop_singlebatch,col=as.factor(n_explain),linetype=as.factor(n_train)))+
+ geom_line()+
+ geom_point()+
+ facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+
+ #scale_y_log10()+
+ scale_x_log10()+
+ ggtitle("Time usage proportional to singlebatch")
+
+
+ggplot(dt_time[p<14],aes(x=n_batches_real,y=timing_shapley_computation,col=as.factor(n_explain),linetype=as.factor(n_train)))+
+ geom_line()+
+ geom_point()+
+ facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+
+ #scale_y_log10()+
+ scale_x_log10()+
+ ggtitle("Time usage")
+
+
+
+
+ggplot(dt_time[p<16& p>2 & approach=="empirical"],aes(x=n_batches_real,y=secs_explain,col=as.factor(n_explain)))+
+ geom_line()+
+ geom_point()+
+ facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+
+ # scale_y_log10()+
+ # scale_x_log10()+
+ ggtitle("Time usage for n_train=100")
+
+
+
+#### Default for ctree + gaussian: Mye å spare minnemessig + lite å tape tidsmessig
+# n_batches <- (2^p-2)
+# max 100, min 10
+
+n_batches_fun <- function(approach,p){
+ n_combinations <- 2^p-2
+
+ if(approach %in% c("ctree","gaussian","copula")){
+ init <- ceiling(n_combinations/10)
+ floor <- max(c(10,init))
+ ret <- min(c(1000,floor))
+ } else {
+ init <- ceiling(n_combinations/100)
+ floor <- max(c(2,init))
+ ret <- min(c(100,floor))
+ }
+ return(ret)
+}
+
+n_batches_fun("empirical",10)
+
+
diff --git a/inst/scripts/bashscript_2023.sh b/inst/scripts/bashscript_2023.sh
new file mode 100644
index 000000000..f7cd3b57f
--- /dev/null
+++ b/inst/scripts/bashscript_2023.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+
+#Create array of inputs - space separator
+#MJ: Define all input vectors here
+script_name="timing_script_2023.R"
+logfile_bash="memory_test_2023_new2.csv"
+logfile_Rscript="timing_test_2023_new2.csv"
+
+
+p_vec=(4 6 8 10 12 14 16)
+n_train_vec=(100 1000) #(100 1000 1000)
+n_explain_vec=(10 100) #(1 2 10 100)
+n_batches_vec=(1 2 4 8 16 32 64) #(1 2 4 8 16 32)
+n_cores_vec=1 #(1 2 4 8 16 24 32)
+approach_vec=("empirical" "gaussian" "ctree" "copula" "independence")
+multicore_method_vec=("sequential")
+reps=3
+
+## get length of $distro array
+len_p_vec=${#p_vec[@]}
+len_n_train_vec=${#n_train_vec[@]}
+len_n_explain_vec=${#n_explain_vec[@]}
+len_n_batches_vec=${#n_batches_vec[@]}
+len_n_cores_vec=${#n_cores_vec[@]}
+len_approach_vec=${#approach_vec[@]}
+len_multicore_method_vec=${#multicore_method_vec[@]}
+
+## Use bash for loop
+for (( i0=0; i0<$reps; i1++ )); do
+for (( i1=0; i1<$len_p_vec; i1++ )); do
+for (( i2=0; i2<$len_n_train_vec; i2++ )); do
+for (( i3=0; i3<$len_n_explain_vec; i3++ )); do
+for (( i4=0; i4<$len_n_batches_vec; i4++ )); do
+for (( i5=0; i5<$len_n_cores_vec; i5++ )); do
+for (( i6=0; i6<$len_approach_vec; i6++ )); do
+for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do
+running_processes=1
+start_new_script=1
+while [[ $running_processes == 1 ]]
+ do
+ if [[ $start_new_script == 1 ]]
+ then
+ sleep 5
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash
+ Rscript --verbose $script_name $i0 ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_explain_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} ${multicore_method_vec[$i7]} $logfile_Rscript &
+ start_new_script=0
+ fi
+
+ sleep 0.5
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash
+ sleep 0.5
+
+ running_processes=$(pgrep -f $script_name -a -c)
+ done
+done; done; done; done; done; done; done; done
diff --git a/inst/scripts/bashscript_2023_tmp.sh b/inst/scripts/bashscript_2023_tmp.sh
new file mode 100644
index 000000000..8528564ce
--- /dev/null
+++ b/inst/scripts/bashscript_2023_tmp.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+
+#Create array of inputs - space separator
+#MJ: Define all input vectors here
+script_name="timing_script_2023.R"
+logfile_bash="memory_test_2023_tmp.csv"
+logfile_Rscript="timing_test_2023_tmp.csv"
+
+
+p_vec=(4 6 8 10 12 14 16)
+n_train_vec=(100) #(100 1000 1000)
+n_explain_vec=(10) #(1 2 10 100)
+n_batches_vec=(1 2 4 8 16 32 64) #(1 2 4 8 16 32)
+n_cores_vec=1 #(1 2 4 8 16 24 32)
+approach_vec=("empirical") #"gaussian" "ctree" "copula" "independence")
+multicore_method_vec=("sequential")
+reps=1
+
+## get length of $distro array
+len_p_vec=${#p_vec[@]}
+len_n_train_vec=${#n_train_vec[@]}
+len_n_explain_vec=${#n_explain_vec[@]}
+len_n_batches_vec=${#n_batches_vec[@]}
+len_n_cores_vec=${#n_cores_vec[@]}
+len_approach_vec=${#approach_vec[@]}
+len_multicore_method_vec=${#multicore_method_vec[@]}
+
+## Use bash for loop
+for (( i0=0; i0<$reps; i1++ )); do
+for (( i1=0; i1<$len_p_vec; i1++ )); do
+for (( i2=0; i2<$len_n_train_vec; i2++ )); do
+for (( i3=0; i3<$len_n_explain_vec; i3++ )); do
+for (( i4=0; i4<$len_n_batches_vec; i4++ )); do
+for (( i5=0; i5<$len_n_cores_vec; i5++ )); do
+for (( i6=0; i6<$len_approach_vec; i6++ )); do
+for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do
+running_processes=1
+start_new_script=1
+while [[ $running_processes == 1 ]]
+ do
+ if [[ $start_new_script == 1 ]]
+ then
+ sleep 5
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash
+ Rscript --verbose $script_name $i0 ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_explain_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} ${multicore_method_vec[$i7]} $logfile_Rscript &
+ start_new_script=0
+ fi
+
+ sleep 0.5
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash
+ sleep 0.5
+
+ running_processes=$(pgrep -f $script_name -a -c)
+ done
+done; done; done; done; done; done; done; done
diff --git a/inst/scripts/compare_shap_python.R b/inst/scripts/compare_shap_python.R
index bb0369122..6a4ed7787 100644
--- a/inst/scripts/compare_shap_python.R
+++ b/inst/scripts/compare_shap_python.R
@@ -1,3 +1,7 @@
+#### NOTE: THIS COMPARISON WAS DONE BASED ON OLD VERSION OF BOTH SHAPR AND SHAP, AND MAY NO LONGER REPRESENT THE
+#### ACTUAL PERFORMANCE DIFFERENCE. THE COMPARISON SHOULD BE UPDATED WITH UP-TO-DATE VERSIONS.
+
+
library(MASS)
library(xgboost)
library(shapr)
diff --git a/inst/scripts/compare_shap_python_new.R b/inst/scripts/compare_shap_python_new.R
new file mode 100644
index 000000000..c15fed9d6
--- /dev/null
+++ b/inst/scripts/compare_shap_python_new.R
@@ -0,0 +1,67 @@
+library(MASS)
+library(xgboost)
+library(shapr)
+library(data.table)
+
+# Python settings
+# Using the virtual environment here "../../Python/.venv/bin/python", as set by
+#Sys.setenv(RETICULATE_PYTHON = "../../Python/.venv/bin/python") in the .Rprofile
+library(reticulate)
+
+# Install some packages
+#py_install("xgboost")
+#py_install("shap")
+#py_install("pandas")
+
+data("Boston")
+
+x_var <- c("lstat", "rm", "dis", "indus")
+y_var <- "medv"
+
+x_train <- as.matrix(tail(Boston[, x_var], -6))
+y_train <- tail(Boston[, y_var], -6)
+x_test <- as.matrix(head(Boston[, x_var], 6))
+
+# Creating a larger test data set (600 observations) for more realistic function time calls.
+# Modifying x_test to repeat the 6 test observations 100 times
+x_test = rep(1,100) %x% x_test
+colnames(x_test) <- colnames(x_train)
+
+# Reading the R format version of the xgboost model to avoid crash reading same xgboost model in R and Python
+model <- readRDS(system.file("model_objects", "xgboost_model_object.rds", package = "shapr"))
+
+pred_test <- predict(model,x_test)
+
+# Spedifying the phi_0, i.e. the expected prediction without any features
+p0 <- mean(predict(model,x_train))# adjustment from the standard mean(y_train) to comply with the shap implementation
+
+time_R_start <- proc.time()
+
+# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using
+# the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default)
+explanation_independence <- explain(model = model,x_explain = x_test,x_train=x_train,
+ approach = "independence", prediction_zero = p0,n_batches = 1)
+
+time_R_indep0 <- proc.time()
+
+
+explanation_largesigma <- explain(model = model,x_explain = x_test,x_train=x_train,
+ approach = "empirical",empirical.type="fixed_sigma",empirical.fixed_sigma=10000,empirical.eta=1,
+ prediction_zero = p0,n_batches=1)
+
+
+time_R_largesigma0 <- proc.time()
+
+(time_R_indep <- time_R_indep0 - time_R_start)
+(time_R_largesigma <- time_R_largesigma0 - time_R_indep0)
+
+# Printing the Shapley values for the test data
+Kshap_indep <- explanation_independence$shapley_values
+Kshap_largesigma <- explanation_largesigma$shapley_values
+
+Kshap_indep
+Kshap_largesigma
+
+mean(abs(as.matrix(Kshap_indep)-as.matrix(Kshap_largesigma)))
+
+reticulate::py_run_file(system.file("scripts", "shap_python_script.py", package = "shapr"))
diff --git a/inst/scripts/devel/Rscript_test.R b/inst/scripts/devel/Rscript_test.R
new file mode 100644
index 000000000..b0bc1da33
--- /dev/null
+++ b/inst/scripts/devel/Rscript_test.R
@@ -0,0 +1,19 @@
+
+
+args <- commandArgs(trailingOnly = TRUE)
+
+p <- as.numeric(args[1])
+n_train <- as.numeric(args[2])
+n_test <- as.numeric(args[3])
+n_batches <- as.numeric(args[4])
+n_cores <- as.numeric(args[5])
+approach <- args[6]
+
+print(.libPaths())
+
+print(p)
+print(n_train)
+print(n_test)
+print(n_batches)
+print(n_cores)
+print(approach)
diff --git a/inst/scripts/devel/Rscript_test_shapr.R b/inst/scripts/devel/Rscript_test_shapr.R
new file mode 100644
index 000000000..8f8b5a504
--- /dev/null
+++ b/inst/scripts/devel/Rscript_test_shapr.R
@@ -0,0 +1,105 @@
+#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library")
+sys_time_initial <- Sys.time()
+
+# libraries
+library(shapr)
+library(future)
+library(MASS)
+library(microbenchmark)
+library(data.table)
+
+# Initial setup
+max_n <- 10^5
+max_p <- 13
+rho <- 0.3
+sigma <- 1
+mu_const <- 0
+beta0 <- 1
+sigma_eps <- 1
+
+mu <- rep(mu_const,max_p)
+beta <- c(beta0,seq_len(max_p)/max_p)
+Sigma <- matrix(rho,max_p,max_p)
+diag(Sigma) <- sigma
+
+set.seed(123)
+x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma)
+y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps)
+
+# Arguments form bash
+args <- commandArgs(trailingOnly = TRUE)
+if(length(args)==0) args = c(1,10,1000,100,6,2,"empirical","multisession","test.csv")
+
+this_rep <- as.numeric(args[1])
+p <- as.numeric(args[2])
+n_train <- as.numeric(args[3])
+n_test <- as.numeric(args[4])
+n_batches <- as.numeric(args[5])
+n_cores <- as.numeric(args[6])
+approach <- args[7]
+multicore_method <- args[8]
+logfilename <- args[9]
+
+set.seed(123)
+
+
+these_p <- sample.int(max_p,size=p)
+these_train <- sample.int(max_n,size=n_train)
+these_test <- sample.int(max_n,size=n_test)
+
+x_train <- as.data.frame(x_all[these_train,these_p])
+x_test <- as.data.frame(x_all[these_test,these_p])
+
+colnames(x_test) <- colnames(x_train) <- paste0("X",seq_len(p))
+
+y_train <- y_all[these_train]
+
+xy_train <- cbind(x_train,y=y_train)
+
+model <- lm(formula = y~.,data=xy_train)
+
+sys_time_start_shapr <- Sys.time()
+explainer <- shapr(x_train, model)
+sys_time_end_shapr <- Sys.time()
+
+prediction_zero <- mean(y_train)
+
+n_batches_use <- min(nrow(explainer$S),n_batches)
+
+future::plan(multicore_method,workers=n_cores)
+
+sys_time_start_explain <- Sys.time()
+explanation <- explain(
+ x_test,
+ approach = approach,
+ explainer = explainer,
+ prediction_zero = prediction_zero,
+ n_batches = n_batches_use
+)
+sys_time_end_explain <- Sys.time()
+future::plan(sequential) # To close multisessions etc
+
+timing <- list(p = p,
+ n_train = n_train,
+ n_test = n_test,
+ n_batches = n_batches,
+ n_cores = n_cores,
+ approach = approach,
+ sys_time_initial = as.character(sys_time_initial),
+ sys_time_start_shapr = as.character(sys_time_start_shapr),
+ sys_time_end_shapr = as.character(sys_time_end_shapr),
+ sys_time_start_explain = as.character(sys_time_start_explain),
+ sys_time_end_explain = as.character(sys_time_end_explain),
+ secs_shapr = as.double(difftime(sys_time_end_shapr,sys_time_start_shapr),units="secs"),
+ secs_explain = as.double(difftime(sys_time_end_explain,sys_time_start_explain),units="secs"),
+ this_rep = this_rep,
+ max_n = max_n,
+ max_p = max_p,
+ rho = rho,
+ sigma = sigma,
+ mu_const = mu_const,
+ beta0 = beta0,
+ sigma_eps = sigma_eps)
+
+#print(unlist(timing))
+data.table::fwrite(timing,logfilename,append = T)
diff --git a/inst/scripts/devel/bashscript_looping.sh b/inst/scripts/devel/bashscript_looping.sh
new file mode 100644
index 000000000..6834840fc
--- /dev/null
+++ b/inst/scripts/devel/bashscript_looping.sh
@@ -0,0 +1,60 @@
+#!/bin/bash
+
+#Create array of inputs - space separator
+#MJ: Define all input vectors here
+p_vec=(10 3 4 5 6 7 8 9 10)
+n_train_vec=(1000 10000)
+n_test_vec=(100 10 20 100)
+n_batches_vec=(1 2 4 8 16 24 32)
+n_cores_vec=(1 2 4 8 16 24 32)
+approach_vec=("empirical" "gaussian" "ctree")
+
+
+## get length of $distro array
+len_p_vec=${#p_vec[@]}
+len_n_train_vec=${#n_train_vec[@]}
+len_n_test_vec=${#n_test_vec[@]}
+len_n_batches_vec=${#n_batches_vec[@]}
+len_n_cores_vec=${#n_cores_vec[@]}
+len_approach_vec=${#approach_vec[@]}
+
+
+## Use bash for loop
+for (( i1=0; i1<$len_p_vec; i1++ )); do
+for (( i2=0; i2<$len_n_train_vec; i2++ )); do
+for (( i3=0; i3<$len_n_test_vec; i3++ )); do
+for (( i4=0; i4<$len_n_batches_vec; i4++ )); do
+for (( i5=0; i5<$len_n_cores_vec; i5++ )); do
+for (( i6=0; i6<$len_approach_vec; i6++ )); do
+# CURRENT STARTS
+ Rscript --verbose Rscript_test_shapr.R ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]}
+# CURRENT ENDS
+done; done; done; done; done; done
+
+
+# SOMETHING LIKE THIS???
+# #START
+run=true
+new=true
+while run
+ do
+ if (("$new")); then
+ Rscript --verbose Rscript_test_shapr.R ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} &
+ new=false
+ else
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -k -c pss -P 4.1.1 | tail -n 1)" | tee -a logfile2
+ sleep 2
+ fi
+ if (___RSCRIPT IS DONE___); then
+ run=false
+ fi
+ done
+## END
+
+
+
+while true
+ do
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -k -c pss -P 4.1.1 | tail -n 1)" | tee -a logfile2
+ sleep 2
+done
diff --git a/inst/scripts/devel/bashscript_looping2.sh b/inst/scripts/devel/bashscript_looping2.sh
new file mode 100644
index 000000000..138d37ca9
--- /dev/null
+++ b/inst/scripts/devel/bashscript_looping2.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+
+#Create array of inputs - space separator
+#MJ: Define all input vectors here
+script_name="Rscript_test_shapr.R"
+logfile_bash="memory_log.csv"
+logfile_Rscript="timing_log.csv"
+
+
+p_vec=(2 3 4 5) # 6 7 8 9 10)
+n_train_vec=(1000 10000)
+n_test_vec=(20) #(10 20 100)
+n_batches_vec=(12) #(1 2 4 8 16 24 32)
+n_cores_vec=(2 12) #(1 2 4 8 16 24 32)
+approach_vec= gaussian #("empirical" "gaussian" "ctree")
+multicore_method_vec= ("multisession" "multicore")
+
+
+## get length of $distro array
+len_p_vec=${#p_vec[@]}
+len_n_train_vec=${#n_train_vec[@]}
+len_n_test_vec=${#n_test_vec[@]}
+len_n_batches_vec=${#n_batches_vec[@]}
+len_n_cores_vec=${#n_cores_vec[@]}
+len_approach_vec=${#approach_vec[@]}
+len_multicore_method_vec=${#multicore_method_vec[@]}
+
+
+## Use bash for loop
+for (( i1=0; i1<$len_p_vec; i1++ )); do
+for (( i2=0; i2<$len_n_train_vec; i2++ )); do
+for (( i3=0; i3<$len_n_test_vec; i3++ )); do
+for (( i4=0; i4<$len_n_batches_vec; i4++ )); do
+for (( i5=0; i5<$len_n_cores_vec; i5++ )); do
+for (( i6=0; i6<$len_approach_vec; i6++ )); do
+for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do
+running_processes=1
+start_new_script=1
+while [[ $running_processes == 1 ]]
+ do
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_test_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash
+ sleep 1
+ if [[ $start_new_script == 1 ]]
+ then
+ Rscript --verbose $script_name ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} $logfile_Rscript &
+ start_new_script=0
+ fi
+ running_processes=$(pgrep -f $script_name -a -c)
+ done
+done; done; done; done; done; done
diff --git a/inst/scripts/devel/bashscript_looping_run.sh b/inst/scripts/devel/bashscript_looping_run.sh
new file mode 100644
index 000000000..f37c34854
--- /dev/null
+++ b/inst/scripts/devel/bashscript_looping_run.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+
+#Create array of inputs - space separator
+#MJ: Define all input vectors here
+script_name="Rscript_test_shapr.R"
+logfile_bash="memory_log_test_big.csv"
+logfile_Rscript="timing_log_test_big.csv"
+
+
+p_vec=(8 9 10 11 12 13)
+n_train_vec=(1000 10000)
+n_test_vec=(100)
+n_batches_vec=(1 2 4 8 16 24 32)
+n_cores_vec=(1 2 4 8 16 24 32)
+approach_vec=("empirical" "gaussian" "ctree")
+multicore_method_vec=("multisession" "multicore")
+reps=5
+
+## get length of $distro array
+len_p_vec=${#p_vec[@]}
+len_n_train_vec=${#n_train_vec[@]}
+len_n_test_vec=${#n_test_vec[@]}
+len_n_batches_vec=${#n_batches_vec[@]}
+len_n_cores_vec=${#n_cores_vec[@]}
+len_approach_vec=${#approach_vec[@]}
+len_multicore_method_vec=${#multicore_method_vec[@]}
+
+
+## Use bash for loop
+for (( i0=0; i1<$reps; i1++ )); do
+for (( i1=0; i1<$len_p_vec; i1++ )); do
+for (( i2=0; i2<$len_n_train_vec; i2++ )); do
+for (( i3=0; i3<$len_n_test_vec; i3++ )); do
+for (( i4=0; i4<$len_n_batches_vec; i4++ )); do
+for (( i5=0; i5<$len_n_cores_vec; i5++ )); do
+for (( i6=0; i6<$len_approach_vec; i6++ )); do
+for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do
+running_processes=1
+start_new_script=1
+while [[ $running_processes == 1 ]]
+ do
+ if [[ $start_new_script == 1 ]]
+ then
+ sleep 5
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_test_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash
+ Rscript --verbose $script_name $i0 ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} ${multicore_method_vec[$i7]} $logfile_Rscript &
+ start_new_script=0
+ fi
+
+ sleep 0.5
+ echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_test_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash
+ sleep 0.5
+
+ running_processes=$(pgrep -f $script_name -a -c)
+ done
+done; done; done; done; done; done; done; done
diff --git a/inst/scripts/devel/compare_explain_batch.R b/inst/scripts/devel/compare_explain_batch.R
index 0e790f3e0..cedf257fb 100644
--- a/inst/scripts/devel/compare_explain_batch.R
+++ b/inst/scripts/devel/compare_explain_batch.R
@@ -78,7 +78,7 @@ explain.independence2 <- function(x, explainer, approach, prediction_zero,
if (!is.null(seed)) set.seed(seed)
# Add arguments to explainer object
- explainer$x_test <- as.matrix(preprocess_data(x, explainer$feature_list)$x_dt)
+ explainer$x_test <- as.matrix(preprocess_data(x, explainer$feature_specs)$x_dt)
explainer$approach <- approach
explainer$n_samples <- n_samples
diff --git a/inst/scripts/devel/devel_parallelization.R b/inst/scripts/devel/devel_parallelization.R
new file mode 100644
index 000000000..6dd6d10bd
--- /dev/null
+++ b/inst/scripts/devel/devel_parallelization.R
@@ -0,0 +1,147 @@
+library(xgboost)
+library(shapr)
+library(future)
+
+data("Boston", package = "MASS")
+
+x_var <- c("lstat", "rm", "dis", "indus","rad","tax","ptratio","black","zn","crim")
+y_var <- "medv"
+
+x_train <- as.matrix(Boston[-1:-6, x_var])
+y_train <- Boston[-1:-6, y_var]
+x_test <- as.matrix(Boston[1:20, x_var])
+
+# Looking at the dependence between the features
+cor(x_train)
+
+# Fitting a basic xgboost model to the training data
+model <- xgboost(
+ data = x_train,
+ label = y_train,
+ nround = 20,
+ verbose = FALSE
+)
+
+# Prepare the data for explanation
+explainer <- shapr(x_train, model)
+
+# Specifying the phi_0, i.e. the expected prediction without any features
+p <- mean(y_train)
+
+
+# No specification (sequential)
+start <- proc.time()
+explanation0 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+time0 <- stop-start
+
+
+# Sequential
+start <- proc.time()
+future::plan("sequential")
+explanation1 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+time1 <- stop-start
+
+# Try to set multicore (in Rstudio this is disabled so falls back to sequential)
+start <- proc.time()
+future::plan("multicore",workers=5) ## defaults to availableCores() workers
+explanation2 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+time2 <- stop-start
+
+# Multisession with 2 workers
+start <- proc.time()
+future::plan("multisession",workers = 2) ## defaults to availableCores() workers
+explanation3 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+time3 <- stop-start
+
+# Multisession with 5 workers
+start <- proc.time()
+future::plan("multisession",workers=5) ## defaults to availableCores() workers
+explanation4 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+time4 <- stop-start
+
+# Multisession with 10 workers
+start <- proc.time()
+future::plan("multisession",workers=10) ## defaults to availableCores() workers
+explanation5 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+time5 <- stop-start
+
+# Multisession with 20 workers
+start <- proc.time()
+future::plan("multisession",workers=20)
+explanation6 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+time6 <- stop-start
+
+# Trying to set up a cluster and run it there
+start <- proc.time()
+cl <- parallel::makeCluster(c(rep("hpc01",5),rep("hpc02",5),rep("hpc03",6)),
+ rscript = "/nr/prog/AppServerDefaults/Ubuntu_18.04_x86_64/bin/Rscript")
+plan(cluster, workers = cl)
+#plan(remote, workers = cl)
+explanation7 <- explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 32
+)
+stop <- proc.time()
+parallel::stopCluster(cl)
+time7 <- stop-start
+
+
+
+
+
+# Printing the Shapley values for the test data.
+# For more information about the interpretation of the values in the table, see ?shapr::explain.
+head(explanation0$dt,2)
+head(explanation1$dt,2)
+head(explanation2$dt,2)
+head(explanation3$dt,2)
+head(explanation4$dt,2)
+head(explanation5$dt,2)
+head(explanation6$dt,2)
+head(explanation7$dt,2)
+
+cbind(time0,time1,time2,time3,time4,time5,time6,time7)
diff --git a/inst/scripts/devel/devel_tmp_new_batch.R b/inst/scripts/devel/devel_tmp_new_batch.R
new file mode 100644
index 000000000..290d5c009
--- /dev/null
+++ b/inst/scripts/devel/devel_tmp_new_batch.R
@@ -0,0 +1,48 @@
+
+
+
+explainer <- explain_setup(
+ x_test,
+ approach = c("empirical","empirical","gaussian","copula"),
+ explainer = explainer,
+ prediction_zero = p,
+ n_batches = 4
+)
+
+
+explainer$approach = c("empirical","empirical","gaussian","copula")
+
+
+
+
+explainer$X[,randomorder:=sample(.N)]
+setorder(explainer$X,randomorder)
+
+aa <- explainer$X[!is.na(approach)][order(randomorder)][order(shapley_weight),batch:=ceiling(.I/.N*n_batches_per_approach)]
+
+aa <- explainer$X[!is.na(approach)][order(randomorder)][order(shapley_weight),batch:=ceiling(.I/.N*5)]
+
+
+bb <- explainer$X[!is.na(approach)][rank(shapley_weight,ties.method = "random")]
+
+
+explainer$X[]
+
+
+n_batches <- max(1, floor(length(index_S) / no_samples * n_batches))
+
+
+S_per_apprach <-
+
+ findInterval(x, quantile(x,type=5), rightmost.closed=TRUE)
+
+# It is fast
+set.seed(1)
+DT <- data.table(x=rep(rnorm(5),2))
+
+library(microbenchmark)
+
+
+microbenchmark(
+ order = DT[order(x),bin:=ceiling(.I/.N*5)],
+ findInterval = DT[, b2 :=findInterval(x, quantile(x,type=5), rightmost.closed=TRUE)],times=20 )
diff --git a/inst/scripts/devel/explain_new.R b/inst/scripts/devel/explain_new.R
new file mode 100644
index 000000000..b6a1e2af7
--- /dev/null
+++ b/inst/scripts/devel/explain_new.R
@@ -0,0 +1,164 @@
+library(xgboost)
+library(shapr)
+
+data("Boston", package = "MASS")
+
+x_var <- c("lstat", "rm", "dis", "indus")
+y_var <- "medv"
+
+x_train <- as.matrix(Boston[-1:-6, x_var])
+y_train <- Boston[-1:-6, y_var]
+x_test <- as.matrix(Boston[1:6, x_var])
+
+# Looking at the dependence between the features
+cor(x_train)
+
+# Fitting a basic xgboost model to the training data
+model <- xgboost(
+ data = x_train,
+ label = y_train,
+ nround = 20,
+ verbose = FALSE
+)
+
+# Prepare the data for explanation
+explainer <- shapr(x_train,model)
+explainer2 <- shapr(x_train,model,is_python=T)
+explainer3 <- shapr(x_train,is_python=T)
+
+
+# Specifying the phi_0, i.e. the expected prediction without any features
+p <- mean(y_train)
+
+# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using
+# the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default)
+
+##### TESTS ####
+
+explanation_new <- explain_new(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer1,
+ prediction_zero = p,
+ n_samples = 5*10^5,n_batches = 1
+)
+
+explanation_new$dt_shapley
+#none lstat rm dis indus
+#1: 22.446 5.190027 -0.9981141 0.4190562 4.2444812
+#2: 22.446 1.828362 -1.3269640 -0.2576771 0.5622163
+#3: 22.446 5.447883 4.6641813 -0.1288418 0.6862445
+#4: 22.446 5.617564 2.5234393 0.6614170 2.1817247
+#5: 22.446 1.715925 5.0150390 0.8001951 1.7526796
+#6: 22.446 2.592836 -2.6699632 0.6478134 1.8333372
+
+explanation_new <- explain_new(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,
+ n_samples = 10^5,n_batches = 4
+)
+
+explanation_new$dt_shapley
+#none lstat rm dis indus
+#1: 22.446 5.194753 -0.9882765 0.4020399 4.2469342
+#2: 22.446 1.809298 -1.3121239 -0.2562250 0.5649886
+#3: 22.446 5.447172 4.6691463 -0.1240268 0.6771758
+#4: 22.446 5.626358 2.5125083 0.6672642 2.1780147
+#5: 22.446 1.712585 5.0095470 0.8175396 1.7441671
+#6: 22.446 2.590425 -2.6672009 0.6596539 1.8211454
+
+explanation_new <- explain_new(
+ x_test,
+ approach = "empirical",
+ explainer = explainer,
+ prediction_zero = p,
+ n_samples = 10^5,n_batches = 1
+)
+
+explanation_new$dt_shapley
+#none lstat rm dis indus
+#1: 22.446 5.2632030 -1.2526613 0.2920444 4.5528644
+#2: 22.446 0.1671901 -0.7088401 0.9689005 0.3786871
+#3: 22.446 5.9888022 5.5450858 0.5660134 -1.4304351
+#4: 22.446 8.2142204 0.7507572 0.1893366 1.8298304
+#5: 22.446 0.5059898 5.6875103 0.8432238 2.2471150
+#6: 22.446 1.9929673 -3.6001958 0.8601984 3.1510531
+
+explanation_new <- explain_new(
+ x_test,
+ approach = "empirical",
+ explainer = explainer,
+ prediction_zero = p,
+ n_samples = 10^5,n_batches = 4
+)
+
+explanation_new$dt_shapley
+#none lstat rm dis indus
+#1: 22.446 5.2632030 -1.2526613 0.2920444 4.5528644
+#2: 22.446 0.1671901 -0.7088401 0.9689005 0.3786871
+#3: 22.446 5.9888022 5.5450858 0.5660134 -1.4304351
+#4: 22.446 8.2142204 0.7507572 0.1893366 1.8298304
+#5: 22.446 0.5059898 5.6875103 0.8432238 2.2471150
+#6: 22.446 1.9929673 -3.6001958 0.8601984 3.1510531
+
+#### TESTS ENDS #####
+
+#
+# print(explanation$dt)
+#
+# setup <- explain_setup(
+# x_test,
+# approach = "gaussian",
+# explainer = explainer,
+# prediction_zero = p
+# )
+#
+# str(explainer,max.level = 1)
+# str(setup,max.level=1)
+#
+explainer <- explain_setup(
+ x_test,
+ approach = "empirical",
+ explainer = explainer,
+ prediction_zero = p,
+ n_batches = 4
+ )
+
+explainer0 <- explain_setup(
+ x_test,
+ approach = c("empirical","copula","ctree","gaussian"),
+ explainer = explainer,
+ prediction_zero = p,
+ n_batches = 7
+)
+
+explainer0$X
+
+#
+#
+# dt <- future.apply::future_lapply(X = explainer$S_batch,
+# FUN = batch_prepare_vS,
+# explainer = explainer,
+# future.seed = explainer$seed)
+# dt <- batch_prepare_vS(explainer$S_batch[[4]],explainer)
+#
+#
+# explanation_new <- explain_new(
+# x_test,
+# approach = "gaussian",
+# explainer = explainer,
+# prediction_zero = p,
+# n_samples = 10^5
+# )
+
+
+
+prepare_data(explainer, index_features = explainer$S_batch[[1]])
+# Printing the Shapley values for the test data.
+# For more information about the interpretation of the values in the table, see ?shapr::explain.
+print(explanation$dt)
+
+# Finally we plot the resulting explanations
+plot(explanation)
diff --git a/inst/scripts/devel/inspect_sim_res.R b/inst/scripts/devel/inspect_sim_res.R
new file mode 100644
index 000000000..3f5c3993a
--- /dev/null
+++ b/inst/scripts/devel/inspect_sim_res.R
@@ -0,0 +1,14 @@
+logfile_bash="memory_log_test_big.csv"
+logfile_Rscript="timing_log_test_big.csv"
+
+
+bash <- fread(file.path("inst/scripts/devel/",logfile_bash))
+Rscript <- fread(file.path("inst/scripts/devel/",logfile_Rscript))
+
+names(bash) <- c("date","time","mem","p","n_train","n_test","n_batches","n_cores","approach","multicore_method","logfilename")
+
+
+bash[,mem_MB:=mem/1024]
+
+bash[,list(max_mem_MB=max(mem_MB)),by=c("p","n_train","n_test","n_batches","n_cores","approach","multicore_method")]
+str(bash)
diff --git a/inst/scripts/devel/memory_log_test_big.csv b/inst/scripts/devel/memory_log_test_big.csv
new file mode 100644
index 000000000..9361ed5cc
--- /dev/null
+++ b/inst/scripts/devel/memory_log_test_big.csv
@@ -0,0 +1,15141 @@
+2022-01-21, 19:45:21, 5729 , 0, 8, 1000, 100, 1, 1, empirical, multisession, timing_log_test_big.csv
+2022-01-21, 19:45:22, 97797 , 0, 8, 1000, 100, 1, 1, empirical, multisession, timing_log_test_big.csv
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+2022-01-23, 14:31:34, 130129353 , 0, 13, 10000, 100, 24, 32, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 14:32:38, 132614209 , 0, 13, 10000, 100, 24, 32, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 14:33:46, 138571011 , 0, 13, 10000, 100, 24, 32, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 14:34:45, 130865710 , 0, 13, 10000, 100, 24, 32, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 14:35:38, 126552671 , 0, 13, 10000, 100, 24, 32, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 14:37:17, 100366803 , 0, 13, 10000, 100, 24, 32, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 14:38:38, 103928922 , 0, 13, 10000, 100, 24, 32, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 14:39:31, 124048140 , 0, 13, 10000, 100, 24, 32, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 14:41:07, 127812730 , 0, 13, 10000, 100, 24, 32, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 14:43:29, 82880224 , 0, 13, 10000, 100, 24, 32, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 14:44:43, 102787935 , 0, 13, 10000, 100, 24, 32, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 14:45:53, 126049764 , 0, 13, 10000, 100, 24, 32, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 14:47:44, 138567821 , 0, 13, 10000, 100, 32, 1, empirical, multisession, timing_log_test_big.csv
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+2022-01-23, 14:50:44, 134286808 , 0, 13, 10000, 100, 32, 1, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 14:51:52, 110075931 , 0, 13, 10000, 100, 32, 1, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 14:53:09, 101608062 , 0, 13, 10000, 100, 32, 1, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 14:53:49, 115388379 , 0, 13, 10000, 100, 32, 1, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 14:54:37, 126230926 , 0, 13, 10000, 100, 32, 1, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 14:55:27, 124852855 , 0, 13, 10000, 100, 32, 1, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 14:56:21, 126420252 , 0, 13, 10000, 100, 32, 1, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 14:57:08, 125336976 , 0, 13, 10000, 100, 32, 1, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 14:58:33, 124072203 , 0, 13, 10000, 100, 32, 1, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 14:59:17, 124708682 , 0, 13, 10000, 100, 32, 1, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:00:05, 62203685 , 0, 13, 10000, 100, 32, 2, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:00:17, 64681545 , 0, 13, 10000, 100, 32, 2, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:00:33, 73324962 , 0, 13, 10000, 100, 32, 2, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:00:47, 78286341 , 0, 13, 10000, 100, 32, 2, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:01:06, 89460172 , 0, 13, 10000, 100, 32, 2, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:01:21, 94301333 , 0, 13, 10000, 100, 32, 2, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:01:41, 104389646 , 0, 13, 10000, 100, 32, 2, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:01:57, 124029025 , 0, 13, 10000, 100, 32, 2, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:02:18, 128771727 , 0, 13, 10000, 100, 32, 2, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:02:28, 127718160 , 0, 13, 10000, 100, 32, 2, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:02:45, 130158501 , 0, 13, 10000, 100, 32, 2, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:02:53, 129843880 , 0, 13, 10000, 100, 32, 2, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:03:05, 122067265 , 0, 13, 10000, 100, 32, 4, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:03:18, 115007965 , 0, 13, 10000, 100, 32, 4, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:03:57, 128395816 , 0, 13, 10000, 100, 32, 4, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:04:03, 127155366 , 0, 13, 10000, 100, 32, 4, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:04:18, 130396486 , 0, 13, 10000, 100, 32, 4, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:04:32, 127829727 , 0, 13, 10000, 100, 32, 4, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:04:52, 122743880 , 0, 13, 10000, 100, 32, 4, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:05:01, 129696346 , 0, 13, 10000, 100, 32, 4, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:05:19, 123521368 , 0, 13, 10000, 100, 32, 4, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:05:33, 125007410 , 0, 13, 10000, 100, 32, 4, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:05:54, 129680331 , 0, 13, 10000, 100, 32, 4, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:06:03, 124777785 , 0, 13, 10000, 100, 32, 4, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:06:18, 129765692 , 0, 13, 10000, 100, 32, 8, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:06:30, 128157777 , 0, 13, 10000, 100, 32, 8, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:06:49, 133593953 , 0, 13, 10000, 100, 32, 8, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:07:16, 129225386 , 0, 13, 10000, 100, 32, 8, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:07:45, 132189810 , 0, 13, 10000, 100, 32, 8, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:07:57, 129803549 , 0, 13, 10000, 100, 32, 8, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:08:17, 133930546 , 0, 13, 10000, 100, 32, 8, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:08:26, 126347342 , 0, 13, 10000, 100, 32, 8, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:08:56, 119754625 , 0, 13, 10000, 100, 32, 8, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:09:21, 130447877 , 0, 13, 10000, 100, 32, 8, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:09:54, 127239504 , 0, 13, 10000, 100, 32, 8, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:10:28, 131109447 , 0, 13, 10000, 100, 32, 8, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:11:12, 132544852 , 0, 13, 10000, 100, 32, 16, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:11:39, 129314362 , 0, 13, 10000, 100, 32, 16, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:12:30, 133255965 , 0, 13, 10000, 100, 32, 16, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:12:59, 132566840 , 0, 13, 10000, 100, 32, 16, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:13:26, 130166710 , 0, 13, 10000, 100, 32, 16, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:13:46, 132151856 , 0, 13, 10000, 100, 32, 16, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:14:26, 128726433 , 0, 13, 10000, 100, 32, 16, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:14:42, 131969651 , 0, 13, 10000, 100, 32, 16, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:15:07, 86412424 , 0, 13, 10000, 100, 32, 16, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:15:46, 116970046 , 0, 13, 10000, 100, 32, 16, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:16:50, 123760155 , 0, 13, 10000, 100, 32, 16, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:18:08, 136258714 , 0, 13, 10000, 100, 32, 16, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:19:18, 137476572 , 0, 13, 10000, 100, 32, 24, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:20:52, 121190182 , 0, 13, 10000, 100, 32, 24, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:22:00, 136832317 , 0, 13, 10000, 100, 32, 24, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:22:42, 132559478 , 0, 13, 10000, 100, 32, 24, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:23:47, 127364511 , 0, 13, 10000, 100, 32, 24, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:24:52, 105642258 , 0, 13, 10000, 100, 32, 24, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:27:05, 110484725 , 0, 13, 10000, 100, 32, 24, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:28:04, 119602266 , 0, 13, 10000, 100, 32, 24, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:29:24, 129365526 , 0, 13, 10000, 100, 32, 24, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:31:03, 129539647 , 0, 13, 10000, 100, 32, 24, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:33:12, 129492550 , 0, 13, 10000, 100, 32, 24, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:34:44, 106271903 , 0, 13, 10000, 100, 32, 24, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:35:55, 133374047 , 0, 13, 10000, 100, 32, 32, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:36:57, 139489295 , 0, 13, 10000, 100, 32, 32, empirical, multisession, timing_log_test_big.csv
+2022-01-23, 15:39:11, 139585391 , 0, 13, 10000, 100, 32, 32, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:40:42, 132448177 , 0, 13, 10000, 100, 32, 32, empirical, multicore, timing_log_test_big.csv
+2022-01-23, 15:42:45, 107035284 , 0, 13, 10000, 100, 32, 32, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:43:27, 114791924 , 0, 13, 10000, 100, 32, 32, gaussian, multisession, timing_log_test_big.csv
+2022-01-23, 15:44:04, 128489982 , 0, 13, 10000, 100, 32, 32, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:45:03, 119422040 , 0, 13, 10000, 100, 32, 32, gaussian, multicore, timing_log_test_big.csv
+2022-01-23, 15:47:02, 133338388 , 0, 13, 10000, 100, 32, 32, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:49:26, 127923326 , 0, 13, 10000, 100, 32, 32, ctree, multisession, timing_log_test_big.csv
+2022-01-23, 15:51:35, 132161860 , 0, 13, 10000, 100, 32, 32, ctree, multicore, timing_log_test_big.csv
+2022-01-23, 15:53:26, 126884391 , 0, 13, 10000, 100, 32, 32, ctree, multicore, timing_log_test_big.csv
diff --git a/inst/scripts/devel/testing_explain_forevast_n_comb.R b/inst/scripts/devel/testing_explain_forevast_n_comb.R
new file mode 100644
index 000000000..48784a6cf
--- /dev/null
+++ b/inst/scripts/devel/testing_explain_forevast_n_comb.R
@@ -0,0 +1,214 @@
+
+
+h3test <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:3],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = i,
+ n_combinations = 300
+)
+
+h2test <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 2,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:2],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = i,
+ n_combinations = 10^7
+)
+
+h1test <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 1,
+ approach = "empirical",
+ prediction_zero = p0_ar[1],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = i,
+ n_combinations = 10^7
+)
+
+w <- h3test$internal$objects$X_list[[1]][["shapley_weight"]]
+
+w[-c(1, length(w))] <- w[-c(1, length(w))] / sum(w[-c(1, length(w))])
+h3test$internal$objects$X_list[[1]][,shapley_weight_norm := w]
+
+
+w <- h1test$internal$objects$X_list[[1]][["shapley_weight"]]
+
+w[-c(1, length(w))] <- w[-c(1, length(w))] / sum(w[-c(1, length(w))])
+h1test$internal$objects$X_list[[1]][,shapley_weight_norm := w]
+
+
+w2 <- h1full$internal$objects$X_list[[1]][["shapley_weight"]]
+
+w2[-c(1, length(w2))] <- w2[-c(1, length(w2))] / sum(w2[-c(1, length(w2))])
+h1full$internal$objects$X_list[[1]][,shapley_weight_norm := w2]
+
+h1test$internal$objects$X_list[[1]][-c(1,.N),]
+h1full$internal$objects$X_list[[1]][-c(1,.N),]
+h3test$internal$objects$X_list[[1]][-c(1,.N),]
+
+
+ncomb <- 50
+
+reps <- 10
+
+set.seed(123)
+h3full <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:3],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = 1)
+
+set.seed(123)
+h1full <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 1,
+ approach = "empirical",
+ prediction_zero = p0_ar[1],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = 1)
+
+
+
+h1list <- h2list <- h3list <- list()
+for (i in 1:reps){
+ h3list[[i]] <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:3],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = i,
+ n_combinations = ncomb
+ )
+
+ h2list[[i]] <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 2,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:2],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = i,
+ n_combinations = ncomb
+ )
+
+ h1list[[i]] <- explain_forecast(model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 1,
+ approach = "empirical",
+ prediction_zero = p0_ar[1],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ seed = i,
+ n_combinations = min(ncomb,31)
+ )
+
+ print(i)
+}
+
+
+
+cols_horizon1 <- h3full$internal$objects$cols_per_horizon[[1]]
+cols_horizon2 <- h3full$internal$objects$cols_per_horizon[[2]]
+cols_horizon3 <- h3full$internal$objects$cols_per_horizon[[3]]
+
+h1mean1 <- h2mean1 <- h2mean2 <- h3mean1 <- h3mean2 <- h3mean3 <- list()
+for(i in 1:reps){
+ h1mean1[[i]] <- as.matrix(h1list[[i]]$shapley_values[horizon==1, ..cols_horizon1])
+
+ h2mean1[[i]] <- as.matrix(h2list[[i]]$shapley_values[horizon==1, ..cols_horizon1])
+ h2mean2[[i]] <- as.matrix(h2list[[i]]$shapley_values[horizon==2, ..cols_horizon2])
+
+ h3mean1[[i]] <- as.matrix(h3list[[i]]$shapley_values[horizon==1, ..cols_horizon1])
+ h3mean2[[i]] <- as.matrix(h3list[[i]]$shapley_values[horizon==2, ..cols_horizon2])
+ h3mean3[[i]] <- as.matrix(h3list[[i]]$shapley_values[horizon==3, ..cols_horizon3])
+
+}
+
+# Horizon 1
+Reduce("+", h1mean1) / reps
+Reduce("+", h2mean1) / reps
+Reduce("+", h3mean1) / reps
+h3full$shapley_values[horizon==1,..cols_horizon1]
+
+# Horizon 2
+Reduce("+", h2mean2) / reps
+Reduce("+", h3mean2) / reps
+h3full$shapley_values[horizon==2,..cols_horizon2]
+
+# Horizon 3
+Reduce("+", h3mean3) / reps
+h3full$shapley_values[horizon==3,..cols_horizon3]
+
+
+
+expect_equal(h2$shapley_values[horizon==1, ..cols_horizon1],
+ h1$shapley_values[horizon==1,..cols_horizon1])
+
+expect_equal(h3$shapley_values[horizon==1, ..cols_horizon1],
+ h1$shapley_values[horizon==1,..cols_horizon1])
+
+cols_horizon2 <- h2$internal$objects$cols_per_horizon[[2]]
+expect_equal(h3$shapley_values[horizon==2, ..cols_horizon2],
+ h2$shapley_values[horizon==2,..cols_horizon2])
diff --git a/inst/scripts/devel/testing_memory_monitoring.R b/inst/scripts/devel/testing_memory_monitoring.R
new file mode 100644
index 000000000..a372c6cf3
--- /dev/null
+++ b/inst/scripts/devel/testing_memory_monitoring.R
@@ -0,0 +1,98 @@
+
+
+library(shapr)
+library(future)
+library(MASS)
+library(microbenchmark)
+library(data.table)
+library(peakRAM)
+
+p_vec <- 20#2:10
+n_train_vec <- 1000
+n_test_vec <- 100#c(2,10,20)
+n_batches_vec <- c(1,2,4,8,16,24,32)#seq(2,20,by=5)
+n_cores_vec <- c(1,2,4,8,16,24,32)#c(1,seq(2,32,by=5))
+approach_vec <- c("empirical","gaussian","ctree")#rev(c("empirical","gaussian"))
+reps <- 2
+
+max_n <- 10^5
+max_p <- 20
+rho <- 0.3
+Sigma <- matrix(rho,max_p,max_p)
+diag(Sigma) <- 1
+mu <- rep(0,max_p)
+beta <- c(1,seq_len(max_p)/max_p)
+sigma_eps <- 1
+
+set.seed(123)
+x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma)
+y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps)
+
+
+
+set.seed(123)
+these_p <- sample.int(max_p,size=p_vec[1])
+these_train <- sample.int(max_n,size=n_train_vec[1])
+these_test <- sample.int(max_n,size=n_test_vec[1])
+
+x_train <- as.data.frame(x_all[these_train,these_p])
+x_test <- as.data.frame(x_all[these_test,these_p])
+
+y_train <- y_all[these_train]
+
+xy_train <- cbind(x_train,y=y_train)
+
+model <- lm(formula = y~.,data=xy_train)
+
+explainer <- shapr(x_train, model,n_combinations = 1000)
+p <- mean(y_train)
+
+
+future::plan("multicore",workers=4)
+#future::plan("multisession",workers=4)
+#future::plan("sequential")
+
+peakRAM(explain(
+ x_test,
+ approach = "gaussian",
+ explainer = explainer,
+ prediction_zero = p,n_batches = 4)
+ )
+
+# ,
+# explain(
+# x_test,
+# approach = "empirical",
+# explainer = explainer,
+# prediction_zero = p,n_batches = 2),
+# explain(
+# x_test,
+# approach = "empirical",
+# explainer = explainer,
+# prediction_zero = p,n_batches = 4))
+
+# explain(
+# x_test,
+# approach = "empirical",
+# explainer = explainer,
+# prediction_zero = p,n_batches = 8),
+# explain(
+# x_test,
+# approach = "empirical",
+# explainer = explainer,
+# prediction_zero = p,n_batches = 16),
+# explain(
+# x_test,
+# approach = "empirical",
+# explainer = explainer,
+# prediction_zero = p,n_batches = 32)
+# )
+
+# s <- proc.time()
+# explain(
+# x_test,
+# approach = "empirical",
+# explainer = explainer,
+# prediction_zero = p,n_batches = 32)
+# print(proc.time()-s)
+#
diff --git a/inst/scripts/devel/testing_parallelization.R b/inst/scripts/devel/testing_parallelization.R
new file mode 100644
index 000000000..24cacc1a7
--- /dev/null
+++ b/inst/scripts/devel/testing_parallelization.R
@@ -0,0 +1,176 @@
+
+
+library(shapr)
+library(future)
+library(MASS)
+library(microbenchmark)
+library(data.table)
+
+p_vec <- 10#2:10
+n_train_vec <- 1000
+n_test_vec <- 100#c(2,10,20)
+n_batches_vec <- c(1,2,4,8,16,24,32)#seq(2,20,by=5)
+n_cores_vec <- c(1,2,4,8,16,24,32)#c(1,seq(2,32,by=5))
+approach_vec <- c("empirical","gaussian","ctree")#rev(c("empirical","gaussian"))
+reps <- 2
+
+max_n <- 10^5
+max_p <- 10
+rho <- 0.3
+Sigma <- matrix(rho,max_p,max_p)
+diag(Sigma) <- 1
+mu <- rep(0,max_p)
+beta <- c(1,seq_len(max_p)/max_p)
+sigma_eps <- 1
+
+set.seed(123)
+x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma)
+y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps)
+
+
+res_dt <- as.data.table(expand.grid(p = p_vec,
+ n_train = n_train_vec,
+ n_test = n_test_vec,
+ n_batches = n_batches_vec,
+ n_cores = n_cores_vec,
+ approach = approach_vec))
+
+res_dt[,n_cores:=ifelse(n_cores>n_batches,n_batches,n_cores)]
+res_dt <- unique(res_dt)
+
+res_dt[,approach:=as.character(approach)]
+res_dt[,time_median:=as.numeric(NA)]
+res_dt[,time_min:=as.numeric(NA)]
+res_dt[,mem_alloc:=as.numeric(NA)]
+
+
+for(i in seq_len(nrow(res_dt))){
+#for(i in sample.int(nrow(res_dt),10)){
+
+ set.seed(123)
+ these_p <- sample.int(max_p,size=res_dt[i,p])
+ these_train <- sample.int(max_n,size=res_dt[i,n_train])
+ these_test <- sample.int(max_n,size=res_dt[i,n_test])
+
+ x_train <- as.data.frame(x_all[these_train,these_p])
+ x_test <- as.data.frame(x_all[these_test,these_p])
+
+ y_train <- y_all[these_train]
+
+ xy_train <- cbind(x_train,y=y_train)
+
+ model <- lm(formula = y~.,data=xy_train)
+
+ explainer <- shapr(x_train, model)
+ p <- mean(y_train)
+
+
+ n_batches_use <- min(nrow(explainer$S),res_dt[i,n_batches])
+ n_cores_use <- res_dt[i,n_cores]
+ approach_use <- res_dt[i,approach]
+
+ #future::plan("multicore",workers=n_cores_use)
+ future::plan("multisession",workers=n_cores_use)
+
+
+ res0 <- bench::mark({
+ explanation <- explain(
+ x_test,
+ approach = approach_use,
+ explainer = explainer,
+ prediction_zero = p,n_batches = n_batches_use
+ )},iterations = reps,time_unit ='s',memory = F,
+ min_time = Inf
+ )
+
+ res_dt[i,c("time_median","time_min","mem_alloc"):= list(res0$median,res0$min,res0$mem_alloc/1024^2),]
+
+ # res_dt[p==res_dt[i,p] &
+ # n_train == res_dt[i,n_train] &
+ # n_test == res_dt[i,n_test] &
+ # n_cores == res_dt[i,n_cores] &
+ # n_batches == res_dt[i,n_batches] &
+ # approach == approach_use,
+ # res:=res0$time[3]/10^6
+ # ]
+
+ print(res_dt[i])
+}
+
+setkey(res_dt,time_median)
+
+#res_dt[approach=="gaussian"]
+
+
+
+
+# p n_train n_test n_batches n_cores approach time_median time_min mem_alloc
+# 1: 10 1000 100 5 10 empirical 8.264136 8.199610 NA
+# 2: 10 1000 100 5 5 empirical 8.277614 8.224627 NA
+# 3: 10 1000 100 5 15 empirical 8.351432 8.189444 NA
+# 4: 10 1000 100 5 20 empirical 8.394858 8.317760 NA
+# 5: 10 1000 100 5 30 empirical 8.496488 8.453119 NA
+# 6: 10 1000 100 10 5 empirical 10.534386 10.523246 NA
+# 7: 10 1000 100 10 10 empirical 11.659772 11.659772 NA
+# 8: 10 1000 100 10 15 empirical 11.767503 11.767503 NA
+# 9: 10 1000 100 10 30 empirical 11.835323 11.835323 NA
+# 10: 10 1000 100 10 20 empirical 11.902262 11.902262 NA
+# 11: 10 1000 100 20 5 empirical 14.750653 14.718519 NA
+# 12: 10 1000 100 20 30 empirical 15.426510 15.398783 NA
+# 13: 10 1000 100 20 15 empirical 15.426532 15.388514 NA
+# 14: 10 1000 100 20 20 empirical 15.468479 15.426808 NA
+# 15: 10 1000 100 20 10 empirical 15.564483 15.536153 NA
+# 16: 10 1000 100 10 2 empirical 16.275958 16.155311 NA
+# 17: 10 1000 100 5 2 empirical 16.520838 16.484130 NA
+# 18: 10 1000 100 20 2 empirical 22.812822 22.733153 NA
+# 19: 10 1000 100 5 1 empirical 32.814998 32.723445 NA
+# 20: 10 1000 100 10 1 empirical 33.740455 33.284869 NA
+# 21: 10 1000 100 10 30 gaussian 42.697496 42.123002 NA
+# 22: 10 1000 100 10 15 gaussian 43.153707 42.400444 NA
+# 23: 10 1000 100 10 20 gaussian 43.331616 42.330915 NA
+# 24: 10 1000 100 10 10 gaussian 43.601197 42.580585 NA
+# 25: 10 1000 100 20 10 gaussian 43.713152 42.444733 NA
+# 26: 10 1000 100 20 1 empirical 44.970672 44.957254 NA
+# 27: 10 1000 100 20 15 gaussian 48.515789 48.364623 NA
+# 28: 10 1000 100 20 30 gaussian 48.980771 48.716296 NA
+# 29: 10 1000 100 20 20 gaussian 49.048357 48.585454 NA
+# 30: 10 1000 100 5 10 gaussian 49.929313 49.906563 NA
+# 31: 10 1000 100 5 5 gaussian 49.952981 49.428697 NA
+# 32: 10 1000 100 20 5 gaussian 49.954880 49.645313 NA
+# 33: 10 1000 100 5 30 gaussian 50.220795 49.894032 NA
+# 34: 10 1000 100 5 20 gaussian 50.480277 50.116526 NA
+# 35: 10 1000 100 5 15 gaussian 50.616905 50.517388 NA
+# 36: 10 1000 100 10 5 gaussian 50.739175 48.893451 NA
+# 37: 10 1000 100 20 20 ctree 79.067415 79.060347 NA
+# 38: 10 1000 100 20 30 ctree 79.178795 78.830831 NA
+# 39: 10 1000 100 20 10 ctree 80.194740 76.259531 NA
+# 40: 10 1000 100 10 20 ctree 84.368049 83.086716 NA
+# 41: 10 1000 100 10 10 ctree 84.583532 84.125999 NA
+# 42: 10 1000 100 20 15 ctree 85.021570 84.921147 NA
+# 43: 10 1000 100 10 30 ctree 86.293475 83.902999 NA
+# 44: 10 1000 100 10 15 ctree 86.549406 85.115549 NA
+# 45: 10 1000 100 20 5 ctree 92.955276 92.538537 NA
+# 46: 10 1000 100 10 5 ctree 94.816191 92.215222 NA
+# 47: 10 1000 100 5 15 ctree 94.846974 94.641546 NA
+# 48: 10 1000 100 10 2 gaussian 95.399388 95.341892 NA
+# 49: 10 1000 100 5 20 ctree 95.887569 95.437676 NA
+# 50: 10 1000 100 5 5 ctree 95.938850 93.705034 NA
+# 51: 10 1000 100 5 30 ctree 96.015618 92.434543 NA
+# 52: 10 1000 100 5 10 ctree 96.238056 94.071784 NA
+# 53: 10 1000 100 20 2 gaussian 96.379812 95.719475 NA
+# 54: 10 1000 100 5 2 gaussian 109.674539 108.807517 NA
+# 55: 10 1000 100 10 1 gaussian 189.596560 188.909395 NA
+# 56: 10 1000 100 5 1 gaussian 191.256527 191.157274 NA
+# 57: 10 1000 100 20 1 gaussian 196.929709 196.358810 NA
+# 58: 10 1000 100 10 2 ctree 200.709682 200.523174 NA
+# 59: 10 1000 100 20 2 ctree 200.942230 200.570071 NA
+# 60: 10 1000 100 5 2 ctree 237.327601 236.488531 NA
+# 61: 10 1000 100 10 1 ctree 395.500767 393.656852 NA
+# 62: 10 1000 100 5 1 ctree 402.635571 401.290227 NA
+# 63: 10 1000 100 20 1 ctree 403.930240 403.903723 NA
+# p n_train n_test n_batches n_cores approach time_median time_min mem_alloc
+
+
+
+
+
diff --git a/inst/scripts/devel/testing_verification_ar_model.R b/inst/scripts/devel/testing_verification_ar_model.R
new file mode 100644
index 000000000..ab5c43d6a
--- /dev/null
+++ b/inst/scripts/devel/testing_verification_ar_model.R
@@ -0,0 +1,38 @@
+library(data.table)
+options(digits = 5) # To avoid round off errors when printing output on different systems
+set.seed(123)
+
+data <- as.data.table(matrix(rnorm(100*3),ncol=3))# first column is noise, the other two are xregs
+
+# Create AR(1)-structure
+y <- rep(0,100)
+y[1] <- data[1,1]/5+data[1,2]+data[1,3]
+for(i in 2:100){
+ y[i] <- y[i-1]+data[i,1]/5+data[i,2]+data[i,3]
+}
+y <- unlist(y)
+plot(y)
+
+dat <- data.table(y=y,xreg1=unlist(data[,2]),xreg2=unlist(data[,3]))
+
+model_arima_temp <- arima(dat$y, c(2,1,0), xreg=dat[,2:3])
+
+
+set.seed(123)
+exp <- explain_forecast(model = model_arima_temp,
+ y = dat$y,
+ xreg = dat[, 2:3],#dat[, 2:3],
+ train_idx = 10:50,
+ explain_idx = 71:72,
+ explain_y_lags = 0,
+ explain_xreg_lags = c(0,0),
+ horizon = 2,
+ approach = "empirical",
+ prediction_zero = c(0,0),
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE,
+ n_combinations = 50
+)
+
+
diff --git a/inst/scripts/devel/timing_log_test_big.csv b/inst/scripts/devel/timing_log_test_big.csv
new file mode 100644
index 000000000..06084d07a
--- /dev/null
+++ b/inst/scripts/devel/timing_log_test_big.csv
@@ -0,0 +1,1476 @@
+p,n_train,n_test,n_batches,n_cores,approach,sys_time_initial,sys_time_start_shapr,sys_time_end_shapr,sys_time_start_explain,sys_time_end_explain,secs_shapr,secs_explain,this_rep,max_n,max_p,rho,sigma,mu_const,beta0,sigma_eps
+8,1000,100,1,1,empirical,2022-01-21 19:45:21,2022-01-21 19:45:22,2022-01-21 19:45:22,2022-01-21 19:45:22,2022-01-21 19:45:28,0.0679423809051514,6.21460437774658,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,1,empirical,2022-01-21 19:45:34,2022-01-21 19:45:34,2022-01-21 19:45:34,2022-01-21 19:45:34,2022-01-21 19:45:41,0.0655725002288818,6.15326380729675,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,1,gaussian,2022-01-21 19:45:46,2022-01-21 19:45:47,2022-01-21 19:45:47,2022-01-21 19:45:47,2022-01-21 19:46:31,0.0679314136505127,43.818482875824,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,1,gaussian,2022-01-21 19:46:37,2022-01-21 19:46:38,2022-01-21 19:46:38,2022-01-21 19:46:38,2022-01-21 19:47:22,0.0672848224639893,44.5887775421143,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,1,ctree,2022-01-21 19:47:28,2022-01-21 19:47:28,2022-01-21 19:47:28,2022-01-21 19:47:29,2022-01-21 19:49:00,0.0699443817138672,91.2768745422363,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,1,ctree,2022-01-21 19:49:06,2022-01-21 19:49:06,2022-01-21 19:49:07,2022-01-21 19:49:07,2022-01-21 19:50:38,0.0691261291503906,91.5022351741791,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,2,empirical,2022-01-21 19:50:44,2022-01-21 19:50:44,2022-01-21 19:50:45,2022-01-21 19:50:45,2022-01-21 19:50:52,0.0679514408111572,6.59367156028748,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,2,empirical,2022-01-21 19:50:58,2022-01-21 19:50:58,2022-01-21 19:50:58,2022-01-21 19:50:59,2022-01-21 19:51:07,0.0681295394897461,8.33681225776672,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,4,empirical,2022-01-21 19:51:29,2022-01-21 19:51:30,2022-01-21 19:51:30,2022-01-21 19:51:31,2022-01-21 19:51:39,0.0709364414215088,7.74393963813782,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,4,empirical,2022-01-21 19:51:36,2022-01-21 19:51:37,2022-01-21 19:51:37,2022-01-21 19:51:37,2022-01-21 19:51:46,0.0693521499633789,9.35610723495483,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,2,gaussian,2022-01-21 19:51:04,2022-01-21 19:51:04,2022-01-21 19:51:05,2022-01-21 19:51:05,2022-01-21 19:51:54,0.0730493068695068,48.3370258808136,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,2,gaussian,2022-01-21 19:51:10,2022-01-21 19:51:11,2022-01-21 19:51:11,2022-01-21 19:51:11,2022-01-21 19:52:01,0.070913553237915,50.250979423523,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,8,empirical,2022-01-21 19:52:09,2022-01-21 19:52:10,2022-01-21 19:52:10,2022-01-21 19:52:11,2022-01-21 19:52:20,0.0875692367553711,8.68195867538452,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,8,empirical,2022-01-21 19:52:16,2022-01-21 19:52:17,2022-01-21 19:52:17,2022-01-21 19:52:17,2022-01-21 19:52:27,0.0852441787719727,9.46109437942505,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,4,gaussian,2022-01-21 19:51:43,2022-01-21 19:51:43,2022-01-21 19:51:43,2022-01-21 19:51:44,2022-01-21 19:52:35,0.10053014755249,51.1583552360535,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,4,gaussian,2022-01-21 19:51:49,2022-01-21 19:51:50,2022-01-21 19:51:50,2022-01-21 19:51:50,2022-01-21 19:52:42,0.0782136917114258,52.3956470489502,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,16,empirical,2022-01-21 19:52:52,2022-01-21 19:52:52,2022-01-21 19:52:52,2022-01-21 19:52:54,2022-01-21 19:53:05,0.0900194644927979,11.7862379550934,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,16,empirical,2022-01-21 19:53:00,2022-01-21 19:53:00,2022-01-21 19:53:00,2022-01-21 19:53:00,2022-01-21 19:53:12,0.103877544403076,11.3551757335663,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,8,gaussian,2022-01-21 19:52:23,2022-01-21 19:52:24,2022-01-21 19:52:24,2022-01-21 19:52:25,2022-01-21 19:53:22,0.0887646675109863,57.4617612361908,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,2,ctree,2022-01-21 19:51:17,2022-01-21 19:51:17,2022-01-21 19:51:17,2022-01-21 19:51:18,2022-01-21 19:53:30,0.0736520290374756,131.975045681,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,8,gaussian,2022-01-21 19:52:30,2022-01-21 19:52:31,2022-01-21 19:52:31,2022-01-21 19:52:31,2022-01-21 19:53:30,0.0811092853546143,59.2656891345978,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,2,ctree,2022-01-21 19:51:23,2022-01-21 19:51:23,2022-01-21 19:51:23,2022-01-21 19:51:24,2022-01-21 19:53:46,0.0772199630737305,142.687088012695,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,24,empirical,2022-01-21 19:53:39,2022-01-21 19:53:40,2022-01-21 19:53:40,2022-01-21 19:53:41,2022-01-21 19:53:53,0.0883660316467285,11.9152135848999,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,24,empirical,2022-01-21 19:53:48,2022-01-21 19:53:49,2022-01-21 19:53:49,2022-01-21 19:53:49,2022-01-21 19:54:00,0.0703213214874268,11.1572158336639,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,16,gaussian,2022-01-21 19:53:08,2022-01-21 19:53:08,2022-01-21 19:53:08,2022-01-21 19:53:10,2022-01-21 19:54:11,0.0837068557739258,61.6379418373108,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,4,ctree,2022-01-21 19:51:56,2022-01-21 19:51:56,2022-01-21 19:51:57,2022-01-21 19:51:57,2022-01-21 19:54:15,0.0905437469482422,138.026141881943,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,16,gaussian,2022-01-21 19:53:16,2022-01-21 19:53:16,2022-01-21 19:53:16,2022-01-21 19:53:16,2022-01-21 19:54:19,0.0814304351806641,62.1634771823883,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,4,ctree,2022-01-21 19:52:03,2022-01-21 19:52:03,2022-01-21 19:52:03,2022-01-21 19:52:03,2022-01-21 19:54:35,0.0948200225830078,152.007485628128,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,32,empirical,2022-01-21 19:54:32,2022-01-21 19:54:33,2022-01-21 19:54:33,2022-01-21 19:54:34,2022-01-21 19:54:48,0.0968301296234131,13.2652621269226,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,32,empirical,2022-01-21 19:54:42,2022-01-21 19:54:43,2022-01-21 19:54:43,2022-01-21 19:54:43,2022-01-21 19:54:54,0.117854833602905,11.0295221805573,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,24,gaussian,2022-01-21 19:53:57,2022-01-21 19:53:57,2022-01-21 19:53:57,2022-01-21 19:53:59,2022-01-21 19:54:57,0.103305101394653,57.8327207565308,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,8,ctree,2022-01-21 19:52:37,2022-01-21 19:52:38,2022-01-21 19:52:38,2022-01-21 19:52:39,2022-01-21 19:55:00,0.0952737331390381,140.792494773865,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,24,gaussian,2022-01-21 19:54:06,2022-01-21 19:54:06,2022-01-21 19:54:06,2022-01-21 19:54:07,2022-01-21 19:55:06,0.105049133300781,59.2937545776367,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,8,ctree,2022-01-21 19:52:44,2022-01-21 19:52:45,2022-01-21 19:52:45,2022-01-21 19:52:45,2022-01-21 19:55:24,0.0825316905975342,158.675955057144,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,1,empirical,2022-01-21 19:55:28,2022-01-21 19:55:29,2022-01-21 19:55:29,2022-01-21 19:55:29,2022-01-21 19:55:44,0.111354351043701,15.2724921703339,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,16,ctree,2022-01-21 19:53:23,2022-01-21 19:53:24,2022-01-21 19:53:24,2022-01-21 19:53:25,2022-01-21 19:55:47,0.114669561386108,141.214350700378,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,32,gaussian,2022-01-21 19:54:52,2022-01-21 19:54:52,2022-01-21 19:54:52,2022-01-21 19:54:54,2022-01-21 19:55:50,0.089949369430542,56.511536359787,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,1,empirical,2022-01-21 19:55:38,2022-01-21 19:55:38,2022-01-21 19:55:38,2022-01-21 19:55:38,2022-01-21 19:55:51,0.112372398376465,12.3302114009857,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,32,gaussian,2022-01-21 19:55:01,2022-01-21 19:55:01,2022-01-21 19:55:01,2022-01-21 19:55:01,2022-01-21 19:55:58,0.07389235496521,56.2764286994934,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,16,ctree,2022-01-21 19:53:32,2022-01-21 19:53:32,2022-01-21 19:53:32,2022-01-21 19:53:32,2022-01-21 19:56:08,0.0753993988037109,155.493281364441,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,24,ctree,2022-01-21 19:54:14,2022-01-21 19:54:15,2022-01-21 19:54:15,2022-01-21 19:54:16,2022-01-21 19:56:21,0.104304552078247,124.821214437485,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,2,empirical,2022-01-21 19:56:21,2022-01-21 19:56:21,2022-01-21 19:56:22,2022-01-21 19:56:22,2022-01-21 19:56:31,0.0804219245910645,8.62554693222046,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,2,empirical,2022-01-21 19:56:29,2022-01-21 19:56:29,2022-01-21 19:56:30,2022-01-21 19:56:30,2022-01-21 19:56:35,0.0955023765563965,5.47262620925903,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,1,gaussian,2022-01-21 19:55:48,2022-01-21 19:55:48,2022-01-21 19:55:48,2022-01-21 19:55:48,2022-01-21 19:56:45,0.0806670188903809,56.8067100048065,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,1,gaussian,2022-01-21 19:55:56,2022-01-21 19:55:57,2022-01-21 19:55:57,2022-01-21 19:55:57,2022-01-21 19:56:54,0.0770695209503174,56.566321849823,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,24,ctree,2022-01-21 19:54:23,2022-01-21 19:54:24,2022-01-21 19:54:24,2022-01-21 19:54:24,2022-01-21 19:56:54,0.0945432186126709,150.246946811676,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,2,gaussian,2022-01-21 19:56:37,2022-01-21 19:56:37,2022-01-21 19:56:37,2022-01-21 19:56:38,2022-01-21 19:57:15,0.0960960388183594,36.7110929489136,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,2,gaussian,2022-01-21 19:56:45,2022-01-21 19:56:45,2022-01-21 19:56:45,2022-01-21 19:56:46,2022-01-21 19:57:24,0.0910992622375488,38.4443778991699,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,4,empirical,2022-01-21 19:57:18,2022-01-21 19:57:19,2022-01-21 19:57:19,2022-01-21 19:57:19,2022-01-21 19:57:25,0.103775978088379,6.2346682548523,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,4,empirical,2022-01-21 19:57:10,2022-01-21 19:57:10,2022-01-21 19:57:10,2022-01-21 19:57:12,2022-01-21 19:57:26,0.109999895095825,14.5601644515991,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,32,ctree,2022-01-21 19:55:18,2022-01-21 19:55:19,2022-01-21 19:55:19,2022-01-21 19:55:19,2022-01-21 19:57:55,0.074582576751709,156.070028543472,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,4,gaussian,2022-01-21 19:57:27,2022-01-21 19:57:27,2022-01-21 19:57:27,2022-01-21 19:57:28,2022-01-21 19:58:10,0.085777759552002,42.0312032699585,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,8,empirical,2022-01-21 19:58:11,2022-01-21 19:58:12,2022-01-21 19:58:12,2022-01-21 19:58:12,2022-01-21 19:58:19,0.165909767150879,6.4951798915863,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,4,gaussian,2022-01-21 19:57:35,2022-01-21 19:57:35,2022-01-21 19:57:35,2022-01-21 19:57:36,2022-01-21 19:58:23,0.0813248157501221,47.1470136642456,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,8,empirical,2022-01-21 19:58:02,2022-01-21 19:58:02,2022-01-21 19:58:02,2022-01-21 19:58:04,2022-01-21 19:58:23,0.108048915863037,18.9510226249695,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,2,ctree,2022-01-21 19:57:01,2022-01-21 19:57:02,2022-01-21 19:57:02,2022-01-21 19:57:02,2022-01-21 19:58:33,0.0978844165802002,90.9949939250946,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,2,ctree,2022-01-21 19:56:53,2022-01-21 19:56:54,2022-01-21 19:56:54,2022-01-21 19:56:55,2022-01-21 19:59:15,0.107846736907959,139.80758357048,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,8,gaussian,2022-01-21 19:58:21,2022-01-21 19:58:21,2022-01-21 19:58:21,2022-01-21 19:58:23,2022-01-21 19:59:17,0.182563066482544,53.5822908878327,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,16,empirical,2022-01-21 19:59:10,2022-01-21 19:59:10,2022-01-21 19:59:11,2022-01-21 19:59:11,2022-01-21 19:59:18,0.102971315383911,7.40506601333618,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,1,32,ctree,2022-01-21 19:55:09,2022-01-21 19:55:10,2022-01-21 19:55:10,2022-01-21 19:55:11,2022-01-21 19:59:23,0.0855953693389893,251.819742918015,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,16,empirical,2022-01-21 19:58:58,2022-01-21 19:58:59,2022-01-21 19:58:59,2022-01-21 19:59:01,2022-01-21 19:59:27,0.185995101928711,25.2913010120392,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,4,ctree,2022-01-21 19:57:52,2022-01-21 19:57:53,2022-01-21 19:57:53,2022-01-21 19:57:53,2022-01-21 19:59:28,0.134212493896484,95.3330371379852,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,8,gaussian,2022-01-21 19:58:30,2022-01-21 19:58:30,2022-01-21 19:58:30,2022-01-21 19:58:31,2022-01-21 19:59:29,0.142381429672241,57.848489522934,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,1,ctree,2022-01-21 19:56:04,2022-01-21 19:56:05,2022-01-21 19:56:05,2022-01-21 19:56:05,2022-01-21 20:00:02,0.0860769748687744,236.681226730347,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,24,empirical,2022-01-21 20:00:06,2022-01-21 20:00:07,2022-01-21 20:00:07,2022-01-21 20:00:08,2022-01-21 20:00:15,0.274724721908569,6.93854188919067,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,16,gaussian,2022-01-21 19:59:20,2022-01-21 19:59:21,2022-01-21 19:59:21,2022-01-21 19:59:23,2022-01-21 20:00:16,0.154306888580322,52.9026510715485,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,1,ctree,2022-01-21 19:56:13,2022-01-21 19:56:13,2022-01-21 19:56:13,2022-01-21 19:56:13,2022-01-21 20:00:20,0.0949101448059082,246.586059093475,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,4,ctree,2022-01-21 19:57:43,2022-01-21 19:57:44,2022-01-21 19:57:44,2022-01-21 19:57:46,2022-01-21 20:00:22,0.135597467422485,155.910915374756,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,24,empirical,2022-01-21 19:59:56,2022-01-21 19:59:57,2022-01-21 19:59:57,2022-01-21 19:59:59,2022-01-21 20:00:24,0.117698669433594,25.2524628639221,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,16,gaussian,2022-01-21 19:59:30,2022-01-21 19:59:30,2022-01-21 19:59:30,2022-01-21 19:59:30,2022-01-21 20:00:26,0.0897903442382813,56.0147714614868,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,8,ctree,2022-01-21 19:58:48,2022-01-21 19:58:49,2022-01-21 19:58:49,2022-01-21 19:58:49,2022-01-21 20:00:28,0.118935823440552,98.2730269432068,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,8,ctree,2022-01-21 19:58:39,2022-01-21 19:58:39,2022-01-21 19:58:39,2022-01-21 19:58:41,2022-01-21 20:00:55,0.163913726806641,134.105771541595,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,24,gaussian,2022-01-21 20:00:17,2022-01-21 20:00:17,2022-01-21 20:00:17,2022-01-21 20:00:20,2022-01-21 20:01:04,0.144383430480957,44.1559345722199,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,32,empirical,2022-01-21 20:01:03,2022-01-21 20:01:04,2022-01-21 20:01:04,2022-01-21 20:01:04,2022-01-21 20:01:10,0.106399059295654,6.23028492927551,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,32,empirical,2022-01-21 20:00:53,2022-01-21 20:00:53,2022-01-21 20:00:53,2022-01-21 20:00:56,2022-01-21 20:01:11,0.112397193908691,14.9856216907501,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,24,gaussian,2022-01-21 20:00:26,2022-01-21 20:00:26,2022-01-21 20:00:26,2022-01-21 20:00:26,2022-01-21 20:01:11,0.0900475978851318,44.6962873935699,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,16,ctree,2022-01-21 19:59:38,2022-01-21 19:59:38,2022-01-21 19:59:39,2022-01-21 19:59:40,2022-01-21 20:01:18,0.0986039638519287,97.7044718265534,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,16,ctree,2022-01-21 19:59:46,2022-01-21 19:59:47,2022-01-21 19:59:47,2022-01-21 19:59:47,2022-01-21 20:01:21,0.131634473800659,93.5434391498566,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,32,gaussian,2022-01-21 20:01:12,2022-01-21 20:01:12,2022-01-21 20:01:12,2022-01-21 20:01:14,2022-01-21 20:01:46,0.0764749050140381,32.3495388031006,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,32,gaussian,2022-01-21 20:01:20,2022-01-21 20:01:21,2022-01-21 20:01:21,2022-01-21 20:01:21,2022-01-21 20:01:54,0.0916368961334229,32.9714848995209,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,24,ctree,2022-01-21 20:00:34,2022-01-21 20:00:35,2022-01-21 20:00:35,2022-01-21 20:00:37,2022-01-21 20:01:56,0.0887670516967773,79.0904116630554,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,1,empirical,2022-01-21 20:01:48,2022-01-21 20:01:48,2022-01-21 20:01:48,2022-01-21 20:01:49,2022-01-21 20:02:03,0.087766170501709,13.9513006210327,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,24,ctree,2022-01-21 20:00:43,2022-01-21 20:00:44,2022-01-21 20:00:44,2022-01-21 20:00:44,2022-01-21 20:02:08,0.11725926399231,83.7143578529358,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,1,empirical,2022-01-21 20:01:56,2022-01-21 20:01:57,2022-01-21 20:01:57,2022-01-21 20:01:57,2022-01-21 20:02:08,0.0754725933074951,11.5400395393372,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,32,ctree,2022-01-21 20:01:28,2022-01-21 20:01:29,2022-01-21 20:01:29,2022-01-21 20:01:31,2022-01-21 20:02:38,0.113322019577026,67.0444369316101,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,2,empirical,2022-01-21 20:02:33,2022-01-21 20:02:33,2022-01-21 20:02:33,2022-01-21 20:02:34,2022-01-21 20:02:45,0.0960612297058105,10.6223297119141,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,2,empirical,2022-01-21 20:02:40,2022-01-21 20:02:40,2022-01-21 20:02:41,2022-01-21 20:02:41,2022-01-21 20:02:47,0.0684645175933838,6.37379550933838,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,2,32,ctree,2022-01-21 20:01:38,2022-01-21 20:01:38,2022-01-21 20:01:39,2022-01-21 20:01:39,2022-01-21 20:02:51,0.0864460468292236,72.778927564621,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,1,gaussian,2022-01-21 20:02:04,2022-01-21 20:02:04,2022-01-21 20:02:04,2022-01-21 20:02:04,2022-01-21 20:03:03,0.0831060409545898,58.4669458866119,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,1,gaussian,2022-01-21 20:02:11,2022-01-21 20:02:11,2022-01-21 20:02:11,2022-01-21 20:02:11,2022-01-21 20:03:11,0.0728819370269775,59.8043076992035,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,4,empirical,2022-01-21 20:03:22,2022-01-21 20:03:23,2022-01-21 20:03:23,2022-01-21 20:03:23,2022-01-21 20:03:28,0.123666048049927,4.63299083709717,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,2,gaussian,2022-01-21 20:02:47,2022-01-21 20:02:48,2022-01-21 20:02:48,2022-01-21 20:02:49,2022-01-21 20:03:29,0.086911678314209,40.0605983734131,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,4,empirical,2022-01-21 20:03:15,2022-01-21 20:03:15,2022-01-21 20:03:15,2022-01-21 20:03:16,2022-01-21 20:03:32,0.112156391143799,15.3883068561554,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,2,gaussian,2022-01-21 20:02:54,2022-01-21 20:02:54,2022-01-21 20:02:55,2022-01-21 20:02:55,2022-01-21 20:03:36,0.0950264930725098,41.5522673130035,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,4,gaussian,2022-01-21 20:03:30,2022-01-21 20:03:30,2022-01-21 20:03:31,2022-01-21 20:03:32,2022-01-21 20:04:05,0.103193998336792,33.7217543125153,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,4,gaussian,2022-01-21 20:03:37,2022-01-21 20:03:38,2022-01-21 20:03:38,2022-01-21 20:03:38,2022-01-21 20:04:14,0.0797321796417236,35.9324431419373,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,8,empirical,2022-01-21 20:04:10,2022-01-21 20:04:11,2022-01-21 20:04:11,2022-01-21 20:04:11,2022-01-21 20:04:16,0.200104236602783,4.6961669921875,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,8,empirical,2022-01-21 20:04:02,2022-01-21 20:04:02,2022-01-21 20:04:02,2022-01-21 20:04:05,2022-01-21 20:04:22,0.184930324554443,17.4330916404724,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,4,ctree,2022-01-21 20:03:53,2022-01-21 20:03:54,2022-01-21 20:03:54,2022-01-21 20:03:55,2022-01-21 20:05:08,0.243716478347778,73.4328377246857,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,2,ctree,2022-01-21 20:03:08,2022-01-21 20:03:08,2022-01-21 20:03:08,2022-01-21 20:03:08,2022-01-21 20:05:09,0.1093909740448,120.792954444885,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,8,gaussian,2022-01-21 20:04:19,2022-01-21 20:04:19,2022-01-21 20:04:19,2022-01-21 20:04:21,2022-01-21 20:05:14,0.154352188110352,53.084475517273,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,16,empirical,2022-01-21 20:05:10,2022-01-21 20:05:11,2022-01-21 20:05:11,2022-01-21 20:05:12,2022-01-21 20:05:18,0.20582103729248,6.22601389884949,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,8,gaussian,2022-01-21 20:04:27,2022-01-21 20:04:28,2022-01-21 20:04:28,2022-01-21 20:04:28,2022-01-21 20:05:23,0.157943964004517,55.0574610233307,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,16,empirical,2022-01-21 20:04:58,2022-01-21 20:05:00,2022-01-21 20:05:00,2022-01-21 20:05:03,2022-01-21 20:05:29,0.304372310638428,26.4898273944855,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,4,ctree,2022-01-21 20:03:45,2022-01-21 20:03:46,2022-01-21 20:03:46,2022-01-21 20:03:48,2022-01-21 20:05:47,0.153909206390381,119.656640529633,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,8,ctree,2022-01-21 20:04:46,2022-01-21 20:04:48,2022-01-21 20:04:48,2022-01-21 20:04:48,2022-01-21 20:06:10,0.1712806224823,81.9681816101074,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,16,gaussian,2022-01-21 20:05:20,2022-01-21 20:05:21,2022-01-21 20:05:21,2022-01-21 20:05:23,2022-01-21 20:06:19,0.186304330825806,55.6254382133484,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,24,empirical,2022-01-21 20:06:16,2022-01-21 20:06:17,2022-01-21 20:06:17,2022-01-21 20:06:18,2022-01-21 20:06:24,0.140614032745361,6.13333296775818,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,16,gaussian,2022-01-21 20:05:30,2022-01-21 20:05:31,2022-01-21 20:05:31,2022-01-21 20:05:31,2022-01-21 20:06:27,0.136568784713745,56.1016058921814,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,24,empirical,2022-01-21 20:06:05,2022-01-21 20:06:06,2022-01-21 20:06:06,2022-01-21 20:06:09,2022-01-21 20:06:33,0.163660526275635,24.5170965194702,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,2,ctree,2022-01-21 20:03:01,2022-01-21 20:03:01,2022-01-21 20:03:01,2022-01-21 20:03:03,2022-01-21 20:06:35,0.106505870819092,212.415378093719,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,8,ctree,2022-01-21 20:04:36,2022-01-21 20:04:37,2022-01-21 20:04:37,2022-01-21 20:04:39,2022-01-21 20:06:47,0.122185945510864,128.019405126572,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,16,ctree,2022-01-21 20:05:52,2022-01-21 20:05:53,2022-01-21 20:05:53,2022-01-21 20:05:53,2022-01-21 20:07:10,0.171620845794678,76.418954372406,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,24,gaussian,2022-01-21 20:06:27,2022-01-21 20:06:28,2022-01-21 20:06:28,2022-01-21 20:06:30,2022-01-21 20:07:15,0.0862655639648438,45.2793593406677,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,24,gaussian,2022-01-21 20:06:37,2022-01-21 20:06:38,2022-01-21 20:06:38,2022-01-21 20:06:38,2022-01-21 20:07:23,0.137756824493408,44.3077807426453,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,32,empirical,2022-01-21 20:07:20,2022-01-21 20:07:21,2022-01-21 20:07:21,2022-01-21 20:07:22,2022-01-21 20:07:26,0.137405157089233,4.74100375175476,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,32,empirical,2022-01-21 20:07:10,2022-01-21 20:07:10,2022-01-21 20:07:11,2022-01-21 20:07:14,2022-01-21 20:07:32,0.131222009658813,18.7855203151703,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,1,ctree,2022-01-21 20:02:18,2022-01-21 20:02:18,2022-01-21 20:02:18,2022-01-21 20:02:18,2022-01-21 20:07:36,0.075833797454834,317.720438480377,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,16,ctree,2022-01-21 20:05:41,2022-01-21 20:05:42,2022-01-21 20:05:42,2022-01-21 20:05:45,2022-01-21 20:07:39,0.272588491439819,114.103365421295,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,24,ctree,2022-01-21 20:06:59,2022-01-21 20:06:59,2022-01-21 20:07:00,2022-01-21 20:07:00,2022-01-21 20:08:04,0.128761529922485,64.2611443996429,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,32,gaussian,2022-01-21 20:07:31,2022-01-21 20:07:32,2022-01-21 20:07:32,2022-01-21 20:07:34,2022-01-21 20:08:10,0.108507633209229,36.2858769893646,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,1,ctree,2022-01-21 20:02:25,2022-01-21 20:02:26,2022-01-21 20:02:26,2022-01-21 20:02:26,2022-01-21 20:08:14,0.124702453613281,348.355218172073,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,32,gaussian,2022-01-21 20:07:41,2022-01-21 20:07:42,2022-01-21 20:07:42,2022-01-21 20:07:42,2022-01-21 20:08:18,0.160996675491333,36.1614592075348,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,24,ctree,2022-01-21 20:06:48,2022-01-21 20:06:49,2022-01-21 20:06:49,2022-01-21 20:06:51,2022-01-21 20:08:23,0.175556421279907,91.6496593952179,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,1,empirical,2022-01-21 20:08:14,2022-01-21 20:08:15,2022-01-21 20:08:15,2022-01-21 20:08:15,2022-01-21 20:08:50,0.0930414199829102,34.4433264732361,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,32,ctree,2022-01-21 20:08:04,2022-01-21 20:08:05,2022-01-21 20:08:05,2022-01-21 20:08:05,2022-01-21 20:08:52,0.13874340057373,46.63427734375,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,4,32,ctree,2022-01-21 20:07:50,2022-01-21 20:07:51,2022-01-21 20:07:51,2022-01-21 20:07:54,2022-01-21 20:08:53,0.141202926635742,58.4276340007782,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,1,empirical,2022-01-21 20:08:23,2022-01-21 20:08:23,2022-01-21 20:08:24,2022-01-21 20:08:24,2022-01-21 20:08:54,0.108072280883789,30.4114615917206,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,2,empirical,2022-01-21 20:08:59,2022-01-21 20:09:00,2022-01-21 20:09:00,2022-01-21 20:09:01,2022-01-21 20:09:13,0.0744998455047607,11.7878661155701,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,2,empirical,2022-01-21 20:09:06,2022-01-21 20:09:06,2022-01-21 20:09:06,2022-01-21 20:09:07,2022-01-21 20:09:15,0.107386827468872,8.36253333091736,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,1,gaussian,2022-01-21 20:08:30,2022-01-21 20:08:31,2022-01-21 20:08:31,2022-01-21 20:08:31,2022-01-21 20:09:35,0.108848571777344,63.6460950374603,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,1,gaussian,2022-01-21 20:08:38,2022-01-21 20:08:39,2022-01-21 20:08:39,2022-01-21 20:08:39,2022-01-21 20:09:45,0.0947568416595459,66.0720264911652,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,4,empirical,2022-01-21 20:09:47,2022-01-21 20:09:48,2022-01-21 20:09:48,2022-01-21 20:09:48,2022-01-21 20:09:54,0.200871467590332,5.78398990631104,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,2,gaussian,2022-01-21 20:09:12,2022-01-21 20:09:13,2022-01-21 20:09:13,2022-01-21 20:09:14,2022-01-21 20:09:59,0.102152824401855,44.7574014663696,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,4,empirical,2022-01-21 20:09:40,2022-01-21 20:09:41,2022-01-21 20:09:41,2022-01-21 20:09:42,2022-01-21 20:10:13,0.1309974193573,31.1193315982819,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,2,gaussian,2022-01-21 20:09:19,2022-01-21 20:09:20,2022-01-21 20:09:20,2022-01-21 20:09:20,2022-01-21 20:10:14,0.0811419486999512,54.2267096042633,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,4,gaussian,2022-01-21 20:09:55,2022-01-21 20:09:56,2022-01-21 20:09:56,2022-01-21 20:09:58,2022-01-21 20:10:40,0.139014482498169,42.466468334198,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,8,empirical,2022-01-21 20:10:38,2022-01-21 20:10:39,2022-01-21 20:10:39,2022-01-21 20:10:40,2022-01-21 20:10:46,0.177822113037109,6.01802349090576,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,4,gaussian,2022-01-21 20:10:03,2022-01-21 20:10:03,2022-01-21 20:10:04,2022-01-21 20:10:04,2022-01-21 20:10:52,0.122958183288574,48.4998507499695,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,8,empirical,2022-01-21 20:10:28,2022-01-21 20:10:29,2022-01-21 20:10:29,2022-01-21 20:10:32,2022-01-21 20:10:58,0.110329151153564,26.5889523029327,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,8,gaussian,2022-01-21 20:10:48,2022-01-21 20:10:49,2022-01-21 20:10:49,2022-01-21 20:10:51,2022-01-21 20:11:36,0.209257364273071,44.82279753685,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,8,gaussian,2022-01-21 20:10:57,2022-01-21 20:10:59,2022-01-21 20:10:59,2022-01-21 20:10:59,2022-01-21 20:11:43,0.266931772232056,43.4305779933929,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,16,empirical,2022-01-21 20:11:44,2022-01-21 20:11:45,2022-01-21 20:11:45,2022-01-21 20:11:46,2022-01-21 20:11:51,0.197437047958374,5.71662092208862,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,4,ctree,2022-01-21 20:10:19,2022-01-21 20:10:20,2022-01-21 20:10:20,2022-01-21 20:10:21,2022-01-21 20:11:55,0.122449398040771,93.899866104126,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,2,ctree,2022-01-21 20:09:33,2022-01-21 20:09:34,2022-01-21 20:09:34,2022-01-21 20:09:34,2022-01-21 20:12:06,0.130840539932251,151.946017503738,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,16,empirical,2022-01-21 20:11:31,2022-01-21 20:11:32,2022-01-21 20:11:32,2022-01-21 20:11:35,2022-01-21 20:12:07,0.197721004486084,31.7347056865692,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,8,ctree,2022-01-21 20:11:18,2022-01-21 20:11:21,2022-01-21 20:11:22,2022-01-21 20:11:22,2022-01-21 20:12:35,0.409997463226318,73.1354198455811,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,4,ctree,2022-01-21 20:10:12,2022-01-21 20:10:13,2022-01-21 20:10:13,2022-01-21 20:10:15,2022-01-21 20:12:53,0.126886606216431,158.704616069794,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,8,ctree,2022-01-21 20:11:07,2022-01-21 20:11:09,2022-01-21 20:11:09,2022-01-21 20:11:13,2022-01-21 20:12:56,0.493088722229004,103.292267084122,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,16,gaussian,2022-01-21 20:12:08,2022-01-21 20:12:09,2022-01-21 20:12:10,2022-01-21 20:12:10,2022-01-21 20:12:59,0.20184850692749,48.7815515995026,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,16,gaussian,2022-01-21 20:11:56,2022-01-21 20:11:57,2022-01-21 20:11:57,2022-01-21 20:12:00,2022-01-21 20:12:59,0.195953607559204,59.3093349933624,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,24,empirical,2022-01-21 20:13:05,2022-01-21 20:13:06,2022-01-21 20:13:06,2022-01-21 20:13:06,2022-01-21 20:13:11,0.167906761169434,5.21187901496887,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,24,empirical,2022-01-21 20:12:55,2022-01-21 20:12:56,2022-01-21 20:12:56,2022-01-21 20:12:59,2022-01-21 20:13:25,0.145366907119751,26.580539226532,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,16,ctree,2022-01-21 20:12:38,2022-01-21 20:12:40,2022-01-21 20:12:41,2022-01-21 20:12:42,2022-01-21 20:13:34,0.337445497512817,52.4844591617584,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,2,ctree,2022-01-21 20:09:26,2022-01-21 20:09:26,2022-01-21 20:09:27,2022-01-21 20:09:28,2022-01-21 20:13:54,0.176681756973267,265.966460704804,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,24,gaussian,2022-01-21 20:13:16,2022-01-21 20:13:17,2022-01-21 20:13:17,2022-01-21 20:13:20,2022-01-21 20:14:01,0.249060869216919,40.5253417491913,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,16,ctree,2022-01-21 20:12:20,2022-01-21 20:12:21,2022-01-21 20:12:22,2022-01-21 20:12:26,2022-01-21 20:14:02,0.254679679870605,95.8836102485657,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,24,gaussian,2022-01-21 20:13:27,2022-01-21 20:13:29,2022-01-21 20:13:29,2022-01-21 20:13:30,2022-01-21 20:14:07,0.472470760345459,37.0459926128387,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,32,empirical,2022-01-21 20:14:14,2022-01-21 20:14:15,2022-01-21 20:14:15,2022-01-21 20:14:15,2022-01-21 20:14:19,0.118376016616821,4.12146234512329,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,32,empirical,2022-01-21 20:14:05,2022-01-21 20:14:06,2022-01-21 20:14:06,2022-01-21 20:14:08,2022-01-21 20:14:33,0.114286184310913,24.9186100959778,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,24,ctree,2022-01-21 20:13:53,2022-01-21 20:13:54,2022-01-21 20:13:55,2022-01-21 20:13:55,2022-01-21 20:14:37,0.266960620880127,42.1022305488587,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,24,ctree,2022-01-21 20:13:38,2022-01-21 20:13:40,2022-01-21 20:13:40,2022-01-21 20:13:44,2022-01-21 20:15:03,0.168293476104736,79.4657056331635,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,32,gaussian,2022-01-21 20:14:25,2022-01-21 20:14:26,2022-01-21 20:14:26,2022-01-21 20:14:29,2022-01-21 20:15:07,0.23208475112915,37.8818883895874,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,32,gaussian,2022-01-21 20:14:35,2022-01-21 20:14:36,2022-01-21 20:14:36,2022-01-21 20:14:36,2022-01-21 20:15:11,0.195303440093994,34.1426935195923,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,1,ctree,2022-01-21 20:08:46,2022-01-21 20:08:47,2022-01-21 20:08:47,2022-01-21 20:08:47,2022-01-21 20:15:25,0.0852367877960205,398.283234596252,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,32,ctree,2022-01-21 20:15:04,2022-01-21 20:15:05,2022-01-21 20:15:05,2022-01-21 20:15:06,2022-01-21 20:15:35,0.212375164031982,28.9642522335053,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,1,ctree,2022-01-21 20:08:53,2022-01-21 20:08:54,2022-01-21 20:08:54,2022-01-21 20:08:54,2022-01-21 20:15:42,0.0759241580963135,408.416339635849,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,8,32,ctree,2022-01-21 20:14:49,2022-01-21 20:14:50,2022-01-21 20:14:51,2022-01-21 20:14:54,2022-01-21 20:15:43,0.282668352127075,49.0856580734253,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,1,empirical,2022-01-21 20:15:13,2022-01-21 20:15:14,2022-01-21 20:15:14,2022-01-21 20:15:15,2022-01-21 20:16:11,0.0885732173919678,56.0222647190094,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,2,empirical,2022-01-21 20:16:06,2022-01-21 20:16:06,2022-01-21 20:16:06,2022-01-21 20:16:06,2022-01-21 20:16:17,0.100633382797241,11.0728497505188,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,1,empirical,2022-01-21 20:15:22,2022-01-21 20:15:22,2022-01-21 20:15:23,2022-01-21 20:15:23,2022-01-21 20:16:19,0.114385604858398,56.4815094470978,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,1,gaussian,2022-01-21 20:15:31,2022-01-21 20:15:31,2022-01-21 20:15:31,2022-01-21 20:15:32,2022-01-21 20:16:56,0.225283861160278,84.2539775371552,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,4,empirical,2022-01-21 20:16:49,2022-01-21 20:16:50,2022-01-21 20:16:51,2022-01-21 20:16:51,2022-01-21 20:17:01,0.270938873291016,10.253130197525,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,2,empirical,2022-01-21 20:15:59,2022-01-21 20:15:59,2022-01-21 20:15:59,2022-01-21 20:16:00,2022-01-21 20:17:11,0.0830924510955811,70.4707696437836,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,1,gaussian,2022-01-21 20:15:39,2022-01-21 20:15:40,2022-01-21 20:15:40,2022-01-21 20:15:40,2022-01-21 20:17:16,0.115403890609741,95.780791759491,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,2,gaussian,2022-01-21 20:16:12,2022-01-21 20:16:13,2022-01-21 20:16:13,2022-01-21 20:16:14,2022-01-21 20:17:27,0.0792553424835205,72.6659426689148,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,2,gaussian,2022-01-21 20:16:19,2022-01-21 20:16:20,2022-01-21 20:16:20,2022-01-21 20:16:20,2022-01-21 20:17:35,0.111799955368042,75.1000399589539,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,4,empirical,2022-01-21 20:16:41,2022-01-21 20:16:42,2022-01-21 20:16:42,2022-01-21 20:16:44,2022-01-21 20:17:55,0.140696287155151,70.410936832428,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,8,empirical,2022-01-21 20:17:48,2022-01-21 20:17:50,2022-01-21 20:17:51,2022-01-21 20:17:52,2022-01-21 20:18:03,0.463979005813599,11.2148358821869,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,4,gaussian,2022-01-21 20:16:58,2022-01-21 20:16:59,2022-01-21 20:16:59,2022-01-21 20:17:01,2022-01-21 20:18:13,0.274870872497559,71.7192182540894,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,4,gaussian,2022-01-21 20:17:06,2022-01-21 20:17:07,2022-01-21 20:17:07,2022-01-21 20:17:08,2022-01-21 20:18:23,0.174767732620239,74.9610559940338,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,8,empirical,2022-01-21 20:17:36,2022-01-21 20:17:37,2022-01-21 20:17:37,2022-01-21 20:17:39,2022-01-21 20:18:25,0.16484522819519,45.1673722267151,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,8,gaussian,2022-01-21 20:17:59,2022-01-21 20:18:02,2022-01-21 20:18:02,2022-01-21 20:18:05,2022-01-21 20:18:57,0.233659982681274,51.3522706031799,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,8,gaussian,2022-01-21 20:18:12,2022-01-21 20:18:13,2022-01-21 20:18:13,2022-01-21 20:18:13,2022-01-21 20:18:57,0.237942457199097,43.4788687229157,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,16,empirical,2022-01-21 20:19:05,2022-01-21 20:19:08,2022-01-21 20:19:09,2022-01-21 20:19:09,2022-01-21 20:19:19,0.34415602684021,9.64531254768372,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,16,empirical,2022-01-21 20:18:52,2022-01-21 20:18:53,2022-01-21 20:18:53,2022-01-21 20:18:56,2022-01-21 20:19:34,0.140913724899292,38.2413604259491,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,4,ctree,2022-01-21 20:17:26,2022-01-21 20:17:27,2022-01-21 20:17:27,2022-01-21 20:17:28,2022-01-21 20:19:55,0.194398641586304,146.996423244476,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,8,ctree,2022-01-21 20:18:38,2022-01-21 20:18:40,2022-01-21 20:18:40,2022-01-21 20:18:40,2022-01-21 20:20:06,0.169980049133301,86.1369121074677,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,16,gaussian,2022-01-21 20:19:35,2022-01-21 20:19:37,2022-01-21 20:19:37,2022-01-21 20:19:37,2022-01-21 20:20:13,0.225265979766846,35.429098367691,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,16,gaussian,2022-01-21 20:19:20,2022-01-21 20:19:22,2022-01-21 20:19:22,2022-01-21 20:19:27,2022-01-21 20:20:14,0.417241334915161,47.14062333107,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,2,ctree,2022-01-21 20:16:34,2022-01-21 20:16:34,2022-01-21 20:16:34,2022-01-21 20:16:35,2022-01-21 20:20:17,0.10867977142334,221.777212619782,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,4,ctree,2022-01-21 20:17:16,2022-01-21 20:17:16,2022-01-21 20:17:16,2022-01-21 20:17:19,2022-01-21 20:20:35,0.153706073760986,195.783322572708,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,8,ctree,2022-01-21 20:18:27,2022-01-21 20:18:28,2022-01-21 20:18:29,2022-01-21 20:18:32,2022-01-21 20:20:39,0.162006139755249,127.041258811951,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,24,empirical,2022-01-21 20:20:47,2022-01-21 20:20:49,2022-01-21 20:20:49,2022-01-21 20:20:49,2022-01-21 20:21:00,0.332635164260864,10.2823584079742,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,16,ctree,2022-01-21 20:20:13,2022-01-21 20:20:14,2022-01-21 20:20:15,2022-01-21 20:20:15,2022-01-21 20:21:06,0.219719171524048,50.7119431495667,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,16,ctree,2022-01-21 20:19:53,2022-01-21 20:19:55,2022-01-21 20:19:56,2022-01-21 20:20:02,2022-01-21 20:21:09,0.451148986816406,66.7785174846649,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,24,empirical,2022-01-21 20:20:28,2022-01-21 20:20:30,2022-01-21 20:20:30,2022-01-21 20:20:34,2022-01-21 20:21:10,0.212979555130005,36.5365943908691,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,24,gaussian,2022-01-21 20:21:04,2022-01-21 20:21:05,2022-01-21 20:21:05,2022-01-21 20:21:07,2022-01-21 20:21:35,0.169111251831055,27.5509021282196,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,24,gaussian,2022-01-21 20:21:14,2022-01-21 20:21:15,2022-01-21 20:21:16,2022-01-21 20:21:16,2022-01-21 20:21:38,0.256148815155029,22.0233566761017,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,2,ctree,2022-01-21 20:16:26,2022-01-21 20:16:27,2022-01-21 20:16:27,2022-01-21 20:16:29,2022-01-21 20:22:01,0.124751806259155,331.955757856369,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,32,empirical,2022-01-21 20:22:01,2022-01-21 20:22:02,2022-01-21 20:22:03,2022-01-21 20:22:03,2022-01-21 20:22:14,0.28948450088501,11.0029306411743,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,24,ctree,2022-01-21 20:21:38,2022-01-21 20:21:39,2022-01-21 20:21:39,2022-01-21 20:21:39,2022-01-21 20:22:16,0.0951733589172363,37.3643593788147,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,32,empirical,2022-01-21 20:21:48,2022-01-21 20:21:48,2022-01-21 20:21:48,2022-01-21 20:21:52,2022-01-21 20:22:30,0.141396999359131,37.8950510025024,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,24,ctree,2022-01-21 20:21:27,2022-01-21 20:21:29,2022-01-21 20:21:29,2022-01-21 20:21:33,2022-01-21 20:22:31,0.302100419998169,57.6968989372254,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,32,gaussian,2022-01-21 20:22:29,2022-01-21 20:22:31,2022-01-21 20:22:31,2022-01-21 20:22:31,2022-01-21 20:22:53,0.175512075424194,22.0318653583527,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,32,gaussian,2022-01-21 20:22:17,2022-01-21 20:22:19,2022-01-21 20:22:19,2022-01-21 20:22:23,2022-01-21 20:22:53,0.284876585006714,30.6179020404816,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,32,ctree,2022-01-21 20:22:55,2022-01-21 20:22:56,2022-01-21 20:22:56,2022-01-21 20:22:56,2022-01-21 20:23:23,0.0971512794494629,26.5797426700592,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,32,ctree,2022-01-21 20:22:45,2022-01-21 20:22:47,2022-01-21 20:22:47,2022-01-21 20:22:51,2022-01-21 20:23:32,0.353890895843506,40.7921936511993,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,1,ctree,2022-01-21 20:15:46,2022-01-21 20:15:46,2022-01-21 20:15:47,2022-01-21 20:15:47,2022-01-21 20:23:42,0.0768370628356934,475.078960418701,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,16,1,ctree,2022-01-21 20:15:52,2022-01-21 20:15:53,2022-01-21 20:15:53,2022-01-21 20:15:53,2022-01-21 20:23:49,0.0899558067321777,476.388217449188,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,1,empirical,2022-01-21 20:23:05,2022-01-21 20:23:06,2022-01-21 20:23:06,2022-01-21 20:23:06,2022-01-21 20:24:08,0.192572832107544,61.8940536975861,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,1,empirical,2022-01-21 20:23:15,2022-01-21 20:23:16,2022-01-21 20:23:16,2022-01-21 20:23:17,2022-01-21 20:24:14,0.228681802749634,57.8439428806305,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,2,empirical,2022-01-21 20:23:59,2022-01-21 20:23:59,2022-01-21 20:23:59,2022-01-21 20:24:00,2022-01-21 20:24:15,0.112568616867065,14.9745237827301,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,4,empirical,2022-01-21 20:24:42,2022-01-21 20:24:43,2022-01-21 20:24:43,2022-01-21 20:24:43,2022-01-21 20:24:56,0.10149884223938,12.6714670658112,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,1,gaussian,2022-01-21 20:23:25,2022-01-21 20:23:26,2022-01-21 20:23:26,2022-01-21 20:23:26,2022-01-21 20:25:09,0.154900312423706,102.915100097656,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,2,empirical,2022-01-21 20:23:52,2022-01-21 20:23:52,2022-01-21 20:23:53,2022-01-21 20:23:54,2022-01-21 20:25:12,0.076507568359375,78.3995487689972,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,1,gaussian,2022-01-21 20:23:33,2022-01-21 20:23:33,2022-01-21 20:23:33,2022-01-21 20:23:33,2022-01-21 20:25:25,0.0845088958740234,111.871523618698,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,2,gaussian,2022-01-21 20:24:06,2022-01-21 20:24:06,2022-01-21 20:24:06,2022-01-21 20:24:07,2022-01-21 20:25:28,0.132264375686646,80.6459562778473,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,2,gaussian,2022-01-21 20:24:13,2022-01-21 20:24:13,2022-01-21 20:24:13,2022-01-21 20:24:13,2022-01-21 20:25:33,0.0865104198455811,79.1543364524841,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,4,empirical,2022-01-21 20:24:34,2022-01-21 20:24:35,2022-01-21 20:24:35,2022-01-21 20:24:37,2022-01-21 20:25:59,0.130098819732666,82.2518985271454,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,8,empirical,2022-01-21 20:25:42,2022-01-21 20:25:44,2022-01-21 20:25:44,2022-01-21 20:25:44,2022-01-21 20:26:01,0.45146656036377,16.7653634548187,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,4,gaussian,2022-01-21 20:24:51,2022-01-21 20:24:52,2022-01-21 20:24:52,2022-01-21 20:24:54,2022-01-21 20:26:16,0.13486385345459,82.1180536746979,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,4,gaussian,2022-01-21 20:25:01,2022-01-21 20:25:02,2022-01-21 20:25:02,2022-01-21 20:25:02,2022-01-21 20:26:22,0.141163349151611,79.802610874176,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,8,empirical,2022-01-21 20:25:31,2022-01-21 20:25:32,2022-01-21 20:25:32,2022-01-21 20:25:35,2022-01-21 20:26:28,0.152444362640381,53.02769947052,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,8,gaussian,2022-01-21 20:25:56,2022-01-21 20:25:57,2022-01-21 20:25:57,2022-01-21 20:26:00,2022-01-21 20:26:50,0.130305051803589,50.0632665157318,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,8,gaussian,2022-01-21 20:26:11,2022-01-21 20:26:12,2022-01-21 20:26:13,2022-01-21 20:26:13,2022-01-21 20:26:59,0.386332273483276,45.6640758514404,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,16,empirical,2022-01-21 20:27:00,2022-01-21 20:27:02,2022-01-21 20:27:02,2022-01-21 20:27:03,2022-01-21 20:27:17,0.379444122314453,13.8426496982574,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,16,empirical,2022-01-21 20:26:49,2022-01-21 20:26:51,2022-01-21 20:26:51,2022-01-21 20:26:55,2022-01-21 20:27:49,0.237590789794922,54.0743391513825,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,4,ctree,2022-01-21 20:25:21,2022-01-21 20:25:22,2022-01-21 20:25:22,2022-01-21 20:25:23,2022-01-21 20:28:02,0.198917865753174,158.905059814453,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,16,gaussian,2022-01-21 20:27:30,2022-01-21 20:27:31,2022-01-21 20:27:31,2022-01-21 20:27:32,2022-01-21 20:28:16,0.469269275665283,43.8945298194885,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,16,gaussian,2022-01-21 20:27:15,2022-01-21 20:27:16,2022-01-21 20:27:16,2022-01-21 20:27:20,2022-01-21 20:28:21,0.30811882019043,60.9312980175018,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,8,ctree,2022-01-21 20:26:36,2022-01-21 20:26:38,2022-01-21 20:26:38,2022-01-21 20:26:39,2022-01-21 20:28:22,0.306416749954224,102.996356248856,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,8,ctree,2022-01-21 20:26:24,2022-01-21 20:26:25,2022-01-21 20:26:25,2022-01-21 20:26:28,2022-01-21 20:28:41,0.205635786056519,133.184597730637,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,4,ctree,2022-01-21 20:25:11,2022-01-21 20:25:12,2022-01-21 20:25:12,2022-01-21 20:25:15,2022-01-21 20:28:49,0.212130546569824,214.058547735214,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,2,ctree,2022-01-21 20:24:27,2022-01-21 20:24:27,2022-01-21 20:24:28,2022-01-21 20:24:28,2022-01-21 20:28:49,0.146969556808472,261.301711797714,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,24,empirical,2022-01-21 20:28:42,2022-01-21 20:28:43,2022-01-21 20:28:44,2022-01-21 20:28:44,2022-01-21 20:28:56,0.360613346099854,11.7977015972137,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,16,ctree,2022-01-21 20:28:12,2022-01-21 20:28:13,2022-01-21 20:28:13,2022-01-21 20:28:13,2022-01-21 20:29:08,0.172926187515259,54.6883406639099,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,24,empirical,2022-01-21 20:28:26,2022-01-21 20:28:28,2022-01-21 20:28:28,2022-01-21 20:28:32,2022-01-21 20:29:09,0.31151819229126,37.6733129024506,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,16,ctree,2022-01-21 20:27:54,2022-01-21 20:27:56,2022-01-21 20:27:57,2022-01-21 20:28:01,2022-01-21 20:29:27,0.406774282455444,86.008882522583,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,24,gaussian,2022-01-21 20:29:10,2022-01-21 20:29:10,2022-01-21 20:29:11,2022-01-21 20:29:11,2022-01-21 20:29:36,0.143285512924194,25.2861435413361,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,24,gaussian,2022-01-21 20:28:59,2022-01-21 20:29:00,2022-01-21 20:29:00,2022-01-21 20:29:03,2022-01-21 20:29:40,0.283975124359131,37.498925447464,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,32,empirical,2022-01-21 20:30:08,2022-01-21 20:30:11,2022-01-21 20:30:11,2022-01-21 20:30:12,2022-01-21 20:30:22,0.352094173431397,10.4392850399017,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,24,ctree,2022-01-21 20:29:41,2022-01-21 20:29:42,2022-01-21 20:29:42,2022-01-21 20:29:42,2022-01-21 20:30:24,0.105332374572754,41.9674677848816,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,2,ctree,2022-01-21 20:24:20,2022-01-21 20:24:20,2022-01-21 20:24:20,2022-01-21 20:24:22,2022-01-21 20:30:30,0.12893557548523,368.428048372269,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,24,ctree,2022-01-21 20:29:29,2022-01-21 20:29:32,2022-01-21 20:29:32,2022-01-21 20:29:37,2022-01-21 20:30:34,0.364339113235474,57.5975983142853,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,32,empirical,2022-01-21 20:29:52,2022-01-21 20:29:53,2022-01-21 20:29:54,2022-01-21 20:29:58,2022-01-21 20:30:38,0.241101264953613,39.8892107009888,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,32,gaussian,2022-01-21 20:30:37,2022-01-21 20:30:38,2022-01-21 20:30:38,2022-01-21 20:30:39,2022-01-21 20:31:00,0.0927791595458984,21.6142842769623,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,32,gaussian,2022-01-21 20:30:25,2022-01-21 20:30:26,2022-01-21 20:30:26,2022-01-21 20:30:30,2022-01-21 20:31:02,0.417242050170898,31.8366224765778,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,32,ctree,2022-01-21 20:31:04,2022-01-21 20:31:04,2022-01-21 20:31:04,2022-01-21 20:31:04,2022-01-21 20:31:34,0.0832962989807129,29.8868131637573,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,32,ctree,2022-01-21 20:30:53,2022-01-21 20:30:56,2022-01-21 20:30:56,2022-01-21 20:31:00,2022-01-21 20:31:43,0.288794040679932,42.1808621883392,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,1,ctree,2022-01-21 20:23:39,2022-01-21 20:23:39,2022-01-21 20:23:39,2022-01-21 20:23:40,2022-01-21 20:32:00,0.0756382942199707,500.141303777695,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,24,1,ctree,2022-01-21 20:23:46,2022-01-21 20:23:46,2022-01-21 20:23:46,2022-01-21 20:23:46,2022-01-21 20:32:06,0.0930509567260742,499.362683057785,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,1,empirical,2022-01-21 20:31:14,2022-01-21 20:31:16,2022-01-21 20:31:16,2022-01-21 20:31:17,2022-01-21 20:32:25,0.247167825698853,67.5796875953674,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,1,empirical,2022-01-21 20:31:29,2022-01-21 20:31:30,2022-01-21 20:31:30,2022-01-21 20:31:30,2022-01-21 20:32:29,0.213907241821289,59.1958515644074,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,2,empirical,2022-01-21 20:32:12,2022-01-21 20:32:12,2022-01-21 20:32:12,2022-01-21 20:32:13,2022-01-21 20:32:30,0.0833723545074463,17.8198385238647,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,4,empirical,2022-01-21 20:32:55,2022-01-21 20:32:56,2022-01-21 20:32:56,2022-01-21 20:32:56,2022-01-21 20:33:10,0.134512662887573,13.8006076812744,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,1,gaussian,2022-01-21 20:31:38,2022-01-21 20:31:38,2022-01-21 20:31:39,2022-01-21 20:31:39,2022-01-21 20:33:23,0.181211709976196,104.488753557205,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,2,empirical,2022-01-21 20:32:05,2022-01-21 20:32:06,2022-01-21 20:32:06,2022-01-21 20:32:07,2022-01-21 20:33:29,0.0951323509216309,82.8130259513855,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,2,gaussian,2022-01-21 20:32:19,2022-01-21 20:32:19,2022-01-21 20:32:19,2022-01-21 20:32:21,2022-01-21 20:33:46,0.0783820152282715,85.7858464717865,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,1,gaussian,2022-01-21 20:31:45,2022-01-21 20:31:46,2022-01-21 20:31:46,2022-01-21 20:31:46,2022-01-21 20:33:55,0.0714077949523926,128.956621408463,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,2,gaussian,2022-01-21 20:32:26,2022-01-21 20:32:26,2022-01-21 20:32:26,2022-01-21 20:32:26,2022-01-21 20:34:01,0.09014892578125,94.9147391319275,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,8,empirical,2022-01-21 20:33:56,2022-01-21 20:33:58,2022-01-21 20:33:58,2022-01-21 20:33:59,2022-01-21 20:34:14,0.356815099716187,15.601455450058,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,4,empirical,2022-01-21 20:32:47,2022-01-21 20:32:48,2022-01-21 20:32:48,2022-01-21 20:32:50,2022-01-21 20:34:20,0.22058367729187,90.3257410526276,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,4,gaussian,2022-01-21 20:33:04,2022-01-21 20:33:05,2022-01-21 20:33:05,2022-01-21 20:33:07,2022-01-21 20:34:30,0.20801830291748,82.5363919734955,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,4,gaussian,2022-01-21 20:33:12,2022-01-21 20:33:13,2022-01-21 20:33:14,2022-01-21 20:33:14,2022-01-21 20:34:34,0.151397466659546,80.183765411377,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,8,empirical,2022-01-21 20:33:45,2022-01-21 20:33:46,2022-01-21 20:33:46,2022-01-21 20:33:49,2022-01-21 20:34:46,0.232907772064209,56.7660956382752,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,8,gaussian,2022-01-21 20:34:12,2022-01-21 20:34:15,2022-01-21 20:34:15,2022-01-21 20:34:19,2022-01-21 20:35:18,0.331713199615479,58.4540123939514,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,8,gaussian,2022-01-21 20:34:27,2022-01-21 20:34:28,2022-01-21 20:34:29,2022-01-21 20:34:29,2022-01-21 20:35:20,0.296703338623047,51.1451034545899,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,16,empirical,2022-01-21 20:35:21,2022-01-21 20:35:22,2022-01-21 20:35:22,2022-01-21 20:35:23,2022-01-21 20:35:34,0.222575426101685,11.3234941959381,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,16,empirical,2022-01-21 20:35:08,2022-01-21 20:35:10,2022-01-21 20:35:10,2022-01-21 20:35:15,2022-01-21 20:35:55,0.379893064498901,40.9224302768707,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,4,ctree,2022-01-21 20:33:33,2022-01-21 20:33:34,2022-01-21 20:33:34,2022-01-21 20:33:35,2022-01-21 20:36:07,0.175532817840576,152.374087095261,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,16,gaussian,2022-01-21 20:35:49,2022-01-21 20:35:50,2022-01-21 20:35:50,2022-01-21 20:35:51,2022-01-21 20:36:29,0.232831001281738,37.9455423355103,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,8,ctree,2022-01-21 20:34:53,2022-01-21 20:34:55,2022-01-21 20:34:55,2022-01-21 20:34:56,2022-01-21 20:36:37,0.362067222595215,101.306230068207,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,16,gaussian,2022-01-21 20:35:35,2022-01-21 20:35:37,2022-01-21 20:35:37,2022-01-21 20:35:43,2022-01-21 20:36:39,0.309902906417847,56.7270946502686,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,2,ctree,2022-01-21 20:32:40,2022-01-21 20:32:40,2022-01-21 20:32:40,2022-01-21 20:32:40,2022-01-21 20:36:50,0.0847711563110352,249.612685203552,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,8,ctree,2022-01-21 20:34:40,2022-01-21 20:34:42,2022-01-21 20:34:42,2022-01-21 20:34:46,2022-01-21 20:37:00,0.16476321220398,134.551515340805,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,4,ctree,2022-01-21 20:33:23,2022-01-21 20:33:25,2022-01-21 20:33:25,2022-01-21 20:33:28,2022-01-21 20:37:10,0.192231416702271,222.240759372711,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,24,empirical,2022-01-21 20:37:02,2022-01-21 20:37:03,2022-01-21 20:37:04,2022-01-21 20:37:04,2022-01-21 20:37:15,0.428291320800781,10.6568012237549,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,16,ctree,2022-01-21 20:36:31,2022-01-21 20:36:32,2022-01-21 20:36:32,2022-01-21 20:36:32,2022-01-21 20:37:28,0.241684198379517,55.7998902797699,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,16,ctree,2022-01-21 20:36:09,2022-01-21 20:36:12,2022-01-21 20:36:12,2022-01-21 20:36:19,2022-01-21 20:37:35,0.598961353302002,75.4088227748871,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,24,empirical,2022-01-21 20:36:44,2022-01-21 20:36:45,2022-01-21 20:36:45,2022-01-21 20:36:50,2022-01-21 20:37:44,0.203548669815063,54.2330090999603,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,24,gaussian,2022-01-21 20:37:32,2022-01-21 20:37:33,2022-01-21 20:37:33,2022-01-21 20:37:34,2022-01-21 20:38:00,0.202137470245361,26.2481873035431,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,24,gaussian,2022-01-21 20:37:21,2022-01-21 20:37:22,2022-01-21 20:37:23,2022-01-21 20:37:26,2022-01-21 20:38:12,0.324201345443726,46.0903024673462,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,32,empirical,2022-01-21 20:38:33,2022-01-21 20:38:35,2022-01-21 20:38:35,2022-01-21 20:38:35,2022-01-21 20:38:44,0.189739942550659,8.40194725990295,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,24,ctree,2022-01-21 20:38:02,2022-01-21 20:38:03,2022-01-21 20:38:03,2022-01-21 20:38:03,2022-01-21 20:38:48,0.221226453781128,45.2243721485138,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,32,empirical,2022-01-21 20:38:16,2022-01-21 20:38:18,2022-01-21 20:38:18,2022-01-21 20:38:22,2022-01-21 20:39:03,0.169388055801392,41.093891620636,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,24,ctree,2022-01-21 20:37:51,2022-01-21 20:37:53,2022-01-21 20:37:53,2022-01-21 20:37:57,2022-01-21 20:39:09,0.248333215713501,72.739577293396,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,2,ctree,2022-01-21 20:32:33,2022-01-21 20:32:33,2022-01-21 20:32:33,2022-01-21 20:32:35,2022-01-21 20:39:19,0.17104172706604,403.866662740707,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,32,gaussian,2022-01-21 20:38:59,2022-01-21 20:39:00,2022-01-21 20:39:00,2022-01-21 20:39:00,2022-01-21 20:39:22,0.151162624359131,21.6640906333923,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,32,gaussian,2022-01-21 20:38:46,2022-01-21 20:38:47,2022-01-21 20:38:47,2022-01-21 20:38:51,2022-01-21 20:39:28,0.195052862167358,37.7409205436707,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,32,ctree,2022-01-21 20:39:30,2022-01-21 20:39:30,2022-01-21 20:39:30,2022-01-21 20:39:30,2022-01-21 20:39:59,0.0839934349060059,28.359222650528,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,32,ctree,2022-01-21 20:39:21,2022-01-21 20:39:22,2022-01-21 20:39:22,2022-01-21 20:39:26,2022-01-21 20:40:11,0.213982820510864,45.0394818782806,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,1,ctree,2022-01-21 20:31:52,2022-01-21 20:31:52,2022-01-21 20:31:52,2022-01-21 20:31:53,2022-01-21 20:40:26,0.0849213600158691,513.462594985962,0,1e+05,13,0.3,1,0,1,1
+8,1000,100,32,1,ctree,2022-01-21 20:31:59,2022-01-21 20:31:59,2022-01-21 20:31:59,2022-01-21 20:31:59,2022-01-21 20:40:39,0.0798075199127197,519.619250059128,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,1,empirical,2022-01-21 20:39:41,2022-01-21 20:39:42,2022-01-21 20:39:43,2022-01-21 20:39:44,2022-01-21 20:41:07,0.514189004898071,83.115002155304,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,1,gaussian,2022-01-21 20:40:05,2022-01-21 20:40:06,2022-01-21 20:40:06,2022-01-21 20:40:07,2022-01-21 20:41:10,0.181352138519287,63.7905759811401,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,1,empirical,2022-01-21 20:39:56,2022-01-21 20:39:57,2022-01-21 20:39:57,2022-01-21 20:39:57,2022-01-21 20:41:11,0.219694137573242,73.331969499588,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,1,gaussian,2022-01-21 20:40:13,2022-01-21 20:40:13,2022-01-21 20:40:14,2022-01-21 20:40:14,2022-01-21 20:41:15,0.0705103874206543,60.8625030517578,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,2,empirical,2022-01-21 20:40:33,2022-01-21 20:40:34,2022-01-21 20:40:34,2022-01-21 20:40:35,2022-01-21 20:41:48,0.0801846981048584,73.6509292125702,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,2,gaussian,2022-01-21 20:40:47,2022-01-21 20:40:47,2022-01-21 20:40:47,2022-01-21 20:40:49,2022-01-21 20:41:56,0.0850775241851807,67.4113500118256,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,2,gaussian,2022-01-21 20:40:54,2022-01-21 20:40:55,2022-01-21 20:40:55,2022-01-21 20:40:55,2022-01-21 20:42:02,0.101362228393555,67.3404161930084,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,2,empirical,2022-01-21 20:40:40,2022-01-21 20:40:41,2022-01-21 20:40:41,2022-01-21 20:40:41,2022-01-21 20:42:12,0.0902249813079834,91.6361484527588,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,4,empirical,2022-01-21 20:41:17,2022-01-21 20:41:18,2022-01-21 20:41:18,2022-01-21 20:41:19,2022-01-21 20:42:37,0.0789525508880615,78.3705594539642,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,4,gaussian,2022-01-21 20:41:32,2022-01-21 20:41:33,2022-01-21 20:41:33,2022-01-21 20:41:34,2022-01-21 20:42:47,0.0763993263244629,73.0424482822418,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,4,gaussian,2022-01-21 20:41:40,2022-01-21 20:41:41,2022-01-21 20:41:41,2022-01-21 20:41:41,2022-01-21 20:42:53,0.0944702625274658,72.0735704898834,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,4,empirical,2022-01-21 20:41:25,2022-01-21 20:41:25,2022-01-21 20:41:25,2022-01-21 20:41:26,2022-01-21 20:42:58,0.0665054321289063,92.3525214195251,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,8,empirical,2022-01-21 20:42:05,2022-01-21 20:42:05,2022-01-21 20:42:05,2022-01-21 20:42:06,2022-01-21 20:43:38,0.0908849239349365,91.9628841876984,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,8,gaussian,2022-01-21 20:42:21,2022-01-21 20:42:22,2022-01-21 20:42:22,2022-01-21 20:42:23,2022-01-21 20:43:47,0.0934679508209229,83.9108798503876,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,8,empirical,2022-01-21 20:42:13,2022-01-21 20:42:13,2022-01-21 20:42:13,2022-01-21 20:42:14,2022-01-21 20:43:51,0.133117914199829,97.358809709549,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,8,gaussian,2022-01-21 20:42:30,2022-01-21 20:42:31,2022-01-21 20:42:31,2022-01-21 20:42:31,2022-01-21 20:43:57,0.150803089141846,85.7089190483093,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,2,ctree,2022-01-21 20:41:10,2022-01-21 20:41:10,2022-01-21 20:41:10,2022-01-21 20:41:11,2022-01-21 20:44:52,0.0881507396697998,221.184534311295,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,16,empirical,2022-01-21 20:43:07,2022-01-21 20:43:08,2022-01-21 20:43:08,2022-01-21 20:43:08,2022-01-21 20:44:55,0.0871436595916748,106.419390439987,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,16,empirical,2022-01-21 20:42:58,2022-01-21 20:42:58,2022-01-21 20:42:58,2022-01-21 20:43:00,2022-01-21 20:44:55,0.117137908935547,115.359238624573,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,16,gaussian,2022-01-21 20:43:17,2022-01-21 20:43:18,2022-01-21 20:43:18,2022-01-21 20:43:20,2022-01-21 20:45:06,0.116492748260498,106.539502382278,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,16,gaussian,2022-01-21 20:43:28,2022-01-21 20:43:29,2022-01-21 20:43:29,2022-01-21 20:43:29,2022-01-21 20:45:12,0.0872268676757813,102.529846906662,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,4,ctree,2022-01-21 20:41:57,2022-01-21 20:41:57,2022-01-21 20:41:57,2022-01-21 20:41:58,2022-01-21 20:45:47,0.0749163627624512,229.18515086174,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,24,empirical,2022-01-21 20:44:13,2022-01-21 20:44:14,2022-01-21 20:44:14,2022-01-21 20:44:14,2022-01-21 20:46:04,0.111527681350708,109.889443635941,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,24,empirical,2022-01-21 20:44:02,2022-01-21 20:44:03,2022-01-21 20:44:03,2022-01-21 20:44:05,2022-01-21 20:46:10,0.093369722366333,125.216494083405,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,24,gaussian,2022-01-21 20:44:25,2022-01-21 20:44:26,2022-01-21 20:44:26,2022-01-21 20:44:28,2022-01-21 20:46:26,0.0979218482971191,117.943421125412,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,24,gaussian,2022-01-21 20:44:39,2022-01-21 20:44:40,2022-01-21 20:44:40,2022-01-21 20:44:40,2022-01-21 20:46:32,0.157740831375122,111.4966173172,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,1,ctree,2022-01-21 20:40:20,2022-01-21 20:40:20,2022-01-21 20:40:20,2022-01-21 20:40:20,2022-01-21 20:46:36,0.0859062671661377,375.549143791199,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,1,ctree,2022-01-21 20:40:26,2022-01-21 20:40:27,2022-01-21 20:40:27,2022-01-21 20:40:27,2022-01-21 20:46:45,0.0841414928436279,377.576674938202,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,8,ctree,2022-01-21 20:42:48,2022-01-21 20:42:49,2022-01-21 20:42:49,2022-01-21 20:42:49,2022-01-21 20:46:47,0.11295485496521,238.121016740799,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,32,empirical,2022-01-21 20:45:33,2022-01-21 20:45:34,2022-01-21 20:45:34,2022-01-21 20:45:34,2022-01-21 20:47:19,0.115691900253296,104.671442508698,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,32,empirical,2022-01-21 20:45:19,2022-01-21 20:45:20,2022-01-21 20:45:20,2022-01-21 20:45:22,2022-01-21 20:47:20,0.107930898666382,117.238768100739,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,32,gaussian,2022-01-21 20:45:48,2022-01-21 20:45:48,2022-01-21 20:45:49,2022-01-21 20:45:51,2022-01-21 20:47:35,0.19084358215332,103.518033742905,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,2,ctree,2022-01-21 20:41:02,2022-01-21 20:41:02,2022-01-21 20:41:03,2022-01-21 20:41:04,2022-01-21 20:47:37,0.134712696075439,392.856748819351,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,32,gaussian,2022-01-21 20:46:05,2022-01-21 20:46:06,2022-01-21 20:46:06,2022-01-21 20:46:06,2022-01-21 20:47:42,0.111264228820801,95.3136060237884,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,16,ctree,2022-01-21 20:43:51,2022-01-21 20:43:52,2022-01-21 20:43:52,2022-01-21 20:43:52,2022-01-21 20:47:46,0.0979311466217041,233.955200910568,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,4,ctree,2022-01-21 20:41:49,2022-01-21 20:41:49,2022-01-21 20:41:49,2022-01-21 20:41:51,2022-01-21 20:48:08,0.0908458232879639,377.439036130905,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,1,empirical,2022-01-21 20:46:50,2022-01-21 20:46:50,2022-01-21 20:46:50,2022-01-21 20:46:51,2022-01-21 20:48:51,0.108370542526245,120.231868982315,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,1,gaussian,2022-01-21 20:47:18,2022-01-21 20:47:19,2022-01-21 20:47:19,2022-01-21 20:47:19,2022-01-21 20:48:54,0.0978591442108154,94.4793810844421,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,24,ctree,2022-01-21 20:45:07,2022-01-21 20:45:08,2022-01-21 20:45:08,2022-01-21 20:45:08,2022-01-21 20:48:58,0.092181921005249,230.083087682724,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,1,empirical,2022-01-21 20:47:04,2022-01-21 20:47:04,2022-01-21 20:47:04,2022-01-21 20:47:05,2022-01-21 20:49:09,0.0792837142944336,124.430074691772,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,1,gaussian,2022-01-21 20:47:32,2022-01-21 20:47:32,2022-01-21 20:47:32,2022-01-21 20:47:32,2022-01-21 20:49:16,0.114082336425781,103.479576587677,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,8,ctree,2022-01-21 20:42:39,2022-01-21 20:42:40,2022-01-21 20:42:40,2022-01-21 20:42:41,2022-01-21 20:49:17,0.109987258911133,395.639775276184,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,2,empirical,2022-01-21 20:48:18,2022-01-21 20:48:19,2022-01-21 20:48:19,2022-01-21 20:48:19,2022-01-21 20:49:21,0.120954990386963,61.8074162006378,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,2,empirical,2022-01-21 20:48:07,2022-01-21 20:48:07,2022-01-21 20:48:07,2022-01-21 20:48:09,2022-01-21 20:49:27,0.132088899612427,77.9539129734039,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,2,gaussian,2022-01-21 20:48:31,2022-01-21 20:48:32,2022-01-21 20:48:32,2022-01-21 20:48:34,2022-01-21 20:49:43,0.170503377914429,69.7559072971344,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,2,gaussian,2022-01-21 20:48:44,2022-01-21 20:48:45,2022-01-21 20:48:45,2022-01-21 20:48:45,2022-01-21 20:49:50,0.142569303512573,64.3325459957123,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,16,ctree,2022-01-21 20:43:40,2022-01-21 20:43:41,2022-01-21 20:43:41,2022-01-21 20:43:43,2022-01-21 20:50:33,0.105155467987061,410.641890048981,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,32,ctree,2022-01-21 20:46:36,2022-01-21 20:46:37,2022-01-21 20:46:37,2022-01-21 20:46:37,2022-01-21 20:50:36,0.11540150642395,238.762038230896,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,4,empirical,2022-01-21 20:49:37,2022-01-21 20:49:38,2022-01-21 20:49:38,2022-01-21 20:49:38,2022-01-21 20:50:36,0.132952928543091,57.566504240036,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,4,empirical,2022-01-21 20:49:25,2022-01-21 20:49:25,2022-01-21 20:49:26,2022-01-21 20:49:27,2022-01-21 20:50:42,0.184035301208496,75.032502412796,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,4,gaussian,2022-01-21 20:49:50,2022-01-21 20:49:50,2022-01-21 20:49:50,2022-01-21 20:49:52,2022-01-21 20:50:56,0.0878503322601318,64.0036766529083,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,4,gaussian,2022-01-21 20:50:02,2022-01-21 20:50:03,2022-01-21 20:50:03,2022-01-21 20:50:03,2022-01-21 20:51:08,0.15785026550293,64.6572296619415,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,2,ctree,2022-01-21 20:49:12,2022-01-21 20:49:12,2022-01-21 20:49:13,2022-01-21 20:49:13,2022-01-21 20:51:18,0.145140886306763,124.979911088944,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,8,empirical,2022-01-21 20:50:57,2022-01-21 20:50:57,2022-01-21 20:50:57,2022-01-21 20:50:58,2022-01-21 20:51:56,0.0942003726959229,58.2835078239441,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,8,empirical,2022-01-21 20:50:44,2022-01-21 20:50:45,2022-01-21 20:50:45,2022-01-21 20:50:47,2022-01-21 20:52:01,0.0997016429901123,74.5951147079468,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,8,gaussian,2022-01-21 20:51:09,2022-01-21 20:51:10,2022-01-21 20:51:10,2022-01-21 20:51:11,2022-01-21 20:52:19,0.0858395099639893,67.3495314121246,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,2,ctree,2022-01-21 20:48:59,2022-01-21 20:48:59,2022-01-21 20:49:00,2022-01-21 20:49:02,2022-01-21 20:52:27,0.130018472671509,205.433418512344,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,24,ctree,2022-01-21 20:44:53,2022-01-21 20:44:54,2022-01-21 20:44:54,2022-01-21 20:44:57,2022-01-21 20:52:33,0.160648822784424,456.004871368408,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,8,gaussian,2022-01-21 20:51:21,2022-01-21 20:51:22,2022-01-21 20:51:22,2022-01-21 20:51:22,2022-01-21 20:52:34,0.117274522781372,71.3184123039246,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,4,ctree,2022-01-21 20:50:30,2022-01-21 20:50:31,2022-01-21 20:50:31,2022-01-21 20:50:32,2022-01-21 20:52:43,0.132680892944336,131.224489688873,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,16,empirical,2022-01-21 20:52:17,2022-01-21 20:52:18,2022-01-21 20:52:18,2022-01-21 20:52:18,2022-01-21 20:53:18,0.182514667510986,59.5530655384064,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,16,empirical,2022-01-21 20:52:03,2022-01-21 20:52:04,2022-01-21 20:52:04,2022-01-21 20:52:06,2022-01-21 20:53:21,0.143407106399536,75.0731844902039,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,16,gaussian,2022-01-21 20:52:31,2022-01-21 20:52:31,2022-01-21 20:52:31,2022-01-21 20:52:33,2022-01-21 20:53:41,0.0980119705200195,67.5845458507538,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,4,ctree,2022-01-21 20:50:16,2022-01-21 20:50:16,2022-01-21 20:50:16,2022-01-21 20:50:18,2022-01-21 20:53:43,0.128473281860352,204.495072841644,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,16,gaussian,2022-01-21 20:52:44,2022-01-21 20:52:45,2022-01-21 20:52:45,2022-01-21 20:52:45,2022-01-21 20:53:50,0.0959305763244629,64.6132051944733,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,1,32,ctree,2022-01-21 20:46:20,2022-01-21 20:46:21,2022-01-21 20:46:21,2022-01-21 20:46:24,2022-01-21 20:53:59,0.0981271266937256,455.146957874298,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,8,ctree,2022-01-21 20:51:48,2022-01-21 20:51:49,2022-01-21 20:51:49,2022-01-21 20:51:49,2022-01-21 20:54:00,0.113694906234741,130.574969768524,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,1,ctree,2022-01-21 20:47:44,2022-01-21 20:47:44,2022-01-21 20:47:45,2022-01-21 20:47:45,2022-01-21 20:54:09,0.112956523895264,384.384921073914,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,1,ctree,2022-01-21 20:47:55,2022-01-21 20:47:56,2022-01-21 20:47:56,2022-01-21 20:47:56,2022-01-21 20:54:20,0.0777542591094971,384.216646432877,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,24,empirical,2022-01-21 20:53:37,2022-01-21 20:53:37,2022-01-21 20:53:38,2022-01-21 20:53:38,2022-01-21 20:54:34,0.137854814529419,56.156763792038,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,24,empirical,2022-01-21 20:53:23,2022-01-21 20:53:24,2022-01-21 20:53:24,2022-01-21 20:53:27,2022-01-21 20:54:36,0.216530323028564,69.1749243736267,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,8,ctree,2022-01-21 20:51:34,2022-01-21 20:51:35,2022-01-21 20:51:35,2022-01-21 20:51:37,2022-01-21 20:54:51,0.138494491577148,193.775209188461,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,24,gaussian,2022-01-21 20:53:50,2022-01-21 20:53:51,2022-01-21 20:53:51,2022-01-21 20:53:53,2022-01-21 20:54:52,0.111333131790161,58.6032857894898,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,24,gaussian,2022-01-21 20:54:02,2022-01-21 20:54:03,2022-01-21 20:54:03,2022-01-21 20:54:03,2022-01-21 20:55:00,0.0959129333496094,56.1699120998383,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,16,ctree,2022-01-21 20:52:57,2022-01-21 20:52:57,2022-01-21 20:52:57,2022-01-21 20:53:00,2022-01-21 20:55:07,0.140775918960571,126.774674892426,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,16,ctree,2022-01-21 20:53:10,2022-01-21 20:53:11,2022-01-21 20:53:11,2022-01-21 20:53:12,2022-01-21 20:55:15,0.17417311668396,123.475018024445,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,32,empirical,2022-01-21 20:54:38,2022-01-21 20:54:38,2022-01-21 20:54:38,2022-01-21 20:54:41,2022-01-21 20:55:32,0.0884103775024414,51.1614468097687,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,32,gaussian,2022-01-21 20:55:02,2022-01-21 20:55:02,2022-01-21 20:55:03,2022-01-21 20:55:04,2022-01-21 20:55:43,0.084935188293457,38.6708788871765,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,32,empirical,2022-01-21 20:54:50,2022-01-21 20:54:51,2022-01-21 20:54:51,2022-01-21 20:54:51,2022-01-21 20:55:44,0.18653392791748,53.2604284286499,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,32,gaussian,2022-01-21 20:55:13,2022-01-21 20:55:14,2022-01-21 20:55:14,2022-01-21 20:55:14,2022-01-21 20:55:53,0.0951611995697021,39.0729277133942,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,24,ctree,2022-01-21 20:54:14,2022-01-21 20:54:14,2022-01-21 20:54:14,2022-01-21 20:54:17,2022-01-21 20:56:03,0.1542067527771,105.781499862671,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,24,ctree,2022-01-21 20:54:26,2022-01-21 20:54:27,2022-01-21 20:54:27,2022-01-21 20:54:27,2022-01-21 20:56:18,0.12476372718811,110.91094994545,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,1,empirical,2022-01-21 20:55:45,2022-01-21 20:55:46,2022-01-21 20:55:46,2022-01-21 20:55:46,2022-01-21 20:57:16,0.079878568649292,90.2866969108582,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,32,ctree,2022-01-21 20:55:35,2022-01-21 20:55:36,2022-01-21 20:55:36,2022-01-21 20:55:36,2022-01-21 20:57:17,0.110094547271729,101.071091890335,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,1,gaussian,2022-01-21 20:56:04,2022-01-21 20:56:04,2022-01-21 20:56:04,2022-01-21 20:56:04,2022-01-21 20:57:25,0.0749552249908447,81.18088722229,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,1,empirical,2022-01-21 20:55:54,2022-01-21 20:55:55,2022-01-21 20:55:55,2022-01-21 20:55:55,2022-01-21 20:57:39,0.0815427303314209,103.489189624786,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,1,gaussian,2022-01-21 20:56:12,2022-01-21 20:56:12,2022-01-21 20:56:12,2022-01-21 20:56:12,2022-01-21 20:57:43,0.08514404296875,90.8046274185181,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,2,empirical,2022-01-21 20:56:46,2022-01-21 20:56:46,2022-01-21 20:56:46,2022-01-21 20:56:46,2022-01-21 20:57:48,0.115150928497314,61.817697763443,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,2,gaussian,2022-01-21 20:56:55,2022-01-21 20:56:55,2022-01-21 20:56:56,2022-01-21 20:56:57,2022-01-21 20:58:11,0.106011152267456,74.1335828304291,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,2,gaussian,2022-01-21 20:57:05,2022-01-21 20:57:06,2022-01-21 20:57:06,2022-01-21 20:57:06,2022-01-21 20:58:25,0.151498794555664,78.4240067005158,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,2,empirical,2022-01-21 20:56:37,2022-01-21 20:56:37,2022-01-21 20:56:37,2022-01-21 20:56:38,2022-01-21 20:58:29,0.102128744125366,111.225126266479,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,4,empirical,2022-01-21 20:57:49,2022-01-21 20:57:49,2022-01-21 20:57:50,2022-01-21 20:57:50,2022-01-21 20:58:32,0.188371181488037,41.8680233955383,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,2,32,ctree,2022-01-21 20:55:24,2022-01-21 20:55:25,2022-01-21 20:55:25,2022-01-21 20:55:27,2022-01-21 20:58:50,0.0877871513366699,203.081659317017,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,4,empirical,2022-01-21 20:57:38,2022-01-21 20:57:39,2022-01-21 20:57:39,2022-01-21 20:57:41,2022-01-21 20:58:52,0.183685064315796,70.5046737194061,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,4,gaussian,2022-01-21 20:58:01,2022-01-21 20:58:02,2022-01-21 20:58:02,2022-01-21 20:58:04,2022-01-21 20:58:54,0.191133975982666,49.4916274547577,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,4,gaussian,2022-01-21 20:58:14,2022-01-21 20:58:15,2022-01-21 20:58:15,2022-01-21 20:58:16,2022-01-21 20:59:02,0.170344591140747,46.8073053359985,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,8,empirical,2022-01-21 20:59:03,2022-01-21 20:59:04,2022-01-21 20:59:04,2022-01-21 20:59:04,2022-01-21 20:59:45,0.103648900985718,41.3012111186981,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,8,empirical,2022-01-21 20:58:54,2022-01-21 20:58:54,2022-01-21 20:58:55,2022-01-21 20:58:57,2022-01-21 21:00:07,0.159465789794922,70.3617722988129,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,8,gaussian,2022-01-21 20:59:13,2022-01-21 20:59:13,2022-01-21 20:59:13,2022-01-21 20:59:15,2022-01-21 21:00:17,0.108479976654053,61.4196181297302,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,4,ctree,2022-01-21 20:58:42,2022-01-21 20:58:43,2022-01-21 20:58:43,2022-01-21 20:58:43,2022-01-21 21:00:21,0.197139501571655,97.4192190170288,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,2,ctree,2022-01-21 20:57:27,2022-01-21 20:57:28,2022-01-21 20:57:28,2022-01-21 20:57:28,2022-01-21 21:00:23,0.149991035461426,174.387036085129,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,8,gaussian,2022-01-21 20:59:24,2022-01-21 20:59:25,2022-01-21 20:59:25,2022-01-21 20:59:25,2022-01-21 21:00:28,0.179873466491699,62.4359295368195,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,16,empirical,2022-01-21 21:00:20,2022-01-21 21:00:21,2022-01-21 21:00:21,2022-01-21 21:00:22,2022-01-21 21:01:10,0.139067888259888,47.9953508377075,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,4,ctree,2022-01-21 20:58:28,2022-01-21 20:58:29,2022-01-21 20:58:29,2022-01-21 20:58:32,2022-01-21 21:01:12,0.208264112472534,160.388398647308,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,16,empirical,2022-01-21 21:00:07,2022-01-21 21:00:08,2022-01-21 21:00:08,2022-01-21 21:00:11,2022-01-21 21:01:29,0.191035747528076,78.1037952899933,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,8,ctree,2022-01-21 20:59:51,2022-01-21 20:59:52,2022-01-21 20:59:53,2022-01-21 20:59:53,2022-01-21 21:01:41,0.289092540740967,107.949241161346,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,16,gaussian,2022-01-21 21:00:31,2022-01-21 21:00:32,2022-01-21 21:00:32,2022-01-21 21:00:35,2022-01-21 21:01:44,0.139708518981934,69.2671241760254,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,16,gaussian,2022-01-21 21:00:44,2022-01-21 21:00:45,2022-01-21 21:00:46,2022-01-21 21:00:46,2022-01-21 21:01:47,0.230913162231445,61.3942022323608,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,2,ctree,2022-01-21 20:57:17,2022-01-21 20:57:18,2022-01-21 20:57:18,2022-01-21 20:57:19,2022-01-21 21:01:59,0.130386114120483,280.008693695068,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,8,ctree,2022-01-21 20:59:37,2022-01-21 20:59:38,2022-01-21 20:59:39,2022-01-21 20:59:42,2022-01-21 21:02:24,0.158949136734009,162.668064832687,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,24,empirical,2022-01-21 21:01:47,2022-01-21 21:01:48,2022-01-21 21:01:48,2022-01-21 21:01:48,2022-01-21 21:02:28,0.165472269058228,40.3758878707886,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,24,empirical,2022-01-21 21:01:33,2022-01-21 21:01:34,2022-01-21 21:01:34,2022-01-21 21:01:38,2022-01-21 21:02:48,0.14913821220398,70.2134475708008,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,16,ctree,2022-01-21 21:01:17,2022-01-21 21:01:18,2022-01-21 21:01:19,2022-01-21 21:01:19,2022-01-21 21:02:56,0.305893421173096,96.9376883506775,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,24,gaussian,2022-01-21 21:01:59,2022-01-21 21:02:00,2022-01-21 21:02:00,2022-01-21 21:02:02,2022-01-21 21:02:59,0.18554425239563,56.98051404953,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,24,gaussian,2022-01-21 21:02:13,2022-01-21 21:02:14,2022-01-21 21:02:14,2022-01-21 21:02:14,2022-01-21 21:03:05,0.0917379856109619,50.3771817684174,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,16,ctree,2022-01-21 21:01:00,2022-01-21 21:01:02,2022-01-21 21:01:02,2022-01-21 21:01:05,2022-01-21 21:03:20,0.268455982208252,135.148362159729,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,32,empirical,2022-01-21 21:03:10,2022-01-21 21:03:11,2022-01-21 21:03:11,2022-01-21 21:03:11,2022-01-21 21:03:50,0.17202615737915,38.376627445221,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,32,empirical,2022-01-21 21:02:59,2022-01-21 21:02:59,2022-01-21 21:03:00,2022-01-21 21:03:02,2022-01-21 21:03:57,0.181943416595459,54.7125444412232,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,24,ctree,2022-01-21 21:02:46,2022-01-21 21:02:47,2022-01-21 21:02:47,2022-01-21 21:02:48,2022-01-21 21:04:12,0.35809850692749,83.8257744312286,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,32,gaussian,2022-01-21 21:03:22,2022-01-21 21:03:22,2022-01-21 21:03:22,2022-01-21 21:03:25,2022-01-21 21:04:14,0.120844125747681,49.3672523498535,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,32,gaussian,2022-01-21 21:03:34,2022-01-21 21:03:35,2022-01-21 21:03:35,2022-01-21 21:03:35,2022-01-21 21:04:19,0.178531885147095,44.2253189086914,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,1,ctree,2022-01-21 20:56:20,2022-01-21 20:56:20,2022-01-21 20:56:21,2022-01-21 20:56:21,2022-01-21 21:04:31,0.0908744335174561,489.950117826462,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,24,ctree,2022-01-21 21:02:29,2022-01-21 21:02:30,2022-01-21 21:02:31,2022-01-21 21:02:34,2022-01-21 21:04:32,0.476623058319092,117.975797176361,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,1,ctree,2022-01-21 20:56:28,2022-01-21 20:56:29,2022-01-21 20:56:29,2022-01-21 20:56:29,2022-01-21 21:04:33,0.0756206512451172,483.763912677765,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,32,ctree,2022-01-21 21:04:05,2022-01-21 21:04:06,2022-01-21 21:04:06,2022-01-21 21:04:06,2022-01-21 21:05:03,0.187911510467529,56.9690225124359,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,4,32,ctree,2022-01-21 21:03:49,2022-01-21 21:03:50,2022-01-21 21:03:50,2022-01-21 21:03:54,2022-01-21 21:05:04,0.238729476928711,70.4672458171845,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,1,gaussian,2022-01-21 21:04:37,2022-01-21 21:04:37,2022-01-21 21:04:37,2022-01-21 21:04:37,2022-01-21 21:05:55,0.0798604488372803,78.0865747928619,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,1,gaussian,2022-01-21 21:04:45,2022-01-21 21:04:45,2022-01-21 21:04:45,2022-01-21 21:04:46,2022-01-21 21:06:19,0.0907771587371826,92.9808647632599,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,2,empirical,2022-01-21 21:05:17,2022-01-21 21:05:17,2022-01-21 21:05:17,2022-01-21 21:05:17,2022-01-21 21:06:23,0.0776762962341309,66.1056880950928,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,2,gaussian,2022-01-21 21:05:24,2022-01-21 21:05:24,2022-01-21 21:05:24,2022-01-21 21:05:26,2022-01-21 21:06:45,0.143327713012695,79.417430639267,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,4,empirical,2022-01-21 21:06:05,2022-01-21 21:06:06,2022-01-21 21:06:06,2022-01-21 21:06:07,2022-01-21 21:07:00,0.157199859619141,52.6849505901337,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,2,gaussian,2022-01-21 21:05:31,2022-01-21 21:05:32,2022-01-21 21:05:32,2022-01-21 21:05:32,2022-01-21 21:07:06,0.111085653305054,94.3574590682984,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,4,gaussian,2022-01-21 21:06:15,2022-01-21 21:06:16,2022-01-21 21:06:16,2022-01-21 21:06:18,2022-01-21 21:07:38,0.204633712768555,79.2583937644959,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,4,gaussian,2022-01-21 21:06:26,2022-01-21 21:06:27,2022-01-21 21:06:28,2022-01-21 21:06:28,2022-01-21 21:07:52,0.264756202697754,84.2117490768433,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,8,empirical,2022-01-21 21:07:20,2022-01-21 21:07:22,2022-01-21 21:07:22,2022-01-21 21:07:22,2022-01-21 21:08:06,0.188937187194824,43.6903123855591,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,4,empirical,2022-01-21 21:05:56,2022-01-21 21:05:57,2022-01-21 21:05:57,2022-01-21 21:05:59,2022-01-21 21:08:14,0.171584844589233,134.929827213287,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,2,empirical,2022-01-21 21:05:10,2022-01-21 21:05:10,2022-01-21 21:05:10,2022-01-21 21:05:11,2022-01-21 21:08:19,0.0868418216705322,188.043449640274,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,1,empirical,2022-01-21 21:04:17,2022-01-21 21:04:18,2022-01-21 21:04:18,2022-01-21 21:04:18,2022-01-21 21:08:27,0.103017568588257,248.826789140701,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,8,empirical,2022-01-21 21:07:06,2022-01-21 21:07:07,2022-01-21 21:07:07,2022-01-21 21:07:10,2022-01-21 21:08:34,0.292656183242798,84.337461233139,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,1,empirical,2022-01-21 21:04:28,2022-01-21 21:04:28,2022-01-21 21:04:28,2022-01-21 21:04:29,2022-01-21 21:08:45,0.0950651168823242,255.985460519791,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,8,gaussian,2022-01-21 21:07:37,2022-01-21 21:07:38,2022-01-21 21:07:39,2022-01-21 21:07:42,2022-01-21 21:08:45,0.399717569351196,62.5868482589722,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,8,gaussian,2022-01-21 21:07:54,2022-01-21 21:07:56,2022-01-21 21:07:56,2022-01-21 21:07:57,2022-01-21 21:08:50,0.593955755233765,53.5022542476654,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,16,empirical,2022-01-21 21:09:01,2022-01-21 21:09:02,2022-01-21 21:09:02,2022-01-21 21:09:03,2022-01-21 21:09:46,0.152786254882813,43.164598941803,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,4,ctree,2022-01-21 21:06:53,2022-01-21 21:06:55,2022-01-21 21:06:55,2022-01-21 21:06:55,2022-01-21 21:09:50,0.256888389587402,174.191545248032,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,16,empirical,2022-01-21 21:08:49,2022-01-21 21:08:50,2022-01-21 21:08:51,2022-01-21 21:08:53,2022-01-21 21:10:18,0.172556400299072,84.9139215946198,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,8,ctree,2022-01-21 21:08:37,2022-01-21 21:08:39,2022-01-21 21:08:39,2022-01-21 21:08:39,2022-01-21 21:10:18,0.248854875564575,99.4150605201721,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,8,ctree,2022-01-21 21:08:17,2022-01-21 21:08:18,2022-01-21 21:08:19,2022-01-21 21:08:23,2022-01-21 21:10:23,0.350428342819214,120.313968896866,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,4,ctree,2022-01-21 21:06:40,2022-01-21 21:06:41,2022-01-21 21:06:42,2022-01-21 21:06:45,2022-01-21 21:10:25,0.427752733230591,220.616188764572,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,16,gaussian,2022-01-21 21:09:16,2022-01-21 21:09:17,2022-01-21 21:09:18,2022-01-21 21:09:22,2022-01-21 21:10:28,0.311072587966919,66.6818714141846,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,2,ctree,2022-01-21 21:05:47,2022-01-21 21:05:48,2022-01-21 21:05:48,2022-01-21 21:05:48,2022-01-21 21:10:29,0.119645595550537,280.962194919586,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,16,gaussian,2022-01-21 21:09:37,2022-01-21 21:09:38,2022-01-21 21:09:39,2022-01-21 21:09:39,2022-01-21 21:10:31,0.337527513504028,51.241792678833,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,24,empirical,2022-01-21 21:10:45,2022-01-21 21:10:46,2022-01-21 21:10:46,2022-01-21 21:10:47,2022-01-21 21:11:14,0.191702842712402,27.2636015415192,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,16,ctree,2022-01-21 21:10:26,2022-01-21 21:10:27,2022-01-21 21:10:27,2022-01-21 21:10:28,2022-01-21 21:11:36,0.164134502410889,67.9979479312897,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,16,ctree,2022-01-21 21:10:04,2022-01-21 21:10:07,2022-01-21 21:10:07,2022-01-21 21:10:12,2022-01-21 21:11:47,0.607365846633911,95.4136109352112,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,24,empirical,2022-01-21 21:10:36,2022-01-21 21:10:36,2022-01-21 21:10:36,2022-01-21 21:10:39,2022-01-21 21:11:51,0.0865225791931152,72.074743270874,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,2,ctree,2022-01-21 21:05:39,2022-01-21 21:05:39,2022-01-21 21:05:40,2022-01-21 21:05:41,2022-01-21 21:11:56,0.115771532058716,374.887598276138,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,24,gaussian,2022-01-21 21:10:58,2022-01-21 21:10:59,2022-01-21 21:10:59,2022-01-21 21:11:03,2022-01-21 21:11:56,0.237247228622437,53.7692830562592,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,24,gaussian,2022-01-21 21:11:16,2022-01-21 21:11:18,2022-01-21 21:11:19,2022-01-21 21:11:19,2022-01-21 21:11:59,0.377049684524536,40.2000601291657,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,32,empirical,2022-01-21 21:12:14,2022-01-21 21:12:15,2022-01-21 21:12:15,2022-01-21 21:12:16,2022-01-21 21:12:46,0.116685628890991,30.0075914859772,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,24,ctree,2022-01-21 21:11:55,2022-01-21 21:11:56,2022-01-21 21:11:57,2022-01-21 21:11:57,2022-01-21 21:12:50,0.166159391403198,53.3284509181976,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,24,ctree,2022-01-21 21:11:38,2022-01-21 21:11:40,2022-01-21 21:11:40,2022-01-21 21:11:44,2022-01-21 21:13:09,0.340238094329834,85.1530411243439,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,32,empirical,2022-01-21 21:12:04,2022-01-21 21:12:05,2022-01-21 21:12:05,2022-01-21 21:12:07,2022-01-21 21:13:15,0.101136445999146,67.9506716728211,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,32,gaussian,2022-01-21 21:12:27,2022-01-21 21:12:28,2022-01-21 21:12:29,2022-01-21 21:12:32,2022-01-21 21:13:21,0.203037977218628,48.8864440917969,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,32,gaussian,2022-01-21 21:12:46,2022-01-21 21:12:47,2022-01-21 21:12:47,2022-01-21 21:12:47,2022-01-21 21:13:22,0.184819459915161,34.9163496494293,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,1,ctree,2022-01-21 21:04:53,2022-01-21 21:04:54,2022-01-21 21:04:54,2022-01-21 21:04:54,2022-01-21 21:13:52,0.105205059051514,537.621712446213,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,32,ctree,2022-01-21 21:13:22,2022-01-21 21:13:22,2022-01-21 21:13:22,2022-01-21 21:13:23,2022-01-21 21:13:57,0.0760343074798584,33.991997718811,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,32,ctree,2022-01-21 21:13:09,2022-01-21 21:13:11,2022-01-21 21:13:11,2022-01-21 21:13:14,2022-01-21 21:14:04,0.259145975112915,49.4829413890839,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,8,1,ctree,2022-01-21 21:05:02,2022-01-21 21:05:03,2022-01-21 21:05:03,2022-01-21 21:05:03,2022-01-21 21:14:06,0.109845399856567,543.068265914917,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,1,gaussian,2022-01-21 21:13:47,2022-01-21 21:13:48,2022-01-21 21:13:48,2022-01-21 21:13:48,2022-01-21 21:15:34,0.124045372009277,106.016930341721,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,2,empirical,2022-01-21 21:14:24,2022-01-21 21:14:25,2022-01-21 21:14:25,2022-01-21 21:14:25,2022-01-21 21:15:42,0.0927612781524658,77.3450326919556,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,1,gaussian,2022-01-21 21:13:56,2022-01-21 21:13:57,2022-01-21 21:13:57,2022-01-21 21:13:57,2022-01-21 21:15:54,0.108012437820435,116.191929101944,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,2,gaussian,2022-01-21 21:14:31,2022-01-21 21:14:32,2022-01-21 21:14:32,2022-01-21 21:14:33,2022-01-21 21:15:57,0.0921623706817627,84.4271998405457,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,4,empirical,2022-01-21 21:15:11,2022-01-21 21:15:12,2022-01-21 21:15:12,2022-01-21 21:15:12,2022-01-21 21:16:24,0.215361595153809,71.0502712726593,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,2,gaussian,2022-01-21 21:14:38,2022-01-21 21:14:39,2022-01-21 21:14:39,2022-01-21 21:14:39,2022-01-21 21:16:26,0.146959543228149,106.851487398148,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,4,gaussian,2022-01-21 21:15:21,2022-01-21 21:15:22,2022-01-21 21:15:22,2022-01-21 21:15:25,2022-01-21 21:16:56,0.251926898956299,90.7974946498871,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,4,gaussian,2022-01-21 21:15:31,2022-01-21 21:15:32,2022-01-21 21:15:32,2022-01-21 21:15:33,2022-01-21 21:17:28,0.341072797775269,114.798723459244,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,8,empirical,2022-01-21 21:16:27,2022-01-21 21:16:29,2022-01-21 21:16:29,2022-01-21 21:16:30,2022-01-21 21:17:29,0.382034778594971,59.2331337928772,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,8,gaussian,2022-01-21 21:16:43,2022-01-21 21:16:45,2022-01-21 21:16:46,2022-01-21 21:16:50,2022-01-21 21:18:11,0.536580562591553,81.4892568588257,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,8,gaussian,2022-01-21 21:17:00,2022-01-21 21:17:03,2022-01-21 21:17:03,2022-01-21 21:17:04,2022-01-21 21:18:15,0.362274885177612,70.5533213615418,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,16,empirical,2022-01-21 21:18:30,2022-01-21 21:18:32,2022-01-21 21:18:33,2022-01-21 21:18:33,2022-01-21 21:19:19,0.223151683807373,45.638147354126,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,8,empirical,2022-01-21 21:16:13,2022-01-21 21:16:14,2022-01-21 21:16:15,2022-01-21 21:16:18,2022-01-21 21:19:25,0.28002142906189,186.538064718246,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,4,empirical,2022-01-21 21:15:02,2022-01-21 21:15:03,2022-01-21 21:15:03,2022-01-21 21:15:05,2022-01-21 21:19:55,0.0735135078430176,290.416796207428,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,4,ctree,2022-01-21 21:15:59,2022-01-21 21:16:00,2022-01-21 21:16:00,2022-01-21 21:16:00,2022-01-21 21:19:59,0.245657682418823,238.891503334045,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,16,empirical,2022-01-21 21:18:08,2022-01-21 21:18:10,2022-01-21 21:18:10,2022-01-21 21:18:14,2022-01-21 21:20:07,0.243280410766602,112.631034851074,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,16,gaussian,2022-01-21 21:18:54,2022-01-21 21:18:56,2022-01-21 21:18:56,2022-01-21 21:19:01,2022-01-21 21:20:12,0.275424003601074,70.3043127059937,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,16,gaussian,2022-01-21 21:19:26,2022-01-21 21:19:30,2022-01-21 21:19:31,2022-01-21 21:19:32,2022-01-21 21:20:14,0.813745021820068,41.8549983501434,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,8,ctree,2022-01-21 21:17:45,2022-01-21 21:17:47,2022-01-21 21:17:48,2022-01-21 21:17:49,2022-01-21 21:20:17,0.379645824432373,148.861283540726,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,8,ctree,2022-01-21 21:17:23,2022-01-21 21:17:26,2022-01-21 21:17:26,2022-01-21 21:17:32,2022-01-21 21:20:37,0.652283191680908,185.617542266846,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,4,ctree,2022-01-21 21:15:45,2022-01-21 21:15:47,2022-01-21 21:15:48,2022-01-21 21:15:51,2022-01-21 21:20:45,0.433119297027588,293.340547323227,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,2,ctree,2022-01-21 21:14:54,2022-01-21 21:14:55,2022-01-21 21:14:55,2022-01-21 21:14:55,2022-01-21 21:21:14,0.108402490615845,379.187018156052,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,16,ctree,2022-01-21 21:20:19,2022-01-21 21:20:20,2022-01-21 21:20:20,2022-01-21 21:20:21,2022-01-21 21:21:19,0.201935529708862,58.4909372329712,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,24,empirical,2022-01-21 21:20:50,2022-01-21 21:20:52,2022-01-21 21:20:52,2022-01-21 21:20:53,2022-01-21 21:21:26,0.336327791213989,32.9388563632965,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,16,ctree,2022-01-21 21:19:53,2022-01-21 21:19:56,2022-01-21 21:19:56,2022-01-21 21:20:03,2022-01-21 21:21:32,0.27570366859436,89.5509271621704,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,2,empirical,2022-01-21 21:14:17,2022-01-21 21:14:18,2022-01-21 21:14:18,2022-01-21 21:14:19,2022-01-21 21:21:49,0.0706212520599365,449.803378820419,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,24,gaussian,2022-01-21 21:21:16,2022-01-21 21:21:19,2022-01-21 21:21:19,2022-01-21 21:21:23,2022-01-21 21:22:10,0.507633209228516,46.8364727497101,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,24,gaussian,2022-01-21 21:21:32,2022-01-21 21:21:33,2022-01-21 21:21:33,2022-01-21 21:21:34,2022-01-21 21:22:11,0.355782270431519,37.2925474643707,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,24,empirical,2022-01-21 21:20:34,2022-01-21 21:20:35,2022-01-21 21:20:35,2022-01-21 21:20:39,2022-01-21 21:22:13,0.149597644805908,93.8490464687347,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,2,ctree,2022-01-21 21:14:46,2022-01-21 21:14:46,2022-01-21 21:14:47,2022-01-21 21:14:48,2022-01-21 21:22:58,0.119531154632568,489.637268066406,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,24,ctree,2022-01-21 21:22:15,2022-01-21 21:22:15,2022-01-21 21:22:15,2022-01-21 21:22:16,2022-01-21 21:23:12,0.124479055404663,56.7212684154511,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,24,ctree,2022-01-21 21:21:59,2022-01-21 21:22:01,2022-01-21 21:22:01,2022-01-21 21:22:06,2022-01-21 21:23:16,0.31951117515564,69.857203245163,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,32,empirical,2022-01-21 21:22:43,2022-01-21 21:22:45,2022-01-21 21:22:46,2022-01-21 21:22:47,2022-01-21 21:23:18,0.514694452285767,31.1206960678101,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,32,gaussian,2022-01-21 21:23:19,2022-01-21 21:23:20,2022-01-21 21:23:21,2022-01-21 21:23:21,2022-01-21 21:23:56,0.180426597595215,35.622038602829,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,32,gaussian,2022-01-21 21:23:02,2022-01-21 21:23:04,2022-01-21 21:23:04,2022-01-21 21:23:08,2022-01-21 21:23:57,0.19591236114502,48.5168907642365,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,32,empirical,2022-01-21 21:22:25,2022-01-21 21:22:27,2022-01-21 21:22:27,2022-01-21 21:22:31,2022-01-21 21:24:03,0.267998456954956,92.2569513320923,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,1,empirical,2022-01-21 21:13:30,2022-01-21 21:13:31,2022-01-21 21:13:31,2022-01-21 21:13:31,2022-01-21 21:24:29,0.0851345062255859,657.828667640686,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,32,ctree,2022-01-21 21:23:58,2022-01-21 21:23:59,2022-01-21 21:23:59,2022-01-21 21:24:00,2022-01-21 21:24:33,0.137048959732056,33.4353752136231,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,32,ctree,2022-01-21 21:23:42,2022-01-21 21:23:45,2022-01-21 21:23:45,2022-01-21 21:23:51,2022-01-21 21:24:41,0.557592868804932,49.9368736743927,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,1,empirical,2022-01-21 21:13:38,2022-01-21 21:13:39,2022-01-21 21:13:39,2022-01-21 21:13:39,2022-01-21 21:24:43,0.0958406925201416,664.118632078171,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,1,ctree,2022-01-21 21:14:04,2022-01-21 21:14:05,2022-01-21 21:14:05,2022-01-21 21:14:05,2022-01-21 21:25:04,0.0676724910736084,658.752881765366,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,16,1,ctree,2022-01-21 21:14:11,2022-01-21 21:14:11,2022-01-21 21:14:11,2022-01-21 21:14:12,2022-01-21 21:25:17,0.0730216503143311,665.175843477249,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,1,gaussian,2022-01-21 21:24:34,2022-01-21 21:24:35,2022-01-21 21:24:35,2022-01-21 21:24:36,2022-01-21 21:26:35,0.30742359161377,119.041927099228,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,2,empirical,2022-01-21 21:25:09,2022-01-21 21:25:09,2022-01-21 21:25:09,2022-01-21 21:25:10,2022-01-21 21:26:36,0.0966546535491943,86.2835886478424,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,1,gaussian,2022-01-21 21:24:42,2022-01-21 21:24:42,2022-01-21 21:24:42,2022-01-21 21:24:43,2022-01-21 21:27:02,0.0847799777984619,139.866026163101,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,2,gaussian,2022-01-21 21:25:16,2022-01-21 21:25:16,2022-01-21 21:25:16,2022-01-21 21:25:18,2022-01-21 21:27:07,0.119027376174927,109.410991430283,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,4,empirical,2022-01-21 21:25:55,2022-01-21 21:25:56,2022-01-21 21:25:56,2022-01-21 21:25:57,2022-01-21 21:27:17,0.184245109558105,80.4567439556122,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,2,gaussian,2022-01-21 21:25:23,2022-01-21 21:25:23,2022-01-21 21:25:24,2022-01-21 21:25:24,2022-01-21 21:27:27,0.121721506118774,123.322007894516,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,4,gaussian,2022-01-21 21:26:13,2022-01-21 21:26:15,2022-01-21 21:26:15,2022-01-21 21:26:15,2022-01-21 21:27:54,0.232003450393677,99.0742924213409,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,4,gaussian,2022-01-21 21:26:04,2022-01-21 21:26:05,2022-01-21 21:26:05,2022-01-21 21:26:08,2022-01-21 21:27:58,0.22277307510376,110.526182889938,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,8,empirical,2022-01-21 21:27:09,2022-01-21 21:27:11,2022-01-21 21:27:11,2022-01-21 21:27:11,2022-01-21 21:28:24,0.226193428039551,72.9715957641602,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,8,gaussian,2022-01-21 21:27:28,2022-01-21 21:27:29,2022-01-21 21:27:30,2022-01-21 21:27:35,2022-01-21 21:28:59,0.508949756622315,83.8114700317383,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,8,gaussian,2022-01-21 21:27:47,2022-01-21 21:27:49,2022-01-21 21:27:49,2022-01-21 21:27:50,2022-01-21 21:29:07,0.217011690139771,76.522379398346,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,16,empirical,2022-01-21 21:29:17,2022-01-21 21:29:18,2022-01-21 21:29:18,2022-01-21 21:29:19,2022-01-21 21:30:34,0.294175863265991,74.7833287715912,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,8,empirical,2022-01-21 21:26:53,2022-01-21 21:26:55,2022-01-21 21:26:55,2022-01-21 21:26:59,2022-01-21 21:31:31,0.339540243148804,271.496822834015,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,16,gaussian,2022-01-21 21:29:43,2022-01-21 21:29:46,2022-01-21 21:29:46,2022-01-21 21:29:53,2022-01-21 21:31:32,0.46642279624939,98.785829782486,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,16,gaussian,2022-01-21 21:30:20,2022-01-21 21:30:24,2022-01-21 21:30:25,2022-01-21 21:30:26,2022-01-21 21:31:33,0.905242443084717,66.3387489318848,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,4,ctree,2022-01-21 21:26:39,2022-01-21 21:26:40,2022-01-21 21:26:40,2022-01-21 21:26:41,2022-01-21 21:31:35,0.436992168426514,293.527332782745,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,8,ctree,2022-01-21 21:28:32,2022-01-21 21:28:35,2022-01-21 21:28:36,2022-01-21 21:28:37,2022-01-21 21:31:41,0.565577268600464,184.314444541931,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,8,ctree,2022-01-21 21:28:08,2022-01-21 21:28:09,2022-01-21 21:28:10,2022-01-21 21:28:14,2022-01-21 21:31:41,0.48603630065918,206.862575531006,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,4,ctree,2022-01-21 21:26:28,2022-01-21 21:26:29,2022-01-21 21:26:29,2022-01-21 21:26:32,2022-01-21 21:32:01,0.323044776916504,328.846762418747,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,16,empirical,2022-01-21 21:28:56,2022-01-21 21:28:58,2022-01-21 21:28:59,2022-01-21 21:29:04,2022-01-21 21:32:28,0.231225490570068,204.396010160446,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,2,ctree,2022-01-21 21:25:38,2022-01-21 21:25:39,2022-01-21 21:25:39,2022-01-21 21:25:39,2022-01-21 21:32:45,0.139758586883545,425.231558322907,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,24,empirical,2022-01-21 21:32:20,2022-01-21 21:32:23,2022-01-21 21:32:23,2022-01-21 21:32:24,2022-01-21 21:32:55,0.23017954826355,31.5129499435425,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,16,ctree,2022-01-21 21:31:39,2022-01-21 21:31:40,2022-01-21 21:31:41,2022-01-21 21:31:41,2022-01-21 21:33:12,0.467198610305786,90.5791802406311,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,16,ctree,2022-01-21 21:31:09,2022-01-21 21:31:11,2022-01-21 21:31:12,2022-01-21 21:31:19,2022-01-21 21:33:15,0.578039646148682,115.481181621552,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,4,empirical,2022-01-21 21:25:47,2022-01-21 21:25:48,2022-01-21 21:25:48,2022-01-21 21:25:50,2022-01-21 21:33:31,0.126460790634155,461.169310092926,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,24,gaussian,2022-01-21 21:33:15,2022-01-21 21:33:17,2022-01-21 21:33:18,2022-01-21 21:33:18,2022-01-21 21:33:55,0.476824283599854,36.0940475463867,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,24,empirical,2022-01-21 21:31:59,2022-01-21 21:32:00,2022-01-21 21:32:01,2022-01-21 21:32:05,2022-01-21 21:33:56,0.360566139221191,110.686193466187,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,24,gaussian,2022-01-21 21:32:46,2022-01-21 21:32:49,2022-01-21 21:32:49,2022-01-21 21:32:56,2022-01-21 21:33:58,0.36862325668335,62.9366602897644,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,2,ctree,2022-01-21 21:25:30,2022-01-21 21:25:31,2022-01-21 21:25:31,2022-01-21 21:25:33,2022-01-21 21:34:29,0.133883953094482,536.059300422668,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,24,ctree,2022-01-21 21:34:03,2022-01-21 21:34:04,2022-01-21 21:34:04,2022-01-21 21:34:04,2022-01-21 21:34:53,0.130608320236206,49.1582133769989,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,24,ctree,2022-01-21 21:33:48,2022-01-21 21:33:50,2022-01-21 21:33:51,2022-01-21 21:33:55,2022-01-21 21:35:04,0.371647357940674,69.4296364784241,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,32,empirical,2022-01-21 21:34:40,2022-01-21 21:34:41,2022-01-21 21:34:42,2022-01-21 21:34:42,2022-01-21 21:35:11,0.251516580581665,28.5495471954346,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,32,gaussian,2022-01-21 21:35:16,2022-01-21 21:35:17,2022-01-21 21:35:17,2022-01-21 21:35:18,2022-01-21 21:35:57,0.216872692108154,39.7184617519379,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,32,gaussian,2022-01-21 21:35:01,2022-01-21 21:35:04,2022-01-21 21:35:05,2022-01-21 21:35:09,2022-01-21 21:36:07,0.288581371307373,58.3261322975159,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,32,empirical,2022-01-21 21:34:18,2022-01-21 21:34:20,2022-01-21 21:34:20,2022-01-21 21:34:26,2022-01-21 21:36:09,0.355702877044678,102.635520219803,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,32,ctree,2022-01-21 21:36:08,2022-01-21 21:36:09,2022-01-21 21:36:09,2022-01-21 21:36:10,2022-01-21 21:36:48,0.209416627883911,38.4663996696472,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,32,ctree,2022-01-21 21:35:51,2022-01-21 21:35:53,2022-01-21 21:35:53,2022-01-21 21:35:59,2022-01-21 21:36:53,0.233638048171997,54.0048789978027,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,2,empirical,2022-01-21 21:25:02,2022-01-21 21:25:02,2022-01-21 21:25:02,2022-01-21 21:25:03,2022-01-21 21:36:59,0.094907283782959,715.46152639389,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,1,ctree,2022-01-21 21:24:48,2022-01-21 21:24:49,2022-01-21 21:24:49,2022-01-21 21:24:49,2022-01-21 21:37:37,0.0688414573669434,768.219142913818,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,1,ctree,2022-01-21 21:24:55,2022-01-21 21:24:56,2022-01-21 21:24:56,2022-01-21 21:24:56,2022-01-21 21:37:53,0.0867717266082764,776.810942173004,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,2,empirical,2022-01-21 21:37:27,2022-01-21 21:37:28,2022-01-21 21:37:28,2022-01-21 21:37:28,2022-01-21 21:39:15,0.0800116062164307,107.160700798035,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,1,gaussian,2022-01-21 21:36:52,2022-01-21 21:36:52,2022-01-21 21:36:53,2022-01-21 21:36:53,2022-01-21 21:39:35,0.101114273071289,162.176388978958,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,4,empirical,2022-01-21 21:38:15,2022-01-21 21:38:16,2022-01-21 21:38:16,2022-01-21 21:38:16,2022-01-21 21:39:58,0.136723756790161,101.457249164581,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,2,gaussian,2022-01-21 21:37:34,2022-01-21 21:37:35,2022-01-21 21:37:35,2022-01-21 21:37:37,2022-01-21 21:40:04,0.164344072341919,147.035058259964,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,1,gaussian,2022-01-21 21:36:59,2022-01-21 21:37:00,2022-01-21 21:37:00,2022-01-21 21:37:00,2022-01-21 21:40:18,0.127327680587769,198.591254711151,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,2,gaussian,2022-01-21 21:37:42,2022-01-21 21:37:43,2022-01-21 21:37:43,2022-01-21 21:37:43,2022-01-21 21:40:27,0.145722150802612,164.032037496567,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,4,gaussian,2022-01-21 21:38:25,2022-01-21 21:38:26,2022-01-21 21:38:26,2022-01-21 21:38:29,2022-01-21 21:40:37,0.164116144180298,127.752472400665,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,4,gaussian,2022-01-21 21:38:38,2022-01-21 21:38:39,2022-01-21 21:38:39,2022-01-21 21:38:40,2022-01-21 21:40:41,0.146379947662354,121.688840150833,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,1,empirical,2022-01-21 21:24:11,2022-01-21 21:24:12,2022-01-21 21:24:12,2022-01-21 21:24:12,2022-01-21 21:40:52,0.195000171661377,999.505385160446,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,24,1,empirical,2022-01-21 21:24:23,2022-01-21 21:24:25,2022-01-21 21:24:25,2022-01-21 21:24:25,2022-01-21 21:40:55,0.256623268127441,989.782598972321,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,8,empirical,2022-01-21 21:39:39,2022-01-21 21:39:42,2022-01-21 21:39:42,2022-01-21 21:39:43,2022-01-21 21:41:16,0.472242593765259,93.2722721099853,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,8,gaussian,2022-01-21 21:40:01,2022-01-21 21:40:03,2022-01-21 21:40:03,2022-01-21 21:40:08,2022-01-21 21:41:38,0.336188554763794,89.9355893135071,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,8,gaussian,2022-01-21 21:40:25,2022-01-21 21:40:28,2022-01-21 21:40:29,2022-01-21 21:40:30,2022-01-21 21:41:49,0.683731079101563,79.5202100276947,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,16,empirical,2022-01-21 21:41:58,2022-01-21 21:42:00,2022-01-21 21:42:01,2022-01-21 21:42:02,2022-01-21 21:42:54,0.502792596817017,52.7055804729462,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,16,gaussian,2022-01-21 21:42:25,2022-01-21 21:42:28,2022-01-21 21:42:29,2022-01-21 21:42:36,2022-01-21 21:44:03,0.841117858886719,87.118138551712,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,16,gaussian,2022-01-21 21:42:57,2022-01-21 21:43:00,2022-01-21 21:43:00,2022-01-21 21:43:02,2022-01-21 21:44:11,0.53568434715271,68.9963009357452,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,8,ctree,2022-01-21 21:41:14,2022-01-21 21:41:16,2022-01-21 21:41:16,2022-01-21 21:41:17,2022-01-21 21:44:17,0.383826494216919,180.081275463104,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,4,ctree,2022-01-21 21:39:04,2022-01-21 21:39:06,2022-01-21 21:39:07,2022-01-21 21:39:07,2022-01-21 21:44:27,0.415200233459473,319.746484041214,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,8,ctree,2022-01-21 21:40:52,2022-01-21 21:40:53,2022-01-21 21:40:54,2022-01-21 21:40:59,2022-01-21 21:45:01,0.641110420227051,241.246463060379,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,4,ctree,2022-01-21 21:38:49,2022-01-21 21:38:50,2022-01-21 21:38:51,2022-01-21 21:38:55,2022-01-21 21:45:20,0.406576156616211,385.773517131805,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,16,empirical,2022-01-21 21:41:37,2022-01-21 21:41:39,2022-01-21 21:41:39,2022-01-21 21:41:45,2022-01-21 21:45:28,0.531155347824097,223.692606687546,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,8,empirical,2022-01-21 21:39:22,2022-01-21 21:39:24,2022-01-21 21:39:24,2022-01-21 21:39:29,2022-01-21 21:45:29,0.322055339813232,359.806304216385,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,16,ctree,2022-01-21 21:44:20,2022-01-21 21:44:22,2022-01-21 21:44:23,2022-01-21 21:44:23,2022-01-21 21:45:53,0.283797264099121,89.9418413639069,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,2,ctree,2022-01-21 21:37:58,2022-01-21 21:37:59,2022-01-21 21:37:59,2022-01-21 21:37:59,2022-01-21 21:46:04,0.124407052993774,484.549674510956,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,16,ctree,2022-01-21 21:43:48,2022-01-21 21:43:53,2022-01-21 21:43:54,2022-01-21 21:44:02,2022-01-21 21:46:05,0.60678243637085,122.520214319229,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,24,empirical,2022-01-21 21:45:21,2022-01-21 21:45:23,2022-01-21 21:45:23,2022-01-21 21:45:24,2022-01-21 21:46:09,0.380441904067993,45.0068895816803,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,24,gaussian,2022-01-21 21:46:13,2022-01-21 21:46:15,2022-01-21 21:46:16,2022-01-21 21:46:16,2022-01-21 21:47:03,0.545144319534302,46.9774036407471,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,24,gaussian,2022-01-21 21:45:53,2022-01-21 21:45:55,2022-01-21 21:45:55,2022-01-21 21:46:01,2022-01-21 21:47:15,0.516844987869263,74.4752373695374,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,24,ctree,2022-01-21 21:47:04,2022-01-21 21:47:06,2022-01-21 21:47:06,2022-01-21 21:47:06,2022-01-21 21:48:14,0.128614187240601,67.0886564254761,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,24,empirical,2022-01-21 21:44:50,2022-01-21 21:44:52,2022-01-21 21:44:53,2022-01-21 21:44:59,2022-01-21 21:48:14,0.461406946182251,194.963154554367,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,4,empirical,2022-01-21 21:38:06,2022-01-21 21:38:07,2022-01-21 21:38:07,2022-01-21 21:38:09,2022-01-21 21:48:22,0.209931373596191,612.431309461594,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,2,ctree,2022-01-21 21:37:50,2022-01-21 21:37:50,2022-01-21 21:37:51,2022-01-21 21:37:52,2022-01-21 21:48:22,0.124840974807739,629.658906459808,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,32,empirical,2022-01-21 21:48:00,2022-01-21 21:48:02,2022-01-21 21:48:03,2022-01-21 21:48:03,2022-01-21 21:48:28,0.61471152305603,24.480751991272,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,24,ctree,2022-01-21 21:46:47,2022-01-21 21:46:49,2022-01-21 21:46:49,2022-01-21 21:46:55,2022-01-21 21:48:41,0.687000751495361,106.168217182159,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,32,gaussian,2022-01-21 21:48:44,2022-01-21 21:48:46,2022-01-21 21:48:46,2022-01-21 21:48:47,2022-01-21 21:49:21,0.4290611743927,34.3318228721619,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,32,empirical,2022-01-21 21:47:33,2022-01-21 21:47:35,2022-01-21 21:47:35,2022-01-21 21:47:40,2022-01-21 21:49:35,0.266908407211304,114.692779064178,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,32,gaussian,2022-01-21 21:48:27,2022-01-21 21:48:28,2022-01-21 21:48:29,2022-01-21 21:48:34,2022-01-21 21:49:39,0.435132741928101,65.0631065368652,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,32,ctree,2022-01-21 21:49:39,2022-01-21 21:49:40,2022-01-21 21:49:40,2022-01-21 21:49:40,2022-01-21 21:50:20,0.103464365005493,39.6891441345215,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,32,ctree,2022-01-21 21:49:22,2022-01-21 21:49:24,2022-01-21 21:49:24,2022-01-21 21:49:29,2022-01-21 21:50:29,0.232711791992188,60.2891592979431,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,1,empirical,2022-01-21 21:49:52,2022-01-21 21:49:54,2022-01-21 21:49:54,2022-01-21 21:49:55,2022-01-21 21:50:45,0.314820528030396,49.5270221233368,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,1,empirical,2022-01-21 21:50:12,2022-01-21 21:50:14,2022-01-21 21:50:14,2022-01-21 21:50:14,2022-01-21 21:50:48,0.376124858856201,33.9582526683807,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,1,ctree,2022-01-21 21:37:06,2022-01-21 21:37:07,2022-01-21 21:37:07,2022-01-21 21:37:07,2022-01-21 21:51:22,0.100116968154907,854.830652475357,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,2,empirical,2022-01-21 21:51:02,2022-01-21 21:51:02,2022-01-21 21:51:02,2022-01-21 21:51:02,2022-01-21 21:51:26,0.140056848526001,23.3518037796021,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,2,empirical,2022-01-21 21:50:54,2022-01-21 21:50:55,2022-01-21 21:50:55,2022-01-21 21:50:56,2022-01-21 21:51:30,0.11153507232666,33.1718173027039,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,1,ctree,2022-01-21 21:37:13,2022-01-21 21:37:13,2022-01-21 21:37:14,2022-01-21 21:37:14,2022-01-21 21:51:35,0.0966775417327881,861.255119562149,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,2,empirical,2022-01-21 21:37:20,2022-01-21 21:37:21,2022-01-21 21:37:21,2022-01-21 21:37:22,2022-01-21 21:51:44,0.154094934463501,862.11428976059,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,4,empirical,2022-01-21 21:51:47,2022-01-21 21:51:47,2022-01-21 21:51:47,2022-01-21 21:51:48,2022-01-21 21:52:12,0.118838787078857,24.670215845108,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,4,empirical,2022-01-21 21:51:39,2022-01-21 21:51:40,2022-01-21 21:51:40,2022-01-21 21:51:41,2022-01-21 21:52:19,0.14535403251648,38.2653274536133,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,8,empirical,2022-01-21 21:52:37,2022-01-21 21:52:37,2022-01-21 21:52:38,2022-01-21 21:52:38,2022-01-21 21:53:09,0.138319969177246,30.8951942920685,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,8,empirical,2022-01-21 21:52:28,2022-01-21 21:52:28,2022-01-21 21:52:29,2022-01-21 21:52:30,2022-01-21 21:53:20,0.18703818321228,49.6046886444092,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,1,gaussian,2022-01-21 21:50:25,2022-01-21 21:50:26,2022-01-21 21:50:26,2022-01-21 21:50:27,2022-01-21 21:53:52,0.227774620056152,205.436056375504,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,1,gaussian,2022-01-21 21:50:33,2022-01-21 21:50:34,2022-01-21 21:50:34,2022-01-21 21:50:34,2022-01-21 21:54:09,0.095834493637085,215.114421606064,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,16,empirical,2022-01-21 21:53:36,2022-01-21 21:53:37,2022-01-21 21:53:37,2022-01-21 21:53:37,2022-01-21 21:54:11,0.156553506851196,33.7957053184509,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,16,empirical,2022-01-21 21:53:25,2022-01-21 21:53:26,2022-01-21 21:53:26,2022-01-21 21:53:29,2022-01-21 21:54:24,0.121114253997803,55.4692192077637,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,2,gaussian,2022-01-21 21:51:09,2022-01-21 21:51:09,2022-01-21 21:51:10,2022-01-21 21:51:11,2022-01-21 21:55:30,0.103042364120483,259.250361919403,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,24,empirical,2022-01-21 21:54:55,2022-01-21 21:54:56,2022-01-21 21:54:56,2022-01-21 21:54:56,2022-01-21 21:55:32,0.212277173995972,35.7914118766785,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,24,empirical,2022-01-21 21:54:40,2022-01-21 21:54:41,2022-01-21 21:54:42,2022-01-21 21:54:45,2022-01-21 21:55:44,0.220999956130981,59.7503657341003,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,2,gaussian,2022-01-21 21:51:16,2022-01-21 21:51:17,2022-01-21 21:51:17,2022-01-21 21:51:17,2022-01-21 21:55:47,0.122857570648193,269.671572208405,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,1,empirical,2022-01-21 21:36:23,2022-01-21 21:36:24,2022-01-21 21:36:24,2022-01-21 21:36:25,2022-01-21 21:56:56,0.225949048995972,1230.64059138298,0,1e+05,13,0.3,1,0,1,1
+8,10000,100,32,1,empirical,2022-01-21 21:36:39,2022-01-21 21:36:41,2022-01-21 21:36:42,2022-01-21 21:36:42,2022-01-21 21:57:04,0.345942974090576,1222.28535413742,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,32,empirical,2022-01-21 21:56:32,2022-01-21 21:56:33,2022-01-21 21:56:33,2022-01-21 21:56:33,2022-01-21 21:57:09,0.202940702438355,35.6931293010712,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,4,gaussian,2022-01-21 21:51:54,2022-01-21 21:51:55,2022-01-21 21:51:55,2022-01-21 21:51:57,2022-01-21 21:57:14,0.115460157394409,316.977005243301,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,4,gaussian,2022-01-21 21:52:02,2022-01-21 21:52:03,2022-01-21 21:52:03,2022-01-21 21:52:03,2022-01-21 21:57:19,0.157488822937012,315.62021446228,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,32,empirical,2022-01-21 21:56:15,2022-01-21 21:56:16,2022-01-21 21:56:16,2022-01-21 21:56:19,2022-01-21 21:57:19,0.19478702545166,60.0290837287903,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,1,empirical,2022-01-21 21:58:07,2022-01-21 21:58:08,2022-01-21 21:58:08,2022-01-21 21:58:09,2022-01-21 21:59:34,0.141458511352539,85.2378029823303,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,8,gaussian,2022-01-21 21:52:55,2022-01-21 21:52:56,2022-01-21 21:52:56,2022-01-21 21:52:56,2022-01-21 21:59:55,0.102338314056396,418.586623191834,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,1,empirical,2022-01-21 21:58:28,2022-01-21 21:58:31,2022-01-21 21:58:32,2022-01-21 21:58:32,2022-01-21 21:59:59,1.18911218643188,87.1899955272675,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,2,empirical,2022-01-21 22:00:44,2022-01-21 22:00:45,2022-01-21 22:00:46,2022-01-21 22:00:46,2022-01-21 22:01:05,0.337473392486572,19.2976343631744,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,2,empirical,2022-01-21 22:00:18,2022-01-21 22:00:19,2022-01-21 22:00:19,2022-01-21 22:00:26,2022-01-21 22:01:26,0.231690406799316,59.5328805446625,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,4,empirical,2022-01-21 22:03:02,2022-01-21 22:03:03,2022-01-21 22:03:03,2022-01-21 22:03:04,2022-01-21 22:03:26,0.305055141448975,22.5605819225311,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,4,empirical,2022-01-21 22:02:35,2022-01-21 22:02:37,2022-01-21 22:02:37,2022-01-21 22:02:39,2022-01-21 22:03:28,0.244565725326538,48.8238210678101,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,2,gaussian,2022-01-21 22:01:03,2022-01-21 22:01:05,2022-01-21 22:01:05,2022-01-21 22:01:07,2022-01-21 22:04:36,0.151224374771118,209.681002616882,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,2,gaussian,2022-01-21 22:01:23,2022-01-21 22:01:24,2022-01-21 22:01:24,2022-01-21 22:01:25,2022-01-21 22:04:43,0.14403223991394,198.822361946106,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,8,empirical,2022-01-21 22:05:23,2022-01-21 22:05:24,2022-01-21 22:05:24,2022-01-21 22:05:25,2022-01-21 22:05:49,0.205525636672974,24.5690989494324,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,8,empirical,2022-01-21 22:05:01,2022-01-21 22:05:02,2022-01-21 22:05:02,2022-01-21 22:05:04,2022-01-21 22:06:03,0.136787891387939,58.4930775165558,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,32,gaussian,2022-01-21 21:57:13,2022-01-21 21:57:14,2022-01-21 21:57:15,2022-01-21 21:57:15,2022-01-21 22:06:06,0.205446004867554,531.462213039398,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,2,ctree,2022-01-21 21:51:32,2022-01-21 21:51:32,2022-01-21 21:51:32,2022-01-21 21:51:33,2022-01-21 22:07:32,0.105569839477539,959.760097503662,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,4,gaussian,2022-01-21 22:03:22,2022-01-21 22:03:23,2022-01-21 22:03:23,2022-01-21 22:03:25,2022-01-21 22:07:57,0.139589309692383,272.022380828857,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,4,gaussian,2022-01-21 22:03:54,2022-01-21 22:03:55,2022-01-21 22:03:56,2022-01-21 22:03:56,2022-01-21 22:07:59,0.177375793457031,242.945282697678,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,16,empirical,2022-01-21 22:07:48,2022-01-21 22:07:50,2022-01-21 22:07:50,2022-01-21 22:07:50,2022-01-21 22:08:06,0.131866216659546,15.4726448059082,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,1,gaussian,2022-01-21 21:59:02,2022-01-21 21:59:08,2022-01-21 21:59:09,2022-01-21 21:59:09,2022-01-21 22:08:13,0.597739219665527,543.518223285675,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,16,empirical,2022-01-21 22:07:24,2022-01-21 22:07:26,2022-01-21 22:07:26,2022-01-21 22:07:29,2022-01-21 22:08:14,0.141277551651001,45.8937659263611,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,8,gaussian,2022-01-21 22:06:13,2022-01-21 22:06:14,2022-01-21 22:06:14,2022-01-21 22:06:14,2022-01-21 22:09:31,0.275805473327637,196.473051071167,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,2,ctree,2022-01-21 22:02:10,2022-01-21 22:02:12,2022-01-21 22:02:13,2022-01-21 22:02:13,2022-01-21 22:09:35,0.650844573974609,441.69122672081,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,24,empirical,2022-01-21 22:10:02,2022-01-21 22:10:03,2022-01-21 22:10:03,2022-01-21 22:10:04,2022-01-21 22:10:25,0.111140489578247,21.0787835121155,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,24,empirical,2022-01-21 22:09:42,2022-01-21 22:09:43,2022-01-21 22:09:43,2022-01-21 22:09:46,2022-01-21 22:10:44,0.188132524490356,58.1565132141113,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,4,ctree,2022-01-21 22:04:41,2022-01-21 22:04:42,2022-01-21 22:04:42,2022-01-21 22:04:43,2022-01-21 22:11:11,0.17656135559082,387.862432956696,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,8,gaussian,2022-01-21 22:05:50,2022-01-21 22:05:51,2022-01-21 22:05:51,2022-01-21 22:05:54,2022-01-21 22:11:37,0.20248556137085,342.39463019371,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,16,gaussian,2022-01-21 22:08:06,2022-01-21 22:08:07,2022-01-21 22:08:08,2022-01-21 22:08:10,2022-01-21 22:12:15,0.251741886138916,244.439931154251,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,32,empirical,2022-01-21 22:12:29,2022-01-21 22:12:30,2022-01-21 22:12:30,2022-01-21 22:12:31,2022-01-21 22:12:50,0.241857767105103,18.8952739238739,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,32,empirical,2022-01-21 22:12:10,2022-01-21 22:12:11,2022-01-21 22:12:11,2022-01-21 22:12:14,2022-01-21 22:13:04,0.169980525970459,50.3863186836243,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,8,ctree,2022-01-21 22:07:01,2022-01-21 22:07:03,2022-01-21 22:07:03,2022-01-21 22:07:03,2022-01-21 22:13:47,0.476006984710693,403.676571130753,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,16,ctree,2022-01-21 22:09:14,2022-01-21 22:09:15,2022-01-21 22:09:16,2022-01-21 22:09:16,2022-01-21 22:14:28,0.198874711990356,312.332421779633,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,24,gaussian,2022-01-21 22:11:06,2022-01-21 22:11:07,2022-01-21 22:11:07,2022-01-21 22:11:08,2022-01-21 22:14:47,0.324310302734375,219.77875828743,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,24,gaussian,2022-01-21 22:10:27,2022-01-21 22:10:29,2022-01-21 22:10:29,2022-01-21 22:11:26,2022-01-21 22:15:05,0.343616008758545,219.071800708771,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,24,ctree,2022-01-21 21:55:59,2022-01-21 21:56:00,2022-01-21 21:56:00,2022-01-21 21:56:00,2022-01-21 22:15:36,0.255512237548828,1175.62971973419,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,1,32,ctree,2022-01-21 21:57:50,2022-01-21 21:57:50,2022-01-21 21:57:51,2022-01-21 21:57:51,2022-01-21 22:16:05,0.216298818588257,1093.91692209244,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,24,ctree,2022-01-21 22:11:52,2022-01-21 22:11:53,2022-01-21 22:11:53,2022-01-21 22:11:53,2022-01-21 22:16:39,0.415038347244263,285.934188365936,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,32,gaussian,2022-01-21 22:13:11,2022-01-21 22:13:11,2022-01-21 22:13:12,2022-01-21 22:13:12,2022-01-21 22:16:45,0.162230730056763,212.970506191254,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,32,gaussian,2022-01-21 22:12:48,2022-01-21 22:12:49,2022-01-21 22:12:49,2022-01-21 22:12:54,2022-01-21 22:16:46,0.274789094924927,232.688220262527,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,1,empirical,2022-01-21 22:14:30,2022-01-21 22:14:31,2022-01-21 22:14:32,2022-01-21 22:14:32,2022-01-21 22:16:50,0.216944694519043,138.203840017319,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,2,ctree,2022-01-21 22:01:48,2022-01-21 22:01:53,2022-01-21 22:01:53,2022-01-21 22:02:07,2022-01-21 22:16:51,0.46225118637085,883.582092761994,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,1,empirical,2022-01-21 22:14:57,2022-01-21 22:14:58,2022-01-21 22:14:58,2022-01-21 22:14:59,2022-01-21 22:17:08,0.200497388839722,129.156509160995,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,2,empirical,2022-01-21 22:17:01,2022-01-21 22:17:02,2022-01-21 22:17:02,2022-01-21 22:17:02,2022-01-21 22:17:18,0.233529567718506,15.48814868927,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,2,empirical,2022-01-21 22:16:46,2022-01-21 22:16:47,2022-01-21 22:16:47,2022-01-21 22:16:49,2022-01-21 22:17:51,0.175598859786987,61.9615774154663,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,32,ctree,2022-01-21 22:14:00,2022-01-21 22:14:04,2022-01-21 22:14:04,2022-01-21 22:14:05,2022-01-21 22:18:41,0.36746621131897,276.714837312698,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,4,ctree,2022-01-21 22:04:15,2022-01-21 22:04:18,2022-01-21 22:04:18,2022-01-21 22:04:22,2022-01-21 22:18:42,0.271044731140137,860.798024177551,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,8,ctree,2022-01-21 22:06:43,2022-01-21 22:06:47,2022-01-21 22:06:48,2022-01-21 22:06:57,2022-01-21 22:18:58,0.485182285308838,721.030483961105,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,4,empirical,2022-01-21 22:18:48,2022-01-21 22:18:49,2022-01-21 22:18:49,2022-01-21 22:18:49,2022-01-21 22:19:03,0.176929712295532,13.4992830753326,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,4,empirical,2022-01-21 22:18:29,2022-01-21 22:18:30,2022-01-21 22:18:31,2022-01-21 22:18:33,2022-01-21 22:19:14,0.15146279335022,41.4915561676025,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,16,ctree,2022-01-21 22:08:47,2022-01-21 22:08:48,2022-01-21 22:08:48,2022-01-21 22:08:56,2022-01-21 22:20:56,0.186589002609253,719.957575082779,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,2,gaussian,2022-01-21 22:17:16,2022-01-21 22:17:17,2022-01-21 22:17:17,2022-01-21 22:17:18,2022-01-21 22:21:27,0.127319812774658,248.970601797104,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,2,gaussian,2022-01-21 22:17:33,2022-01-21 22:17:33,2022-01-21 22:17:34,2022-01-21 22:17:34,2022-01-21 22:21:42,0.303115367889404,247.949193477631,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,4,gaussian,2022-01-21 22:19:07,2022-01-21 22:19:08,2022-01-21 22:19:08,2022-01-21 22:19:10,2022-01-21 22:21:50,0.137515306472778,159.775513410568,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,8,empirical,2022-01-21 22:21:33,2022-01-21 22:21:34,2022-01-21 22:21:34,2022-01-21 22:21:35,2022-01-21 22:21:53,0.410574197769165,17.8846871852875,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,8,empirical,2022-01-21 22:21:02,2022-01-21 22:21:03,2022-01-21 22:21:04,2022-01-21 22:21:08,2022-01-21 22:21:58,0.326882839202881,50.183333158493,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,4,gaussian,2022-01-21 22:19:24,2022-01-21 22:19:25,2022-01-21 22:19:25,2022-01-21 22:19:25,2022-01-21 22:22:00,0.134849786758423,154.92019534111,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,1,gaussian,2022-01-21 22:15:18,2022-01-21 22:15:20,2022-01-21 22:15:20,2022-01-21 22:15:20,2022-01-21 22:22:04,0.273510456085205,403.633595228195,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,24,ctree,2022-01-21 22:11:29,2022-01-21 22:11:30,2022-01-21 22:11:31,2022-01-21 22:11:37,2022-01-21 22:22:38,0.227318286895752,660.360733270645,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,1,gaussian,2022-01-21 22:15:42,2022-01-21 22:15:43,2022-01-21 22:15:43,2022-01-21 22:15:44,2022-01-21 22:22:40,0.187559843063355,416.699294805527,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,16,empirical,2022-01-21 22:23:45,2022-01-21 22:23:46,2022-01-21 22:23:46,2022-01-21 22:23:47,2022-01-21 22:24:09,0.345642805099487,21.8081345558167,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,16,empirical,2022-01-21 22:23:18,2022-01-21 22:23:20,2022-01-21 22:23:20,2022-01-21 22:23:25,2022-01-21 22:24:15,0.343698978424072,50.4578335285187,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,4,ctree,2022-01-21 22:20:27,2022-01-21 22:20:29,2022-01-21 22:20:29,2022-01-21 22:20:30,2022-01-21 22:24:25,0.429012537002564,235.408743143082,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,8,gaussian,2022-01-21 22:21:57,2022-01-21 22:21:58,2022-01-21 22:21:58,2022-01-21 22:22:01,2022-01-21 22:24:35,0.177173137664795,154.475384950638,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,2,ctree,2022-01-21 22:18:09,2022-01-21 22:18:09,2022-01-21 22:18:10,2022-01-21 22:18:10,2022-01-21 22:24:51,0.168188810348511,401.378915071487,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,8,gaussian,2022-01-21 22:22:14,2022-01-21 22:22:15,2022-01-21 22:22:15,2022-01-21 22:22:15,2022-01-21 22:24:56,0.211163282394409,160.510772228241,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,32,ctree,2022-01-21 22:13:35,2022-01-21 22:13:37,2022-01-21 22:13:37,2022-01-21 22:13:41,2022-01-21 22:25:02,0.452039480209351,681.259838581085,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,4,ctree,2022-01-21 22:19:54,2022-01-21 22:19:55,2022-01-21 22:19:56,2022-01-21 22:20:00,2022-01-21 22:26:05,0.428579330444336,365.407131910324,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,24,empirical,2022-01-21 22:26:09,2022-01-21 22:26:12,2022-01-21 22:26:12,2022-01-21 22:26:13,2022-01-21 22:26:30,0.41444730758667,17.750617980957,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,24,empirical,2022-01-21 22:25:42,2022-01-21 22:25:43,2022-01-21 22:25:43,2022-01-21 22:25:47,2022-01-21 22:26:40,0.21017599105835,53.4012956619263,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,16,gaussian,2022-01-21 22:24:13,2022-01-21 22:24:14,2022-01-21 22:24:15,2022-01-21 22:24:18,2022-01-21 22:26:47,0.262914657592773,149.178348064423,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,8,ctree,2022-01-21 22:22:55,2022-01-21 22:22:56,2022-01-21 22:22:56,2022-01-21 22:22:57,2022-01-21 22:26:50,0.159201383590698,233.722866296768,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,16,gaussian,2022-01-21 22:24:40,2022-01-21 22:24:41,2022-01-21 22:24:41,2022-01-21 22:24:42,2022-01-21 22:27:09,0.257635116577148,147.821232318878,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,1,ctree,2022-01-21 22:00:00,2022-01-21 22:00:01,2022-01-21 22:00:01,2022-01-21 22:00:02,2022-01-21 22:27:40,0.206462144851685,1658.67733025551,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,32,empirical,2022-01-21 22:28:24,2022-01-21 22:28:26,2022-01-21 22:28:26,2022-01-21 22:28:26,2022-01-21 22:28:43,0.189979553222656,16.4995386600494,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,8,ctree,2022-01-21 22:22:34,2022-01-21 22:22:36,2022-01-21 22:22:36,2022-01-21 22:22:39,2022-01-21 22:28:45,0.194582939147949,366.230029344559,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,32,empirical,2022-01-21 22:27:51,2022-01-21 22:27:52,2022-01-21 22:27:52,2022-01-21 22:27:56,2022-01-21 22:28:46,0.310261726379395,50.0165915489197,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,2,ctree,2022-01-21 22:17:50,2022-01-21 22:17:51,2022-01-21 22:17:51,2022-01-21 22:17:53,2022-01-21 22:29:01,0.205642700195313,667.131421327591,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,16,ctree,2022-01-21 22:25:17,2022-01-21 22:25:18,2022-01-21 22:25:19,2022-01-21 22:25:19,2022-01-21 22:29:03,0.205032825469971,223.933054924011,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,2,1,ctree,2022-01-21 21:59:42,2022-01-21 21:59:43,2022-01-21 21:59:43,2022-01-21 21:59:43,2022-01-21 22:29:05,0.192127704620361,1761.23698711395,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,24,gaussian,2022-01-21 22:26:35,2022-01-21 22:26:36,2022-01-21 22:26:36,2022-01-21 22:26:40,2022-01-21 22:29:07,0.280040502548218,146.89582657814,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,24,gaussian,2022-01-21 22:26:53,2022-01-21 22:26:54,2022-01-21 22:26:54,2022-01-21 22:26:55,2022-01-21 22:29:13,0.193342447280884,138.21481347084,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,24,ctree,2022-01-21 22:27:30,2022-01-21 22:27:31,2022-01-21 22:27:32,2022-01-21 22:27:32,2022-01-21 22:30:53,0.284122467041016,201.362976789475,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,16,ctree,2022-01-21 22:24:57,2022-01-21 22:24:58,2022-01-21 22:24:59,2022-01-21 22:25:02,2022-01-21 22:30:54,0.250876903533936,352.183906078339,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,32,gaussian,2022-01-21 22:28:49,2022-01-21 22:28:50,2022-01-21 22:28:50,2022-01-21 22:28:54,2022-01-21 22:30:55,0.219277620315552,121.395689725876,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,32,gaussian,2022-01-21 22:29:08,2022-01-21 22:29:09,2022-01-21 22:29:09,2022-01-21 22:29:09,2022-01-21 22:31:03,0.145594358444214,113.794147491455,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,24,ctree,2022-01-21 22:27:11,2022-01-21 22:27:12,2022-01-21 22:27:13,2022-01-21 22:27:16,2022-01-21 22:31:50,0.311970710754395,274.085108757019,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,32,ctree,2022-01-21 22:29:40,2022-01-21 22:29:41,2022-01-21 22:29:41,2022-01-21 22:29:41,2022-01-21 22:31:55,0.211369752883911,133.464083433151,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,2,empirical,2022-01-21 22:31:47,2022-01-21 22:31:48,2022-01-21 22:31:48,2022-01-21 22:31:48,2022-01-21 22:32:05,0.126852989196777,16.9229083061218,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,1,empirical,2022-01-21 22:30:03,2022-01-21 22:30:05,2022-01-21 22:30:05,2022-01-21 22:30:05,2022-01-21 22:32:44,0.36572790145874,158.928721666336,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,2,empirical,2022-01-21 22:31:35,2022-01-21 22:31:36,2022-01-21 22:31:36,2022-01-21 22:31:38,2022-01-21 22:33:02,0.172738313674927,84.8098220825195,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,1,empirical,2022-01-21 22:30:28,2022-01-21 22:30:30,2022-01-21 22:30:30,2022-01-21 22:30:30,2022-01-21 22:33:04,0.256429672241211,153.903678894043,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,4,empirical,2022-01-21 22:32:57,2022-01-21 22:32:58,2022-01-21 22:32:59,2022-01-21 22:32:59,2022-01-21 22:33:13,0.255167722702026,14.6000862121582,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,32,ctree,2022-01-21 22:29:23,2022-01-21 22:29:23,2022-01-21 22:29:24,2022-01-21 22:29:27,2022-01-21 22:33:27,0.177758693695068,240.008117675781,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,4,empirical,2022-01-21 22:32:44,2022-01-21 22:32:45,2022-01-21 22:32:45,2022-01-21 22:32:47,2022-01-21 22:33:40,0.147699356079102,52.8816111087799,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,1,ctree,2022-01-21 22:16:03,2022-01-21 22:16:03,2022-01-21 22:16:04,2022-01-21 22:16:04,2022-01-21 22:34:39,0.255949020385742,1114.83027148247,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,8,empirical,2022-01-21 22:34:30,2022-01-21 22:34:32,2022-01-21 22:34:33,2022-01-21 22:34:33,2022-01-21 22:34:53,0.517178535461426,19.2127959728241,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,8,empirical,2022-01-21 22:34:08,2022-01-21 22:34:10,2022-01-21 22:34:10,2022-01-21 22:34:14,2022-01-21 22:34:58,0.311906814575195,44.1680457592011,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,4,1,ctree,2022-01-21 22:16:26,2022-01-21 22:16:30,2022-01-21 22:16:30,2022-01-21 22:16:30,2022-01-21 22:35:20,0.145765066146851,1129.97586965561,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,4,gaussian,2022-01-21 22:33:11,2022-01-21 22:33:12,2022-01-21 22:33:12,2022-01-21 22:33:15,2022-01-21 22:35:50,0.253901481628418,154.907715559006,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,4,gaussian,2022-01-21 22:33:25,2022-01-21 22:33:26,2022-01-21 22:33:27,2022-01-21 22:33:27,2022-01-21 22:36:21,0.228247404098511,174.19814491272,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,2,gaussian,2022-01-21 22:31:59,2022-01-21 22:32:00,2022-01-21 22:32:00,2022-01-21 22:32:01,2022-01-21 22:36:32,0.104673147201538,270.136843681335,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,8,gaussian,2022-01-21 22:34:53,2022-01-21 22:34:54,2022-01-21 22:34:54,2022-01-21 22:34:58,2022-01-21 22:37:00,0.317301511764526,121.768710613251,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,8,gaussian,2022-01-21 22:35:11,2022-01-21 22:35:13,2022-01-21 22:35:13,2022-01-21 22:35:14,2022-01-21 22:37:11,0.24849534034729,117.243046760559,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,16,empirical,2022-01-21 22:36:59,2022-01-21 22:37:01,2022-01-21 22:37:01,2022-01-21 22:37:02,2022-01-21 22:37:15,0.271949529647827,13.7412049770355,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,2,gaussian,2022-01-21 22:32:09,2022-01-21 22:32:10,2022-01-21 22:32:10,2022-01-21 22:32:10,2022-01-21 22:37:18,0.193725109100342,308.168631076813,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,16,empirical,2022-01-21 22:36:31,2022-01-21 22:36:33,2022-01-21 22:36:33,2022-01-21 22:36:38,2022-01-21 22:37:24,0.286097288131714,46.4927845001221,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,1,gaussian,2022-01-21 22:30:46,2022-01-21 22:30:46,2022-01-21 22:30:47,2022-01-21 22:30:47,2022-01-21 22:38:40,0.1725754737854,473.471204280853,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,1,gaussian,2022-01-21 22:31:00,2022-01-21 22:31:01,2022-01-21 22:31:01,2022-01-21 22:31:01,2022-01-21 22:39:01,0.213510274887085,480.263280630112,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,16,gaussian,2022-01-21 22:37:17,2022-01-21 22:37:19,2022-01-21 22:37:19,2022-01-21 22:37:22,2022-01-21 22:39:26,0.287959814071655,124.199512958527,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,8,ctree,2022-01-21 22:36:01,2022-01-21 22:36:04,2022-01-21 22:36:04,2022-01-21 22:36:05,2022-01-21 22:39:38,0.491583108901978,212.733715772629,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,16,gaussian,2022-01-21 22:37:36,2022-01-21 22:37:37,2022-01-21 22:37:37,2022-01-21 22:37:38,2022-01-21 22:39:39,0.302842855453491,121.034599304199,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,24,empirical,2022-01-21 22:39:38,2022-01-21 22:39:40,2022-01-21 22:39:40,2022-01-21 22:39:40,2022-01-21 22:39:53,0.283218383789063,13.1604814529419,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,24,empirical,2022-01-21 22:39:09,2022-01-21 22:39:10,2022-01-21 22:39:11,2022-01-21 22:39:17,2022-01-21 22:40:06,0.521362543106079,49.1333849430084,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,4,ctree,2022-01-21 22:33:54,2022-01-21 22:33:55,2022-01-21 22:33:55,2022-01-21 22:33:56,2022-01-21 22:40:17,0.245711803436279,381.208502531052,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,8,ctree,2022-01-21 22:35:36,2022-01-21 22:35:38,2022-01-21 22:35:38,2022-01-21 22:35:43,2022-01-21 22:40:38,0.375783920288086,294.619994878769,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,24,gaussian,2022-01-21 22:39:59,2022-01-21 22:40:00,2022-01-21 22:40:01,2022-01-21 22:40:05,2022-01-21 22:42:05,0.236182689666748,120.445372343063,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,16,ctree,2022-01-21 22:38:31,2022-01-21 22:38:33,2022-01-21 22:38:34,2022-01-21 22:38:35,2022-01-21 22:42:06,0.33737325668335,211.672404527664,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,4,ctree,2022-01-21 22:33:39,2022-01-21 22:33:41,2022-01-21 22:33:41,2022-01-21 22:33:43,2022-01-21 22:42:06,0.152687311172485,503.346331834793,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,24,gaussian,2022-01-21 22:40:18,2022-01-21 22:40:20,2022-01-21 22:40:20,2022-01-21 22:40:20,2022-01-21 22:42:20,0.185132026672363,119.76815199852,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,32,empirical,2022-01-21 22:42:17,2022-01-21 22:42:18,2022-01-21 22:42:19,2022-01-21 22:42:19,2022-01-21 22:42:30,0.329325675964355,10.8604383468628,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,16,ctree,2022-01-21 22:38:01,2022-01-21 22:38:03,2022-01-21 22:38:03,2022-01-21 22:38:09,2022-01-21 22:42:33,0.311623573303223,264.84286570549,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,32,empirical,2022-01-21 22:41:52,2022-01-21 22:41:54,2022-01-21 22:41:54,2022-01-21 22:42:00,2022-01-21 22:42:37,0.454149007797241,37.1734952926636,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,2,ctree,2022-01-21 22:32:32,2022-01-21 22:32:33,2022-01-21 22:32:33,2022-01-21 22:32:33,2022-01-21 22:43:25,0.259046316146851,652.208637714386,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,24,ctree,2022-01-21 22:41:17,2022-01-21 22:41:19,2022-01-21 22:41:19,2022-01-21 22:41:19,2022-01-21 22:44:12,0.218549966812134,172.9876101017,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,32,gaussian,2022-01-21 22:42:33,2022-01-21 22:42:33,2022-01-21 22:42:34,2022-01-21 22:42:36,2022-01-21 22:44:18,0.147002696990967,101.280863046646,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,32,gaussian,2022-01-21 22:42:48,2022-01-21 22:42:49,2022-01-21 22:42:49,2022-01-21 22:42:49,2022-01-21 22:44:23,0.14326548576355,93.6687562465668,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,24,ctree,2022-01-21 22:40:46,2022-01-21 22:40:48,2022-01-21 22:40:48,2022-01-21 22:40:53,2022-01-21 22:44:45,0.319091796875,232.262341737747,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,32,ctree,2022-01-21 22:43:37,2022-01-21 22:43:39,2022-01-21 22:43:40,2022-01-21 22:43:41,2022-01-21 22:45:14,0.371606349945068,93.1474964618683,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,2,ctree,2022-01-21 22:32:20,2022-01-21 22:32:21,2022-01-21 22:32:21,2022-01-21 22:32:23,2022-01-21 22:45:26,0.255661487579346,782.361234426498,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,32,ctree,2022-01-21 22:43:08,2022-01-21 22:43:10,2022-01-21 22:43:10,2022-01-21 22:43:15,2022-01-21 22:45:50,0.454951763153076,155.522286653519,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,2,empirical,2022-01-21 22:45:31,2022-01-21 22:45:32,2022-01-21 22:45:32,2022-01-21 22:45:32,2022-01-21 22:45:53,0.167878866195679,21.1563355922699,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,4,empirical,2022-01-21 22:46:25,2022-01-21 22:46:26,2022-01-21 22:46:26,2022-01-21 22:46:26,2022-01-21 22:46:45,0.302005529403687,18.4034526348114,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,4,empirical,2022-01-21 22:46:16,2022-01-21 22:46:17,2022-01-21 22:46:17,2022-01-21 22:46:19,2022-01-21 22:47:50,0.162072896957397,91.4509291648865,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,2,empirical,2022-01-21 22:45:20,2022-01-21 22:45:21,2022-01-21 22:45:21,2022-01-21 22:45:23,2022-01-21 22:47:52,0.239159345626831,149.316841840744,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,8,empirical,2022-01-21 22:47:54,2022-01-21 22:47:56,2022-01-21 22:47:56,2022-01-21 22:47:57,2022-01-21 22:48:20,0.356923818588257,22.9967947006226,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,8,empirical,2022-01-21 22:47:35,2022-01-21 22:47:37,2022-01-21 22:47:37,2022-01-21 22:47:41,2022-01-21 22:48:44,0.542655229568481,62.5721321105957,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,1,empirical,2022-01-21 22:44:07,2022-01-21 22:44:09,2022-01-21 22:44:10,2022-01-21 22:44:10,2022-01-21 22:49:22,0.4885573387146,311.512584686279,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,1,empirical,2022-01-21 22:44:25,2022-01-21 22:44:26,2022-01-21 22:44:26,2022-01-21 22:44:27,2022-01-21 22:49:42,0.149182796478271,315.930994510651,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,4,gaussian,2022-01-21 22:46:35,2022-01-21 22:46:36,2022-01-21 22:46:36,2022-01-21 22:46:38,2022-01-21 22:50:11,0.320524454116821,213.06708574295,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,4,gaussian,2022-01-21 22:46:48,2022-01-21 22:46:49,2022-01-21 22:46:49,2022-01-21 22:46:49,2022-01-21 22:50:41,0.181196689605713,232.186753034592,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,8,gaussian,2022-01-21 22:48:18,2022-01-21 22:48:20,2022-01-21 22:48:20,2022-01-21 22:48:26,2022-01-21 22:50:51,0.626577854156494,144.440980195999,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,16,empirical,2022-01-21 22:50:39,2022-01-21 22:50:43,2022-01-21 22:50:43,2022-01-21 22:50:44,2022-01-21 22:51:01,0.550537586212158,17.2531402111053,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,8,gaussian,2022-01-21 22:48:39,2022-01-21 22:48:42,2022-01-21 22:48:42,2022-01-21 22:48:43,2022-01-21 22:51:04,0.529030561447144,140.757581233978,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,2,gaussian,2022-01-21 22:45:41,2022-01-21 22:45:42,2022-01-21 22:45:42,2022-01-21 22:45:43,2022-01-21 22:51:14,0.124796390533447,330.832180023193,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,16,empirical,2022-01-21 22:50:06,2022-01-21 22:50:08,2022-01-21 22:50:09,2022-01-21 22:50:15,2022-01-21 22:51:19,0.780651330947876,64.0196824073792,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,2,gaussian,2022-01-21 22:45:51,2022-01-21 22:45:52,2022-01-21 22:45:52,2022-01-21 22:45:52,2022-01-21 22:52:09,0.153620004653931,377.223563194275,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,16,gaussian,2022-01-21 22:51:10,2022-01-21 22:51:11,2022-01-21 22:51:11,2022-01-21 22:51:15,2022-01-21 22:52:47,0.254809617996216,91.6232008934021,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,16,gaussian,2022-01-21 22:51:37,2022-01-21 22:51:40,2022-01-21 22:51:41,2022-01-21 22:51:42,2022-01-21 22:53:01,0.68389630317688,79.4774351119995,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,24,empirical,2022-01-21 22:53:47,2022-01-21 22:53:49,2022-01-21 22:53:50,2022-01-21 22:53:51,2022-01-21 22:54:12,0.660815000534058,21.2565610408783,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,24,empirical,2022-01-21 22:53:14,2022-01-21 22:53:15,2022-01-21 22:53:16,2022-01-21 22:53:21,2022-01-21 22:54:27,0.398909568786621,66.5011882781982,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,8,ctree,2022-01-21 22:49:34,2022-01-21 22:49:37,2022-01-21 22:49:37,2022-01-21 22:49:38,2022-01-21 22:54:30,0.44926643371582,291.756458282471,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,8,ctree,2022-01-21 22:49:05,2022-01-21 22:49:08,2022-01-21 22:49:09,2022-01-21 22:49:14,2022-01-21 22:54:50,0.588739156723023,335.885450839996,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,16,ctree,2022-01-21 22:52:52,2022-01-21 22:52:54,2022-01-21 22:52:55,2022-01-21 22:52:56,2022-01-21 22:55:31,0.405108451843262,155.535728693008,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,16,ctree,2022-01-21 22:52:15,2022-01-21 22:52:18,2022-01-21 22:52:20,2022-01-21 22:52:27,2022-01-21 22:55:44,1.1738178730011,197.750079154968,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,1,gaussian,2022-01-21 22:44:37,2022-01-21 22:44:38,2022-01-21 22:44:38,2022-01-21 22:44:38,2022-01-21 22:55:51,0.133188247680664,672.828920841217,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,1,gaussian,2022-01-21 22:44:48,2022-01-21 22:44:50,2022-01-21 22:44:50,2022-01-21 22:44:50,2022-01-21 22:55:54,0.238940715789795,664.258563518524,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,4,ctree,2022-01-21 22:47:20,2022-01-21 22:47:21,2022-01-21 22:47:21,2022-01-21 22:47:22,2022-01-21 22:56:00,0.413654804229736,518.207196235657,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,24,gaussian,2022-01-21 22:54:29,2022-01-21 22:54:30,2022-01-21 22:54:30,2022-01-21 22:54:35,2022-01-21 22:56:14,0.443702220916748,99.0784475803375,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,24,gaussian,2022-01-21 22:54:56,2022-01-21 22:55:00,2022-01-21 22:55:00,2022-01-21 22:55:02,2022-01-21 22:56:20,0.511928558349609,78.5577299594879,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,1,ctree,2022-01-21 22:31:11,2022-01-21 22:31:12,2022-01-21 22:31:12,2022-01-21 22:31:12,2022-01-21 22:56:29,0.109898328781128,1517.13978362083,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,32,empirical,2022-01-21 22:56:38,2022-01-21 22:56:40,2022-01-21 22:56:40,2022-01-21 22:56:40,2022-01-21 22:56:55,0.234601020812988,15.2211899757385,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,4,ctree,2022-01-21 22:47:03,2022-01-21 22:47:04,2022-01-21 22:47:05,2022-01-21 22:47:08,2022-01-21 22:57:12,0.387108325958252,604.210825204849,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,32,empirical,2022-01-21 22:56:24,2022-01-21 22:56:26,2022-01-21 22:56:26,2022-01-21 22:56:31,2022-01-21 22:57:26,0.307132720947266,55.1622960567474,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,24,ctree,2022-01-21 22:56:05,2022-01-21 22:56:06,2022-01-21 22:56:07,2022-01-21 22:56:07,2022-01-21 22:58:08,0.132061719894409,121.364614963532,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,8,1,ctree,2022-01-21 22:31:22,2022-01-21 22:31:23,2022-01-21 22:31:23,2022-01-21 22:31:23,2022-01-21 22:58:13,0.120223999023438,1609.75892066956,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,24,ctree,2022-01-21 22:55:37,2022-01-21 22:55:41,2022-01-21 22:55:41,2022-01-21 22:55:48,2022-01-21 22:58:21,0.708167791366577,152.317051649094,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,32,gaussian,2022-01-21 22:57:02,2022-01-21 22:57:03,2022-01-21 22:57:04,2022-01-21 22:57:09,2022-01-21 22:58:40,0.346423149108887,90.6698167324066,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,32,gaussian,2022-01-21 22:57:31,2022-01-21 22:57:33,2022-01-21 22:57:34,2022-01-21 22:57:35,2022-01-21 22:58:46,0.689708232879639,71.2142984867096,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,2,ctree,2022-01-21 22:46:07,2022-01-21 22:46:08,2022-01-21 22:46:08,2022-01-21 22:46:08,2022-01-21 22:59:10,0.228174209594727,782.302576780319,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,32,ctree,2022-01-21 22:58:39,2022-01-21 22:58:40,2022-01-21 22:58:40,2022-01-21 22:58:41,2022-01-21 22:59:37,0.253966808319092,56.2891671657562,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,32,ctree,2022-01-21 22:58:11,2022-01-21 22:58:13,2022-01-21 22:58:14,2022-01-21 22:58:19,2022-01-21 23:00:02,0.597131729125977,102.375074863434,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,2,empirical,2022-01-21 23:00:12,2022-01-21 23:00:12,2022-01-21 23:00:12,2022-01-21 23:00:12,2022-01-21 23:00:36,0.101426362991333,23.8866410255432,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,2,ctree,2022-01-21 22:45:59,2022-01-21 22:45:59,2022-01-21 22:45:59,2022-01-21 22:46:01,2022-01-21 23:01:07,0.219992160797119,905.683482170105,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,4,empirical,2022-01-21 23:01:03,2022-01-21 23:01:04,2022-01-21 23:01:04,2022-01-21 23:01:04,2022-01-21 23:01:23,0.158360242843628,19.0518290996552,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,8,empirical,2022-01-21 23:02:31,2022-01-21 23:02:33,2022-01-21 23:02:34,2022-01-21 23:02:35,2022-01-21 23:03:08,0.616332769393921,33.3605146408081,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,4,empirical,2022-01-21 23:00:53,2022-01-21 23:00:54,2022-01-21 23:00:54,2022-01-21 23:00:56,2022-01-21 23:03:19,0.173843383789063,142.868554592133,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,2,empirical,2022-01-21 23:00:04,2022-01-21 23:00:05,2022-01-21 23:00:05,2022-01-21 23:00:06,2022-01-21 23:03:55,0.0894968509674072,228.905282974243,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,8,empirical,2022-01-21 23:02:11,2022-01-21 23:02:13,2022-01-21 23:02:13,2022-01-21 23:02:17,2022-01-21 23:03:55,0.285260677337647,98.3122055530548,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,4,gaussian,2022-01-21 23:01:22,2022-01-21 23:01:23,2022-01-21 23:01:24,2022-01-21 23:01:24,2022-01-21 23:05:19,0.214018106460571,235.138467073441,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,4,gaussian,2022-01-21 23:01:13,2022-01-21 23:01:14,2022-01-21 23:01:14,2022-01-21 23:01:17,2022-01-21 23:05:19,0.236996173858643,242.379977464676,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,8,gaussian,2022-01-21 23:02:56,2022-01-21 23:02:58,2022-01-21 23:02:59,2022-01-21 23:03:05,2022-01-21 23:05:47,0.69096851348877,162.256893634796,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,16,empirical,2022-01-21 23:05:32,2022-01-21 23:05:35,2022-01-21 23:05:36,2022-01-21 23:05:37,2022-01-21 23:06:06,0.658001899719238,28.8231976032257,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,8,gaussian,2022-01-21 23:03:25,2022-01-21 23:03:28,2022-01-21 23:03:29,2022-01-21 23:03:30,2022-01-21 23:06:09,0.725349187850952,159.656928300858,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,16,empirical,2022-01-21 23:04:54,2022-01-21 23:04:58,2022-01-21 23:04:59,2022-01-21 23:05:06,2022-01-21 23:06:45,0.702497005462647,98.9050166606903,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,2,gaussian,2022-01-21 23:00:19,2022-01-21 23:00:20,2022-01-21 23:00:20,2022-01-21 23:00:21,2022-01-21 23:06:55,0.132981061935425,393.448242902756,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,1,empirical,2022-01-21 22:58:54,2022-01-21 22:58:55,2022-01-21 22:58:55,2022-01-21 22:58:55,2022-01-21 23:06:55,0.15290379524231,480.150446891785,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,1,empirical,2022-01-21 22:59:07,2022-01-21 22:59:08,2022-01-21 22:59:08,2022-01-21 22:59:08,2022-01-21 23:07:24,0.135558366775513,496.058737754822,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,2,gaussian,2022-01-21 23:00:27,2022-01-21 23:00:27,2022-01-21 23:00:28,2022-01-21 23:00:28,2022-01-21 23:07:41,0.183258771896362,433.611840724945,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,16,gaussian,2022-01-21 23:06:11,2022-01-21 23:06:12,2022-01-21 23:06:13,2022-01-21 23:06:17,2022-01-21 23:08:20,0.300452709197998,123.592937707901,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,16,gaussian,2022-01-21 23:06:39,2022-01-21 23:06:42,2022-01-21 23:06:42,2022-01-21 23:06:43,2022-01-21 23:08:28,0.427873134613037,104.869209527969,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,24,empirical,2022-01-21 23:08:55,2022-01-21 23:08:58,2022-01-21 23:08:59,2022-01-21 23:09:00,2022-01-21 23:09:23,0.662010192871094,22.8727989196777,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,8,ctree,2022-01-21 23:04:22,2022-01-21 23:04:25,2022-01-21 23:04:26,2022-01-21 23:04:27,2022-01-21 23:09:42,0.91907525062561,314.682576417923,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,24,empirical,2022-01-21 23:08:27,2022-01-21 23:08:28,2022-01-21 23:08:29,2022-01-21 23:08:35,2022-01-21 23:09:52,0.622437715530396,77.2132482528687,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,8,ctree,2022-01-21 23:03:56,2022-01-21 23:03:58,2022-01-21 23:03:59,2022-01-21 23:04:04,2022-01-21 23:10:29,0.372728586196899,384.311139583588,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,16,ctree,2022-01-21 23:07:48,2022-01-21 23:07:51,2022-01-21 23:07:52,2022-01-21 23:07:53,2022-01-21 23:11:22,0.732694625854492,209.310985565186,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,4,ctree,2022-01-21 23:01:52,2022-01-21 23:01:55,2022-01-21 23:01:55,2022-01-21 23:01:56,2022-01-21 23:11:24,0.377866744995117,568.152215003967,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,24,gaussian,2022-01-21 23:10:10,2022-01-21 23:10:12,2022-01-21 23:10:13,2022-01-21 23:10:14,2022-01-21 23:11:24,0.535529136657715,70.1305189132691,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,24,gaussian,2022-01-21 23:09:48,2022-01-21 23:09:50,2022-01-21 23:09:50,2022-01-21 23:09:55,2022-01-21 23:11:29,0.369015216827393,94.3659627437592,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,1,gaussian,2022-01-21 22:59:22,2022-01-21 22:59:23,2022-01-21 22:59:23,2022-01-21 22:59:23,2022-01-21 23:11:46,0.354739189147949,742.093995809555,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,1,gaussian,2022-01-21 22:59:36,2022-01-21 22:59:36,2022-01-21 22:59:37,2022-01-21 22:59:37,2022-01-21 23:12:04,0.201120615005493,747.384329557419,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,16,ctree,2022-01-21 23:07:13,2022-01-21 23:07:16,2022-01-21 23:07:17,2022-01-21 23:07:23,2022-01-21 23:12:09,0.567658185958862,285.673801422119,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,4,ctree,2022-01-21 23:01:36,2022-01-21 23:01:38,2022-01-21 23:01:38,2022-01-21 23:01:42,2022-01-21 23:12:23,0.232872486114502,640.908837795258,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,32,empirical,2022-01-21 23:12:27,2022-01-21 23:12:30,2022-01-21 23:12:31,2022-01-21 23:12:32,2022-01-21 23:12:48,1.05334401130676,16.4307479858398,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,32,empirical,2022-01-21 23:11:53,2022-01-21 23:11:54,2022-01-21 23:11:55,2022-01-21 23:11:58,2022-01-21 23:13:02,0.275591135025024,63.8064095973969,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,24,ctree,2022-01-21 23:11:30,2022-01-21 23:11:31,2022-01-21 23:11:31,2022-01-21 23:11:32,2022-01-21 23:13:05,0.232386589050293,93.2567884922028,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,24,ctree,2022-01-21 23:10:59,2022-01-21 23:11:04,2022-01-21 23:11:05,2022-01-21 23:11:12,2022-01-21 23:13:35,0.741848707199097,142.907009840012,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,32,gaussian,2022-01-21 23:13:16,2022-01-21 23:13:18,2022-01-21 23:13:19,2022-01-21 23:13:19,2022-01-21 23:14:15,0.341586112976074,55.6804964542389,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,32,gaussian,2022-01-21 23:13:02,2022-01-21 23:13:03,2022-01-21 23:13:03,2022-01-21 23:13:07,2022-01-21 23:14:27,0.443306922912598,80.574419260025,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,2,ctree,2022-01-21 23:00:44,2022-01-21 23:00:45,2022-01-21 23:00:45,2022-01-21 23:00:45,2022-01-21 23:14:45,0.141685724258423,839.488871097565,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,1,ctree,2022-01-21 22:44:59,2022-01-21 22:45:00,2022-01-21 22:45:00,2022-01-21 22:45:00,2022-01-21 23:15:13,0.166231155395508,1813.10887289047,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,32,ctree,2022-01-21 23:14:16,2022-01-21 23:14:18,2022-01-21 23:14:18,2022-01-21 23:14:18,2022-01-21 23:15:29,0.159716844558716,70.7030262947083,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,32,ctree,2022-01-21 23:13:47,2022-01-21 23:13:51,2022-01-21 23:13:51,2022-01-21 23:13:59,2022-01-21 23:15:42,0.58564019203186,102.828152894974,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,16,1,ctree,2022-01-21 22:45:10,2022-01-21 22:45:11,2022-01-21 22:45:11,2022-01-21 22:45:11,2022-01-21 23:15:45,0.211512327194214,1833.67788076401,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,2,empirical,2022-01-21 23:15:58,2022-01-21 23:15:58,2022-01-21 23:15:58,2022-01-21 23:15:59,2022-01-21 23:16:27,0.115360021591187,28.3638520240784,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,2,ctree,2022-01-21 23:00:36,2022-01-21 23:00:37,2022-01-21 23:00:37,2022-01-21 23:00:39,2022-01-21 23:16:48,0.199713230133057,969.217360973358,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,4,empirical,2022-01-21 23:16:47,2022-01-21 23:16:48,2022-01-21 23:16:48,2022-01-21 23:16:48,2022-01-21 23:17:09,0.316717624664307,20.2307696342468,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,8,empirical,2022-01-21 23:18:08,2022-01-21 23:18:10,2022-01-21 23:18:11,2022-01-21 23:18:11,2022-01-21 23:18:53,0.361157894134522,41.7231628894806,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,4,empirical,2022-01-21 23:16:38,2022-01-21 23:16:39,2022-01-21 23:16:39,2022-01-21 23:16:41,2022-01-21 23:19:56,0.139171600341797,194.434577465057,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,8,empirical,2022-01-21 23:17:51,2022-01-21 23:17:53,2022-01-21 23:17:54,2022-01-21 23:17:57,2022-01-21 23:20:03,0.339982986450195,125.338730812073,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,2,empirical,2022-01-21 23:15:50,2022-01-21 23:15:51,2022-01-21 23:15:51,2022-01-21 23:15:52,2022-01-21 23:21:17,0.107144594192505,324.658476829529,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,8,gaussian,2022-01-21 23:18:31,2022-01-21 23:18:34,2022-01-21 23:18:34,2022-01-21 23:18:41,2022-01-21 23:21:48,0.6770920753479,186.761570453644,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,16,empirical,2022-01-21 23:21:28,2022-01-21 23:21:33,2022-01-21 23:21:34,2022-01-21 23:21:34,2022-01-21 23:22:05,0.769256353378296,30.0707681179047,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,4,gaussian,2022-01-21 23:17:08,2022-01-21 23:17:09,2022-01-21 23:17:10,2022-01-21 23:17:10,2022-01-21 23:22:07,0.356031656265259,297.543975114822,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,4,gaussian,2022-01-21 23:16:58,2022-01-21 23:16:59,2022-01-21 23:16:59,2022-01-21 23:17:02,2022-01-21 23:22:08,0.3267822265625,305.888471364975,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,8,gaussian,2022-01-21 23:19:01,2022-01-21 23:19:05,2022-01-21 23:19:06,2022-01-21 23:19:07,2022-01-21 23:22:09,0.639943361282349,181.976780653,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,16,empirical,2022-01-21 23:20:48,2022-01-21 23:20:51,2022-01-21 23:20:52,2022-01-21 23:20:58,2022-01-21 23:22:36,0.788543701171875,98.364565372467,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,2,gaussian,2022-01-21 23:16:05,2022-01-21 23:16:06,2022-01-21 23:16:06,2022-01-21 23:16:07,2022-01-21 23:23:15,0.171708822250366,427.113997459412,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,2,gaussian,2022-01-21 23:16:13,2022-01-21 23:16:14,2022-01-21 23:16:14,2022-01-21 23:16:14,2022-01-21 23:23:58,0.118020296096802,463.605856895447,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,16,gaussian,2022-01-21 23:22:12,2022-01-21 23:22:13,2022-01-21 23:22:13,2022-01-21 23:22:18,2022-01-21 23:24:13,0.205774784088135,114.940622806549,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,16,gaussian,2022-01-21 23:22:35,2022-01-21 23:22:38,2022-01-21 23:22:38,2022-01-21 23:22:39,2022-01-21 23:24:14,0.787665367126465,95.1328811645508,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,24,empirical,2022-01-21 23:24:54,2022-01-21 23:24:57,2022-01-21 23:24:58,2022-01-21 23:24:59,2022-01-21 23:25:29,0.920004606246948,30.150486946106,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,1,empirical,2022-01-21 23:14:33,2022-01-21 23:14:34,2022-01-21 23:14:34,2022-01-21 23:14:35,2022-01-21 23:25:46,0.340062141418457,671.17283654213,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,8,ctree,2022-01-21 23:20:14,2022-01-21 23:20:17,2022-01-21 23:20:17,2022-01-21 23:20:19,2022-01-21 23:25:52,0.716333866119385,333.34334564209,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,1,empirical,2022-01-21 23:14:51,2022-01-21 23:14:53,2022-01-21 23:14:53,2022-01-21 23:14:54,2022-01-21 23:26:05,0.190628051757813,671.602699279785,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,24,empirical,2022-01-21 23:24:23,2022-01-21 23:24:25,2022-01-21 23:24:25,2022-01-21 23:24:31,2022-01-21 23:26:22,0.386958837509155,110.625061988831,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,8,ctree,2022-01-21 23:19:36,2022-01-21 23:19:40,2022-01-21 23:19:40,2022-01-21 23:19:47,2022-01-21 23:26:51,0.722717046737671,424.375296592712,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,16,ctree,2022-01-21 23:23:54,2022-01-21 23:23:58,2022-01-21 23:23:58,2022-01-21 23:23:59,2022-01-21 23:27:05,0.375831365585327,185.630403995514,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,16,ctree,2022-01-21 23:23:12,2022-01-21 23:23:16,2022-01-21 23:23:17,2022-01-21 23:23:25,2022-01-21 23:27:33,0.929568529129028,248.059343338013,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,24,gaussian,2022-01-21 23:26:15,2022-01-21 23:26:18,2022-01-21 23:26:18,2022-01-21 23:26:19,2022-01-21 23:27:46,0.484882593154907,86.7589671611786,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,4,ctree,2022-01-21 23:17:35,2022-01-21 23:17:37,2022-01-21 23:17:38,2022-01-21 23:17:38,2022-01-21 23:27:46,0.38923192024231,607.946191072464,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,24,gaussian,2022-01-21 23:25:42,2022-01-21 23:25:44,2022-01-21 23:25:45,2022-01-21 23:25:51,2022-01-21 23:27:54,0.337574243545532,123.366424798965,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,32,empirical,2022-01-21 23:28:26,2022-01-21 23:28:29,2022-01-21 23:28:29,2022-01-21 23:28:30,2022-01-21 23:28:49,0.422631978988647,19.1013867855072,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,1,gaussian,2022-01-21 23:15:08,2022-01-21 23:15:08,2022-01-21 23:15:09,2022-01-21 23:15:09,2022-01-21 23:28:50,0.344911575317383,820.982033967972,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,1,gaussian,2022-01-21 23:15:23,2022-01-21 23:15:24,2022-01-21 23:15:24,2022-01-21 23:15:24,2022-01-21 23:29:33,0.264777898788452,849.050582885742,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,32,empirical,2022-01-21 23:27:59,2022-01-21 23:28:01,2022-01-21 23:28:01,2022-01-21 23:28:06,2022-01-21 23:29:35,0.129807233810425,88.9248158931732,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,4,ctree,2022-01-21 23:17:20,2022-01-21 23:17:22,2022-01-21 23:17:22,2022-01-21 23:17:26,2022-01-21 23:29:36,0.635373115539551,730.156134605408,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,24,ctree,2022-01-21 23:27:39,2022-01-21 23:27:41,2022-01-21 23:27:41,2022-01-21 23:27:41,2022-01-21 23:30:13,0.296672582626343,151.561917066574,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,32,gaussian,2022-01-21 23:29:29,2022-01-21 23:29:31,2022-01-21 23:29:31,2022-01-21 23:29:32,2022-01-21 23:30:29,0.697615385055542,56.9771840572357,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,32,gaussian,2022-01-21 23:29:07,2022-01-21 23:29:08,2022-01-21 23:29:08,2022-01-21 23:29:14,2022-01-21 23:30:39,0.479684352874756,84.5930240154266,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,24,ctree,2022-01-21 23:27:11,2022-01-21 23:27:15,2022-01-21 23:27:16,2022-01-21 23:27:22,2022-01-21 23:30:57,0.700387001037598,215.529710769653,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,32,ctree,2022-01-21 23:30:41,2022-01-21 23:30:42,2022-01-21 23:30:42,2022-01-21 23:30:42,2022-01-21 23:31:52,0.25790548324585,69.5653491020203,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,2,ctree,2022-01-21 23:16:29,2022-01-21 23:16:31,2022-01-21 23:16:31,2022-01-21 23:16:31,2022-01-21 23:31:57,0.202917337417603,926.150607585907,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,1,ctree,2022-01-21 22:59:46,2022-01-21 22:59:46,2022-01-21 22:59:47,2022-01-21 22:59:47,2022-01-21 23:32:01,0.300891637802124,1934.46302509308,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,32,ctree,2022-01-21 23:30:17,2022-01-21 23:30:21,2022-01-21 23:30:22,2022-01-21 23:30:28,2022-01-21 23:32:06,0.595257759094238,98.6756672859192,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,24,1,ctree,2022-01-21 22:59:56,2022-01-21 22:59:57,2022-01-21 22:59:57,2022-01-21 22:59:57,2022-01-21 23:32:22,0.228708744049072,1944.59611845017,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,2,ctree,2022-01-21 23:16:21,2022-01-21 23:16:22,2022-01-21 23:16:22,2022-01-21 23:16:24,2022-01-21 23:33:10,0.143362998962402,1006.09124326706,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,1,ctree,2022-01-21 23:15:35,2022-01-21 23:15:35,2022-01-21 23:15:36,2022-01-21 23:15:36,2022-01-21 23:41:05,0.308120727539063,1528.82672333717,0,1e+05,13,0.3,1,0,1,1
+9,1000,100,32,1,ctree,2022-01-21 23:15:43,2022-01-21 23:15:44,2022-01-21 23:15:44,2022-01-21 23:15:44,2022-01-21 23:42:46,0.137923240661621,1622.01966881752,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,2,empirical,2022-01-21 23:45:03,2022-01-21 23:45:08,2022-01-21 23:45:08,2022-01-21 23:45:08,2022-01-21 23:47:40,0.259602069854736,151.541785955429,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,2,empirical,2022-01-21 23:44:35,2022-01-21 23:44:36,2022-01-21 23:44:36,2022-01-21 23:44:44,2022-01-21 23:47:54,0.0859007835388184,189.539999723434,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,1,empirical,2022-01-21 23:42:58,2022-01-21 23:42:59,2022-01-21 23:42:59,2022-01-21 23:42:59,2022-01-21 23:48:07,0.126839876174927,307.438761472702,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,1,empirical,2022-01-21 23:42:39,2022-01-21 23:42:40,2022-01-21 23:42:40,2022-01-21 23:42:40,2022-01-21 23:48:09,0.125292539596558,329.293765306473,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,2,gaussian,2022-01-21 23:45:24,2022-01-21 23:45:26,2022-01-21 23:45:26,2022-01-21 23:45:31,2022-01-21 23:48:32,0.491873025894165,180.712240934372,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,2,gaussian,2022-01-21 23:45:41,2022-01-21 23:45:43,2022-01-21 23:45:43,2022-01-21 23:45:43,2022-01-21 23:48:49,0.383482933044434,185.265007019043,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,4,empirical,2022-01-21 23:47:07,2022-01-21 23:47:11,2022-01-21 23:47:11,2022-01-21 23:47:11,2022-01-21 23:49:54,0.176090955734253,163.357348680496,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,4,empirical,2022-01-21 23:46:43,2022-01-21 23:46:44,2022-01-21 23:46:44,2022-01-21 23:46:46,2022-01-21 23:50:07,0.154705286026001,200.106759309769,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,4,gaussian,2022-01-21 23:47:32,2022-01-21 23:47:34,2022-01-21 23:47:34,2022-01-21 23:47:37,2022-01-21 23:50:41,0.110007047653198,184.219397306442,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,4,gaussian,2022-01-21 23:47:54,2022-01-21 23:47:55,2022-01-21 23:47:55,2022-01-21 23:47:55,2022-01-21 23:50:51,0.128975868225098,175.251116991043,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,8,empirical,2022-01-21 23:49:18,2022-01-21 23:49:20,2022-01-21 23:49:20,2022-01-21 23:49:20,2022-01-21 23:52:05,0.158977031707764,165.097994804382,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,8,empirical,2022-01-21 23:48:53,2022-01-21 23:48:54,2022-01-21 23:48:54,2022-01-21 23:48:56,2022-01-21 23:52:18,0.176050424575806,201.731809139252,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,8,gaussian,2022-01-21 23:50:00,2022-01-21 23:50:01,2022-01-21 23:50:01,2022-01-21 23:50:01,2022-01-21 23:53:18,0.17525577545166,196.500575065613,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,16,empirical,2022-01-21 23:51:19,2022-01-21 23:51:20,2022-01-21 23:51:20,2022-01-21 23:51:21,2022-01-21 23:54:11,0.19235634803772,170.304957151413,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,16,empirical,2022-01-21 23:51:01,2022-01-21 23:51:02,2022-01-21 23:51:02,2022-01-21 23:51:04,2022-01-21 23:54:31,0.138709306716919,206.372300386429,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,16,gaussian,2022-01-21 23:52:13,2022-01-21 23:52:14,2022-01-21 23:52:15,2022-01-21 23:52:15,2022-01-21 23:55:37,0.24111819267273,201.864651203156,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,24,empirical,2022-01-21 23:53:44,2022-01-21 23:53:45,2022-01-21 23:53:46,2022-01-21 23:53:46,2022-01-21 23:56:42,0.143988370895386,176.26457285881,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,24,empirical,2022-01-21 23:53:20,2022-01-21 23:53:21,2022-01-21 23:53:21,2022-01-21 23:53:24,2022-01-21 23:57:08,0.12809419631958,224.134032964706,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,24,gaussian,2022-01-21 23:54:27,2022-01-21 23:54:28,2022-01-21 23:54:28,2022-01-21 23:54:29,2022-01-21 23:57:28,0.213754653930664,179.775923728943,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,32,empirical,2022-01-21 23:56:04,2022-01-21 23:56:05,2022-01-21 23:56:05,2022-01-21 23:56:05,2022-01-21 23:58:46,0.16818642616272,160.449657917023,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,32,empirical,2022-01-21 23:55:41,2022-01-21 23:55:42,2022-01-21 23:55:43,2022-01-21 23:55:46,2022-01-21 23:58:58,0.107287168502808,192.195042133331,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,32,gaussian,2022-01-21 23:56:48,2022-01-21 23:56:49,2022-01-21 23:56:49,2022-01-21 23:56:51,2022-01-21 23:59:34,0.136141061782837,162.692548513412,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,32,gaussian,2022-01-21 23:57:12,2022-01-21 23:57:13,2022-01-21 23:57:13,2022-01-21 23:57:14,2022-01-21 23:59:41,0.144119262695313,147.837868213654,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,16,ctree,2022-01-21 23:52:56,2022-01-21 23:52:57,2022-01-21 23:52:57,2022-01-21 23:52:58,2022-01-22 00:00:40,0.252333164215088,462.654721975327,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,24,ctree,2022-01-21 23:55:17,2022-01-21 23:55:19,2022-01-21 23:55:19,2022-01-21 23:55:20,2022-01-22 00:02:00,0.332910299301147,399.990538358688,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,2,empirical,2022-01-22 00:00:28,2022-01-22 00:00:28,2022-01-22 00:00:29,2022-01-22 00:00:29,2022-01-22 00:03:10,0.220245361328125,161.383081912994,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,2,empirical,2022-01-22 00:00:09,2022-01-22 00:00:10,2022-01-22 00:00:10,2022-01-22 00:00:12,2022-01-22 00:03:38,0.135678052902222,206.263612508774,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,4,empirical,2022-01-22 00:02:16,2022-01-22 00:02:17,2022-01-22 00:02:17,2022-01-22 00:02:23,2022-01-22 00:05:11,0.177535772323608,168.04819059372,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,2,32,ctree,2022-01-21 23:57:50,2022-01-21 23:57:51,2022-01-21 23:57:51,2022-01-21 23:57:51,2022-01-22 00:05:17,0.139546871185303,445.846391201019,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,4,empirical,2022-01-22 00:02:45,2022-01-22 00:02:47,2022-01-22 00:02:47,2022-01-22 00:02:48,2022-01-22 00:05:22,0.240315198898315,154.110302448273,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,1,empirical,2022-01-21 23:58:11,2022-01-21 23:58:11,2022-01-21 23:58:12,2022-01-21 23:58:12,2022-01-22 00:05:30,0.157306671142578,437.728199005127,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,1,gaussian,2022-01-21 23:58:59,2022-01-21 23:59:00,2022-01-21 23:59:00,2022-01-21 23:59:01,2022-01-22 00:05:33,0.204936265945435,392.501891851425,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,2,gaussian,2022-01-22 00:00:56,2022-01-22 00:00:57,2022-01-22 00:00:57,2022-01-22 00:00:59,2022-01-22 00:05:40,0.156799554824829,280.752032995224,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,1,empirical,2022-01-21 23:58:41,2022-01-21 23:58:43,2022-01-21 23:58:43,2022-01-21 23:58:43,2022-01-22 00:05:49,0.161206007003784,425.689492940903,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,2,gaussian,2022-01-22 00:01:14,2022-01-22 00:01:15,2022-01-22 00:01:15,2022-01-22 00:01:15,2022-01-22 00:06:25,0.144938945770264,309.708197116852,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,4,gaussian,2022-01-22 00:03:16,2022-01-22 00:03:17,2022-01-22 00:03:18,2022-01-22 00:03:21,2022-01-22 00:06:29,0.261320352554321,187.884455919266,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,4,gaussian,2022-01-22 00:03:41,2022-01-22 00:03:43,2022-01-22 00:03:43,2022-01-22 00:03:44,2022-01-22 00:06:37,0.224979162216187,172.63569688797,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,1,gaussian,2022-01-21 23:59:24,2022-01-21 23:59:28,2022-01-21 23:59:28,2022-01-21 23:59:28,2022-01-22 00:06:46,0.124329090118408,437.600485563278,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,8,empirical,2022-01-22 00:05:21,2022-01-22 00:05:23,2022-01-22 00:05:23,2022-01-22 00:05:26,2022-01-22 00:07:45,0.263403415679932,138.696480989456,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,8,empirical,2022-01-22 00:05:50,2022-01-22 00:05:51,2022-01-22 00:05:51,2022-01-22 00:05:51,2022-01-22 00:08:15,0.144243001937866,143.470705509186,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,16,empirical,2022-01-22 00:07:46,2022-01-22 00:07:47,2022-01-22 00:07:48,2022-01-22 00:07:51,2022-01-22 00:10:47,0.311504364013672,176.461219787598,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,4,ctree,2022-01-22 00:04:45,2022-01-22 00:04:47,2022-01-22 00:04:48,2022-01-22 00:04:49,2022-01-22 00:11:18,0.452795267105103,389.520493984222,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,4,ctree,2022-01-22 00:04:13,2022-01-22 00:04:15,2022-01-22 00:04:16,2022-01-22 00:04:19,2022-01-22 00:11:29,0.525002002716065,429.647247076035,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,16,empirical,2022-01-22 00:08:12,2022-01-22 00:08:14,2022-01-22 00:08:14,2022-01-22 00:08:14,2022-01-22 00:11:50,0.135880470275879,216.064201831818,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,24,empirical,2022-01-22 00:10:56,2022-01-22 00:10:57,2022-01-22 00:10:57,2022-01-22 00:11:02,2022-01-22 00:13:45,0.21747899055481,163.167289972305,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,2,ctree,2022-01-22 00:01:55,2022-01-22 00:01:56,2022-01-22 00:01:56,2022-01-22 00:01:57,2022-01-22 00:13:51,0.174901962280273,714.155141115189,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,24,empirical,2022-01-22 00:11:26,2022-01-22 00:11:27,2022-01-22 00:11:27,2022-01-22 00:11:28,2022-01-22 00:14:04,0.284063339233398,156.44540309906,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,8,ctree,2022-01-22 00:07:23,2022-01-22 00:07:24,2022-01-22 00:07:25,2022-01-22 00:07:25,2022-01-22 00:14:12,0.218754529953003,407.031522989273,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,16,ctree,2022-01-22 00:10:24,2022-01-22 00:10:26,2022-01-22 00:10:26,2022-01-22 00:10:27,2022-01-22 00:16:14,0.350234508514404,346.560861587524,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,32,empirical,2022-01-22 00:13:54,2022-01-22 00:13:55,2022-01-22 00:13:55,2022-01-22 00:14:00,2022-01-22 00:16:51,0.133309602737427,171.693699598312,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,32,empirical,2022-01-22 00:14:28,2022-01-22 00:14:29,2022-01-22 00:14:29,2022-01-22 00:14:30,2022-01-22 00:17:02,0.24024772644043,152.427399158478,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,32,gaussian,2022-01-22 00:15:38,2022-01-22 00:15:39,2022-01-22 00:15:40,2022-01-22 00:15:41,2022-01-22 00:17:37,1.03340101242065,116.364461421967,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,32,gaussian,2022-01-22 00:15:12,2022-01-22 00:15:23,2022-01-22 00:15:24,2022-01-22 00:15:37,2022-01-22 00:17:44,0.205392122268677,126.366458415985,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,24,ctree,2022-01-22 00:13:22,2022-01-22 00:13:24,2022-01-22 00:13:24,2022-01-22 00:13:25,2022-01-22 00:18:12,0.377152442932129,287.093768596649,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,24,ctree,2022-01-22 00:12:53,2022-01-22 00:12:55,2022-01-22 00:12:55,2022-01-22 00:12:59,2022-01-22 00:19:02,0.371990203857422,362.543828725815,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,32,ctree,2022-01-22 00:16:27,2022-01-22 00:16:28,2022-01-22 00:16:28,2022-01-22 00:16:29,2022-01-22 00:19:16,0.173864364624023,167.117560386658,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,32,ctree,2022-01-22 00:16:03,2022-01-22 00:16:05,2022-01-22 00:16:05,2022-01-22 00:16:09,2022-01-22 00:21:27,0.223615884780884,318.196619749069,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,2,empirical,2022-01-22 00:18:47,2022-01-22 00:18:48,2022-01-22 00:18:48,2022-01-22 00:18:48,2022-01-22 00:22:03,0.213789224624634,194.670427560806,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,4,empirical,2022-01-22 00:20:17,2022-01-22 00:20:19,2022-01-22 00:20:19,2022-01-22 00:20:19,2022-01-22 00:23:19,0.228528738021851,179.822612285614,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,4,empirical,2022-01-22 00:20:00,2022-01-22 00:20:01,2022-01-22 00:20:01,2022-01-22 00:20:04,2022-01-22 00:23:28,0.105710744857788,204.595075845718,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,2,gaussian,2022-01-22 00:19:03,2022-01-22 00:19:04,2022-01-22 00:19:04,2022-01-22 00:19:06,2022-01-22 00:24:05,0.113917112350464,299.086584329605,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,4,gaussian,2022-01-22 00:20:38,2022-01-22 00:20:39,2022-01-22 00:20:39,2022-01-22 00:20:42,2022-01-22 00:24:10,0.263284921646118,207.605838537216,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,2,empirical,2022-01-22 00:18:32,2022-01-22 00:18:32,2022-01-22 00:18:32,2022-01-22 00:18:34,2022-01-22 00:24:33,0.14330005645752,358.819204330444,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,2,gaussian,2022-01-22 00:19:16,2022-01-22 00:19:17,2022-01-22 00:19:17,2022-01-22 00:19:17,2022-01-22 00:24:48,0.238066911697388,330.341274023056,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,4,gaussian,2022-01-22 00:21:08,2022-01-22 00:21:10,2022-01-22 00:21:11,2022-01-22 00:21:11,2022-01-22 00:24:51,0.394684791564941,219.658683538437,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,8,empirical,2022-01-22 00:22:36,2022-01-22 00:22:37,2022-01-22 00:22:38,2022-01-22 00:22:42,2022-01-22 00:24:53,0.394059181213379,131.442513942719,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,8,empirical,2022-01-22 00:23:12,2022-01-22 00:23:13,2022-01-22 00:23:14,2022-01-22 00:23:14,2022-01-22 00:25:18,0.318946599960327,124.100888729095,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,1,gaussian,2022-01-22 00:17:34,2022-01-22 00:17:35,2022-01-22 00:17:35,2022-01-22 00:17:35,2022-01-22 00:25:49,0.121601343154907,493.762038946152,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,8,gaussian,2022-01-22 00:23:47,2022-01-22 00:23:50,2022-01-22 00:23:50,2022-01-22 00:23:55,2022-01-22 00:25:51,0.514194965362549,116.117251634598,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,8,gaussian,2022-01-22 00:24:23,2022-01-22 00:24:25,2022-01-22 00:24:26,2022-01-22 00:24:26,2022-01-22 00:26:09,0.586534261703491,102.69692158699,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,1,gaussian,2022-01-22 00:17:49,2022-01-22 00:17:50,2022-01-22 00:17:50,2022-01-22 00:17:50,2022-01-22 00:26:33,0.12436580657959,522.362556695938,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,1,empirical,2022-01-22 00:16:51,2022-01-22 00:16:52,2022-01-22 00:16:52,2022-01-22 00:16:53,2022-01-22 00:27:37,0.319175958633423,643.956770658493,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,16,empirical,2022-01-22 00:26:26,2022-01-22 00:26:27,2022-01-22 00:26:27,2022-01-22 00:26:28,2022-01-22 00:28:12,0.174864292144775,104.851170301437,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,16,empirical,2022-01-22 00:26:02,2022-01-22 00:26:04,2022-01-22 00:26:04,2022-01-22 00:26:07,2022-01-22 00:28:13,0.160935640335083,125.941323518753,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,1,empirical,2022-01-22 00:17:12,2022-01-22 00:17:14,2022-01-22 00:17:14,2022-01-22 00:17:14,2022-01-22 00:28:26,0.235838890075684,671.502711772919,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,16,gaussian,2022-01-22 00:26:57,2022-01-22 00:26:59,2022-01-22 00:27:00,2022-01-22 00:27:04,2022-01-22 00:29:26,0.365767240524292,142.308668613434,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,8,ctree,2022-01-22 00:25:31,2022-01-22 00:25:33,2022-01-22 00:25:34,2022-01-22 00:25:35,2022-01-22 00:29:31,0.405417919158936,236.971807718277,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,16,gaussian,2022-01-22 00:27:34,2022-01-22 00:27:37,2022-01-22 00:27:37,2022-01-22 00:27:38,2022-01-22 00:29:36,0.536219120025635,117.436851501465,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,4,ctree,2022-01-22 00:22:07,2022-01-22 00:22:09,2022-01-22 00:22:10,2022-01-22 00:22:10,2022-01-22 00:29:39,0.415358781814575,449.0838367939,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,8,ctree,2022-01-22 00:25:00,2022-01-22 00:25:02,2022-01-22 00:25:02,2022-01-22 00:25:06,2022-01-22 00:30:25,0.367226362228394,318.389145851135,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,24,empirical,2022-01-22 00:29:36,2022-01-22 00:29:37,2022-01-22 00:29:38,2022-01-22 00:29:41,2022-01-22 00:31:32,0.330016851425171,110.997332572937,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,24,empirical,2022-01-22 00:29:54,2022-01-22 00:29:55,2022-01-22 00:29:55,2022-01-22 00:29:56,2022-01-22 00:31:36,0.393418550491333,99.948324918747,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,4,ctree,2022-01-22 00:21:41,2022-01-22 00:21:42,2022-01-22 00:21:42,2022-01-22 00:21:46,2022-01-22 00:31:52,0.3515465259552,605.213157176971,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,1,ctree,2022-01-21 23:59:38,2022-01-21 23:59:39,2022-01-21 23:59:39,2022-01-21 23:59:39,2022-01-22 00:32:05,0.130911350250244,1946.13237524033,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,24,gaussian,2022-01-22 00:30:23,2022-01-22 00:30:25,2022-01-22 00:30:25,2022-01-22 00:30:29,2022-01-22 00:32:36,0.260666370391846,126.316056013107,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,16,ctree,2022-01-22 00:29:11,2022-01-22 00:29:14,2022-01-22 00:29:15,2022-01-22 00:29:16,2022-01-22 00:32:43,0.903154373168945,206.772490739822,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,24,gaussian,2022-01-22 00:30:55,2022-01-22 00:30:56,2022-01-22 00:30:57,2022-01-22 00:30:57,2022-01-22 00:32:52,0.43907618522644,114.813937187195,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,4,1,ctree,2022-01-21 23:59:54,2022-01-21 23:59:54,2022-01-21 23:59:54,2022-01-21 23:59:55,2022-01-22 00:33:20,0.141476631164551,2005.1497604847,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,2,ctree,2022-01-22 00:19:44,2022-01-22 00:19:45,2022-01-22 00:19:45,2022-01-22 00:19:45,2022-01-22 00:33:35,0.198578834533691,829.780259370804,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,16,ctree,2022-01-22 00:28:23,2022-01-22 00:28:26,2022-01-22 00:28:26,2022-01-22 00:28:31,2022-01-22 00:33:37,0.29680061340332,306.306308031082,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,32,empirical,2022-01-22 00:33:06,2022-01-22 00:33:06,2022-01-22 00:33:07,2022-01-22 00:33:07,2022-01-22 00:34:11,0.221571683883667,64.3871071338654,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,32,empirical,2022-01-22 00:32:46,2022-01-22 00:32:47,2022-01-22 00:32:47,2022-01-22 00:32:50,2022-01-22 00:34:34,0.379937410354614,104.152796506882,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,32,gaussian,2022-01-22 00:33:28,2022-01-22 00:33:30,2022-01-22 00:33:30,2022-01-22 00:33:34,2022-01-22 00:35:23,0.369773149490356,108.813843011856,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,24,ctree,2022-01-22 00:32:18,2022-01-22 00:32:20,2022-01-22 00:32:20,2022-01-22 00:32:21,2022-01-22 00:35:26,0.485552072525024,184.968723297119,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,32,gaussian,2022-01-22 00:34:02,2022-01-22 00:34:03,2022-01-22 00:34:03,2022-01-22 00:34:04,2022-01-22 00:35:34,0.156780004501343,90.2864699363709,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,24,ctree,2022-01-22 00:31:42,2022-01-22 00:31:45,2022-01-22 00:31:46,2022-01-22 00:31:51,2022-01-22 00:35:58,0.570005416870117,247.049502849579,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,32,ctree,2022-01-22 00:35:14,2022-01-22 00:35:16,2022-01-22 00:35:16,2022-01-22 00:35:17,2022-01-22 00:36:47,0.295845746994019,90.1422259807587,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,2,ctree,2022-01-22 00:19:30,2022-01-22 00:19:31,2022-01-22 00:19:31,2022-01-22 00:19:33,2022-01-22 00:37:07,0.260430574417114,1053.96999835968,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,32,ctree,2022-01-22 00:34:39,2022-01-22 00:34:41,2022-01-22 00:34:41,2022-01-22 00:34:47,2022-01-22 00:37:21,0.325368404388428,154.057362318039,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,2,empirical,2022-01-22 00:37:05,2022-01-22 00:37:05,2022-01-22 00:37:06,2022-01-22 00:37:06,2022-01-22 00:41:26,0.198206663131714,260.210177183151,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,4,empirical,2022-01-22 00:38:06,2022-01-22 00:38:06,2022-01-22 00:38:07,2022-01-22 00:38:07,2022-01-22 00:42:00,0.194207191467285,232.913385391235,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,4,gaussian,2022-01-22 00:38:18,2022-01-22 00:38:19,2022-01-22 00:38:19,2022-01-22 00:38:21,2022-01-22 00:42:37,0.308275938034058,255.396221637726,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,4,gaussian,2022-01-22 00:38:33,2022-01-22 00:38:34,2022-01-22 00:38:34,2022-01-22 00:38:34,2022-01-22 00:43:12,0.303925037384033,277.403713464737,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,8,empirical,2022-01-22 00:40:04,2022-01-22 00:40:07,2022-01-22 00:40:08,2022-01-22 00:40:09,2022-01-22 00:43:13,0.621465921401978,184.616212844849,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,8,empirical,2022-01-22 00:39:35,2022-01-22 00:39:37,2022-01-22 00:39:38,2022-01-22 00:39:42,2022-01-22 00:43:26,0.543291091918945,224.043934345245,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,2,gaussian,2022-01-22 00:37:17,2022-01-22 00:37:18,2022-01-22 00:37:18,2022-01-22 00:37:19,2022-01-22 00:43:32,0.127069711685181,372.482070684433,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,4,empirical,2022-01-22 00:37:55,2022-01-22 00:37:56,2022-01-22 00:37:56,2022-01-22 00:37:58,2022-01-22 00:43:47,0.133932113647461,349.239078521729,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,8,gaussian,2022-01-22 00:40:44,2022-01-22 00:40:47,2022-01-22 00:40:48,2022-01-22 00:40:54,2022-01-22 00:43:49,0.605763673782349,175.793090820313,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,8,gaussian,2022-01-22 00:41:28,2022-01-22 00:41:32,2022-01-22 00:41:33,2022-01-22 00:41:34,2022-01-22 00:44:06,0.720996856689453,151.639906644821,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,2,gaussian,2022-01-22 00:37:27,2022-01-22 00:37:27,2022-01-22 00:37:27,2022-01-22 00:37:28,2022-01-22 00:44:15,0.15282416343689,407.644268751144,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,16,empirical,2022-01-22 00:44:14,2022-01-22 00:44:16,2022-01-22 00:44:16,2022-01-22 00:44:17,2022-01-22 00:45:51,0.456368923187256,94.2123739719391,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,16,empirical,2022-01-22 00:43:52,2022-01-22 00:43:53,2022-01-22 00:43:53,2022-01-22 00:43:57,2022-01-22 00:46:17,0.151166439056396,139.408195734024,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,1,gaussian,2022-01-22 00:36:02,2022-01-22 00:36:03,2022-01-22 00:36:03,2022-01-22 00:36:03,2022-01-22 00:46:53,0.161900520324707,650.075906276703,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,16,gaussian,2022-01-22 00:44:56,2022-01-22 00:44:58,2022-01-22 00:44:59,2022-01-22 00:45:05,2022-01-22 00:46:58,0.818410634994507,112.681494474411,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,2,empirical,2022-01-22 00:36:52,2022-01-22 00:36:53,2022-01-22 00:36:53,2022-01-22 00:36:54,2022-01-22 00:47:06,0.218976020812988,611.386895179749,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,16,gaussian,2022-01-22 00:45:51,2022-01-22 00:45:55,2022-01-22 00:45:55,2022-01-22 00:45:56,2022-01-22 00:47:17,0.346254110336304,80.6972205638886,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,1,gaussian,2022-01-22 00:36:14,2022-01-22 00:36:15,2022-01-22 00:36:15,2022-01-22 00:36:15,2022-01-22 00:47:19,0.181397914886475,663.9934425354,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,8,ctree,2022-01-22 00:43:15,2022-01-22 00:43:17,2022-01-22 00:43:18,2022-01-22 00:43:18,2022-01-22 00:48:19,0.415080785751343,300.199335813522,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,8,ctree,2022-01-22 00:42:23,2022-01-22 00:42:26,2022-01-22 00:42:27,2022-01-22 00:42:33,2022-01-22 00:49:17,0.745680570602417,403.496193647385,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,4,ctree,2022-01-22 00:39:13,2022-01-22 00:39:15,2022-01-22 00:39:15,2022-01-22 00:39:15,2022-01-22 00:49:30,0.237137794494629,615.247409820557,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,24,empirical,2022-01-22 00:48:24,2022-01-22 00:48:26,2022-01-22 00:48:26,2022-01-22 00:48:27,2022-01-22 00:50:00,0.407120943069458,93.3155579566956,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,24,empirical,2022-01-22 00:47:44,2022-01-22 00:47:46,2022-01-22 00:47:46,2022-01-22 00:47:51,2022-01-22 00:50:20,0.290511131286621,148.672857046127,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,16,ctree,2022-01-22 00:47:17,2022-01-22 00:47:18,2022-01-22 00:47:18,2022-01-22 00:47:18,2022-01-22 00:50:25,0.206394672393799,186.436529159546,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,16,ctree,2022-01-22 00:46:50,2022-01-22 00:46:52,2022-01-22 00:46:53,2022-01-22 00:46:58,2022-01-22 00:50:39,0.467600345611572,221.192490339279,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,24,gaussian,2022-01-22 00:49:16,2022-01-22 00:49:19,2022-01-22 00:49:19,2022-01-22 00:49:26,2022-01-22 00:51:08,0.606553316116333,101.851763248444,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,24,gaussian,2022-01-22 00:50:05,2022-01-22 00:50:07,2022-01-22 00:50:07,2022-01-22 00:50:08,2022-01-22 00:51:17,0.542969942092896,69.0861201286316,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,4,ctree,2022-01-22 00:38:54,2022-01-22 00:38:56,2022-01-22 00:38:56,2022-01-22 00:39:01,2022-01-22 00:51:44,0.448035478591919,763.012451410294,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,1,ctree,2022-01-22 00:18:17,2022-01-22 00:18:18,2022-01-22 00:18:18,2022-01-22 00:18:19,2022-01-22 00:52:59,0.179383993148804,2080.43359661102,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,8,1,ctree,2022-01-22 00:18:04,2022-01-22 00:18:04,2022-01-22 00:18:05,2022-01-22 00:18:05,2022-01-22 00:52:59,0.107439041137695,2094.62745237351,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,32,empirical,2022-01-22 00:51:55,2022-01-22 00:51:57,2022-01-22 00:51:58,2022-01-22 00:51:59,2022-01-22 00:53:21,0.435993909835815,81.7191488742829,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,24,ctree,2022-01-22 00:51:12,2022-01-22 00:51:14,2022-01-22 00:51:14,2022-01-22 00:51:14,2022-01-22 00:53:44,0.280441045761108,149.494758367538,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,32,empirical,2022-01-22 00:51:30,2022-01-22 00:51:32,2022-01-22 00:51:32,2022-01-22 00:51:36,2022-01-22 00:53:47,0.313796043395996,130.518866539001,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,24,ctree,2022-01-22 00:50:42,2022-01-22 00:50:44,2022-01-22 00:50:45,2022-01-22 00:50:51,2022-01-22 00:54:13,0.647085189819336,202.169643163681,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,32,gaussian,2022-01-22 00:52:33,2022-01-22 00:52:36,2022-01-22 00:52:36,2022-01-22 00:52:43,2022-01-22 00:54:24,0.516229391098023,100.847553491592,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,32,gaussian,2022-01-22 00:53:22,2022-01-22 00:53:25,2022-01-22 00:53:25,2022-01-22 00:53:26,2022-01-22 00:54:29,0.473212957382202,63.3109831809998,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,2,ctree,2022-01-22 00:37:45,2022-01-22 00:37:46,2022-01-22 00:37:46,2022-01-22 00:37:47,2022-01-22 00:55:09,0.120632648468018,1042.07389187813,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,1,empirical,2022-01-22 00:35:36,2022-01-22 00:35:37,2022-01-22 00:35:37,2022-01-22 00:35:37,2022-01-22 00:55:43,0.151748657226563,1205.67083859444,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,32,ctree,2022-01-22 00:54:29,2022-01-22 00:54:29,2022-01-22 00:54:30,2022-01-22 00:54:30,2022-01-22 00:55:44,0.18700122833252,74.0043988227844,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,1,empirical,2022-01-22 00:35:49,2022-01-22 00:35:50,2022-01-22 00:35:50,2022-01-22 00:35:51,2022-01-22 00:55:58,0.147314786911011,1207.83577370644,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,32,ctree,2022-01-22 00:54:05,2022-01-22 00:54:08,2022-01-22 00:54:08,2022-01-22 00:54:14,2022-01-22 00:56:04,0.579286336898804,110.02873301506,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,2,ctree,2022-01-22 00:37:36,2022-01-22 00:37:36,2022-01-22 00:37:37,2022-01-22 00:37:38,2022-01-22 00:57:12,0.125193357467651,1173.81370997429,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,4,empirical,2022-01-22 00:57:08,2022-01-22 00:57:09,2022-01-22 00:57:10,2022-01-22 00:57:10,2022-01-22 01:01:19,0.385452747344971,248.512441396713,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,2,empirical,2022-01-22 00:56:14,2022-01-22 00:56:14,2022-01-22 00:56:14,2022-01-22 00:56:15,2022-01-22 01:01:45,0.140118598937988,330.112222671509,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,4,gaussian,2022-01-22 00:57:20,2022-01-22 00:57:21,2022-01-22 00:57:22,2022-01-22 00:57:25,2022-01-22 01:02:15,0.251033544540405,290.476743936539,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,8,empirical,2022-01-22 00:58:58,2022-01-22 00:59:00,2022-01-22 00:59:00,2022-01-22 00:59:01,2022-01-22 01:02:25,0.459428548812866,204.051424503326,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,4,gaussian,2022-01-22 00:57:34,2022-01-22 00:57:35,2022-01-22 00:57:35,2022-01-22 00:57:36,2022-01-22 01:02:42,0.20646858215332,305.704801559448,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,2,gaussian,2022-01-22 00:56:21,2022-01-22 00:56:22,2022-01-22 00:56:22,2022-01-22 00:56:23,2022-01-22 01:02:55,0.126375675201416,391.518209457397,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,8,gaussian,2022-01-22 00:59:31,2022-01-22 00:59:34,2022-01-22 00:59:35,2022-01-22 00:59:42,2022-01-22 01:02:56,0.781108140945435,194.267905473709,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,8,gaussian,2022-01-22 01:00:11,2022-01-22 01:00:14,2022-01-22 01:00:14,2022-01-22 01:00:15,2022-01-22 01:03:25,0.160382270812988,189.745049238205,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,2,gaussian,2022-01-22 00:56:30,2022-01-22 00:56:30,2022-01-22 00:56:30,2022-01-22 00:56:31,2022-01-22 01:04:12,0.125285863876343,461.467194318771,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,8,empirical,2022-01-22 00:58:32,2022-01-22 00:58:33,2022-01-22 00:58:34,2022-01-22 00:58:38,2022-01-22 01:04:38,0.398452281951904,359.780193328857,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,16,empirical,2022-01-22 01:03:22,2022-01-22 01:03:25,2022-01-22 01:03:26,2022-01-22 01:03:27,2022-01-22 01:05:35,0.360058546066284,127.964381217957,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,16,gaussian,2022-01-22 01:04:05,2022-01-22 01:04:07,2022-01-22 01:04:07,2022-01-22 01:04:15,2022-01-22 01:06:41,0.443737745285034,146.113445043564,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,4,empirical,2022-01-22 00:56:57,2022-01-22 00:56:58,2022-01-22 00:56:59,2022-01-22 00:57:01,2022-01-22 01:06:52,0.218158960342407,590.905384302139,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,16,gaussian,2022-01-22 01:04:56,2022-01-22 01:05:00,2022-01-22 01:05:01,2022-01-22 01:05:01,2022-01-22 01:07:03,0.713358402252197,121.663578271866,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,16,empirical,2022-01-22 01:02:51,2022-01-22 01:02:54,2022-01-22 01:02:54,2022-01-22 01:03:00,2022-01-22 01:07:20,0.226186275482178,260.132481575012,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,8,ctree,2022-01-22 01:01:58,2022-01-22 01:02:01,2022-01-22 01:02:02,2022-01-22 01:02:03,2022-01-22 01:08:30,0.720283031463623,386.861576557159,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,1,gaussian,2022-01-22 00:55:13,2022-01-22 00:55:14,2022-01-22 00:55:14,2022-01-22 00:55:15,2022-01-22 01:08:52,0.132827997207642,817.01242351532,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,24,empirical,2022-01-22 01:08:11,2022-01-22 01:08:16,2022-01-22 01:08:17,2022-01-22 01:08:19,2022-01-22 01:09:35,0.915817737579346,76.3794054985046,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,8,ctree,2022-01-22 01:01:01,2022-01-22 01:01:06,2022-01-22 01:01:07,2022-01-22 01:01:15,2022-01-22 01:09:39,0.852621555328369,504.664010763168,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,1,gaussian,2022-01-22 00:55:32,2022-01-22 00:55:33,2022-01-22 00:55:34,2022-01-22 00:55:34,2022-01-22 01:09:41,0.339427709579468,846.283426046372,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,24,empirical,2022-01-22 01:07:30,2022-01-22 01:07:33,2022-01-22 01:07:33,2022-01-22 01:07:40,2022-01-22 01:10:38,0.375697374343872,178.268216371536,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,4,ctree,2022-01-22 00:58:09,2022-01-22 00:58:11,2022-01-22 00:58:12,2022-01-22 00:58:13,2022-01-22 01:10:39,0.506494522094727,746.505525588989,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,24,gaussian,2022-01-22 01:09:15,2022-01-22 01:09:18,2022-01-22 01:09:19,2022-01-22 01:09:27,2022-01-22 01:11:15,0.608818769454956,107.476801633835,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,24,gaussian,2022-01-22 01:10:05,2022-01-22 01:10:08,2022-01-22 01:10:09,2022-01-22 01:10:09,2022-01-22 01:11:18,0.640880584716797,68.3192081451416,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,16,ctree,2022-01-22 01:06:52,2022-01-22 01:06:54,2022-01-22 01:06:55,2022-01-22 01:06:55,2022-01-22 01:11:24,0.543595552444458,268.161667823792,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,16,ctree,2022-01-22 01:06:01,2022-01-22 01:06:04,2022-01-22 01:06:05,2022-01-22 01:06:11,2022-01-22 01:11:46,0.677626848220825,335.324812412262,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,4,ctree,2022-01-22 00:57:52,2022-01-22 00:57:55,2022-01-22 00:57:55,2022-01-22 00:57:59,2022-01-22 01:12:10,0.375102758407593,850.867648124695,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,2,empirical,2022-01-22 00:56:06,2022-01-22 00:56:07,2022-01-22 00:56:07,2022-01-22 00:56:08,2022-01-22 01:13:04,0.0894236564636231,1016.30822968483,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,32,empirical,2022-01-22 01:12:22,2022-01-22 01:12:26,2022-01-22 01:12:26,2022-01-22 01:12:28,2022-01-22 01:13:42,0.62375283241272,74.4546775817871,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,24,ctree,2022-01-22 01:11:28,2022-01-22 01:11:29,2022-01-22 01:11:30,2022-01-22 01:11:30,2022-01-22 01:13:43,0.480569124221802,133.051090240479,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,24,ctree,2022-01-22 01:11:10,2022-01-22 01:11:11,2022-01-22 01:11:12,2022-01-22 01:11:16,2022-01-22 01:14:05,0.300862550735474,169.09902882576,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,32,empirical,2022-01-22 01:11:51,2022-01-22 01:11:53,2022-01-22 01:11:54,2022-01-22 01:11:58,2022-01-22 01:14:35,0.436030387878418,157.347808122635,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,32,gaussian,2022-01-22 01:13:57,2022-01-22 01:13:59,2022-01-22 01:14:00,2022-01-22 01:14:01,2022-01-22 01:14:59,0.61557149887085,58.2910091876984,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,32,gaussian,2022-01-22 01:13:17,2022-01-22 01:13:20,2022-01-22 01:13:20,2022-01-22 01:13:29,2022-01-22 01:14:59,0.608458995819092,90.1108300685883,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,2,ctree,2022-01-22 00:56:47,2022-01-22 00:56:48,2022-01-22 00:56:48,2022-01-22 00:56:48,2022-01-22 01:15:27,0.154180765151978,1118.53525209427,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,1,ctree,2022-01-22 00:36:27,2022-01-22 00:36:27,2022-01-22 00:36:27,2022-01-22 00:36:28,2022-01-22 01:16:09,0.15717601776123,2381.60752701759,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,32,ctree,2022-01-22 01:15:03,2022-01-22 01:15:04,2022-01-22 01:15:04,2022-01-22 01:15:04,2022-01-22 01:16:26,0.120380163192749,82.1608276367188,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,16,1,ctree,2022-01-22 00:36:40,2022-01-22 00:36:41,2022-01-22 00:36:41,2022-01-22 00:36:41,2022-01-22 01:16:27,0.185147523880005,2386.15934276581,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,32,ctree,2022-01-22 01:14:44,2022-01-22 01:14:47,2022-01-22 01:14:48,2022-01-22 01:14:54,2022-01-22 01:16:42,0.87923264503479,107.627303123474,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,2,ctree,2022-01-22 00:56:38,2022-01-22 00:56:39,2022-01-22 00:56:39,2022-01-22 00:56:41,2022-01-22 01:18:04,0.118601560592651,1283.59071850777,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,4,empirical,2022-01-22 01:17:57,2022-01-22 01:17:58,2022-01-22 01:17:59,2022-01-22 01:17:59,2022-01-22 01:22:29,0.272570610046387,270.00651550293,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,1,empirical,2022-01-22 00:54:41,2022-01-22 00:54:42,2022-01-22 00:54:42,2022-01-22 00:54:42,2022-01-22 01:22:58,0.172960996627808,1695.70917034149,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,4,gaussian,2022-01-22 01:18:09,2022-01-22 01:18:11,2022-01-22 01:18:11,2022-01-22 01:18:14,2022-01-22 01:23:28,0.243377923965454,314.42053771019,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,8,empirical,2022-01-22 01:19:39,2022-01-22 01:19:41,2022-01-22 01:19:41,2022-01-22 01:19:42,2022-01-22 01:23:30,0.67338228225708,228.16231918335,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,1,empirical,2022-01-22 00:54:57,2022-01-22 00:54:58,2022-01-22 00:54:59,2022-01-22 00:54:59,2022-01-22 01:23:39,0.145205020904541,1719.32288908958,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,8,gaussian,2022-01-22 01:20:08,2022-01-22 01:20:11,2022-01-22 01:20:12,2022-01-22 01:20:18,2022-01-22 01:23:47,0.779103994369507,208.754786729813,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,4,gaussian,2022-01-22 01:18:23,2022-01-22 01:18:25,2022-01-22 01:18:25,2022-01-22 01:18:25,2022-01-22 01:24:04,0.166044473648071,338.406321763992,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,8,gaussian,2022-01-22 01:20:42,2022-01-22 01:20:45,2022-01-22 01:20:45,2022-01-22 01:20:46,2022-01-22 01:24:16,0.684644222259522,210.197974205017,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,2,empirical,2022-01-22 01:17:00,2022-01-22 01:17:01,2022-01-22 01:17:01,2022-01-22 01:17:01,2022-01-22 01:24:19,0.156692266464233,437.709321022034,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,2,gaussian,2022-01-22 01:17:17,2022-01-22 01:17:17,2022-01-22 01:17:17,2022-01-22 01:17:18,2022-01-22 01:25:41,0.126760005950928,503.505681991577,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,2,gaussian,2022-01-22 01:17:08,2022-01-22 01:17:09,2022-01-22 01:17:09,2022-01-22 01:17:11,2022-01-22 01:26:22,0.260280132293701,551.535883188248,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,16,empirical,2022-01-22 01:24:16,2022-01-22 01:24:19,2022-01-22 01:24:20,2022-01-22 01:24:21,2022-01-22 01:26:26,0.820237874984741,124.791858434677,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,8,empirical,2022-01-22 01:19:16,2022-01-22 01:19:17,2022-01-22 01:19:18,2022-01-22 01:19:22,2022-01-22 01:26:58,0.213090896606445,455.892606496811,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,16,gaussian,2022-01-22 01:25:01,2022-01-22 01:25:04,2022-01-22 01:25:05,2022-01-22 01:25:14,2022-01-22 01:27:48,0.600769519805908,154.320029258728,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,16,gaussian,2022-01-22 01:25:58,2022-01-22 01:26:02,2022-01-22 01:26:03,2022-01-22 01:26:05,2022-01-22 01:28:02,0.930123090744019,117.771133184433,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,16,empirical,2022-01-22 01:23:29,2022-01-22 01:23:32,2022-01-22 01:23:32,2022-01-22 01:23:40,2022-01-22 01:28:14,0.515447616577148,274.445198297501,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,8,ctree,2022-01-22 01:22:27,2022-01-22 01:22:31,2022-01-22 01:22:32,2022-01-22 01:22:34,2022-01-22 01:30:22,0.95165228843689,468.72024679184,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,8,ctree,2022-01-22 01:21:30,2022-01-22 01:21:34,2022-01-22 01:21:35,2022-01-22 01:21:44,2022-01-22 01:31:04,0.981574535369873,560.419023513794,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,24,empirical,2022-01-22 01:29:22,2022-01-22 01:29:25,2022-01-22 01:29:25,2022-01-22 01:29:26,2022-01-22 01:31:23,0.737452745437622,116.889065504074,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,4,empirical,2022-01-22 01:17:45,2022-01-22 01:17:47,2022-01-22 01:17:47,2022-01-22 01:17:50,2022-01-22 01:31:49,0.201099872589111,839.631712436676,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,1,gaussian,2022-01-22 01:16:04,2022-01-22 01:16:06,2022-01-22 01:16:07,2022-01-22 01:16:07,2022-01-22 01:31:54,0.317160129547119,946.763425827026,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,16,ctree,2022-01-22 01:28:06,2022-01-22 01:28:08,2022-01-22 01:28:09,2022-01-22 01:28:09,2022-01-22 01:32:22,0.370973587036133,252.829816818237,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,1,gaussian,2022-01-22 01:16:23,2022-01-22 01:16:24,2022-01-22 01:16:24,2022-01-22 01:16:25,2022-01-22 01:32:32,0.259876728057861,967.826942682266,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,16,ctree,2022-01-22 01:27:09,2022-01-22 01:27:12,2022-01-22 01:27:13,2022-01-22 01:27:21,2022-01-22 01:32:34,0.809694051742554,312.536510705948,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,24,gaussian,2022-01-22 01:30:27,2022-01-22 01:30:30,2022-01-22 01:30:31,2022-01-22 01:30:40,2022-01-22 01:32:53,0.975281715393066,133.432362556458,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,24,gaussian,2022-01-22 01:31:33,2022-01-22 01:31:37,2022-01-22 01:31:38,2022-01-22 01:31:39,2022-01-22 01:32:57,0.975476026535034,78.014899969101,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,4,ctree,2022-01-22 01:18:57,2022-01-22 01:19:00,2022-01-22 01:19:00,2022-01-22 01:19:02,2022-01-22 01:33:13,0.730637788772583,851.774129629135,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,24,empirical,2022-01-22 01:28:36,2022-01-22 01:28:37,2022-01-22 01:28:38,2022-01-22 01:28:44,2022-01-22 01:33:16,0.608855962753296,271.502300739288,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,32,empirical,2022-01-22 01:33:56,2022-01-22 01:34:00,2022-01-22 01:34:01,2022-01-22 01:34:02,2022-01-22 01:35:11,0.851229667663574,68.9212827682495,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,4,ctree,2022-01-22 01:18:39,2022-01-22 01:18:40,2022-01-22 01:18:41,2022-01-22 01:18:44,2022-01-22 01:35:15,0.326074600219727,990.224272966385,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,32,empirical,2022-01-22 01:33:22,2022-01-22 01:33:24,2022-01-22 01:33:24,2022-01-22 01:33:29,2022-01-22 01:36:30,0.282434701919556,181.30904173851,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,24,ctree,2022-01-22 01:33:01,2022-01-22 01:33:02,2022-01-22 01:33:03,2022-01-22 01:33:03,2022-01-22 01:36:46,0.29549241065979,223.263365983963,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,32,gaussian,2022-01-22 01:35:39,2022-01-22 01:35:42,2022-01-22 01:35:43,2022-01-22 01:35:44,2022-01-22 01:36:47,0.593777418136597,62.2703466415405,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,32,gaussian,2022-01-22 01:34:48,2022-01-22 01:34:51,2022-01-22 01:34:52,2022-01-22 01:35:00,2022-01-22 01:36:49,1.072110414505,109.304872989655,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,24,ctree,2022-01-22 01:32:39,2022-01-22 01:32:40,2022-01-22 01:32:40,2022-01-22 01:32:44,2022-01-22 01:36:52,0.229806184768677,247.217267990112,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,32,ctree,2022-01-22 01:36:58,2022-01-22 01:36:59,2022-01-22 01:36:59,2022-01-22 01:37:00,2022-01-22 01:38:20,0.140708446502686,80.3763806819916,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,2,ctree,2022-01-22 01:17:35,2022-01-22 01:17:36,2022-01-22 01:17:36,2022-01-22 01:17:37,2022-01-22 01:38:37,0.154358386993408,1260.15559983254,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,32,ctree,2022-01-22 01:36:45,2022-01-22 01:36:46,2022-01-22 01:36:46,2022-01-22 01:36:49,2022-01-22 01:38:37,0.238625526428223,108.586423873901,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,1,ctree,2022-01-22 00:55:46,2022-01-22 00:55:47,2022-01-22 00:55:47,2022-01-22 00:55:47,2022-01-22 01:38:46,0.137654066085815,2579.23980212212,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,24,1,ctree,2022-01-22 00:55:57,2022-01-22 00:55:58,2022-01-22 00:55:58,2022-01-22 00:55:58,2022-01-22 01:38:47,0.102860450744629,2569.46539926529,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,2,empirical,2022-01-22 01:16:52,2022-01-22 01:16:53,2022-01-22 01:16:53,2022-01-22 01:16:54,2022-01-22 01:38:55,0.100463628768921,1320.99148344994,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,1,empirical,2022-01-22 01:37:20,2022-01-22 01:37:22,2022-01-22 01:37:22,2022-01-22 01:37:22,2022-01-22 01:39:05,0.34293794631958,102.677614450455,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,1,empirical,2022-01-22 01:37:56,2022-01-22 01:37:59,2022-01-22 01:37:59,2022-01-22 01:38:00,2022-01-22 01:39:15,0.538186073303223,75.0914919376373,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,2,empirical,2022-01-22 01:39:07,2022-01-22 01:39:07,2022-01-22 01:39:07,2022-01-22 01:39:08,2022-01-22 01:39:58,0.151895761489868,50.6365342140198,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,2,empirical,2022-01-22 01:38:59,2022-01-22 01:39:00,2022-01-22 01:39:00,2022-01-22 01:39:01,2022-01-22 01:40:07,0.12798285484314,65.1254358291626,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,2,ctree,2022-01-22 01:17:25,2022-01-22 01:17:26,2022-01-22 01:17:26,2022-01-22 01:17:28,2022-01-22 01:40:09,0.188083648681641,1360.05182433128,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,4,empirical,2022-01-22 01:39:57,2022-01-22 01:39:58,2022-01-22 01:39:58,2022-01-22 01:39:58,2022-01-22 01:40:59,0.134471893310547,60.5440368652344,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,4,empirical,2022-01-22 01:39:48,2022-01-22 01:39:49,2022-01-22 01:39:49,2022-01-22 01:39:51,2022-01-22 01:41:07,0.168824911117554,76.013284444809,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,8,empirical,2022-01-22 01:40:55,2022-01-22 01:40:56,2022-01-22 01:40:56,2022-01-22 01:40:56,2022-01-22 01:42:12,0.159416913986206,75.9276638031006,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,8,empirical,2022-01-22 01:40:44,2022-01-22 01:40:45,2022-01-22 01:40:45,2022-01-22 01:40:47,2022-01-22 01:42:19,0.288350582122803,91.5134017467499,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,16,empirical,2022-01-22 01:41:57,2022-01-22 01:41:58,2022-01-22 01:41:58,2022-01-22 01:42:01,2022-01-22 01:43:42,0.214263916015625,101.665854930878,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,16,empirical,2022-01-22 01:42:12,2022-01-22 01:42:14,2022-01-22 01:42:14,2022-01-22 01:42:14,2022-01-22 01:43:51,0.115813732147217,97.0400414466858,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,24,empirical,2022-01-22 01:43:33,2022-01-22 01:43:34,2022-01-22 01:43:35,2022-01-22 01:43:39,2022-01-22 01:45:29,0.321691751480103,109.873937129974,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,24,empirical,2022-01-22 01:43:51,2022-01-22 01:43:52,2022-01-22 01:43:52,2022-01-22 01:43:52,2022-01-22 01:45:39,0.241383790969849,106.319730520248,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,1,empirical,2022-01-22 01:15:18,2022-01-22 01:15:20,2022-01-22 01:15:20,2022-01-22 01:15:21,2022-01-22 01:46:11,0.387478828430176,1850.03498315811,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,1,empirical,2022-01-22 01:15:38,2022-01-22 01:15:41,2022-01-22 01:15:41,2022-01-22 01:15:42,2022-01-22 01:46:36,0.496640205383301,1853.96723556519,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,32,empirical,2022-01-22 01:45:50,2022-01-22 01:45:52,2022-01-22 01:45:52,2022-01-22 01:45:56,2022-01-22 01:47:57,0.39066481590271,121.211019992828,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,1,32,empirical,2022-01-22 01:46:18,2022-01-22 01:46:19,2022-01-22 01:46:19,2022-01-22 01:46:20,2022-01-22 01:48:31,0.385215997695923,131.002678155899,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,1,ctree,2022-01-22 01:16:36,2022-01-22 01:16:36,2022-01-22 01:16:36,2022-01-22 01:16:37,2022-01-22 01:51:13,0.144690990447998,2075.92581558228,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,1,empirical,2022-01-22 01:48:49,2022-01-22 01:48:51,2022-01-22 01:48:51,2022-01-22 01:48:52,2022-01-22 01:51:29,0.344641208648682,157.168018341064,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,1,empirical,2022-01-22 01:49:19,2022-01-22 01:49:21,2022-01-22 01:49:21,2022-01-22 01:49:22,2022-01-22 01:51:58,0.386120557785034,156.260537624359,0,1e+05,13,0.3,1,0,1,1
+9,10000,100,32,1,ctree,2022-01-22 01:16:45,2022-01-22 01:16:45,2022-01-22 01:16:45,2022-01-22 01:16:45,2022-01-22 01:52:19,0.109169960021973,2133.36749792099,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,2,empirical,2022-01-22 01:52:06,2022-01-22 01:52:08,2022-01-22 01:52:08,2022-01-22 01:52:10,2022-01-22 01:53:38,0.232431173324585,87.2419457435608,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,2,empirical,2022-01-22 01:52:39,2022-01-22 01:52:40,2022-01-22 01:52:40,2022-01-22 01:52:41,2022-01-22 01:53:41,0.205278396606445,60.3414344787598,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,4,empirical,2022-01-22 01:55:33,2022-01-22 01:55:34,2022-01-22 01:55:35,2022-01-22 01:55:40,2022-01-22 01:57:15,0.23747444152832,95.2346889972687,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,4,empirical,2022-01-22 01:56:13,2022-01-22 01:56:15,2022-01-22 01:56:15,2022-01-22 01:56:16,2022-01-22 01:57:22,0.3500075340271,66.3186814785004,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,8,empirical,2022-01-22 01:59:13,2022-01-22 01:59:14,2022-01-22 01:59:15,2022-01-22 01:59:18,2022-01-22 02:00:43,0.362035751342773,85.0005538463593,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,8,empirical,2022-01-22 01:59:47,2022-01-22 01:59:48,2022-01-22 01:59:49,2022-01-22 01:59:49,2022-01-22 02:00:57,0.305766344070435,67.8213777542114,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,16,empirical,2022-01-22 02:02:51,2022-01-22 02:02:52,2022-01-22 02:02:53,2022-01-22 02:02:56,2022-01-22 02:04:21,0.224912881851196,85.0768251419068,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,16,empirical,2022-01-22 02:03:20,2022-01-22 02:03:22,2022-01-22 02:03:22,2022-01-22 02:03:22,2022-01-22 02:04:30,0.235606670379639,67.4479074478149,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,24,empirical,2022-01-22 02:06:00,2022-01-22 02:06:02,2022-01-22 02:06:02,2022-01-22 02:06:05,2022-01-22 02:07:47,0.38435173034668,101.877918243408,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,24,empirical,2022-01-22 02:06:37,2022-01-22 02:06:38,2022-01-22 02:06:38,2022-01-22 02:06:39,2022-01-22 02:07:49,0.330096244812012,70.4685909748077,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,32,empirical,2022-01-22 02:09:43,2022-01-22 02:09:45,2022-01-22 02:09:45,2022-01-22 02:09:50,2022-01-22 02:11:19,0.3041090965271,88.6841707229614,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,2,32,empirical,2022-01-22 02:10:18,2022-01-22 02:10:20,2022-01-22 02:10:20,2022-01-22 02:10:21,2022-01-22 02:12:07,0.270083665847778,106.64613199234,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,1,empirical,2022-01-22 02:13:10,2022-01-22 02:13:11,2022-01-22 02:13:12,2022-01-22 02:13:12,2022-01-22 02:16:42,0.249302387237549,210.346002101898,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,1,empirical,2022-01-22 02:13:48,2022-01-22 02:13:53,2022-01-22 02:13:53,2022-01-22 02:13:54,2022-01-22 02:17:22,0.260806798934937,208.156105279922,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,2,empirical,2022-01-22 02:16:54,2022-01-22 02:16:55,2022-01-22 02:16:56,2022-01-22 02:16:56,2022-01-22 02:17:46,0.376712083816528,50.1760177612305,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,2,empirical,2022-01-22 02:16:26,2022-01-22 02:16:28,2022-01-22 02:16:28,2022-01-22 02:16:31,2022-01-22 02:18:17,0.314223527908325,105.823621749878,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,4,empirical,2022-01-22 02:19:16,2022-01-22 02:19:17,2022-01-22 02:19:17,2022-01-22 02:19:19,2022-01-22 02:20:18,0.204575061798096,58.8597540855408,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,4,empirical,2022-01-22 02:20:08,2022-01-22 02:20:09,2022-01-22 02:20:09,2022-01-22 02:20:10,2022-01-22 02:20:32,0.178043127059937,22.7060980796814,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,8,empirical,2022-01-22 02:22:28,2022-01-22 02:22:29,2022-01-22 02:22:30,2022-01-22 02:22:33,2022-01-22 02:23:42,0.180992364883423,69.2829351425171,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,8,empirical,2022-01-22 02:22:58,2022-01-22 02:23:01,2022-01-22 02:23:01,2022-01-22 02:23:02,2022-01-22 02:23:49,0.512573003768921,46.6333136558533,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,16,empirical,2022-01-22 02:26:42,2022-01-22 02:26:43,2022-01-22 02:26:43,2022-01-22 02:26:43,2022-01-22 02:27:57,0.306085348129272,73.472410440445,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,16,empirical,2022-01-22 02:26:14,2022-01-22 02:26:16,2022-01-22 02:26:16,2022-01-22 02:26:19,2022-01-22 02:28:41,0.154666185379028,142.405807495117,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,24,empirical,2022-01-22 02:30:39,2022-01-22 02:30:41,2022-01-22 02:30:42,2022-01-22 02:30:46,2022-01-22 02:31:54,0.37065052986145,68.0808618068695,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,24,empirical,2022-01-22 02:31:21,2022-01-22 02:31:23,2022-01-22 02:31:23,2022-01-22 02:31:24,2022-01-22 02:32:24,0.326589107513428,60.2514832019806,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,32,empirical,2022-01-22 02:36:09,2022-01-22 02:36:11,2022-01-22 02:36:12,2022-01-22 02:36:12,2022-01-22 02:36:55,0.437098979949951,43.1515862941742,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,4,ctree,2022-01-22 02:21:59,2022-01-22 02:22:00,2022-01-22 02:22:01,2022-01-22 02:22:01,2022-01-22 02:37:54,0.479377508163452,952.167794942856,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,32,empirical,2022-01-22 02:35:37,2022-01-22 02:35:40,2022-01-22 02:35:41,2022-01-22 02:36:06,2022-01-22 02:38:01,0.623153924942017,114.225393533707,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,2,empirical,2022-01-22 02:44:37,2022-01-22 02:44:38,2022-01-22 02:44:39,2022-01-22 02:44:39,2022-01-22 02:45:15,0.204447269439697,35.7962510585785,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,24,ctree,2022-01-22 02:34:23,2022-01-22 02:34:25,2022-01-22 02:34:25,2022-01-22 02:34:26,2022-01-22 02:45:17,0.404924869537354,650.690148353577,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,1,empirical,2022-01-22 02:40:44,2022-01-22 02:40:46,2022-01-22 02:40:46,2022-01-22 02:40:47,2022-01-22 02:46:43,0.344234466552734,356.187374591827,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,2,empirical,2022-01-22 02:44:10,2022-01-22 02:44:11,2022-01-22 02:44:11,2022-01-22 02:44:16,2022-01-22 02:47:17,0.33298659324646,180.438060998917,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,1,empirical,2022-01-22 02:41:27,2022-01-22 02:41:28,2022-01-22 02:41:28,2022-01-22 02:41:28,2022-01-22 02:47:33,0.211798667907715,364.525406360626,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,4,empirical,2022-01-22 02:47:19,2022-01-22 02:47:22,2022-01-22 02:47:22,2022-01-22 02:47:23,2022-01-22 02:47:55,0.26728343963623,31.9319834709168,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,4,empirical,2022-01-22 02:46:44,2022-01-22 02:46:45,2022-01-22 02:46:45,2022-01-22 02:46:48,2022-01-22 02:48:38,0.163733720779419,109.999467134476,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,4,32,ctree,2022-01-22 02:39:23,2022-01-22 02:39:25,2022-01-22 02:39:25,2022-01-22 02:39:25,2022-01-22 02:50:23,0.28056001663208,657.837820529938,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,8,empirical,2022-01-22 02:49:44,2022-01-22 02:49:46,2022-01-22 02:49:46,2022-01-22 02:49:49,2022-01-22 02:51:03,0.378391742706299,74.2485461235046,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,8,empirical,2022-01-22 02:50:27,2022-01-22 02:50:29,2022-01-22 02:50:29,2022-01-22 02:50:29,2022-01-22 02:51:11,0.288201093673706,41.4705760478973,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,16,empirical,2022-01-22 02:55:09,2022-01-22 02:55:16,2022-01-22 02:55:17,2022-01-22 02:55:19,2022-01-22 02:56:18,1.01343655586243,59.1026678085327,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,16,empirical,2022-01-22 02:53:58,2022-01-22 02:54:02,2022-01-22 02:54:02,2022-01-22 02:54:09,2022-01-22 02:56:40,0.692918300628662,150.14314699173,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,4,gaussian,2022-01-22 02:47:53,2022-01-22 02:47:54,2022-01-22 02:47:54,2022-01-22 02:47:57,2022-01-22 02:57:14,0.211225271224976,556.901450157166,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,8,gaussian,2022-01-22 02:51:45,2022-01-22 02:51:47,2022-01-22 02:51:48,2022-01-22 02:51:49,2022-01-22 02:57:59,0.486351490020752,370.704549789429,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,4,gaussian,2022-01-22 02:48:16,2022-01-22 02:48:19,2022-01-22 02:48:19,2022-01-22 02:48:20,2022-01-22 02:58:59,0.472658157348633,639.603382587433,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,24,empirical,2022-01-22 02:59:08,2022-01-22 02:59:10,2022-01-22 02:59:11,2022-01-22 02:59:17,2022-01-22 03:01:00,0.499860525131226,103.697935581207,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,24,empirical,2022-01-22 03:00:04,2022-01-22 03:00:07,2022-01-22 03:00:08,2022-01-22 03:00:09,2022-01-22 03:01:01,0.549014329910278,52.0763595104218,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,32,empirical,2022-01-22 03:04:15,2022-01-22 03:04:19,2022-01-22 03:04:20,2022-01-22 03:04:28,2022-01-22 03:06:12,0.664488554000855,104.000945568085,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,32,empirical,2022-01-22 03:05:42,2022-01-22 03:05:45,2022-01-22 03:05:46,2022-01-22 03:05:47,2022-01-22 03:06:14,0.560811758041382,27.7492747306824,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,8,ctree,2022-01-22 02:53:08,2022-01-22 02:53:11,2022-01-22 02:53:12,2022-01-22 02:53:13,2022-01-22 03:06:44,0.607256412506104,811.02317070961,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,16,ctree,2022-01-22 02:58:23,2022-01-22 02:58:25,2022-01-22 02:58:26,2022-01-22 02:58:26,2022-01-22 03:07:00,0.417230606079102,513.509533166885,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,4,ctree,2022-01-22 02:49:08,2022-01-22 02:49:09,2022-01-22 02:49:10,2022-01-22 02:49:10,2022-01-22 03:11:49,0.193442821502686,1359.09394526482,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,24,ctree,2022-01-22 03:03:08,2022-01-22 03:03:11,2022-01-22 03:03:12,2022-01-22 03:03:13,2022-01-22 03:12:57,0.896903514862061,583.915278196335,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,32,ctree,2022-01-22 03:08:40,2022-01-22 03:08:43,2022-01-22 03:08:43,2022-01-22 03:08:44,2022-01-22 03:13:43,0.509770631790161,298.823926687241,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,4,ctree,2022-01-22 02:48:40,2022-01-22 02:48:42,2022-01-22 02:48:42,2022-01-22 02:48:46,2022-01-22 03:14:14,0.184979677200317,1528.42556667328,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,2,empirical,2022-01-22 03:13:34,2022-01-22 03:13:36,2022-01-22 03:13:36,2022-01-22 03:13:37,2022-01-22 03:14:40,0.389299392700195,63.1538534164429,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,24,ctree,2022-01-22 03:02:02,2022-01-22 03:02:06,2022-01-22 03:02:06,2022-01-22 03:02:14,2022-01-22 03:15:31,0.577649831771851,796.840286016464,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,32,ctree,2022-01-22 03:07:46,2022-01-22 03:07:49,2022-01-22 03:07:50,2022-01-22 03:07:57,2022-01-22 03:16:19,0.64387583732605,502.070887327194,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,4,empirical,2022-01-22 03:15:51,2022-01-22 03:15:52,2022-01-22 03:15:53,2022-01-22 03:15:53,2022-01-22 03:16:34,0.39448881149292,40.8158376216888,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,1,gaussian,2022-01-22 02:42:52,2022-01-22 02:42:54,2022-01-22 02:42:54,2022-01-22 02:42:54,2022-01-22 03:17:06,0.308839797973633,2051.89924573898,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,2,empirical,2022-01-22 03:13:06,2022-01-22 03:13:07,2022-01-22 03:13:08,2022-01-22 03:13:11,2022-01-22 03:17:13,0.289663314819336,242.524348497391,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,4,empirical,2022-01-22 03:15:32,2022-01-22 03:15:34,2022-01-22 03:15:34,2022-01-22 03:15:37,2022-01-22 03:17:50,0.209911584854126,132.961334228516,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,1,empirical,2022-01-22 03:09:35,2022-01-22 03:09:37,2022-01-22 03:09:38,2022-01-22 03:09:39,2022-01-22 03:17:56,0.532514572143555,496.4817237854,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,1,empirical,2022-01-22 03:10:32,2022-01-22 03:10:42,2022-01-22 03:10:42,2022-01-22 03:10:43,2022-01-22 03:18:39,0.61293625831604,475.975988864899,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,8,empirical,2022-01-22 03:18:07,2022-01-22 03:18:08,2022-01-22 03:18:09,2022-01-22 03:18:09,2022-01-22 03:18:44,0.236171960830689,35.3581767082214,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,8,2,ctree,2022-01-22 02:46:13,2022-01-22 02:46:15,2022-01-22 02:46:16,2022-01-22 02:46:16,2022-01-22 03:18:54,0.38081431388855,1957.60978984833,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,8,empirical,2022-01-22 03:17:41,2022-01-22 03:17:43,2022-01-22 03:17:43,2022-01-22 03:17:47,2022-01-22 03:19:04,0.362355470657349,76.3169219493866,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,16,empirical,2022-01-22 03:21:02,2022-01-22 03:21:06,2022-01-22 03:21:07,2022-01-22 03:21:13,2022-01-22 03:22:44,0.596693277359009,91.0323338508606,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,16,empirical,2022-01-22 03:22:11,2022-01-22 03:22:15,2022-01-22 03:22:16,2022-01-22 03:22:17,2022-01-22 03:22:53,0.781660795211792,36.5028355121613,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,8,gaussian,2022-01-22 03:18:36,2022-01-22 03:18:39,2022-01-22 03:18:39,2022-01-22 03:18:43,2022-01-22 03:23:57,0.515699148178101,314.193381071091,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,8,gaussian,2022-01-22 03:19:02,2022-01-22 03:19:04,2022-01-22 03:19:04,2022-01-22 03:19:05,2022-01-22 03:24:36,0.454651355743408,330.895944356918,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,4,gaussian,2022-01-22 03:16:09,2022-01-22 03:16:10,2022-01-22 03:16:10,2022-01-22 03:16:13,2022-01-22 03:24:56,0.529733896255493,523.202776432037,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,4,gaussian,2022-01-22 03:16:31,2022-01-22 03:16:33,2022-01-22 03:16:33,2022-01-22 03:16:34,2022-01-22 03:25:52,0.366849422454834,558.215231180191,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,16,gaussian,2022-01-22 03:24:16,2022-01-22 03:24:19,2022-01-22 03:24:20,2022-01-22 03:24:22,2022-01-22 03:27:59,0.906281471252441,217.435868263245,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,24,empirical,2022-01-22 03:28:37,2022-01-22 03:28:40,2022-01-22 03:28:41,2022-01-22 03:28:42,2022-01-22 03:29:09,0.51256799697876,27.5531740188599,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,24,empirical,2022-01-22 03:27:57,2022-01-22 03:27:59,2022-01-22 03:27:59,2022-01-22 03:28:05,2022-01-22 03:29:21,0.314983606338501,75.8100733757019,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,8,ctree,2022-01-22 03:20:12,2022-01-22 03:20:14,2022-01-22 03:20:15,2022-01-22 03:20:16,2022-01-22 03:34:56,0.736507892608643,880.105220556259,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,16,ctree,2022-01-22 03:26:53,2022-01-22 03:27:05,2022-01-22 03:27:07,2022-01-22 03:27:08,2022-01-22 03:34:59,1.68570113182068,471.237874746323,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,16,ctree,2022-01-22 03:25:20,2022-01-22 03:25:24,2022-01-22 03:25:26,2022-01-22 03:25:35,2022-01-22 03:35:00,1.0910701751709,565.137838602066,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,24,gaussian,2022-01-22 03:30:27,2022-01-22 03:30:31,2022-01-22 03:30:33,2022-01-22 03:30:34,2022-01-22 03:35:11,1.46600914001465,276.781050443649,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,32,empirical,2022-01-22 03:35:42,2022-01-22 03:35:44,2022-01-22 03:35:45,2022-01-22 03:35:46,2022-01-22 03:36:09,0.583730697631836,23.1291429996491,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,32,empirical,2022-01-22 03:35:10,2022-01-22 03:35:12,2022-01-22 03:35:13,2022-01-22 03:35:18,2022-01-22 03:36:20,0.338057518005371,61.9942181110382,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,8,ctree,2022-01-22 03:19:34,2022-01-22 03:19:37,2022-01-22 03:19:37,2022-01-22 03:19:43,2022-01-22 03:37:48,0.73034930229187,1084.6105401516,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,24,ctree,2022-01-22 03:32:39,2022-01-22 03:32:43,2022-01-22 03:32:44,2022-01-22 03:33:09,2022-01-22 03:39:51,1.05011582374573,402.112931966782,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,24,ctree,2022-01-22 03:34:38,2022-01-22 03:34:40,2022-01-22 03:34:40,2022-01-22 03:34:41,2022-01-22 03:40:17,0.312652111053467,336.034219503403,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,32,gaussian,2022-01-22 03:36:21,2022-01-22 03:36:23,2022-01-22 03:36:23,2022-01-22 03:36:28,2022-01-22 03:40:17,0.323652505874634,228.625579595566,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,32,gaussian,2022-01-22 03:37:14,2022-01-22 03:37:18,2022-01-22 03:37:19,2022-01-22 03:37:21,2022-01-22 03:40:39,0.98668909072876,197.94747376442,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,4,ctree,2022-01-22 03:17:16,2022-01-22 03:17:18,2022-01-22 03:17:18,2022-01-22 03:17:19,2022-01-22 03:41:33,0.258048534393311,1453.51244306564,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,32,ctree,2022-01-22 03:40:10,2022-01-22 03:40:12,2022-01-22 03:40:12,2022-01-22 03:40:13,2022-01-22 03:43:11,0.475870132446289,178.015317678452,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,4,ctree,2022-01-22 03:16:52,2022-01-22 03:16:55,2022-01-22 03:16:55,2022-01-22 03:17:00,2022-01-22 03:43:30,0.500282287597656,1590.80685448647,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,32,ctree,2022-01-22 03:39:01,2022-01-22 03:39:06,2022-01-22 03:39:06,2022-01-22 03:39:18,2022-01-22 03:43:55,0.87313985824585,276.822570800781,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,2,empirical,2022-01-22 03:43:34,2022-01-22 03:43:35,2022-01-22 03:43:35,2022-01-22 03:43:35,2022-01-22 03:44:26,0.200089454650879,51.1402425765991,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,1,gaussian,2022-01-22 03:11:29,2022-01-22 03:11:30,2022-01-22 03:11:30,2022-01-22 03:11:31,2022-01-22 03:44:40,0.187605142593384,1989.52359509468,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,4,empirical,2022-01-22 03:44:48,2022-01-22 03:44:49,2022-01-22 03:44:50,2022-01-22 03:44:50,2022-01-22 03:45:42,0.373626232147217,51.8829953670502,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,4,empirical,2022-01-22 03:44:35,2022-01-22 03:44:36,2022-01-22 03:44:36,2022-01-22 03:44:38,2022-01-22 03:47:51,0.204853296279907,192.799675703049,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,8,empirical,2022-01-22 03:47:04,2022-01-22 03:47:07,2022-01-22 03:47:07,2022-01-22 03:47:08,2022-01-22 03:48:06,0.583279609680176,57.5668113231659,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,8,empirical,2022-01-22 03:46:35,2022-01-22 03:46:36,2022-01-22 03:46:37,2022-01-22 03:46:41,2022-01-22 03:48:48,0.367329120635986,127.040172576904,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,2,empirical,2022-01-22 03:43:11,2022-01-22 03:43:13,2022-01-22 03:43:13,2022-01-22 03:43:18,2022-01-22 03:49:13,0.55579400062561,355.404944419861,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,2,ctree,2022-01-22 03:15:08,2022-01-22 03:15:09,2022-01-22 03:15:10,2022-01-22 03:15:10,2022-01-22 03:49:34,0.585629463195801,2063.59565925598,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,16,empirical,2022-01-22 03:51:37,2022-01-22 03:51:42,2022-01-22 03:51:43,2022-01-22 03:51:44,2022-01-22 03:52:44,0.479313135147095,60.1241667270661,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,1,empirical,2022-01-22 03:40:43,2022-01-22 03:40:44,2022-01-22 03:40:45,2022-01-22 03:40:45,2022-01-22 03:53:05,0.249307870864868,739.79953455925,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,16,empirical,2022-01-22 03:50:37,2022-01-22 03:50:40,2022-01-22 03:50:41,2022-01-22 03:50:48,2022-01-22 03:53:13,0.737832069396973,144.867150068283,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,8,gaussian,2022-01-22 03:47:34,2022-01-22 03:47:37,2022-01-22 03:47:38,2022-01-22 03:47:43,2022-01-22 03:53:43,0.588129758834839,359.696888685226,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,1,empirical,2022-01-22 03:41:03,2022-01-22 03:41:04,2022-01-22 03:41:05,2022-01-22 03:41:05,2022-01-22 03:53:45,0.339154243469238,759.80712223053,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,8,gaussian,2022-01-22 03:48:18,2022-01-22 03:48:21,2022-01-22 03:48:22,2022-01-22 03:48:23,2022-01-22 03:54:39,0.639863252639771,376.704631090164,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,4,gaussian,2022-01-22 03:45:01,2022-01-22 03:45:03,2022-01-22 03:45:03,2022-01-22 03:45:07,2022-01-22 03:55:16,0.397824764251709,609.590039253235,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,4,gaussian,2022-01-22 03:45:19,2022-01-22 03:45:21,2022-01-22 03:45:21,2022-01-22 03:45:21,2022-01-22 03:56:16,0.309081792831421,654.494213104248,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,16,gaussian,2022-01-22 03:52:57,2022-01-22 03:53:01,2022-01-22 03:53:01,2022-01-22 03:53:10,2022-01-22 03:57:48,0.800508499145508,277.265662431717,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,16,gaussian,2022-01-22 03:54:06,2022-01-22 03:54:10,2022-01-22 03:54:10,2022-01-22 03:54:12,2022-01-22 03:58:30,0.724850416183472,258.238918542862,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,24,empirical,2022-01-22 03:58:35,2022-01-22 03:58:38,2022-01-22 03:58:38,2022-01-22 03:58:39,2022-01-22 03:59:11,0.346559762954712,32.0990943908691,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,24,empirical,2022-01-22 03:57:21,2022-01-22 03:57:25,2022-01-22 03:57:25,2022-01-22 03:57:33,2022-01-22 03:59:40,0.641945362091065,126.86918592453,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,2,ctree,2022-01-22 03:14:42,2022-01-22 03:14:43,2022-01-22 03:14:43,2022-01-22 03:14:46,2022-01-22 04:00:33,0.229181289672852,2746.98105549812,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,2,gaussian,2022-01-22 03:43:48,2022-01-22 03:43:49,2022-01-22 03:43:50,2022-01-22 03:43:52,2022-01-22 04:03:26,0.215842485427856,1174.06771945953,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,24,gaussian,2022-01-22 04:01:03,2022-01-22 04:01:08,2022-01-22 04:01:09,2022-01-22 04:01:10,2022-01-22 04:04:20,0.841955423355103,189.700965881348,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,8,ctree,2022-01-22 03:49:46,2022-01-22 03:49:49,2022-01-22 03:49:50,2022-01-22 03:49:51,2022-01-22 04:05:36,0.558454275131226,944.906455278397,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,16,ctree,2022-01-22 03:56:18,2022-01-22 03:56:23,2022-01-22 03:56:24,2022-01-22 03:56:25,2022-01-22 04:06:11,0.63016676902771,586.453827142716,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,2,gaussian,2022-01-22 03:44:01,2022-01-22 03:44:01,2022-01-22 03:44:02,2022-01-22 03:44:02,2022-01-22 04:06:18,0.165026903152466,1336.52430844307,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,32,empirical,2022-01-22 04:05:56,2022-01-22 04:05:58,2022-01-22 04:05:59,2022-01-22 04:06:01,2022-01-22 04:06:28,0.87062668800354,27.5880634784699,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,32,empirical,2022-01-22 04:04:51,2022-01-22 04:04:54,2022-01-22 04:04:55,2022-01-22 04:05:01,2022-01-22 04:06:35,0.24402117729187,94.7127361297608,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,16,ctree,2022-01-22 03:55:17,2022-01-22 03:55:22,2022-01-22 03:55:23,2022-01-22 03:55:31,2022-01-22 04:06:37,0.853739500045776,665.81511425972,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,8,ctree,2022-01-22 03:49:06,2022-01-22 03:49:09,2022-01-22 03:49:10,2022-01-22 03:49:16,2022-01-22 04:07:37,0.691263914108276,1101.05474829674,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,24,ctree,2022-01-22 04:04:11,2022-01-22 04:04:13,2022-01-22 04:04:13,2022-01-22 04:04:13,2022-01-22 04:09:16,0.275013446807861,302.969065666199,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,24,ctree,2022-01-22 04:02:54,2022-01-22 04:03:07,2022-01-22 04:03:09,2022-01-22 04:03:56,2022-01-22 04:10:02,1.94342827796936,366.072500944138,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,32,gaussian,2022-01-22 04:06:52,2022-01-22 04:06:54,2022-01-22 04:06:55,2022-01-22 04:07:01,2022-01-22 04:10:22,0.54408597946167,200.706781864166,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,32,gaussian,2022-01-22 04:07:56,2022-01-22 04:08:02,2022-01-22 04:08:03,2022-01-22 04:08:06,2022-01-22 04:10:41,1.27220463752747,155.589918613434,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,4,ctree,2022-01-22 03:46:07,2022-01-22 03:46:10,2022-01-22 03:46:10,2022-01-22 03:46:10,2022-01-22 04:12:17,0.354359865188599,1567.05265402794,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,32,ctree,2022-01-22 04:10:35,2022-01-22 04:10:36,2022-01-22 04:10:36,2022-01-22 04:10:37,2022-01-22 04:13:34,0.238357305526733,177.855332612991,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,32,ctree,2022-01-22 04:09:36,2022-01-22 04:09:42,2022-01-22 04:09:43,2022-01-22 04:09:53,2022-01-22 04:13:59,0.945101976394653,245.957392692566,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,4,ctree,2022-01-22 03:45:41,2022-01-22 03:45:43,2022-01-22 03:45:44,2022-01-22 03:45:48,2022-01-22 04:14:26,0.652153253555298,1718.03585600853,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,2,empirical,2022-01-22 04:14:05,2022-01-22 04:14:06,2022-01-22 04:14:06,2022-01-22 04:14:06,2022-01-22 04:15:05,0.165839672088623,59.1394057273865,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,4,empirical,2022-01-22 04:15:11,2022-01-22 04:15:13,2022-01-22 04:15:13,2022-01-22 04:15:14,2022-01-22 04:16:21,0.32716965675354,67.030036687851,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,1,gaussian,2022-01-22 03:41:25,2022-01-22 03:41:27,2022-01-22 03:41:27,2022-01-22 03:41:27,2022-01-22 04:17:06,0.424708366394043,2138.78034353256,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,1,gaussian,2022-01-22 03:41:50,2022-01-22 03:41:52,2022-01-22 03:41:52,2022-01-22 03:41:53,2022-01-22 04:18:24,0.405516624450684,2191.29393172264,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,8,empirical,2022-01-22 04:17:32,2022-01-22 04:17:34,2022-01-22 04:17:34,2022-01-22 04:17:36,2022-01-22 04:18:41,0.810015201568604,65.1195733547211,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,4,empirical,2022-01-22 04:14:59,2022-01-22 04:15:01,2022-01-22 04:15:01,2022-01-22 04:15:04,2022-01-22 04:19:22,0.374434947967529,258.052938699722,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,8,empirical,2022-01-22 04:17:05,2022-01-22 04:17:08,2022-01-22 04:17:08,2022-01-22 04:17:13,2022-01-22 04:19:54,0.547721147537231,160.733810424805,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,2,empirical,2022-01-22 04:13:53,2022-01-22 04:13:54,2022-01-22 04:13:54,2022-01-22 04:13:56,2022-01-22 04:21:21,0.254103422164917,445.001267910004,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,16,empirical,2022-01-22 04:22:09,2022-01-22 04:22:14,2022-01-22 04:22:15,2022-01-22 04:22:17,2022-01-22 04:23:12,0.977073431015015,54.7836308479309,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,16,empirical,2022-01-22 04:21:07,2022-01-22 04:21:10,2022-01-22 04:21:11,2022-01-22 04:21:18,2022-01-22 04:23:37,0.667179346084595,138.789150476456,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,2,ctree,2022-01-22 03:44:23,2022-01-22 03:44:24,2022-01-22 03:44:24,2022-01-22 03:44:24,2022-01-22 04:24:24,0.283277750015259,2400.35080480576,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,8,gaussian,2022-01-22 04:18:08,2022-01-22 04:18:12,2022-01-22 04:18:13,2022-01-22 04:18:19,2022-01-22 04:24:58,0.546968460083008,398.43504691124,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,8,gaussian,2022-01-22 04:18:52,2022-01-22 04:18:55,2022-01-22 04:18:55,2022-01-22 04:18:56,2022-01-22 04:25:42,0.552104234695435,405.455137014389,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,4,gaussian,2022-01-22 04:15:27,2022-01-22 04:15:29,2022-01-22 04:15:29,2022-01-22 04:15:33,2022-01-22 04:27:26,0.503604888916016,713.526695728302,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,4,gaussian,2022-01-22 04:15:49,2022-01-22 04:15:50,2022-01-22 04:15:51,2022-01-22 04:15:51,2022-01-22 04:27:38,0.298052310943604,707.226191520691,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,1,empirical,2022-01-22 04:11:01,2022-01-22 04:11:03,2022-01-22 04:11:03,2022-01-22 04:11:04,2022-01-22 04:27:45,0.344135522842407,1000.90395665169,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,16,gaussian,2022-01-22 04:23:33,2022-01-22 04:23:39,2022-01-22 04:23:40,2022-01-22 04:23:49,2022-01-22 04:28:12,1.18532633781433,263.216118335724,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,16,gaussian,2022-01-22 04:24:37,2022-01-22 04:24:42,2022-01-22 04:24:43,2022-01-22 04:24:45,2022-01-22 04:28:31,0.984174489974976,225.848697423935,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,1,empirical,2022-01-22 04:11:29,2022-01-22 04:11:32,2022-01-22 04:11:32,2022-01-22 04:11:33,2022-01-22 04:28:32,0.441158771514893,1018.56839704514,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,24,empirical,2022-01-22 04:29:17,2022-01-22 04:29:22,2022-01-22 04:29:23,2022-01-22 04:29:24,2022-01-22 04:30:18,0.819403409957886,53.6927182674408,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,24,empirical,2022-01-22 04:28:26,2022-01-22 04:28:28,2022-01-22 04:28:29,2022-01-22 04:28:35,2022-01-22 04:31:09,0.50626277923584,154.623038053513,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,16,ctree,2022-01-22 04:27:14,2022-01-22 04:27:19,2022-01-22 04:27:20,2022-01-22 04:27:22,2022-01-22 04:35:24,0.96224308013916,481.980944395065,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,24,gaussian,2022-01-22 04:30:33,2022-01-22 04:30:37,2022-01-22 04:30:38,2022-01-22 04:30:47,2022-01-22 04:35:41,1.10155034065247,294.364867448807,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,8,ctree,2022-01-22 04:20:18,2022-01-22 04:20:21,2022-01-22 04:20:22,2022-01-22 04:20:23,2022-01-22 04:35:50,0.672575235366821,927.401400566101,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,16,ctree,2022-01-22 04:25:53,2022-01-22 04:25:57,2022-01-22 04:25:59,2022-01-22 04:26:08,2022-01-22 04:36:21,1.11445498466492,612.33871793747,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,24,gaussian,2022-01-22 04:31:41,2022-01-22 04:31:46,2022-01-22 04:31:47,2022-01-22 04:31:49,2022-01-22 04:36:51,1.0779721736908,301.52494430542,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,32,empirical,2022-01-22 04:37:11,2022-01-22 04:37:14,2022-01-22 04:37:15,2022-01-22 04:37:17,2022-01-22 04:37:53,0.782464265823364,35.699024438858,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,2,gaussian,2022-01-22 04:14:15,2022-01-22 04:14:17,2022-01-22 04:14:17,2022-01-22 04:14:19,2022-01-22 04:37:53,0.196419477462769,1413.82299041748,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,8,ctree,2022-01-22 04:19:37,2022-01-22 04:19:40,2022-01-22 04:19:41,2022-01-22 04:19:47,2022-01-22 04:38:11,0.93701171875,1104.41647624969,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,32,empirical,2022-01-22 04:36:11,2022-01-22 04:36:13,2022-01-22 04:36:14,2022-01-22 04:36:20,2022-01-22 04:38:26,0.86063814163208,125.708092451096,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,2,ctree,2022-01-22 03:44:11,2022-01-22 03:44:13,2022-01-22 03:44:13,2022-01-22 03:44:15,2022-01-22 04:38:42,0.121263265609741,3267.3486225605,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,2,gaussian,2022-01-22 04:14:27,2022-01-22 04:14:28,2022-01-22 04:14:28,2022-01-22 04:14:28,2022-01-22 04:38:56,0.145580530166626,1467.49732446671,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,32,gaussian,2022-01-22 04:38:37,2022-01-22 04:38:39,2022-01-22 04:38:39,2022-01-22 04:38:45,2022-01-22 04:42:25,0.142096996307373,219.530455827713,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,24,ctree,2022-01-22 04:35:02,2022-01-22 04:35:06,2022-01-22 04:35:07,2022-01-22 04:35:08,2022-01-22 04:43:04,0.678585052490234,475.959749221802,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,24,ctree,2022-01-22 04:33:26,2022-01-22 04:33:29,2022-01-22 04:33:29,2022-01-22 04:33:47,2022-01-22 04:44:01,0.610734462738037,614.076422452927,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,32,ctree,2022-01-22 04:42:26,2022-01-22 04:42:27,2022-01-22 04:42:28,2022-01-22 04:42:28,2022-01-22 04:45:14,0.215010643005371,165.800561189652,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,4,ctree,2022-01-22 04:16:39,2022-01-22 04:16:41,2022-01-22 04:16:41,2022-01-22 04:16:42,2022-01-22 04:45:46,0.502440214157105,1744.52444386482,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,32,ctree,2022-01-22 04:40:36,2022-01-22 04:40:42,2022-01-22 04:40:43,2022-01-22 04:40:56,2022-01-22 04:45:55,1.08466792106628,299.564634084702,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,4,ctree,2022-01-22 04:16:15,2022-01-22 04:16:17,2022-01-22 04:16:17,2022-01-22 04:16:21,2022-01-22 04:46:58,0.19093656539917,1836.83525872231,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,16,1,ctree,2022-01-22 03:12:14,2022-01-22 03:12:15,2022-01-22 03:12:15,2022-01-22 03:12:16,2022-01-22 04:47:21,0.14710259437561,5704.73913574219,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,1,gaussian,2022-01-22 04:12:36,2022-01-22 04:12:39,2022-01-22 04:12:39,2022-01-22 04:12:40,2022-01-22 04:49:25,0.554125308990479,2204.86403536797,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,1,gaussian,2022-01-22 04:11:57,2022-01-22 04:12:00,2022-01-22 04:12:01,2022-01-22 04:12:02,2022-01-22 04:50:23,0.468873977661133,2301.6923494339,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,2,ctree,2022-01-22 04:14:48,2022-01-22 04:14:49,2022-01-22 04:14:49,2022-01-22 04:14:49,2022-01-22 04:51:20,0.188418626785278,2191.39847159386,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,2,ctree,2022-01-22 04:14:36,2022-01-22 04:14:38,2022-01-22 04:14:38,2022-01-22 04:14:41,2022-01-22 04:59:38,0.2060866355896,2697.50974178314,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,1,ctree,2022-01-22 03:42:22,2022-01-22 03:42:23,2022-01-22 03:42:23,2022-01-22 03:42:24,2022-01-22 05:08:25,0.160650491714478,5161.34821200371,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,24,1,ctree,2022-01-22 03:42:49,2022-01-22 03:42:51,2022-01-22 03:42:52,2022-01-22 03:42:52,2022-01-22 05:10:50,0.462807416915894,5277.98386788368,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,1,ctree,2022-01-22 04:13:38,2022-01-22 04:13:40,2022-01-22 04:13:40,2022-01-22 04:13:40,2022-01-22 05:33:20,0.265472888946533,4779.43022942543,0,1e+05,13,0.3,1,0,1,1
+10,1000,100,32,1,ctree,2022-01-22 04:13:14,2022-01-22 04:13:16,2022-01-22 04:13:16,2022-01-22 04:13:17,2022-01-22 05:33:55,0.322280406951904,4837.77612161636,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,8,32,empirical,2022-01-22 06:12:43,2022-01-22 06:12:44,2022-01-22 06:12:45,2022-01-22 06:12:45,2022-01-22 06:18:44,0.549720287322998,358.897489786148,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,16,empirical,2022-01-22 06:31:17,2022-01-22 06:31:19,2022-01-22 06:31:19,2022-01-22 06:31:24,2022-01-22 06:37:45,0.382375955581665,381.614962339401,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,16,empirical,2022-01-22 06:32:00,2022-01-22 06:32:04,2022-01-22 06:32:04,2022-01-22 06:32:05,2022-01-22 06:38:00,0.454639911651611,355.269972085953,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,24,empirical,2022-01-22 06:38:22,2022-01-22 06:38:24,2022-01-22 06:38:24,2022-01-22 06:38:29,2022-01-22 06:42:58,0.280443906784058,269.579582214356,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,24,empirical,2022-01-22 06:39:01,2022-01-22 06:39:08,2022-01-22 06:39:08,2022-01-22 06:39:09,2022-01-22 06:43:09,0.167315721511841,240.405614614487,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,8,empirical,2022-01-22 06:25:44,2022-01-22 06:25:46,2022-01-22 06:25:46,2022-01-22 06:25:47,2022-01-22 06:43:24,0.417171239852905,1057.1796169281,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,32,empirical,2022-01-22 06:45:28,2022-01-22 06:45:30,2022-01-22 06:45:30,2022-01-22 06:45:31,2022-01-22 06:48:32,0.452541828155518,180.846414089203,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,32,empirical,2022-01-22 06:44:41,2022-01-22 06:44:42,2022-01-22 06:44:42,2022-01-22 06:45:03,2022-01-22 06:49:37,0.1501145362854,274.034828186035,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,24,ctree,2022-01-22 06:43:55,2022-01-22 06:44:00,2022-01-22 06:44:01,2022-01-22 06:44:02,2022-01-22 06:51:36,1.01762580871582,454.107393741608,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,32,ctree,2022-01-22 06:50:12,2022-01-22 06:50:14,2022-01-22 06:50:15,2022-01-22 06:50:16,2022-01-22 06:53:17,0.931843996047974,181.305830955505,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,16,32,ctree,2022-01-22 06:48:51,2022-01-22 06:48:59,2022-01-22 06:49:00,2022-01-22 06:49:17,2022-01-22 06:54:07,0.874159574508667,290.188654899597,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,8,gaussian,2022-01-22 06:57:18,2022-01-22 06:57:21,2022-01-22 06:57:21,2022-01-22 06:57:27,2022-01-22 07:05:28,0.39708685874939,481.765605211258,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,24,empirical,2022-01-22 07:10:57,2022-01-22 07:10:59,2022-01-22 07:10:59,2022-01-22 07:11:00,2022-01-22 07:16:01,0.440148115158081,300.573085784912,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,24,empirical,2022-01-22 07:09:47,2022-01-22 07:09:52,2022-01-22 07:09:53,2022-01-22 07:10:10,2022-01-22 07:16:17,0.583382368087769,367.776007175446,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,24,gaussian,2022-01-22 07:13:30,2022-01-22 07:13:35,2022-01-22 07:13:36,2022-01-22 07:13:38,2022-01-22 07:16:49,1.32819700241089,191.068200826645,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,32,empirical,2022-01-22 07:17:16,2022-01-22 07:17:18,2022-01-22 07:17:18,2022-01-22 07:17:23,2022-01-22 07:23:56,0.328133821487427,393.07698392868,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,32,empirical,2022-01-22 07:17:56,2022-01-22 07:18:06,2022-01-22 07:18:07,2022-01-22 07:18:07,2022-01-22 07:23:58,0.558789491653442,350.839649915695,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,2,gaussian,2022-01-22 06:53:52,2022-01-22 06:53:53,2022-01-22 06:53:53,2022-01-22 06:53:55,2022-01-22 07:24:36,0.326735734939575,1840.53780341148,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,32,ctree,2022-01-22 07:24:02,2022-01-22 07:24:03,2022-01-22 07:24:04,2022-01-22 07:24:04,2022-01-22 07:27:09,0.378990411758423,185.192625999451,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,24,32,ctree,2022-01-22 07:22:59,2022-01-22 07:23:21,2022-01-22 07:23:21,2022-01-22 07:23:33,2022-01-22 07:27:26,0.262618064880371,232.805879831314,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,8,gaussian,2022-01-22 07:31:00,2022-01-22 07:31:04,2022-01-22 07:31:04,2022-01-22 07:31:09,2022-01-22 07:40:05,0.517565488815308,535.357954978943,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,4,gaussian,2022-01-22 07:28:44,2022-01-22 07:28:45,2022-01-22 07:28:45,2022-01-22 07:28:47,2022-01-22 07:43:54,0.274258136749268,906.078526973724,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,16,gaussian,2022-01-22 07:39:39,2022-01-22 07:39:42,2022-01-22 07:39:42,2022-01-22 07:39:43,2022-01-22 07:44:01,0.843225002288818,257.609613180161,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,16,gaussian,2022-01-22 07:38:08,2022-01-22 07:38:15,2022-01-22 07:38:15,2022-01-22 07:38:29,2022-01-22 07:44:15,0.740493059158325,345.924526691437,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,4,empirical,2022-01-22 07:28:32,2022-01-22 07:28:33,2022-01-22 07:28:33,2022-01-22 07:28:33,2022-01-22 07:46:53,0.142195701599121,1099.26915645599,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,24,empirical,2022-01-22 07:45:10,2022-01-22 07:45:16,2022-01-22 07:45:17,2022-01-22 07:45:18,2022-01-22 07:52:06,0.981189966201782,408.092513561249,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,4,empirical,2022-01-22 07:28:20,2022-01-22 07:28:21,2022-01-22 07:28:22,2022-01-22 07:28:24,2022-01-22 07:55:50,0.192413091659546,1646.58459353447,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,32,empirical,2022-01-22 07:52:40,2022-01-22 07:52:43,2022-01-22 07:52:43,2022-01-22 07:52:50,2022-01-22 07:58:43,0.342799663543701,352.674590826035,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,32,empirical,2022-01-22 07:53:51,2022-01-22 07:54:16,2022-01-22 07:54:27,2022-01-22 07:54:28,2022-01-22 07:58:51,11.702737569809,263.852354288101,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,32,gaussian,2022-01-22 07:55:23,2022-01-22 07:55:26,2022-01-22 07:55:26,2022-01-22 07:55:33,2022-01-22 07:59:37,0.640182971954346,243.275604248047,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,2,gaussian,2022-01-22 07:27:53,2022-01-22 07:27:53,2022-01-22 07:27:53,2022-01-22 07:27:54,2022-01-22 07:59:37,0.12651801109314,1903.59405994415,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,32,gaussian,2022-01-22 07:57:27,2022-01-22 07:57:37,2022-01-22 07:57:37,2022-01-22 07:57:39,2022-01-22 07:59:51,0.725312948226929,132.025541305542,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,32,ctree,2022-01-22 07:59:56,2022-01-22 07:59:57,2022-01-22 07:59:57,2022-01-22 07:59:57,2022-01-22 08:03:06,0.159965038299561,189.171859502792,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,32,ctree,2022-01-22 07:59:40,2022-01-22 07:59:42,2022-01-22 07:59:42,2022-01-22 07:59:47,2022-01-22 08:03:30,0.26979660987854,223.707008123398,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,1,empirical,2022-01-22 08:00:21,2022-01-22 08:00:23,2022-01-22 08:00:24,2022-01-22 08:00:24,2022-01-22 08:04:09,0.343303680419922,224.362646341324,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,1,empirical,2022-01-22 08:01:03,2022-01-22 08:01:05,2022-01-22 08:01:06,2022-01-22 08:01:07,2022-01-22 08:04:42,0.75458025932312,214.968354463577,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,2,empirical,2022-01-22 07:27:29,2022-01-22 07:27:29,2022-01-22 07:27:29,2022-01-22 07:27:30,2022-01-22 08:05:27,0.140658617019653,2276.14828109741,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,2,empirical,2022-01-22 08:03:45,2022-01-22 08:03:45,2022-01-22 08:03:45,2022-01-22 08:03:46,2022-01-22 08:05:45,0.122052192687988,119.600993394852,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,2,empirical,2022-01-22 08:03:36,2022-01-22 08:03:37,2022-01-22 08:03:37,2022-01-22 08:03:38,2022-01-22 08:05:58,0.120152235031128,139.18771147728,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,4,empirical,2022-01-22 08:04:44,2022-01-22 08:04:45,2022-01-22 08:04:45,2022-01-22 08:04:45,2022-01-22 08:07:13,0.162909269332886,147.285476207733,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,4,empirical,2022-01-22 08:04:34,2022-01-22 08:04:34,2022-01-22 08:04:35,2022-01-22 08:04:36,2022-01-22 08:07:19,0.154385089874268,162.961228132248,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,8,empirical,2022-01-22 08:05:46,2022-01-22 08:05:46,2022-01-22 08:05:46,2022-01-22 08:05:49,2022-01-22 08:08:50,0.141325235366821,180.752852916718,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,8,empirical,2022-01-22 08:05:59,2022-01-22 08:06:00,2022-01-22 08:06:00,2022-01-22 08:06:01,2022-01-22 08:08:59,0.140075445175171,178.269796609879,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,16,empirical,2022-01-22 08:07:18,2022-01-22 08:07:19,2022-01-22 08:07:19,2022-01-22 08:07:22,2022-01-22 08:10:39,0.2239990234375,196.892966985703,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,16,empirical,2022-01-22 08:07:36,2022-01-22 08:07:37,2022-01-22 08:07:37,2022-01-22 08:07:38,2022-01-22 08:11:32,0.199705362319946,234.011049509048,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,1,24,empirical,2022-01-22 08:09:24,2022-01-22 08:09:25,2022-01-22 08:09:25,2022-01-22 08:09:28,2022-01-22 08:13:24,0.303143978118897,235.908289194107,0,1e+05,13,0.3,1,0,1,1
+10,10000,100,32,1,empirical,2022-01-22 07:25:08,2022-01-22 07:25:10,2022-01-22 07:25:10,2022-01-22 07:25:11,2022-01-22 08:17:43,0.346535682678223,3152.29618883133,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,1,empirical,2022-01-22 08:16:04,2022-01-22 08:16:07,2022-01-22 08:16:07,2022-01-22 08:16:08,2022-01-22 08:21:00,0.668867826461792,292.229100704193,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,1,empirical,2022-01-22 08:16:36,2022-01-22 08:16:38,2022-01-22 08:16:38,2022-01-22 08:16:39,2022-01-22 08:21:46,0.303682565689087,307.089729070664,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,2,empirical,2022-01-22 08:19:29,2022-01-22 08:19:30,2022-01-22 08:19:30,2022-01-22 08:19:33,2022-01-22 08:21:59,0.219695091247559,145.378130197525,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,2,empirical,2022-01-22 08:19:57,2022-01-22 08:19:59,2022-01-22 08:19:59,2022-01-22 08:20:00,2022-01-22 08:22:17,0.360942840576172,136.975060462952,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,4,empirical,2022-01-22 08:22:50,2022-01-22 08:22:52,2022-01-22 08:22:52,2022-01-22 08:22:54,2022-01-22 08:25:17,0.30582594871521,142.196585416794,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,4,empirical,2022-01-22 08:23:31,2022-01-22 08:23:33,2022-01-22 08:23:33,2022-01-22 08:23:33,2022-01-22 08:26:10,0.156835794448853,157.198087453842,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,8,empirical,2022-01-22 08:26:36,2022-01-22 08:26:37,2022-01-22 08:26:38,2022-01-22 08:26:41,2022-01-22 08:29:17,0.351461172103882,156.074484109879,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,8,empirical,2022-01-22 08:27:10,2022-01-22 08:27:12,2022-01-22 08:27:12,2022-01-22 08:27:12,2022-01-22 08:29:50,0.226331472396851,157.663980484009,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,16,empirical,2022-01-22 08:30:11,2022-01-22 08:30:13,2022-01-22 08:30:13,2022-01-22 08:30:18,2022-01-22 08:33:49,0.404173612594605,211.679840803146,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,16,empirical,2022-01-22 08:30:56,2022-01-22 08:30:58,2022-01-22 08:30:58,2022-01-22 08:30:59,2022-01-22 08:34:08,0.366115570068359,188.986455440521,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,24,empirical,2022-01-22 08:34:24,2022-01-22 08:34:26,2022-01-22 08:34:26,2022-01-22 08:34:31,2022-01-22 08:37:52,0.419590473175049,201.074247121811,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,24,empirical,2022-01-22 08:35:01,2022-01-22 08:35:04,2022-01-22 08:35:04,2022-01-22 08:35:05,2022-01-22 08:38:16,0.367833137512207,191.385853528976,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,32,empirical,2022-01-22 08:38:23,2022-01-22 08:38:25,2022-01-22 08:38:25,2022-01-22 08:38:30,2022-01-22 08:41:31,0.40959358215332,180.730140686035,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,2,32,empirical,2022-01-22 08:39:05,2022-01-22 08:39:10,2022-01-22 08:39:10,2022-01-22 08:39:11,2022-01-22 08:42:04,0.218723297119141,173.828474998474,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,1,empirical,2022-01-22 08:42:56,2022-01-22 08:42:58,2022-01-22 08:42:59,2022-01-22 08:42:59,2022-01-22 08:48:43,0.54443883895874,343.096045970917,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,1,empirical,2022-01-22 08:43:47,2022-01-22 08:43:49,2022-01-22 08:43:50,2022-01-22 08:43:50,2022-01-22 08:49:22,0.326147556304932,331.385711669922,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,2,empirical,2022-01-22 08:47:42,2022-01-22 08:47:44,2022-01-22 08:47:45,2022-01-22 08:47:45,2022-01-22 08:49:59,0.317653179168701,133.886053800583,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,2,empirical,2022-01-22 08:47:13,2022-01-22 08:47:15,2022-01-22 08:47:16,2022-01-22 08:47:19,2022-01-22 08:50:04,0.161678552627563,165.61696434021,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,4,empirical,2022-01-22 08:50:50,2022-01-22 08:50:51,2022-01-22 08:50:51,2022-01-22 08:50:54,2022-01-22 08:52:39,0.4151771068573,104.645393610001,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,4,empirical,2022-01-22 08:51:27,2022-01-22 08:51:28,2022-01-22 08:51:29,2022-01-22 08:51:29,2022-01-22 08:53:00,0.160355091094971,90.6087260246277,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,8,empirical,2022-01-22 08:55:02,2022-01-22 08:55:04,2022-01-22 08:55:05,2022-01-22 08:55:08,2022-01-22 08:57:02,0.460837841033936,113.219378709793,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,8,empirical,2022-01-22 08:55:37,2022-01-22 08:55:40,2022-01-22 08:55:40,2022-01-22 08:55:41,2022-01-22 08:57:28,0.379063606262207,106.964731454849,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,16,empirical,2022-01-22 08:59:58,2022-01-22 09:00:01,2022-01-22 09:00:01,2022-01-22 09:00:23,2022-01-22 09:02:41,0.472664594650269,138.272471189499,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,16,empirical,2022-01-22 09:00:56,2022-01-22 09:00:58,2022-01-22 09:00:58,2022-01-22 09:00:59,2022-01-22 09:03:13,0.327378749847412,133.840874671936,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,24,empirical,2022-01-22 09:05:26,2022-01-22 09:05:29,2022-01-22 09:05:29,2022-01-22 09:05:34,2022-01-22 09:08:14,0.391673803329468,159.168850898743,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,24,empirical,2022-01-22 09:06:19,2022-01-22 09:06:21,2022-01-22 09:06:21,2022-01-22 09:06:22,2022-01-22 09:08:49,0.45950174331665,147.115752220154,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,32,empirical,2022-01-22 09:11:33,2022-01-22 09:11:36,2022-01-22 09:11:36,2022-01-22 09:11:42,2022-01-22 09:13:56,0.506219148635864,133.893241882324,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,4,32,empirical,2022-01-22 09:12:30,2022-01-22 09:12:32,2022-01-22 09:12:33,2022-01-22 09:12:34,2022-01-22 09:14:49,0.79218339920044,134.524483203888,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,1,empirical,2022-01-22 09:17:42,2022-01-22 09:17:44,2022-01-22 09:17:44,2022-01-22 09:17:45,2022-01-22 09:27:00,0.457140684127808,555.385914087296,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,2,empirical,2022-01-22 09:24:17,2022-01-22 09:24:23,2022-01-22 09:24:23,2022-01-22 09:24:24,2022-01-22 09:27:36,0.304661273956299,192.208995580673,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,1,empirical,2022-01-22 09:19:08,2022-01-22 09:19:20,2022-01-22 09:19:20,2022-01-22 09:19:21,2022-01-22 09:27:57,0.458425283432007,516.102925777435,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,2,empirical,2022-01-22 09:23:42,2022-01-22 09:23:44,2022-01-22 09:23:44,2022-01-22 09:23:47,2022-01-22 09:28:35,0.384088754653931,287.245557069778,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,4,empirical,2022-01-22 09:28:02,2022-01-22 09:28:03,2022-01-22 09:28:03,2022-01-22 09:28:04,2022-01-22 09:29:09,0.211258172988892,65.4639945030213,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,4,empirical,2022-01-22 09:27:36,2022-01-22 09:27:38,2022-01-22 09:27:38,2022-01-22 09:27:41,2022-01-22 09:29:49,0.370321750640869,128.275465965271,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,8,empirical,2022-01-22 09:30:47,2022-01-22 09:30:48,2022-01-22 09:30:49,2022-01-22 09:30:56,2022-01-22 09:32:19,0.258855819702148,82.3746254444122,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,8,empirical,2022-01-22 09:31:30,2022-01-22 09:31:32,2022-01-22 09:31:33,2022-01-22 09:31:33,2022-01-22 09:32:40,0.435195207595825,66.9115686416626,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,16,empirical,2022-01-22 09:36:02,2022-01-22 09:36:05,2022-01-22 09:36:06,2022-01-22 09:36:11,2022-01-22 09:38:06,0.566511631011963,115.21001958847,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,16,empirical,2022-01-22 09:37:01,2022-01-22 09:37:03,2022-01-22 09:37:03,2022-01-22 09:37:04,2022-01-22 09:38:27,0.299057245254517,82.5114333629608,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,24,empirical,2022-01-22 09:42:43,2022-01-22 09:42:47,2022-01-22 09:42:48,2022-01-22 09:42:58,2022-01-22 09:45:10,0.455637216567993,131.892152070999,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,24,empirical,2022-01-22 09:43:56,2022-01-22 09:43:59,2022-01-22 09:43:59,2022-01-22 09:44:01,2022-01-22 09:45:37,0.317171573638916,96.0130724906922,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,32,empirical,2022-01-22 09:49:56,2022-01-22 09:50:02,2022-01-22 09:50:02,2022-01-22 09:50:09,2022-01-22 09:51:58,0.358751535415649,108.590593099594,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,8,32,empirical,2022-01-22 09:51:11,2022-01-22 09:51:14,2022-01-22 09:51:15,2022-01-22 09:51:16,2022-01-22 09:52:59,0.662763118743897,103.211233139038,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,2,empirical,2022-01-22 10:03:44,2022-01-22 10:03:46,2022-01-22 10:03:46,2022-01-22 10:03:47,2022-01-22 10:06:19,0.213312864303589,152.167365074158,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,4,empirical,2022-01-22 10:07:08,2022-01-22 10:07:09,2022-01-22 10:07:09,2022-01-22 10:07:10,2022-01-22 10:08:11,0.571666479110718,60.6217052936554,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,2,empirical,2022-01-22 10:03:24,2022-01-22 10:03:25,2022-01-22 10:03:25,2022-01-22 10:03:27,2022-01-22 10:11:33,0.169464349746704,485.991571187973,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,4,empirical,2022-01-22 10:06:39,2022-01-22 10:06:40,2022-01-22 10:06:40,2022-01-22 10:06:43,2022-01-22 10:11:40,0.23082709312439,297.450491189957,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,8,empirical,2022-01-22 10:10:48,2022-01-22 10:10:50,2022-01-22 10:10:51,2022-01-22 10:10:51,2022-01-22 10:12:39,0.434882640838623,107.411097288132,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,8,empirical,2022-01-22 10:09:53,2022-01-22 10:09:57,2022-01-22 10:09:57,2022-01-22 10:10:03,2022-01-22 10:13:38,0.648483514785767,215.688893079758,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,1,empirical,2022-01-22 09:58:56,2022-01-22 09:58:59,2022-01-22 09:58:59,2022-01-22 09:59:00,2022-01-22 10:13:59,0.407754421234131,899.119991540909,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,1,empirical,2022-01-22 09:57:50,2022-01-22 09:57:53,2022-01-22 09:57:54,2022-01-22 09:57:55,2022-01-22 10:14:22,0.580220699310303,987.149334192276,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,16,empirical,2022-01-22 10:17:50,2022-01-22 10:17:56,2022-01-22 10:17:57,2022-01-22 10:17:57,2022-01-22 10:18:54,0.251925945281982,56.9248580932617,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,16,empirical,2022-01-22 10:16:01,2022-01-22 10:16:04,2022-01-22 10:16:05,2022-01-22 10:16:36,2022-01-22 10:19:39,0.807049036026001,183.412191152573,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,24,empirical,2022-01-22 10:23:25,2022-01-22 10:23:35,2022-01-22 10:23:35,2022-01-22 10:23:52,2022-01-22 10:26:56,0.313836812973022,184.364561080933,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,24,empirical,2022-01-22 10:25:31,2022-01-22 10:25:38,2022-01-22 10:25:39,2022-01-22 10:25:45,2022-01-22 10:27:21,0.730576515197754,95.9319317340851,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,32,empirical,2022-01-22 10:35:42,2022-01-22 10:35:50,2022-01-22 10:35:51,2022-01-22 10:35:52,2022-01-22 10:37:13,0.57447075843811,80.3200747966766,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,16,32,empirical,2022-01-22 10:33:57,2022-01-22 10:34:04,2022-01-22 10:34:05,2022-01-22 10:34:23,2022-01-22 10:37:51,0.892342567443848,208.491036176682,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,2,empirical,2022-01-22 10:48:57,2022-01-22 10:48:58,2022-01-22 10:48:58,2022-01-22 10:48:58,2022-01-22 10:51:14,0.139031171798706,135.397399425507,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,4,empirical,2022-01-22 10:53:03,2022-01-22 10:53:05,2022-01-22 10:53:05,2022-01-22 10:53:05,2022-01-22 10:54:18,0.278486728668213,73.3427712917328,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,4,empirical,2022-01-22 10:52:10,2022-01-22 10:52:11,2022-01-22 10:52:11,2022-01-22 10:52:18,2022-01-22 10:58:41,0.130438804626465,382.672886133194,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,8,empirical,2022-01-22 10:57:32,2022-01-22 10:57:34,2022-01-22 10:57:35,2022-01-22 10:57:36,2022-01-22 10:59:06,0.52803373336792,90.3082437515259,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,2,empirical,2022-01-22 10:48:34,2022-01-22 10:48:37,2022-01-22 10:48:37,2022-01-22 10:48:53,2022-01-22 10:59:39,0.414208889007568,645.782402992249,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,8,empirical,2022-01-22 10:56:32,2022-01-22 10:56:35,2022-01-22 10:56:36,2022-01-22 10:56:42,2022-01-22 10:59:51,0.761104345321655,189.024942874908,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,16,empirical,2022-01-22 11:01:40,2022-01-22 11:01:44,2022-01-22 11:01:45,2022-01-22 11:01:46,2022-01-22 11:03:15,0.884268999099731,89.2238826751709,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,16,empirical,2022-01-22 11:00:52,2022-01-22 11:00:56,2022-01-22 11:00:56,2022-01-22 11:01:03,2022-01-22 11:03:38,0.683446884155273,155.434013128281,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,1,empirical,2022-01-22 10:45:51,2022-01-22 10:45:53,2022-01-22 10:45:53,2022-01-22 10:45:54,2022-01-22 11:03:45,0.428142547607422,1071.30545711517,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,1,empirical,2022-01-22 10:43:56,2022-01-22 10:44:01,2022-01-22 10:44:01,2022-01-22 10:44:03,2022-01-22 11:05:44,0.606060981750488,1301.2333483696,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,24,empirical,2022-01-22 11:10:19,2022-01-22 11:10:22,2022-01-22 11:10:23,2022-01-22 11:10:24,2022-01-22 11:11:22,0.765867710113525,57.6616673469544,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,24,empirical,2022-01-22 11:09:13,2022-01-22 11:09:19,2022-01-22 11:09:20,2022-01-22 11:09:34,2022-01-22 11:11:40,0.645092725753784,126.192297697067,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,8,gaussian,2022-01-22 10:58:19,2022-01-22 10:58:21,2022-01-22 10:58:21,2022-01-22 10:58:26,2022-01-22 11:14:30,0.355068206787109,963.877534866333,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,32,empirical,2022-01-22 11:22:04,2022-01-22 11:22:08,2022-01-22 11:22:09,2022-01-22 11:22:10,2022-01-22 11:22:57,0.620054483413696,46.5232944488525,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,32,empirical,2022-01-22 11:20:49,2022-01-22 11:20:52,2022-01-22 11:20:53,2022-01-22 11:20:59,2022-01-22 11:22:58,0.614947080612183,118.759776830673,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,24,24,ctree,2022-01-22 11:15:34,2022-01-22 11:15:41,2022-01-22 11:15:43,2022-01-22 11:16:05,2022-01-22 11:36:12,1.47764706611633,1206.56102824211,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,2,empirical,2022-01-22 11:36:53,2022-01-22 11:36:54,2022-01-22 11:36:55,2022-01-22 11:36:55,2022-01-22 11:38:44,0.279355049133301,108.533129453659,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,4,empirical,2022-01-22 11:38:22,2022-01-22 11:38:22,2022-01-22 11:38:23,2022-01-22 11:38:23,2022-01-22 11:39:54,0.222272157669067,91.0347166061401,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,8,empirical,2022-01-22 11:40:18,2022-01-22 11:40:20,2022-01-22 11:40:21,2022-01-22 11:40:21,2022-01-22 11:42:05,0.549343824386597,103.083040714264,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,8,empirical,2022-01-22 11:39:50,2022-01-22 11:39:51,2022-01-22 11:39:52,2022-01-22 11:39:56,2022-01-22 11:43:21,0.221651554107666,205.402804136276,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,4,empirical,2022-01-22 11:38:09,2022-01-22 11:38:10,2022-01-22 11:38:10,2022-01-22 11:38:12,2022-01-22 11:43:31,0.168190956115723,318.949892759323,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,16,empirical,2022-01-22 11:45:31,2022-01-22 11:45:36,2022-01-22 11:45:37,2022-01-22 11:45:38,2022-01-22 11:47:05,1.00594711303711,86.7194800376892,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,16,empirical,2022-01-22 11:44:20,2022-01-22 11:44:22,2022-01-22 11:44:23,2022-01-22 11:44:30,2022-01-22 11:47:06,0.523659706115723,156.78627872467,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,2,empirical,2022-01-22 11:36:32,2022-01-22 11:36:34,2022-01-22 11:36:34,2022-01-22 11:36:37,2022-01-22 11:47:31,0.165961503982544,654.281175374985,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,24,empirical,2022-01-22 11:54:52,2022-01-22 11:54:57,2022-01-22 11:54:58,2022-01-22 11:55:00,2022-01-22 11:56:24,1.14682364463806,83.9669575691223,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,24,empirical,2022-01-22 11:53:45,2022-01-22 11:53:49,2022-01-22 11:53:52,2022-01-22 11:54:02,2022-01-22 11:56:55,2.42309260368347,173.221267461777,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,1,empirical,2022-01-22 11:32:41,2022-01-22 11:32:44,2022-01-22 11:32:44,2022-01-22 11:32:45,2022-01-22 11:57:30,0.692607164382935,1484.48212981224,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,1,empirical,2022-01-22 11:33:23,2022-01-22 11:33:25,2022-01-22 11:33:25,2022-01-22 11:33:26,2022-01-22 12:02:20,0.282714128494263,1734.03425574303,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,32,empirical,2022-01-22 12:06:47,2022-01-22 12:06:52,2022-01-22 12:06:53,2022-01-22 12:06:55,2022-01-22 12:07:42,0.352906465530396,47.5443353652954,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,32,empirical,2022-01-22 12:05:04,2022-01-22 12:05:07,2022-01-22 12:05:08,2022-01-22 12:05:17,2022-01-22 12:07:43,0.731136083602905,145.705512523651,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,32,ctree,2022-01-22 12:16:21,2022-01-22 12:16:24,2022-01-22 12:16:24,2022-01-22 12:16:25,2022-01-22 12:25:11,0.612917900085449,525.298412322998,0,1e+05,13,0.3,1,0,1,1
+11,1000,100,32,24,ctree,2022-01-22 12:03:24,2022-01-22 12:03:42,2022-01-22 12:03:44,2022-01-22 12:03:47,2022-01-22 12:25:46,1.86805367469788,1319.6498568058,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,4,2,empirical,2022-01-22 17:15:03,2022-01-22 17:15:04,2022-01-22 17:15:04,2022-01-22 17:15:05,2022-01-22 17:20:47,0.349697113037109,342.335664749146,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,4,4,empirical,2022-01-22 17:18:13,2022-01-22 17:18:15,2022-01-22 17:18:15,2022-01-22 17:18:16,2022-01-22 17:22:24,0.327213048934937,248.467654705048,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,2,empirical,2022-01-22 17:51:26,2022-01-22 17:51:28,2022-01-22 17:51:28,2022-01-22 17:51:29,2022-01-22 17:57:17,0.341325283050537,347.88488650322,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,2,empirical,2022-01-22 17:50:45,2022-01-22 17:50:47,2022-01-22 17:50:47,2022-01-22 17:50:50,2022-01-22 17:58:58,0.566452026367188,487.494567871094,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,1,empirical,2022-01-22 17:45:30,2022-01-22 17:45:35,2022-01-22 17:45:36,2022-01-22 17:45:36,2022-01-22 17:59:05,0.363771200180054,808.551822900772,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,4,empirical,2022-01-22 17:54:54,2022-01-22 17:54:55,2022-01-22 17:54:56,2022-01-22 17:55:00,2022-01-22 17:59:07,0.538132190704346,246.953627109528,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,4,empirical,2022-01-22 17:55:29,2022-01-22 17:55:31,2022-01-22 17:55:31,2022-01-22 17:55:32,2022-01-22 17:59:40,0.322488069534302,248.050733804703,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,1,empirical,2022-01-22 17:46:27,2022-01-22 17:46:30,2022-01-22 17:46:30,2022-01-22 17:46:31,2022-01-22 18:00:03,0.636071443557739,811.378388404846,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,8,empirical,2022-01-22 17:58:58,2022-01-22 17:59:01,2022-01-22 17:59:01,2022-01-22 17:59:05,2022-01-22 18:01:51,0.477749347686768,166.109085559845,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,8,empirical,2022-01-22 17:59:37,2022-01-22 17:59:40,2022-01-22 17:59:41,2022-01-22 17:59:41,2022-01-22 18:02:13,0.694354295730591,151.790697574615,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,16,empirical,2022-01-22 18:04:30,2022-01-22 18:04:34,2022-01-22 18:04:35,2022-01-22 18:04:41,2022-01-22 18:08:19,0.872366189956665,217.755168676376,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,16,empirical,2022-01-22 18:05:50,2022-01-22 18:05:53,2022-01-22 18:05:54,2022-01-22 18:05:55,2022-01-22 18:09:42,0.94979190826416,226.951153039932,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,24,empirical,2022-01-22 18:14:15,2022-01-22 18:14:19,2022-01-22 18:14:19,2022-01-22 18:14:20,2022-01-22 18:17:42,0.739112854003906,201.530950307846,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,8,32,empirical,2022-01-22 18:22:49,2022-01-22 18:22:52,2022-01-22 18:22:53,2022-01-22 18:22:54,2022-01-22 18:26:05,1.13713765144348,190.900821208954,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,2,empirical,2022-01-22 18:36:57,2022-01-22 18:36:59,2022-01-22 18:37:00,2022-01-22 18:37:00,2022-01-22 18:42:14,0.579819202423096,313.423630237579,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,4,empirical,2022-01-22 18:41:08,2022-01-22 18:41:10,2022-01-22 18:41:11,2022-01-22 18:41:11,2022-01-22 18:44:36,0.601732969284058,204.581836462021,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,4,empirical,2022-01-22 18:40:26,2022-01-22 18:40:31,2022-01-22 18:40:31,2022-01-22 18:40:36,2022-01-22 18:46:19,0.438101053237915,343.334706306458,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,2,empirical,2022-01-22 18:36:26,2022-01-22 18:36:28,2022-01-22 18:36:29,2022-01-22 18:36:32,2022-01-22 18:47:34,0.373157739639282,662.379744052887,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,8,empirical,2022-01-22 18:44:40,2022-01-22 18:44:43,2022-01-22 18:44:43,2022-01-22 18:44:48,2022-01-22 18:48:23,0.592904329299927,214.69881272316,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,8,empirical,2022-01-22 18:45:36,2022-01-22 18:45:39,2022-01-22 18:45:39,2022-01-22 18:45:40,2022-01-22 18:49:58,0.501163959503174,257.76019525528,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,1,empirical,2022-01-22 18:30:00,2022-01-22 18:30:03,2022-01-22 18:30:04,2022-01-22 18:30:06,2022-01-22 18:53:43,0.874485969543457,1417.68450570107,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,16,empirical,2022-01-22 18:50:26,2022-01-22 18:50:36,2022-01-22 18:50:38,2022-01-22 18:50:53,2022-01-22 18:53:52,1.89371299743652,178.651078939438,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,16,empirical,2022-01-22 18:51:27,2022-01-22 18:51:34,2022-01-22 18:51:35,2022-01-22 18:51:37,2022-01-22 18:53:54,0.81693696975708,137.530794858933,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,1,empirical,2022-01-22 18:31:23,2022-01-22 18:31:26,2022-01-22 18:31:27,2022-01-22 18:31:27,2022-01-22 18:55:31,0.658213615417481,1443.05756092072,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,24,empirical,2022-01-22 19:01:47,2022-01-22 19:01:54,2022-01-22 19:01:56,2022-01-22 19:01:59,2022-01-22 19:05:18,2.0645956993103,199.504716157913,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,16,32,empirical,2022-01-22 19:14:53,2022-01-22 19:14:58,2022-01-22 19:15:00,2022-01-22 19:15:01,2022-01-22 19:18:06,1.19364953041077,185.220926761627,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,2,empirical,2022-01-22 19:33:57,2022-01-22 19:33:58,2022-01-22 19:33:59,2022-01-22 19:33:59,2022-01-22 19:39:19,0.302567005157471,320.244542598724,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,4,empirical,2022-01-22 19:37:55,2022-01-22 19:37:57,2022-01-22 19:37:57,2022-01-22 19:37:58,2022-01-22 19:42:18,0.411279439926147,260.641203165054,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,8,empirical,2022-01-22 19:42:00,2022-01-22 19:42:06,2022-01-22 19:42:06,2022-01-22 19:42:06,2022-01-22 19:45:33,0.25588870048523,206.726095199585,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,4,empirical,2022-01-22 19:37:12,2022-01-22 19:37:16,2022-01-22 19:37:18,2022-01-22 19:37:22,2022-01-22 19:45:47,2.54053568840027,505.435400724411,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,8,empirical,2022-01-22 19:41:42,2022-01-22 19:41:53,2022-01-22 19:41:54,2022-01-22 19:42:22,2022-01-22 19:50:08,1.03908658027649,465.98491859436,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,16,empirical,2022-01-22 19:47:32,2022-01-22 19:47:35,2022-01-22 19:47:36,2022-01-22 19:47:42,2022-01-22 19:51:34,0.818039178848267,232.413943529129,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,16,empirical,2022-01-22 19:49:46,2022-01-22 19:49:49,2022-01-22 19:49:50,2022-01-22 19:49:50,2022-01-22 19:52:20,0.785335063934326,149.103640794754,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,2,empirical,2022-01-22 19:33:33,2022-01-22 19:33:34,2022-01-22 19:33:35,2022-01-22 19:33:40,2022-01-22 19:53:07,0.177771806716919,1167.03664064407,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,24,empirical,2022-01-22 19:56:10,2022-01-22 19:56:20,2022-01-22 19:56:22,2022-01-22 19:56:33,2022-01-22 20:01:29,1.43938851356506,296.15135717392,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,24,empirical,2022-01-22 19:58:25,2022-01-22 19:58:31,2022-01-22 19:58:33,2022-01-22 19:58:35,2022-01-22 20:01:30,1.93052744865417,174.487854003906,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,1,empirical,2022-01-22 19:27:44,2022-01-22 19:27:49,2022-01-22 19:27:50,2022-01-22 19:27:51,2022-01-22 20:05:03,1.09261703491211,2231.47869825363,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,32,empirical,2022-01-22 20:10:44,2022-01-22 20:10:56,2022-01-22 20:10:58,2022-01-22 20:11:57,2022-01-22 20:16:59,2.12010335922241,301.716490030289,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,24,32,empirical,2022-01-22 20:13:31,2022-01-22 20:13:39,2022-01-22 20:13:41,2022-01-22 20:13:44,2022-01-22 20:17:08,2.00761938095093,204.593585729599,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,2,empirical,2022-01-22 20:33:33,2022-01-22 20:33:35,2022-01-22 20:33:36,2022-01-22 20:33:36,2022-01-22 20:38:37,0.481948614120483,300.715023756027,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,4,empirical,2022-01-22 20:37:41,2022-01-22 20:37:44,2022-01-22 20:37:58,2022-01-22 20:37:58,2022-01-22 20:40:02,13.9503815174103,123.559878826141,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,4,empirical,2022-01-22 20:37:17,2022-01-22 20:37:18,2022-01-22 20:37:18,2022-01-22 20:37:21,2022-01-22 20:45:11,0.256648778915405,470.425592660904,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,8,empirical,2022-01-22 20:42:08,2022-01-22 20:42:14,2022-01-22 20:42:14,2022-01-22 20:42:15,2022-01-22 20:45:14,0.838595390319824,179.268458604813,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,8,empirical,2022-01-22 20:41:11,2022-01-22 20:41:12,2022-01-22 20:41:13,2022-01-22 20:41:18,2022-01-22 20:47:44,0.301345109939575,385.813750505447,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,16,empirical,2022-01-22 20:49:31,2022-01-22 20:49:35,2022-01-22 20:49:36,2022-01-22 20:49:37,2022-01-22 20:51:36,0.850199699401856,119.07880282402,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,16,empirical,2022-01-22 20:48:08,2022-01-22 20:48:12,2022-01-22 20:48:13,2022-01-22 20:48:20,2022-01-22 20:51:37,1.39012312889099,197.221575498581,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,2,empirical,2022-01-22 20:33:10,2022-01-22 20:33:17,2022-01-22 20:33:18,2022-01-22 20:33:22,2022-01-22 20:54:38,0.360357284545898,1276.76272368431,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,24,empirical,2022-01-22 20:58:40,2022-01-22 20:58:56,2022-01-22 20:58:56,2022-01-22 20:58:58,2022-01-22 21:01:11,0.342028379440308,133.387796640396,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,24,empirical,2022-01-22 20:56:27,2022-01-22 20:56:32,2022-01-22 20:56:34,2022-01-22 20:56:44,2022-01-22 21:03:28,1.55437779426575,403.614232540131,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,1,empirical,2022-01-22 20:28:23,2022-01-22 20:28:30,2022-01-22 20:28:32,2022-01-22 20:28:33,2022-01-22 21:15:47,1.68647599220276,2833.6894595623,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,32,empirical,2022-01-22 21:13:30,2022-01-22 21:13:42,2022-01-22 21:13:43,2022-01-22 21:13:44,2022-01-22 21:15:49,0.637606859207153,124.904546737671,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,32,empirical,2022-01-22 21:11:23,2022-01-22 21:11:40,2022-01-22 21:11:41,2022-01-22 21:12:14,2022-01-22 21:20:10,1.17925953865051,476.281431674957,0,1e+05,13,0.3,1,0,1,1
+12,1000,100,32,1,empirical,2022-01-22 20:25:06,2022-01-22 20:25:12,2022-01-22 20:25:14,2022-01-22 20:25:17,2022-01-22 21:29:23,1.76262354850769,3846.27600169182,0,1e+05,13,0.3,1,0,1,1
+13,1000,100,1,2,empirical,2022-01-23 05:20:17,2022-01-23 05:20:17,2022-01-23 05:20:17,2022-01-23 05:20:18,2022-01-23 05:24:06,0.130674123764038,228.618875026703,0,1e+05,13,0.3,1,0,1,1
+13,1000,100,16,16,empirical,2022-01-23 07:46:35,2022-01-23 07:46:42,2022-01-23 07:46:43,2022-01-23 07:46:43,2022-01-23 07:53:27,0.78925633430481,403.846161127091,0,1e+05,13,0.3,1,0,1,1
+13,1000,100,16,32,empirical,2022-01-23 08:11:19,2022-01-23 08:11:30,2022-01-23 08:11:33,2022-01-23 08:11:34,2022-01-23 08:17:42,2.57792806625366,367.90896821022,0,1e+05,13,0.3,1,0,1,1
+13,1000,100,24,4,empirical,2022-01-23 08:33:22,2022-01-23 08:33:26,2022-01-23 08:33:27,2022-01-23 08:33:31,2022-01-23 08:51:04,0.321960210800171,1053.26301193237,0,1e+05,13,0.3,1,0,1,1
+13,1000,100,32,8,empirical,2022-01-23 09:46:17,2022-01-23 09:46:20,2022-01-23 09:46:21,2022-01-23 09:46:21,2022-01-23 09:58:47,1.05202484130859,745.312472581863,0,1e+05,13,0.3,1,0,1,1
+13,1000,100,32,16,empirical,2022-01-23 09:54:20,2022-01-23 09:54:23,2022-01-23 09:54:24,2022-01-23 09:54:24,2022-01-23 10:00:31,0.986858129501343,366.976919174194,0,1e+05,13,0.3,1,0,1,1
+13,1000,100,32,4,empirical,2022-01-23 09:40:45,2022-01-23 09:40:46,2022-01-23 09:40:46,2022-01-23 09:40:49,2022-01-23 10:01:17,0.369621515274048,1228.32821273804,0,1e+05,13,0.3,1,0,1,1
diff --git a/inst/scripts/devel/verifying_arima_model_output.R b/inst/scripts/devel/verifying_arima_model_output.R
new file mode 100644
index 000000000..7a63bcbf5
--- /dev/null
+++ b/inst/scripts/devel/verifying_arima_model_output.R
@@ -0,0 +1,76 @@
+library(shapr)
+
+options(digits = 5) # To avoid round off errors when printing output on different systems
+set.seed(123)
+
+n <- 10^3
+
+xreg <- cbind(rnorm(n,mean=1,sd=1),
+ rnorm(n,mean=2,sd=1))
+
+noise <- rnorm(n,mean=0,sd=0.5)
+
+# Create AR(1)-structure
+beta <- c(1.5,0)
+alpha <- 0.5 # AR-coefficient
+mu <- 1
+
+y <- rep(0,n)
+y[1] <- mu +beta[1]*xreg[1,1]+beta[2]*xreg[1,2]+noise[1]
+
+
+for(i in 2:n){
+ y[i] <- mu +alpha*y[i-1]+beta[1]*xreg[i,1]+beta[2]*xreg[i,2]+noise[i]
+}
+plot(y,type="l")
+
+# In practice this model is y = 1 + y[i] + 1.5*xreg1 with independent features
+
+#model_arima_temp <- arima(y, c(3,1,2), xreg=xreg)
+model_arima_temp <- arima(y, c(1,0,0), xreg=xreg)
+
+colnames(xreg) <- c("var1","var2")
+
+train_idx <- 1:(n-10)
+explain_idx <- n-5:4
+
+
+set.seed(123)
+exp <- explain_forecast(model = model_arima_temp,
+ y = y,
+ xreg = xreg,
+ train_idx = train_idx,
+ explain_idx = explain_idx,
+ explain_y_lags = 1,
+ explain_xreg_lags = c(0,1),
+ horizon = 1,
+ approach = "empirical",
+ prediction_zero = rep(mean(y),1),
+ group_lags = FALSE,
+ n_batches = 1)
+
+# These two should be approximately equal
+# For y
+exp$shapley_values$Y1.1
+model_arima_temp$coef[1]*(y[explain_idx]-mean(y))
+#[1] -0.13500 0.20643
+#[1] -0.079164 0.208118
+
+
+# for xreg1
+exp$shapley_values$var1.F1
+model_arima_temp$coef[3]*(xreg[explain_idx+1,1]-mean(xreg[,1]))
+#[1] -0.030901 1.179386
+#[1] -0.12034 1.19589
+
+# for xreg2
+exp$shapley_values$var2.F1
+0
+#[1] 0.011555 0.031911
+#[1] 0
+
+
+# Close enough (maybe increase sample size n to make sure they converge as they should?)
+
+
+
diff --git a/inst/scripts/empirical_memory_testing2.R b/inst/scripts/empirical_memory_testing2.R
new file mode 100644
index 000000000..ca57a8d5f
--- /dev/null
+++ b/inst/scripts/empirical_memory_testing2.R
@@ -0,0 +1,145 @@
+#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library")
+sys_time_initial <- Sys.time()
+
+# libraries
+library(shapr)
+library(future)
+library(MASS)
+library(microbenchmark)
+library(data.table)
+library(profmem)
+
+# Initial setup
+max_n <- 10^5
+max_p <- 16
+rho <- 0.3
+sigma <- 1
+mu_const <- 0
+beta0 <- 1
+sigma_eps <- 1
+
+mu <- rep(mu_const,max_p)
+beta <- c(beta0,seq_len(max_p)/max_p)
+Sigma <- matrix(rho,max_p,max_p)
+diag(Sigma) <- sigma
+
+set.seed(123)
+x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma)
+y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps)
+
+# Arguments from bash
+#args <- commandArgs(trailingOnly = TRUE)
+#if(length(args)==0) args = c(1,10,1000,100,10,1,"empirical","sequential","timing_test_2023.csv")
+
+
+this_rep <- 1
+p <- 6
+n_train <- 100
+n_explain <- 100
+n_batches <- 100
+n_cores <- 1
+approach <- "empirical"
+multicore_method <- "sequential"
+logfilename <- "bla"
+
+set.seed(123)
+
+
+these_p <- sample.int(max_p,size=p)
+these_train <- sample.int(max_n,size=n_train)
+these_explain <- sample.int(max_n,size=n_explain)
+
+x_train <- as.data.frame(x_all[these_train,these_p,drop=F])
+x_explain <- as.data.frame(x_all[these_explain,these_p,drop=F])
+
+colnames(x_explain) <- colnames(x_train) <- paste0("X",seq_len(p))
+
+y_train <- y_all[these_train]
+
+xy_train <- cbind(x_train,y=y_train)
+
+model <- lm(formula = y~.,data=xy_train)
+
+prediction_zero <- mean(y_train)
+
+n_batches_use <- min(2^p-2,n_batches)
+
+
+explanation_many <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = approach,
+ n_batches = n_batches_use,
+ prediction_zero = prediction_zero
+ )
+
+
+#explanation_single <- explain(
+# model = model,
+# x_explain = x_explain,
+# x_train = x_train,
+# approach = approach,
+# n_batches = 1,
+# prediction_zero = prediction_zero
+#)
+
+
+
+#S_batch_many <- copy(explanation_many$internal$objects$S_batch)
+#internal_many <- copy(explanation_many$internal)
+
+#S_batch_single <- list(`1`=sort(unlist(copy(explanation_many$internal$objects$S_batch),use.names=FALSE)))
+#internal_single <- copy(explanation_many$internal)
+
+feature_specs <- shapr:::get_feature_specs(NULL, model)
+
+
+internal <- setup(
+ x_train = x_train,
+ x_explain = x_explain,
+ approach = approach,
+ prediction_zero = prediction_zero,
+ n_combinations = 2^p,
+ group = NULL,
+ n_samples = 1e3,
+ n_batches = n_batches_use,
+ seed = 123,
+ keep_samp_for_vS = FALSE,
+ feature_specs = feature_specs)
+
+internal <- setup_computation(internal, model, NULL)
+
+S_batch_many <- internal$objects$S_batch
+S_batch_single <- list(`1`=sort(unlist(copy(S_batch_many),use.names=FALSE)))
+
+testfunc <- function(S, internal) {
+ dt <- shapr:::batch_prepare_vS(S = S, internal = internal) # Make it optional to store and return the dt_list
+ return(S)
+}
+
+internal$parameters$empirical.fixed_sigma <- rep(0.1,2)
+
+#pp_many <- profmem({
+s <- proc.time()
+ret <- future.apply::future_lapply(
+ X = S_batch_many,
+ FUN = testfunc,
+ internal = internal)
+proc.time()-s
+#},threshold=10^4)
+
+#pp_single <- profmem({
+s <- proc.time()
+ ret <- future.apply::future_lapply(
+ X = S_batch_single,
+ FUN = testfunc,
+ internal = internal)
+proc.time()-s
+#},threshold=10^4)
+
+plot(pp_many$bytes)
+points(pp_single$bytes,col=2)
+
+sum(pp_many$bytes)
+sum(pp_single$bytes)
diff --git a/inst/scripts/example_annabelle.R b/inst/scripts/example_annabelle.R
new file mode 100644
index 000000000..feede50bb
--- /dev/null
+++ b/inst/scripts/example_annabelle.R
@@ -0,0 +1,77 @@
+library(shapr)
+library(data.table)
+library(MASS)
+
+# ------------------------------
+
+Boston$rad <- as.factor(Boston$rad)
+Boston$chas <- as.factor(Boston$chas)
+x_var <- c("rad", "chas")
+y_var <- "medv"
+
+ind_x_test <- 1:4
+train <- Boston[-ind_x_test, c(x_var, y_var)]
+x_test <- Boston[ind_x_test, x_var]
+x_train = train[, x_var]
+
+model <- lm(medv ~ rad + chas, data = train)
+
+# ------------------------------
+# To test the categorical method when we know the results
+
+data = fread("../shapr/data.csv")
+data$feat_1_ = factor(data$feat_1_)
+data$feat_2_ = factor(data$feat_2_)
+data$feat_3_ = factor(data$feat_3_)
+
+x_train = data[1:1000, c("feat_1_", "feat_2_", "feat_3_")]
+x_test = data[1001:1005, c("feat_1_", "feat_2_", "feat_3_")]
+
+joint_prob_dt = fread("../shapr/joint_prob_dt.csv")
+
+p <- mean(data[1:1000,][['response']])
+
+joint_prob_dt[, feat_1_ := as.factor(feat_1_)]
+joint_prob_dt[, feat_2_ := as.factor(feat_2_)]
+joint_prob_dt[, feat_3_ := as.factor(feat_3_)]
+
+train = data[1:1000,]
+
+model <- lm(response ~ feat_1_ + feat_2_ + feat_3_, data = train)
+
+# ------------------------------
+
+temp = explain(
+ x_train = x_train,
+ x_explain = x_test,
+ model = model,
+ approach = "categorical",
+ prediction_zero = p,
+ joint_probability_dt = joint_prob_dt
+)
+print(temp)
+# none rad chas
+# 1: -0.030511 13.231 10.887
+# 2: -0.030511 15.709 11.035
+# 3: -0.030511 15.709 11.035
+# 4: -0.030511 16.624 10.883
+
+# Without joint prob dt
+# none feat_1_ feat_2_ feat_3_
+# 1: -0.030516 0.20455 0.29895 0.1381985
+# 2: -0.030516 0.23079 0.35300 -0.0480793
+# 3: -0.030516 0.13084 0.32979 -0.8297798
+# 4: -0.030516 0.23133 -0.88754 0.1923399
+# 5: -0.030516 0.27954 -0.84447 -0.0049256
+
+# With joint prob dt
+# none feat_1_ feat_2_ feat_3_
+# 1: -0.03051645 0.2211416 0.3030599 0.1174976222
+# 2: -0.03051648 0.2312988 0.3611456 -0.0567361622
+# 3: -0.03051644 0.1437691 0.3371903 -0.8501081617
+# 4: -0.03051647 0.2446707 -0.8627886 0.1542449764
+# 5: -0.03051649 0.2140973 -0.7843376 0.0003764934
+
+# none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
+# 1: 40.752 6.1998 7.8422 2.852 70.2288
+# 2: 40.752 -3.7270 9.8283 5.626 4.1224
diff --git a/inst/scripts/example_ctree_method.R b/inst/scripts/example_ctree_method.R
index 712abe1c5..6f0d26f12 100644
--- a/inst/scripts/example_ctree_method.R
+++ b/inst/scripts/example_ctree_method.R
@@ -78,7 +78,7 @@ model_cat <- xgboost::xgboost(
nround = 20,
verbose = FALSE
)
-model_cat$feature_list <- dummylist$feature_list
+model_cat$feature_specs <- dummylist$feature_specs
explainer_cat <- shapr(dummylist$traindata_new, model_cat)
diff --git a/inst/scripts/example_custom_model.R b/inst/scripts/example_custom_model.R
index da6633fe8..34a6377a4 100644
--- a/inst/scripts/example_custom_model.R
+++ b/inst/scripts/example_custom_model.R
@@ -50,15 +50,15 @@ predict_model.gbm <- function(x, newdata) {
}
get_model_specs.gbm <- function(x){
- feature_list = list()
- feature_list$labels <- labels(x$Terms)
- m <- length(feature_list$labels)
+ feature_specs = list()
+ feature_specs$labels <- labels(x$Terms)
+ m <- length(feature_specs$labels)
- feature_list$classes <- attr(x$Terms,"dataClasses")[-1]
- feature_list$factor_levels <- setNames(vector("list", m), feature_list$labels)
- feature_list$factor_levels[feature_list$classes=="factor"] <- NA # the model object doesn't contain factor levels info
+ feature_specs$classes <- attr(x$Terms,"dataClasses")[-1]
+ feature_specs$factor_levels <- setNames(vector("list", m), feature_specs$labels)
+ feature_specs$factor_levels[feature_specs$classes=="factor"] <- NA # the model object doesn't contain factor levels info
- return(feature_list)
+ return(feature_specs)
}
# Prepare the data for explanation
diff --git a/inst/scripts/explain_memory_testing.R b/inst/scripts/explain_memory_testing.R
new file mode 100644
index 000000000..7c3030ffc
--- /dev/null
+++ b/inst/scripts/explain_memory_testing.R
@@ -0,0 +1,113 @@
+#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library")
+sys_time_initial <- Sys.time()
+
+# libraries
+library(shapr)
+library(future)
+library(MASS)
+library(microbenchmark)
+library(data.table)
+library(profmem)
+
+# Initial setup
+max_n <- 10^5
+max_p <- 16
+rho <- 0.3
+sigma <- 1
+mu_const <- 0
+beta0 <- 1
+sigma_eps <- 1
+
+mu <- rep(mu_const,max_p)
+beta <- c(beta0,seq_len(max_p)/max_p)
+Sigma <- matrix(rho,max_p,max_p)
+diag(Sigma) <- sigma
+
+set.seed(123)
+x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma)
+y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps)
+
+# Arguments from bash
+#args <- commandArgs(trailingOnly = TRUE)
+#if(length(args)==0) args = c(1,10,1000,100,10,1,"empirical","sequential","timing_test_2023.csv")
+
+
+this_rep <- 1
+p <- 4
+n_train <- 1000
+n_explain <- 50
+n_batches <- 10
+n_cores <- 1
+approach <- "empirical"
+multicore_method <- "sequential"
+logfilename <- "bla"
+
+set.seed(123)
+
+
+these_p <- sample.int(max_p,size=p)
+these_train <- sample.int(max_n,size=n_train)
+these_explain <- sample.int(max_n,size=n_explain)
+
+x_train <- as.data.frame(x_all[these_train,these_p,drop=F])
+x_explain <- as.data.frame(x_all[these_explain,these_p,drop=F])
+
+colnames(x_explain) <- colnames(x_train) <- paste0("X",seq_len(p))
+
+y_train <- y_all[these_train]
+
+xy_train <- cbind(x_train,y=y_train)
+
+model <- lm(formula = y~.,data=xy_train)
+
+prediction_zero <- mean(y_train)
+
+n_batches_use <- min(2^p-2,n_batches)
+
+
+sys_time_start_explain <- Sys.time()
+
+pp.old <- profmem({
+explanation <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = approach,
+ n_batches = n_batches_use,
+ prediction_zero = prediction_zero
+)
+},threshold=10^4)
+
+sys_time_end_explain <- Sys.time()
+
+pp[!is.na(pp$bytes) &pp$bytes >6*10^5,]
+
+plot(pp$bytes)
+points(pp.old$bytes,col=2)
+#"bytes"]
+pp.old[!is.na(pp.old$bytes) &pp.old$bytes >6*10^5,"bytes"]
+
+secs_explain <- as.double(difftime(sys_time_end_explain,sys_time_start_explain),units="secs")
+print(secs_explain)
+
+timing <- list(p = p,
+ n_train = n_train,
+ n_explain = n_explain,
+ n_batches = n_batches,
+ n_cores = n_cores,
+ approach = approach,
+ sys_time_initial = as.character(sys_time_initial),
+ sys_time_start_explain = as.character(sys_time_start_explain),
+ sys_time_end_explain = as.character(sys_time_end_explain),
+ secs_explain = secs_explain,
+ this_rep = this_rep,
+ max_n = max_n,
+ max_p = max_p,
+ rho = rho,
+ sigma = sigma,
+ mu_const = mu_const,
+ beta0 = beta0,
+ sigma_eps = sigma_eps)
+
+#print(unlist(timing))
+data.table::fwrite(timing,logfilename,append = T)
diff --git a/inst/scripts/memory_test_2023.csv b/inst/scripts/memory_test_2023.csv
new file mode 100644
index 000000000..4daa220ed
--- /dev/null
+++ b/inst/scripts/memory_test_2023.csv
@@ -0,0 +1,3013 @@
+2023-01-17, 15:06:10, 6610 , 0, 2, 100, 10, 1, 1, empirical, sequential, timing_test_2023.csv
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+2023-01-17, 16:26:00, 6618 , 0, 16, 1000, 100, 32, 1, ctree, sequential, timing_test_2023.csv
+2023-01-17, 16:26:01, 6601 , 0, 16, 1000, 100, 32, 1, ctree, sequential, timing_test_2023.csv
+2023-01-17, 16:26:07, 6612 , 0, 16, 1000, 100, 32, 1, independence, sequential, timing_test_2023.csv
+2023-01-17, 16:26:07, 6611 , 0, 16, 1000, 100, 32, 1, independence, sequential, timing_test_2023.csv
+2023-01-18, 15:53:39, 6612 , 0, 4, 100, 10, 1, 1, empirical, sequential, timing_test_2023.csv
+2023-01-18, 15:53:40, 135086 , 0, 4, 100, 10, 1, 1, empirical, sequential, timing_test_2023.csv
+2023-01-18, 15:53:45, 6623 , 0, 4, 100, 10, 1, 1, gaussian, sequential, timing_test_2023.csv
+2023-01-18, 15:53:46, 122348 , 0, 4, 100, 10, 1, 1, gaussian, sequential, timing_test_2023.csv
+2023-01-18, 15:53:52, 6623 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:53:52, 133068 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:53:53, 256802 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:53:54, 6613 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:54:00, 6609 , 0, 4, 100, 10, 1, 1, independence, sequential, timing_test_2023.csv
+2023-01-18, 15:54:00, 133109 , 0, 4, 100, 10, 1, 1, independence, sequential, timing_test_2023.csv
+2023-01-18, 15:54:06, 6605 , 0, 4, 100, 10, 2, 1, empirical, sequential, timing_test_2023.csv
+2023-01-18, 15:54:07, 122717 , 0, 4, 100, 10, 2, 1, empirical, sequential, timing_test_2023.csv
+2023-01-18, 15:54:12, 6614 , 0, 4, 100, 10, 2, 1, gaussian, sequential, timing_test_2023.csv
+2023-01-18, 15:54:13, 133271 , 0, 4, 100, 10, 2, 1, gaussian, sequential, timing_test_2023.csv
+2023-01-18, 15:54:18, 6599 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:54:19, 133701 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:54:20, 256149 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:54:21, 6603 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv
+2023-01-18, 15:54:27, 6609 , 0, 4, 100, 10, 2, 1, independence, sequential, timing_test_2023.csv
+2023-01-18, 15:54:27, 122741 , 0, 4, 100, 10, 2, 1, independence, sequential, timing_test_2023.csv
diff --git a/inst/scripts/problematic_plots_jens.R b/inst/scripts/problematic_plots_jens.R
new file mode 100644
index 000000000..2aa26c896
--- /dev/null
+++ b/inst/scripts/problematic_plots_jens.R
@@ -0,0 +1,117 @@
+devtools::load_all()
+library(data.table)
+data("airquality")
+data <- data.table::as.data.table(airquality)
+data <- data[complete.cases(data), ]
+x_var <- c("Solar.R", "Wind", "Temp", "Month")
+y_var <- "Ozone"
+
+ind_x_explain <- 1:100
+x_train <- data[, ..x_var]
+y_train <- data[, get(y_var)]
+x_explain <- data[ind_x_explain, ..x_var]
+
+# convert to factors
+data[,Month_factor := as.factor(month.abb[Month])]
+# data[, Temp_factor := fcase(Temp < 71, "low",
+# Temp %between% c(71, 84), "medium",
+# Temp > 84, "high")]
+# data[, Temp_factor := as.factor(Temp_factor)]
+
+
+
+data[, Temp_factor := as.factor(round(Temp, -1))]
+data_train_cat <- copy(data)
+data_explain_cat <- data[ind_x_explain,]
+
+x_var_cat <- c("Solar.R", "Wind", "Temp_factor", "Month_factor")
+x_train_cat <- data_train_cat[, ..x_var_cat]
+
+
+# Example 1 - No errors -------------------------------------------------------------------------------------------
+x_explain_cat <- data_explain_cat[, ..x_var_cat]
+# x_explain_cat[, Wind := 10]
+#x_explain_cat[, Month_factor := Month_factor[1]]
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + ")))
+model_lm_cat <- lm(lm_formula,data_train_cat)
+
+p0 <- mean(y_train)
+explanation_cat <- explain(
+ model = model_lm_cat,
+ x_explain = x_explain_cat,
+ x_train = x_train_cat,
+ approach = "ctree",
+ prediction_zero = p0
+)
+
+
+plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter")
+plot(explanation_cat, plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE)
+
+
+
+# Example 2 - One factor value -----------------------------------------------------------------------------------
+x_explain_cat <- data_explain_cat[, ..x_var_cat]
+x_explain_cat[, Month_factor := Month_factor[1]]
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + ")))
+model_lm_cat <- lm(lm_formula,data_train_cat)
+
+p0 <- mean(y_train)
+explanation_cat <- explain(
+ model = model_lm_cat,
+ x_explain = x_explain_cat,
+ x_train = x_train_cat,
+ approach = "ctree",
+ prediction_zero = p0
+)
+
+# Works fine
+plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter")
+# Wrong x-labels due to breaks being different from when scatter_hist = TRUE
+plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE)
+
+
+
+# Example 3 - few test observations ------------------------------------------------------------------------------
+
+x_explain_cat <- data_explain_cat[, ..x_var_cat]
+x_explain_cat <- x_explain_cat[1:3, ]
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + ")))
+model_lm_cat <- lm(lm_formula,data_train_cat)
+
+p0 <- mean(y_train)
+explanation_cat <- explain(
+ model = model_lm_cat,
+ x_explain = x_explain_cat,
+ x_train = x_train_cat,
+ approach = "ctree",
+ prediction_zero = p0
+)
+
+# Only 4 ticks in the x-axis for the factor
+plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter")
+# Wrong x-labels due to breaks being different from when scatter_hist = TRUE
+plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE)
+
+
+# Example 4 - few observations - to many x-ticks with same label -----------------------------------------
+
+x_explain_cat <- data_explain_cat[, ..x_var_cat]
+x_explain_cat <- x_explain_cat[1:4, ]
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + ")))
+model_lm_cat <- lm(lm_formula,data_train_cat)
+
+p0 <- mean(y_train)
+explanation_cat <- explain(
+ model = model_lm_cat,
+ x_explain = x_explain_cat,
+ x_train = x_train_cat,
+ approach = "ctree",
+ prediction_zero = p0
+)
+
+# Duplicated labels on the x-axis
+plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter")
+# Wrong x-labels due to breaks being different from when scatter_hist = TRUE
+plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE)
+
diff --git a/inst/scripts/readme_example.R b/inst/scripts/readme_example.R
index 91d3565bf..480f599d7 100644
--- a/inst/scripts/readme_example.R
+++ b/inst/scripts/readme_example.R
@@ -1,44 +1,45 @@
library(xgboost)
library(shapr)
-data("Boston", package = "MASS")
+data("airquality")
+data <- data.table::as.data.table(airquality)
+data <- data[complete.cases(data), ]
-x_var <- c("lstat", "rm", "dis", "indus")
-y_var <- "medv"
+x_var <- c("Solar.R", "Wind", "Temp", "Month")
+y_var <- "Ozone"
-x_train <- as.matrix(Boston[-1:-6, x_var])
-y_train <- Boston[-1:-6, y_var]
-x_test <- as.matrix(Boston[1:6, x_var])
+ind_x_explain <- 1:6
+x_train <- data[-ind_x_explain, ..x_var]
+y_train <- data[-ind_x_explain, get(y_var)]
+x_explain <- data[ind_x_explain, ..x_var]
# Looking at the dependence between the features
cor(x_train)
# Fitting a basic xgboost model to the training data
model <- xgboost(
- data = x_train,
+ data = as.matrix(x_train),
label = y_train,
nround = 20,
verbose = FALSE
)
-# Prepare the data for explanation
-explainer <- shapr(x_train, model)
-
# Specifying the phi_0, i.e. the expected prediction without any features
-p <- mean(y_train)
+p0 <- mean(y_train)
# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using
# the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default)
explanation <- explain(
- x_test,
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
approach = "empirical",
- explainer = explainer,
- prediction_zero = p
+ prediction_zero = p0
)
# Printing the Shapley values for the test data.
# For more information about the interpretation of the values in the table, see ?shapr::explain.
-print(explanation$dt)
+print(explanation$shapley_values)
# Finally we plot the resulting explanations
plot(explanation)
diff --git a/inst/scripts/testing_samling_ncombinations.R b/inst/scripts/testing_samling_ncombinations.R
new file mode 100644
index 000000000..65e066d98
--- /dev/null
+++ b/inst/scripts/testing_samling_ncombinations.R
@@ -0,0 +1,126 @@
+library(xgboost)
+#library(shapr)
+# remotes::install_github("NorskRegnesentral/shapr@devel")
+library(shapr)
+library(data.table)
+n = c(100, 1000, 2000)
+p = c(5, 10, 10)
+n_combinations = c(20, 800, 800)
+
+res = list()
+for (i in seq_along(n)) {
+ set.seed(123)
+ cat("n =", n[i], "p =", p[i], "n_combinations =", n_combinations[i], "\n")
+ x_train = data.table(matrix(rnorm(n[i]*p[i]), nrow = n[i], ncol = p[i]))
+ x_test = data.table(matrix(rnorm(10*p[i]), nrow = 10, ncol = p[i]))
+ beta = rnorm(p[i])
+ y = rnorm(n[i], as.matrix(x_train) %*% beta)
+ dt = data.table(cbind(x_train, data.table(y=y)))
+ model = lm(y ~ ., data = dt)
+ p_mean = mean(y)
+
+ res[[i]] = bench::mark(
+ x = shapr::explain(
+ x_train,
+ x_test,
+ model = model,
+ approach = "empirical",
+ prediction_zero = p_mean,
+ n_combinations = n_combinations[i]
+ )
+ )
+}
+
+devtools::load_all()
+res2 = list()
+for (i in seq_along(n)) {
+
+
+ set.seed(123)
+ cat("n =", n[i], "p =", p[i], "n_combinations =", n_combinations[i], "\n")
+ x_train = data.table(matrix(rnorm(n[i] * p[i]), nrow = n[i], ncol = p[i]))
+ x_test = data.table(matrix(rnorm(10 * p[i]), nrow = 10, ncol = p[i]))
+ beta = rnorm(p[i])
+ y = rnorm(n[i], as.matrix(x_train) %*% beta)
+ dt = data.table(cbind(x_train, data.table(y = y)))
+ model = lm(y ~ ., data = dt)
+ p_mean = mean(y)
+
+ res2[[i]] = bench::mark(
+ explain(
+ x_train,
+ x_test,
+ model = model,
+ approach = "empirical",
+ prediction_zero = p_mean,
+ n_combinations = n_combinations[i]
+ )
+ )
+}
+
+saveRDS(res, "inst/scripts/testing_samling_ncombinations.rds")
+saveRDS(res2, "inst/scripts/testing_samling_ncombinations2.rds")
+
+
+
+i = 2
+set.seed(123)
+cat("n =", n[i], "p =", p[i], "n_combinations =", n_combinations[i], "\n")
+x_train = data.table(matrix(rnorm(n[i] * p[i]), nrow = n[i], ncol = p[i]))
+x_test = data.table(matrix(rnorm(10 * p[i]), nrow = 10, ncol = p[i]))
+beta = rnorm(p[i])
+y = rnorm(n[i], as.matrix(x_train) %*% beta)
+dt = data.table(cbind(x_train, data.table(y = y)))
+model = lm(y ~ ., data = dt)
+p_mean = mean(y)
+x1 = Sys.time()
+system.time({res = explain(
+ x_train,
+ x_test,
+ model = model,
+ approach = "empirical",
+ prediction_zero = p_mean,
+ n_combinations = 1000
+)})
+
+devtools::load_all()
+system.time({res2 = explain(
+ x_train,
+ x_test,
+ model = model,
+ approach = "empirical",
+ prediction_zero = p_mean,
+ n_combinations = 800
+)})
+
+
+
+system.time({res3 = explain(
+ x_train,
+ x_test,
+ model = model,
+ approach = "empirical",
+ prediction_zero = p_mean,
+ n_combinations = NULL
+)})
+
+x2 = Sys.time()
+x2-x1
+# devel branch user system elapsed
+# 2.43 0.25 2.56
+
+
+library(profvis)
+
+res = profvis({res = explain(
+ x_train,
+ x_test,
+ model = model,
+ approach = "empirical",
+ prediction_zero = p_mean,
+ n_combinations = n_combinations[i]
+)})
+res
+
+time2 - time1
+time4 - time3
diff --git a/inst/scripts/time_series_annabelle.R b/inst/scripts/time_series_annabelle.R
new file mode 100644
index 000000000..26e1f8b38
--- /dev/null
+++ b/inst/scripts/time_series_annabelle.R
@@ -0,0 +1,89 @@
+library(data.table)
+library(shapr)
+
+devtools::load_all()
+
+set.seed(1)
+n_train = 1000
+n_test = 6
+n_features = 200
+# x = rnorm((n_train + n_test) * (n_features + 5), mean = 1, sd = 2)
+# x = matrix(x, nrow = n_train + n_test, byrow = T)
+# x1 = t(apply(x, 1, cumsum))
+# x = data.table(x[, c(1:n_features, n_features + 5)])
+
+alpha <- 1
+beta <- 0
+theta <- 0.8
+
+data = NULL
+for(n in 1:(n_train + n_test)){
+ set.seed(n)
+ e <- rnorm(n_features + 6, mean = 0, sd = 1)
+
+ m_1 <- 0
+ for(i in 2:length(e)){
+ m_1[i] <- alpha + beta * i + theta * m_1[i - 1] + e[i]
+ }
+ data = rbind(data, m_1)
+}
+
+
+x <- data[, c(2:(n_features + 1), n_features + 5)]
+x <- data.table(x)
+
+plot(ts((t(x)[,1])))
+points(ts((t(x)[,1])), pch = 19)
+
+Q1_days <- 1:(floor(n_features / 4))
+Q2_days <- 1:(floor(n_features / 4)) + max(Q1_days)
+Q3_days <- 1:(floor(n_features / 4)) + max(Q2_days)
+Q4_days <- (max(Q3_days) + 1):n_features
+
+group <- list(Q1 = paste0("V", Q1_days),
+ Q2 = paste0("V", Q2_days),
+ Q3 = paste0("V", Q3_days),
+ Q4 = paste0("V", Q4_days))
+
+response = paste0("V", n_features + 1)
+formula = as.formula(paste0(response, "~ ", paste0("V", 1:n_features, collapse = " + ")))
+
+model = lm(formula, data = x)
+
+x_all <- x[, 1:n_features]
+y_all <- x[[response]]
+
+all_pred <- predict(model, x_all)
+mean((all_pred-y_all)^2)
+# [1] 1.8074
+
+# ---------------
+
+x_explain = x_all[-c(1:n_train), ]
+x_train = x_all[1:n_train, ]
+
+p0 <- mean(y_all[-c(1:n_train)])
+
+# ---------------
+
+explanation_group <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "timeseries",
+ prediction_zero = p0,
+ group = group,
+ timeseries.fixed_sigma_vec = 2
+ # timeseries.bounds = c(-1, 2)
+)
+
+explanation_group
+# none Q1 Q2 Q3 Q4
+# 1: 5.1217 -0.0019489 0.201396 -0.208099 0.74808
+# 2: 5.1217 0.0164650 -0.148537 0.639499 0.38405
+# 3: 5.1217 -0.4625373 0.564378 -0.281495 0.61380
+# 4: 5.1217 -0.1859842 -0.121323 0.048696 -0.25682
+# 5: 5.1217 -1.2290037 -0.896415 1.096474 -0.10961
+# 6: 5.1217 -0.0435240 -0.049311 0.898789 -1.36716
+
+plot(explanation_group, plot_phi0 = F)
diff --git a/inst/scripts/timing_script_2023.R b/inst/scripts/timing_script_2023.R
new file mode 100644
index 000000000..d43db74f6
--- /dev/null
+++ b/inst/scripts/timing_script_2023.R
@@ -0,0 +1,113 @@
+#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library")
+sys_time_initial <- Sys.time()
+
+# libraries
+library(shapr)
+library(future)
+library(MASS)
+library(microbenchmark)
+library(data.table)
+
+# Initial setup
+max_n <- 10^5
+max_p <- 16
+rho <- 0.3
+sigma <- 1
+mu_const <- 0
+beta0 <- 1
+sigma_eps <- 1
+
+mu <- rep(mu_const,max_p)
+beta <- c(beta0,seq_len(max_p)/max_p)
+Sigma <- matrix(rho,max_p,max_p)
+diag(Sigma) <- sigma
+
+set.seed(123)
+x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma)
+y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps)
+
+# Arguments from bash
+args <- commandArgs(trailingOnly = TRUE)
+if(length(args)==0) args = c(0,4,100,10,16,1,"empirical","sequential","timing_test_2023_new2.csv")
+
+
+this_rep <- as.numeric(args[1])
+p <- as.numeric(args[2])
+n_train <- as.numeric(args[3])
+n_explain <- as.numeric(args[4])
+n_batches <- as.numeric(args[5])
+n_cores <- as.numeric(args[6])
+approach <- args[7]
+multicore_method <- args[8]
+logfilename <- args[9]
+
+set.seed(123)
+
+
+these_p <- sample.int(max_p,size=p)
+these_train <- sample.int(max_n,size=n_train)
+these_explain <- sample.int(max_n,size=n_explain)
+
+x_train <- as.data.frame(x_all[these_train,these_p,drop=F])
+x_explain <- as.data.frame(x_all[these_explain,these_p,drop=F])
+
+colnames(x_explain) <- colnames(x_train) <- paste0("X",seq_len(p))
+
+y_train <- y_all[these_train]
+
+xy_train <- cbind(x_train,y=y_train)
+
+model <- lm(formula = y~.,data=xy_train)
+
+prediction_zero <- mean(y_train)
+
+n_batches_use <- min(2^p-2,n_batches)
+
+
+sys_time_start_explain <- Sys.time()
+
+explanation <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = approach,
+ n_batches = n_batches_use,
+ prediction_zero = prediction_zero,
+ n_combinations = 10^4
+)
+
+sys_time_end_explain <- Sys.time()
+
+secs_explain <- as.double(difftime(sys_time_end_explain,sys_time_start_explain),units="secs")
+print(secs_explain)
+
+explanation$timing$timing_secs
+
+timing <- list(p = p,
+ n_train = n_train,
+ n_explain = n_explain,
+ n_batches = n_batches,
+ n_cores = n_cores,
+ approach = approach,
+ n_combinations = explanation$internal$parameters$used_n_combinations,
+ sys_time_initial = as.character(sys_time_initial),
+ sys_time_start_explain = as.character(sys_time_start_explain),
+ sys_time_end_explain = as.character(sys_time_end_explain),
+ secs_explain = secs_explain,
+ this_rep = this_rep,
+ max_n = max_n,
+ max_p = max_p,
+ rho = rho,
+ sigma = sigma,
+ mu_const = mu_const,
+ beta0 = beta0,
+ sigma_eps = sigma_eps,
+ timing_setup = explanation$timing$timing_secs["setup"],
+ timing_test_prediction = explanation$timing$timing_secs["test_prediction"],
+ timing_setup_computation = explanation$timing$timing_secs["setup_computation"],
+ timing_compute_vS = explanation$timing$timing_secs["compute_vS"],
+ timing_postprocessing = explanation$timing$timing_secs["postprocessing"],
+ timing_shapley_computation = explanation$timing$timing_secs["shapley_computation"])
+
+#print(unlist(timing))
+data.table::fwrite(timing,logfilename,append = T)
diff --git a/inst/scripts/timing_test_2023.csv b/inst/scripts/timing_test_2023.csv
new file mode 100644
index 000000000..ee6e1a6e9
--- /dev/null
+++ b/inst/scripts/timing_test_2023.csv
@@ -0,0 +1,296 @@
+2,100,10,1,1,empirical,2023-01-17 15:06:10,2023-01-17 15:06:10,2023-01-17 15:06:10,0.316483736038208,0,1e+05,13,0.3,1,0,1,1
+2,100,10,1,1,gaussian,2023-01-17 15:06:16,2023-01-17 15:06:16,2023-01-17 15:06:17,0.311131954193115,0,1e+05,13,0.3,1,0,1,1
+2,100,10,1,1,ctree,2023-01-17 15:06:22,2023-01-17 15:06:23,2023-01-17 15:06:24,1.30662298202515,0,1e+05,13,0.3,1,0,1,1
+2,100,10,1,1,independence,2023-01-17 15:06:29,2023-01-17 15:06:30,2023-01-17 15:06:30,0.276997566223145,0,1e+05,13,0.3,1,0,1,1
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+4,100,10,1,1,independence,2023-01-17 15:17:32,2023-01-17 15:17:32,2023-01-17 15:17:32,0.306885242462158,0,1e+05,13,0.3,1,0,1,1
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+4,100,10,2,1,gaussian,2023-01-17 15:17:44,2023-01-17 15:17:44,2023-01-17 15:17:45,0.562438249588013,0,1e+05,13,0.3,1,0,1,1
+4,100,10,2,1,ctree,2023-01-17 15:17:50,2023-01-17 15:17:51,2023-01-17 15:17:52,1.93632698059082,0,1e+05,13,0.3,1,0,1,1
+4,100,10,2,1,independence,2023-01-17 15:17:59,2023-01-17 15:17:59,2023-01-17 15:17:59,0.318980693817139,0,1e+05,13,0.3,1,0,1,1
+4,100,10,4,1,empirical,2023-01-17 15:18:05,2023-01-17 15:18:05,2023-01-17 15:18:06,0.46616268157959,0,1e+05,13,0.3,1,0,1,1
+4,100,10,4,1,gaussian,2023-01-17 15:18:11,2023-01-17 15:18:11,2023-01-17 15:18:12,0.5863196849823,0,1e+05,13,0.3,1,0,1,1
+4,100,10,4,1,ctree,2023-01-17 15:18:17,2023-01-17 15:18:17,2023-01-17 15:18:19,1.89016652107239,0,1e+05,13,0.3,1,0,1,1
+4,100,10,4,1,independence,2023-01-17 15:18:25,2023-01-17 15:18:26,2023-01-17 15:18:26,0.400885820388794,0,1e+05,13,0.3,1,0,1,1
+4,100,10,8,1,empirical,2023-01-17 15:18:32,2023-01-17 15:18:32,2023-01-17 15:18:33,0.66105318069458,0,1e+05,13,0.3,1,0,1,1
+4,100,10,8,1,gaussian,2023-01-17 15:18:39,2023-01-17 15:18:39,2023-01-17 15:18:40,0.759965419769287,0,1e+05,13,0.3,1,0,1,1
+4,100,10,8,1,ctree,2023-01-17 15:18:46,2023-01-17 15:18:46,2023-01-17 15:18:48,1.92150068283081,0,1e+05,13,0.3,1,0,1,1
+4,100,10,8,1,independence,2023-01-17 15:18:54,2023-01-17 15:18:55,2023-01-17 15:18:55,0.451948881149292,0,1e+05,13,0.3,1,0,1,1
+4,100,10,16,1,empirical,2023-01-17 15:19:01,2023-01-17 15:19:01,2023-01-17 15:19:02,0.945892810821533,0,1e+05,13,0.3,1,0,1,1
+4,100,10,16,1,gaussian,2023-01-17 15:19:08,2023-01-17 15:19:08,2023-01-17 15:19:09,0.871599435806274,0,1e+05,13,0.3,1,0,1,1
+4,100,10,16,1,ctree,2023-01-17 15:19:15,2023-01-17 15:19:15,2023-01-17 15:19:17,1.99962306022644,0,1e+05,13,0.3,1,0,1,1
+4,100,10,16,1,independence,2023-01-17 15:19:23,2023-01-17 15:19:24,2023-01-17 15:19:24,0.485783338546753,0,1e+05,13,0.3,1,0,1,1
+4,100,10,32,1,empirical,2023-01-17 15:19:30,2023-01-17 15:19:30,2023-01-17 15:19:31,0.889486312866211,0,1e+05,13,0.3,1,0,1,1
+4,100,10,32,1,gaussian,2023-01-17 15:19:37,2023-01-17 15:19:37,2023-01-17 15:19:38,0.845785856246948,0,1e+05,13,0.3,1,0,1,1
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+4,100,10,32,1,independence,2023-01-17 15:19:53,2023-01-17 15:19:53,2023-01-17 15:19:53,0.513545036315918,0,1e+05,13,0.3,1,0,1,1
+4,100,100,1,1,empirical,2023-01-17 15:19:59,2023-01-17 15:19:59,2023-01-17 15:20:00,0.785656213760376,0,1e+05,13,0.3,1,0,1,1
+4,100,100,1,1,gaussian,2023-01-17 15:20:06,2023-01-17 15:20:06,2023-01-17 15:20:09,2.64088177680969,0,1e+05,13,0.3,1,0,1,1
+4,100,100,1,1,ctree,2023-01-17 15:20:14,2023-01-17 15:20:15,2023-01-17 15:20:21,5.92083716392517,0,1e+05,13,0.3,1,0,1,1
+4,100,100,1,1,independence,2023-01-17 15:20:26,2023-01-17 15:20:26,2023-01-17 15:20:27,0.486744165420532,0,1e+05,13,0.3,1,0,1,1
+4,100,100,2,1,empirical,2023-01-17 15:20:32,2023-01-17 15:20:32,2023-01-17 15:20:34,1.20081543922424,0,1e+05,13,0.3,1,0,1,1
+4,100,100,2,1,gaussian,2023-01-17 15:20:39,2023-01-17 15:20:40,2023-01-17 15:20:42,2.56840300559998,0,1e+05,13,0.3,1,0,1,1
+4,100,100,2,1,ctree,2023-01-17 15:20:48,2023-01-17 15:20:48,2023-01-17 15:20:54,6.00723266601563,0,1e+05,13,0.3,1,0,1,1
+4,100,100,2,1,independence,2023-01-17 15:20:59,2023-01-17 15:21:00,2023-01-17 15:21:00,0.644494771957398,0,1e+05,13,0.3,1,0,1,1
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+4,100,100,4,1,ctree,2023-01-17 15:21:23,2023-01-17 15:21:24,2023-01-17 15:21:30,6.38518142700195,0,1e+05,13,0.3,1,0,1,1
+4,100,100,4,1,independence,2023-01-17 15:21:36,2023-01-17 15:21:36,2023-01-17 15:21:37,0.937007904052734,0,1e+05,13,0.3,1,0,1,1
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+4,100,100,8,1,gaussian,2023-01-17 15:21:53,2023-01-17 15:21:53,2023-01-17 15:21:56,3.16380023956299,0,1e+05,13,0.3,1,0,1,1
+4,100,100,8,1,ctree,2023-01-17 15:22:02,2023-01-17 15:22:02,2023-01-17 15:22:09,6.76805973052979,0,1e+05,13,0.3,1,0,1,1
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diff --git a/inst/scripts/vilde/Rplot02.pdf b/inst/scripts/vilde/Rplot02.pdf
new file mode 100644
index 000000000..e683d6036
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diff --git a/inst/scripts/vilde/airquality_example.R b/inst/scripts/vilde/airquality_example.R
new file mode 100644
index 000000000..9c162bfe2
--- /dev/null
+++ b/inst/scripts/vilde/airquality_example.R
@@ -0,0 +1,37 @@
+
+data("airquality")
+airquality <- airquality[complete.cases(airquality), ]
+# Split data into test- and training data
+train <- head(airquality, -50)
+test <- tail(airquality, 50)
+
+# Fit a linear model
+model <- lm(Ozone ~ Solar.R + Wind+ Temp + Month, data = x_train)
+
+p <- mean(train$Ozone)
+
+x <- explain(
+ train,
+ test,
+ model = model,
+ approach = "empirical",
+ prediction_zero = p
+)
+
+if (requireNamespace("ggplot2", quietly = TRUE)) {
+ # The default plotting option is a bar plot of the Shapley values
+ # We draw bar plots for the first 4 observations
+ plot(x, index_x_explain = 1:4)
+
+ # We can also make waterfall plots
+ plot(x, plot_type = "waterfall", index_x_explain = 1:4)
+ plot(x, plot_type = "waterfall", index_x_explain = 1:4, top_k_features = 2) # top_k_features = 2 shows the 2 features with largest contribution
+
+ # Or scatter plots showing the distribution of the shapley values and feature values
+ plot(x, plot_type = "scatter")
+ plot(x, plot_type = "scatter", scatter_features = "Temp") # if we only want the scatter plot for a specific feature
+
+ # Or a beeswarm plot summarising the Shapley values and feature values for all features
+ plot(x, plot_type = "beeswarm")
+ plot(x, plot_type = "beeswarm", col = c("red", "black")) # we can change colors
+}
diff --git a/inst/scripts/vilde/arrow_waterfall.pdf b/inst/scripts/vilde/arrow_waterfall.pdf
new file mode 100644
index 000000000..2aa6ef77f
Binary files /dev/null and b/inst/scripts/vilde/arrow_waterfall.pdf differ
diff --git a/inst/scripts/vilde/bug_example.pdf b/inst/scripts/vilde/bug_example.pdf
new file mode 100644
index 000000000..944842872
Binary files /dev/null and b/inst/scripts/vilde/bug_example.pdf differ
diff --git a/inst/scripts/vilde/check_progress.R b/inst/scripts/vilde/check_progress.R
new file mode 100644
index 000000000..aee0f765c
--- /dev/null
+++ b/inst/scripts/vilde/check_progress.R
@@ -0,0 +1,58 @@
+library(progressr)
+library(future.apply)
+library(xgboost)
+library(shapr)
+library(data.table)
+
+data("Boston", package = "MASS")
+
+x_var <- c("lstat", "rm", "dis", "indus", "age", "ptratio")
+y_var <- "medv"
+
+x_train <- as.matrix(Boston[-1:-15, x_var])
+y_train <- Boston[-1:-15, y_var]
+x_test <- as.matrix(Boston[1:100, x_var])
+
+# Fitting a basic xgboost model to the training data
+model <- xgboost(
+ data = x_train,
+ label = y_train,
+ nround = 20,
+ verbose = FALSE
+)
+p <- mean(y_train)
+
+plan(multisession, workers=3)
+
+# when we simply call explain(), no progress bar is shown
+x <- explain(x_train, x_test, model, approach="gaussian", prediction_zero=p, n_batches = 4)
+
+# the handler specifies what kind of progress bar is shown
+# Wrapping explain() in with_progress() gives a progress bar when calling explain()
+handlers("txtprogressbar")
+x <- with_progress(
+ explain(x_train, x_test, model, approach="empirical", prediction_zero=p, n_batches = 5)
+ )
+
+# with global=TRUE the progress bar is displayed whenever the explain-function is called, and there is no need to use with_progress()
+handlers(global = TRUE)
+x <- explain(x_train, x_test, model, approach="gaussian", prediction_zero=p, n_batches = 4)
+
+# there are different options for what kind of progress bar should be displayed
+handlers("txtprogressbar") #this is the default
+x <- explain(x_train, x_test, model, approach="independence", prediction_zero=p, n_batches = 4)
+
+handlers("progress")
+x <- explain(x_train, x_test, model, approach="independence", prediction_zero=p, n_batches = 4)
+
+# you can edit the symbol used to draw completed progress in the progress bar (as well as other features) with handler_progress()
+handlers(handler_progress(complete = "#"))
+x <- explain(x_train, x_test, model, approach="copula", prediction_zero=p, n_batches = 4)
+
+plan("sequential")
+
+handlers("progress")
+x <- explain(x_train, x_test, model, approach=c(rep("ctree",4),"independence","independence"), prediction_zero=p, n_batches = 4)
+
+
+
diff --git a/inst/scripts/vilde/sketch_for_waterfall_plot.R b/inst/scripts/vilde/sketch_for_waterfall_plot.R
new file mode 100644
index 000000000..dc9e9278f
--- /dev/null
+++ b/inst/scripts/vilde/sketch_for_waterfall_plot.R
@@ -0,0 +1,68 @@
+library(xgboost)
+library(shapr)
+library(ggplot2)
+library(data.table)
+
+data("Boston", package = "MASS")
+
+x_var <- c("lstat", "rm", "dis", "indus")
+y_var <- "medv"
+
+x_train <- as.matrix(Boston[-1:-6, x_var])
+y_train <- Boston[-1:-6, y_var]
+x_test <- as.matrix(Boston[1:6, x_var])
+
+# Looking at the dependence between the features
+cor(x_train)
+
+# Fitting a basic xgboost model to the training data
+model <- xgboost(
+ data = x_train,
+ label = y_train,
+ nround = 20,
+ verbose = FALSE
+)
+p <- mean(y_train)
+
+# Prepare the data for explanation
+res <- explain_final(x_train,x_test,model,approach="independence",prediction_zero=p,n_batches = 4)
+plot(res)
+
+i<- 1 # index for observation we want to plot
+dt <- data.table(feat_name = paste0(colnames(res$shapley_values[,-1]), " = ", format(res$internal$data$x_explain[i,], 2) ),
+ shapley_value = as.numeric(res$shapley_values[i,-1])
+ )
+dt
+expected <- as.numeric(res$shapley_values[i,])[1]
+observed <- res$pred_explain[i]
+
+dt[, sign := ifelse(shapley_value > 0, "Increases", "Decreases")]
+dt[, rank := frank(abs(shapley_value))]
+setorder(dt, rank)
+dt[, end := cumsum(shapley_value)+expected]
+dt[, start := c(expected, head(end, -1))]
+dt[, description := factor(feat_name, levels = unique(feat_name[order(abs(shapley_value))]))]
+dt
+
+p <- ggplot(dt, aes(x = description, fill = sign)) +
+ geom_rect(aes(x=description, xmin = rank - 0.45, xmax = rank + 0.45, ymin = end,ymax = start)) +
+ scale_fill_manual(values=c("steelblue", "lightsteelblue")) +
+ geom_segment(x=-0.1, xend = 0.56, y=expected, yend=expected, linetype="dashed", col="dark grey") +
+ labs(
+ y = "Feature contribution",
+ x = "Feature",
+ fill = "",
+ title = "Shapley value prediction explanation"
+ ) +
+ geom_text(aes(label = format(shapley_value,digits=2), x=rank, y=start + (end-start)/2)) +
+ annotate("text",label=paste0("E(italic(f(x)))==", format(expected,digits=3)), y=expected, x=-Inf,parse = TRUE) +
+ coord_flip(clip = 'off', xlim=c(0.5, 4)) +
+ theme(plot.margin = unit(c(1,1,3,1), "lines")) +
+ geom_segment(x=-0.1, xend = 4.46, y=observed, yend=observed, linetype="dashed", col="dark grey") +
+ annotate("text",label=paste0("italic(f(x))==", format(observed,digits=3)), y=observed, x=Inf, parse = TRUE) +
+ geom_segment(aes(x=ifelse(rank==last(rank), as.numeric(rank), as.numeric(rank)-0.45), xend = ifelse(rank==last(rank), as.numeric(rank), as.numeric(rank)+1.45),
+ y=end, yend=end), linetype="dashed", col="dark grey")
+p
+
+
+
diff --git a/inst/scripts/vilde/waterfall_plot.R b/inst/scripts/vilde/waterfall_plot.R
new file mode 100644
index 000000000..531f1e4c1
--- /dev/null
+++ b/inst/scripts/vilde/waterfall_plot.R
@@ -0,0 +1,79 @@
+library(xgboost)
+library(shapr)
+library(ggplot2)
+library(data.table)
+
+#test plotting w Boston data
+data("Boston", package = "MASS")
+x_var <- c("lstat", "rm", "dis", "indus", "crim", "age")
+y_var <- "medv"
+b <- 150
+x_train <- as.matrix(Boston[-1:-b, x_var])
+y_train <- Boston[-1:-b, y_var]
+x_test <- as.matrix(Boston[1:b, x_var])
+
+model <- xgboost(
+ data = x_train,
+ label = y_train,
+ nround = 20,
+ verbose = FALSE
+)
+p <- mean(y_train)
+x <- explain_final(x_train,x_test,model,approach="independence",prediction_zero=p,n_batches = 4)
+plot.shapr(x,
+ plot_type = "bar",
+ digits = 3,
+ plot_phi0 = TRUE,
+ index_x_explain = NULL,
+ top_k_features = NULL,
+ col = c("#00BA38","#F8766D"), #first increasing color, then decreasing color
+ plot_order = "largest_first",
+ features_to_plot = NULL,
+ histogram = TRUE,
+ )
+
+# data("AdultUCI", package = "arules")
+# names(AdultUCI) <- gsub("-","_",names(AdultUCI))
+# data <- na.omit(AdultUCI)
+# data$income <-ifelse(data$income==2,1,0)
+# x_var <- c("age", "workclass", "hours_per_week","native_country")
+# y_var <- "income"
+# x_train <- as.matrix(data[-1:-b, x_var])
+# y_train <- data[-1:-b, y_var]
+# x_test <- as.matrix(data[1:b, x_var])
+
+#test plotting with simulated data
+test <- data.frame(x1 = rnorm(5000, mean=10, sd=4),
+ x2 = rnorm(5000, mean=-60, sd=2),
+ x3 = rnorm(5000, mean=100, sd=1),
+ x4 = rnorm(5000, mean=0, sd=1),
+ y = rnorm(5000, mean=-5, sd=2))
+
+x_var <- c("x1", "x2", "x3", "x4")
+y_var <- "y"
+b <- 350
+x_train <- as.matrix(test[-1:-b, x_var])
+y_train <- test[-1:-b, y_var]
+x_test <- as.matrix(test[1:b, x_var])
+
+# Fitting a basic xgboost model to the training data
+model <- xgboost(
+ data = x_train,
+ label = y_train,
+ nround = 20,
+ verbose = FALSE
+)
+p <- mean(y_train)
+
+plot.shapr(x,
+ plot_type = "bar",
+ digits = 3,
+ plot_phi0 = TRUE,
+ index_x_explain = NULL,
+ top_k_features = NULL,
+ col = c("#00BA38","#F8766D"), #first increasing color, then decreasing color
+ plot_order = "largest_first",
+ features_to_plot = NULL,
+ histogram = TRUE
+ )
+
diff --git a/inst/scripts/vilde/waterfall_plot.pdf b/inst/scripts/vilde/waterfall_plot.pdf
new file mode 100644
index 000000000..bdae1d34a
Binary files /dev/null and b/inst/scripts/vilde/waterfall_plot.pdf differ
diff --git a/inst/scripts/vilde/waterfall_plot_featurename_fixed.pdf b/inst/scripts/vilde/waterfall_plot_featurename_fixed.pdf
new file mode 100644
index 000000000..e3c3d2b36
Binary files /dev/null and b/inst/scripts/vilde/waterfall_plot_featurename_fixed.pdf differ
diff --git a/logfile b/logfile
new file mode 100644
index 000000000..9f6e0f3f5
--- /dev/null
+++ b/logfile
@@ -0,0 +1,26 @@
+DAY: 2022-01-20, TIME:14:17:23 MEMORY:
+ 6.8M
+DAY: 2022-01-20, TIME:14:17:33 MEMORY:
+ 6.8M
+DAY: 2022-01-20, TIME:14:17:44 MEMORY:
+ 6.8M
+DAY: 2022-01-20, TIME:14:17:54 MEMORY:
+ 6.8M
+DAY: 2022-01-20, TIME:14:18:04 MEMORY:
+ 6.8M
+DAY: 2022-01-20, TIME:14:18:14 MEMORY:
+ 6.9M
+DAY: 2022-01-20, TIME:14:18:24 MEMORY:
+ 388.5M
+DAY: 2022-01-20, TIME:14:18:34 MEMORY:
+ 256.6M
+DAY: 2022-01-20, TIME:14:18:44 MEMORY:
+ 256.5M
+DAY: 2022-01-20, TIME:14:18:54 MEMORY:
+ 6.8M
+2022-01-20, 14:47:06 ,
+ 6.8M
+2022-01-20, 14:47:35,
+ 6.9M
+2022-01-20, 14:55:32,
+ 7.0M
diff --git a/logfile2 b/logfile2
new file mode 100644
index 000000000..0e002077f
--- /dev/null
+++ b/logfile2
@@ -0,0 +1,5 @@
+2022-01-20, 14:59:20 5.7M
+2022-01-20, 14:59:22 5.7M
+2022-01-20, 14:59:24 5.7M
+2022-01-20, 14:59:26 5.7M
+2022-01-20, 14:59:28 5.6M
diff --git a/man/apply_dummies.Rd b/man/apply_dummies.Rd
deleted file mode 100644
index 315a9282c..000000000
--- a/man/apply_dummies.Rd
+++ /dev/null
@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/features.R
-\name{apply_dummies}
-\alias{apply_dummies}
-\title{Apply dummy variables - this is an internal function intended only to be used in
-predict_model.xgb.Booster()}
-\usage{
-apply_dummies(feature_list, testdata)
-}
-\arguments{
-\item{feature_list}{List. The \code{feature_list} object in the output object after running
-\code{\link[shapr:make_dummies]{make_dummies}}}
-
-\item{testdata}{data.table or data.frame. New data that has the same
-feature names, types, and levels as \code{feature_list}.}
-}
-\value{
-A data.table with all features but where the factors in \code{testdata} are
-one-hot encoded variables as specified in feature_list
-}
-\description{
-Apply dummy variables - this is an internal function intended only to be used in
-predict_model.xgb.Booster()
-}
-\author{
-Annabelle Redelmeier, Martin Jullum
-}
-\keyword{internal}
diff --git a/man/check_features.Rd b/man/check_features.Rd
deleted file mode 100644
index 5207bd7a9..000000000
--- a/man/check_features.Rd
+++ /dev/null
@@ -1,42 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/preprocess_data.R
-\name{check_features}
-\alias{check_features}
-\title{Checks that two extracted feature lists have exactly the same properties}
-\usage{
-check_features(f_list_1, f_list_2, use_1_as_truth = T)
-}
-\arguments{
-\item{f_list_1, f_list_2}{List. As extracted from either \code{get_data_specs} or \code{get_model_specs}.}
-
-\item{use_1_as_truth}{Logical. If TRUE, \code{f_list_2} is compared to \code{f_list_1}, i.e. additional elements
-is allowed in \code{f_list_2}, and if \code{f_list_1}'s feature classes contains NAs, feature class check is
-ignored regardless of what is specified in \code{f_list_1}. If FALSE, \code{f_list_1} and \code{f_list_2} are
-equated and they need to contain exactly the same elements. Set to TRUE when comparing a model and data, and FALSE
-when comparing two data sets.}
-}
-\value{
-List. The \code{f_list_1} is returned as inserted if there all check are carried out. If some info is
-missing from \code{f_list_1}, the function continues consistency checking using \code{f_list_2} and returns that.
-}
-\description{
-Checks that two extracted feature lists have exactly the same properties
-}
-\examples{
-# Load example data
-if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- # Split data into test- and training data
- x_train <- data.table::as.data.table(head(Boston))
- x_train[, rad := as.factor(rad)]
- data_features <- get_data_specs(x_train)
- model <- lm(medv ~ lstat + rm + rad + indus, data = x_train)
-
- model_features <- get_model_specs(model)
- check_features(model_features, data_features)
-}
-}
-\author{
-Martin Jullum
-}
-\keyword{internal}
diff --git a/man/check_groups.Rd b/man/check_groups.Rd
index 21366aad9..4618eb3b7 100644
--- a/man/check_groups.Rd
+++ b/man/check_groups.Rd
@@ -1,18 +1,13 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/features.R
+% Please edit documentation in R/setup.R
\name{check_groups}
\alias{check_groups}
\title{Check that the group parameter has the right form and content}
\usage{
-check_groups(feature_labels, group)
+check_groups(feature_names, group)
}
\arguments{
-\item{feature_labels}{Vector of characters. Contains the feature labels used by the model}
-
-\item{group}{List. If \code{NULL} regular feature wise Shapley values are computed.
-If provided, group wise Shapley values are computed. \code{group} then has length equal to
-the number of groups. The list element contains character vectors with the features included
-in each of the different groups.}
+\item{feature_names}{Vector of characters. Contains the feature labels used by the model}
}
\value{
Error or NULL
diff --git a/man/compute_shapley.Rd b/man/compute_shapley_new.Rd
similarity index 59%
rename from man/compute_shapley.Rd
rename to man/compute_shapley_new.Rd
index 7ec98017c..7396b6d9e 100644
--- a/man/compute_shapley.Rd
+++ b/man/compute_shapley_new.Rd
@@ -1,15 +1,15 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/predictions.R
-\name{compute_shapley}
-\alias{compute_shapley}
+% Please edit documentation in R/finalize_explanation.R
+\name{compute_shapley_new}
+\alias{compute_shapley_new}
\title{Compute shapley values}
\usage{
-compute_shapley(explainer, contribution_mat)
+compute_shapley_new(internal, dt_vS)
}
\arguments{
-\item{explainer}{An \code{explain} object.}
+\item{dt_vS}{The contribution matrix.}
-\item{contribution_mat}{The contribution matrix.}
+\item{explainer}{An \code{explain} object.}
}
\value{
A \code{data.table} with shapley values for each test observation.
diff --git a/man/compute_vS.Rd b/man/compute_vS.Rd
new file mode 100644
index 000000000..1988ef5c5
--- /dev/null
+++ b/man/compute_vS.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/compute_vS.R
+\name{compute_vS}
+\alias{compute_vS}
+\title{Computes \code{v(S)} for all features subsets \code{S}.}
+\usage{
+compute_vS(internal, model, predict_model, method = "future")
+}
+\arguments{
+\item{internal}{List.
+Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}}
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.}
+
+\item{model}{Objects.
+The model object that ought to be explained.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{method}{Character
+Indicates whether the lappy method (default) or loop method should be used.}
+}
+\description{
+Computes \code{v(S)} for all features subsets \code{S}.
+}
diff --git a/man/create_S_batch.Rd b/man/create_S_batch.Rd
deleted file mode 100644
index adee91bff..000000000
--- a/man/create_S_batch.Rd
+++ /dev/null
@@ -1,26 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/explanation.R
-\name{create_S_batch}
-\alias{create_S_batch}
-\title{Compute Shapley values in batches}
-\usage{
-create_S_batch(explainer, n_batches, index_S = NULL)
-}
-\arguments{
-\item{explainer}{The binary matrix \code{S} returned from \code{\link{shapr}}.}
-
-\item{n_batches}{Numeric value specifying how many batches \code{S} should be split into.}
-
-\item{index_S}{Numeric vector specifying which rows of \code{S} that should be considered.}
-}
-\value{
-A list of length \code{n_batches}.
-}
-\description{
-Create a list of indexes used to compute Shapley values in batches.
-}
-\details{
-If \code{index_S} is not \code{NULL} then the number of batches is scaled such that the
-total number of batches is equal \code{n_batches} and not within the rows specified by\code{index_S}.
-}
-\keyword{internal}
diff --git a/man/create_ctree.Rd b/man/create_ctree.Rd
index ade2b927c..3c3db21f6 100644
--- a/man/create_ctree.Rd
+++ b/man/create_ctree.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sampling.R
+% Please edit documentation in R/approach_ctree.R
\name{create_ctree}
\alias{create_ctree}
\title{Make all conditional inference trees}
@@ -16,16 +16,16 @@ create_ctree(
\arguments{
\item{given_ind}{Numeric value. Indicates which features are conditioned on.}
-\item{x_train}{Numeric vector. Indicates the specific values of features for individual i.}
+\item{mincriterion}{Numeric scalar or vector. (default = 0.95)
+Either a scalar or vector of length equal to the number of features in the model.
+Value is equal to 1 - \eqn{\alpha} where \eqn{\alpha} is the nominal level of the conditional independence tests.
+If it is a vector, this indicates which value to use when conditioning on various numbers of features.}
-\item{mincriterion}{Numeric value or vector equal to 1 - alpha where alpha is the nominal level of the conditional
-independence tests.
-Can also be a vector equal to the length of the number of features indicating which mincriterion to use
-when conditioning on various numbers of features.}
+\item{minsplit}{Numeric scalar. (default = 20)
+Determines minimum value that the sum of the left and right daughter nodes required for a split.}
-\item{minsplit}{Numeric value. Equal to the value that the sum of the left and right daughter nodes need to exceed.}
-
-\item{minbucket}{Numeric value. Equal to the minimum sum of weights in a terminal node.}
+\item{minbucket}{Numeric scalar. (default = 7)
+Determines the minimum sum of weights in a terminal node required for a split}
\item{use_partykit}{String. In some semi-rare cases \code{partyk::ctree} runs into an error related to the LINPACK
used by R. To get around this problem, one may fall back to using the newer (but slower) \code{partykit::ctree}
@@ -40,26 +40,6 @@ List with conditional inference tree and the variables conditioned/not condition
\description{
Make all conditional inference trees
}
-\examples{
-if (requireNamespace("MASS", quietly = TRUE) & requireNamespace("party", quietly = TRUE)) {
- m <- 10
- n <- 40
- n_samples <- 50
- mu <- rep(1, m)
- cov_mat <- cov(matrix(rnorm(n * m), n, m))
- x_train <- data.table::data.table(MASS::mvrnorm(n, mu, cov_mat))
- given_ind <- c(4, 7)
- mincriterion <- 0.95
- minsplit <- 20
- minbucket <- 7
- sample <- TRUE
- create_ctree(
- given_ind = given_ind, x_train = x_train,
- mincriterion = mincriterion, minsplit = minsplit,
- minbucket = minbucket, use_partykit = "on_error"
- )
-}
-}
\author{
Annabelle Redelmeier, Martin Jullum
}
diff --git a/man/default_doc.Rd b/man/default_doc.Rd
new file mode 100644
index 000000000..eb2ee0e0d
--- /dev/null
+++ b/man/default_doc.Rd
@@ -0,0 +1,36 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/documentation.R
+\name{default_doc}
+\alias{default_doc}
+\title{Unexported documentation helper function.}
+\usage{
+default_doc()
+}
+\arguments{
+\item{internal}{List.
+Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}}
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.}
+
+\item{model}{Objects.
+The model object that ought to be explained.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{output_size}{TODO: Document}
+
+\item{extra}{TODO: Document}
+
+\item{...}{Further arguments passed to \code{approach}-specific functions.}
+}
+\value{
+List \code{internal}
+It holds all parameters, data, and computed objects used within \code{\link[=explain]{explain()}}.
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.
+}
+\description{
+Unexported documentation helper function.
+}
+\keyword{internal}
diff --git a/man/default_doc_explain.Rd b/man/default_doc_explain.Rd
new file mode 100644
index 000000000..3893ddce2
--- /dev/null
+++ b/man/default_doc_explain.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/documentation.R
+\name{default_doc_explain}
+\alias{default_doc_explain}
+\title{Exported documentation helper function.}
+\usage{
+default_doc_explain()
+}
+\arguments{
+\item{internal}{Not used.}
+}
+\description{
+Exported documentation helper function.
+}
+\keyword{internal}
diff --git a/man/explain.Rd b/man/explain.Rd
index 1dbb5c216..781255a3c 100644
--- a/man/explain.Rd
+++ b/man/explain.Rd
@@ -1,234 +1,215 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/explanation.R
+% Please edit documentation in R/explain.R
\name{explain}
\alias{explain}
-\alias{explain.independence}
-\alias{explain.empirical}
-\alias{explain.gaussian}
-\alias{explain.copula}
-\alias{explain.ctree}
-\alias{explain.combined}
-\alias{explain.ctree_comb_mincrit}
\title{Explain the output of machine learning models with more accurately estimated Shapley values}
\usage{
explain(
- x,
- explainer,
+ model,
+ x_explain,
+ x_train,
approach,
prediction_zero,
+ n_combinations = NULL,
+ group = NULL,
n_samples = 1000,
- n_batches = 1,
- ...
-)
-
-\method{explain}{independence}(
- x,
- explainer,
- approach,
- prediction_zero,
- n_samples = 1000,
- n_batches = 1,
- seed = 1,
- ...
-)
-
-\method{explain}{empirical}(
- x,
- explainer,
- approach,
- prediction_zero,
- n_samples = 1000,
- n_batches = 1,
- seed = 1,
- w_threshold = 0.95,
- type = "fixed_sigma",
- fixed_sigma_vec = 0.1,
- n_samples_aicc = 1000,
- eval_max_aicc = 20,
- start_aicc = 0.1,
- cov_mat = NULL,
- ...
-)
-
-\method{explain}{gaussian}(
- x,
- explainer,
- approach,
- prediction_zero,
- n_samples = 1000,
- n_batches = 1,
- seed = 1,
- mu = NULL,
- cov_mat = NULL,
- ...
-)
-
-\method{explain}{copula}(
- x,
- explainer,
- approach,
- prediction_zero,
- n_samples = 1000,
- n_batches = 1,
- seed = 1,
- ...
-)
-
-\method{explain}{ctree}(
- x,
- explainer,
- approach,
- prediction_zero,
- n_samples = 1000,
- n_batches = 1,
- seed = 1,
- mincriterion = 0.95,
- minsplit = 20,
- minbucket = 7,
- sample = TRUE,
- ...
-)
-
-\method{explain}{combined}(
- x,
- explainer,
- approach,
- prediction_zero,
- n_samples = 1000,
- n_batches = 1,
- seed = 1,
- mu = NULL,
- cov_mat = NULL,
- ...
-)
-
-\method{explain}{ctree_comb_mincrit}(
- x,
- explainer,
- approach,
- prediction_zero,
- n_samples,
- n_batches = 1,
+ n_batches = NULL,
seed = 1,
- mincriterion,
+ keep_samp_for_vS = FALSE,
+ predict_model = NULL,
+ get_model_specs = NULL,
+ timing = TRUE,
...
)
}
\arguments{
-\item{x}{A matrix or data.frame. Contains the the features, whose
-predictions ought to be explained (test data).}
+\item{model}{The model whose predictions we want to explain.
+Run \code{\link[=get_supported_models]{get_supported_models()}}
+for a table of which models \code{explain} supports natively. Unsupported models
+can still be explained by passing \code{predict_model} and (optionally) \code{get_model_specs},
+see details for more information.}
+
+\item{x_explain}{A matrix or data.frame/data.table.
+Contains the the features, whose predictions ought to be explained.}
-\item{explainer}{An \code{explainer} object to use for explaining the observations.
-See \code{\link{shapr}}.}
+\item{x_train}{Matrix or data.frame/data.table.
+Contains the data used to estimate the (conditional) distributions for the features
+needed to properly estimate the conditional expectations in the Shapley formula.}
\item{approach}{Character vector of length \code{1} or \code{n_features}.
\code{n_features} equals the total number of features in the model. All elements should,
-either be \code{"gaussian"}, \code{"copula"}, \code{"empirical"}, \code{"ctree"}, or \code{"independence"}.
+either be \code{"gaussian"}, \code{"copula"}, \code{"empirical"}, \code{"ctree"}, \code{"categorical"}, \code{"timeseries"}, or \code{"independence"}.
See details for more information.}
-\item{prediction_zero}{Numeric. The prediction value for unseen data, typically equal to the mean of
-the response.}
-
-\item{n_samples}{Positive integer. Indicating the maximum number of samples to use in the
+\item{prediction_zero}{Numeric.
+The prediction value for unseen data, i.e. an estimate of the expected prediction without conditioning on any
+features.
+Typically we set this value equal to the mean of the response variable in our training data, but other choices
+such as the mean of the predictions in the training data are also reasonable.}
+
+\item{n_combinations}{Integer.
+If \code{group = NULL}, \code{n_combinations} represents the number of unique feature combinations to sample.
+If \code{group != NULL}, \code{n_combinations} represents the number of unique group combinations to sample.
+If \code{n_combinations = NULL}, the exact method is used and all combinations are considered.
+The maximum number of combinations equals \code{2^m}, where \code{m} is the number of features.}
+
+\item{group}{List.
+If \code{NULL} regular feature wise Shapley values are computed.
+If provided, group wise Shapley values are computed. \code{group} then has length equal to
+the number of groups. The list element contains character vectors with the features included
+in each of the different groups.}
+
+\item{n_samples}{Positive integer.
+Indicating the maximum number of samples to use in the
Monte Carlo integration for every conditional expectation. See also details.}
-\item{n_batches}{Positive integer.
+\item{n_batches}{Positive integer (or NULL).
Specifies how many batches the total number of feature combinations should be split into when calculating the
contribution function for each test observation.
-The default value is 1.
-Increasing the number of batches may significantly reduce the RAM allocation for models with many features.
+The default value is NULL which uses a reasonable trade-off between RAM allocation and computation speed,
+which depends on \code{approach} and \code{n_combinations}.
+For models with many features, increasing the number of batches reduces the RAM allocation significantly.
This typically comes with a small increase in computation time.}
-\item{...}{Additional arguments passed to \code{\link{prepare_and_predict}}}
-
-\item{seed}{Positive integer. If \code{NULL} the seed will be inherited from the calling environment.}
-
-\item{w_threshold}{Numeric vector of length 1, with \code{0 < w_threshold <= 1} representing the minimum proportion
-of the total empirical weight that data samples should use. If e.g. \code{w_threshold = .8} we will choose the
-\code{K} samples with the largest weight so that the sum of the weights accounts for 80\% of the total weight.
-\code{w_threshold} is the \eqn{\eta} parameter in equation (15) of Aas et al (2021).}
-
-\item{type}{Character. Should be equal to either \code{"independence"},
-\code{"fixed_sigma"}, \code{"AICc_each_k"} or \code{"AICc_full"}.}
-
-\item{fixed_sigma_vec}{Numeric. Represents the kernel bandwidth. Note that this argument is only
-applicable when \code{approach = "empirical"}, and \code{type = "fixed_sigma"}}
-
-\item{n_samples_aicc}{Positive integer. Number of samples to consider in AICc optimization.
-Note that this argument is only applicable when \code{approach = "empirical"}, and \code{type}
-is either equal to \code{"AICc_each_k"} or \code{"AICc_full"}}
-
-\item{eval_max_aicc}{Positive integer. Maximum number of iterations when
-optimizing the AICc. Note that this argument is only applicable when
-\code{approach = "empirical"}, and \code{type} is either equal to
-\code{"AICc_each_k"} or \code{"AICc_full"}}
-
-\item{start_aicc}{Numeric. Start value of \code{sigma} when optimizing the AICc. Note that this argument
-is only applicable when \code{approach = "empirical"}, and \code{type} is either equal to
-\code{"AICc_each_k"} or \code{"AICc_full"}}
-
-\item{cov_mat}{Numeric matrix. (Optional) Containing the covariance matrix of the data
-generating distribution. \code{NULL} means it is estimated from the data if needed
-(in the Gaussian approach).}
-
-\item{mu}{Numeric vector. (Optional) Containing the mean of the data generating distribution.
-If \code{NULL} the expected values are estimated from the data. Note that this is only used
-when \code{approach = "gaussian"}.}
-
-\item{mincriterion}{Numeric value or vector where length of vector is the number of features in model.
-Value is equal to 1 - alpha where alpha is the nominal level of the conditional
-independence tests.
-If it is a vector, this indicates which mincriterion to use
-when conditioning on various numbers of features.}
-
-\item{minsplit}{Numeric value. Equal to the value that the sum of the left and right daughter nodes need to exceed.}
-
-\item{minbucket}{Numeric value. Equal to the minimum sum of weights in a terminal node.}
-
-\item{sample}{Boolean. If TRUE, then the method always samples \code{n_samples} from the leaf (with replacement).
-If FALSE and the number of obs in the leaf is less than \code{n_samples}, the method will take all observations
-in the leaf. If FALSE and the number of obs in the leaf is more than \code{n_samples}, the method will sample
-\code{n_samples} (with replacement). This means that there will always be sampling in the leaf unless
+\item{seed}{Positive integer.
+Specifies the seed before any randomness based code is being run.
+If \code{NULL} the seed will be inherited from the calling environment.}
+
+\item{keep_samp_for_vS}{Logical.
+Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned
+(in \code{internal$output})}
+
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+(Run \code{\link[=get_supported_models]{get_supported_models()}} for a list of natively supported
+models.)
+The function must have two arguments, \code{model} and \code{newdata} which specify, respectively, the model
+and a data.frame/data.table to compute predictions for. The function must give the prediction as a numeric vector.
+\code{NULL} (the default) uses functions specified internally.
+Can also be used to override the default function for natively supported model classes.}
+
+\item{get_model_specs}{Function.
+An optional function for checking model/data consistency when \code{model} is not natively supported.
+(Run \code{\link[=get_supported_models]{get_supported_models()}} for a list of natively supported
+models.)
+The function takes \code{model} as argument and provides a list with 3 elements:
+\describe{
+\item{labels}{Character vector with the names of each feature.}
+\item{classes}{Character vector with the classes of each features.}
+\item{factor_levels}{Character vector with the levels for any categorical features.}
+}
+If \code{NULL} (the default) internal functions are used for natively supported model classes, and the checking is
+disabled for unsupported model classes.
+Can also be used to override the default function for natively supported model classes.}
+
+\item{timing}{Logical.
+Whether the timing of the different parts of the \code{explain()} should saved in the model object.}
+
+\item{...}{
+ Arguments passed on to \code{\link[=setup_approach.empirical]{setup_approach.empirical}}, \code{\link[=setup_approach.independence]{setup_approach.independence}}, \code{\link[=setup_approach.gaussian]{setup_approach.gaussian}}, \code{\link[=setup_approach.copula]{setup_approach.copula}}, \code{\link[=setup_approach.ctree]{setup_approach.ctree}}, \code{\link[=setup_approach.categorical]{setup_approach.categorical}}, \code{\link[=setup_approach.timeseries]{setup_approach.timeseries}}
+ \describe{
+ \item{\code{empirical.type}}{Character. (default = \code{"fixed_sigma"})
+Should be equal to either \code{"independence"},\code{"fixed_sigma"}, \code{"AICc_each_k"} \code{"AICc_full"}.
+TODO: Describe better what the methods do here.}
+ \item{\code{empirical.eta}}{Numeric. (default = 0.95)
+Needs to be \verb{0 < eta <= 1}.
+Represents the minimum proportion of the total empirical weight that data samples should use.
+If e.g. \code{eta = .8} we will choose the \code{K} samples with the largest weight so that the sum of the weights
+accounts for 80\\% of the total weight.
+\code{eta} is the \eqn{\eta} parameter in equation (15) of Aas et al (2021).}
+ \item{\code{empirical.fixed_sigma}}{Positive numeric scalar. (default = 0.1)
+Represents the kernel bandwidth in the distance computation used when conditioning on all different combinations.
+Only used when \code{empirical.type = "fixed_sigma"}}
+ \item{\code{empirical.n_samples_aicc}}{Positive integer. (default = 1000)
+Number of samples to consider in AICc optimization.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+ \item{\code{empirical.eval_max_aicc}}{Positive integer. (default = 20)
+Maximum number of iterations when optimizing the AICc.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+ \item{\code{empirical.start_aicc}}{Numeric. (default = 0.1)
+Start value of the \code{sigma} parameter when optimizing the AICc.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+ \item{\code{empirical.cov_mat}}{Numeric matrix. (Optional, default = NULL)
+Containing the covariance matrix of the data generating distribution used to define the Mahalanobis distance.
+\code{NULL} means it is estimated from \code{x_train}.}
+ \item{\code{internal}}{Not used.}
+ \item{\code{gaussian.mu}}{Numeric vector. (Optional)
+Containing the mean of the data generating distribution.
+\code{NULL} means it is estimated from the \code{x_train}.}
+ \item{\code{gaussian.cov_mat}}{Numeric matrix. (Optional)
+Containing the covariance matrix of the data generating distribution.
+\code{NULL} means it is estimated from the \code{x_train}.}
+ \item{\code{ctree.mincriterion}}{Numeric scalar or vector. (default = 0.95)
+Either a scalar or vector of length equal to the number of features in the model.
+Value is equal to 1 - \eqn{\alpha} where \eqn{\alpha} is the nominal level of the conditional independence tests.
+If it is a vector, this indicates which value to use when conditioning on various numbers of features.}
+ \item{\code{ctree.minsplit}}{Numeric scalar. (default = 20)
+Determines minimum value that the sum of the left and right daughter nodes required for a split.}
+ \item{\code{ctree.minbucket}}{Numeric scalar. (default = 7)
+Determines the minimum sum of weights in a terminal node required for a split}
+ \item{\code{ctree.sample}}{Boolean. (default = TRUE)
+If TRUE, then the method always samples \code{n_samples} observations from the leaf nodes (with replacement).
+If FALSE and the number of observations in the leaf node is less than \code{n_samples},
+the method will take all observations in the leaf.
+If FALSE and the number of observations in the leaf node is more than \code{n_samples},
+the method will sample \code{n_samples} observations (with replacement).
+This means that there will always be sampling in the leaf unless
\code{sample} = FALSE AND the number of obs in the node is less than \code{n_samples}.}
+ \item{\code{categorical.joint_prob_dt}}{Data.table. (Optional)
+Containing the joint probability distribution for each combination of feature
+values.
+\code{NULL} means it is estimated from the \code{x_train} and \code{x_explain}.}
+ \item{\code{categorical.epsilon}}{Numeric value. (Optional)
+If \code{joint_probability_dt} is not supplied, probabilities/frequencies are
+estimated using \code{x_train}. If certain observations occur in \code{x_train} and NOT in \code{x_explain},
+then epsilon is used as the proportion of times that these observations occurs in the training data.
+In theory, this proportion should be zero, but this causes an error later in the Shapley computation.}
+ \item{\code{timeseries.fixed_sigma_vec}}{Numeric. (Default = 2)
+Represents the kernel bandwidth in the distance computation. TODO: What length should it have? 1?}
+ \item{\code{timeseries.bounds}}{Numeric vector of length two. (Default = c(NULL, NULL))
+If one or both of these bounds are not NULL, we restrict the sampled time series to be
+between these bounds.
+This is useful if the underlying time series are scaled between 0 and 1, for example.}
+ }}
}
\value{
Object of class \code{c("shapr", "list")}. Contains the following items:
\describe{
- \item{dt}{data.table}
- \item{model}{Model object}
- \item{p}{Numeric vector}
- \item{x_test}{data.table}
+\item{shapley_values}{data.table with the estimated Shapley values}
+\item{internal}{List with the different parameters, data and functions used internally}
+\item{pred_explain}{Numeric vector with the predictions for the explained observations.}
}
-Note that the returned items \code{model}, \code{p} and \code{x_test} are mostly added due
-to the implementation of \code{plot.shapr}. If you only want to look at the numerical results
-it is sufficient to focus on \code{dt}. \code{dt} is a data.table where the number of rows equals
+\code{shapley_values} is a data.table where the number of rows equals
the number of observations you'd like to explain, and the number of columns equals \code{m +1},
where \code{m} equals the total number of features in your model.
-If \code{dt[i, j + 1] > 0} it indicates that the j-th feature increased the prediction for
-the i-th observation. Likewise, if \code{dt[i, j + 1] < 0} it indicates that the j-th feature
-decreased the prediction for the i-th observation. The magnitude of the value is also important
-to notice. E.g. if \code{dt[i, k + 1]} and \code{dt[i, j + 1]} are greater than \code{0},
-where \code{j != k}, and \code{dt[i, k + 1]} > \code{dt[i, j + 1]} this indicates that feature
+If \code{shapley_values[i, j + 1] > 0} it indicates that the j-th feature increased the prediction for
+the i-th observation. Likewise, if \code{shapley_values[i, j + 1] < 0} it indicates that the j-th feature
+decreased the prediction for the i-th observation.
+The magnitude of the value is also important to notice. E.g. if \code{shapley_values[i, k + 1]} and
+\code{shapley_values[i, j + 1]} are greater than \code{0}, where \code{j != k}, and
+\code{shapley_values[i, k + 1]} > \code{shapley_values[i, j + 1]} this indicates that feature
\code{j} and \code{k} both increased the value of the prediction, but that the effect of the k-th
feature was larger than the j-th feature.
-The first column in \code{dt}, called `none`, is the prediction value not assigned to any of the features
+The first column in \code{dt}, called \code{none}, is the prediction value not assigned to any of the features
(\ifelse{html}{\eqn{\phi}\out{0}}{\eqn{\phi_0}}).
It's equal for all observations and set by the user through the argument \code{prediction_zero}.
+The difference between the prediction and \code{none} is distributed among the other features.
In theory this value should be the expected prediction without conditioning on any features.
Typically we set this value equal to the mean of the response variable in our training data, but other choices
-such as the mean of the predictions in the training data are also reasonable.
+such as the mean of the predictions in the training data are also reasonable. \code{\link[=explain]{explain()}} \code{\link[=explain]{explain()}}
}
\description{
-Explain the output of machine learning models with more accurately estimated Shapley values
+Computes dependence-aware Shapley values for observations in \code{x_explain} from the specified
+\code{model} by using the method specified in \code{approach} to estimate the conditional expectation.
}
\details{
-The most important thing to notice is that \code{shapr} has implemented five different
+The most important thing to notice is that \code{shapr} has implemented six different
approaches for estimating the conditional distributions of the data, namely \code{"empirical"},
-\code{"gaussian"}, \code{"copula"}, \code{"ctree"} and \code{"independence"}.
-In addition, the user also has the option of combining the four approaches.
+\code{"gaussian"}, \code{"copula"}, \code{"ctree"}, \code{"categorical"}, \code{"timeseries"}, and \code{"independence"}.
+In addition, the user also has the option of combining the different approaches.
E.g., if you're in a situation where you have trained a model that consists of 10 features,
and you'd like to use the \code{"gaussian"} approach when you condition on a single feature,
the \code{"empirical"} approach if you condition on 2-5 features, and \code{"copula"} version
@@ -241,82 +222,109 @@ For \code{approach="ctree"}, \code{n_samples} corresponds to the number of sampl
from the leaf node (see an exception related to the \code{sample} argument).
For \code{approach="empirical"}, \code{n_samples} is the \eqn{K} parameter in equations (14-15) of
Aas et al. (2021), i.e. the maximum number of observations (with largest weights) that is used, see also the
-\code{w_threshold} argument.
+\code{empirical.eta} argument.
}
\examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
- # Load example data
- data("Boston", package = "MASS")
-
- # Split data into test- and training data
- x_train <- head(Boston, -3)
- x_test <- tail(Boston, 3)
-
- # Fit a linear model
- model <- lm(medv ~ lstat + rm + dis + indus, data = x_train)
-
- # Create an explainer object
- explainer <- shapr(x_train, model)
-
- # Explain predictions
- p <- mean(x_train$medv)
-
- # Empirical approach
- explain1 <- explain(x_test, explainer,
- approach = "empirical",
- prediction_zero = p, n_samples = 1e2
- )
-
- # Gaussian approach
- explain2 <- explain(x_test, explainer,
- approach = "gaussian",
- prediction_zero = p, n_samples = 1e2
- )
-
- # Gaussian copula approach
- explain3 <- explain(x_test, explainer,
- approach = "copula",
- prediction_zero = p, n_samples = 1e2
- )
-
- # ctree approach
- explain4 <- explain(x_test, explainer,
- approach = "ctree",
- prediction_zero = p
- )
-
- # Combined approach
- approach <- c("gaussian", "gaussian", "empirical", "empirical")
- explain5 <- explain(x_test, explainer,
- approach = approach,
- prediction_zero = p, n_samples = 1e2
- )
-
- # Print the Shapley values
- print(explain1$dt)
-
- # Plot the results
- if (requireNamespace("ggplot2", quietly = TRUE)) {
- plot(explain1)
- }
-
- # Group-wise explanations
- group <- list(A = c("lstat", "rm"), B = c("dis", "indus"))
- explainer_group <- shapr(x_train, model, group = group)
- explain_groups <- explain(
- x_test,
- explainer_group,
- approach = "empirical",
- prediction_zero = p,
- n_samples = 1e2
- )
- print(explain_groups$dt)
+
+# Load example data
+data("airquality")
+airquality <- airquality[complete.cases(airquality), ]
+x_var <- c("Solar.R", "Wind", "Temp", "Month")
+y_var <- "Ozone"
+
+# Split data into test- and training data
+data_train <- head(airquality, -3)
+data_explain <- tail(airquality, 3)
+
+x_train <- data_train[, x_var]
+x_explain <- data_explain[, x_var]
+
+# Fit a linear model
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var, collapse = " + ")))
+model <- lm(lm_formula, data = data_train)
+
+# Explain predictions
+p <- mean(data_train[, y_var])
+
+# Empirical approach
+explain1 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "empirical",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Gaussian approach
+explain2 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "gaussian",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Gaussian copula approach
+explain3 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "copula",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# ctree approach
+explain4 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "ctree",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Combined approach
+approach <- c("gaussian", "gaussian", "empirical", "empirical")
+explain5 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = approach,
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Print the Shapley values
+print(explain1$shapley_values)
+
+# Plot the results
+if (requireNamespace("ggplot2", quietly = TRUE)) {
+ plot(explain1)
+ plot(explain1, plot_type = "waterfall")
}
+
+# Group-wise explanations
+group_list <- list(A = c("Temp", "Month"), B = c("Wind", "Solar.R"))
+
+explain_groups <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ group = group_list,
+ approach = "empirical",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+print(explain_groups$shapley_values)
+
}
\references{
-Aas, K., Jullum, M., & Løland, A. (2021). Explaining individual predictions when features are dependent:
- More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502.
+Aas, K., Jullum, M., & Lland, A. (2021). Explaining individual predictions when features are dependent:
+More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502.
}
\author{
-Camilla Lingjaerde, Nikolai Sellereite, Martin Jullum, Annabelle Redelmeier
+Martin Jullum
}
diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd
new file mode 100644
index 000000000..998697055
--- /dev/null
+++ b/man/explain_forecast.Rd
@@ -0,0 +1,283 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/explain_forecast.R
+\name{explain_forecast}
+\alias{explain_forecast}
+\title{Explain a forecast from a time series model using Shapley values.}
+\usage{
+explain_forecast(
+ model,
+ y,
+ xreg = NULL,
+ train_idx = NULL,
+ explain_idx,
+ explain_y_lags,
+ explain_xreg_lags = explain_y_lags,
+ horizon,
+ approach,
+ prediction_zero,
+ n_combinations = NULL,
+ group_lags = TRUE,
+ group = NULL,
+ n_samples = 1000,
+ n_batches = NULL,
+ seed = 1,
+ keep_samp_for_vS = FALSE,
+ predict_model = NULL,
+ get_model_specs = NULL,
+ timing = TRUE,
+ ...
+)
+}
+\arguments{
+\item{model}{The model whose predictions we want to explain.
+Run \code{\link[=get_supported_models]{get_supported_models()}}
+for a table of which models \code{explain} supports natively. Unsupported models
+can still be explained by passing \code{predict_model} and (optionally) \code{get_model_specs},
+see details for more information.}
+
+\item{y}{Matrix, data.frame/data.table or a numeric vector.
+Contains the endogenous variables used to estimate the (conditional) distributions
+needed to properly estimate the conditional expectations in the Shapley formula
+including the observations to be explained.}
+
+\item{xreg}{Matrix, data.frame/data.table or a numeric vector.
+Contains the exogenous variables used to estimate the (conditional) distributions
+needed to properly estimate the conditional expectations in the Shapley formula
+including the observations to be explained.
+As exogenous variables are used contemporaneusly when producing a forecast,
+this item should contain nrow(y) + horizon rows.}
+
+\item{train_idx}{Numeric vector
+The row indices in data and reg denoting points in time to use when estimating the conditional expectations in
+the Shapley value formula.
+If \code{train_idx = NULL} (default) all indices not selected to be explained will be used.}
+
+\item{explain_idx}{Numeric vector
+The row indices in data and reg denoting points in time to explain.}
+
+\item{explain_y_lags}{Numeric vector.
+Denotes the number of lags that should be used for each variable in \code{y} when making a forecast.}
+
+\item{explain_xreg_lags}{Numeric vector.
+If \code{xreg != NULL}, denotes the number of lags that should be used for each variable in \code{xreg} when making a forecast.}
+
+\item{horizon}{Numeric.
+The forecast horizon to explain. Passed to the \code{predict_model} function.}
+
+\item{approach}{Character vector of length \code{1} or \code{n_features}.
+\code{n_features} equals the total number of features in the model. All elements should,
+either be \code{"gaussian"}, \code{"copula"}, \code{"empirical"}, \code{"ctree"}, \code{"categorical"}, \code{"timeseries"}, or \code{"independence"}.
+See details for more information.}
+
+\item{prediction_zero}{Numeric.
+The prediction value for unseen data, i.e. an estimate of the expected prediction without conditioning on any
+features.
+Typically we set this value equal to the mean of the response variable in our training data, but other choices
+such as the mean of the predictions in the training data are also reasonable.}
+
+\item{n_combinations}{Integer.
+If \code{group = NULL}, \code{n_combinations} represents the number of unique feature combinations to sample.
+If \code{group != NULL}, \code{n_combinations} represents the number of unique group combinations to sample.
+If \code{n_combinations = NULL}, the exact method is used and all combinations are considered.
+The maximum number of combinations equals \code{2^m}, where \code{m} is the number of features.}
+
+\item{group_lags}{Logical.
+If \code{TRUE} all lags of each variable are grouped together and explained as a group.
+If \code{FALSE} all lags of each variable are explained individually.}
+
+\item{group}{List.
+If \code{NULL} regular feature wise Shapley values are computed.
+If provided, group wise Shapley values are computed. \code{group} then has length equal to
+the number of groups. The list element contains character vectors with the features included
+in each of the different groups.}
+
+\item{n_samples}{Positive integer.
+Indicating the maximum number of samples to use in the
+Monte Carlo integration for every conditional expectation. See also details.}
+
+\item{n_batches}{Positive integer (or NULL).
+Specifies how many batches the total number of feature combinations should be split into when calculating the
+contribution function for each test observation.
+The default value is NULL which uses a reasonable trade-off between RAM allocation and computation speed,
+which depends on \code{approach} and \code{n_combinations}.
+For models with many features, increasing the number of batches reduces the RAM allocation significantly.
+This typically comes with a small increase in computation time.}
+
+\item{seed}{Positive integer.
+Specifies the seed before any randomness based code is being run.
+If \code{NULL} the seed will be inherited from the calling environment.}
+
+\item{keep_samp_for_vS}{Logical.
+Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned
+(in \code{internal$output})}
+
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+(Run \code{\link[=get_supported_models]{get_supported_models()}} for a list of natively supported
+models.)
+The function must have two arguments, \code{model} and \code{newdata} which specify, respectively, the model
+and a data.frame/data.table to compute predictions for. The function must give the prediction as a numeric vector.
+\code{NULL} (the default) uses functions specified internally.
+Can also be used to override the default function for natively supported model classes.}
+
+\item{get_model_specs}{Function.
+An optional function for checking model/data consistency when \code{model} is not natively supported.
+(Run \code{\link[=get_supported_models]{get_supported_models()}} for a list of natively supported
+models.)
+The function takes \code{model} as argument and provides a list with 3 elements:
+\describe{
+\item{labels}{Character vector with the names of each feature.}
+\item{classes}{Character vector with the classes of each features.}
+\item{factor_levels}{Character vector with the levels for any categorical features.}
+}
+If \code{NULL} (the default) internal functions are used for natively supported model classes, and the checking is
+disabled for unsupported model classes.
+Can also be used to override the default function for natively supported model classes.}
+
+\item{timing}{Logical.
+Whether the timing of the different parts of the \code{explain()} should saved in the model object.}
+
+\item{...}{
+ Arguments passed on to \code{\link[=setup_approach.empirical]{setup_approach.empirical}}, \code{\link[=setup_approach.independence]{setup_approach.independence}}, \code{\link[=setup_approach.gaussian]{setup_approach.gaussian}}, \code{\link[=setup_approach.copula]{setup_approach.copula}}, \code{\link[=setup_approach.ctree]{setup_approach.ctree}}, \code{\link[=setup_approach.categorical]{setup_approach.categorical}}, \code{\link[=setup_approach.timeseries]{setup_approach.timeseries}}
+ \describe{
+ \item{\code{empirical.type}}{Character. (default = \code{"fixed_sigma"})
+Should be equal to either \code{"independence"},\code{"fixed_sigma"}, \code{"AICc_each_k"} \code{"AICc_full"}.
+TODO: Describe better what the methods do here.}
+ \item{\code{empirical.eta}}{Numeric. (default = 0.95)
+Needs to be \verb{0 < eta <= 1}.
+Represents the minimum proportion of the total empirical weight that data samples should use.
+If e.g. \code{eta = .8} we will choose the \code{K} samples with the largest weight so that the sum of the weights
+accounts for 80\\% of the total weight.
+\code{eta} is the \eqn{\eta} parameter in equation (15) of Aas et al (2021).}
+ \item{\code{empirical.fixed_sigma}}{Positive numeric scalar. (default = 0.1)
+Represents the kernel bandwidth in the distance computation used when conditioning on all different combinations.
+Only used when \code{empirical.type = "fixed_sigma"}}
+ \item{\code{empirical.n_samples_aicc}}{Positive integer. (default = 1000)
+Number of samples to consider in AICc optimization.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+ \item{\code{empirical.eval_max_aicc}}{Positive integer. (default = 20)
+Maximum number of iterations when optimizing the AICc.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+ \item{\code{empirical.start_aicc}}{Numeric. (default = 0.1)
+Start value of the \code{sigma} parameter when optimizing the AICc.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+ \item{\code{empirical.cov_mat}}{Numeric matrix. (Optional, default = NULL)
+Containing the covariance matrix of the data generating distribution used to define the Mahalanobis distance.
+\code{NULL} means it is estimated from \code{x_train}.}
+ \item{\code{internal}}{Not used.}
+ \item{\code{gaussian.mu}}{Numeric vector. (Optional)
+Containing the mean of the data generating distribution.
+\code{NULL} means it is estimated from the \code{x_train}.}
+ \item{\code{gaussian.cov_mat}}{Numeric matrix. (Optional)
+Containing the covariance matrix of the data generating distribution.
+\code{NULL} means it is estimated from the \code{x_train}.}
+ \item{\code{ctree.mincriterion}}{Numeric scalar or vector. (default = 0.95)
+Either a scalar or vector of length equal to the number of features in the model.
+Value is equal to 1 - \eqn{\alpha} where \eqn{\alpha} is the nominal level of the conditional independence tests.
+If it is a vector, this indicates which value to use when conditioning on various numbers of features.}
+ \item{\code{ctree.minsplit}}{Numeric scalar. (default = 20)
+Determines minimum value that the sum of the left and right daughter nodes required for a split.}
+ \item{\code{ctree.minbucket}}{Numeric scalar. (default = 7)
+Determines the minimum sum of weights in a terminal node required for a split}
+ \item{\code{ctree.sample}}{Boolean. (default = TRUE)
+If TRUE, then the method always samples \code{n_samples} observations from the leaf nodes (with replacement).
+If FALSE and the number of observations in the leaf node is less than \code{n_samples},
+the method will take all observations in the leaf.
+If FALSE and the number of observations in the leaf node is more than \code{n_samples},
+the method will sample \code{n_samples} observations (with replacement).
+This means that there will always be sampling in the leaf unless
+\code{sample} = FALSE AND the number of obs in the node is less than \code{n_samples}.}
+ \item{\code{categorical.joint_prob_dt}}{Data.table. (Optional)
+Containing the joint probability distribution for each combination of feature
+values.
+\code{NULL} means it is estimated from the \code{x_train} and \code{x_explain}.}
+ \item{\code{categorical.epsilon}}{Numeric value. (Optional)
+If \code{joint_probability_dt} is not supplied, probabilities/frequencies are
+estimated using \code{x_train}. If certain observations occur in \code{x_train} and NOT in \code{x_explain},
+then epsilon is used as the proportion of times that these observations occurs in the training data.
+In theory, this proportion should be zero, but this causes an error later in the Shapley computation.}
+ \item{\code{timeseries.fixed_sigma_vec}}{Numeric. (Default = 2)
+Represents the kernel bandwidth in the distance computation. TODO: What length should it have? 1?}
+ \item{\code{timeseries.bounds}}{Numeric vector of length two. (Default = c(NULL, NULL))
+If one or both of these bounds are not NULL, we restrict the sampled time series to be
+between these bounds.
+This is useful if the underlying time series are scaled between 0 and 1, for example.}
+ }}
+}
+\value{
+Object of class \code{c("shapr", "list")}. Contains the following items:
+\describe{
+\item{shapley_values}{data.table with the estimated Shapley values}
+\item{internal}{List with the different parameters, data and functions used internally}
+\item{pred_explain}{Numeric vector with the predictions for the explained observations.}
+}
+
+\code{shapley_values} is a data.table where the number of rows equals
+the number of observations you'd like to explain, and the number of columns equals \code{m +1},
+where \code{m} equals the total number of features in your model.
+
+If \code{shapley_values[i, j + 1] > 0} it indicates that the j-th feature increased the prediction for
+the i-th observation. Likewise, if \code{shapley_values[i, j + 1] < 0} it indicates that the j-th feature
+decreased the prediction for the i-th observation.
+The magnitude of the value is also important to notice. E.g. if \code{shapley_values[i, k + 1]} and
+\code{shapley_values[i, j + 1]} are greater than \code{0}, where \code{j != k}, and
+\code{shapley_values[i, k + 1]} > \code{shapley_values[i, j + 1]} this indicates that feature
+\code{j} and \code{k} both increased the value of the prediction, but that the effect of the k-th
+feature was larger than the j-th feature.
+
+The first column in \code{dt}, called \code{none}, is the prediction value not assigned to any of the features
+(\ifelse{html}{\eqn{\phi}\out{0}}{\eqn{\phi_0}}).
+It's equal for all observations and set by the user through the argument \code{prediction_zero}.
+The difference between the prediction and \code{none} is distributed among the other features.
+In theory this value should be the expected prediction without conditioning on any features.
+Typically we set this value equal to the mean of the response variable in our training data, but other choices
+such as the mean of the predictions in the training data are also reasonable. \code{\link[=explain]{explain()}} \code{\link[=explain]{explain()}}
+}
+\description{
+Computes dependence-aware Shapley values for observations in \code{explain_idx} from the specified
+\code{model} by using the method specified in \code{approach} to estimate the conditional expectation.
+}
+\details{
+This function explains a forecast of length \code{horizon}. The argument \code{train_idx}
+is analogous to x_train in \code{explain()}, however, it just contains the time indices of where
+in the data the forecast should start for each training sample. In the same way \code{explain_idx}
+defines the time index (indices) which will precede a forecast to be explained.
+
+As any autoregressive forecast model will require a set of lags to make a forecast at an
+arbitrary point in time, \code{explain_y_lags} and \code{explain_xreg_lags} define how many lags
+are required to "refit" the model at any given time index. This allows the different
+approaches to work in the same way they do for time-invariant models.
+}
+\examples{
+
+# Load example data
+data("airquality")
+data <- data.table::as.data.table(airquality)
+
+# Fit an AR(2) model.
+model_ar_temp <- ar(data$Temp, order = 2)
+
+# Calculate the zero prediction values for a three step forecast.
+p0_ar <- rep(mean(data$Temp), 3)
+
+# Empirical approach, explaining forecasts starting at T = 152 and T = 153.
+explain_forecast(
+ model = model_ar_temp,
+ y = data[, "Temp"],
+ train_idx = 2:151,
+ explain_idx = 152:153,
+ explain_y_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar,
+ group_lags = FALSE
+)
+
+}
+\references{
+Aas, K., Jullum, M., & Lland, A. (2021). Explaining individual predictions when features are dependent:
+More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502.
+}
+\author{
+Martin Jullum
+}
diff --git a/man/feature_combinations.Rd b/man/feature_combinations.Rd
index 810d2b865..f6b6c4220 100644
--- a/man/feature_combinations.Rd
+++ b/man/feature_combinations.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/features.R
+% Please edit documentation in R/setup_computation.R
\name{feature_combinations}
\alias{feature_combinations}
\title{Define feature combinations, and fetch additional information about each unique combination}
diff --git a/man/feature_group.Rd b/man/feature_group.Rd
index 1f4729773..ce6775245 100644
--- a/man/feature_group.Rd
+++ b/man/feature_group.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/features.R
+% Please edit documentation in R/setup_computation.R
\name{feature_group}
\alias{feature_group}
\title{Analogue to feature_exact, but for groups instead.}
diff --git a/man/feature_group_not_exact.Rd b/man/feature_group_not_exact.Rd
index 5c84614e7..da4d90d66 100644
--- a/man/feature_group_not_exact.Rd
+++ b/man/feature_group_not_exact.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/features.R
+% Please edit documentation in R/setup_computation.R
\name{feature_group_not_exact}
\alias{feature_group_not_exact}
\title{Analogue to feature_not_exact, but for groups instead.}
@@ -10,10 +10,6 @@ feature_group_not_exact(group_num, n_combinations = 200, weight_zero_m = 10^6)
\item{group_num}{List. Contains vector of integers indicating the feature numbers for the
different groups.}
-\item{n_combinations}{Integer. The number of feature combinations to sample. If \code{NULL},
-the exact method is used and all combinations are considered. The maximum number of
-combinations equals \code{2^ncol(x)}.}
-
\item{weight_zero_m}{Positive integer. Represents the Shapley weight for two special
cases, i.e. the case where you have either \code{0} or \code{m} features/feature groups.}
}
diff --git a/man/figures/README-basic_example-1.png b/man/figures/README-basic_example-1.png
index 03c2761e3..95378c7c3 100644
Binary files a/man/figures/README-basic_example-1.png and b/man/figures/README-basic_example-1.png differ
diff --git a/man/finalize_explanation.Rd b/man/finalize_explanation.Rd
new file mode 100644
index 000000000..7e419f57a
--- /dev/null
+++ b/man/finalize_explanation.Rd
@@ -0,0 +1,172 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/finalize_explanation.R
+\name{finalize_explanation}
+\alias{finalize_explanation}
+\title{Computes the Shapley values given \code{v(S)}}
+\usage{
+finalize_explanation(vS_list, internal)
+}
+\arguments{
+\item{vS_list}{List
+Output from \code{\link[=compute_vS]{compute_vS()}}}
+
+\item{internal}{List.
+Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}}
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.}
+}
+\value{
+Object of class \code{c("shapr", "list")}. Contains the following items:
+\describe{
+\item{shapley_values}{data.table with the estimated Shapley values}
+\item{internal}{List with the different parameters, data and functions used internally}
+\item{pred_explain}{Numeric vector with the predictions for the explained observations.}
+}
+
+\code{shapley_values} is a data.table where the number of rows equals
+the number of observations you'd like to explain, and the number of columns equals \code{m +1},
+where \code{m} equals the total number of features in your model.
+
+If \code{shapley_values[i, j + 1] > 0} it indicates that the j-th feature increased the prediction for
+the i-th observation. Likewise, if \code{shapley_values[i, j + 1] < 0} it indicates that the j-th feature
+decreased the prediction for the i-th observation.
+The magnitude of the value is also important to notice. E.g. if \code{shapley_values[i, k + 1]} and
+\code{shapley_values[i, j + 1]} are greater than \code{0}, where \code{j != k}, and
+\code{shapley_values[i, k + 1]} > \code{shapley_values[i, j + 1]} this indicates that feature
+\code{j} and \code{k} both increased the value of the prediction, but that the effect of the k-th
+feature was larger than the j-th feature.
+
+The first column in \code{dt}, called \code{none}, is the prediction value not assigned to any of the features
+(\ifelse{html}{\eqn{\phi}\out{0}}{\eqn{\phi_0}}).
+It's equal for all observations and set by the user through the argument \code{prediction_zero}.
+The difference between the prediction and \code{none} is distributed among the other features.
+In theory this value should be the expected prediction without conditioning on any features.
+Typically we set this value equal to the mean of the response variable in our training data, but other choices
+such as the mean of the predictions in the training data are also reasonable. \code{\link[=explain]{explain()}} \code{\link[=explain]{explain()}}
+}
+\description{
+Computes dependence-aware Shapley values for observations in \code{x_explain} from the specified
+\code{model} by using the method specified in \code{approach} to estimate the conditional expectation.
+}
+\details{
+The most important thing to notice is that \code{shapr} has implemented six different
+approaches for estimating the conditional distributions of the data, namely \code{"empirical"},
+\code{"gaussian"}, \code{"copula"}, \code{"ctree"}, \code{"categorical"}, \code{"timeseries"}, and \code{"independence"}.
+In addition, the user also has the option of combining the different approaches.
+E.g., if you're in a situation where you have trained a model that consists of 10 features,
+and you'd like to use the \code{"gaussian"} approach when you condition on a single feature,
+the \code{"empirical"} approach if you condition on 2-5 features, and \code{"copula"} version
+if you condition on more than 5 features this can be done by simply passing
+\code{approach = c("gaussian", rep("empirical", 4), rep("copula", 5))}. If
+\code{"approach[i]" = "gaussian"} means that you'd like to use the \code{"gaussian"} approach
+when conditioning on \code{i} features.
+
+For \code{approach="ctree"}, \code{n_samples} corresponds to the number of samples
+from the leaf node (see an exception related to the \code{sample} argument).
+For \code{approach="empirical"}, \code{n_samples} is the \eqn{K} parameter in equations (14-15) of
+Aas et al. (2021), i.e. the maximum number of observations (with largest weights) that is used, see also the
+\code{empirical.eta} argument.
+}
+\examples{
+
+# Load example data
+data("airquality")
+airquality <- airquality[complete.cases(airquality), ]
+x_var <- c("Solar.R", "Wind", "Temp", "Month")
+y_var <- "Ozone"
+
+# Split data into test- and training data
+data_train <- head(airquality, -3)
+data_explain <- tail(airquality, 3)
+
+x_train <- data_train[, x_var]
+x_explain <- data_explain[, x_var]
+
+# Fit a linear model
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var, collapse = " + ")))
+model <- lm(lm_formula, data = data_train)
+
+# Explain predictions
+p <- mean(data_train[, y_var])
+
+# Empirical approach
+explain1 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "empirical",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Gaussian approach
+explain2 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "gaussian",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Gaussian copula approach
+explain3 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "copula",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# ctree approach
+explain4 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "ctree",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Combined approach
+approach <- c("gaussian", "gaussian", "empirical", "empirical")
+explain5 <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = approach,
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+# Print the Shapley values
+print(explain1$shapley_values)
+
+# Plot the results
+if (requireNamespace("ggplot2", quietly = TRUE)) {
+ plot(explain1)
+ plot(explain1, plot_type = "waterfall")
+}
+
+# Group-wise explanations
+group_list <- list(A = c("Temp", "Month"), B = c("Wind", "Solar.R"))
+
+explain_groups <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ group = group_list,
+ approach = "empirical",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+print(explain_groups$shapley_values)
+
+}
+\references{
+Aas, K., Jullum, M., & Lland, A. (2021). Explaining individual predictions when features are dependent:
+More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502.
+}
+\author{
+Martin Jullum
+}
diff --git a/man/gaussian_transform.Rd b/man/gaussian_transform.Rd
index 3da08882f..c0af625c4 100644
--- a/man/gaussian_transform.Rd
+++ b/man/gaussian_transform.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/transformation.R
+% Please edit documentation in R/approach_copula.R
\name{gaussian_transform}
\alias{gaussian_transform}
\title{Transforms a sample to standardized normal distribution}
diff --git a/man/gaussian_transform_separate.Rd b/man/gaussian_transform_separate.Rd
index 420af791a..eef1e0c6a 100644
--- a/man/gaussian_transform_separate.Rd
+++ b/man/gaussian_transform_separate.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/transformation.R
+% Please edit documentation in R/approach_copula.R
\name{gaussian_transform_separate}
\alias{gaussian_transform_separate}
\title{Transforms new data to standardized normal (dimension 1) based on other data transformations}
diff --git a/man/get_cov_mat.Rd b/man/get_cov_mat.Rd
new file mode 100644
index 000000000..c3e7386d7
--- /dev/null
+++ b/man/get_cov_mat.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/approach_gaussian.R
+\name{get_cov_mat}
+\alias{get_cov_mat}
+\title{get_cov_mat}
+\usage{
+get_cov_mat(x_train, min_eigen_value = 1e-06)
+}
+\arguments{
+\item{x_train}{Matrix or data.frame/data.table.
+Contains the data used to estimate the (conditional) distributions for the features
+needed to properly estimate the conditional expectations in the Shapley formula.}
+
+\item{min_eigen_value}{Numeric
+Specifies the smallest allowed eigen value before the covariance matrix of \code{x_train} is assumed to not be
+positive definite, and \code{\link[Matrix:nearPD]{Matrix::nearPD()}} is used to find the nearest one.}
+}
+\description{
+get_cov_mat
+}
diff --git a/man/get_data_forecast.Rd b/man/get_data_forecast.Rd
new file mode 100644
index 000000000..74fc0bf78
--- /dev/null
+++ b/man/get_data_forecast.Rd
@@ -0,0 +1,46 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/explain_forecast.R
+\name{get_data_forecast}
+\alias{get_data_forecast}
+\title{Set up data for explain_forecast}
+\usage{
+get_data_forecast(
+ y,
+ xreg,
+ train_idx,
+ explain_idx,
+ explain_y_lags,
+ explain_xreg_lags,
+ horizon
+)
+}
+\arguments{
+\item{y}{A matrix or numeric vector containing the endogenous variables for the model.
+One variable per column, one observation per row.}
+
+\item{xreg}{A matrix containing exogenous regressors for the model.
+One variable per column, one observation per row. Should have nrow(data) + horizon rows.}
+
+\item{train_idx}{The observations indices in data to use as training examples.}
+
+\item{explain_idx}{The observations indices in data to explain.}
+
+\item{explain_y_lags}{Numeric vector
+Indicates the number of lags of y to include in the explanation.}
+
+\item{explain_xreg_lags}{Numeric vector
+Indicates the number of lags of xreg to include in the explanation.}
+
+\item{horizon}{The forecast horizon to explain.}
+}
+\value{
+A list containing
+\itemize{
+\item The data.frames x_train and x_explain which holds the lagged data examples.
+\item A numeric, n_endo denoting how many columns are endogenous in x_train and x_explain.
+\item A list, group with groupings of each variable to explain per variable and not per variable and lag.
+}
+}
+\description{
+Set up data for explain_forecast
+}
diff --git a/man/get_data_specs.Rd b/man/get_data_specs.Rd
index ae0bd6d8b..8c4b98b55 100644
--- a/man/get_data_specs.Rd
+++ b/man/get_data_specs.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/preprocess_data.R
+% Please edit documentation in R/setup.R
\name{get_data_specs}
\alias{get_data_specs}
\title{Fetches feature information from a given data set}
@@ -12,10 +12,10 @@ get_data_specs(x)
\value{
A list with the following elements:
\describe{
- \item{labels}{character vector with the feature names to compute Shapley values for}
- \item{classes}{a named character vector with the labels as names and the class types as elements}
- \item{factor_levels}{a named list with the labels as names and character vectors with the factor levels as elements
- (NULL if the feature is not a factor)}
+\item{labels}{character vector with the feature names to compute Shapley values for}
+\item{classes}{a named character vector with the labels as names and the class types as elements}
+\item{factor_levels}{a named list with the labels as names and character vectors with the factor levels as elements
+(NULL if the feature is not a factor)}
}
}
\description{
@@ -23,19 +23,19 @@ Fetches feature information from a given data set
}
\details{
This function is used to extract the feature information to be checked against the corresponding
-information extracted from the model and other data sets. The function is called from
-\code{\link[shapr:preprocess_data]{preprocess_data}}
-and \code{\link[shapr:make_dummies]{make_dummies}}
+information extracted from the model and other data sets. The function is called from internally
}
\examples{
# Load example data
-if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- # Split data into test- and training data
- x_train <- data.table::as.data.table(head(Boston))
- x_train[, rad := as.factor(rad)]
- get_data_specs(x_train)
-}
+data("airquality")
+airquality <- airquality[complete.cases(airquality), ]
+# Split data into test- and training data
+x_train <- head(airquality, -3)
+x_explain <- tail(airquality, 3)
+# Split data into test- and training data
+x_train <- data.table::as.data.table(head(airquality))
+x_train[, Temp := as.factor(Temp)]
+get_data_specs(x_train)
}
\author{
Martin Jullum
diff --git a/man/get_extra_parameters.Rd b/man/get_extra_parameters.Rd
new file mode 100644
index 000000000..de1acfa35
--- /dev/null
+++ b/man/get_extra_parameters.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/setup.R
+\name{get_extra_parameters}
+\alias{get_extra_parameters}
+\title{This includes both extra parameters and other objects}
+\usage{
+get_extra_parameters(internal)
+}
+\description{
+This includes both extra parameters and other objects
+}
+\keyword{internal}
diff --git a/man/get_list_approaches.Rd b/man/get_list_approaches.Rd
deleted file mode 100644
index 06218ec3f..000000000
--- a/man/get_list_approaches.Rd
+++ /dev/null
@@ -1,26 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/explanation.R
-\name{get_list_approaches}
-\alias{get_list_approaches}
-\title{Helper function used in \code{\link{explain.combined}}}
-\usage{
-get_list_approaches(n_features, approach)
-}
-\arguments{
-\item{n_features}{Integer vector. Note that
-\code{length(n_features) <= 2^m}, where \code{m} equals the number
-of features.}
-
-\item{approach}{Character vector of length \code{m}. All elements should be
-either \code{"empirical"}, \code{"gaussian"} or \code{"copula"}.}
-}
-\value{
-List
-}
-\description{
-Helper function used in \code{\link{explain.combined}}
-}
-\author{
-Nikolai Sellereite
-}
-\keyword{internal}
diff --git a/man/get_model_specs.Rd b/man/get_model_specs.Rd
index 5db8b40ab..dcad6b450 100644
--- a/man/get_model_specs.Rd
+++ b/man/get_model_specs.Rd
@@ -1,23 +1,34 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/models.R
+% Please edit documentation in R/model.R, R/model_ar.R, R/model_arima.R,
+% R/model_glm.R, R/model_lm.R, R/model_mgcv_gam.R, R/model_ranger.R,
+% R/model_xgboost.R
\name{get_model_specs}
\alias{get_model_specs}
\alias{get_model_specs.default}
-\alias{get_model_specs.lm}
+\alias{get_model_specs.ar}
+\alias{get_model_specs.Arima}
+\alias{get_model_specs.forecast_ARIMA}
\alias{get_model_specs.glm}
+\alias{get_model_specs.lm}
\alias{get_model_specs.gam}
\alias{get_model_specs.ranger}
\alias{get_model_specs.xgb.Booster}
-\title{Fetches feature information from a given model object}
+\title{Fetches feature information from natively supported models}
\usage{
get_model_specs(x)
\method{get_model_specs}{default}(x)
-\method{get_model_specs}{lm}(x)
+\method{get_model_specs}{ar}(x)
+
+\method{get_model_specs}{Arima}(x)
+
+\method{get_model_specs}{forecast_ARIMA}(x)
\method{get_model_specs}{glm}(x)
+\method{get_model_specs}{lm}(x)
+
\method{get_model_specs}{gam}(x)
\method{get_model_specs}{ranger}(x)
@@ -30,30 +41,38 @@ get_model_specs(x)
\value{
A list with the following elements:
\describe{
- \item{labels}{character vector with the feature names to compute Shapley values for}
- \item{classes}{a named character vector with the labels as names and the class type as elements}
- \item{factor_levels}{a named list with the labels as names and character vectors with the factor levels as elements
- (NULL if the feature is not a factor)}
+\item{labels}{character vector with the feature names to compute Shapley values for}
+\item{classes}{a named character vector with the labels as names and the class type as elements}
+\item{factor_levels}{a named list with the labels as names and character vectors with the factor levels as elements
+(NULL if the feature is not a factor)}
}
}
\description{
-Fetches feature information from a given model object
+This function is used to extract the feature information from the model to be checked against the
+corresponding feature information in the data passed to \verb{[explain()]}.
+
+NOTE: You should never need to call this function explicitly.
+It is exported just to be easier accessible for users, see details.
}
\details{
-This function is used to extract the feature information to be checked against data passed to \code{shapr}
-and \code{explain}. The function is called from \code{preprocess_data}.
+If you are explaining a model not supported natively, you may (optionally) enable such checking by
+creating this function yourself and passing it on to \verb{[explain()]}.
}
\examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
- # Load example data
- data("Boston", package = "MASS")
- # Split data into test- and training data
- x_train <- data.table::as.data.table(head(Boston))
- x_train[, rad := as.factor(rad)]
- model <- lm(medv ~ lstat + rm + rad + indus, data = x_train)
+# Load example data
+data("airquality")
+airquality <- airquality[complete.cases(airquality), ]
+# Split data into test- and training data
+x_train <- head(airquality, -3)
+x_explain <- tail(airquality, 3)
+# Fit a linear model
+model <- lm(Ozone ~ Solar.R + Wind + Temp + Month, data = x_train)
+get_model_specs(model)
- get_model_specs(model)
}
+\seealso{
+For model classes not supported natively, you NEED to create an analogue to \verb{[predict_model()]}. See it's
+help file for details.
}
\author{
Martin Jullum
diff --git a/man/get_mu_vec.Rd b/man/get_mu_vec.Rd
new file mode 100644
index 000000000..5c25d7dbd
--- /dev/null
+++ b/man/get_mu_vec.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/approach_gaussian.R
+\name{get_mu_vec}
+\alias{get_mu_vec}
+\title{get_mu_vec}
+\usage{
+get_mu_vec(x_train)
+}
+\arguments{
+\item{x_train}{Matrix or data.frame/data.table.
+Contains the data used to estimate the (conditional) distributions for the features
+needed to properly estimate the conditional expectations in the Shapley formula.}
+}
+\description{
+get_mu_vec
+}
diff --git a/man/get_predict_model.Rd b/man/get_predict_model.Rd
new file mode 100644
index 000000000..2fda6c8aa
--- /dev/null
+++ b/man/get_predict_model.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/get_predict_model.R
+\name{get_predict_model}
+\alias{get_predict_model}
+\title{Get predict_model function}
+\usage{
+get_predict_model(predict_model, model)
+}
+\arguments{
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{model}{Objects.
+The model object that ought to be explained.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+}
+\description{
+Get predict_model function
+}
+\keyword{internal}
diff --git a/man/get_supported_approaches.Rd b/man/get_supported_approaches.Rd
new file mode 100644
index 000000000..8a7de6c5e
--- /dev/null
+++ b/man/get_supported_approaches.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/setup.R
+\name{get_supported_approaches}
+\alias{get_supported_approaches}
+\title{Gets the implemented approaches}
+\usage{
+get_supported_approaches()
+}
+\value{
+Character vector.
+The names of the implemented approaches that can be passed to argument \code{approach} in \code{\link[=explain]{explain()}}.
+}
+\description{
+Gets the implemented approaches
+}
diff --git a/man/get_supported_models.Rd b/man/get_supported_models.Rd
index 7bcd24ab9..985ae7966 100644
--- a/man/get_supported_models.Rd
+++ b/man/get_supported_models.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/models.R
+% Please edit documentation in R/model.R
\name{get_supported_models}
\alias{get_supported_models}
\title{Provides a data.table with the supported models}
diff --git a/man/inv_gaussian_transform.Rd b/man/inv_gaussian_transform.Rd
index 540fb79b2..76f058772 100644
--- a/man/inv_gaussian_transform.Rd
+++ b/man/inv_gaussian_transform.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/transformation.R
+% Please edit documentation in R/approach_copula.R
\name{inv_gaussian_transform}
\alias{inv_gaussian_transform}
\title{Transforms new data to a standardized normal distribution}
diff --git a/man/lag_data.Rd b/man/lag_data.Rd
new file mode 100644
index 000000000..874af4eab
--- /dev/null
+++ b/man/lag_data.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/explain_forecast.R
+\name{lag_data}
+\alias{lag_data}
+\title{Lag a matrix of variables a specific number of lags for each variables.}
+\usage{
+lag_data(x, lags)
+}
+\arguments{
+\item{x}{The matrix of variables (one variable per column).}
+
+\item{lags}{A numeric vector denoting how many lags each variable should have.}
+}
+\value{
+A list with two items
+\itemize{
+\item A matrix, lagged with the lagged data.
+\item A list, group, with groupings of the lagged data per variable.
+}
+}
+\description{
+Lag a matrix of variables a specific number of lags for each variables.
+}
diff --git a/man/make_dummies.Rd b/man/make_dummies.Rd
deleted file mode 100644
index e68c0e037..000000000
--- a/man/make_dummies.Rd
+++ /dev/null
@@ -1,50 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/features.R
-\name{make_dummies}
-\alias{make_dummies}
-\title{Initiate the making of dummy variables}
-\usage{
-make_dummies(traindata, testdata)
-}
-\arguments{
-\item{traindata}{data.table or data.frame.}
-
-\item{testdata}{data.table or data.frame. New data that has the same
-feature names, types, and levels as \code{traindata}.}
-}
-\value{
-A list that contains the following entries:
-\describe{
-\item{feature_list}{List. Output from \code{check_features}}
-\item{train_dummies}{A data.frame containing all of the factors in \code{traindata} as
-one-hot encoded variables.}
-\item{test_dummies}{A data.frame containing all of the factors in \code{testdata} as
-one-hot encoded variables.}
-\item{traindata_new}{Original traindata with correct column ordering and factor levels. To be passed to
-\code{\link[shapr:shapr]{shapr}.}}
-\item{testdata_new}{Original testdata with correct column ordering and factor levels. To be passed to
-\code{\link[shapr:explain]{explain}.}}
-}
-}
-\description{
-Initiate the making of dummy variables
-}
-\examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
- x_train <- as.data.frame(Boston[401:411, x_var])
- y_train <- Boston[401:408, y_var]
- x_test <- as.data.frame(Boston[1:4, x_var])
-
- # convert to factors for illustational purpose
- x_train$rm <- factor(round(x_train$rm))
- x_test$rm <- factor(round(x_test$rm), levels = levels(x_train$rm))
-
- dummylist <- make_dummies(traindata = x_train, testdata = x_test)
-}
-}
-\author{
-Annabelle Redelmeier, Martin Jullum
-}
diff --git a/man/model_checker.Rd b/man/model_checker.Rd
index 63e4ea90b..313902a13 100644
--- a/man/model_checker.Rd
+++ b/man/model_checker.Rd
@@ -1,27 +1,38 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/models.R
+% Please edit documentation in R/model.R, R/model_ar.R, R/model_arima.R,
+% R/model_glm.R, R/model_lm.R, R/model_mgcv_gam.R, R/model_ranger.R,
+% R/model_xgboost.R
\name{model_checker}
\alias{model_checker}
\alias{model_checker.default}
-\alias{model_checker.lm}
+\alias{model_checker.ar}
+\alias{model_checker.Arima}
+\alias{model_checker.forecast_ARIMA}
\alias{model_checker.glm}
-\alias{model_checker.ranger}
+\alias{model_checker.lm}
\alias{model_checker.gam}
+\alias{model_checker.ranger}
\alias{model_checker.xgb.Booster}
-\title{Check that the type of model is supported by the explanation method}
+\title{Check that the type of model is supported by the native implementation of the model class}
\usage{
model_checker(x)
\method{model_checker}{default}(x)
-\method{model_checker}{lm}(x)
+\method{model_checker}{ar}(x)
+
+\method{model_checker}{Arima}(x)
+
+\method{model_checker}{forecast_ARIMA}(x)
\method{model_checker}{glm}(x)
-\method{model_checker}{ranger}(x)
+\method{model_checker}{lm}(x)
\method{model_checker}{gam}(x)
+\method{model_checker}{ranger}(x)
+
\method{model_checker}{xgb.Booster}(x)
}
\arguments{
@@ -35,21 +46,7 @@ The function checks whether the model given by \code{x} is supported.
If \code{x} is not a supported model the function will return an error message, otherwise it return NULL
(meaning all types of models with this class is supported)
}
-\details{
-See \code{\link{predict_model}} for more information about
-what type of models \code{shapr} currently support.
-}
-\examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
- # Load example data
- data("Boston", package = "MASS")
- # Split data into test- and training data
- x_train <- head(Boston, -3)
- # Fit a linear model
- model <- lm(medv ~ lstat + rm + dis + indus, data = x_train)
-
- # Checking the model object
- model_checker(x = model)
-}
+\seealso{
+See \code{\link[=predict_model]{predict_model()}} for more information about what type of models \code{shapr} currently support.
}
\keyword{internal}
diff --git a/man/observation_impute.Rd b/man/observation_impute.Rd
index dd48d6c1f..813869b28 100644
--- a/man/observation_impute.Rd
+++ b/man/observation_impute.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/observations.R
+% Please edit documentation in R/approach_empirical.R
\name{observation_impute}
\alias{observation_impute}
\title{Generate permutations of training data using test observations}
@@ -8,29 +8,25 @@ observation_impute(
W_kernel,
S,
x_train,
- x_test,
- w_threshold = 0.7,
+ x_explain,
+ empirical.eta = 0.7,
n_samples = 1000
)
}
\arguments{
\item{W_kernel}{Numeric matrix. Contains all nonscaled weights between training and test
-observations for all feature combinations. The dimension equals \code{n_train x m}.}
+observations for all feature combinations. The dimension equals \verb{n_train x m}.}
-\item{S}{Integer matrix of dimension \code{n_combinations x m}, where \code{n_combinations}
+\item{S}{Integer matrix of dimension \verb{n_combinations x m}, where \code{n_combinations}
and \code{m} equals the total number of sampled/non-sampled feature combinations and
the total number of unique features, respectively. Note that \code{m = ncol(x_train)}.}
\item{x_train}{Numeric matrix}
-\item{x_test}{Numeric matrix}
+\item{x_explain}{Numeric matrix}
-\item{w_threshold}{Numeric vector of length 1, with \code{0 < w_threshold <= 1} representing the minimum proportion
-of the total empirical weight that data samples should use. If e.g. \code{w_threshold = .8} we will choose the
-\code{K} samples with the largest weight so that the sum of the weights accounts for 80\% of the total weight.
-\code{w_threshold} is the \eqn{\eta} parameter in equation (15) of Aas et al (2021).}
-
-\item{n_samples}{Positive integer. Indicating the maximum number of samples to use in the
+\item{n_samples}{Positive integer.
+Indicating the maximum number of samples to use in the
Monte Carlo integration for every conditional expectation. See also details.}
}
\value{
@@ -39,10 +35,6 @@ data.table
\description{
Generate permutations of training data using test observations
}
-\references{
-Aas, K., Jullum, M., & Løland, A. (2021). Explaining individual predictions when features are dependent:
- More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502.
-}
\author{
Nikolai Sellereite
}
diff --git a/man/plot.shapr.Rd b/man/plot.shapr.Rd
index 1eccfd78b..f45485d4e 100644
--- a/man/plot.shapr.Rd
+++ b/man/plot.shapr.Rd
@@ -6,27 +6,88 @@
\usage{
\method{plot}{shapr}(
x,
+ plot_type = "bar",
digits = 3,
- plot_phi0 = TRUE,
- index_x_test = NULL,
+ index_x_explain = NULL,
top_k_features = NULL,
+ col = NULL,
+ bar_plot_phi0 = TRUE,
+ bar_plot_order = "largest_first",
+ scatter_features = NULL,
+ scatter_hist = TRUE,
...
)
}
\arguments{
-\item{x}{An \code{shapr} object. See \code{\link{explain}}.}
+\item{x}{An \code{shapr} object.
+The output from \code{\link[=explain]{explain()}}.}
-\item{digits}{Integer. Number of significant digits to use in the feature description}
+\item{plot_type}{Character.
+Specifies the type of plot to produce.
+\code{"bar"} (the default) gives a regular horizontal bar plot of the Shapley value magnitudes.
+\code{"waterfall"} gives a waterfall plot indicating the changes in the prediction score due to each features
+contribution (their Shapley values).
+\code{"scatter"} plots the feature values on the x-axis and Shapley values on the y-axis, as well as
+(optionally) a background scatter_hist showing the distribution of the feature data.
+\code{"beeswarm"} summarises the distribution of the Shapley values along the x-axis for all the features.
+Each point gives the shapley value of a given instance, where the points are colored by the feature value
+of that instance.}
-\item{plot_phi0}{Logical. Whether to include \code{phi0} in the plot}
+\item{digits}{Integer.
+Number of significant digits to use in the feature description.
+Applicable for \code{plot_type} \code{"bar"} and \code{"waterfall"}}
-\item{index_x_test}{Integer vector. Which of the test observations to plot. E.g. if you have
-explained 10 observations using \code{\link{explain}}, you can generate a plot for the first 5
-observations by setting \code{index_x_test = 1:5}.}
+\item{index_x_explain}{Integer vector.
+Which of the test observations to plot. E.g. if you have
+explained 10 observations using \code{\link[=explain]{explain()}}, you can generate a plot for the first 5
+observations by setting \code{index_x_explain = 1:5}.}
-\item{top_k_features}{Integer. How many features to include in the plot. E.g. if you have 15
-features in your model you can plot the 5 most important features, for each explanation, by setting
-\code{top_k_features = 1:5}.}
+\item{top_k_features}{Integer.
+How many features to include in the plot.
+E.g. if you have 15 features in your model you can plot the 5 most important features,
+for each explanation, by setting \code{top_k_features = 1:5}.
+Applicable for \code{plot_type} \code{"bar"} and \code{"waterfall"}}
+
+\item{col}{Character vector (length depends on plot type).
+The color codes (hex codes or other names understood by \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}}) for positive and negative
+Shapley values, respectively.
+The default is \code{col=NULL}, plotting with the default colors respective to the plot type.
+For \code{plot_type = "bar"} and \code{plot_type = "waterfall"}, the default is \code{c("#00BA38","#F8766D")}.
+For \code{plot_type = "beeswarm"}, the default is \code{c("#F8766D","yellow","#00BA38")}.
+For \code{plot_type = "scatter"}, the default is \code{"#619CFF"}.
+
+If you want to alter the colors i the plot, the length of the \code{col} vector depends on plot type.
+For \code{plot_type = "bar"} or \code{plot_type = "waterfall"}, two colors should be provided, first for positive and
+then for negative Shapley values.
+For \code{plot_type = "beeswarm"}, either two or three colors can be given.
+If two colors are given, then the first color determines the color that points with high feature values will have,
+and the second determines the color of points with low feature values.
+If three colors are given, then the first colors high feature values, the second colors mid-range feature values,
+and the third colors low feature values.
+For instance, \code{col = c("red", "yellow", "blue")} will make high values red, mid-range values yellow,
+and low values blue.
+For \code{plot_type = "scatter"}, a single color is to be given, which determines the color of the points on the
+scatter plot.}
+
+\item{bar_plot_phi0}{Logical.
+Whether to include \code{phi0} in the plot for \code{plot_type = "bar"}.}
+
+\item{bar_plot_order}{Character.
+Specifies what order to plot the features with respect to the magnitude of the shapley values with
+\code{plot_type = "bar"}:
+\code{"largest_first"} (the default) plots the features ordered from largest to smallest absolute Shapley value.
+\code{"smallest_first"} plots the features ordered from smallest to largest absolute Shapley value.
+\code{"original"} plots the features in the original order of the data table.}
+
+\item{scatter_features}{Integer or character vector.
+Only used for \code{plot_type = "scatter"}.
+Specifies what features to include in (scatter) plot. Can be a numerical vector indicating feature index, or a
+character vector, indicating the name(s) of the feature(s) to plot.}
+
+\item{scatter_hist}{Logical.
+Only used for \code{plot_type = "scatter"}.
+Whether to include a scatter_hist indicating the distribution of the data when making the scatter plot. Note that the
+bins are scaled so that when all the bins are stacked they fit the span of the y-axis of the plot.}
\item{...}{Currently not used.}
}
@@ -37,41 +98,96 @@ ggplot object with plots of the Shapley value explanations
Plots the individual prediction explanations.
}
\details{
-See \code{vignette("understanding_shapr", package = "shapr")} for an example of
+See the examples below, or \code{vignette("understanding_shapr", package = "shapr")} for an examples of
how you should use the function.
}
\examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
- #' # Load example data
- data("Boston", package = "MASS")
-
- # Split data into test- and training data
- x_train <- head(Boston, -3)
- x_test <- tail(Boston, 3)
-
- # Fit a linear model
- model <- lm(medv ~ lstat + rm + dis + indus, data = x_train)
-
- # Create an explainer object
- explainer <- shapr(x_train, model)
-
- # Explain predictions
- p <- mean(x_train$medv)
-
- # Empirical approach
- explanation <- explain(x_test,
- explainer,
- approach = "empirical",
- prediction_zero = p,
- n_samples = 1e2
- )
-
- if (requireNamespace("ggplot2", quietly = TRUE)) {
- # Plot the explantion (this function)
- plot(explanation)
- }
+
+data("airquality")
+airquality <- airquality[complete.cases(airquality), ]
+x_var <- c("Solar.R", "Wind", "Temp", "Month")
+y_var <- "Ozone"
+
+# Split data into test- and training data
+data_train <- head(airquality, -50)
+data_explain <- tail(airquality, 50)
+
+x_train <- data_train[, x_var]
+x_explain <- data_explain[, x_var]
+
+# Fit a linear model
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var, collapse = " + ")))
+model <- lm(lm_formula, data = data_train)
+
+# Explain predictions
+p <- mean(data_train[, y_var])
+
+# Empirical approach
+x <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "empirical",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+if (requireNamespace("ggplot2", quietly = TRUE)) {
+ # The default plotting option is a bar plot of the Shapley values
+ # We draw bar plots for the first 4 observations
+ plot(x, index_x_explain = 1:4)
+
+ # We can also make waterfall plots
+ plot(x, plot_type = "waterfall", index_x_explain = 1:4)
+ # And only showing the 2 features with largest contribution
+ plot(x, plot_type = "waterfall", index_x_explain = 1:4, top_k_features = 2)
+
+ # Or scatter plots showing the distribution of the shapley values and feature values
+ plot(x, plot_type = "scatter")
+ # And only for a specific feature
+ plot(x, plot_type = "scatter", scatter_features = "Temp")
+
+ # Or a beeswarm plot summarising the Shapley values and feature values for all features
+ plot(x, plot_type = "beeswarm")
+ plot(x, plot_type = "beeswarm", col = c("red", "black")) # we can change colors
}
+
+# Example of scatter and beeswarm plot with factor variables
+airquality$Month_factor <- as.factor(month.abb[airquality$Month])
+airquality <- airquality[complete.cases(airquality), ]
+x_var <- c("Solar.R", "Wind", "Temp", "Month_factor")
+y_var <- "Ozone"
+
+# Split data into test- and training data
+data_train <- airquality
+data_explain <- tail(airquality, 50)
+
+x_train <- data_train[, x_var]
+x_explain <- data_explain[, x_var]
+
+# Fit a linear model
+lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var, collapse = " + ")))
+model <- lm(lm_formula, data = data_train)
+
+# Explain predictions
+p <- mean(data_train[, y_var])
+
+# Empirical approach
+x <- explain(
+ model = model,
+ x_explain = x_explain,
+ x_train = x_train,
+ approach = "ctree",
+ prediction_zero = p,
+ n_samples = 1e2
+)
+
+if (requireNamespace("ggplot2", quietly = TRUE)) {
+ plot(x, plot_type = "scatter")
+ plot(x, plot_type = "beeswarm")
+}
+
}
\author{
-Martin Jullum
+Martin Jullum, Vilde Ung
}
diff --git a/man/predict_model.Rd b/man/predict_model.Rd
index 94fd2d43e..9e2f06996 100644
--- a/man/predict_model.Rd
+++ b/man/predict_model.Rd
@@ -1,80 +1,105 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/models.R
+% Please edit documentation in R/model.R, R/model_ar.R, R/model_arima.R,
+% R/model_glm.R, R/model_lm.R, R/model_mgcv_gam.R, R/model_ranger.R,
+% R/model_xgboost.R
\name{predict_model}
\alias{predict_model}
\alias{predict_model.default}
-\alias{predict_model.lm}
+\alias{predict_model.ar}
+\alias{predict_model.Arima}
+\alias{predict_model.forecast_ARIMA}
\alias{predict_model.glm}
+\alias{predict_model.lm}
+\alias{predict_model.gam}
\alias{predict_model.ranger}
\alias{predict_model.xgb.Booster}
-\alias{predict_model.gam}
-\title{Generate predictions for different model classes}
+\title{Generate predictions for input data with specified model}
\usage{
-predict_model(x, newdata)
+predict_model(x, newdata, ...)
-\method{predict_model}{default}(x, newdata)
+\method{predict_model}{default}(x, newdata, ...)
-\method{predict_model}{lm}(x, newdata)
+\method{predict_model}{ar}(x, newdata, newreg, horizon, ...)
-\method{predict_model}{glm}(x, newdata)
+\method{predict_model}{Arima}(
+ x,
+ newdata,
+ newreg,
+ horizon,
+ explain_idx,
+ explain_lags,
+ y,
+ xreg,
+ ...
+)
-\method{predict_model}{ranger}(x, newdata)
+\method{predict_model}{forecast_ARIMA}(x, newdata, newreg, horizon, ...)
-\method{predict_model}{xgb.Booster}(x, newdata)
+\method{predict_model}{glm}(x, newdata, ...)
-\method{predict_model}{gam}(x, newdata)
+\method{predict_model}{lm}(x, newdata, ...)
+
+\method{predict_model}{gam}(x, newdata, ...)
+
+\method{predict_model}{ranger}(x, newdata, ...)
+
+\method{predict_model}{xgb.Booster}(x, newdata, ...)
}
\arguments{
\item{x}{Model object for the model to be explained.}
-\item{newdata}{A data frame (or matrix) in which to look for variables with which to predict.}
+\item{newdata}{A data.frame/data.table with the features to predict from.}
+
+\item{...}{\code{newreg} and \code{horizon} parameters used in models passed to \verb{[explain_forecast()]}}
}
\value{
-Numeric
+Numeric. Vector of size equal to the number of rows in \code{newdata}.
}
\description{
-Performs prediction of response \code{\link[stats]{lm}}, \code{\link[stats]{glm}},
-\code{\link[ranger]{ranger}}, \code{\link[mgcv:gam]{mgcv::gam}} and
-\code{\link[xgboost:xgb.train]{xgboost::xgb.train}} with binary or continuous
+Performs prediction of response
+\code{\link[stats:lm]{stats::lm()}},
+\code{\link[stats:glm]{stats::glm()}},
+\code{\link[ranger:ranger]{ranger::ranger()}},
+\code{\link[mgcv:gam]{mgcv::gam()}} and
+\code{\link[xgboost:xgb.train]{xgboost::xgb.train()}} with binary or continuous
response. See details for more information.
+
+NOTE: You should never need to call this function explicitly.
+It is exported just to be easier accessible for users, see details.
}
\details{
The following models are currently supported:
\itemize{
-\item \code{\link[stats:lm]{stats::lm}}
-\item \code{\link[stats:glm]{stats::glm}}
-\item \code{\link[ranger:ranger]{ranger::ranger}}
-\item \code{\link[mgcv:gam]{mgcv::gam}}
-\item \code{\link[xgboost:xgb.train]{xgboost::xgb.train}}
-}
-
-The returned object \code{p} always satisfies the following properties:
-\itemize{
-\item \code{is.atomic(p)} equals \code{TRUE}
-\item \code{is.double(p)} equals \code{TRUE}
+\item \code{\link[stats:lm]{stats::lm()}}
+\item \code{\link[stats:glm]{stats::glm()}}
+\item \code{\link[ranger:ranger]{ranger::ranger()}}
+\item \code{\link[mgcv:gam]{mgcv::gam()}}
+\item \code{\link[xgboost:xgb.train]{xgboost::xgb.train()}}
}
If you have a binary classification model we'll always return the probability prediction
for a single class.
-For more details on how to explain other types of models (i.e. custom models), see the Advanced usage section
+If you are explaining a model not supported natively, you need to create the \verb{[predict_model()]} function yourself,
+and pass it on to as an argument to \verb{[explain()]}.
+
+For more details on how to explain such non-supported models (i.e. custom models), see the Advanced usage section
of the vignette: \cr
From R: \code{vignette("understanding_shapr", package = "shapr")} \cr
Web: \url{https://norskregnesentral.github.io/shapr/articles/understanding_shapr.html#explain-custom-models}
}
\examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
- # Load example data
- data("Boston", package = "MASS")
- # Split data into test- and training data
- x_train <- head(Boston, -3)
- x_test <- tail(Boston, 3)
- # Fit a linear model
- model <- lm(medv ~ lstat + rm + dis + indus, data = x_train)
-
- # Predicting for a model with a standardized format
- predict_model(x = model, newdata = x_test)
-}
+# Load example data
+data("airquality")
+airquality <- airquality[complete.cases(airquality), ]
+# Split data into test- and training data
+x_train <- head(airquality, -3)
+x_explain <- tail(airquality, 3)
+# Fit a linear model
+model <- lm(Ozone ~ Solar.R + Wind + Temp + Month, data = x_train)
+
+# Predicting for a model with a standardized format
+predict_model(x = model, newdata = x_explain)
}
\author{
Martin Jullum
diff --git a/man/prediction.Rd b/man/prediction.Rd
deleted file mode 100644
index cfc9c9232..000000000
--- a/man/prediction.Rd
+++ /dev/null
@@ -1,46 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/predictions.R
-\name{prediction}
-\alias{prediction}
-\title{Calculate Shapley weights for test data}
-\usage{
-prediction(dt, prediction_zero, explainer)
-}
-\arguments{
-\item{dt}{data.table}
-
-\item{prediction_zero}{Numeric. The value to use for \code{phi_0}.}
-
-\item{explainer}{An object of class \code{explainer}. See \code{\link{shapr}}.}
-}
-\value{
-An object of class \code{c("shapr", "list")}. For more details see \code{\link{explain}}.
-}
-\description{
-This function should only be called internally, and not be used as
-a stand-alone function.
-}
-\details{
-If \code{dt} does not contain three columns called \code{id}, \code{id_combination} and \code{w}
-the function will fail. \code{id} represents a unique key for a given test observation,
-and \code{id_combination} is a unique key for which feature combination the row represents. \code{w}
-represents the Shapley value of feature combination given by \code{id_combination}. In addition
-to these three columns, \code{dt} should also have columns which matches the variables used
-when training the model.
-
-I.e. you have fitted a linear model using the features \code{x1},
-\code{x2} and \code{x3}, and you want to explain 5 test observations using the exact method, i.e.
-setting \code{exact = TRUE} in \code{\link{shapr}}, the following properties should be satisfied
-\enumerate{
-\item \code{colnames(dt)} equals \code{c("x1", "x2", "x3", "id", "id_combination", ""w)}
-\item \code{dt[, max(id)]} equals the number of test observations
-\item \code{dt[, min(id)]} equals 1L.
-\item \code{dt[, max(id_combination)]} equals \code{2^m} where m equals the number of features.
-\item \code{dt[, min(id_combination)]} equals 1L.
-\item \code{dt[, type(w)]} equals \code{double}.
-}
-}
-\author{
-Nikolai Sellereite
-}
-\keyword{internal}
diff --git a/man/prepare_and_predict.Rd b/man/prepare_and_predict.Rd
deleted file mode 100644
index 5c261768c..000000000
--- a/man/prepare_and_predict.Rd
+++ /dev/null
@@ -1,36 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/explanation.R
-\name{prepare_and_predict}
-\alias{prepare_and_predict}
-\title{Calculate Shapley values}
-\usage{
-prepare_and_predict(explainer, n_batches, prediction_zero, ...)
-}
-\arguments{
-\item{explainer}{An \code{explainer} object to use for explaining the observations.
-See \code{\link{shapr}}.}
-
-\item{n_batches}{Positive integer.
-Specifies how many batches the total number of feature combinations should be split into when calculating the
-contribution function for each test observation.
-The default value is 1.
-Increasing the number of batches may significantly reduce the RAM allocation for models with many features.
-This typically comes with a small increase in computation time.}
-
-\item{prediction_zero}{Numeric. The prediction value for unseen data, typically equal to the mean of
-the response.}
-
-\item{...}{Arguments passed to \code{\link{prepare_data}} with exception of \code{only_return_contrib_dt},
-which is only passed to explain. If \code{TRUE} the
-\code{data.table} from \code{\link{prediction}} is returned, else an object of class \code{shapr}.
-Each column (except for \code{row_id}) correspond to the vector \code{v_D} in Equation 7 in the reference.
-The Shapley values can be calculated by \code{t(explainer$W \%*\% dt_contrib[, -"row_id"]))}}
-}
-\value{
-A list. See \code{\link{explain}} for more information.
-}
-\description{
-Sample covariate values, predict and calculate Shapley values. The sampling and prediction can be done in batches
-if \code{n_batches} is greater than 1.
-}
-\keyword{internal}
diff --git a/man/prepare_data.Rd b/man/prepare_data.Rd
index a4857a3e7..ac2fe8e00 100644
--- a/man/prepare_data.Rd
+++ b/man/prepare_data.Rd
@@ -1,56 +1,50 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/observations.R
+% Please edit documentation in R/approach.R, R/approach_categorical.R,
+% R/approach_copula.R, R/approach_ctree.R, R/approach_empirical.R,
+% R/approach_gaussian.R, R/approach_independence.R, R/approach_timeseries.R
\name{prepare_data}
\alias{prepare_data}
-\alias{prepare_data.independence}
-\alias{prepare_data.empirical}
-\alias{prepare_data.gaussian}
+\alias{prepare_data.categorical}
\alias{prepare_data.copula}
\alias{prepare_data.ctree}
+\alias{prepare_data.empirical}
+\alias{prepare_data.gaussian}
+\alias{prepare_data.independence}
+\alias{prepare_data.timeseries}
\title{Generate data used for predictions}
\usage{
-prepare_data(x, ...)
+prepare_data(internal, ...)
+
+\method{prepare_data}{categorical}(internal, index_features = NULL, ...)
+
+\method{prepare_data}{copula}(internal, index_features = NULL, ...)
-\method{prepare_data}{independence}(x, index_features = NULL, ...)
+\method{prepare_data}{ctree}(internal, index_features = NULL, ...)
-\method{prepare_data}{empirical}(x, index_features = NULL, ...)
+\method{prepare_data}{empirical}(internal, index_features = NULL, ...)
-\method{prepare_data}{gaussian}(x, index_features = NULL, ...)
+\method{prepare_data}{gaussian}(internal, index_features = NULL, ...)
-\method{prepare_data}{copula}(x, index_features = NULL, ...)
+\method{prepare_data}{independence}(internal, index_features = NULL, ...)
-\method{prepare_data}{ctree}(
- x,
- index_features = NULL,
- mc_cores = 1,
- mc_cores_create_ctree = mc_cores,
- mc_cores_sample_ctree = mc_cores,
- ...
-)
+\method{prepare_data}{timeseries}(internal, index_features = NULL, ...)
}
\arguments{
-\item{x}{Explainer object. See \code{\link{explain}} for more information.}
+\item{internal}{List.
+Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}}
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.}
\item{...}{Currently not used.}
-\item{index_features}{List. Default is NULL but if either various methods are being used or various mincriterion are
-used for different numbers of conditioned features, this will be a list with the features to pass.}
-
-\item{mc_cores}{Integer. Only for class \code{ctree} currently. The number of cores to use in paralellization of the
-tree building (\code{create_ctree}) and tree sampling (\code{sample_ctree}). Defaults to 1. Note: Uses
-parallel::mclapply which relies on forking, i.e. uses only 1 core on Windows systems.}
-
-\item{mc_cores_create_ctree}{Integer. Same as \code{mc_cores}, but specific for the tree building function
-#' Defaults to \code{mc_cores}.}
+\item{index_features}{Positive integer vector. Specifies the indices of combinations to apply to the present method.
+\code{NULL} means all combinations. Only used internally.}
-\item{mc_cores_sample_ctree}{Integer. Same as \code{mc_cores}, but specific for the tree building prediction
-function.
-Defaults to \code{mc_cores}.}
+\item{x}{Explainer object. See \code{\link[=explain]{explain()}} for more information.}
\item{seed}{Positive integer. If \code{NULL} the seed will be inherited from the calling environment.}
}
\value{
-A `data.table` containing simulated data passed to \code{\link{prediction}}.
+A data.table containing simulated data passed to prediction().
}
\description{
Generate data used for predictions
diff --git a/man/preprocess_data.Rd b/man/preprocess_data.Rd
deleted file mode 100644
index 5576add6f..000000000
--- a/man/preprocess_data.Rd
+++ /dev/null
@@ -1,44 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/preprocess_data.R
-\name{preprocess_data}
-\alias{preprocess_data}
-\title{Process (check and update) data according to specified feature list}
-\usage{
-preprocess_data(x, feature_list)
-}
-\arguments{
-\item{x}{matrix, data.frame or data.table. The data to check input for and update
-according to the specification in \code{feature_list}.}
-
-\item{feature_list}{List. Output from running \code{\link[shapr:get_data_specs]{get_data_specs}} or
-\code{\link[shapr:get_model_specs]{get_model_specs}}}
-}
-\value{
-List with two named elements: \code{x_dt}: Checked and updated data \code{x} in data.table format, and
-\code{update_feature_list} the output from \code{\link[shapr:check_features]{check_features}}
-}
-\description{
-Process (check and update) data according to specified feature list
-}
-\details{
-This function takes care of all preprocessing and checking of the provided data in \code{x} against
-the feature_list which is typically the output from \code{\link[shapr:get_model_specs]{get_model_specs}}
-}
-\examples{
-# Load example data
-if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- # Split data into test- and training data
- x_train <- data.table::as.data.table(head(Boston))
- x_train[, rad := as.factor(rad)]
- data_features <- get_data_specs(x_train)
- model <- lm(medv ~ lstat + rm + rad + indus, data = x_train)
-
- model_features <- get_model_specs(model)
- preprocess_data(x_train, model_features)
-}
-}
-\author{
-Martin Jullum
-}
-\keyword{internal}
diff --git a/man/process_factor_data.Rd b/man/process_factor_data.Rd
new file mode 100644
index 000000000..50248a79b
--- /dev/null
+++ b/man/process_factor_data.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{process_factor_data}
+\alias{process_factor_data}
+\title{Treat factors as numeric values}
+\usage{
+process_factor_data(dt, factor_cols)
+}
+\arguments{
+\item{dt}{data.table to plot}
+
+\item{factor_cols}{Columns that are factors or character}
+}
+\value{
+A list of a lookup table with each factor and level and its numeric value, a data.table
+very similar to the input data, but now with numeric values for factors, and the maximum feature value.
+}
+\description{
+Factors are given a numeric value above the highest numeric value in the data. The value of the different levels
+are sorted by factor and then level.
+}
diff --git a/man/process_groups.Rd b/man/process_groups.Rd
deleted file mode 100644
index d52862aba..000000000
--- a/man/process_groups.Rd
+++ /dev/null
@@ -1,31 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/preprocess_data.R
-\name{process_groups}
-\alias{process_groups}
-\title{Process (check and update names) the group list}
-\usage{
-process_groups(group, feature_labels)
-}
-\arguments{
-\item{group}{List. If \code{NULL} regular feature wise Shapley values are computed.
-If provided, group wise Shapley values are computed. \code{group} then has length equal to
-the number of groups. The list element contains character vectors with the features included
-in each of the different groups.}
-
-\item{feature_labels}{Vector of characters. Contains the feature labels used by the model}
-}
-\value{
-List with two named elements: \code{group}: The input, but with group names if non-existing,
-\code{group_num} a corresponding group list with names replaced by feature number
-}
-\description{
-Process (check and update names) the group list
-}
-\details{
-This function takes care of all preprocessing and checking of the provided data in \code{x} against
-the feature_list which is typically the output from \code{\link[shapr:get_model_specs]{get_model_specs}}
-}
-\author{
-Martin Jullum
-}
-\keyword{internal}
diff --git a/man/reg_forecast_setup.Rd b/man/reg_forecast_setup.Rd
new file mode 100644
index 000000000..85c02f5f1
--- /dev/null
+++ b/man/reg_forecast_setup.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/explain_forecast.R
+\name{reg_forecast_setup}
+\alias{reg_forecast_setup}
+\title{Set up exogenous regressors for explanation in a forecast model.}
+\usage{
+reg_forecast_setup(x, horizon, group)
+}
+\arguments{
+\item{x}{A matrix with the exogenous variables.}
+
+\item{horizon}{The forecast horizon.}
+
+\item{group}{The list of endogenous groups, to append exogenous groups to.}
+}
+\value{
+A list containing
+\itemize{
+\item fcast A matrix containing the exogenous observations needed for each observation.
+\item group The list group with the exogenous groups appended.
+}
+}
+\description{
+Set up exogenous regressors for explanation in a forecast model.
+}
diff --git a/man/sample_combinations.Rd b/man/sample_combinations.Rd
index b669e5a64..2e9889b7c 100644
--- a/man/sample_combinations.Rd
+++ b/man/sample_combinations.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sampling.R
+% Please edit documentation in R/approach_empirical.R
\name{sample_combinations}
\alias{sample_combinations}
\title{Helper function to sample a combination of training and testing rows, which does not risk
diff --git a/man/sample_copula.Rd b/man/sample_copula.Rd
index 245fc9dea..c180f25ca 100644
--- a/man/sample_copula.Rd
+++ b/man/sample_copula.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sampling.R
+% Please edit documentation in R/approach_copula.R
\name{sample_copula}
\alias{sample_copula}
\title{Sample conditional variables using the Gaussian copula approach}
@@ -10,9 +10,9 @@ sample_copula(
mu,
cov_mat,
m,
- x_test_gaussian,
+ x_explain_gaussian,
x_train,
- x_test
+ x_explain
)
}
\arguments{
@@ -21,10 +21,11 @@ sample_copula(
\item{m}{Positive integer. The total number of features.}
-\item{x_test_gaussian}{Numeric matrix. Contains the observation whose predictions ought to be explained (test data),
+\item{x_explain_gaussian}{Numeric matrix. Contains the observation whose predictions ought
+to be explained (test data),
after quantile-transforming them to standard Gaussian variables.}
-\item{x_test}{Numeric matrix. Contains the features of the observation whose
+\item{x_explain}{Numeric matrix. Contains the features of the observation whose
predictions ought to be explained (test data).}
}
\value{
diff --git a/man/sample_ctree.Rd b/man/sample_ctree.Rd
index 4a47183ec..f95f74383 100644
--- a/man/sample_ctree.Rd
+++ b/man/sample_ctree.Rd
@@ -1,10 +1,10 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sampling.R
+% Please edit documentation in R/approach_ctree.R
\name{sample_ctree}
\alias{sample_ctree}
\title{Sample ctree variables from a given conditional inference tree}
\usage{
-sample_ctree(tree, n_samples, x_test, x_train, p, sample)
+sample_ctree(tree, n_samples, x_explain, x_train, n_features, sample)
}
\arguments{
\item{tree}{List. Contains tree which is an object of type ctree built from the party package.
@@ -12,12 +12,12 @@ Also contains given_ind, the features to condition upon.}
\item{n_samples}{Numeric. Indicates how many samples to use for MCMC.}
-\item{x_test}{Matrix, data.frame or data.table with the features of the observation whose
-predictions ought to be explained (test data). Dimension \code{1xp} or \code{px1}.}
+\item{x_explain}{Matrix, data.frame or data.table with the features of the observation whose
+predictions ought to be explained (test data). Dimension \verb{1\\timesp} or \verb{p\\times1}.}
\item{x_train}{Matrix, data.frame or data.table with training data.}
-\item{p}{Positive integer. The number of features.}
+\item{n_features}{Positive integer. The number of features.}
\item{sample}{Boolean. True indicates that the method samples from the terminal node
of the tree whereas False indicates that the method takes all the observations if it is
@@ -29,35 +29,6 @@ data.table with \code{n_samples} (conditional) Gaussian samples
\description{
Sample ctree variables from a given conditional inference tree
}
-\examples{
-if (requireNamespace("MASS", quietly = TRUE) & requireNamespace("party", quietly = TRUE)) {
- m <- 10
- n <- 40
- n_samples <- 50
- mu <- rep(1, m)
- cov_mat <- cov(matrix(rnorm(n * m), n, m))
- x_train <- data.table::data.table(MASS::mvrnorm(n, mu, cov_mat))
- x_test <- MASS::mvrnorm(1, mu, cov_mat)
- x_test_dt <- data.table::setDT(as.list(x_test))
- given_ind <- c(4, 7)
- dependent_ind <- (1:dim(x_train)[2])[-given_ind]
- x <- x_train[, given_ind, with = FALSE]
- y <- x_train[, dependent_ind, with = FALSE]
- df <- data.table::data.table(cbind(y, x))
- colnames(df) <- c(paste0("Y", 1:ncol(y)), paste0("V", given_ind))
- ynam <- paste0("Y", 1:ncol(y))
- fmla <- as.formula(paste(paste(ynam, collapse = "+"), "~ ."))
- datact <- party::ctree(fmla, data = df, controls = party::ctree_control(
- minbucket = 7,
- mincriterion = 0.95
- ))
- tree <- list(tree = datact, given_ind = given_ind, dependent_ind = dependent_ind)
- shapr:::sample_ctree(
- tree = tree, n_samples = n_samples, x_test = x_test_dt, x_train = x_train,
- p = length(x_test), sample = TRUE
- )
-}
-}
\author{
Annabelle Redelmeier
}
diff --git a/man/sample_gaussian.Rd b/man/sample_gaussian.Rd
index 1b81f0842..f91312e85 100644
--- a/man/sample_gaussian.Rd
+++ b/man/sample_gaussian.Rd
@@ -1,10 +1,10 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sampling.R
+% Please edit documentation in R/approach_gaussian.R
\name{sample_gaussian}
\alias{sample_gaussian}
\title{Sample conditional Gaussian variables}
\usage{
-sample_gaussian(index_given, n_samples, mu, cov_mat, m, x_test)
+sample_gaussian(index_given, n_samples, mu, cov_mat, m, x_explain)
}
\arguments{
\item{index_given}{Integer vector. The indices of the features to condition upon. Note that
@@ -12,7 +12,7 @@ sample_gaussian(index_given, n_samples, mu, cov_mat, m, x_test)
\item{m}{Positive integer. The total number of features.}
-\item{x_test}{Numeric matrix. Contains the features of the observation whose
+\item{x_explain}{Numeric matrix. Contains the features of the observation whose
predictions ought to be explained (test data).}
}
\value{
diff --git a/man/setup.Rd b/man/setup.Rd
new file mode 100644
index 000000000..fa75a9b42
--- /dev/null
+++ b/man/setup.Rd
@@ -0,0 +1,147 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/setup.R
+\name{setup}
+\alias{setup}
+\title{check_setup}
+\usage{
+setup(
+ x_train,
+ x_explain,
+ approach,
+ prediction_zero,
+ output_size = 1,
+ n_combinations,
+ group,
+ n_samples,
+ n_batches,
+ seed,
+ keep_samp_for_vS,
+ feature_specs,
+ type = "normal",
+ horizon = NULL,
+ y = NULL,
+ xreg = NULL,
+ train_idx = NULL,
+ explain_idx = NULL,
+ explain_y_lags = NULL,
+ explain_xreg_lags = NULL,
+ group_lags = NULL,
+ timing,
+ init_time,
+ is_python = FALSE,
+ ...
+)
+}
+\arguments{
+\item{x_train}{Matrix or data.frame/data.table.
+Contains the data used to estimate the (conditional) distributions for the features
+needed to properly estimate the conditional expectations in the Shapley formula.}
+
+\item{x_explain}{A matrix or data.frame/data.table.
+Contains the the features, whose predictions ought to be explained.}
+
+\item{approach}{Character vector of length \code{1} or \code{n_features}.
+\code{n_features} equals the total number of features in the model. All elements should,
+either be \code{"gaussian"}, \code{"copula"}, \code{"empirical"}, \code{"ctree"}, \code{"categorical"}, \code{"timeseries"}, or \code{"independence"}.
+See details for more information.}
+
+\item{prediction_zero}{Numeric.
+The prediction value for unseen data, i.e. an estimate of the expected prediction without conditioning on any
+features.
+Typically we set this value equal to the mean of the response variable in our training data, but other choices
+such as the mean of the predictions in the training data are also reasonable.}
+
+\item{output_size}{TODO: Document}
+
+\item{n_combinations}{Integer.
+If \code{group = NULL}, \code{n_combinations} represents the number of unique feature combinations to sample.
+If \code{group != NULL}, \code{n_combinations} represents the number of unique group combinations to sample.
+If \code{n_combinations = NULL}, the exact method is used and all combinations are considered.
+The maximum number of combinations equals \code{2^m}, where \code{m} is the number of features.}
+
+\item{group}{List.
+If \code{NULL} regular feature wise Shapley values are computed.
+If provided, group wise Shapley values are computed. \code{group} then has length equal to
+the number of groups. The list element contains character vectors with the features included
+in each of the different groups.}
+
+\item{n_samples}{Positive integer.
+Indicating the maximum number of samples to use in the
+Monte Carlo integration for every conditional expectation. See also details.}
+
+\item{n_batches}{Positive integer (or NULL).
+Specifies how many batches the total number of feature combinations should be split into when calculating the
+contribution function for each test observation.
+The default value is NULL which uses a reasonable trade-off between RAM allocation and computation speed,
+which depends on \code{approach} and \code{n_combinations}.
+For models with many features, increasing the number of batches reduces the RAM allocation significantly.
+This typically comes with a small increase in computation time.}
+
+\item{seed}{Positive integer.
+Specifies the seed before any randomness based code is being run.
+If \code{NULL} the seed will be inherited from the calling environment.}
+
+\item{keep_samp_for_vS}{Logical.
+Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned
+(in \code{internal$output})}
+
+\item{feature_specs}{List. The output from \code{\link[=get_model_specs]{get_model_specs()}} or \code{\link[=get_data_specs]{get_data_specs()}}.
+Contains the 3 elements:
+\describe{
+\item{labels}{Character vector with the names of each feature.}
+\item{classes}{Character vector with the classes of each features.}
+\item{factor_levels}{Character vector with the levels for any categorical features.}
+}}
+
+\item{type}{Character.
+Either "normal" or "forecast" corresponding to function \code{setup()} is called from,
+correspondingly the type of explanation that should be generated.}
+
+\item{horizon}{Numeric.
+The forecast horizon to explain. Passed to the \code{predict_model} function.}
+
+\item{y}{Matrix, data.frame/data.table or a numeric vector.
+Contains the endogenous variables used to estimate the (conditional) distributions
+needed to properly estimate the conditional expectations in the Shapley formula
+including the observations to be explained.}
+
+\item{xreg}{Matrix, data.frame/data.table or a numeric vector.
+Contains the exogenous variables used to estimate the (conditional) distributions
+needed to properly estimate the conditional expectations in the Shapley formula
+including the observations to be explained.
+As exogenous variables are used contemporaneusly when producing a forecast,
+this item should contain nrow(y) + horizon rows.}
+
+\item{train_idx}{Numeric vector
+The row indices in data and reg denoting points in time to use when estimating the conditional expectations in
+the Shapley value formula.
+If \code{train_idx = NULL} (default) all indices not selected to be explained will be used.}
+
+\item{explain_idx}{Numeric vector
+The row indices in data and reg denoting points in time to explain.}
+
+\item{explain_y_lags}{Numeric vector.
+Denotes the number of lags that should be used for each variable in \code{y} when making a forecast.}
+
+\item{explain_xreg_lags}{Numeric vector.
+If \code{xreg != NULL}, denotes the number of lags that should be used for each variable in \code{xreg} when making a forecast.}
+
+\item{group_lags}{Logical.
+If \code{TRUE} all lags of each variable are grouped together and explained as a group.
+If \code{FALSE} all lags of each variable are explained individually.}
+
+\item{timing}{Logical.
+Whether the timing of the different parts of the \code{explain()} should saved in the model object.}
+
+\item{init_time}{POSIXct-object
+Output from \code{Sys.time()} called at the start of \code{explain()}. Used initialize the timing.}
+
+\item{is_python}{Logical. Indicates whether the function is called from the Python wrapper. Default is FALSE which is
+never changed when calling the function via \code{explain()} in R. The parameter is later used to disallow
+running the AICc-versions of the empirical as that requires data based optimization.}
+
+\item{...}{Further arguments passed to \code{approach}-specific functions.}
+}
+\description{
+check_setup
+}
diff --git a/man/setup_approach.Rd b/man/setup_approach.Rd
new file mode 100644
index 000000000..137a8a0e8
--- /dev/null
+++ b/man/setup_approach.Rd
@@ -0,0 +1,154 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/approach.R, R/approach_categorical.R,
+% R/approach_copula.R, R/approach_ctree.R, R/approach_empirical.R,
+% R/approach_gaussian.R, R/approach_independence.R, R/approach_timeseries.R
+\name{setup_approach}
+\alias{setup_approach}
+\alias{setup_approach.categorical}
+\alias{setup_approach.copula}
+\alias{setup_approach.ctree}
+\alias{setup_approach.empirical}
+\alias{setup_approach.gaussian}
+\alias{setup_approach.independence}
+\alias{setup_approach.timeseries}
+\title{Set up the framework chosen approach}
+\usage{
+setup_approach(internal, ...)
+
+\method{setup_approach}{categorical}(
+ internal,
+ categorical.joint_prob_dt = NULL,
+ categorical.epsilon = 0.001,
+ ...
+)
+
+\method{setup_approach}{copula}(internal, ...)
+
+\method{setup_approach}{ctree}(
+ internal,
+ ctree.mincriterion = 0.95,
+ ctree.minsplit = 20,
+ ctree.minbucket = 7,
+ ctree.sample = TRUE,
+ ...
+)
+
+\method{setup_approach}{empirical}(
+ internal,
+ empirical.type = "fixed_sigma",
+ empirical.eta = 0.95,
+ empirical.fixed_sigma = 0.1,
+ empirical.n_samples_aicc = 1000,
+ empirical.eval_max_aicc = 20,
+ empirical.start_aicc = 0.1,
+ empirical.cov_mat = NULL,
+ model = NULL,
+ predict_model = NULL,
+ ...
+)
+
+\method{setup_approach}{gaussian}(internal, gaussian.mu = NULL, gaussian.cov_mat = NULL, ...)
+
+\method{setup_approach}{independence}(internal, ...)
+
+\method{setup_approach}{timeseries}(
+ internal,
+ timeseries.fixed_sigma_vec = 2,
+ timeseries.bounds = c(NULL, NULL),
+ ...
+)
+}
+\arguments{
+\item{internal}{Not used.}
+
+\item{...}{\code{approach}-specific arguments. See below.}
+
+\item{categorical.joint_prob_dt}{Data.table. (Optional)
+Containing the joint probability distribution for each combination of feature
+values.
+\code{NULL} means it is estimated from the \code{x_train} and \code{x_explain}.}
+
+\item{categorical.epsilon}{Numeric value. (Optional)
+If \code{joint_probability_dt} is not supplied, probabilities/frequencies are
+estimated using \code{x_train}. If certain observations occur in \code{x_train} and NOT in \code{x_explain},
+then epsilon is used as the proportion of times that these observations occurs in the training data.
+In theory, this proportion should be zero, but this causes an error later in the Shapley computation.}
+
+\item{ctree.mincriterion}{Numeric scalar or vector. (default = 0.95)
+Either a scalar or vector of length equal to the number of features in the model.
+Value is equal to 1 - \eqn{\alpha} where \eqn{\alpha} is the nominal level of the conditional independence tests.
+If it is a vector, this indicates which value to use when conditioning on various numbers of features.}
+
+\item{ctree.minsplit}{Numeric scalar. (default = 20)
+Determines minimum value that the sum of the left and right daughter nodes required for a split.}
+
+\item{ctree.minbucket}{Numeric scalar. (default = 7)
+Determines the minimum sum of weights in a terminal node required for a split}
+
+\item{ctree.sample}{Boolean. (default = TRUE)
+If TRUE, then the method always samples \code{n_samples} observations from the leaf nodes (with replacement).
+If FALSE and the number of observations in the leaf node is less than \code{n_samples},
+the method will take all observations in the leaf.
+If FALSE and the number of observations in the leaf node is more than \code{n_samples},
+the method will sample \code{n_samples} observations (with replacement).
+This means that there will always be sampling in the leaf unless
+\code{sample} = FALSE AND the number of obs in the node is less than \code{n_samples}.}
+
+\item{empirical.type}{Character. (default = \code{"fixed_sigma"})
+Should be equal to either \code{"independence"},\code{"fixed_sigma"}, \code{"AICc_each_k"} \code{"AICc_full"}.
+TODO: Describe better what the methods do here.}
+
+\item{empirical.eta}{Numeric. (default = 0.95)
+Needs to be \verb{0 < eta <= 1}.
+Represents the minimum proportion of the total empirical weight that data samples should use.
+If e.g. \code{eta = .8} we will choose the \code{K} samples with the largest weight so that the sum of the weights
+accounts for 80\\% of the total weight.
+\code{eta} is the \eqn{\eta} parameter in equation (15) of Aas et al (2021).}
+
+\item{empirical.fixed_sigma}{Positive numeric scalar. (default = 0.1)
+Represents the kernel bandwidth in the distance computation used when conditioning on all different combinations.
+Only used when \code{empirical.type = "fixed_sigma"}}
+
+\item{empirical.n_samples_aicc}{Positive integer. (default = 1000)
+Number of samples to consider in AICc optimization.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+
+\item{empirical.eval_max_aicc}{Positive integer. (default = 20)
+Maximum number of iterations when optimizing the AICc.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+
+\item{empirical.start_aicc}{Numeric. (default = 0.1)
+Start value of the \code{sigma} parameter when optimizing the AICc.
+Only used for \code{empirical.type} is either \code{"AICc_each_k"} or \code{"AICc_full"}.}
+
+\item{empirical.cov_mat}{Numeric matrix. (Optional, default = NULL)
+Containing the covariance matrix of the data generating distribution used to define the Mahalanobis distance.
+\code{NULL} means it is estimated from \code{x_train}.}
+
+\item{model}{Objects.
+The model object that ought to be explained.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{gaussian.mu}{Numeric vector. (Optional)
+Containing the mean of the data generating distribution.
+\code{NULL} means it is estimated from the \code{x_train}.}
+
+\item{gaussian.cov_mat}{Numeric matrix. (Optional)
+Containing the covariance matrix of the data generating distribution.
+\code{NULL} means it is estimated from the \code{x_train}.}
+
+\item{timeseries.fixed_sigma_vec}{Numeric. (Default = 2)
+Represents the kernel bandwidth in the distance computation. TODO: What length should it have? 1?}
+
+\item{timeseries.bounds}{Numeric vector of length two. (Default = c(NULL, NULL))
+If one or both of these bounds are not NULL, we restrict the sampled time series to be
+between these bounds.
+This is useful if the underlying time series are scaled between 0 and 1, for example.}
+}
+\description{
+The different choices of \code{approach} takes different (optional) parameters, which are forwarded from \code{\link[=explain]{explain()}}.
+}
diff --git a/man/setup_computation.Rd b/man/setup_computation.Rd
new file mode 100644
index 000000000..f731787e5
--- /dev/null
+++ b/man/setup_computation.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/setup_computation.R
+\name{setup_computation}
+\alias{setup_computation}
+\title{Sets up everything for the Shapley values computation in \code{\link[=explain]{explain()}}}
+\usage{
+setup_computation(internal, model, predict_model)
+}
+\arguments{
+\item{internal}{List.
+Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}}
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.}
+
+\item{model}{Objects.
+The model object that ought to be explained.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+}
+\value{
+List \code{internal}
+It holds all parameters, data, and computed objects used within \code{\link[=explain]{explain()}}.
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.
+}
+\description{
+Sets up everything for the Shapley values computation in \code{\link[=explain]{explain()}}
+}
diff --git a/man/shapley_weights.Rd b/man/shapley_weights.Rd
index be20a139d..109e68de3 100644
--- a/man/shapley_weights.Rd
+++ b/man/shapley_weights.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/shapley.R
+% Please edit documentation in R/setup_computation.R
\name{shapley_weights}
\alias{shapley_weights}
\title{Calculate Shapley weight}
@@ -13,7 +13,7 @@ shapley_weights(m, N, n_components, weight_zero_m = 10^6)
groups, without replacement, from a sample space consisting of \code{m} different features/feature groups.}
\item{n_components}{Positive integer. Represents the number of features/feature groups you want to sample from
-a feature space consisting of \code{m} unique features/feature groups. Note that \code{ 0 < = n_components <= m}.}
+a feature space consisting of \code{m} unique features/feature groups. Note that \verb{ 0 < = n_components <= m}.}
\item{weight_zero_m}{Positive integer. Represents the Shapley weight for two special
cases, i.e. the case where you have either \code{0} or \code{m} features/feature groups.}
diff --git a/man/shapr-package.Rd b/man/shapr-package.Rd
new file mode 100644
index 000000000..f2ecda286
--- /dev/null
+++ b/man/shapr-package.Rd
@@ -0,0 +1,39 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/shapr-package.R
+\docType{package}
+\name{shapr-package}
+\alias{shapr}
+\alias{shapr-package}
+\title{shapr: Prediction Explanation with Dependence-Aware Shapley Values}
+\description{
+Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements the method described in Aas, Jullum and Løland (2019) \href{https://arxiv.org/abs/1903.10464}{arXiv:1903.10464}, which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values.
+}
+\seealso{
+Useful links:
+\itemize{
+ \item \url{https://norskregnesentral.github.io/shapr/}
+ \item \url{https://github.com/NorskRegnesentral/shapr}
+ \item Report bugs at \url{https://github.com/NorskRegnesentral/shapr/issues}
+}
+
+}
+\author{
+\strong{Maintainer}: Martin Jullum \email{Martin.Jullum@nr.no} (\href{https://orcid.org/0000-0003-3908-5155}{ORCID})
+
+Authors:
+\itemize{
+ \item Nikolai Sellereite \email{nikolaisellereite@gmail.com} (\href{https://orcid.org/0000-0002-4671-0337}{ORCID})
+ \item Annabelle Redelmeier \email{Annabelle.Redelmeier@nr.no}
+ \item Jon Lachmann \email{Jon@lachmann.nu}
+}
+
+Other contributors:
+\itemize{
+ \item Anders Løland \email{Anders.Loland@nr.no} [contributor]
+ \item Jens Christian Wahl \email{Jens.Christian.Wahl@nr.no} [contributor]
+ \item Camilla Lingjærde [contributor]
+ \item Norsk Regnesentral [copyright holder, funder]
+}
+
+}
+\keyword{internal}
diff --git a/man/shapr.Rd b/man/shapr.Rd
deleted file mode 100644
index dac7775c7..000000000
--- a/man/shapr.Rd
+++ /dev/null
@@ -1,93 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/shapley.R
-\name{shapr}
-\alias{shapr}
-\title{Create an explainer object with Shapley weights for test data.}
-\usage{
-shapr(x, model, n_combinations = NULL, group = NULL)
-}
-\arguments{
-\item{x}{Numeric matrix or data.frame/data.table. Contains the data used to estimate the (conditional)
-distributions for the features needed to properly estimate the conditional expectations in the Shapley formula.}
-
-\item{model}{The model whose predictions we want to explain. Run
-\code{\link[shapr:get_supported_models]{shapr:::get_supported_models()}}
-for a table of which models \code{shapr} supports natively.}
-
-\item{n_combinations}{Integer. The number of feature combinations to sample. If \code{NULL},
-the exact method is used and all combinations are considered. The maximum number of
-combinations equals \code{2^ncol(x)}.}
-
-\item{group}{List. If \code{NULL} regular feature wise Shapley values are computed.
-If provided, group wise Shapley values are computed. \code{group} then has length equal to
-the number of groups. The list element contains character vectors with the features included
-in each of the different groups.}
-}
-\value{
-Named list that contains the following items:
-\describe{
- \item{exact}{Boolean. Equals \code{TRUE} if \code{n_combinations = NULL} or
- \code{n_combinations < 2^ncol(x)}, otherwise \code{FALSE}.}
- \item{n_features}{Positive integer. The number of columns in \code{x}}
- \item{S}{Binary matrix. The number of rows equals the number of unique combinations, and
- the number of columns equals the total number of features. I.e. let's say we have a case with
- three features. In that case we have \code{2^3 = 8} unique combinations. If the j-th
- observation for the i-th row equals \code{1} it indicates that the j-th feature is present in
- the i-th combination. Otherwise it equals \code{0}.}
- \item{W}{Matrix. This matrix is equal to the matrix \code{R_D} in Equation 7 in the reference
- of \code{link{explain}}. The Shapley value for a test observation will be equal to the matrix-vector product
- of \code{W} and the contribution vector.}
- \item{X}{data.table. Returned object from \code{\link{feature_combinations}}}
- \item{x_train}{data.table. Transformed \code{x} into a data.table.}
- \item{feature_list}{List. The \code{updated_feature_list} output from
- \code{\link[shapr:preprocess_data]{preprocess_data}}}
-}
-
-In addition to the items above, \code{model} and \code{n_combinations} are also present in the returned object.
-}
-\description{
-Create an explainer object with Shapley weights for test data.
-}
-\examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
- # Load example data
- data("Boston", package = "MASS")
- df <- Boston
-
- # Example using the exact method
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
- df0 <- df[, x_var]
- model <- lm(medv ~ lstat + rm + dis + indus, data = df)
- explainer <- shapr(df0, model)
-
- print(nrow(explainer$X))
- # 16 (which equals 2^4)
-
- # Example using approximation
- y_var <- "medv"
- model <- lm(medv ~ ., data = df)
- explainer <- shapr(df, model, n_combinations = 1e3)
-
- print(nrow(explainer$X))
-
- # Example using approximation where n_combinations > 2^m
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
- model <- lm(medv ~ lstat + rm + dis + indus, data = df)
- explainer <- shapr(df0, model, n_combinations = 1e3)
-
- print(nrow(explainer$X))
- # 16 (which equals 2^4)
-
- # Example using groups
- group <- list(A=x_var[1:2], B=x_var[3:4])
-
- explainer_group <- shapr(df0, model, group = group)
- print(nrow(explainer_group$X))
- # 4 (which equals 2^(#groups))
-}
-}
-\author{
-Nikolai Sellereite
-}
diff --git a/man/test_predict_model.Rd b/man/test_predict_model.Rd
new file mode 100644
index 000000000..f428150e0
--- /dev/null
+++ b/man/test_predict_model.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/get_predict_model.R
+\name{test_predict_model}
+\alias{test_predict_model}
+\title{Model testing function}
+\usage{
+test_predict_model(x_test, predict_model, model, internal)
+}
+\arguments{
+\item{predict_model}{Function.
+The prediction function used when \code{model} is not natively supported.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{model}{Objects.
+The model object that ought to be explained.
+See the documentation of \code{\link[=explain]{explain()}} for details.}
+
+\item{internal}{List.
+Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}}
+The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{output}.}
+}
+\description{
+Model testing function
+}
+\keyword{internal}
diff --git a/man/update_data.Rd b/man/update_data.Rd
deleted file mode 100644
index c23df278e..000000000
--- a/man/update_data.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/preprocess_data.R
-\name{update_data}
-\alias{update_data}
-\title{Updates data by reference according to the updater argument.}
-\usage{
-update_data(data, updater)
-}
-\arguments{
-\item{data}{data.table. Data that ought to be updated.}
-
-\item{updater}{List. The object should be the output from
-\code{\link[shapr:check_features]{check_features}}.}
-}
-\value{
-NULL.
-}
-\description{
-\code{data} is updated, i.e. unused columns and factor levels are removed as described in
-\code{updater}. This is done by reference, i.e. updates the object being passed to data even if nothing is
-returned by the function itself.
-}
-\examples{
-# Load example data
-if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- # Split data into test- and training data
- x_train <- data.table::as.data.table(head(Boston))
- x_train[, rad := as.factor(rad)]
- data_features <- get_data_specs(x_train)
- model <- lm(medv ~ lstat + rm + rad + indus, data = x_train)
-
- model_features <- get_model_specs(model)
- updater <- check_features(model_features, data_features)
- update_data(x_train, updater)
-}
-}
-\author{
-Martin Jullum
-}
-\keyword{internal}
diff --git a/man/weight_matrix.Rd b/man/weight_matrix.Rd
index 185e5deef..734160661 100644
--- a/man/weight_matrix.Rd
+++ b/man/weight_matrix.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/shapley.R
+% Please edit documentation in R/setup_computation.R
\name{weight_matrix}
\alias{weight_matrix}
\title{Calculate weighted matrix}
@@ -16,7 +16,7 @@ except combination \code{1} and \code{2^m}.}
\item{is_groupwise}{Logical. Indicating whether group wise Shapley values are to be computed.}
}
\value{
-Numeric matrix. See \code{\link{weight_matrix_cpp}} for more information.
+Numeric matrix. See \code{\link[=weight_matrix_cpp]{weight_matrix_cpp()}} for more information.
}
\description{
Calculate weighted matrix
diff --git a/shapr.Rproj b/shapr.Rproj
index e7b674747..a16a99568 100644
--- a/shapr.Rproj
+++ b/shapr.Rproj
@@ -17,5 +17,6 @@ StripTrailingWhitespace: Yes
BuildType: Package
PackageUseDevtools: Yes
+PackageCleanBeforeInstall: No
PackageInstallArgs: --no-multiarch --with-keep.source --no-lock
-PackageRoxygenize: rd,collate,namespace,vignette
+PackageRoxygenize: rd,collate,namespace
diff --git a/shapr_timing.csv b/shapr_timing.csv
new file mode 100644
index 000000000..a53117d22
--- /dev/null
+++ b/shapr_timing.csv
@@ -0,0 +1,2 @@
+p,n_train,n_test,n_batches,n_cores,approach,min_time,mean_time,median_time,max_time,sys_time_start,sys_time_end,reps,max_n,max_p,rho,sigma,mu_const,beta0,sigma_eps
+4,100,2,6,2,gaussian,0.55644755,0.7457012925,0.7457012925,0.934955035,2022-01-21 11:32:11,2022-01-21 11:32:14,2,1e+05,10,0.3,1,0,1,1
diff --git a/tests/testthat/_snaps/forecast-output.md b/tests/testthat/_snaps/forecast-output.md
new file mode 100644
index 000000000..1049a2e5f
--- /dev/null
+++ b/tests/testthat/_snaps/forecast-output.md
@@ -0,0 +1,1861 @@
+# forecast_output_ar_numeric
+
+ Code
+ (out <- code)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ explain_idx horizon none Temp.1 Temp.2
+ 1: 152 1 77.88 -0.3972 -1.3912
+ 2: 153 1 77.88 -6.6177 -0.1835
+ 3: 152 2 77.88 -0.3285 -1.2034
+ 4: 153 2 77.88 -6.0208 -0.3371
+ 5: 152 3 77.88 -0.2915 -1.0552
+ 6: 153 3 77.88 -5.2122 -0.2553
+
+# forecast_output_arima_numeric
+
+ Code
+ (out <- code)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ explain_idx horizon none Temp.1 Temp.2 Wind.1 Wind.2 Wind.F1 Wind.F2
+ 1: 149 1 77.88 -0.9588 -5.044 1.0543 -2.8958 -2.6627 NA
+ 2: 150 1 77.88 1.1553 -3.137 -2.8802 0.7196 -1.4930 NA
+ 3: 149 2 77.88 0.1327 -5.048 0.3337 -2.8249 -2.3014 -1.1764
+ 4: 150 2 77.88 1.6007 -2.399 -2.8146 0.4646 -0.7938 0.4662
+ 5: 149 3 77.88 -1.3878 -5.014 0.7964 -1.3881 -1.9652 -0.3295
+ 6: 150 3 77.88 1.6690 -2.556 -2.3821 0.3835 -0.8644 -0.1648
+ Wind.F3
+ 1: NA
+ 2: NA
+ 3: NA
+ 4: NA
+ 5: 0.5630
+ 6: -0.7615
+
+# forecast_output_arima_numeric_no_xreg
+
+ Code
+ (out <- code)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ explain_idx horizon none Temp.1 Temp.2
+ 1: 149 1 77.88 -1.7273 -7.033
+ 2: 150 1 77.88 -0.2229 -4.492
+ 3: 149 2 77.88 -1.7273 -7.033
+ 4: 150 2 77.88 -0.2229 -4.492
+ 5: 149 3 77.88 -1.7273 -7.033
+ 6: 150 3 77.88 -0.2229 -4.492
+
+# forecast_output_forecast_ARIMA_group_numeric
+
+ Code
+ (out <- code)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ explain_idx horizon none Temp Wind
+ 1: 149 1 77.88 -5.3063 -5.201
+ 2: 150 1 77.88 -1.4435 -4.192
+ 3: 149 2 77.88 -3.6824 -7.202
+ 4: 150 2 77.88 -0.2568 -3.220
+ 5: 149 3 77.88 -6.5216 -2.204
+ 6: 150 3 77.88 -1.2125 -3.463
+
+# forecast_output_arima_numeric_no_lags
+
+ Code
+ (out <- code)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Warning
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
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+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ data length [2] is not a sub-multiple or multiple of the number of rows [3]
+ Output
+ explain_idx horizon none Wind.F1 Wind.F2 Wind.F3
+ 1: 149 1 77.88 -9.391 NA NA
+ 2: 150 1 77.88 -4.142 NA NA
+ 3: 149 2 77.88 -4.699 -4.6989 NA
+ 4: 150 2 77.88 -2.074 -2.0745 NA
+ 5: 149 3 77.88 -3.130 -4.6234 -3.130
+ 6: 150 3 77.88 -1.381 -0.7147 -1.381
+
diff --git a/tests/testthat/_snaps/forecast-output/forecast_output_ar_numeric.rds b/tests/testthat/_snaps/forecast-output/forecast_output_ar_numeric.rds
new file mode 100644
index 000000000..aa82c500f
Binary files /dev/null and b/tests/testthat/_snaps/forecast-output/forecast_output_ar_numeric.rds differ
diff --git a/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric.rds b/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric.rds
new file mode 100644
index 000000000..1fc18b15f
Binary files /dev/null and b/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric.rds differ
diff --git a/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_no_lags.rds b/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_no_lags.rds
new file mode 100644
index 000000000..cba9f0c06
Binary files /dev/null and b/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_no_lags.rds differ
diff --git a/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_no_xreg.rds b/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_no_xreg.rds
new file mode 100644
index 000000000..ad9be5cde
Binary files /dev/null and b/tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_no_xreg.rds differ
diff --git a/tests/testthat/_snaps/forecast-output/forecast_output_forecast_ARIMA_group_numeric.rds b/tests/testthat/_snaps/forecast-output/forecast_output_forecast_ARIMA_group_numeric.rds
new file mode 100644
index 000000000..940357268
Binary files /dev/null and b/tests/testthat/_snaps/forecast-output/forecast_output_forecast_ARIMA_group_numeric.rds differ
diff --git a/tests/testthat/_snaps/forecast-setup.md b/tests/testthat/_snaps/forecast-setup.md
new file mode 100644
index 000000000..8ae2d017f
--- /dev/null
+++ b/tests/testthat/_snaps/forecast-setup.md
@@ -0,0 +1,274 @@
+# error with custom model without providing predict_model
+
+ Code
+ model_custom_arima_temp <- model_arima_temp
+ class(model_custom_arima_temp) <- "whatever"
+ explain_forecast(model = model_custom_arima_temp, y = data[1:150, "Temp"],
+ xreg = data[, "Wind"], train_idx = 2:148, explain_idx = 149:150,
+ explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Message
+ Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
+ Consistency checks between model and data is therefore disabled.
+
+ Error
+ You passed a model to explain() which is not natively supported, and did not supply the 'predict_model' function to explain().
+ See ?shapr::explain or the vignette for more information on how to run shapr with custom models.
+
+# erroneous input: `x_train/x_explain`
+
+ Code
+ y_wrong_format <- data[, c("Temp", "Wind")]
+ explain_forecast(model = model_arima_temp, y = y_wrong_format, xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
+ n_batches = 1)
+ Error
+ `y` has 2 columns (Temp,Wind).
+ `explain_y_lags` has length 1.
+ These two should match.
+
+---
+
+ Code
+ xreg_wrong_format <- data[, c("Temp", "Wind")]
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = xreg_wrong_format,
+ train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
+ n_batches = 1)
+ Error
+ `xreg` has 2 columns (Temp,Wind).
+ `explain_xreg_lags` has length 1.
+ These two should match.
+
+---
+
+ Code
+ xreg_no_column_names <- data[, "Wind"]
+ names(xreg_no_column_names) <- NULL
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = xreg_no_column_names,
+ train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
+ n_batches = 1)
+ Error
+ `xreg` misses column names.
+
+# erroneous input: `model`
+
+ Code
+ explain_forecast(y = data[1:150, "Temp"], xreg = data[, "Wind"], train_idx = 2:
+ 148, explain_idx = 149:150, explain_y_lags = 2, explain_xreg_lags = 2,
+ horizon = 3, approach = "independence", prediction_zero = p0_ar, n_batches = 1)
+ Error
+ argument "model" is missing, with no default
+
+# erroneous input: `prediction_zero`
+
+ Code
+ p0_wrong_length <- p0_ar[1:2]
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_wrong_length,
+ n_batches = 1)
+ Error
+ `prediction_zero` (77.8823529411765, 77.8823529411765) must be numeric and match the output size of the model (3).
+
+# erroneous input: `n_combinations`
+
+ Code
+ horizon <- 3
+ explain_y_lags <- 2
+ explain_xreg_lags <- 2
+ n_combinations <- horizon + explain_y_lags + explain_xreg_lags - 1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags,
+ explain_xreg_lags = explain_xreg_lags, horizon = horizon, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1, n_combinations = n_combinations,
+ group_lags = FALSE)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Error
+ `n_combinations` (6) has to be greater than the number of components to decompose the forecast onto:
+ `horizon` (3) + `explain_y_lags` (2) + sum(`explain_xreg_lags`) (2).
+
+---
+
+ Code
+ horizon <- 3
+ explain_y_lags <- 2
+ explain_xreg_lags <- 2
+ n_combinations <- 1 + 1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags,
+ explain_xreg_lags = explain_xreg_lags, horizon = horizon, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1, n_combinations = n_combinations,
+ group_lags = TRUE)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Error
+ `n_combinations` (2) has to be greater than the number of components to decompose the forecast onto:
+ ncol(`xreg`) (1) + 1
+
+# erroneous input: `train_idx`
+
+ Code
+ train_idx_too_short <- 2
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = train_idx_too_short, explain_idx = 149:150,
+ explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `train_idx` must be a vector of positive finite integers and length > 1.
+
+---
+
+ Code
+ train_idx_not_integer <- c(3:5) + 0.1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = train_idx_not_integer, explain_idx = 149:150,
+ explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `train_idx` must be a vector of positive finite integers and length > 1.
+
+---
+
+ Code
+ train_idx_out_of_range <- 1:5
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = train_idx_out_of_range, explain_idx = 149:150,
+ explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ The train (`train_idx`) and explain (`explain_idx`) indices must fit in the lagged data.
+ The lagged data begins at index 2 and ends at index 150.
+
+# erroneous input: `explain_idx`
+
+ Code
+ explain_idx_not_integer <- c(3:5) + 0.1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = explain_idx_not_integer,
+ explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `explain_idx` must be a vector of positive finite integers.
+
+---
+
+ Code
+ explain_idx_out_of_range <- 1:5
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = explain_idx_out_of_range,
+ explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ The train (`train_idx`) and explain (`explain_idx`) indices must fit in the lagged data.
+ The lagged data begins at index 2 and ends at index 150.
+
+# erroneous input: `explain_y_lags`
+
+ Code
+ explain_y_lags_negative <- -1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags_negative,
+ explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
+ n_batches = 1)
+ Error
+ `explain_y_lags` must be a vector of positive finite integers.
+
+---
+
+ Code
+ explain_y_lags_not_integer <- 2.1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags_not_integer,
+ explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
+ n_batches = 1)
+ Error
+ `explain_y_lags` must be a vector of positive finite integers.
+
+---
+
+ Code
+ explain_y_lags_more_than_one <- c(1, 2)
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags_more_than_one,
+ explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
+ n_batches = 1)
+ Error
+ `y` has 1 columns (Temp).
+ `explain_y_lags` has length 2.
+ These two should match.
+
+---
+
+ Code
+ explain_y_lags_zero <- 0
+ explain_forecast(model = model_arima_temp_noxreg, y = data[1:150, "Temp"],
+ train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 0, horizon = 3,
+ approach = "independence", prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `explain_y_lags=0` is not allowed for models without exogeneous variables
+
+# erroneous input: `explain_x_lags`
+
+ Code
+ explain_xreg_lags_negative <- -2
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = explain_xreg_lags_negative, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `explain_xreg_lags` must be a vector of positive finite integers.
+
+---
+
+ Code
+ explain_xreg_lags_not_integer <- 2.1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = explain_xreg_lags_not_integer, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `explain_xreg_lags` must be a vector of positive finite integers.
+
+---
+
+ Code
+ explain_x_lags_wrong_length <- c(1, 2)
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = explain_x_lags_wrong_length, horizon = 3, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `xreg` has 1 columns (Wind).
+ `explain_xreg_lags` has length 2.
+ These two should match.
+
+# erroneous input: `horizon`
+
+ Code
+ horizon_negative <- -2
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = 2, horizon = horizon_negative, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `horizon` must be a vector (or scalar) of positive integers.
+
+---
+
+ Code
+ horizon_not_integer <- 2.1
+ explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
+ "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
+ explain_xreg_lags = 2, horizon = horizon_not_integer, approach = "independence",
+ prediction_zero = p0_ar, n_batches = 1)
+ Error
+ `horizon` must be a vector (or scalar) of positive integers.
+
diff --git a/tests/testthat/_snaps/output.md b/tests/testthat/_snaps/output.md
new file mode 100644
index 000000000..bdde7a3df
--- /dev/null
+++ b/tests/testthat/_snaps/output.md
@@ -0,0 +1,274 @@
+# output_lm_numeric_independence
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -4.537 8.269 17.517 -5.581 -3.066
+ 2: 42.44 2.250 -3.345 -5.232 -5.581 -1.971
+ 3: 42.44 3.708 -18.610 -1.440 -2.541 1.316
+
+# output_lm_numeric_empirical
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -13.252 15.541 12.826 -5.77179 3.259
+ 2: 42.44 2.758 -3.325 -7.992 -7.12800 1.808
+ 3: 42.44 6.805 -22.126 3.730 -0.09235 -5.885
+
+# output_lm_numeric_empirical_n_combinations
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -5.795 15.320 8.557 -7.547 2.066
+ 2: 42.44 3.266 -3.252 -7.693 -7.663 1.462
+ 3: 42.44 4.290 -24.395 6.739 -1.006 -3.197
+
+# output_lm_numeric_empirical_independence
+
+ Code
+ (out <- code)
+ Warning
+ Using empirical.type = 'independence' for approach = 'empirical' is deprecated.
+ Please use approach = 'independence' instead.
+ Message
+
+ Success with message:
+ empirical.eta force set to 1 for empirical.type = 'independence'
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -4.537 8.269 17.517 -5.581 -3.066
+ 2: 42.44 2.250 -3.345 -5.232 -5.581 -1.971
+ 3: 42.44 3.708 -18.610 -1.440 -2.541 1.316
+
+# output_lm_numeric_empirical_AICc_each
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -15.66 6.823 17.5092 0.2463 3.6847
+ 2: 42.44 10.70 -1.063 -10.6804 -13.0305 0.1983
+ 3: 42.44 14.65 -19.946 0.9675 -7.3433 -5.8946
+
+# output_lm_numeric_empirical_AICc_full
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -14.98 6.3170 17.4103 0.2876 3.5623
+ 2: 42.44 12.42 0.1482 -10.2338 -16.4096 0.1967
+ 3: 42.44 15.74 -19.7250 0.9992 -8.6950 -5.8886
+
+# output_lm_numeric_gaussian
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -8.545 7.779 14.586 0.4475 -1.6653
+ 2: 42.44 4.826 -4.295 -11.655 -1.1250 -1.6309
+ 3: 42.44 7.163 -25.491 0.368 -0.5455 0.9377
+
+# output_lm_numeric_copula
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -6.371 7.355 14.470 -0.6108 -2.241
+ 2: 42.44 4.115 -4.159 -9.980 -1.9378 -1.917
+ 3: 42.44 5.932 -25.086 1.857 -1.3624 1.090
+
+# output_lm_numeric_ctree
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -9.124 9.509 17.139 -1.4711 -3.451
+ 2: 42.44 5.342 -6.097 -8.232 -2.8129 -2.079
+ 3: 42.44 6.901 -21.079 -4.687 0.1494 1.146
+
+# output_lm_categorical_ctree
+
+ Code
+ (out <- code)
+ Output
+ none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
+ 1: 42.44 -6.206 15.38 -6.705 -2.973
+ 2: 42.44 -5.764 -17.71 21.866 -13.219
+ 3: 42.44 7.101 -21.78 1.730 -5.413
+
+# output_lm_categorical_categorical
+
+ Code
+ (out <- code)
+ Output
+ none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
+ 1: 42.44 13.656 -19.73 4.369 -16.659
+ 2: 42.44 -5.448 11.31 -11.445 5.078
+ 3: 42.44 -7.493 -12.27 19.672 -14.744
+
+# output_lm_categorical_independence
+
+ Code
+ (out <- code)
+ Output
+ none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
+ 1: 42.44 -5.252 13.95 -7.041 -2.167
+ 2: 42.44 -5.252 -15.61 20.086 -14.050
+ 3: 42.44 4.833 -15.61 0.596 -8.178
+
+# output_lm_ts_timeseries
+
+ Code
+ (out <- code)
+ Output
+ none S1 S2 S3 S4
+ 1: 4.895 -0.5261 0.7831 -0.21023 -0.3885
+ 2: 4.895 -0.6310 1.6288 -0.04498 -2.9298
+
+# output_lm_numeric_comb1
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -8.580 7.999 14.3608 0.3719 -1.5505
+ 2: 42.44 5.078 -5.014 -12.0644 -0.8963 -0.9825
+ 3: 42.44 7.276 -25.448 0.3953 -0.3868 0.5959
+
+# output_lm_numeric_comb2
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -8.957 9.400 17.061 -1.4678210 -3.435
+ 2: 42.44 5.307 -5.932 -8.126 -2.9311283 -2.198
+ 3: 42.44 6.913 -20.969 -4.539 -0.0004225 1.026
+
+# output_lm_numeric_comb3
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -4.537 8.269 17.517 -5.581 -3.066
+ 2: 42.44 2.250 -3.345 -5.232 -5.581 -1.971
+ 3: 42.44 3.708 -18.610 -1.440 -2.541 1.316
+
+# output_lm_mixed_independence
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -4.730 7.750 17.753 -2.601 -7.588
+ 2: 42.44 2.338 -3.147 -5.310 -1.676 -7.588
+ 3: 42.44 3.857 -17.469 -1.466 1.099 3.379
+
+# output_lm_mixed_ctree
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -9.165 11.815 13.184 -0.4473 -4.802
+ 2: 42.44 3.652 -5.782 -6.524 -0.4349 -6.295
+ 3: 42.44 6.268 -21.441 -7.323 1.6330 10.262
+
+# output_lm_mixed_comb
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -9.153 11.492 13.340 -0.2291 -4.866
+ 2: 42.44 3.936 -5.765 -6.403 -0.4462 -6.704
+ 3: 42.44 6.129 -21.416 -7.208 1.5514 10.344
+
+# output_custom_lm_numeric_independence_1
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -4.537 8.269 17.517 -5.581 -3.066
+ 2: 42.44 2.250 -3.345 -5.232 -5.581 -1.971
+ 3: 42.44 3.708 -18.610 -1.440 -2.541 1.316
+
+# output_custom_lm_numeric_independence_2
+
+ Code
+ (out <- code)
+ Message
+ Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -4.537 8.269 17.517 -5.581 -3.066
+ 2: 42.44 2.250 -3.345 -5.232 -5.581 -1.971
+ 3: 42.44 3.708 -18.610 -1.440 -2.541 1.316
+
+# output_custom_xgboost_mixed_dummy_ctree
+
+ Code
+ (out <- code)
+ Message
+ Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -5.603 13.05 20.43 0.08508 -0.2664
+ 2: 42.44 4.645 -12.57 -16.65 1.29133 -2.1574
+ 3: 42.44 5.451 -14.01 -19.72 1.32503 6.3851
+
+# output_lm_numeric_interaction
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind
+ 1: 42.44 -13.818 10.579
+ 2: 42.44 4.642 -6.287
+ 3: 42.44 4.452 -34.602
+
+# output_lm_numeric_ctree_parallelized
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -9.124 9.509 17.139 -1.4711 -3.451
+ 2: 42.44 5.342 -6.097 -8.232 -2.8129 -2.079
+ 3: 42.44 6.901 -21.079 -4.687 0.1494 1.146
+
+# output_lm_numeric_independence_more_batches
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -4.537 8.269 17.517 -5.581 -3.066
+ 2: 42.44 2.250 -3.345 -5.232 -5.581 -1.971
+ 3: 42.44 3.708 -18.610 -1.440 -2.541 1.316
+
+# output_lm_numeric_empirical_progress
+
+ Code
+ (out <- code)
+ Output
+ none Solar.R Wind Temp Month Day
+ 1: 42.44 -13.252 15.541 12.826 -5.77179 3.259
+ 2: 42.44 2.758 -3.325 -7.992 -7.12800 1.808
+ 3: 42.44 6.805 -22.126 3.730 -0.09235 -5.885
+
diff --git a/tests/testthat/_snaps/output/output_custom_lm_numeric_independence_1.rds b/tests/testthat/_snaps/output/output_custom_lm_numeric_independence_1.rds
new file mode 100644
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diff --git a/tests/testthat/_snaps/output/output_custom_lm_numeric_independence_2.rds b/tests/testthat/_snaps/output/output_custom_lm_numeric_independence_2.rds
new file mode 100644
index 000000000..30dc540db
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diff --git a/tests/testthat/_snaps/output/output_custom_xgboost_mixed_dummy_ctree.rds b/tests/testthat/_snaps/output/output_custom_xgboost_mixed_dummy_ctree.rds
new file mode 100644
index 000000000..c1308a564
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diff --git a/tests/testthat/_snaps/output/output_lm_categorical_ctree.rds b/tests/testthat/_snaps/output/output_lm_categorical_ctree.rds
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diff --git a/tests/testthat/_snaps/output/output_lm_categorical_independence.rds b/tests/testthat/_snaps/output/output_lm_categorical_independence.rds
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+
+
diff --git a/tests/testthat/_snaps/plot/bar-plot-digits-5.svg b/tests/testthat/_snaps/plot/bar-plot-digits-5.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/bar-plot-index-x-explain-1.svg b/tests/testthat/_snaps/plot/bar-plot-index-x-explain-1.svg
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@@ -0,0 +1,90 @@
+
+
diff --git a/tests/testthat/_snaps/plot/bar-plot-new-colors.svg b/tests/testthat/_snaps/plot/bar-plot-new-colors.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/bar-plot-no-phi0.svg b/tests/testthat/_snaps/plot/bar-plot-no-phi0.svg
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@@ -0,0 +1,200 @@
+
+
diff --git a/tests/testthat/_snaps/plot/bar-plot-order-original.svg b/tests/testthat/_snaps/plot/bar-plot-order-original.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/bar-plot-top-3-features.svg b/tests/testthat/_snaps/plot/bar-plot-top-3-features.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/beeswarm-plot-default.svg b/tests/testthat/_snaps/plot/beeswarm-plot-default.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/beeswarm-plot-index-x-explain-1-2.svg b/tests/testthat/_snaps/plot/beeswarm-plot-index-x-explain-1-2.svg
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@@ -0,0 +1,73 @@
+
+
diff --git a/tests/testthat/_snaps/plot/beeswarm-plot-new-colors.svg b/tests/testthat/_snaps/plot/beeswarm-plot-new-colors.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/scatter-plot-default.svg b/tests/testthat/_snaps/plot/scatter-plot-default.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/scatter-plot-index-x-explain-1-2.svg b/tests/testthat/_snaps/plot/scatter-plot-index-x-explain-1-2.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/scatter-plot-new-color.svg b/tests/testthat/_snaps/plot/scatter-plot-new-color.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/scatter-plot-no-hist.svg b/tests/testthat/_snaps/plot/scatter-plot-no-hist.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/scatter-plot-one-feature.svg b/tests/testthat/_snaps/plot/scatter-plot-one-feature.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/waterfall-plot-default.svg b/tests/testthat/_snaps/plot/waterfall-plot-default.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/waterfall-plot-digits-5.svg b/tests/testthat/_snaps/plot/waterfall-plot-digits-5.svg
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+
+
diff --git a/tests/testthat/_snaps/plot/waterfall-plot-index-x-explain-1.svg b/tests/testthat/_snaps/plot/waterfall-plot-index-x-explain-1.svg
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@@ -0,0 +1,97 @@
+
+
diff --git a/tests/testthat/_snaps/plot/waterfall-plot-new-colors.svg b/tests/testthat/_snaps/plot/waterfall-plot-new-colors.svg
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@@ -0,0 +1,223 @@
+
+
diff --git a/tests/testthat/_snaps/plot/waterfall-plot-top-3-features.svg b/tests/testthat/_snaps/plot/waterfall-plot-top-3-features.svg
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@@ -0,0 +1,199 @@
+
+
diff --git a/tests/testthat/_snaps/setup.md b/tests/testthat/_snaps/setup.md
new file mode 100644
index 000000000..3ede34f5e
--- /dev/null
+++ b/tests/testthat/_snaps/setup.md
@@ -0,0 +1,755 @@
+# error with custom model without providing predict_model
+
+ Code
+ model_custom_lm_mixed <- model_lm_mixed
+ class(model_custom_lm_mixed) <- "whatever"
+ explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
+ approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
+ Message
+ Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
+ Consistency checks between model and data is therefore disabled.
+
+ Error
+ You passed a model to explain() which is not natively supported, and did not supply the 'predict_model' function to explain().
+ See ?shapr::explain or the vignette for more information on how to run shapr with custom models.
+
+# messages with missing detail in get_model_specs
+
+ Code
+ explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
+ approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
+ get_model_specs = NA, n_batches = 1, timing = FALSE)
+ Message
+ Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -4.730 7.750 17.753 -2.601 -7.588
+ 2: 42.44 2.338 -3.147 -5.310 -1.676 -7.588
+ 3: 42.44 3.857 -17.469 -1.466 1.099 3.379
+
+---
+
+ Code
+ custom_get_model_specs_no_lab <- (function(x) {
+ feature_specs <- list(labels = NA, classes = NA, factor_levels = NA)
+ })
+ explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
+ approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
+ get_model_specs = custom_get_model_specs_no_lab, n_batches = 1, timing = FALSE)
+ Message
+ Note: Feature names extracted from the model contains NA.
+ Consistency checks between model and data is therefore disabled.
+
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -4.730 7.750 17.753 -2.601 -7.588
+ 2: 42.44 2.338 -3.147 -5.310 -1.676 -7.588
+ 3: 42.44 3.857 -17.469 -1.466 1.099 3.379
+
+---
+
+ Code
+ custom_gms_no_classes <- (function(x) {
+ feature_specs <- list(labels = labels(x$terms), classes = NA, factor_levels = NA)
+ })
+ explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
+ approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
+ get_model_specs = custom_gms_no_classes, n_batches = 1, timing = FALSE)
+ Message
+ Note: Feature classes extracted from the model contains NA.
+ Assuming feature classes from the data are correct.
+
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -4.730 7.750 17.753 -2.601 -7.588
+ 2: 42.44 2.338 -3.147 -5.310 -1.676 -7.588
+ 3: 42.44 3.857 -17.469 -1.466 1.099 3.379
+
+---
+
+ Code
+ custom_gms_no_factor_levels <- (function(x) {
+ feature_specs <- list(labels = labels(x$terms), classes = attr(x$terms,
+ "dataClasses")[-1], factor_levels = NA)
+ })
+ explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
+ approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
+ get_model_specs = custom_gms_no_factor_levels, n_batches = 1, timing = FALSE)
+ Message
+ Note: Feature factor levels extracted from the model contains NA.
+ Assuming feature factor levels from the data are correct.
+
+ Output
+ none Solar.R Wind Temp Day Month_factor
+ 1: 42.44 -4.730 7.750 17.753 -2.601 -7.588
+ 2: 42.44 2.338 -3.147 -5.310 -1.676 -7.588
+ 3: 42.44 3.857 -17.469 -1.466 1.099 3.379
+
+# erroneous input: `x_train/x_explain`
+
+ Code
+ x_train_wrong_format <- c(a = 1, b = 2)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_wrong_format,
+ approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
+ Error
+ x_train should be a matrix or a data.frame/data.table.
+
+---
+
+ Code
+ x_explain_wrong_format <- c(a = 1, b = 2)
+ explain(model = model_lm_numeric, x_explain = x_explain_wrong_format, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
+ Error
+ x_explain should be a matrix or a data.frame/data.table.
+
+---
+
+ Code
+ x_train_wrong_format <- c(a = 1, b = 2)
+ x_explain_wrong_format <- c(a = 3, b = 4)
+ explain(model = model_lm_numeric, x_explain = x_explain_wrong_format, x_train = x_train_wrong_format,
+ approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
+ Error
+ x_train should be a matrix or a data.frame/data.table.
+ x_explain should be a matrix or a data.frame/data.table.
+
+---
+
+ Code
+ x_train_no_column_names <- as.data.frame(x_train_numeric)
+ names(x_train_no_column_names) <- NULL
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_no_column_names,
+ approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
+ Error
+ x_train misses column names.
+
+---
+
+ Code
+ x_explain_no_column_names <- as.data.frame(x_explain_numeric)
+ names(x_explain_no_column_names) <- NULL
+ explain(model = model_lm_numeric, x_explain = x_explain_no_column_names,
+ x_train = x_train_numeric, approach = "independence", prediction_zero = p0,
+ n_batches = 1, timing = FALSE)
+ Error
+ x_explain misses column names.
+
+---
+
+ Code
+ x_train_no_column_names <- as.data.frame(x_train_numeric)
+ x_explain_no_column_names <- as.data.frame(x_explain_numeric)
+ names(x_explain_no_column_names) <- NULL
+ explain(model = model_lm_numeric, x_explain = x_explain_no_column_names,
+ x_train = x_train_no_column_names, approach = "independence",
+ prediction_zero = p0, n_batches = 1, timing = FALSE)
+ Error
+ x_explain misses column names.
+
+# erroneous input: `model`
+
+ Code
+ explain(x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence",
+ prediction_zero = p0, n_batches = 1, timing = FALSE)
+ Error
+ argument "model" is missing, with no default
+
+# erroneous input: `approach`
+
+ Code
+ approach_non_character <- 1
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = approach_non_character, prediction_zero = p0, n_batches = 1,
+ timing = FALSE)
+ Error
+ `approach` must be one of the following:
+ categorical, copula, ctree, empirical, gaussian, independence, timeseries
+ or a vector of length equal to the number of features ( 5 ) with only the above strings.
+
+---
+
+ Code
+ approach_incorrect_length <- c("empirical", "gaussian")
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = approach_incorrect_length, prediction_zero = p0, n_batches = 1,
+ timing = FALSE)
+ Error
+ `approach` must be one of the following:
+ categorical, copula, ctree, empirical, gaussian, independence, timeseries
+ or a vector of length equal to the number of features ( 5 ) with only the above strings.
+
+---
+
+ Code
+ approach_incorrect_character <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = approach_incorrect_character, prediction_zero = p0, n_batches = 1,
+ timing = FALSE)
+ Error
+ `approach` must be one of the following:
+ categorical, copula, ctree, empirical, gaussian, independence, timeseries
+ or a vector of length equal to the number of features ( 5 ) with only the above strings.
+
+# erroneous input: `prediction_zero`
+
+ Code
+ p0_non_numeric_1 <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0_non_numeric_1, n_batches = 1,
+ timing = FALSE)
+ Error
+ `prediction_zero` (bla) must be numeric and match the output size of the model (1).
+
+---
+
+ Code
+ p0_non_numeric_2 <- NULL
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0_non_numeric_2, n_batches = 1,
+ timing = FALSE)
+ Error
+ `prediction_zero` () must be numeric and match the output size of the model (1).
+
+---
+
+ Code
+ p0_too_long <- c(1, 2)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0_too_long, n_batches = 1,
+ timing = FALSE)
+ Error
+ `prediction_zero` (1, 2) must be numeric and match the output size of the model (1).
+
+---
+
+ Code
+ p0_is_NA <- as.numeric(NA)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0_is_NA, n_batches = 1, timing = FALSE)
+ Error
+ `prediction_zero` (NA) must be numeric and match the output size of the model (1).
+
+# erroneous input: `n_combinations`
+
+ Code
+ n_combinations_non_numeric_1 <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_numeric_1,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_combinations_non_numeric_2 <- TRUE
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_numeric_2,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_combinations_non_integer <- 10.5
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_integer,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_combinations_too_long <- c(1, 2)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_combinations = n_combinations_too_long,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_combinations_is_NA <- as.numeric(NA)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_combinations = n_combinations_is_NA,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_combinations_non_positive <- 0
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_positive,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_combinations <- ncol(x_explain_numeric) - 1
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ prediction_zero = p0, approach = "gaussian", n_combinations = n_combinations,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` has to be greater than the number of features.
+
+---
+
+ Code
+ groups <- list(A = c("Solar.R", "Wind"), B = c("Temp", "Month"), C = "Day")
+ n_combinations <- length(groups) - 1
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ prediction_zero = p0, approach = "gaussian", group = groups, n_combinations = n_combinations,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_combinations` has to be greater than the number of groups.
+
+# erroneous input: `group`
+
+ Code
+ group_non_list <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, group = group_non_list,
+ n_batches = 1, timing = FALSE)
+ Error
+ `group` must be NULL or a list
+
+---
+
+ Code
+ group_with_non_characters <- list(A = 1, B = 2)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, group = group_with_non_characters,
+ n_batches = 1, timing = FALSE)
+ Error
+ All components of group should be a character.
+
+---
+
+ Code
+ group_with_non_data_features <- list(A = c("Solar.R", "Wind",
+ "not_a_data_feature"), B = c("Temp", "Month", "Day"))
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, group = group_with_non_data_features,
+ n_batches = 1, timing = FALSE)
+ Error
+ The group feature(s) not_a_data_feature are not
+ among the features in the data: Solar.R, Wind, Temp, Month, Day. Delete from group.
+
+---
+
+ Code
+ group_missing_data_features <- list(A = c("Solar.R"), B = c("Temp", "Month",
+ "Day"))
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, group = group_missing_data_features,
+ n_batches = 1, timing = FALSE)
+ Error
+ The data feature(s) Wind do not
+ belong to one of the groups. Add to a group.
+
+---
+
+ Code
+ group_dup_data_features <- list(A = c("Solar.R", "Solar.R", "Wind"), B = c(
+ "Temp", "Month", "Day"))
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, group = group_dup_data_features,
+ n_batches = 1, timing = FALSE)
+ Error
+ Feature(s) Solar.R are found in more than one group or multiple times per group.
+ Make sure each feature is only represented in one group, and only once.
+
+---
+
+ Code
+ single_group <- list(A = c("Solar.R", "Wind", "Temp", "Month", "Day"))
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, group = single_group,
+ n_batches = 1, timing = FALSE)
+ Error
+ You have specified only a single group named A, containing the features: Solar.R, Wind, Temp, Month, Day.
+ The predictions must be decomposed in at least two groups to be meaningful.
+
+# erroneous input: `n_samples`
+
+ Code
+ n_samples_non_numeric_1 <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_samples = n_samples_non_numeric_1,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_samples` must be a single positive integer.
+
+---
+
+ Code
+ n_samples_non_numeric_2 <- TRUE
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_samples = n_samples_non_numeric_2,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_samples` must be a single positive integer.
+
+---
+
+ Code
+ n_samples_non_integer <- 10.5
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_samples = n_samples_non_integer,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_samples` must be a single positive integer.
+
+---
+
+ Code
+ n_samples_too_long <- c(1, 2)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_samples = n_samples_too_long,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_samples` must be a single positive integer.
+
+---
+
+ Code
+ n_samples_is_NA <- as.numeric(NA)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_samples = n_samples_is_NA,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_samples` must be a single positive integer.
+
+---
+
+ Code
+ n_samples_non_positive <- 0
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_samples = n_samples_non_positive,
+ n_batches = 1, timing = FALSE)
+ Error
+ `n_samples` must be a single positive integer.
+
+# erroneous input: `n_batches`
+
+ Code
+ n_batches_non_numeric_1 <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = n_batches_non_numeric_1,
+ timing = FALSE)
+ Error
+ `n_batches` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_batches_non_numeric_2 <- TRUE
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = n_batches_non_numeric_2,
+ timing = FALSE)
+ Error
+ `n_batches` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_batches_non_integer <- 10.5
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = n_batches_non_integer,
+ timing = FALSE)
+ Error
+ `n_batches` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_batches_too_long <- c(1, 2)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = n_batches_too_long,
+ timing = FALSE)
+ Error
+ `n_batches` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_batches_is_NA <- as.numeric(NA)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = n_batches_is_NA,
+ timing = FALSE)
+ Error
+ `n_batches` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_batches_non_positive <- 0
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = n_batches_non_positive,
+ timing = FALSE)
+ Error
+ `n_batches` must be NULL or a single positive integer.
+
+---
+
+ Code
+ n_combinations <- 10
+ n_batches_too_large <- 11
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_combinations = n_combinations,
+ n_batches = n_batches_too_large, timing = FALSE)
+ Error
+ `n_batches` (11) must be smaller than the number feature combinations/`n_combinations` (10)
+
+---
+
+ Code
+ n_batches_too_large_2 <- 32
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, n_batches = n_batches_too_large_2,
+ timing = FALSE)
+ Error
+ `n_batches` (32) must be smaller than the number feature combinations/`n_combinations` (32)
+
+# erroneous input: `seed`
+
+ Code
+ seed_not_integer_interpretable <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, seed = seed_not_integer_interpretable,
+ n_batches = 1, timing = FALSE)
+ Warning
+ NAs introduced by coercion
+ Error
+ supplied seed is not a valid integer
+
+# erroneous input: `keep_samp_for_vS`
+
+ Code
+ keep_samp_for_vS_non_logical_1 <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, keep_samp_for_vS = keep_samp_for_vS_non_logical_1,
+ n_batches = 1, timing = FALSE)
+ Error
+ `keep_samp_for_vS` must be single logical.
+
+---
+
+ Code
+ keep_samp_for_vS_non_logical_2 <- NULL
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, keep_samp_for_vS = keep_samp_for_vS_non_logical_2,
+ n_batches = 1, timing = FALSE)
+ Error
+ `keep_samp_for_vS` must be single logical.
+
+---
+
+ Code
+ keep_samp_for_vS_too_long <- c(TRUE, FALSE)
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, keep_samp_for_vS = keep_samp_for_vS_too_long,
+ n_batches = 1, timing = FALSE)
+ Error
+ `keep_samp_for_vS` must be single logical.
+
+# erroneous input: `predict_model`
+
+ Code
+ predict_model_nonfunction <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, predict_model = predict_model_nonfunction,
+ n_batches = 1, timing = FALSE)
+ Error
+ `predict_model` must be NULL or a function.
+
+---
+
+ Code
+ predict_model_non_num_output <- (function(model, x) {
+ "bla"
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, predict_model = predict_model_non_num_output,
+ n_batches = 1, timing = FALSE)
+ Error
+ The predict_model function of class `lm` does not return a numeric output of the desired length
+ for single output models or a data.table of the correct
+ dimensions for a multiple output model.
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+
+ for more information on running shapr with custom models.
+
+---
+
+ Code
+ predict_model_wrong_output_len <- (function(model, x) {
+ rep(1, nrow(x) + 1)
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, predict_model = predict_model_wrong_output_len,
+ n_batches = 1, timing = FALSE)
+ Error
+ The predict_model function of class `lm` does not return a numeric output of the desired length
+ for single output models or a data.table of the correct
+ dimensions for a multiple output model.
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+
+ for more information on running shapr with custom models.
+
+---
+
+ Code
+ predict_model_invalid_argument <- (function(model) {
+ rep(1, nrow(x))
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, predict_model = predict_model_invalid_argument,
+ n_batches = 1, timing = FALSE)
+ Error
+ The predict_model function of class `lm` is invalid.
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+ for more information on running shapr with custom models.
+ A basic function test threw the following error:
+ Error in predict_model(model, x_test): unused argument (x_test)
+
+---
+
+ Code
+ predict_model_error <- (function(model, x) {
+ 1 + "bla"
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, predict_model = predict_model_error,
+ n_batches = 1, timing = FALSE)
+ Error
+ The predict_model function of class `lm` is invalid.
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+ for more information on running shapr with custom models.
+ A basic function test threw the following error:
+ Error in 1 + "bla": non-numeric argument to binary operator
+
+# erroneous input: `get_model_specs`
+
+ Code
+ get_model_specs_nonfunction <- "bla"
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, get_model_specs = get_model_specs_nonfunction,
+ n_batches = 1, timing = FALSE)
+ Error
+ `get_model_specs` must be NULL, NA or a function.
+
+---
+
+ Code
+ get_ms_output_not_list <- (function(x) {
+ "bla"
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, get_model_specs = get_ms_output_not_list,
+ n_batches = 1, timing = FALSE)
+ Error
+ The `get_model_specs` function of class `lm` does not return a list of length 3 with elements "labels","classes","factor_levels".
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+ for more information on running shapr with custom models and the required output format of get_model_specs.
+
+---
+
+ Code
+ get_ms_output_too_long <- (function(x) {
+ list(1, 2, 3, 4)
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, get_model_specs = get_ms_output_too_long,
+ n_batches = 1, timing = FALSE)
+ Error
+ The `get_model_specs` function of class `lm` does not return a list of length 3 with elements "labels","classes","factor_levels".
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+ for more information on running shapr with custom models and the required output format of get_model_specs.
+
+---
+
+ Code
+ get_ms_output_wrong_names <- (function(x) {
+ list(labels = 1, classes = 2, not_a_name = 3)
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, get_model_specs = get_ms_output_wrong_names,
+ n_batches = 1, timing = FALSE)
+ Error
+ The `get_model_specs` function of class `lm` does not return a list of length 3 with elements "labels","classes","factor_levels".
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+ for more information on running shapr with custom models and the required output format of get_model_specs.
+
+---
+
+ Code
+ get_model_specs_error <- (function(x) {
+ 1 + "bla"
+ })
+ explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
+ approach = "independence", prediction_zero = p0, get_model_specs = get_model_specs_error,
+ n_batches = 1, timing = FALSE)
+ Error
+ The get_model_specs function of class `lm` is invalid.
+ See the 'Advanced usage' section of the vignette:
+ vignette('understanding_shapr', package = 'shapr')
+ for more information on running shapr with custom models.
+ Note that `get_model_specs` is not required (can be set to NULL)
+ unless you require consistency checks between model and data.
+ A basic function test threw the following error:
+ Error in 1 + "bla": non-numeric argument to binary operator
+
+# incompatible input: `data/approach`
+
+ Code
+ non_factor_approach_1 <- "gaussian"
+ explain(model = model_lm_mixed, x_explain = x_explain_mixed, x_train = x_explain_mixed,
+ approach = non_factor_approach_1, prediction_zero = p0, n_batches = 1,
+ timing = FALSE)
+ Error
+ The following feature(s) are factor(s): Month_factor.
+ approach = 'gaussian' does not support factor features.
+ Please change approach to one of 'independence' (not recommended), 'ctree', 'categorical'.
+
+---
+
+ Code
+ non_factor_approach_2 <- "empirical"
+ explain(model = model_lm_mixed, x_explain = x_explain_mixed, x_train = x_explain_mixed,
+ approach = non_factor_approach_2, prediction_zero = p0, n_batches = 1,
+ timing = FALSE)
+ Error
+ The following feature(s) are factor(s): Month_factor.
+ approach = 'empirical' does not support factor features.
+ Please change approach to one of 'independence' (not recommended), 'ctree', 'categorical'.
+
+---
+
+ Code
+ non_factor_approach_3 <- "copula"
+ explain(model = model_lm_mixed, x_explain = x_explain_mixed, x_train = x_explain_mixed,
+ approach = non_factor_approach_3, prediction_zero = p0, n_batches = 1,
+ timing = FALSE)
+ Error
+ The following feature(s) are factor(s): Month_factor.
+ approach = 'copula' does not support factor features.
+ Please change approach to one of 'independence' (not recommended), 'ctree', 'categorical'.
+
diff --git a/tests/testthat/helper-ar-arima.R b/tests/testthat/helper-ar-arima.R
new file mode 100644
index 000000000..47944e87b
--- /dev/null
+++ b/tests/testthat/helper-ar-arima.R
@@ -0,0 +1,17 @@
+options(digits = 5) # To avoid round off errors when printing output on different systems
+
+
+
+data <- data.table::as.data.table(airquality)
+
+model_ar_temp <- ar(data$Temp, order = 2)
+model_ar_temp$n.ahead <- 3
+
+p0_ar <- rep(mean(data$Temp), 3)
+
+model_arima_temp <- arima(data$Temp[1:150], c(2, 1, 0), xreg = data$Wind[1:150])
+
+model_arima_temp_noxreg <- arima(data$Temp[1:150], c(2, 1, 0))
+
+# When loading this here we avoid the "Registered S3 method overwritten" when calling forecast
+model_forecast_ARIMA_temp <- forecast::Arima(data$Temp[1:150], order = c(2, 1, 0), xreg = data$Wind[1:150])
diff --git a/tests/testthat/helper-lm.R b/tests/testthat/helper-lm.R
new file mode 100644
index 000000000..8b886cdbf
--- /dev/null
+++ b/tests/testthat/helper-lm.R
@@ -0,0 +1,47 @@
+options(digits = 5) # To avoid round off errors when printing output on different systems
+
+set.seed(12345)
+
+data <- data.table::as.data.table(airquality)
+data[, Month_factor := as.factor(Month)]
+data[, Ozone_sub30 := (Ozone < 30) * 1]
+data[, Ozone_sub30_factor := as.factor(Ozone_sub30)]
+data[, Solar.R_factor := as.factor(cut(Solar.R, 10))]
+data[, Wind_factor := as.factor(round(Wind))]
+
+data_complete <- data[complete.cases(airquality), ]
+data_complete <- data_complete[sample(seq_len(.N))] # Sh
+
+y_var_numeric <- "Ozone"
+y_var_binary <- "Ozone_sub30"
+y_var_binaryfactor <- "Ozone_sub30_factor"
+
+x_var_numeric <- c("Solar.R", "Wind", "Temp", "Month", "Day")
+x_var_mixed <- c("Solar.R", "Wind", "Temp", "Day", "Month_factor")
+x_var_categorical <- c("Month_factor", "Ozone_sub30_factor", "Solar.R_factor", "Wind_factor")
+
+data_train <- head(data_complete, -3)
+data_explain <- tail(data_complete, 3)
+
+x_train_numeric <- data_train[, ..x_var_numeric]
+x_train_mixed <- data_train[, ..x_var_mixed]
+x_train_categorical <- data_train[, ..x_var_categorical]
+
+x_explain_numeric <- data_explain[, ..x_var_numeric]
+x_explain_mixed <- data_explain[, ..x_var_mixed]
+x_explain_categorical <- data_explain[, ..x_var_categorical]
+
+lm_formula_numeric <- as.formula(paste0(y_var_numeric, " ~ ", paste0(x_var_numeric, collapse = " + ")))
+lm_formula_mixed <- as.formula(paste0(y_var_numeric, " ~ ", paste0(x_var_mixed, collapse = " + ")))
+lm_formula_interaction <- Ozone ~ Solar.R * Wind
+# lm_formula_numeric_col_order <- as.formula(paste0(y_var_numeric, " ~ ",
+# paste0(sort(x_var_numeric), collapse = " + ")))
+lm_formula_categorical <- as.formula(paste0(y_var_numeric, " ~ ", paste0(x_var_categorical, collapse = " + ")))
+
+model_lm_numeric <- lm(lm_formula_numeric, data = data_complete)
+model_lm_categorical <- lm(lm_formula_categorical, data = data_complete)
+model_lm_numeric_col_order <- lm(lm_formula_numeric, data = rev(data_complete))
+model_lm_mixed <- lm(lm_formula_mixed, data = data_complete)
+model_lm_interaction <- lm(lm_formula_interaction, data = data_complete)
+
+p0 <- data_train[, mean(get(y_var_numeric))]
diff --git a/tests/testthat/helper-ts.R b/tests/testthat/helper-ts.R
new file mode 100644
index 000000000..0ce92e9fa
--- /dev/null
+++ b/tests/testthat/helper-ts.R
@@ -0,0 +1,40 @@
+options(digits = 5) # To avoid round off errors when printing output on different systems
+
+set.seed(1234)
+
+data_ts <- data.frame(matrix(NA, ncol = 41, nrow = 4))
+for (n in 1:100) {
+ set.seed(n)
+ e <- rnorm(42, mean = 0, sd = 1)
+
+ m_1 <- 0
+ for (i in 2:length(e)) {
+ m_1[i] <- 1 + 0.8 * m_1[i - 1] + e[i]
+ }
+ data_ts[n, ] <- m_1[-1]
+}
+data_ts <- data.table::as.data.table(data_ts)
+
+x_var_ts <- paste0("X", 1:40)
+y_var_ts <- "X41"
+
+ind_x_explain <- 1:2
+data_ts_train <- data_ts[-ind_x_explain]
+
+# Creating a predictive model (for illustration just predicting the next point in the time series with a linear model)
+lm_ts_formula <- as.formula(X41 ~ .)
+model_lm_ts <- lm(lm_ts_formula, data_ts_train)
+
+x_explain_ts <- data_ts[ind_x_explain, ..x_var_ts]
+x_train_ts <- data_ts[-ind_x_explain, ..x_var_ts]
+
+# Spitting the time series into 4 segments
+group_ts <- list(
+ S1 = paste0("X", 1:10),
+ S2 = paste0("X", 11:20),
+ S3 = paste0("X", 21:30),
+ S4 = paste0("X", 31:40)
+)
+
+
+p0_ts <- mean(unlist(data_ts_train[, ..y_var_ts]))
diff --git a/tests/testthat/manual_test_scripts/test_custom_models.R b/tests/testthat/manual_test_scripts/test_custom_models.R
deleted file mode 100644
index 248e29843..000000000
--- a/tests/testthat/manual_test_scripts/test_custom_models.R
+++ /dev/null
@@ -1,117 +0,0 @@
-# Test custom models
-
-# Doing all testing from shapr
-# Because new functions have to be created (to use gbm with shapr), we cannot use a classic testthat set up because
-# shapr will not see the functions created inside of the test environment. Therefore we have to test these functions
-# a bit differently (and more manual) than other tests.
-
-library(testthat)
-library(shapr)
-library(gbm)
-library(MASS)
-
-# Data -----------
-data("Boston", package = "MASS")
-y_var <- "medv"
-x_train <- tail(Boston, -6)
-y_train <- tail(Boston[, y_var], -6)
-y_train_binary <- as.factor(tail((Boston[, y_var] > 20) * 1, -6))
-
-# convert to factors for testing purposes
-x_train$rad <- factor(round(x_train$rad))
-x_train$chas <- factor(round(x_train$chas))
-
-train_df <- cbind(x_train, y_train, y_train_binary)
-
-
-x_var_numeric <- c("lstat", "rm", "dis", "indus")
-x_var_factor <- c("lstat", "rm", "dis", "indus", "rad", "chas")
-
-train_df_used_numeric <- x_train[, x_var_numeric]
-train_df_used_factor <- x_train[, x_var_factor]
-
-formula_numeric <- as.formula(paste0("y_train ~ ", paste0(x_var_numeric, collapse = "+")))
-formula_factor <- as.formula(paste0("y_train ~ ", paste0(x_var_factor, collapse = "+")))
-
-# Custom model with only numeric features
-model_custom <- gbm::gbm(formula_numeric, data = train_df, distribution = "gaussian")
-expect_error(shapr(train_df_used_numeric, model_custom)) # Required model objects defined
-get_model_specs.gbm <- function(x) {
- feature_list <- list()
- feature_list$labels <- labels(x$Terms)
- m <- length(feature_list$labels)
- feature_list$classes <- attr(x$Terms, "dataClasses")[-1]
- feature_list$factor_levels <- setNames(vector("list", m), feature_list$labels)
- feature_list$factor_levels[feature_list$classes == "factor"] <- NA # the model object don't contain factor levels info
- return(feature_list)
-}
-expect_error(shapr(train_df_used_numeric, model_custom)) # predict_model objects not defined
-
-predict_model.gbm <- function(x, newdata) {
- if (!requireNamespace("gbm", quietly = TRUE)) {
- stop("The gbm package is required for predicting train models")
- }
- model_type <- ifelse(
- x$distribution$name %in% c("bernoulli", "adaboost"),
- "classification",
- "regression"
- )
- if (model_type == "classification") {
- predict(x, as.data.frame(newdata), type = "response", n.trees = x$n.trees)
- } else {
- predict(x, as.data.frame(newdata), n.trees = x$n.trees)
- }
-}
-
-expect_silent(shapr(train_df_used_numeric, model_custom)) # Both defined, so pass silently
-
-rm(get_model_specs.gbm)
-
-expect_message(shapr(train_df_used_numeric, model_custom)) # Only predict_model defined, so warning
-rm(predict_model.gbm)
-
-
-# Custom model with factors
-model_custom <- gbm::gbm(formula_factor, data = train_df, distribution = "gaussian")
-expect_error(shapr(train_df_used_factor, model_custom)) # Required model objects defined
-get_model_specs.gbm <- function(x) {
- feature_list <- list()
- feature_list$labels <- labels(x$Terms)
- m <- length(feature_list$labels)
- feature_list$classes <- attr(x$Terms, "dataClasses")[-1]
- feature_list$factor_levels <- setNames(vector("list", m), feature_list$labels)
- feature_list$factor_levels[feature_list$classes == "factor"] <- NA # model object doesn't contain factor level info
- return(feature_list)
-}
-expect_error(shapr(train_df_used_factor, model_custom)) # predict_model objects not defined
-
-predict_model.gbm <- function(x, newdata) {
- if (!requireNamespace("gbm", quietly = TRUE)) {
- stop("The gbm package is required for predicting train models")
- }
- model_type <- ifelse(
- x$distribution$name %in% c("bernoulli", "adaboost"),
- "classification",
- "regression"
- )
- if (model_type == "classification") {
- predict(x, as.data.frame(newdata), type = "response", n.trees = x$n.trees)
- } else {
- predict(x, as.data.frame(newdata), n.trees = x$n.trees)
- }
-}
-expect_message(shapr(train_df_used_factor, model_custom)) # Both defined, so pass with message as factor_level is NA
-
-rm(get_model_specs.gbm)
-
-expect_message(shapr(train_df_used_factor, model_custom)) # Only predict_model defined, so warning message returned
-
-rm(predict_model.gbm)
-
-predict_model.gbm <- function(x, newdata) NULL
-
-# Erroneous predict_model defined, so throw error + messages
-expect_message(expect_error(shapr(train_df_used_factor, model_custom)))
-
-
-rm(predict_model.gbm)
diff --git a/tests/testthat/model_objects/lm_model_object.rds b/tests/testthat/model_objects/lm_model_object.rds
deleted file mode 100644
index 07053d343..000000000
Binary files a/tests/testthat/model_objects/lm_model_object.rds and /dev/null differ
diff --git a/tests/testthat/test-a-shapley.R b/tests/testthat/test-a-shapley.R
deleted file mode 100644
index eccfe4547..000000000
--- a/tests/testthat/test-a-shapley.R
+++ /dev/null
@@ -1,271 +0,0 @@
-context("test-shapley.R")
-
-suppressWarnings(RNGversion(vstr = "3.5.0"))
-
-test_that("Basic test functions in shapley.R", {
-
- # Load data -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- x_train <- tail(Boston[, x_var], 50)
-
- # Load premade lm model. Path needs to be relative to testthat directory in the package
- model <- readRDS("model_objects/lm_model_object.rds")
-
- # Prepare the data for explanation
- explainer <- shapr(x_train, model)
-
- expect_known_value(explainer,
- file = "test_objects/shapley_explainer_obj.rds",
- update = F
- )
- }
-})
-
-
-test_that("Testing data input to shapr in shapley.R", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
-
- y_var <- "medv"
- x_train <- tail(Boston, -6)
- y_train <- tail(Boston[, y_var], -6)
- y_train_binary <- as.factor(tail((Boston[, y_var] > 20) * 1, -6))
-
- # convert to factors for testing purposes
- x_train$rad <- factor(round(x_train$rad))
- x_train$chas <- factor(round(x_train$chas))
-
- train_df <- cbind(x_train, y_train, y_train_binary)
-
-
- x_var_numeric <- c("lstat", "rm", "dis", "indus")
- x_var_factor <- c("lstat", "rm", "dis", "indus", "rad", "chas")
-
- train_df_used_numeric <- x_train[, x_var_numeric]
- train_df_used_factor <- x_train[, x_var_factor]
-
- formula_numeric <- as.formula(paste0("y_train ~ ", paste0(x_var_numeric, collapse = "+")))
- formula_factor <- as.formula(paste0("y_train ~ ", paste0(x_var_factor, collapse = "+")))
-
- formula_binary_numeric <- as.formula(paste0("y_train_binary ~ ", paste0(x_var_numeric, collapse = "+")))
- formula_binary_factor <- as.formula(paste0("y_train_binary ~ ", paste0(x_var_factor, collapse = "+")))
-
- dummylist <- make_dummies(traindata = x_train[, x_var_factor], testdata = x_train[, x_var_factor])
-
- # List of models to run silently
- l_numeric <- list(
- stats::lm(formula_numeric, data = train_df),
- stats::glm(formula_numeric, data = train_df)
- )
-
- if (requireNamespace("mgcv", quietly = TRUE)) {
- l_numeric[[length(l_numeric) + 1]] <- mgcv::gam(formula_numeric, data = train_df)
- }
-
- l_factor <- list(
- stats::lm(formula_factor, data = train_df),
- stats::glm(formula_factor, data = train_df)
- )
-
- if (requireNamespace("mgcv", quietly = TRUE)) {
- l_factor[[length(l_factor) + 1]] <- mgcv::gam(formula_factor, data = train_df)
- }
-
- if (requireNamespace("xgboost", quietly = TRUE)) {
- l_factor[[length(l_factor) + 1]] <- xgboost::xgboost(
- data = dummylist$train_dummies,
- label = y_train,
- nrounds = 3, verbose = FALSE
- )
- l_factor[[length(l_factor)]]$feature_list <- dummylist$feature_list
- }
-
-
- for (i in seq_along(l_numeric)) {
- expect_silent(shapr(train_df_used_numeric, l_numeric[[i]])) # No modification
- expect_message(shapr(train_df, l_numeric[[i]])) # Features dropped
- }
-
- for (i in seq_along(l_factor)) {
- expect_silent(shapr(train_df_used_factor, l_factor[[i]])) # No modification
- expect_message(shapr(train_df, l_factor[[i]])) # Features dropped
- }
-
-
- # Testing errors on incompatible model and data
- # Missing features
- model <- stats::lm(formula_factor, data = train_df)
- data_error <- train_df[, -3]
- expect_error(shapr(data_error, model))
-
- # Duplicated column names
- data_error <- train_df_used_factor
- data_error <- cbind(data_error, lstat = 1)
- expect_error(shapr(data_error, model))
-
- # Empty column names in data
- data_error <- train_df
- colnames(data_error) <- NULL
- expect_error(shapr(data_error, model))
-
- # Empty column names in model (ok if found in data -- and we trust it)
- if (requireNamespace("xgboost", quietly = TRUE)) {
- data_with_colnames <- data_without_colnames <- as.matrix(train_df_used_numeric)
- colnames(data_without_colnames) <- NULL
-
- model_xgb <- xgboost::xgboost(
- data = data_without_colnames, label = y_train,
- nrounds = 3, verbose = FALSE
- )
- expect_message(shapr(data_with_colnames, model_xgb))
- }
-
- # Data feature with incorrect class
- data_error <- train_df_used_factor
- data_error$lstat <- as.logical(data_error$lstat > 15)
- expect_error(shapr(data_error, model))
-
- # non-matching factor levels
- data_error <- head(train_df_used_factor)
- data_error$rad <- droplevels(data_error$rad)
- expect_error(shapr(data_error, model))
- }
-})
-
-test_that("Basic test functions for grouping in shapley.R", {
-
- # Load data -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- x_train <- tail(Boston[, x_var], 50)
-
- # Load premade lm model. Path needs to be relative to testthat directory in the package
- model <- readRDS("model_objects/lm_model_object.rds")
-
- group1_num <- list(
- c(1, 3),
- c(2, 4)
- )
-
- group1 <- lapply(group1_num, function(x) {
- x_var[x]
- })
-
-
- group2_num <- list(
- c(1),
- c(2),
- c(3),
- c(4)
- )
-
- group2 <- lapply(group2_num, function(x) {
- x_var[x]
- })
-
- # Prepare the data for explanation
- explainer1 <- shapr(x_train, model, group = group1)
- explainer2 <- shapr(x_train, model, group = group2)
-
- set.seed(123)
- explainer1_2 <- shapr(x_train, model, group = group1, n_combinations = 5)
- set.seed(1234)
- explainer2_2 <- shapr(x_train, model, group = group2, n_combinations = 5)
-
- expect_known_value(explainer1,
- file = "test_objects/shapley_explainer_group1_obj.rds",
- update = F
- )
- expect_known_value(explainer2,
- file = "test_objects/shapley_explainer_group2_obj.rds",
- update = F
- )
- expect_known_value(explainer1_2,
- file = "test_objects/shapley_explainer_group1_2_obj.rds",
- update = F
- )
- expect_known_value(explainer2_2,
- file = "test_objects/shapley_explainer_group2_2_obj.rds",
- update = F
- )
-
- }
-})
-
-
-test_that("Testing data input to shapr for grouping in shapley.R", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
-
- x_var <- c("lstat", "rm", "dis", "indus")
- not_x_var <- "crim"
-
- x_train <- as.matrix(tail(Boston[, x_var], -6))
- xy_train <- tail(Boston, -6)
- group_num <- list(
- c(1, 3),
- c(2, 4)
- )
-
- group <- lapply(group_num, function(x) {
- x_var[x]
- })
- names(group) <- c("A", "B")
-
- group_no_names <- lapply(group_num, function(x) {
- x_var[x]
- })
-
- group_error_1 <- list(
- c(x_var[1:2], not_x_var),
- x_var[3:4]
- )
-
- group_error_2 <- list(
- x_var[1],
- x_var[3:4]
- )
-
- group_error_3 <- list(
- x_var[c(1, 2)],
- x_var[c(1, 3, 4)]
- )
-
- group_error_4 <- list(
- x_var[c(1, 2)],
- x_var[c(1, 3, 4)]
- )
-
-
- # Fitting models
- formula <- as.formula(paste0("medv ~ ", paste0(x_var, collapse = "+")))
- model <- stats::lm(formula = formula, data = xy_train)
-
-
- # Expect silent
- expect_silent(shapr(x = x_train, model = model, group = group))
-
- # Expect message for missing names
- expect_message(shapr(x = x_train, model = model, group = group_no_names))
-
-
- # Expect error when group is not a list
- expect_error(shapr(x_train, model, group = x_var))
-
-
- # Expect error that group does not include names of features
- expect_error(shapr(x = x_train, model = model, group = group_num))
-
- # Expect error when x_train/model does not use a feature mentioned in the group
- expect_error(shapr(x_train, model, group = group_error_1))
-
- # Expect error when group does not contain a feature used by the model
- expect_error(shapr(x_train, model, group = group_error_2))
-
- # Expect error when group does duplicated features
- expect_error(shapr(x_train, model, group = group_error_3))
- }
-})
diff --git a/tests/testthat/test-explanation.R b/tests/testthat/test-explanation.R
deleted file mode 100644
index de74a3b7d..000000000
--- a/tests/testthat/test-explanation.R
+++ /dev/null
@@ -1,716 +0,0 @@
-context("test-explanation.R")
-
-# For using same Random numer generator as CircelCI (R version 3.5.x)
-suppressWarnings(RNGversion(vstr = "3.5.0"))
-
-test_that("Test get_list_approaches", {
- m <- 4
- n_features <- c(0, 1, 1, 1, 2, 2, 2, 3)
- approach <- c("gaussian", "copula", "copula")
- l <- get_list_approaches(n_features, approach)
-
- expect_true(is.list(l))
- expect_equal(names(l), c("gaussian", "copula"))
- expect_equal(l$gaussian, 1:4)
- expect_equal(l$copula, 5:8)
-})
-
-test_that("Test functions in explanation.R", {
-
- # Load data -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- y_train <- tail(Boston[, y_var], 50)
- x_test <- as.matrix(head(Boston[, x_var], 2))
-
- # Prepare the data for explanation. Path needs to be relative to testthat directory in the package
- explainer <- readRDS(file = "test_objects/shapley_explainer_obj.rds")
- p0 <- mean(y_train)
-
- # Test way to insert test data (shapr=4.0)
- expect_silent(explain(x_test, explainer, approach = "gaussian", prediction_zero = p0))
- expect_silent(explain(head(x_test), explainer, approach = "gaussian", prediction_zero = p0))
- expect_silent(explain(x_test[, 1:4], explainer, approach = "gaussian", prediction_zero = p0))
- expect_silent(explain(x_test[1:2, ], explainer, approach = "gaussian", prediction_zero = p0))
-
-
- # Creating list with lots of different explainer objects
- ex_list <- list()
-
- # Ex 1: Explain predictions (gaussian)
- ex_list[[1]] <- explain(x_test, explainer, approach = "gaussian", prediction_zero = p0)
-
- # Ex 2: Explain predictions (copula)
- ex_list[[2]] <- explain(x_test, explainer, approach = "copula", prediction_zero = p0)
-
- # Ex 3: Explain predictions (empirical, independence):
- ex_list[[3]] <- explain(x_test, explainer, approach = "independence", prediction_zero = p0)
-
- # Ex 4: Explain predictions (empirical, fixed sigma)
- ex_list[[4]] <- explain(x_test, explainer, approach = "empirical", prediction_zero = p0, type = "fixed_sigma")
-
- # Ex 5: Explain predictions (empirical, AICc)
- ex_list[[5]] <- explain(x_test, explainer, approach = "empirical", prediction_zero = p0, type = "AICc_each_k")
-
- # Ex 6: Explain predictions (empirical, AICc full)
- ex_list[[6]] <- explain(x_test, explainer, approach = "empirical", prediction_zero = p0, type = "AICc_full")
-
- # Ex 7: Explain combined - empirical and gaussian
- ex_list[[7]] <- explain(x_test, explainer, approach = c("empirical", rep("gaussian", 3)), prediction_zero = p0)
-
- # Ex 8: Explain combined II - all gaussian
- ex_list[[8]] <- explain(x_test, explainer, approach = c(rep("gaussian", 4)), prediction_zero = p0)
-
- # Ex 9: Explain combined III - all copula
- ex_list[[9]] <- explain(x_test, explainer, approach = rep("copula", 4), prediction_zero = p0)
-
- # Ex 10: gaussian and copula XX (works with seed)
- approach <- c(rep("gaussian", 2), rep("copula", 2))
- ex_list[[10]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 11: empirical and gaussian
- approach <- c(rep("empirical", 2), rep("gaussian", 2))
- ex_list[[11]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 12: empirical and copula
- approach <- c(rep("empirical", 2), rep("copula", 2))
- ex_list[[12]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 13: copula and empirical XX (works now)
- approach <- c(rep("copula", 2), rep("empirical", 2))
- ex_list[[13]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 14: gaussian and copula XX (works with seed)
- approach <- c(rep("gaussian", 1), rep("copula", 3))
- ex_list[[14]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 15: empirical and copula
- approach <- c(rep("empirical", 1), rep("copula", 3))
- ex_list[[15]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 16: gaussian and empirical XX (works now)
- approach <- c(rep("gaussian", 1), rep("empirical", 3))
- ex_list[[16]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 17: gaussian and empirical XX (works now!)
- approach <- c(rep("gaussian", 2), rep("empirical", 2))
- ex_list[[17]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
- # Ex 18: Explain combined II - all empirical
- approach <- c(rep("empirical", 4))
- ex_list[[18]] <- explain(x_test, explainer, approach = approach, prediction_zero = p0)
-
-
- if (requireNamespace("party", quietly = TRUE)) {
-
- # Ex 19: Explain predictions (ctree, sample = FALSE, default parameters)
- ex_list[[19]] <- explain(x_test, explainer, approach = "ctree", prediction_zero = p0, sample = FALSE)
-
- # Ex 20: Explain predictions (ctree, sample = TRUE, default parameters)
- ex_list[[20]] <- explain(x_test, explainer, approach = "ctree", prediction_zero = p0, sample = TRUE)
-
- # Ex 21: Explain predictions (ctree, sample = FALSE, other ctree parameters)
- ex_list[[21]] <- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = FALSE,
- mincriterion = 0.9, minsplit = 20, minbucket = 25
- )
-
- # Ex 22: Explain predictions (ctree, sample = TRUE, other ctree parameters)
- ex_list[[22]] <- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mincriterion = 0.9, minsplit = 20, minbucket = 25
- )
-
- # Ex 23: Explain combined - ctree and gaussian, sample = FALSE
- ex_list[[23]] <- explain(x_test, explainer,
- approach = c("ctree", rep("gaussian", 3)),
- prediction_zero = p0, sample = FALSE
- )
-
- # Ex 24: Explain combined II - ctree and gaussian, sample = FALSE
- ex_list[[24]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 2), rep("gaussian", 2)),
- prediction_zero = p0, sample = FALSE
- )
-
- # Ex 25: Explain combined III - ctree and gaussian, sample = FALSE
- ex_list[[25]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 3), rep("gaussian", 1)),
- prediction_zero = p0, sample = FALSE
- )
-
- # Ex 26: Explain combined IV - ctree all, sample = FALSE
- ex_list[[26]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 4)),
- prediction_zero = p0, sample = FALSE
- )
-
- # Ex 27: Explain combined - ctree and empirical, sample = FALSE
- ex_list[[27]] <- explain(x_test, explainer,
- approach = c("ctree", rep("empirical", 3)),
- prediction_zero = p0, sample = FALSE
- )
-
- # Ex 28: Explain combined II - ctree and empirical, sample = FALSE
- ex_list[[28]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 2), rep("empirical", 2)),
- prediction_zero = p0, sample = FALSE
- )
-
- # Ex 29: Explain combined III - ctree and empirical, sample = FALSE
- ex_list[[29]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 3), rep("empirical", 1)),
- prediction_zero = p0, sample = FALSE
- )
-
- # Ex 30: Explain combined - ctree and gaussian, sample = TRUE
- ex_list[[30]] <- explain(x_test, explainer,
- approach = c("ctree", rep("gaussian", 3)),
- prediction_zero = p0, sample = TRUE
- )
-
- # Ex 31: Explain combined II - ctree and gaussian, sample = TRUE
- ex_list[[31]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 2), rep("gaussian", 2)),
- prediction_zero = p0, sample = TRUE
- )
-
- # Ex 32: Explain combined III - ctree and gaussian, sample = TRUE
- ex_list[[32]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 3), rep("gaussian", 1)),
- prediction_zero = p0, sample = TRUE
- )
-
- # Ex 33: Explain combined IV - ctree all, sample = TRUE
- ex_list[[33]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 4)),
- prediction_zero = p0, sample = TRUE
- )
-
- # Ex 34: Explain combined - ctree and empirical, sample = TRUE
- ex_list[[34]] <- explain(x_test, explainer,
- approach = c("ctree", rep("empirical", 3)),
- prediction_zero = p0, sample = TRUE
- )
-
- # Ex 35: Explain combined II - ctree and empirical, sample = TRUE
- ex_list[[35]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 2), rep("empirical", 2)),
- prediction_zero = p0, sample = TRUE
- )
-
- # Ex 36: Explain combined III - ctree and empirical, sample = TRUE
- ex_list[[36]] <- explain(x_test, explainer,
- approach = c(rep("ctree", 3), rep("empirical", 1)),
- prediction_zero = p0, sample = TRUE
- )
-
- # Ex 37: Explain different ctree mincriterion for different number of dependent variables, sample = TRUE
- ex_list[[37]] <- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mincriterion = c(0.05, 0.05, 0.95, 0.95)
- )
-
- # Ex 38: Explain different ctree mincriterion for different number of dependent variables, sample = TRUE
- ex_list[[38]] <- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mincriterion = rep(0.95, 4)
- )
-
- # Ex 39: Test that ctree with mincriterion equal to same probability four times gives the same as only passing one
- # probability to mincriterion
- expect_equal(
- (explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mincriterion = rep(0.95, 4)
- ))$dt,
- (explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mincriterion = 0.95
- ))$dt
- )
-
-
- # Ex 40: Test that ctree with the same mincriterion repeated four times is the same as passing mincriterion once
- expect_equal(
- (explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = FALSE,
- mincriterion = c(rep(0.95, 2), rep(0.95, 2))
- ))$dt,
- (explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = FALSE,
- mincriterion = 0.95
- ))$dt
- )
-
- # Checking that explanations with different paralellizations gives the same result (only unix systems!)
-
- if (.Platform$OS.type == "unix") {
- explain_base_nosample <- explain(x_test, explainer, approach = "ctree", prediction_zero = p0, sample = FALSE)
-
- multicore <- 2
-
- expect_equal(
- explain_base_nosample,
- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = FALSE,
- mc_cores = multicore
- )
- )
-
- expect_equal(
- explain_base_nosample,
- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = FALSE,
- mc_cores_create_ctree = 1, mc_cores_sample_ctree = multicore
- )
- )
-
- expect_equal(
- explain_base_nosample,
- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = FALSE,
- mc_cores_create_ctree = multicore, mc_cores_sample_ctree = 1
- )
- )
-
- explain_base_sample <- explain(x_test, explainer, approach = "ctree", prediction_zero = p0, sample = TRUE)
-
- # Consistent results when only paralellizing create_ctree, and not sample_ctree
- expect_equal(
- explain_base_sample,
- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mc_cores_create_ctree = multicore, mc_cores_sample_ctree = 1
- )
- )
-
- # Consistent results when ran twice with same seed
- expect_equal(
- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mc_cores = multicore
- ),
- explain(x_test, explainer,
- approach = "ctree", prediction_zero = p0, sample = TRUE,
- mc_cores = multicore
- )
- )
- }
- # Checking that all explain objects produce the same as before
- expect_known_value(ex_list,
- file = "test_objects/explanation_explain_obj_list.rds",
- update = FALSE
- )
- } else {
- # Tests using only the first 17 elements of explanation_explain_obj_list.rds
- expect_known_value(ex_list,
- file = "test_objects/explanation_explain_obj_list_no_ctree.rds",
- update = FALSE
- )
- }
-
-
- ### Additional test to test that only the produced shapley values are the same as before
- fixed_explain_obj_list <- readRDS("test_objects/explanation_explain_obj_list_fixed.rds")
- for (i in 1:length(ex_list)) {
- expect_equal(ex_list[[i]]$dt, fixed_explain_obj_list[[i]]$dt)
- }
-
-
- # Checks that an error is returned
- expect_error(
- explain(1, explainer, approach = "gaussian", prediction_zero = p0)
- )
- expect_error(
- explain(list(), explainer, approach = "gaussian", prediction_zero = p0)
- )
- expect_error(
- explain(x_test, explainer, approach = "Gaussian", prediction_zero = p0)
- )
- expect_error(
- explain(x_test, explainer, approach = rep("gaussian", ncol(x_test) + 1), prediction_zero = p0)
- )
-
- # Check that the same results are obtained if you pass the covariance matrix or whether it is computed internally
- # Gaussian approach
- explained_gaus_cov <- explain(x_test, explainer, approach = "gaussian",
- prediction_zero = p0, cov_mat = cov(explainer$x_train))
- explained_gaus_no_cov <- explain(x_test, explainer, approach = "gaussian",
- prediction_zero = p0)
-
- # Empirical approach
- explained_emp_cov <- explain(x_test, explainer, approach = "empirical",
- prediction_zero = p0, cov_mat = cov(explainer$x_train))
- explained_emp_no_cov <- explain(x_test, explainer, approach = "empirical",
- prediction_zero = p0)
-
- expect_equal(explained_gaus_cov, explained_gaus_no_cov)
- expect_equal(explained_emp_cov, explained_emp_no_cov)
-
- }
-})
-
-test_that("Testing data input to explain in explanation.R", {
-
- # Setup for training data and explainer object
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- # Training data
- x_train <- as.matrix(tail(Boston[, x_var], -6))
- y_train <- tail(Boston[, y_var], -6)
- xy_train_full_df <- tail(Boston[, ], -6)
-
- # Test data
- x_test <- as.matrix(head(Boston[, x_var], 6))
- x_test_full <- as.matrix(head(Boston[, ], 6))
- x_test_reordered <- as.matrix(head(Boston[, rev(x_var)], 6))
- xy_test_full_df <- head(Boston[, ], 6)
- xy_test_missing_lstat_df <- xy_test_full_df[, !(colnames(xy_test_full_df) == "lstat")]
- xy_test_full_df_no_colnames <- xy_test_full_df
- colnames(xy_test_full_df_no_colnames) <- NULL
-
- formula <- as.formula(paste0("medv ~ ", paste0(x_var, collapse = "+")))
- p0 <- mean(y_train)
-
- # Test data
- all_test_data <- list(
- x_test,
- x_test_reordered,
- x_test_full
- )
-
- # Linear model
- list_models <- list(
- lm(
- formula = formula,
- data = xy_train_full_df
- )
- )
-
- all_explainers <- list(
- shapr(x_train, list_models[[1]])
- )
-
- # explainer 1
- # Expect message due to no label/factor checking
- list_explanation <- list()
- list_explanation[[1]] <- expect_silent(
- explain(
- all_test_data[[1]],
- all_explainers[[1]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- # Expect message due to no label/factor checking
- list_explanation[[2]] <- expect_silent(
- explain(
- all_test_data[[2]],
- all_explainers[[1]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- # Expect message due to removal of data
- list_explanation[[3]] <- expect_message(
- explain(
- all_test_data[[3]],
- all_explainers[[1]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- for (i in 2:length(list_explanation)) {
- expect_equal(list_explanation[[i - 1]], list_explanation[[i]])
- }
-
-
- if (requireNamespace("xgboost", quietly = TRUE)) {
- list_models[[length(list_models) + 1]] <- xgboost::xgboost(
- data = x_train,
- label = y_train,
- nround = 5,
- verbose = FALSE
- )
-
- all_explainers[[length(all_explainers) + 1]] <- shapr(x_train, list_models[[length(list_models)]])
-
- # explainer 2
- # Expect silent
- list_explanation <- list()
- list_explanation[[1]] <- expect_silent(
- explain(
- all_test_data[[1]],
- all_explainers[[length(all_explainers)]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- # Expect silent
- list_explanation[[2]] <- expect_silent(
- explain(
- all_test_data[[2]],
- all_explainers[[length(all_explainers)]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- # Expect message due to removal of data
- list_explanation[[3]] <- expect_message(
- explain(
- all_test_data[[3]],
- all_explainers[[length(all_explainers)]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- for (i in 2:length(list_explanation)) {
- expect_equal(list_explanation[[i - 1]], list_explanation[[i]])
- }
- }
-
-
- if (requireNamespace("ranger", quietly = TRUE)) {
- list_models[[length(list_models) + 1]] <- ranger::ranger(
- formula = formula,
- data = xy_train_full_df,
- num.trees = 50
- )
-
- all_explainers[[length(all_explainers) + 1]] <- shapr(x_train, list_models[[length(list_models)]])
-
- # explainer 3
- # Expect silent
- list_explanation <- list()
- list_explanation[[1]] <- expect_silent(
- explain(
- all_test_data[[1]],
- all_explainers[[length(all_explainers)]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- # Expect silent
- list_explanation[[2]] <- expect_silent(
- explain(
- all_test_data[[2]],
- all_explainers[[length(all_explainers)]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- # Expect message due removal of data
- list_explanation[[3]] <- expect_message(
- explain(
- all_test_data[[3]],
- all_explainers[[length(all_explainers)]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- for (i in 2:length(list_explanation)) {
- expect_equal(list_explanation[[i - 1]], list_explanation[[i]])
- }
- }
-
- for (i in seq_along(all_explainers)) {
-
- # Expect error when test data misses used variable
- expect_error(
- explain(
- xy_test_missing_lstat_df,
- all_explainers[[i]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
-
- # Expect error when test data misses column names
- expect_error(
- explain(
- xy_test_full_df_no_colnames,
- all_explainers[[i]],
- approach = "empirical",
- prediction_zero = p0,
- n_samples = 1e2
- )
- )
- }
- }
-})
-
-
-test_that("Testing that differnet data ordering gives same explanations", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- # Training data
- x_train <- as.matrix(tail(Boston[, x_var], -6))
- y_train <- tail(Boston[, y_var], -6)
- xy_train_full_df <- tail(Boston, -6)
-
- # Test data
- x_test <- as.matrix(head(Boston[, x_var], 6))
-
- formula <- as.formula(paste0("medv ~ ", paste0(x_var, collapse = "+")))
- p0 <- mean(y_train)
-
- model <- lm(formula = formula, data = xy_train_full_df)
-
- explainer_1 <- shapr(x_train[, 1:4], model = model)
- explainer_2 <- shapr(x_train[, 4:1], model = model)
-
- explained_1_1 <- explain(x = x_test[, 1:4], explainer = explainer_1, approach = "empirical", prediction_zero = p0)
- explained_1_2 <- explain(x = x_test[, 4:1], explainer = explainer_1, approach = "empirical", prediction_zero = p0)
- explained_2_1 <- explain(x = x_test[, 1:4], explainer = explainer_2, approach = "empirical", prediction_zero = p0)
- explained_2_2 <- explain(x = x_test[, 4:1], explainer = explainer_2, approach = "empirical", prediction_zero = p0)
-
- expect_identical(explained_1_1, explained_1_2)
- expect_identical(explained_1_1, explained_2_1)
- expect_identical(explained_1_1, explained_2_2)
- }
-})
-
-
-test_that("Test functions related to groups in explanation.R", {
-
- # Load data -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- y_train <- tail(Boston[, y_var], 50)
- x_test <- as.matrix(head(Boston[, x_var], 2))
-
- # Prepare the data for explanation. Path needs to be relative to testthat directory in the package
- explainer0 <- readRDS(file = "test_objects/shapley_explainer_obj.rds")
- explainer1 <- readRDS(file = "test_objects/shapley_explainer_group1_obj.rds")
- explainer2 <- readRDS(file = "test_objects/shapley_explainer_group2_obj.rds")
-
- # Creating list with lots of different explainer objects
- p0 <- mean(y_train)
-
- ex_list <- list()
-
- # Ex 1: Explain predictions (gaussian)
- ex_list[[1]] <- explain(x_test, explainer1, approach = "gaussian", prediction_zero = p0)
-
- # Ex 2: Explain predictions (empirical)
- ex_list[[2]] <- explain(x_test, explainer1, approach = "empirical", prediction_zero = p0)
-
- # Ex 3: Explain predictions (copula)
- ex_list[[3]] <- explain(x_test, explainer1, approach = "copula", prediction_zero = p0)
-
- # Ex 4: Explain predictions (gaussian, empirical)
- ex_list[[4]] <- explain(x_test, explainer1, approach = c(
- "gaussian", "empirical", "gaussian",
- "empirical"
- ), prediction_zero = p0)
-
- # Ex 5: Explain predictions (copula)
- ex_list[[5]] <- explain(x_test, explainer2, approach = "gaussian", prediction_zero = p0)
-
- # Checking that all explain objects produce the same as before
- expect_known_value(ex_list, file = "test_objects/explanation_explain_group_obj_list.rds")
-
- ### Additional test that only the produced shapley values are the same as before
- fixed_explain_obj_list <- readRDS("test_objects/explanation_explain_group_obj_list.rds")
- for (i in 1:length(ex_list)) {
- expect_equal(ex_list[[i]]$dt, fixed_explain_obj_list[[i]]$dt)
- }
-
- # Here we check if not grouping (explanation0) and grouping with one feature per group (explanation2)
- # gives the same answer
- explanation0 <- explain(x_test, explainer0, approach = "empirical", prediction_zero = p0)
- explanation2 <- explain(x_test, explainer2, approach = "empirical", prediction_zero = p0)
- names(explanation2$dt)[-1] <- unlist(explainer2$group)
- expect_equal(explanation0$dt, explanation2$dt)
- }
-})
-
-
-test_that("test functions related to running explain in batch", {
-
- explainer <- list()
-
- n_comb <- 20
- n_batches <- 1
- explainer$S <- matrix(1, nrow = n_comb, ncol = 5)
- S_batch <- create_S_batch(explainer, n_batches = n_batches)
- n_batches <- 1
- expect_length(S_batch, n_batches)
-
- n_batches <- 5
- S_batch <- create_S_batch(explainer, n_batches = n_batches)
- expect_length(S_batch, 5)
- expect_equal(unlist(S_batch, use.names = FALSE), 1:n_comb)
-
-
- S_batch <- create_S_batch(explainer, n_batches = n_batches, index_S = 1:10)
- expect_equal(unlist(S_batch, use.names = FALSE), 1:10)
-
-})
-
-
-test_that("prepare_and_predict", {
-
- if (requireNamespace("MASS", quietly = TRUE)) {
-
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- y_train <- tail(Boston[, y_var], 50)
- x_test <- as.matrix(head(Boston[, x_var], 2))
-
- # Prepare the data for explanation. Path needs to be relative to testthat directory in the package
- explainer <- readRDS(file = "test_objects/shapley_explainer_obj.rds")
-
- p0 <- mean(y_train)
- explainer$x_test <- as.matrix(preprocess_data(x_test, explainer$feature_list)$x_dt)
- explainer$approach <- "independence"
- explainer$n_samples <- 100
-
- res <- prepare_and_predict(explainer, n_batches = 1, p0)
-
- expect_true(is.list(res))
- expect_s3_class(res, "shapr")
- expect_equal(names(res), c("dt", "model", "p", "x_test", "is_groupwise"))
-
-
- # return the contribution matrix
- res <- prepare_and_predict(explainer, n_batches = 1, p0, only_return_contrib_dt = TRUE)
- expect_s3_class(res, "data.table")
-
- }
-})
-
-test_that("errors with non valid n_batches", {
-
- explainer <- list()
- explainer$S <- matrix(1, nrow = 10, ncol = 2)
- x_test <- data.table()
- expect_error(explain(x_test, explainer, n_batches = 0))
- expect_error(explain(x_test, explainer, n_batches = 11))
-
-})
diff --git a/tests/testthat/test-features.R b/tests/testthat/test-features.R
deleted file mode 100644
index 667c06964..000000000
--- a/tests/testthat/test-features.R
+++ /dev/null
@@ -1,372 +0,0 @@
-context("test-features.R")
-
-test_that("Test feature_combinations", {
-
- # Example 1 -----------
- m <- 3
- exact <- TRUE
- w <- 10^6
- x1 <- feature_combinations(m = m, exact = exact, weight_zero_m = w)
- x2 <- feature_exact(m, w)
-
- # Example 2 -----------
- m <- 10
- exact <- FALSE
- n_combinations <- 50
- w <- 10^6
- set.seed(1)
- y1 <- feature_combinations(
- m = m,
- exact = exact,
- n_combinations = n_combinations,
- weight_zero_m = w
- )
-
- set.seed(1)
- y2 <- feature_not_exact(
- m = m,
- n_combinations = n_combinations,
- weight_zero_m = w
- )
- y2[, p := NULL]
-
- # Example 3 -----------
- m <- 3
- exact <- FALSE
- n_combinations <- 1e4
- w <- 10^6
- set.seed(1)
- y3 <- feature_combinations(
- m = m,
- exact = exact,
- n_combinations = n_combinations,
- weight_zero_m = w
- )
-
- # Test results -----------
- expect_equal(x1, x2)
- expect_equal(y1, y2)
- expect_equal(nrow(y3), 2^3)
- expect_error(feature_combinations(100))
- expect_error(feature_combinations(100, n_combinations = NULL))
-})
-
-test_that("Test feature_exact", {
-
- # Example -----------
- m <- 3
- weight_zero_m <- 10^6
- x <- feature_exact(m, weight_zero_m)
-
- # Define results -----------
- cnms <- c("id_combination", "features", "n_features", "N", "shapley_weight")
- classes <- c("integer", "list", "integer", "integer", "double")
- lfeatures <- list(
- integer(0),
- 1L,
- 2L,
- 3L,
- c(1L, 2L),
- c(1L, 3L),
- c(2L, 3L),
- c(1L, 2L, 3L)
- )
- n_components <- c(0, rep(1, 3), rep(2, 3), 3)
- n <- c(1, rep(3, 6), 1)
-
- # Tests -----------
- expect_true(data.table::is.data.table(x))
- expect_equal(names(x), cnms)
- expect_equal(unname(sapply(x, typeof)), classes)
- expect_equal(x[["id_combination"]], seq(nrow(x)))
- expect_equal(x[["features"]], lfeatures)
- expect_equal(x[["n_features"]], n_components)
- expect_equal(x[["N"]], n)
-})
-
-test_that("Test feature_not_exact", {
-
- # Example -----------
- m <- 10
- exact <- FALSE
- n_combinations <- 50
- w <- 10^6
- set.seed(1)
- x <- feature_not_exact(
- m = m,
- n_combinations = n_combinations,
- weight_zero_m = w
- )
- set.seed(1)
-
- cnms <- c("id_combination", "features", "n_features", "N", "shapley_weight", "p")
- classes <- c("integer", "list", "integer", "integer", "integer", "double")
- n <- sapply(seq(m - 1), choose, n = m)
- w_all <- shapley_weights(m = m, N = n, n_components = seq(m - 1)) * n
- w_default <- w_all / sum(w_all)
-
- # Test results -----------
- expect_true(data.table::is.data.table(x))
- expect_equal(names(x), cnms)
- expect_equal(unname(sapply(x, typeof)), classes)
- expect_true(nrow(x) <= n_combinations + 2)
- expect_equal(x[["id_combination"]], seq(nrow(x)))
- for (i in x[, .I]) {
- f <- x[["features"]][[i]]
- if (length(f) == 0) {
- expect_equal(x[["n_features"]][[i]], 0)
- expect_equal(x[["N"]][[i]], 1)
- expect_equal(x[["shapley_weight"]][[i]], w)
- expect_equal(x[["p"]][[i]], NA_real_)
- } else if (length(f) == m) {
- expect_equal(f, seq(m))
- expect_equal(x[["n_features"]][[i]], m)
- expect_equal(x[["N"]][[i]], 1)
- expect_equal(x[["shapley_weight"]][[i]], w)
- expect_equal(x[["p"]][[i]], NA_real_)
- } else {
- k <- length(f)
- expect_equal(f, sort(f))
- expect_equal(x[["n_features"]][[i]], k)
- expect_equal(x[["N"]][[i]], choose(m, k))
- expect_equal(x[["p"]][[i]], w_default[x[["n_features"]][[i]]])
- expect_equal(between(x[["shapley_weight"]][[i]], 1L, n_combinations), TRUE)
- }
- }
-})
-
-test_that("Test helper_feature", {
-
- # Example -----------
- m <- 5
- feature_sample <- list(
- integer(0),
- 1:2,
- 3:5,
- 1:2,
- 1:5
- )
- x <- helper_feature(m, feature_sample)
-
- # Define results -----------
- x2 <- c(1, 2, 1, 2, 1)
- x3 <- c(FALSE, FALSE, FALSE, TRUE, FALSE)
-
- # Test results -----------
- cnms <- c("sample_frequence", "is_duplicate")
- classes <- c("integer", "logical")
- expect_true(data.table::is.data.table(x))
- expect_equal(names(x), cnms)
- expect_equal(nrow(x), length(feature_sample))
- expect_equal(classes, unname(sapply(x, typeof)))
- expect_equal(x[["sample_frequence"]], x2)
- expect_equal(x[["is_duplicate"]], x3)
-})
-
-
-test_that("Test make_dummies", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- x_train <- as.data.frame(Boston[401:411, x_var])
- y_train <- Boston[401:408, y_var]
- x_test <- as.data.frame(Boston[1:4, x_var])
-
- # convert to factors for illustrational purpose
- x_train$rm <- factor(round(x_train$rm))
- x_test$rm <- factor(round(x_test$rm), levels = levels(x_train$rm))
-
- factor_feat <- sapply(x_train, is.factor)
- nb_factor_feat <- sum(factor_feat)
-
- dummylist <- make_dummies(traindata = x_train, testdata = x_train)
-
- # Tests
- expect_type(dummylist, "list")
-
- expect_equal(length(dummylist$feature_list$contrasts_list), nb_factor_feat)
-
- expect_equal(length(dummylist$feature_list$labels), ncol(x_train))
-
- expect_equal(sum(dummylist$feature_list$classes == "factor"), nb_factor_feat)
-
- expect_equal(ncol(dummylist$feature_list$contrasts_list$rm), length(levels(x_train$rm)))
-
- # 1) What if train has more features than test but features in test are contained in train
- x_train1 <- cbind(x_train, 1)
- x_test1 <- x_test
- expect_error(make_dummies(traindata = x_train1, testdata = x_test1))
-
- # 2) What if train has different feature types than test
- x_train2 <- x_train
- x_test2 <- x_test
- x_test2$rm <- as.numeric(x_test2$rm)
- expect_error(make_dummies(traindata = x_train2, testdata = x_test2))
-
- # 3) What if test has more features than train but features in train are contained in test
- x_train3 <- x_train
- x_test3 <- cbind(x_test, 1)
- expect_error(make_dummies(traindata = x_train3, testdata = x_test3))
-
- # 4) What if train and test only have numerical features
- x_train4 <- x_train
- x_train4$rm <- as.numeric(x_train4$rm)
- x_test4 <- x_test
- x_test4$rm <- as.numeric(x_test4$rm)
- expect_type(make_dummies(traindata = x_train4, testdata = x_test4), "list")
-
- # 5) What if train and test only have categorical features
- x_train5 <- x_train
- x_train5 <- x_train5[, "rm", drop = FALSE]
- x_test5 <- x_test
- x_test5 <- x_test5[, "rm", drop = FALSE]
- expect_type(make_dummies(traindata = x_train5, testdata = x_test5), "list")
-
- # 6) What if test has the same levels as train but random ordering of levels
- x_train6 <- x_train
- x_train6$rm <- factor(x_train6$rm, levels = 4:9)
- x_test6 <- x_test
- x_test6$rm <- factor(x_test6$rm, levels = c(8, 9, 7, 4, 5, 6))
- expect_type(make_dummies(traindata = x_train6, testdata = x_test6), "list")
-
- # 7) What if test has different levels than train
- x_train7 <- x_train
- x_train7$rm <- factor(x_train7$rm, levels = 4:9)
- x_test7 <- x_test
- x_test7$rm <- factor(x_test7$rm, levels = 6:8)
- expect_error(make_dummies(traindata = x_train7, testdata = x_test7))
-
- # 8) What if train and test have different feature names
- x_train8 <- x_train
- x_test8 <- x_test
- names(x_test8) <- c("lstat2", "rm2", "dis2", "indus2")
- expect_error(make_dummies(traindata = x_train8, testdata = x_test8))
-
- # 9) What if one variables has an empty name
- x_train9 <- x_train
- colnames(x_train9) <- c("", "rm", "dis", "indus")
- x_test9 <- x_test
- colnames(x_test9) <- c("", "rm", "dis", "indus")
- expect_error(make_dummies(traindata = x_train9, testdata = x_test9))
-
- # 10) What if traindata has a column that repeats
- x_train10 <- cbind(x_train, lstat = x_train$lstat)
- x_test10 <- cbind(x_test, lstat = x_test$lstat)
- expect_error(make_dummies(traindata = x_train10, testdata = x_test10))
-
- # 11) What if traindata has no column names
- x_train11 <- x_train
- colnames(x_train11) <- NULL
- x_test11 <- x_test
- colnames(x_test11) <- NULL
- expect_error(make_dummies(traindata = x_train11, testdata = x_test11))
-
- # 12 Test that traindata_new and testdata_new will be the same as the original
- # x_train and x_test. The only time this is different is if the levels of train
- # and test are different. See below.
- dummylist12 <- make_dummies(traindata = x_train, testdata = x_test)
- #
- expect_true(all(data.frame(dummylist12$traindata_new) == x_train))
- expect_true(all(levels(dummylist12$traindata_new$rm) == levels(x_train$rm)))
- expect_true(all(data.frame(dummylist12$testdata_new) == x_test))
- expect_true(all(levels(dummylist12$testdata_new$rm) == levels(x_test$rm)))
-
-
- # 13 Different levels same as check # 12
- #
- x_train13 <- x_train
- x_train13$rm <- factor(x_train13$rm, levels = 4:9)
- x_test13 <- x_test
- x_test13$rm <- factor(x_test13$rm, levels = c(8, 9, 7, 4, 5, 6))
- dummylist13 <- make_dummies(traindata = x_train13, testdata = x_test13)
- #
- expect_true(all(data.frame(dummylist13$traindata_new) == x_train13))
- expect_true(all(levels(dummylist13$traindata_new$rm) == levels(x_train13$rm)))
- expect_true(all(data.frame(dummylist13$testdata_new) == x_test13))
- # Important !!!!
- expect_false(all(levels(dummylist13$testdata_new$rm) == levels(x_test13$rm)))
- }
-})
-
-test_that("Test apply_dummies", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- x_train <- as.data.frame(Boston[401:411, x_var])
- y_train <- Boston[401:408, y_var]
- x_test <- as.data.frame(Boston[1:4, x_var])
-
- # convert to factors for illustrational purpose
- x_train$rm <- factor(round(x_train$rm))
- x_test$rm <- factor(round(x_test$rm), levels = levels(x_train$rm))
-
- numeric_feat <- !sapply(x_train, is.factor)
- nb_numeric_feat <- sum(numeric_feat)
-
- dummylist <- make_dummies(traindata = x_train, testdata = x_test)
-
- x_test_dummies <- apply_dummies(feature_list = dummylist$feature_list, testdata = x_test)
-
- # Tests
- expect_type(x_test_dummies, "double")
-
- expect_equal(
- ncol(x_test_dummies),
- nb_numeric_feat + ncol(dummylist$feature_list$contrasts_list$rm)
- )
-
- # Test that make_dummies() and apply_dummies() gives the same output
- # for a given traindata and testdata
- expect_true(all(dummylist$test_dummies == x_test_dummies))
-
- # 1) What if you re-arrange the columns in x_train
- x_test1 <- x_test[, c(2, 3, 1, 4)]
- diff_column_placements <- apply_dummies(dummylist$feature_list, testdata = x_test1)
- expect_equal(colnames(diff_column_placements), colnames(x_test_dummies))
-
- # 2) What if you put in less features then the original traindata
- x_test2 <- x_test[, c(2, 1)]
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test2))
-
- # 3) What if you change the feature types of testdata
- x_test3 <- sapply(x_test, as.numeric)
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test3))
-
- # 4) What if you add a feature
- x_test4 <- cbind(x_train[, c(1, 2)], new_var = x_train[, 2], x_train[, c(3, 4)])
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test4))
-
- # 6) What if test has the same levels as train but random ordering of levels
- x_test6 <- x_test
- x_test6$rm <- factor(x_test6$rm, levels = c(8, 9, 7, 4, 5, 6))
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test6))
-
- # 7) What if test has different levels than train
- x_test7 <- x_test
- x_test7$rm <- factor(x_test7$rm, levels = 6:8)
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test7))
-
- # 8) What if train and test have different feature names
- x_test8 <- x_test
- names(x_test8) <- c("lstat2", "rm2", "dis2", "indus2")
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test8))
-
- # 9) What if one variables has an empty name
- x_test9 <- x_test
- colnames(x_test9) <- c("", "rm", "dis", "indus")
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test9))
-
- # 10) What if traindata has a column that repeats
- x_test10 <- cbind(x_test, lstat = x_test$lstat)
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test10))
-
- # 11) What if testdata has no column names
- x_test11 <- x_test
- colnames(x_test11) <- NULL
- expect_error(apply_dummies(dummylist$feature_list, testdata = x_test11))
- }
-})
diff --git a/tests/testthat/test-forecast-output.R b/tests/testthat/test-forecast-output.R
new file mode 100644
index 000000000..c2bcc000b
--- /dev/null
+++ b/tests/testthat/test-forecast-output.R
@@ -0,0 +1,236 @@
+test_that("forecast_output_ar_numeric", {
+ expect_snapshot_rds(
+ explain_forecast(
+ model = model_ar_temp,
+ y = data[, "Temp"],
+ train_idx = 2:151,
+ explain_idx = 152:153,
+ explain_y_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar,
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "forecast_output_ar_numeric"
+ )
+})
+
+test_that("forecast_output_arima_numeric", {
+ expect_snapshot_rds(
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar,
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "forecast_output_arima_numeric"
+ )
+})
+
+test_that("forecast_output_arima_numeric_no_xreg", {
+ expect_snapshot_rds(
+ explain_forecast(
+ model = model_arima_temp_noxreg,
+ y = data[1:150, "Temp"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar,
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "forecast_output_arima_numeric_no_xreg"
+ )
+})
+
+test_that("forecast_output_forecast_ARIMA_group_numeric", {
+ expect_snapshot_rds(
+ explain_forecast(
+ model = model_forecast_ARIMA_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar,
+ group_lags = TRUE,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "forecast_output_forecast_ARIMA_group_numeric"
+ )
+})
+
+
+test_that("ARIMA gives the same output with different horizons", {
+ h3 <- explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:3],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE, n_combinations = 50
+ )
+
+
+ h2 <- explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 2,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:2],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE, n_combinations = 50
+ )
+
+ h1 <- explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 1,
+ approach = "empirical",
+ prediction_zero = p0_ar[1],
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE, n_combinations = 50
+ )
+
+ cols_horizon1 <- h2$internal$objects$cols_per_horizon[[1]]
+ expect_equal(
+ h2$shapley_values[horizon == 1, ..cols_horizon1],
+ h1$shapley_values[horizon == 1, ..cols_horizon1]
+ )
+
+ expect_equal(
+ h3$shapley_values[horizon == 1, ..cols_horizon1],
+ h1$shapley_values[horizon == 1, ..cols_horizon1]
+ )
+
+ cols_horizon2 <- h2$internal$objects$cols_per_horizon[[2]]
+ expect_equal(
+ h3$shapley_values[horizon == 2, ..cols_horizon2],
+ h2$shapley_values[horizon == 2, ..cols_horizon2]
+ )
+})
+
+test_that("ARIMA gives the same output with different horizons with grouping", {
+ h3 <- explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:3],
+ group_lags = TRUE,
+ n_batches = 1,
+ timing = FALSE, n_combinations = 50
+ )
+
+
+ h2 <- explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 2,
+ approach = "empirical",
+ prediction_zero = p0_ar[1:2],
+ group_lags = TRUE,
+ n_batches = 1,
+ timing = FALSE, n_combinations = 50
+ )
+
+ h1 <- explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 1,
+ approach = "empirical",
+ prediction_zero = p0_ar[1],
+ group_lags = TRUE,
+ n_batches = 1,
+ timing = FALSE, n_combinations = 50
+ )
+
+ expect_equal(
+ h2$shapley_values[horizon == 1],
+ h1$shapley_values[horizon == 1]
+ )
+
+ expect_equal(
+ h3$shapley_values[horizon == 1],
+ h1$shapley_values[horizon == 1]
+ )
+
+ expect_equal(
+ h3$shapley_values[horizon == 2],
+ h2$shapley_values[horizon == 2]
+ )
+})
+
+test_that("forecast_output_arima_numeric_no_lags", {
+ # TODO: Need to check out this output. It gives lots of warnings, which indicates something might be wrong.
+ expect_snapshot_rds(
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 0,
+ explain_xreg_lags = 0,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ group_lags = FALSE,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "forecast_output_arima_numeric_no_lags"
+ )
+})
diff --git a/tests/testthat/test-forecast-setup.R b/tests/testthat/test-forecast-setup.R
new file mode 100644
index 000000000..70a49eafb
--- /dev/null
+++ b/tests/testthat/test-forecast-setup.R
@@ -0,0 +1,563 @@
+test_that("error with custom model without providing predict_model", {
+ set.seed(123)
+
+
+ expect_snapshot(
+ {
+ # Custom model with no predict_model
+ model_custom_arima_temp <- model_arima_temp
+ class(model_custom_arima_temp) <- "whatever"
+
+ explain_forecast(
+ model = model_custom_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+
+test_that("erroneous input: `x_train/x_explain`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # not vector or one-column data.table/matrix
+ y_wrong_format <- data[, c("Temp", "Wind")]
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = y_wrong_format,
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # not correct dimension
+ xreg_wrong_format <- data[, c("Temp", "Wind")]
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = xreg_wrong_format,
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # missing column names x_train
+ xreg_no_column_names <- data[, "Wind"]
+ names(xreg_no_column_names) <- NULL
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = xreg_no_column_names,
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `model`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # no model passed
+ explain_forecast(
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+
+test_that("erroneous input: `prediction_zero`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # incorrect length
+ p0_wrong_length <- p0_ar[1:2]
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_wrong_length,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `n_combinations`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # Too low n_combinations (smaller than # features)
+ horizon <- 3
+ explain_y_lags <- 2
+ explain_xreg_lags <- 2
+
+ n_combinations <- horizon + explain_y_lags + explain_xreg_lags - 1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = explain_y_lags,
+ explain_xreg_lags = explain_xreg_lags,
+ horizon = horizon,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1,
+ n_combinations = n_combinations,
+ group_lags = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # Too low n_combinations (smaller than # groups)
+ horizon <- 3
+ explain_y_lags <- 2
+ explain_xreg_lags <- 2
+
+ n_combinations <- 1 + 1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = explain_y_lags,
+ explain_xreg_lags = explain_xreg_lags,
+ horizon = horizon,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1,
+ n_combinations = n_combinations,
+ group_lags = TRUE
+ )
+ },
+ error = TRUE
+ )
+})
+
+
+test_that("erroneous input: `train_idx`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # train_idx too short length
+ train_idx_too_short <- 2
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = train_idx_too_short,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # train_idx not containing integers
+ train_idx_not_integer <- c(3:5) + 0.1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = train_idx_not_integer,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # train_idx being out of range
+ train_idx_out_of_range <- 1:5
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = train_idx_out_of_range,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `explain_idx`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # explain_idx not containing integers
+ explain_idx_not_integer <- c(3:5) + 0.1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = explain_idx_not_integer,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # explain_idx being out of range
+ explain_idx_out_of_range <- 1:5
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = explain_idx_out_of_range,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `explain_y_lags`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # explain_y_lags not positive
+ explain_y_lags_negative <- -1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = explain_y_lags_negative,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # explain_y_lags not integer valued
+ explain_y_lags_not_integer <- 2.1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = explain_y_lags_not_integer,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # explain_y_lags more than single integer
+ explain_y_lags_more_than_one <- c(1, 2)
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = explain_y_lags_more_than_one,
+ explain_xreg_lags = 2,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # explain_y_lags is zero for model without xreg
+ explain_y_lags_zero <- 0
+
+ explain_forecast(
+ model = model_arima_temp_noxreg,
+ y = data[1:150, "Temp"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 0,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+
+test_that("erroneous input: `explain_x_lags`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # explain_xreg_lags not positive
+ explain_xreg_lags_negative <- -2
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = explain_xreg_lags_negative,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # explain_xreg_lags not integer valued
+ explain_xreg_lags_not_integer <- 2.1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = explain_xreg_lags_not_integer,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # explain_x_lags wrong length
+ explain_x_lags_wrong_length <- c(1, 2) # only 1 xreg variable defined
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = explain_x_lags_wrong_length,
+ horizon = 3,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `horizon`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # horizon not positive
+ horizon_negative <- -2
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = horizon_negative,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # horizon not integer valued
+ horizon_not_integer <- 2.1
+
+ explain_forecast(
+ model = model_arima_temp,
+ y = data[1:150, "Temp"],
+ xreg = data[, "Wind"],
+ train_idx = 2:148,
+ explain_idx = 149:150,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = horizon_not_integer,
+ approach = "independence",
+ prediction_zero = p0_ar,
+ n_batches = 1
+ )
+ },
+ error = TRUE
+ )
+})
+
+
+test_that("Forecast data setup produces expected results", {
+ mock_y <- matrix(1:100, 100, dimnames = list(NULL, "Y1"))
+ mock_xreg <- matrix(101:205, 105, dimnames = list(NULL, "X1"))
+
+ formatted_data <- get_data_forecast(
+ mock_y,
+ mock_xreg,
+ train_idx = 2:99,
+ explain_idx = 100,
+ explain_y_lags = 2,
+ explain_xreg_lags = 2,
+ horizon = 5
+ )
+
+ # Y1 lag 1, Y2 lag 2, X1 lag 1, X1 lag 2, X1 f1, f2, ... f5.
+ x_explain <- c(100, 99, 200, 199, 201, 202, 203, 204, 205)
+ expect_equal(x_explain, as.numeric(formatted_data$x_explain))
+
+ # The data is just linearly increasing, idx 99 should be idx 100 - 1 at each value.
+ expect_equal(x_explain - 1, as.numeric(formatted_data$x_train[98, ]))
+})
diff --git a/tests/testthat/test-models.R b/tests/testthat/test-models.R
deleted file mode 100644
index e6fbf231f..000000000
--- a/tests/testthat/test-models.R
+++ /dev/null
@@ -1,492 +0,0 @@
-context("test-models.R")
-
-test_that("Test predict_model (regression)", {
-
- # Data -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
- x_train <- tail(Boston[, x_var], -6)
- y_train <- tail(Boston[, y_var], -6)
- x_test <- head(Boston[, x_var], 6)
- str_formula <- "y_train ~ lstat + rm + dis + indus"
- train_df <- cbind(y_train, x_train)
-
- # List of models
- l <- list(
- stats::lm(str_formula, data = train_df),
- stats::glm(str_formula, data = train_df)
- )
-
- if (requireNamespace("ranger", quietly = TRUE)) {
- l[[length(l) + 1]] <- ranger::ranger(str_formula, data = train_df)
- }
- if (requireNamespace("xgboost", quietly = TRUE)) {
- l[[length(l) + 1]] <- xgboost::xgboost(data = as.matrix(x_train), label = y_train, nrounds = 3, verbose = FALSE)
- }
- if (requireNamespace("mgcv", quietly = TRUE)) {
- l[[length(l) + 1]] <- mgcv::gam(as.formula(str_formula), data = train_df)
- }
-
- # Tests
- for (i in seq_along(l)) {
-
- # Input equals data.frame
- expect_true(
- is.vector(predict_model(l[[i]], x_test))
- )
- expect_true(
- is.atomic(predict_model(l[[i]], x_test))
- )
- expect_true(
- is.double(predict_model(l[[i]], x_test))
- )
- expect_true(
- length(predict_model(l[[i]], x_test)) == nrow(x_test)
- )
-
- # Input equals matrix
- expect_true(
- is.double(predict_model(l[[i]], as.matrix(x_test)))
- )
- expect_true(
- is.atomic(predict_model(l[[i]], as.matrix(x_test)))
- )
- expect_true(
- is.vector(predict_model(l[[i]], as.matrix(x_test)))
- )
- expect_true(
- length(predict_model(l[[i]], as.matrix(x_test))) == nrow(x_test)
- )
- }
- }
-})
-
-test_that("Test predict_model (binary classification)", {
-
- # Data -----------
-
- if (requireNamespace("datasets", quietly = TRUE)) {
- data("iris", package = "datasets")
- x_var <- c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
- y_var <- "Species"
- iris$Species <- as.character(iris$Species)
- iris <- iris[which(iris$Species != "virginica"), ]
- iris$Species <- as.factor(iris$Species)
- x_train <- tail(iris[, x_var], -6)
- y_train <- tail(iris[, y_var], -6)
- x_test <- head(iris[, x_var], 6)
- str_formula <- "y_train ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width"
- train_df <- cbind(y_train, x_train)
-
- # List of models
- l <- list(
- suppressWarnings(stats::glm(str_formula, data = train_df, family = "binomial"))
- )
-
- if (requireNamespace("mgcv", quietly = TRUE)) {
- l[[length(l) + 1]] <- suppressWarnings(mgcv::gam(as.formula(str_formula), data = train_df, family = "binomial"))
- }
- if (requireNamespace("ranger", quietly = TRUE)) {
- l[[length(l) + 1]] <- ranger::ranger(str_formula, data = train_df, probability = TRUE)
- }
- if (requireNamespace("xgboost", quietly = TRUE)) {
- l[[length(l) + 1]] <- xgboost::xgboost(
- data = as.matrix(x_train),
- label = as.integer(y_train) - 1,
- nrounds = 2,
- verbose = FALSE,
- objective = "binary:logistic",
- eval_metric = "error"
- )
- }
-
- # Tests
- for (i in seq_along(l)) {
-
- # Input equals data.frame
- expect_true(
- is.vector(predict_model(l[[i]], x_test))
- )
- expect_true(
- is.atomic(predict_model(l[[i]], x_test))
- )
- expect_true(
- is.double(predict_model(l[[i]], x_test))
- )
- expect_true(
- length(predict_model(l[[i]], x_test)) == nrow(x_test)
- )
- expect_true(
- all(data.table::between(predict_model(l[[i]], x_test), 0, 1))
- )
-
- # Input equals matrix
- expect_true(
- is.double(predict_model(l[[i]], as.matrix(x_test)))
- )
- expect_true(
- is.atomic(predict_model(l[[i]], as.matrix(x_test)))
- )
- expect_true(
- is.vector(predict_model(l[[i]], as.matrix(x_test)))
- )
- expect_true(
- length(predict_model(l[[i]], as.matrix(x_test))) == nrow(x_test)
- )
- expect_true(
- all(data.table::between(predict_model(l[[i]], as.matrix(x_test)), 0, 1))
- )
-
- # Check that output is equal
- expect_equal(
- predict_model(l[[i]], x_test), predict_model(l[[i]], as.matrix(x_test))
- )
- }
-
- # Errors
- l <- list()
-
- if (requireNamespace("ranger", quietly = TRUE)) {
- l[[length(l) + 1]] <- ranger::ranger(str_formula, data = train_df)
- }
- if (requireNamespace("xgboost", quietly = TRUE)) {
- l[[length(l) + 1]] <- xgboost::xgboost(
- data = as.matrix(x_train),
- label = as.integer(y_train) - 1,
- nrounds = 2,
- verbose = FALSE,
- objective = "reg:logistic"
- )
- }
-
- # Tests
- for (i in seq_along(l)) {
-
- # Input equals data.frame
- expect_error(
- get_model_specs(l[[i]])
- )
-
- # Input equals matrix
- expect_error(
- get_model_specs(l[[i]])
- )
- }
- }
-})
-
-test_that("Test predict_model (multi-classification)", {
-
- # Data -----------
- if (requireNamespace("datasets", quietly = TRUE)) {
- data("iris", package = "datasets")
- x_var <- c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
- y_var <- "Species"
- x_train <- tail(iris[, x_var], -6)
- y_train <- tail(iris[, y_var], -6)
- x_test <- head(iris[, x_var], 6)
- str_formula <- "y_train ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width"
- train_df <- cbind(y_train, x_train)
-
- # List of models
- l <- list()
-
- if (requireNamespace("ranger", quietly = TRUE)) {
- l[[length(l) + 1]] <- ranger::ranger(
- str_formula,
- data = train_df
- )
- l[[length(l) + 1]] <- ranger::ranger(
- str_formula,
- data = train_df,
- probability = TRUE
- )
- }
- if (requireNamespace("xgboost", quietly = TRUE)) {
- l[[length(l) + 1]] <- xgboost::xgboost(
- as.matrix(x_train),
- label = as.integer(y_train) - 1,
- nrounds = 2,
- verbose = FALSE,
- objective = "multi:softprob",
- eval_metric = "merror",
- num_class = 3
- )
- l[[length(l) + 1]] <- xgboost::xgboost(
- as.matrix(x_train),
- label = as.integer(y_train) - 1,
- nrounds = 2,
- verbose = FALSE,
- objective = "multi:softmax",
- eval_metric = "merror",
- num_class = 3
- )
- }
-
-
- # Tests
- for (i in seq_along(l)) {
-
- # Input equals data.frame
- expect_error(
- get_model_specs(l[[i]], x_test)
- )
-
- # Input equals matrix
- expect_error(
- get_model_specs(l[[i]], as.matrix(x_test))
- )
- }
- }
-})
-
-test_that("Test check_features + update_data", {
-
- # Data -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- y_var <- "medv"
- x_train <- tail(Boston, -6)
- y_train <- tail(Boston[, y_var], -6)
- y_train_binary <- as.factor(tail((Boston[, y_var] > 20) * 1, -6))
-
- # convert to factors for testing purposes
- x_train$rad <- factor(round(x_train$rad))
- x_train$chas <- factor(round(x_train$chas))
-
- train_df <- cbind(x_train, y_train, y_train_binary)
-
- x_var_numeric <- c("lstat", "rm", "dis", "indus")
- x_var_factor <- c("lstat", "rm", "dis", "indus", "rad", "chas")
-
- formula_numeric <- as.formula(paste0("y_train ~ ", paste0(x_var_numeric, collapse = "+")))
- formula_factor <- as.formula(paste0("y_train ~ ", paste0(x_var_factor, collapse = "+")))
-
- formula_binary_numeric <- as.formula(paste0("y_train_binary ~ ", paste0(x_var_numeric, collapse = "+")))
- formula_binary_factor <- as.formula(paste0("y_train_binary ~ ", paste0(x_var_factor, collapse = "+")))
-
- dummylist <- make_dummies(traindata = x_train[, x_var_factor], testdata = x_train[, x_var_factor])
-
- # List of models to run silently
- l_silent <- list(
- stats::lm(formula_numeric, data = train_df),
- stats::lm(formula_factor, data = train_df),
- stats::glm(formula_numeric, data = train_df),
- stats::glm(formula_factor, data = train_df),
- stats::glm(formula_binary_numeric, data = train_df, family = "binomial"),
- stats::glm(formula_binary_factor, data = train_df, family = "binomial")
- )
- l_message <- list()
-
-
- if (requireNamespace("mgcv", quietly = TRUE)) {
- l_silent[[length(l_silent) + 1]] <- mgcv::gam(formula_numeric, data = train_df)
- l_silent[[length(l_silent) + 1]] <- mgcv::gam(formula_factor, data = train_df)
- l_silent[[length(l_silent) + 1]] <- mgcv::gam(formula_binary_numeric, data = train_df, family = "binomial")
- l_silent[[length(l_silent) + 1]] <- mgcv::gam(formula_binary_factor, data = train_df, family = "binomial")
- }
-
- if (requireNamespace("xgboost", quietly = TRUE)) {
- l_silent[[length(l_silent) + 1]] <- xgboost::xgboost(
- data = dummylist$train_dummies, label = y_train,
- nrounds = 3, verbose = FALSE
- )
- l_silent[[length(l_silent)]]$feature_list <- dummylist$feature_list
-
- l_silent[[length(l_silent) + 1]] <- xgboost::xgboost(
- data = dummylist$train_dummies,
- label = as.integer(y_train_binary) - 1,
- nrounds = 3,
- verbose = FALSE,
- objective = "binary:logistic",
- eval_metric = "error"
- )
- l_silent[[length(l_silent)]]$feature_list <- dummylist$feature_list
-
- l_message[[length(l_message) + 1]] <- xgboost::xgboost(
- data = as.matrix(x_train[, x_var_numeric]),
- label = y_train, nrounds = 3, verbose = FALSE
- )
- }
-
- if (requireNamespace("ranger", quietly = TRUE)) {
- l_message[[length(l_message) + 1]] <- ranger::ranger(formula_numeric, data = train_df)
- l_message[[length(l_message) + 1]] <- ranger::ranger(formula_factor, data = train_df)
- l_message[[length(l_message) + 1]] <- ranger::ranger(formula_binary_numeric, data = train_df, probability = TRUE)
- l_message[[length(l_message) + 1]] <- ranger::ranger(formula_binary_factor, data = train_df, probability = TRUE)
- }
-
- data_features <- get_data_specs(train_df)
- for (i in seq_along(l_silent)) {
- model_features <- get_model_specs(l_silent[[i]])
- expect_silent(check_features(model_features, data_features))
- }
-
- for (i in seq_along(l_message)) {
- model_features <- get_model_specs(l_message[[i]])
- expect_message(check_features(model_features, data_features))
- }
-
-
- # Checking all stops in check_features
- data_features_ok <- get_data_specs(train_df)
-
- # Non-matching labels
- data_features_error <- get_data_specs(train_df)
- data_features_error$labels <- NULL
- expect_error(check_features(data_features_ok, data_features_error))
- expect_message(check_features(data_features_error, data_features_ok, use_1_as_truth = T))
- expect_error(check_features(data_features_error, data_features_ok, use_1_as_truth = F))
-
-
- # Missing features
- data_features_error <- get_data_specs(train_df[, -3])
- expect_error(check_features(data_features_ok, data_features_error))
- expect_error(check_features(data_features_error, data_features_ok, use_1_as_truth = F))
-
- # Duplicated column names
- data_features_error <- get_data_specs(cbind(crim = train_df[, 1], train_df))
- expect_error(check_features(data_features_error, data_features_error))
-
- # Empty column names
- train_df_0 <- train_df
- names(train_df_0)[1] <- ""
- data_features_error <- get_data_specs(train_df_0)
- expect_error(check_features(data_features_error, data_features_error))
-
- # feature class is NA
- data_features_error <- data_features_ok
- data_features_error$classes <- rep(NA, length(data_features_error$classes))
- expect_message(check_features(data_features_error, data_features_ok))
-
- # feature classes are different
- data_features_error <- data_features_ok
- data_features_error$classes <- rev(data_features_error$classes)
- names(data_features_error$classes) <- names(data_features_ok$classes)
- expect_error(check_features(data_features_ok, data_features_error))
-
- # invalid feature class
- data_features_error <- data_features_ok
- data_features_error$classes[1] <- "logical"
- expect_error(check_features(data_features_error, data_features_error))
-
- # non-matching factor levels
- data_features_error <- data_features_ok
- data_features_error$factor_levels$chas <- c(data_features_error$factor_levels$chas, "2")
- expect_error(check_features(data_features_ok, data_features_error))
-
- #### Now turning to update_data tests ####
-
- model_features_ok <- get_model_specs(l_silent[[2]])
-
- # Checking null output and message to remove features
- train_dt <- as.data.table(train_df)
- data_to_update <- copy(train_dt)
- expect_message(expect_null(update_data(data_to_update, model_features_ok)))
-
- # Checking that features are indeed removed
- expect_equal(names(data_to_update), model_features_ok$labels)
-
- # Second call with same input should do nothing
- expect_silent(expect_null(update_data(data_to_update, model_features_ok)))
-
- # Checking null output and message to shuffle factor levels
- data_to_update_2 <- head(copy(train_dt), 20)
- data_to_update_2$rad <- droplevels(data_to_update_2$rad)
- org_levels_rad <- levels(data_to_update_2$rad)
-
- expect_message(expect_null(update_data(data_to_update_2, model_features_ok)))
-
- # Checking that levels are indeed updated
- expect_true(length(org_levels_rad) < length(levels(data_to_update_2$rad)))
- expect_equal(model_features_ok$factor_levels$rad, levels(data_to_update_2$rad))
- }
-})
-
-test_that("Test missing colnames", {
-
- # Data -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
- x_train <- as.matrix(tail(Boston[, x_var], -6))
- y_train <- tail(Boston[, y_var], -6)
- x_test <- as.matrix(head(Boston[, x_var]))
-
- x_train_nonames <- x_train
- colnames(x_train_nonames) <- NULL
- x_test_nonames <- x_test
- colnames(x_test_nonames) <- NULL
-
- if (requireNamespace("xgboost", quietly = TRUE)) {
- model <- xgboost::xgboost(
- data = x_train, label = y_train, nrounds = 3, verbose = FALSE
- )
-
- model_nonames <- xgboost::xgboost(
- data = x_train_nonames, label = y_train, nrounds = 3, verbose = FALSE
- )
-
- # missing colnames in model
- expect_error(shapr(model_nonames, x_train))
-
- # missing colnames in training data
- expect_error(shapr(model, x_train_nonames))
-
- # missing colnames in both model and training data
- expect_error(shapr(model_nonames, x_train_nonames))
-
- # missing colnames in test data
- explain <- shapr(x_train, model)
- p <- mean(y_train)
- expect_error(
- explain(
- x_test_nonames,
- approach = "empirical",
- explainer = explainer,
- prediction_zero = p
- )
- )
- }
- }
-})
-
-test_that("Test get_supported_models", {
- org_models <- get_supported_models()
-
- # Adding to .GlobalEnv
- get_model_specs.test <<- function() 1
-
- new_models <- get_supported_models()
-
- # Removin form .GlobalEnv
- rm("get_model_specs.test",pos = ".GlobalEnv")
-
- expect_false("test" %in% org_models$model_class)
- expect_true("test" %in% new_models$model_class)
-})
-
-test_that("Test get_model_specs", {
-
- # Data -----------
- if (requireNamespace("MASS", quietly = TRUE) & requireNamespace("gbm", quietly = TRUE)) {
- data("Boston", package = "MASS")
- y_var <- "medv"
- x_train <- tail(Boston, -6)
- y_train <- tail(Boston[, y_var], -6)
-
- train_df <- cbind(x_train, y_train)
- x_var_numeric <- c("lstat", "rm", "dis", "indus")
- formula_numeric <- as.formula(paste0("y_train ~ ", paste0(x_var_numeric, collapse = "+")))
-
- # Unsupported model
- model_unsupported <- gbm::gbm(formula_numeric, distribution = "gaussian", data = train_df)
- expect_error(get_model_specs(model_unsupported))
-
- # This is further tested for custom models in the script tests/manual_test_scripts/test_custom_models.R
- # run expect_silent(source("tests/testthat/manual_test_scripts/test_custom_models.R")) manually to run those tests
- }
-})
diff --git a/tests/testthat/test-observations.R b/tests/testthat/test-observations.R
deleted file mode 100644
index 60fc04208..000000000
--- a/tests/testthat/test-observations.R
+++ /dev/null
@@ -1,122 +0,0 @@
-context("test-observations.R")
-
-test_that("Test observation_impute", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- # Examples
- n <- 20
- m <- 2
- sigma <- cov(matrix(MASS::mvrnorm(m * n, 0, 1), nrow = n))
- x_train <- as.matrix(MASS::mvrnorm(n, mu = rep(0, m), Sigma = sigma), ncol = m)
- x_test <- t(as.matrix(MASS::mvrnorm(1, mu = rep(0, m), sigma)))
- colnames(x_train) <- colnames(x_test) <- paste0("X", seq(m))
- S <- matrix(c(1, 0, 0, 1), nrow = m)
- W_kernel <- matrix(rnorm(n * ncol(S), mean = 1 / n, sd = 1 / n^2), nrow = n)
- r <- observation_impute(W_kernel, S, x_train, x_test)
-
- # Test the default argument n_samples
- expect_equal(
- observation_impute(W_kernel, S, x_train, x_test, n_samples = 1e3),
- observation_impute(W_kernel, S, x_train, x_test)
- )
-
- # Test the default argument w_threshold
- expect_equal(
- observation_impute(W_kernel, S, x_train, x_test, w_threshold = .7),
- observation_impute(W_kernel, S, x_train, x_test)
- )
-
- # Test that w_threshold reduces number of rows
- expect_true(
- nrow(observation_impute(W_kernel, S, x_train, x_test, w_threshold = .7)) >
- nrow(observation_impute(W_kernel, S, x_train, x_test, w_threshold = 0.5))
- )
-
- # Test that n_samples reduces number of rows
- expect_true(
- nrow(observation_impute(W_kernel, S, x_train, x_test)) >
- nrow(observation_impute(W_kernel, S, x_train, x_test, n_samples = 10))
- )
-
- # Tests error
- expect_error(observation_impute(1, S, x_train, x_test))
- expect_error(observation_impute(W_kernel, 1, x_train, x_test))
- expect_error(observation_impute(W_kernel, tail(S, -1), x_train, x_test))
- expect_error(observation_impute(tail(W_kernel, -1), S, x_train, x_test))
-
- # Test single result
- cnms <- c(colnames(x_train), "id_combination", "w")
- expect_true(data.table::is.data.table(r))
- expect_true(ncol(r) == m + 2)
- expect_true(all(colnames(r) == cnms))
- expect_true(all(unlist(lapply(r, is.numeric))))
- expect_true(is.integer(r$id_combination))
- }
-})
-
-
-test_that("Check correct index_feature usage in prepare_data", {
-
- data("Boston", package = "MASS")
- x_var <- c("lstat", "rm", "dis", "indus")
- y_var <- "medv"
-
- y_train <- tail(Boston[, y_var], 50)
- x <- as.matrix(head(Boston[, x_var], 2))
- n_samples <- 100
- index_features <- 4:7
- w_threshold = 0.95
- type = "fixed_sigma"
- fixed_sigma_vec = 0.1
- n_samples_aicc = 1000
- eval_max_aicc = 20
- start_aicc = 0.1
- mincriterion = 0.95
- minsplit = 20
- minbucket = 7
- sample = TRUE
-
- explainer <- readRDS(file = "test_objects/shapley_explainer_obj.rds")
- explainer$x_test <- as.matrix(preprocess_data(x, explainer$feature_list)$x_dt)
- explainer$n_samples <- n_samples
-
- explainer$approach <- "independence"
- dt <- prepare_data(explainer, index_features = index_features)
- expect_identical(sort(dt[,unique(id_combination)]),index_features)
-
- explainer$type <- type
- explainer$fixed_sigma_vec <- fixed_sigma_vec
- explainer$n_samples_aicc <- n_samples_aicc
- explainer$eval_max_aicc <- eval_max_aicc
- explainer$start_aicc <- start_aicc
- explainer$w_threshold <- w_threshold
- explainer$cov_mat <- stats::cov(explainer$x_train)
-
- explainer$approach <- "empirical"
- dt <- prepare_data(explainer, index_features = index_features)
- expect_identical(sort(dt[,unique(id_combination)]),index_features)
-
- explainer$mu <- unname(colMeans(explainer$x_train))
- explainer$approach <- "gaussian"
- dt <- prepare_data(explainer, index_features = index_features)
- expect_identical(sort(dt[,unique(id_combination)]),index_features)
-
- explainer$x_test_gaussian <- explainer$x_test # Shortcut
- explainer$approach <- "copula"
- dt <- prepare_data(explainer, index_features = index_features)
- expect_identical(sort(dt[,unique(id_combination)]),index_features)
-
- explainer$x_test_gaussian <- explainer$x_test # Shortcut
- explainer$approach <- "copula"
- dt <- prepare_data(explainer, index_features = index_features)
- expect_identical(sort(dt[,unique(id_combination)]),index_features)
-
- explainer$mincriterion <- mincriterion
- explainer$minsplit <- minsplit
- explainer$minbucket <- minbucket
- explainer$sample <- sample
- explainer$approach <- "ctree"
- explainer$x_test <- preprocess_data(x, explainer$feature_list)$x_dt
- dt <- prepare_data(explainer, index_features = index_features)
- expect_identical(sort(dt[,unique(id_combination)]),index_features)
-
-})
diff --git a/tests/testthat/test-output.R b/tests/testthat/test-output.R
new file mode 100644
index 000000000..5118564a6
--- /dev/null
+++ b/tests/testthat/test-output.R
@@ -0,0 +1,498 @@
+# lm_numeric with different approaches
+
+test_that("output_lm_numeric_independence", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_independence"
+ )
+})
+
+test_that("output_lm_numeric_empirical", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_empirical"
+ )
+})
+
+test_that("output_lm_numeric_empirical_n_combinations", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_combinations = 20,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_empirical_n_combinations"
+ )
+})
+
+test_that("output_lm_numeric_empirical_independence", {
+ set.seed(123)
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ empirical.type = "independence",
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_empirical_independence"
+ )
+})
+
+test_that("output_lm_numeric_empirical_AICc_each", {
+ set.seed(123)
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_combinations = 8,
+ empirical.type = "AICc_each_k",
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_empirical_AICc_each"
+ )
+})
+
+test_that("output_lm_numeric_empirical_AICc_full", {
+ set.seed(123)
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_combinations = 8,
+ empirical.type = "AICc_full",
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_empirical_AICc_full"
+ )
+})
+
+test_that("output_lm_numeric_gaussian", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "gaussian",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_gaussian"
+ )
+})
+
+test_that("output_lm_numeric_copula", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "copula",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_copula"
+ )
+})
+
+test_that("output_lm_numeric_ctree", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_ctree"
+ )
+})
+
+test_that("output_lm_categorical_ctree", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_categorical,
+ x_explain = x_explain_categorical,
+ x_train = x_train_categorical,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_categorical_ctree"
+ )
+})
+
+test_that("output_lm_categorical_categorical", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_categorical,
+ x_explain = x_explain_categorical,
+ x_train = x_train_categorical,
+ approach = "categorical",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_categorical_method"
+ )
+})
+
+test_that("output_lm_categorical_independence", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_categorical,
+ x_explain = x_explain_categorical,
+ x_train = x_train_categorical,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_categorical_independence"
+ )
+})
+
+test_that("output_lm_ts_timeseries", {
+ expect_snapshot_rds(
+ explanation_timeseries <- explain(
+ model = model_lm_ts,
+ x_explain = x_explain_ts,
+ x_train = x_train_ts,
+ approach = "timeseries",
+ prediction_zero = p0_ts,
+ group = group_ts,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_timeseries_method"
+ )
+})
+
+test_that("output_lm_numeric_comb1", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = c("gaussian", "empirical", "ctree", "independence", "empirical"),
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_comb1"
+ )
+})
+
+test_that("output_lm_numeric_comb2", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = c("ctree", "copula", "independence", "copula", "empirical"),
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_comb2"
+ )
+})
+
+test_that("output_lm_numeric_comb3", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = c("independence", "empirical", "gaussian", "empirical", "gaussian"),
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_comb3"
+ )
+})
+
+
+# lm_mixed with different approaches
+
+test_that("output_lm_mixed_independence", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_train_mixed,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_mixed_independence"
+ )
+})
+
+test_that("output_lm_mixed_ctree", {
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_train_mixed,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_mixed_ctree"
+ )
+})
+
+test_that("output_lm_mixed_comb", {
+ set.seed(123)
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_train_mixed,
+ approach = c("ctree", "independence", "ctree", "independence", "independence"),
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_mixed_comb"
+ )
+})
+
+
+
+### Custom model by passing predict_model
+test_that("output_custom_lm_numeric_independence_1", {
+ set.seed(123)
+ custom_pred_func <- function(x, newdata) {
+ beta <- coef(x)
+ X <- cbind(1, newdata)
+ return(as.vector(beta %*% t(X)))
+ }
+
+ model_custom_lm_numeric <- model_lm_numeric
+
+ expect_snapshot_rds(
+ explain(
+ model = model_custom_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = custom_pred_func,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_custom_lm_numeric_independence_1"
+ )
+})
+
+test_that("output_custom_lm_numeric_independence_2", {
+ set.seed(123)
+ custom_pred_func <- function(x, newdata) {
+ beta <- coef(x)
+ X <- cbind(1, newdata)
+ return(as.vector(beta %*% t(X)))
+ }
+
+ model_custom_lm_numeric <- model_lm_numeric
+ class(model_custom_lm_numeric) <- "whatever"
+
+
+ expect_snapshot_rds(
+ (custom <- explain(
+ model = model_custom_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = custom_pred_func,
+ n_batches = 1,
+ timing = FALSE
+ )),
+ "output_custom_lm_numeric_independence_2"
+ )
+
+ native <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+
+ # Check that the printed Shapley values are identical
+ expect_equal(
+ custom$shapley_values,
+ native$shapley_values
+ )
+})
+
+test_that("output_custom_xgboost_mixed_dummy_ctree", {
+ if (requireNamespace("xgboost", quietly = TRUE)) {
+ x_train_mixed_dummy <- model.matrix(~ . + 0, x_train_mixed)
+ x_explain_mixed_dummy <- model.matrix(~ . + 0, x_explain_mixed)
+
+ y_train <- data_train[, get(y_var_numeric)]
+
+ # Fitting a basic xgboost model to the training data
+ model_xgboost_mixed_dummy <- xgboost::xgboost(
+ data = x_train_mixed_dummy,
+ label = y_train,
+ nround = 20,
+ verbose = FALSE
+ )
+
+ predict_model.xgboost_dummy <- function(x, newdata) {
+ newdata_dummy <- model.matrix(~ . + 0, newdata)
+
+ predict(x, newdata_dummy)
+ }
+
+ # Check that created predict_model works as intended
+ expect_equal(
+ predict_model.xgboost_dummy(model_xgboost_mixed_dummy, x_explain_mixed),
+ predict(model_xgboost_mixed_dummy, x_explain_mixed_dummy)
+ )
+
+ # Specifying the phi_0, i.e. the expected prediction without any features
+ p0 <- data_train[, mean(get(y_var_numeric))]
+
+
+ expect_snapshot_rds(
+ {
+ custom <- explain(
+ model = model_xgboost_mixed_dummy,
+ x_train = x_train_mixed,
+ x_explain = x_explain_mixed,
+ approach = "ctree",
+ prediction_zero = p0,
+ predict_model = predict_model.xgboost_dummy,
+ get_model_specs = NA,
+ n_batches = 1,
+ timing = FALSE
+ )
+ # custom$internal$objects$predict_model <- "Del on purpose" # Avoids issues with xgboost package updates
+ custom
+ },
+ "output_custom_xgboost_mixed_dummy_ctree"
+ )
+ }
+})
+
+test_that("output_lm_numeric_interaction", {
+ x_train_interaction <- x_train_numeric[, mget(all.vars(formula(model_lm_interaction))[-1])]
+ x_explain_interaction <- x_explain_numeric[, mget(all.vars(formula(model_lm_interaction))[-1])]
+ expect_snapshot_rds(
+ explain(
+ model = model_lm_interaction,
+ x_explain = x_explain_interaction,
+ x_train = x_train_interaction,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ ),
+ "output_lm_numeric_interaction"
+ )
+})
+
+test_that("output_lm_numeric_ctree_parallelized", {
+ future::plan("multisession", workers = 2)
+ expect_snapshot_rds(
+ {
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ "output_lm_numeric_ctree_parallelized"
+ )
+ future::plan("sequential")
+})
+
+test_that("output_lm_numeric_independence_more_batches", {
+ expect_snapshot_rds(
+ {
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+ },
+ "output_lm_numeric_independence_n_batches_10"
+ )
+})
+
+# Nothing special here, as the test does not record the actual progress output.
+# It just checks whether calling on progressr does not produce an error or unexpected output.
+test_that("output_lm_numeric_empirical_progress", {
+ progressr::handlers("txtprogressbar")
+ expect_snapshot_rds(
+ {
+ progressr::with_progress({
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+ })
+ },
+ "output_lm_numeric_empirical_progress"
+ )
+})
diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R
index 7c321037f..1304338e0 100644
--- a/tests/testthat/test-plot.R
+++ b/tests/testthat/test-plot.R
@@ -1,49 +1,131 @@
-context("test-plot.R")
-
-test_that("Test plot.shapr", {
- if (requireNamespace("ggplot2", quietly = TRUE)) {
-
- # Example -----------
- x <- matrix(c(
- 4.98, 9.14, 4.03, 2.94, 5.33,
- 6.575, 6.421, 7.185, 6.998, 7.147,
- 4.0900, 4.9671, 4.9671, 6.0622, 6.0622,
- 2.31, 7.07, 7.07, 2.18, 2.18
- ),
- ncol = 4
- )
-
- colnames(x) <- c("lstat", "rm", "dis", "indus")
-
- explanation <- list()
- explanation$p <- c(31.30145, 23.25194, 33.11547, 33.43015, 31.72984)
- explanation$dt <- data.table::data.table(
- "none" = rep(22.00, 5),
- "lstat" = c(5.2632, 0.1672, 5.9888, 8.2142, 0.5060),
- "rm" = c(-1.2527, -0.7088, 5.5451, 0.7508, 5.6875),
- "dis" = c(0.2920, 0.9689, 0.5660, 0.1893, 0.8432),
- "indus" = c(4.5529, 0.3787, -1.4304, 1.8298, 2.2471)
- )
- explanation$x_test <- x
- explanation$is_groupwise <- FALSE
- attr(explanation, "class") <- c("shapr", "list")
-
-
- # Test -----------
- p <- plot(explanation, plot_phi0 = FALSE)
-
- expect_equal(colnames(x), unique(as.character(p$data$variable)))
- expect_equal(explanation$p, unique(p$data$pred))
- expect_equal(sort(as.vector(as.matrix(explanation$dt[, -c("none")]))), sort(p$data$phi))
-
- p <- plot(explanation, plot_phi0 = TRUE)
-
- expect_equal(colnames(explanation$dt), unique(as.character(p$data$variable)))
- expect_equal(explanation$p, unique(p$data$pred))
- expect_equal(sort(as.vector(as.matrix(explanation$dt))), sort(p$data$phi))
-
- p <- plot(explanation, plot_phi0 = TRUE, top_k_features = 2)
-
- expect_equal(2, max(p$data$rank))
- }
+set.seed(123) #
+
+explain_mixed <- explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_train_mixed,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+)
+
+test_that("checking default outputs", {
+ skip_if_not_installed("vdiffr")
+
+ vdiffr::expect_doppelganger(
+ title = "bar_plot_default",
+ fig = plot(explain_mixed)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "waterfall_plot_default",
+ fig = plot(explain_mixed, plot_type = "waterfall")
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "scatter_plot_default",
+ fig = plot(explain_mixed, plot_type = "scatter")
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "beeswarm_plot_default",
+ fig = plot(explain_mixed, plot_type = "beeswarm")
+ )
+})
+
+test_that("bar_plot_new_arguments", {
+ skip_if_not_installed("vdiffr")
+
+ vdiffr::expect_doppelganger(
+ title = "bar_plot_digits_5",
+ fig = plot(explain_mixed, digits = 5)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "bar_plot_no_phi0",
+ fig = plot(explain_mixed, bar_plot_phi0 = FALSE)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "bar_plot_index_x_explain_1",
+ fig = plot(explain_mixed, index_x_explain = 1)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "bar_plot_top_3_features",
+ fig = plot(explain_mixed, top_k_features = 3)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "bar_plot_new_colors",
+ fig = plot(explain_mixed, col = c("red", "black"))
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "bar_plot_order_original",
+ fig = plot(explain_mixed, bar_plot_order = "original")
+ )
+})
+
+test_that("waterfall_plot_new_arguments", {
+ skip_if_not_installed("vdiffr")
+
+ vdiffr::expect_doppelganger(
+ title = "waterfall_plot_digits_5",
+ fig = plot(explain_mixed, plot_type = "waterfall", digits = 5)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "waterfall_plot_index_x_explain_1",
+ fig = plot(explain_mixed, plot_type = "waterfall", index_x_explain = 1)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "waterfall_plot_top_3_features",
+ fig = plot(explain_mixed, plot_type = "waterfall", top_k_features = 3)
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "waterfall_plot_new_colors",
+ fig = plot(explain_mixed, plot_type = "waterfall", col = c("red", "black"))
+ )
+})
+
+test_that("scatter_plot_new_arguments", {
+ skip_if_not_installed("vdiffr")
+
+ vdiffr::expect_doppelganger(
+ title = "scatter_plot_index_x_explain_1_2",
+ fig = plot(explain_mixed, plot_type = "scatter", index_x_explain = c(1, 2))
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "scatter_plot_new_color",
+ fig = plot(explain_mixed, plot_type = "scatter", col = "black")
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "scatter_plot_one_feature",
+ fig = plot(explain_mixed, plot_type = "scatter", scatter_features = "Temp")
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "scatter_plot_no_hist",
+ fig = plot(explain_mixed, plot_type = "scatter", scatter_hist = FALSE)
+ )
+})
+
+test_that("beeswarm_plot_new_arguments", {
+ skip_if_not_installed("vdiffr")
+
+ vdiffr::expect_doppelganger(
+ title = "beeswarm_plot_new_colors",
+ fig = plot(explain_mixed, plot_type = "beeswarm", col = c("red", "black"))
+ )
+
+ vdiffr::expect_doppelganger(
+ title = "beeswarm_plot_index_x_explain_1_2",
+ fig = plot(explain_mixed, plot_type = "beeswarm", index_x_explain = c(1, 2))
+ )
})
diff --git a/tests/testthat/test-predictions.R b/tests/testthat/test-predictions.R
deleted file mode 100644
index cace8fe5f..000000000
--- a/tests/testthat/test-predictions.R
+++ /dev/null
@@ -1,49 +0,0 @@
-context("test-predictions.R")
-
-test_that("Test prediction", {
-
- # Example -----------
- if (requireNamespace("MASS", quietly = TRUE)) {
- data("Boston", package = "MASS")
- dt_train <- data.table::as.data.table(Boston)
- features <- c("lstat", "rm", "dis", "indus")
- n_combinations <- 10
- n_features <- 4
- prediction_zero <- .5
- n_xtest <- 8
- explainer <- list()
- explainer$model <- stats::lm(formula = "medv ~ lstat + rm + dis + indus", data = head(dt_train, -n_xtest))
- explainer$x_test <- tail(dt_train[, .SD, .SDcols = features], n_xtest)
- explainer$W <- matrix(1, nrow = n_features + 1, ncol = n_combinations)
- explainer$is_groupwise <- FALSE
- explainer$S <- matrix(1, nrow = n_combinations, ncol = n_features)
- dt <- dt_train[, .SD, .SDcols = features][rep(1:.N, 4)]
- dt[, id := rep_len(1:n_xtest, .N)]
- dt[, id_combination := rep_len(1:n_combinations, .N), id]
- dt[, w := runif(.N)]
- max_id_combination <- dt[, max(id_combination)]
- dt <- dt[!(id_combination == max_id_combination)]
- dt_lastrows <- data.table::data.table(
- explainer$x_test,
- id = 1:n_xtest,
- id_combination = max_id_combination,
- w = 1.0
- )
- dt <- rbind(dt, dt_lastrows, dt_lastrows, dt_lastrows)
-
- x <- prediction(dt, prediction_zero, explainer)
-
- # Test -----------
- lnms <- c("p", "dt_mat")
- expect_equal(class(x), "list")
- expect_equal(names(x), lnms)
- expect_equal(x$p, predict_model(explainer$model, explainer$x_test))
- expect_true(data.table::is.data.table(x$dt_mat))
-
- # t(W %*% x$dt_mat) = shapley values
- expect_equal(ncol(explainer$W), nrow(x$dt_mat))
-
- # Tests errors
- expect_error(prediction(dt[id < n_xtest], prediction_zero, explainer))
- }
-})
diff --git a/tests/testthat/test-sampling.R b/tests/testthat/test-sampling.R
deleted file mode 100644
index 08597c291..000000000
--- a/tests/testthat/test-sampling.R
+++ /dev/null
@@ -1,290 +0,0 @@
-context("test-sample_combinations.R")
-
-test_that("Test sample_combinations", {
-
- # Example -----------
- ntrain <- 10
- ntest <- 10
- nsamples <- 7
- joint_sampling <- FALSE
- cnms <- c("samp_train", "samp_test")
-
- set.seed(123) # Ensuring consistency in every test
- x <- sample_combinations(ntrain, ntest, nsamples, joint_sampling)
-
- # Tests -----------
- expect_true(is.data.frame(x))
- expect_equal(names(x), cnms)
- expect_equal(nrow(x), nsamples)
-
- # Expect all unique values when nsamples < ntrain
- expect_true(length(unique(x$samp_train)) == nsamples)
- expect_true(length(unique(x$samp_test)) == nsamples)
-
- expect_true(max(x$samp_train) <= ntrain)
- expect_true(max(x$samp_test) <= ntest)
-
- # Example -----------
- ntrain <- 5
- ntest <- 5
- nsamples <- 7
- joint_sampling <- FALSE
-
- x <- sample_combinations(ntrain, ntest, nsamples, joint_sampling)
-
- # Tests -----------
- expect_true(max(x$samp_train) <= ntrain)
- expect_true(max(x$samp_test) <= ntest)
- expect_equal(nrow(x), nsamples)
-
- # Example -----------
- ntrain <- 5
- ntest <- 5
- nsamples <- 7
- joint_sampling <- TRUE
-
- x <- sample_combinations(ntrain, ntest, nsamples, joint_sampling)
-
- # Tests -----------
- expect_true(max(x$samp_train) <= ntrain)
- expect_true(max(x$samp_test) <= ntest)
- expect_equal(nrow(x), nsamples)
-})
-
-test_that("test sample_gaussian", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- # Example -----------
- m <- 10
- n_samples <- 50
- mu <- rep(1, m)
- cov_mat <- cov(matrix(rnorm(n_samples * m), n_samples, m))
- x_test <- matrix(MASS::mvrnorm(1, mu, cov_mat), nrow = 1)
- cnms <- paste0("x", seq(m))
- colnames(x_test) <- cnms
- index_given <- c(4, 7)
- r <- sample_gaussian(index_given, n_samples, mu, cov_mat, m, x_test)
-
- # Test output format ------------------
- expect_true(data.table::is.data.table(r))
- expect_equal(ncol(r), m)
- expect_equal(nrow(r), n_samples)
- expect_equal(colnames(r), cnms)
-
- # Check that the given features are not resampled, but kept as is.
- for (i in seq(m)) {
- var_name <- cnms[i]
-
- if (i %in% index_given) {
- expect_equal(
- unique(r[[var_name]]), x_test[, var_name][[1]]
- )
- } else {
- expect_true(
- length(unique(r[[var_name]])) == n_samples
- )
- }
- }
-
- # Example 2 -------------
- # Check that conditioning upon all variables simply returns the test observation.
- r <- sample_gaussian(1:m, n_samples, mu, cov_mat, m, x_test)
- expect_identical(r, data.table::as.data.table(x_test))
-
- # Tests for errors ------------------
- expect_error(
- sample_gaussian(m + 1, n_samples, mu, cov_mat, m, x_test)
- )
- expect_error(
- sample_gaussian(m + 1, n_samples, mu, cov_mat, m, as.vector(x_test))
- )
- }
-})
-
-test_that("test sample_copula", {
- if (requireNamespace("MASS", quietly = TRUE)) {
- # Example 1 --------------
- # Check that the given features are not resampled, but kept as is.
- m <- 10
- n <- 40
- n_samples <- 50
- mu <- rep(1, m)
- set.seed(123) # Ensuring consistency in every test
- cov_mat <- cov(matrix(rnorm(n * m), n, m))
- x_train <- MASS::mvrnorm(n, mu, cov_mat)
- x_test <- MASS::mvrnorm(1, mu, cov_mat)
- x_test_gaussian <- MASS::mvrnorm(1, mu, cov_mat)
- index_given <- 3:6
- set.seed(1)
- ret <- sample_copula(index_given, n_samples, mu, cov_mat, m, x_test_gaussian, x_train, x_test)
- X_given <- x_test[index_given]
- res1.1 <- as.data.table(matrix(rep(X_given, each = n_samples), nrow = n_samples))
- res1.2 <- as.data.table(ret[, ..index_given])
- colnames(res1.1) <- colnames(res1.2)
-
- # Example 2 --------------
- # Check that conditioning upon all variables simply returns the test observation.
- index_given <- 1:m
- x2 <- as.data.table(matrix(x_test, ncol = m, nrow = 1))
- res2 <- sample_copula(index_given, n_samples, mu, cov_mat, m, x_test_gaussian, x_train, x_test)
-
- # Example 3 --------------
- # Check that the colnames are preserved.
- index_given <- c(1, 2, 3, 5, 6)
- x_test <- t(as.data.frame(x_test))
- colnames(x_test) <- 1:m
- res3 <- sample_copula(index_given, n_samples, mu, cov_mat, m, x_test_gaussian, x_train, x_test)
-
- # Tests ------------------
- expect_equal(res1.1, res1.2)
- expect_equal(x2, res2)
- expect_identical(colnames(res3), colnames(x_test))
- expect_error(sample_copula(m + 1, n_samples, mu, cov_mat, m, x_test_gaussian, x_train, x_test))
- expect_true(data.table::is.data.table(res2))
- }
-})
-
-test_that("test create_ctree", {
- if (requireNamespace("MASS", quietly = TRUE) & requireNamespace("party", quietly = TRUE)) {
-
- # Example 1-----------
- m <- 10
- n <- 40
- n_samples <- 50
- mu <- rep(1, m)
- set.seed(123) # Ensuring consistency in every test
- cov_mat <- cov(matrix(rnorm(n * m), n, m))
- x_train <- data.table::data.table(MASS::mvrnorm(n, mu, cov_mat))
-
- given_ind <- c(4, 7)
-
- mincriterion <- 0.95
- minsplit <- 20
- minbucket <- 7
- sample <- TRUE
-
- # build the tree
- r <- create_ctree(
- given_ind = given_ind,
- x_train = x_train,
- mincriterion = mincriterion,
- minsplit = minsplit,
- minbucket = minbucket,
- use_partykit = "on_error"
- )
-
- dependent_ind <- (1:dim(x_train)[2])[-given_ind]
- # Test output format ------------------
- expect_true(is.list(r))
- expect_equal(length(r), 3)
- expect_equal(class(r$tree)[1], "BinaryTree")
- expect_equal(r$given_ind, given_ind)
- expect_equal(r$dependent_ind, dependent_ind)
-
- df <- data.table(cbind(
- party::response(object = r$tree)$Y1,
- party::response(object = r$tree)$Y2,
- party::response(object = r$tree)$Y3,
- party::response(object = r$tree)$Y4,
- party::response(object = r$tree)$Y5,
- party::response(object = r$tree)$Y6,
- party::response(object = r$tree)$Y7,
- party::response(object = r$tree)$Y8
- ))
-
- names(df) <- paste0("V", dependent_ind)
- expect_equal(df, x_train[, dependent_ind, with = FALSE])
-
- # Example 2 -------------
- # Check that conditioning upon all variables returns empty tree.
-
- given_ind <- 1:10
- mincriterion <- 0.95
- minsplit <- 20
- minbucket <- 7
- sample <- TRUE
-
- # build the tree
- r <- create_ctree(
- given_ind = given_ind,
- x_train = x_train,
- mincriterion = mincriterion,
- minsplit = minsplit,
- minbucket = minbucket,
- use_partykit = "on_error"
- )
-
- expect_equal(length(r), 3)
- expect_true(is.list(r))
- expect_true(is.list(r$tree))
- expect_equal(r$given_ind, given_ind)
- expect_equal(r$dependent_ind, (1:dim(x_train)[2])[-given_ind])
- }
-})
-
-test_that("test sample_ctree", {
- if (requireNamespace("MASS", quietly = TRUE) & requireNamespace("party", quietly = TRUE)) {
- # Example -----------
- m <- 10
- n <- 40
- n_samples <- 50
- mu <- rep(1, m)
- set.seed(123) # Ensuring consistency in every test
- cov_mat <- cov(matrix(rnorm(n * m), n, m))
- x_train <- data.table::data.table(MASS::mvrnorm(n, mu, cov_mat))
- x_test <- MASS::mvrnorm(1, mu, cov_mat)
- x_test_dt <- data.table::setDT(as.list(x_test))
-
- given_ind <- c(4, 7)
-
- # build the tree
- dependent_ind <- (1:dim(x_train)[2])[-given_ind]
-
- x <- x_train[, given_ind, with = FALSE]
- y <- x_train[, dependent_ind, with = FALSE]
-
- df <- data.table::data.table(cbind(y, x))
-
- colnames(df) <- c(paste0("Y", 1:ncol(y)), paste0("V", given_ind))
-
- ynam <- paste0("Y", 1:ncol(y))
- fmla <- as.formula(paste(paste(ynam, collapse = "+"), "~ ."))
-
- datact <- party::ctree(fmla,
- data = df, controls =
- party::ctree_control(
- minbucket = 7,
- mincriterion = 0.95
- )
- )
-
-
- tree <- list(tree = datact, given_ind = given_ind, dependent_ind = dependent_ind)
-
- # new
- r <- sample_ctree(
- tree = tree, n_samples = n_samples, x_test = x_test_dt,
- x_train = x_train,
- p = length(x_test), sample = TRUE
- )
-
- # Test output format ------------------
- expect_true(data.table::is.data.table(r))
- expect_equal(ncol(r), m)
- expect_equal(nrow(r), n_samples)
- expect_equal(colnames(r), colnames(x_test_dt))
-
- # Example 2 -------------
- # Check that conditioning upon all variables simply returns the test observation.
-
- given_ind <- 1:10
- dependent_ind <- (1:dim(x_train)[2])[-given_ind]
- datact <- list()
- tree <- list(tree = datact, given_ind = given_ind, dependent_ind = dependent_ind)
- r <- sample_ctree(
- tree = tree, n_samples = n_samples, x_test = x_test_dt,
- x_train = x_train,
- p = length(x_test), sample = TRUE
- )
- expect_identical(r, data.table::as.data.table(x_test_dt))
- }
-})
diff --git a/tests/testthat/test-setup.R b/tests/testthat/test-setup.R
new file mode 100644
index 000000000..022ae8d84
--- /dev/null
+++ b/tests/testthat/test-setup.R
@@ -0,0 +1,1546 @@
+test_that("error with custom model without providing predict_model", {
+ set.seed(123)
+
+
+ expect_snapshot(
+ {
+ # Custom model with no predict_model
+
+ model_custom_lm_mixed <- model_lm_mixed
+ class(model_custom_lm_mixed) <- "whatever"
+
+ explain(
+ model = model_custom_lm_mixed,
+ x_train = x_train_mixed,
+ x_explain = x_explain_mixed,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("messages with missing detail in get_model_specs", {
+ set.seed(123)
+
+ model_custom_lm_mixed <- model_lm_mixed
+ class(model_custom_lm_mixed) <- "whatever"
+
+ custom_predict_model <- function(x, newdata) {
+ beta <- coef(x)
+ X <- model.matrix(~., newdata)
+ return(as.vector(beta %*% t(X)))
+ }
+
+ expect_snapshot({
+ # Custom model with no get_model_specs
+ explain(
+ model = model_custom_lm_mixed,
+ x_train = x_train_mixed,
+ x_explain = x_explain_mixed,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = custom_predict_model,
+ get_model_specs = NA,
+ n_batches = 1,
+ timing = FALSE
+ )
+ })
+
+
+ expect_snapshot({
+ # Custom model where get_model_specs gives NA-labels
+ custom_get_model_specs_no_lab <- function(x) {
+ feature_specs <- list(labels = NA, classes = NA, factor_levels = NA)
+ }
+
+ explain(
+ model = model_custom_lm_mixed,
+ x_train = x_train_mixed,
+ x_explain = x_explain_mixed,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = custom_predict_model,
+ get_model_specs = custom_get_model_specs_no_lab,
+ n_batches = 1,
+ timing = FALSE
+ )
+ })
+
+
+ expect_snapshot({
+ # Custom model where get_model_specs gives NA-classes
+ custom_gms_no_classes <- function(x) {
+ feature_specs <- list(labels = labels(x$terms), classes = NA, factor_levels = NA)
+ }
+
+ explain(
+ model = model_custom_lm_mixed,
+ x_train = x_train_mixed,
+ x_explain = x_explain_mixed,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = custom_predict_model,
+ get_model_specs = custom_gms_no_classes,
+ n_batches = 1,
+ timing = FALSE
+ )
+ })
+
+
+ expect_snapshot({
+ # Custom model where get_model_specs gives NA-factor levels
+ custom_gms_no_factor_levels <- function(x) {
+ feature_specs <- list(
+ labels = labels(x$terms),
+ classes = attr(x$terms, "dataClasses")[-1],
+ factor_levels = NA
+ )
+ }
+
+ explain(
+ model = model_custom_lm_mixed,
+ x_train = x_train_mixed,
+ x_explain = x_explain_mixed,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = custom_predict_model,
+ get_model_specs = custom_gms_no_factor_levels,
+ n_batches = 1,
+ timing = FALSE
+ )
+ })
+})
+
+test_that("erroneous input: `x_train/x_explain`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # not matrix or data.table 1
+ x_train_wrong_format <- c(a = 1, b = 2)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_wrong_format,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # not matrix or data.table 2
+ x_explain_wrong_format <- c(a = 1, b = 2)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_wrong_format,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # not matrix or data.table 3
+ x_train_wrong_format <- c(a = 1, b = 2)
+ x_explain_wrong_format <- c(a = 3, b = 4)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_wrong_format,
+ x_train = x_train_wrong_format,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # missing column names x_train
+ x_train_no_column_names <- as.data.frame(x_train_numeric)
+ names(x_train_no_column_names) <- NULL
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_no_column_names,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # missing column names x_explain
+ x_explain_no_column_names <- as.data.frame(x_explain_numeric)
+ names(x_explain_no_column_names) <- NULL
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_no_column_names,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # missing column names in both x_train and x_explain
+ x_train_no_column_names <- as.data.frame(x_train_numeric)
+ x_explain_no_column_names <- as.data.frame(x_explain_numeric)
+ names(x_explain_no_column_names) <- NULL
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_no_column_names,
+ x_train = x_train_no_column_names,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `model`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # no model passed
+ explain(
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `approach`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # not a character (vector)
+ approach_non_character <- 1
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = approach_non_character,
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # incorrect length
+ approach_incorrect_length <- c("empirical", "gaussian")
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = approach_incorrect_length,
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # incorrect character
+ approach_incorrect_character <- "bla"
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = approach_incorrect_character,
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `prediction_zero`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # non-numeric 1
+ p0_non_numeric_1 <- "bla"
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0_non_numeric_1,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # non-numeric 2
+ p0_non_numeric_2 <- NULL
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0_non_numeric_2,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # length > 1
+ p0_too_long <- c(1, 2)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0_too_long,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # NA-numeric
+ p0_is_NA <- as.numeric(NA)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0_is_NA,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `n_combinations`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # non-numeric 1
+ n_combinations_non_numeric_1 <- "bla"
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_combinations = n_combinations_non_numeric_1,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # non-numeric 2
+ n_combinations_non_numeric_2 <- TRUE
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_combinations = n_combinations_non_numeric_2,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # non-integer
+ n_combinations_non_integer <- 10.5
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_combinations = n_combinations_non_integer,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+
+ expect_snapshot(
+ {
+ # length > 1
+ n_combinations_too_long <- c(1, 2)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_combinations = n_combinations_too_long,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # NA-numeric
+ n_combinations_is_NA <- as.numeric(NA)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_combinations = n_combinations_is_NA,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # Non-positive
+ n_combinations_non_positive <- 0
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_combinations = n_combinations_non_positive,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # Too low n_combinations (smaller than # features
+ n_combinations <- ncol(x_explain_numeric) - 1
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ prediction_zero = p0,
+ approach = "gaussian",
+ n_combinations = n_combinations,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # Too low n_combinations (smaller than # groups
+ groups <- list(
+ A = c("Solar.R", "Wind"),
+ B = c("Temp", "Month"),
+ C = "Day"
+ )
+
+ n_combinations <- length(groups) - 1
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ prediction_zero = p0,
+ approach = "gaussian",
+ group = groups,
+ n_combinations = n_combinations,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `group`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # not a list
+ group_non_list <- "bla"
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ group = group_non_list,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # non-characters in list
+ group_with_non_characters <- list(A = 1, B = 2)
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ group = group_with_non_characters,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # group features not in data
+ group_with_non_data_features <- list(
+ A = c("Solar.R", "Wind", "not_a_data_feature"),
+ B = c("Temp", "Month", "Day")
+ )
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ group = group_with_non_data_features,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # missing feature in group
+ group_missing_data_features <- list(
+ A = c("Solar.R"),
+ B = c("Temp", "Month", "Day")
+ )
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ group = group_missing_data_features,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # missing feature in group
+ group_dup_data_features <- list(
+ A = c("Solar.R", "Solar.R", "Wind"),
+ B = c("Temp", "Month", "Day")
+ )
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ group = group_dup_data_features,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # a single group only
+ single_group <- list(A = c("Solar.R", "Wind", "Temp", "Month", "Day"))
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ group = single_group,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `n_samples`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # non-numeric 1
+ n_samples_non_numeric_1 <- "bla"
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_samples = n_samples_non_numeric_1,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # non-numeric 2
+ n_samples_non_numeric_2 <- TRUE
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_samples = n_samples_non_numeric_2,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # non-integer
+ n_samples_non_integer <- 10.5
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_samples = n_samples_non_integer,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # length > 1
+ expect_snapshot(
+ {
+ n_samples_too_long <- c(1, 2)
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_samples = n_samples_too_long,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # NA-numeric
+ expect_snapshot(
+ {
+ n_samples_is_NA <- as.numeric(NA)
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_samples = n_samples_is_NA,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # Non-positive
+ expect_snapshot(
+ {
+ n_samples_non_positive <- 0
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_samples = n_samples_non_positive,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `n_batches`", {
+ set.seed(123)
+
+ # non-numeric 1
+ expect_snapshot(
+ {
+ n_batches_non_numeric_1 <- "bla"
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = n_batches_non_numeric_1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # non-numeric 2
+ expect_snapshot(
+ {
+ n_batches_non_numeric_2 <- TRUE
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = n_batches_non_numeric_2,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # non-integer
+ expect_snapshot(
+ {
+ n_batches_non_integer <- 10.5
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = n_batches_non_integer,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # length > 1
+ expect_snapshot(
+ {
+ n_batches_too_long <- c(1, 2)
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = n_batches_too_long,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # NA-numeric
+ expect_snapshot(
+ {
+ n_batches_is_NA <- as.numeric(NA)
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = n_batches_is_NA,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # Non-positive
+ expect_snapshot(
+ {
+ n_batches_non_positive <- 0
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = n_batches_non_positive,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # Larger than number of n_combinations
+ expect_snapshot(
+ {
+ n_combinations <- 10
+ n_batches_too_large <- 11
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_combinations = n_combinations,
+ n_batches = n_batches_too_large,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # Larger than number of n_combinations without specification
+ expect_snapshot(
+ {
+ n_batches_too_large_2 <- 32
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ n_batches = n_batches_too_large_2,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `seed`", {
+ set.seed(123)
+
+ # Not interpretable as integer
+ expect_snapshot(
+ {
+ seed_not_integer_interpretable <- "bla"
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ seed = seed_not_integer_interpretable,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `keep_samp_for_vS`", {
+ set.seed(123)
+
+ # non-logical 1
+ expect_snapshot(
+ {
+ keep_samp_for_vS_non_logical_1 <- "bla"
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ keep_samp_for_vS = keep_samp_for_vS_non_logical_1,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ # non-logical 2
+ expect_snapshot(
+ {
+ keep_samp_for_vS_non_logical_2 <- NULL
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ keep_samp_for_vS = keep_samp_for_vS_non_logical_2,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+ # length > 1
+ expect_snapshot(
+ {
+ keep_samp_for_vS_too_long <- c(TRUE, FALSE)
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ keep_samp_for_vS = keep_samp_for_vS_too_long,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `predict_model`", {
+ set.seed(123)
+
+ # not a function
+ expect_snapshot(
+ {
+ predict_model_nonfunction <- "bla"
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = predict_model_nonfunction,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # non-numeric output
+ predict_model_non_num_output <- function(model, x) {
+ "bla"
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = predict_model_non_num_output,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # incorrect output length
+ predict_model_wrong_output_len <- function(model, x) {
+ rep(1, nrow(x) + 1)
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = predict_model_wrong_output_len,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # invalid function format
+ predict_model_invalid_argument <- function(model) {
+ rep(1, nrow(x))
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = predict_model_invalid_argument,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # error within function
+ predict_model_error <- function(model, x) {
+ 1 + "bla"
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ predict_model = predict_model_error,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("erroneous input: `get_model_specs`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # not a function
+ get_model_specs_nonfunction <- "bla"
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ get_model_specs = get_model_specs_nonfunction,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+
+ expect_snapshot(
+ {
+ # wrong output (not list)
+ get_ms_output_not_list <- function(x) {
+ "bla"
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ get_model_specs = get_ms_output_not_list,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # wrong output (wrong length)
+ get_ms_output_too_long <- function(x) {
+ list(1, 2, 3, 4)
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ get_model_specs = get_ms_output_too_long,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # wrong output (wrong length)
+ get_ms_output_wrong_names <- function(x) {
+ list(
+ labels = 1,
+ classes = 2,
+ not_a_name = 3
+ )
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ get_model_specs = get_ms_output_wrong_names,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # wrong output (wrong length)
+ get_model_specs_error <- function(x) {
+ 1 + "bla"
+ }
+
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "independence",
+ prediction_zero = p0,
+ get_model_specs = get_model_specs_error,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("incompatible input: `data/approach`", {
+ set.seed(123)
+
+ expect_snapshot(
+ {
+ # factor model/data with approach gaussian
+ non_factor_approach_1 <- "gaussian"
+ explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_explain_mixed,
+ approach = non_factor_approach_1,
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # factor model/data with approach empirical
+ non_factor_approach_2 <- "empirical"
+ explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_explain_mixed,
+ approach = non_factor_approach_2,
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+
+ expect_snapshot(
+ {
+ # factor model/data with approach copula
+ non_factor_approach_3 <- "copula"
+ explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_explain_mixed,
+ approach = non_factor_approach_3,
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+ },
+ error = TRUE
+ )
+})
+
+test_that("Correct dimension of S when sampling combinations", {
+ n_combinations <- 10
+
+ res <- explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_explain_mixed,
+ prediction_zero = p0,
+ approach = "ctree",
+ n_combinations = n_combinations,
+ n_batches = 1,
+ timing = FALSE
+ )
+
+ expect_equal(nrow(res$internal$objects$S), n_combinations)
+})
+
+test_that("Error with too low `n_combinations`", {
+ n_combinations <- ncol(x_explain_numeric) - 1
+
+ expect_error(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_explain_numeric,
+ prediction_zero = p0,
+ approach = "gaussian",
+ n_combinations = n_combinations,
+ n_batches = 1,
+ timing = FALSE
+ )
+ )
+
+ # Same for groups
+ groups <- list(
+ A = c("Solar.R", "Wind"),
+ B = c("Temp", "Month"),
+ C = "Day"
+ )
+
+ n_combinations <- length(groups) - 1
+
+ expect_error(
+ explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_explain_numeric,
+ prediction_zero = p0,
+ approach = "gaussian",
+ group = groups,
+ n_combinations = n_combinations,
+ n_batches = 1,
+ timing = FALSE
+ )
+ )
+})
+
+test_that("Correct dimension of S when sampling combinations with groups", {
+ n_combinations <- 5
+
+ groups <- list(
+ A = c("Solar.R", "Wind"),
+ B = c("Temp", "Month_factor"),
+ C = "Day"
+ )
+
+ res <- explain(
+ model = model_lm_mixed,
+ x_explain = x_explain_mixed,
+ x_train = x_explain_mixed,
+ prediction_zero = p0,
+ approach = "ctree",
+ group = groups,
+ n_combinations = n_combinations,
+ n_batches = 1,
+ timing = FALSE
+ )
+
+ expect_equal(nrow(res$internal$objects$S), n_combinations)
+})
+
+test_that("data feature ordering is output_lm_numeric_column_order", {
+ explain.original <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+
+ explain.new_data_feature_order <- explain(
+ model = model_lm_numeric,
+ x_explain = rev(x_explain_numeric),
+ x_train = rev(x_train_numeric),
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+
+ explain.new_model_feat_order <- explain(
+ model = model_lm_numeric_col_order,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 1,
+ timing = FALSE
+ )
+
+ # Same Shapley values, but different order
+ expect_false(identical(
+ explain.original$shapley_values,
+ explain.new_data_feature_order$shapley_values
+ ))
+ expect_equal(
+ explain.original$shapley_values[, mget(sort(names(explain.original$shapley_values)))],
+ explain.new_data_feature_order$shapley_values[, mget(sort(names(explain.new_data_feature_order$shapley_values)))]
+ )
+
+ # Same Shapley values in same order
+ expect_equal(explain.original, explain.new_model_feat_order)
+})
+
+test_that("parallelization gives same output for any approach", {
+ # Empirical is seed independent
+ explain.empirical_sequential <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+
+ future::plan("multisession", workers = 2) # Parallelized with 2 cores
+ explain.empirical_multisession <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+
+ future::plan("sequential") # Resetting to sequential computation
+
+ # Identical results
+ expect_equal(
+ explain.empirical_sequential,
+ explain.empirical_multisession
+ )
+
+
+ # ctree is seed NOT independent
+ explain.ctree_sequential <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+
+ future::plan("multisession", workers = 5) # Parallelized with 2 cores
+ explain.ctree_multisession <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+
+ future::plan("sequential") # Resetting to sequential computation
+
+ # Identical results also for seed dependent methods.
+ expect_equal(
+ explain.ctree_sequential,
+ explain.ctree_multisession
+ )
+})
+
+test_that("different n_batches gives same/different shapley values for different approaches", {
+ # approach "empirical" is seed independent
+ explain.empirical_n_batches_5 <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 5,
+ timing = FALSE
+ )
+
+ explain.empirical_n_batches_10 <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "empirical",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+
+
+ # Difference in the objects (n_batches and related)
+ expect_false(identical(
+ explain.empirical_n_batches_5,
+ explain.empirical_n_batches_10
+ ))
+ # Same Shapley values
+ expect_equal(
+ explain.empirical_n_batches_5$shapley_values,
+ explain.empirical_n_batches_10$shapley_values
+ )
+
+
+ # approach "ctree" is seed dependent
+ explain.ctree_n_batches_5 <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 5,
+ timing = FALSE
+ )
+
+ explain.ctree_n_batches_10 <- explain(
+ model = model_lm_numeric,
+ x_explain = x_explain_numeric,
+ x_train = x_train_numeric,
+ approach = "ctree",
+ prediction_zero = p0,
+ n_batches = 10,
+ timing = FALSE
+ )
+
+ # Difference in the objects (n_batches and related)
+ expect_false(identical(
+ explain.ctree_n_batches_5,
+ explain.ctree_n_batches_10
+ ))
+ # NEITHER same Shapley values
+ expect_false(identical(
+ explain.ctree_n_batches_5$shapley_values,
+ explain.ctree_n_batches_10$shapley_values
+ ))
+})
diff --git a/tests/testthat/test-src_impute_data.R b/tests/testthat/test-src_impute_data.R
deleted file mode 100644
index a05d2e0ff..000000000
--- a/tests/testthat/test-src_impute_data.R
+++ /dev/null
@@ -1,77 +0,0 @@
-context("test-src_impute_data.R")
-
-test_that("Test observation_impute_cpp", {
-
- # Example data -----------
- if (requireNamespace("datasets", quietly = TRUE)) {
- data("mtcars", package = "datasets")
- rownames(mtcars) <- NULL
- mtcars <- as.matrix(mtcars)
-
- # Example -----------
- m <- 3
- n_combinations <- 2^m
- mtcars <- mtcars[1:15, seq(m)]
- ntrain <- 14
- xtrain <- mtcars[seq(ntrain), ]
- xtest <- mtcars[-seq(ntrain), , drop = FALSE]
- S <- matrix(0L, n_combinations, m)
- features <- list(
- integer(), 1, 2, 3, c(1, 2), c(1, 3), c(2, 3), c(1, 2, 3)
- )
- for (i in seq_along(features)) {
- feature_i <- features[[i]]
- if (length(feature_i) > 0) {
- S[i, features[[i]]] <- 1L
- }
- }
-
- # Tests (invalid input) -----------
- expect_error(
- observation_impute_cpp(
- index_xtrain = c(1, 2),
- index_s = c(1, 2, 3),
- xtrain = xtrain,
- xtest = xtest,
- S = S
- )
- )
- expect_error(
- observation_impute_cpp(
- index_xtrain = c(1, 2),
- index_s = c(2, 3),
- xtrain = xtrain[, 1:2],
- xtest = xtest,
- S = S
- )
- )
-
- # Tests (valid input) -----------
- index_xtrain <- c(1, 2)
- index_s <- c(4, 5)
- x <- observation_impute_cpp(
- index_xtrain = index_xtrain,
- index_s = index_s,
- xtrain = xtrain,
- xtest = xtest,
- S = S
- )
-
- expect_equal(nrow(x), length(index_s))
- expect_equal(ncol(x), ncol(xtrain))
- expect_true(is.matrix(x))
- expect_true(is.double(x))
-
- for (i in 1:nrow(x)) {
- feature_i <- features[[index_s[i]]]
-
- for (j in seq(m)) {
- if (j %in% feature_i) {
- expect_equal(x[i, j], unname(xtest[1, j]))
- } else {
- expect_equal(x[i, j], unname(xtrain[index_xtrain[i], j]))
- }
- }
- }
- }
-})
diff --git a/tests/testthat/test-src_weighted_matrix.R b/tests/testthat/test-src_weighted_matrix.R
deleted file mode 100644
index 7c8973e30..000000000
--- a/tests/testthat/test-src_weighted_matrix.R
+++ /dev/null
@@ -1,79 +0,0 @@
-context("test-src_weighted_matrix.R")
-
-test_that("Test weight_matrix_cpp", {
-
- ## Example -----------
- m <- 3
- n <- 2^m
- subsets <- unlist(
- lapply(
- 0:m,
- utils::combn,
- x = m,
- simplify = FALSE
- ),
- recursive = FALSE
- )
- w_all <- shapley_weights(m, choose(m, 0:m), 0:m)
- w_all[!is.finite(w_all)] <- 10^6
- w <- w_all[sapply(subsets, length) + 1]
- x <- weight_matrix_cpp(
- subsets = subsets,
- m = m,
- n = n,
- w = w
- )
-
- ## Exact results -----------
- Z <- matrix(0, nrow = n, ncol = m + 1)
- Z[, 1] <- 1
- for (i in seq_along(subsets)) {
- f <- subsets[[i]]
- if (length(f) > 0) {
- Z[i, f + 1] <- 1
- }
- }
- W <- matrix(0, nrow = n, ncol = n)
- diag(W) <- w
- res <- solve(t(Z) %*% W %*% Z) %*% (t(Z) %*% W)
-
- ## Test results -----------
- expect_true(is.matrix(x))
- expect_true(is.double(x))
- expect_equal(nrow(x), m + 1)
- expect_equal(ncol(x), n)
- expect_equal(x, res)
-})
-
-test_that("Test feature_matrix_cpp", {
-
- ## Example -----------
- features <- list(
- integer(0),
- 1:2,
- 10,
- 4:8,
- 3:7
- )
- m <- 10
- x <- feature_matrix_cpp(features, m)
-
- ## Test results -----------
- expect_true(is.matrix(x))
- expect_equal(ncol(x), m)
- expect_equal(nrow(x), length(features))
- expect_true(max(x) <= 1)
- expect_true(min(x) >= 0)
- expect_equal(sapply(features, length), rowSums(x))
- for (i in seq_along(features)) {
- feature_i <- features[[i]]
- n_features <- length(feature_i)
- if (n_features == 0) {
- expect_equal(x[i, ], rep(0, m))
- } else {
- expect_equal(x[i, feature_i], rep(1, n_features))
- expect_equal(x[i, -feature_i], rep(0, m - n_features))
- }
- }
- expect_error(feature_matrix_cpp(list(1, 2:3), 3))
-})
diff --git a/tests/testthat/test-transformation.R b/tests/testthat/test-transformation.R
deleted file mode 100644
index bbba509c6..000000000
--- a/tests/testthat/test-transformation.R
+++ /dev/null
@@ -1,52 +0,0 @@
-context("test-transformation.R")
-
-test_that("Test inv_gaussian_transform", {
-
- # Example -----------
- zx <- rnorm(50)
- n_z <- 30
-
- x <- inv_gaussian_transform(zx, n_z)
-
- # Tests -----------
- expect_true(is.atomic(x))
- expect_true(is.double(x))
-
- expect_equal(length(x), n_z)
- expect_true(min(x) >= min(zx[-c(1:n_z)]))
- expect_true(max(x) <= max(zx[-c(1:n_z)]))
-
- # Erros -----------
- expect_error(inv_gaussian_transform(zx, length(zx)))
- expect_error(inv_gaussian_transform(zx, length(zx) + 1))
-})
-
-test_that("Test gaussian_transform_separate", {
-
- # Example -----------
- yx <- rnorm(50)
- n_y <- 30
-
- x <- gaussian_transform_separate(yx, n_y)
-
- # Tests -----------
- expect_true(is.atomic(x))
- expect_true(is.double(x))
- expect_equal(length(x), n_y)
-
- # Erros -----------
- expect_error(gaussian_transform_separate(yx, length(yx)))
- expect_error(gaussian_transform_separate(yx, length(yx) + 1))
-})
-
-test_that("Test gaussian_transform", {
-
- # Example -----------
- y <- rnorm(50)
- x <- gaussian_transform(y)
-
- # Tests -----------
- expect_true(is.atomic(x))
- expect_true(is.double(x))
- expect_equal(length(x), length(y))
-})
diff --git a/tests/testthat/test_objects/explanation_explain_group_obj_list.rds b/tests/testthat/test_objects/explanation_explain_group_obj_list.rds
deleted file mode 100644
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diff --git a/tests/testthat/test_objects/explanation_explain_obj_list.rds b/tests/testthat/test_objects/explanation_explain_obj_list.rds
deleted file mode 100644
index ec323afaa..000000000
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diff --git a/tests/testthat/test_objects/explanation_explain_obj_list_fixed.rds b/tests/testthat/test_objects/explanation_explain_obj_list_fixed.rds
deleted file mode 100644
index 9dd1133ff..000000000
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diff --git a/tests/testthat/test_objects/explanation_explain_obj_list_no_ctree.rds b/tests/testthat/test_objects/explanation_explain_obj_list_no_ctree.rds
deleted file mode 100644
index 97452ec56..000000000
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diff --git a/tests/testthat/test_objects/shapley_explainer_group1_2_obj.rds b/tests/testthat/test_objects/shapley_explainer_group1_2_obj.rds
deleted file mode 100644
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diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd
index b52158d0e..4b36ddffd 100644
--- a/vignettes/understanding_shapr.Rmd
+++ b/vignettes/understanding_shapr.Rmd
@@ -4,9 +4,12 @@ author: "Camilla Lingjærde, Martin Jullum & Nikolai Sellereite"
output: rmarkdown::html_vignette
bibliography: ../inst/REFERENCES.bib
vignette: >
+ %\VignetteEncoding{UTF-8}
%\VignetteIndexEntry{`shapr`: Explaining individual machine learning predictions with Shapley values}
%\VignetteEngine{knitr::rmarkdown}
- %\VignetteEncoding{UTF-8}
+editor_options:
+ markdown:
+ wrap: 72
---
```{r, include = FALSE}
@@ -14,15 +17,16 @@ knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
- fig.height = 3
+ fig.height = 3,
+ warning = FALSE,
+ message = FALSE
)
```
```{r setup, include=FALSE, warning=FALSE}
library(shapr)
```
-
-
+
> [Introduction](#intro)
> [Overview of Package](#overview)
@@ -33,24 +37,28 @@ library(shapr)
> [Advanced usage](#advanced)
-> [Comparison to Lundberg & Lee's implementation](#compare)
+> [Scalability and efficency](#scalability)
+> [Comparison to Lundberg & Lee's implementation](#compare)
# Introduction
-
-
-The `shapr` package implements an extended version of the Kernel SHAP method for approximating
-Shapley values (@lundberg2017unified), in which dependence between the features is taken into
-account (@aas2019explaining). Estimation of Shapley values is of interest when attempting to
-explain complex machine learning models. Of existing work on interpreting individual predictions,
-Shapley values is regarded to be the only model-agnostic explanation method with a solid theoretical
-foundation (@lundberg2017unified). Kernel SHAP is a computationally efficient approximation to
-Shapley values in higher dimensions, but it assumes independent features. @aas2019explaining extend
-the Kernel SHAP method to handle dependent features, resulting in more accurate approximations to
-the true Shapley values. See the [paper](https://arxiv.org/abs/1903.10464) (@aas2019explaining) for further details.
+The `shapr` package implements an extended version of the Kernel SHAP
+method for approximating Shapley values (@lundberg2017unified), in which
+dependence between the features is taken into account
+(@aas2019explaining). Estimation of Shapley values is of interest when
+attempting to explain complex machine learning models. Of existing work
+on interpreting individual predictions, Shapley values is regarded to be
+the only model-agnostic explanation method with a solid theoretical
+foundation (@lundberg2017unified). Kernel SHAP is a computationally
+efficient approximation to Shapley values in higher dimensions, but it
+assumes independent features. @aas2019explaining extend the Kernel SHAP
+method to handle dependent features, resulting in more accurate
+approximations to the true Shapley values. See the
+[paper](https://www.sciencedirect.com/sdfe/reader/pii/S0004370221000539/pdf)
+(@aas2019explaining) for further details.
@@ -60,149 +68,179 @@ the true Shapley values. See the [paper](https://arxiv.org/abs/1903.10464) (@aas
## Functions
-Here is an overview of the main functions. You can read their documentation and see examples
-with `?function_name`.
-
------------------------------ ------------------------------------------------------
-Function Name Description
------------------------------ ------------------------------------------------------
-`shapr` Get Shapley weights for test data.
+Here is an overview of the main functions. You can read their
+documentation and see examples with `?function_name`.
-`explain` Computes kernel SHAP values for test data.
-
-`plot.shapr` Plots the individual prediction explanations. Uses facet_wrap of ggplot.
------------------------------ --------------------------------------------------
+| Function Name | Description |
+|:---------------------|:-------------------------------------------------|
+| `explain` | Computes kernel SHAP values for test data. |
+| `explain_forecast` | Analogous to `explain`, but for explaining forecasts from time series models. |
+| `plot.shapr` | Plots the individual prediction explanations. Uses the `ggplot` and `ggbeeswarm` package. |
: Main functions in the `shapr` package.
-
-
# The Kernel SHAP Method -Assume a predictive model $f(\boldsymbol{x})$ for a response value $y$ with features -$\boldsymbol{x}\in \mathbb{R}^M$, trained on a training set, and that we want to explain the -predictions for new sets of data. This may be done using ideas from cooperative game theory, -letting a single prediction take the place of the game being played and the features the place of -the players. Letting $N$ denote the set of all $M$ players, and $S \subseteq N$ be a subset -of $|S|$ players, the "contribution" function $v(S)$ describes the total expected sum of payoffs -the members of $S$ can obtain by cooperation. The Shapley value (@Shapley53) is one way to -distribute the total gains to the players, assuming that they all collaborate. The amount that -player $i$ gets is then +Assume a predictive model $f(\boldsymbol{x})$ for a response value $y$ +with features $\boldsymbol{x}\in \mathbb{R}^M$, trained on a training +set, and that we want to explain the predictions for new sets of data. +This may be done using ideas from cooperative game theory, letting a +single prediction take the place of the game being played and the +features the place of the players. Letting $N$ denote the set of all $M$ +players, and $S \subseteq N$ be a subset of $|S|$ players, the +"contribution" function $v(S)$ describes the total expected sum of +payoffs the members of $S$ can obtain by cooperation. The Shapley value +(@Shapley53) is one way to distribute the total gains to the players, +assuming that they all collaborate. The amount that player $i$ gets is +then $$\phi_i(v) = \phi_i = \sum_{S \subseteq N \setminus\{i\}} \frac{|S| ! (M-| S| - 1)!}{M!}(v(S\cup \{i\})-v(S)),$$ +that is, a weighted mean over all subsets $S$ of players not containing +player $i$. @lundberg2017unified define the contribution function for a +certain subset $S$ of these features $\boldsymbol{x}_S$ as +$v(S) = \mbox{E}[f(\boldsymbol{x})|\boldsymbol{x}_S]$, the expected +output of the predictive model conditional on the feature values of the +subset. @lundberg2017unified names this type of Shapley values SHAP +(SHapley Additive exPlanation) values. Since the conditional +expectations can be written as -that is, a weighted mean over all subsets $S$ of players not containing player $i$. -@lundberg2017unified define the contribution function for a certain subset $S$ of these features -$\boldsymbol{x}_S$ as $v(S) = \mbox{E}[f(\boldsymbol{x})|\boldsymbol{x}_S]$, the expected output -of the predictive model conditional on the feature values of the subset. @lundberg2017unified names -this type of Shapley values SHAP (SHapley Additive exPlanation) values. Since the conditional -expectations can be written as - +```{=tex} \begin{equation} \label{eq:CondExp} E[f(\boldsymbol{x})|\boldsymbol{x}_s=\boldsymbol{x}_S^*] = E[f(\boldsymbol{x}_{\bar{S}},\boldsymbol{x}_S)|\boldsymbol{x}_S=\boldsymbol{x}_S^*] = \int f(\boldsymbol{x}_{\bar{S}},\boldsymbol{x}_S^*)\,p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)d\boldsymbol{x}_{\bar{S}}, \end{equation} - -the conditional distributions $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ are -needed to compute the contributions. The Kernel SHAP method of @lundberg2017unified assumes feature -independence, so that $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)=p(\boldsymbol{x}_{\bar{S}})$. -If samples -$\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$, from $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ -are available, the conditional expectation in above can be approximated by - +``` +the conditional distributions +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ are +needed to compute the contributions. The Kernel SHAP method of +@lundberg2017unified assumes feature independence, so that +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)=p(\boldsymbol{x}_{\bar{S}})$. +If samples $\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$, from +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ are +available, the conditional expectation in above can be approximated by + +```{=tex} \begin{equation} v_{\text{KerSHAP}}(S) = \frac{1}{K}\sum_{k=1}^K f(\boldsymbol{x}_{\bar{S}}^{k},\boldsymbol{x}_S^*). \end{equation} - -In Kernel SHAP, $\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$ are sampled from the $\bar{S}$-part of -the training data, *independently* of $\boldsymbol{x}_{S}$. This is motivated by using the -training set as the empirical distribution of $\boldsymbol{x}_{\bar{S}}$, and assuming that -$\boldsymbol{x}_{\bar{S}}$ is independent of $\boldsymbol{x}_S=\boldsymbol{x}_S^*$. -Due to the independence assumption, if the features in a given model are highly dependent, the -Kernel SHAP method may give a completely wrong answer. This can be avoided by estimating the -conditional distribution $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ directly -and generating samples from this distribution. With this small change, the contributions and -Shapley values may then be approximated as in the ordinary Kernel SHAP framework. @aas2019explaining -propose three different approaches for estimating the conditional probabilities. The methods may -also be combined, such that e.g. one method is used when conditioning on a small number of features, -while another method is used otherwise. +``` +In Kernel SHAP, $\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$ are sampled +from the $\bar{S}$-part of the training data, *independently* of +$\boldsymbol{x}_{S}$. This is motivated by using the training set as the +empirical distribution of $\boldsymbol{x}_{\bar{S}}$, and assuming that +$\boldsymbol{x}_{\bar{S}}$ is independent of +$\boldsymbol{x}_S=\boldsymbol{x}_S^*$. Due to the independence +assumption, if the features in a given model are highly dependent, the +Kernel SHAP method may give a completely wrong answer. This can be +avoided by estimating the conditional distribution +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ +directly and generating samples from this distribution. With this small +change, the contributions and Shapley values may then be approximated as +in the ordinary Kernel SHAP framework. @aas2019explaining propose three +different approaches for estimating the conditional probabilities which +are implemented: `empirical`, `gaussian` and `copula`. The package also +implements the method `ctree` method from @redelmeier2020explaining. The +original `independence` approach of @lundberg2017unified is also +available. The methods may also be combined, such that e.g. one method +is used when conditioning on a small number of features, while another +method is used otherwise. ## Multivariate Gaussian Distribution Approach -The first approach arises from the assumption that the feature vector $\boldsymbol{x}$ stems from a -multivariate Gaussian distribution with some mean vector $\boldsymbol{\mu}$ and covariance matrix -$\boldsymbol{\Sigma}$. Under this assumption, the conditional distribution -$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$ -is also multivariate Gaussian -$\text{N}_{|\bar{\mathcal{S}}|}(\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}},\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}})$, -with analytical expressions for the conditional mean vector $\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ -and covariance matrix $\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$, see @aas2019explaining for details. -Hence, instead of sampling from the marginal empirical distribution of $\boldsymbol{x}_{\bar{\mathcal{S}}}$ -approximated by the training data, we can sample from the Gaussian conditional distribution, which is fitted -using the training data. Using the resulting samples -$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, the conditional expectations be approximated -as in the Kernel SHAP. +The first approach arises from the assumption that the feature vector +$\boldsymbol{x}$ stems from a multivariate Gaussian distribution with +some mean vector $\boldsymbol{\mu}$ and covariance matrix +$\boldsymbol{\Sigma}$. Under this assumption, the conditional +distribution +$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$ +is also multivariate Gaussian\ +$\text{N}_{|\bar{\mathcal{S}}|}(\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}},\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}})$, +with analytical expressions for the conditional mean vector +$\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ and covariance matrix +$\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$, see +@aas2019explaining for details. Hence, instead of sampling from the +marginal empirical distribution of $\boldsymbol{x}_{\bar{\mathcal{S}}}$ +approximated by the training data, we can sample from the Gaussian +conditional distribution, which is fitted using the training data. Using +the resulting samples +$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, the conditional +expectations be approximated as in the Kernel SHAP. ## Gaussian Copula Approach -If the features are far from multivariate Gaussian, an alternative approach is to instead represent -the marginals by their empirical distributions, and model the dependence structure by a Gaussian -copula. Assuming a Gaussian copula, we may convert the marginals of the training data to Gaussian -features using their empirical distributions, and then fit a multivariate Gaussian distribution to these. - -To produce samples from the conditional distribution -$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$, -we convert the marginals of $\boldsymbol{x}_{\mathcal{S}}$ to Gaussians, sample from the conditional -Gaussian distribution as above, and convert the marginals of the samples back to the original -distribution. Those samples are then used to approximate the sample from the resulting multivariate -Gaussian conditional distribution. While other copulas may be used, the Gaussian copula has the -benefit that we may use the analytical expressions for the conditionals -$\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ and $\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$. -Finally, we may convert the marginals back to their original distribution, and use the resulting -samples to approximate the conditional expectations as in the Kernel SHAP. +If the features are far from multivariate Gaussian, an alternative +approach is to instead represent the marginals by their empirical +distributions, and model the dependence structure by a Gaussian copula. +Assuming a Gaussian copula, we may convert the marginals of the training +data to Gaussian features using their empirical distributions, and then +fit a multivariate Gaussian distribution to these. + +To produce samples from the conditional distribution +$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$, +we convert the marginals of $\boldsymbol{x}_{\mathcal{S}}$ to Gaussians, +sample from the conditional Gaussian distribution as above, and convert +the marginals of the samples back to the original distribution. Those +samples are then used to approximate the sample from the resulting +multivariate Gaussian conditional distribution. While other copulas may +be used, the Gaussian copula has the benefit that we may use the +analytical expressions for the conditionals +$\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ and +$\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$. Finally, we may +convert the marginals back to their original distribution, and use the +resulting samples to approximate the conditional expectations as in the +Kernel SHAP. ## Empirical Conditional Distribution Approach -If both the dependence structure and the marginal distributions of $\boldsymbol{x}$ are very far -from the Gaussian, neither of the two aforementioned methods will work very well. Few methods -exists for the non-parametric estimation of conditional densities, and the classic kernel -estimator (@rosenblatt1956) for non-parametric density estimation suffers greatly from the -curse of dimensionality and does not provide a way to generate samples from the estimated -distribution. For such situations, @aas2019explaining propose an empirical conditional approach -to sample approximately from $p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$. -The idea is to compute weights $w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^i),\ i=1,...,n_{\text{train}}$ -for all training instances based on their Mahalanobis distances (in the $S$ subset only) to the -instance $\boldsymbol{x}^*$ to be explained. Instead of sampling from this weighted (conditional) -empirical distribution, @aas2019explaining suggests a more efficient variant, using only the $K$ -instances with the largest weights: +If both the dependence structure and the marginal distributions of +$\boldsymbol{x}$ are very far from the Gaussian, neither of the two +aforementioned methods will work very well. Few methods exists for the +non-parametric estimation of conditional densities, and the classic +kernel estimator (@rosenblatt1956) for non-parametric density estimation +suffers greatly from the curse of dimensionality and does not provide a +way to generate samples from the estimated distribution. For such +situations, @aas2019explaining propose an empirical conditional approach +to sample approximately from +$p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$. +The idea is to compute weights +$w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^i),\ i=1,...,n_{\text{train}}$ +for all training instances based on their Mahalanobis distances (in the +$S$ subset only) to the instance $\boldsymbol{x}^*$ to be explained. +Instead of sampling from this weighted (conditional) empirical +distribution, @aas2019explaining suggests a more efficient variant, +using only the $K$ instances with the largest weights: $$v_{\text{condKerSHAP}}(\mathcal{S}) = \frac{\sum_{k=1}^K w_{\mathcal{S}}(\boldsymbol{x}^*, \boldsymbol{x}^{[k]}) f(\boldsymbol{x}_{\bar{\mathcal{S}}}^{[k]}, -\boldsymbol{x}_{\mathcal{S}}^*)}{\sum_{k=1}^K w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^{[k]})},$$ - -The number of samples $K$ to be used in the approximate prediction can for instance be chosen such -that the $K$ largest weights accounts for a fraction $\eta$, for example $0.9$, of the total weight. -If $K$ exceeds a certain limit, for instance $5,000$, it might be set to that limit. A bandwidth -parameter $\sigma$ used to scale the weights, must also be specified. This choice may be viewed as -a bias-variance trade-off. A small $\sigma$ puts most of the weight to a few of the closest -training observations and thereby gives low bias, but high variance. When $\sigma \rightarrow \infty$, -this method converges to the original Kernel SHAP assuming feature independence. Typically, when -the features are highly dependent, a small $\sigma$ is typically needed such that the bias does -not dominate. @aas2019explaining show that a proper criterion for selecting $\sigma$ is a -small-sample-size corrected version of the AIC known as AICc. As calculation of it is -computationally intensive, an approximate version of the selection criterion is also suggested. +\boldsymbol{x}_{\mathcal{S}}^*)}{\sum_{k=1}^K w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^{[k]})},$$ + +The number of samples $K$ to be used in the approximate prediction can +for instance be chosen such that the $K$ largest weights accounts for a +fraction $\eta$, for example $0.9$, of the total weight. If $K$ exceeds +a certain limit, for instance $5,000$, it might be set to that limit. A +bandwidth parameter $\sigma$ used to scale the weights, must also be +specified. This choice may be viewed as a bias-variance trade-off. A +small $\sigma$ puts most of the weight to a few of the closest training +observations and thereby gives low bias, but high variance. When +$\sigma \rightarrow \infty$, this method converges to the original +Kernel SHAP assuming feature independence. Typically, when the features +are highly dependent, a small $\sigma$ is typically needed such that the +bias does not dominate. @aas2019explaining show that a proper criterion +for selecting $\sigma$ is a small-sample-size corrected version of the +AIC known as AICc. As calculation of it is computationally intensive, an +approximate version of the selection criterion is also suggested. Details on this is found in @aas2019explaining. @@ -211,16 +249,58 @@ Details on this is found in @aas2019explaining. ## Conditional Inference Tree Approach -The previous three methods can only handle numerical data. This means that if the data contains categorical/discrete/ordinal features, the features first have to be one-hot encoded. When the number of levels/features is large, this is not feasible. An approach that handles mixed (i.e numerical, categorical, discrete, ordinal) features and both univariate and multivariate responses is conditional inference trees (@hothorn2006unbiased). - -Conditional inference trees is a special tree fitting procedure that relies on hypothesis tests to choose both the splitting feature and the splitting point. The tree fitting procedure is sequential: first a splitting feature is chosen (the feature that is least independent of the response), and then a splitting point is chosen for this feature. This decreases the chance of being biased towards features with many splits (@hothorn2006unbiased). - -We use conditional inference trees (*ctree*) to model the conditional distribution, $p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$, found in the Shapley methodology. -First, we fit a different conditional inference tree to each conditional distribution. -Once a tree is fit for given dependent features, the end node of $\boldsymbol{x}_{\mathcal{S}}^*$ is found. Then, we sample from this end node and use the resulting samples, -$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, when approximating the conditional expectations as in Kernel SHAP. See @Redelmeier2020ctree for more details. - -The conditional inference trees are fit using the *party* and *partykit* packages (@partykit_package). +The previous three methods can only handle numerical data. This means +that if the data contains categorical/discrete/ordinal features, the +features first have to be one-hot encoded. When the number of +levels/features is large, this is not feasible. An approach that handles +mixed (i.e numerical, categorical, discrete, ordinal) features and both +univariate and multivariate responses is conditional inference trees +(@hothorn2006unbiased). + +Conditional inference trees is a special tree fitting procedure that +relies on hypothesis tests to choose both the splitting feature and the +splitting point. The tree fitting procedure is sequential: first a +splitting feature is chosen (the feature that is least independent of +the response), and then a splitting point is chosen for this feature. +This decreases the chance of being biased towards features with many +splits (@hothorn2006unbiased). + +We use conditional inference trees (*ctree*) to model the conditional +distribution, +$p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$, +found in the Shapley methodology. First, we fit a different conditional +inference tree to each conditional distribution. Once a tree is fit for +given dependent features, the end node of +$\boldsymbol{x}_{\mathcal{S}}^*$ is found. Then, we sample from this end +node and use the resulting samples, +$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, when approximating +the conditional expectations as in Kernel SHAP. See +@redelmeier2020explaining for more details. + +The conditional inference trees are fit using the *party* and *partykit* +packages (@partykit_package). + +## Categorical Approach + +When the features are all categorical, we can estimate the conditional +expectations using basic statistical formulas. For example, if we have +three features, $x_1, x_2, x_3$ with three levels each (indicated as 1, +2, 3), and we are provided with a table of counts indicating how many +times each combination of feature values occurs, we can estimate the +marginal and conditional probabilities as follows. Marginal +probabilities are estimated by dividing the number of times a given +feature (or features) takes on a certain value in the data set with the +total number of observations in the data set. Condititional +probabilities (for example, $P(X_1 = 1 | X_2 = 1)$) are estimated by +first subsetting the data set to reflect the conditioning (i.e., +extracting all rows where $X_2 = 1$), and then dividing the number of +times the feature on the left hand side of $|$ takes the given value in +this subset by the total number of observations in this subset. Once the +marginal and conditional probabilities are estimated for all +combinations of feature values, each conditional expectation can be +calculated. For example, the expected value of $X_1$ given $X_2 = 1$ and +$X_3 = 2$ is +$$E(X_1|X_2, X_3) = \sum_{x}x P(X_1 = x | X_2=1, X_3=2) = \sum_{x} x \frac{P(X_1 = x, X_2 = 1, X_3 = 2)}{P(X_2=1, X_3=2)}.$$. @@ -228,480 +308,845 @@ The conditional inference trees are fit using the *party* and *partykit* package # Examples -`shapr` supports computation of Shapley values with any predictive model which takes a set of -numeric features and produces a numeric outcome. Note that the ctree method takes both numeric and categorical variables. Check under "Advanced usage" for an example of how this can be done. +`shapr` supports computation of Shapley values with any predictive model +which takes a set of numeric features and produces a numeric outcome. +Note that the ctree method takes both numeric and categorical variables. +Check under "Advanced usage" for an example of how this can be done. -The following example shows how a simple `xgboost` model is trained using the Boston Housing Data, -and how `shapr` can be used to explain the individual predictions. Note that the empirical conditional -distribution approach is the default (i.e. `approach = "empirical"`), and that the Gaussian, -Gaussian copula, and ctree approaches can be used instead by setting the argument `approach` to either -`"gaussian"`, `"copula"`, or `"ctree`". +The following example shows how a simple `xgboost` model is trained +using the `airquality` dataset, and how `shapr` can be used to explain +the individual predictions. Note that the empirical conditional +distribution approach is the default (i.e. `approach = "empirical"`). +The Gaussian, Gaussian copula, ctree or independence approaches can be +used instead by setting the argument `approach` to either `"gaussian"`, +`"copula"`, `"ctree"`, `"categorical"` or `"independence"` in the code +below. ```{r, warning=FALSE} library(xgboost) -library(shapr) +library(data.table) -data("Boston", package = "MASS") +data("airquality") +data <- data.table::as.data.table(airquality) +data <- data[complete.cases(data), ] -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" +x_var <- c("Solar.R", "Wind", "Temp", "Month") +y_var <- "Ozone" -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) +ind_x_explain <- 1:6 +x_train <- data[-ind_x_explain, ..x_var] +y_train <- data[-ind_x_explain, get(y_var)] +x_explain <- data[ind_x_explain, ..x_var] # Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, +model <- xgboost::xgboost( + data = as.matrix(x_train), label = y_train, nround = 20, verbose = FALSE ) -# Prepare the data for explanation -explainer <- shapr(x_train, model) - # Specifying the phi_0, i.e. the expected prediction without any features -p <- mean(y_train) +p0 <- mean(y_train) # Computing the actual Shapley values with kernelSHAP accounting for feature dependence using # the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default) explanation <- explain( - x_test, + model = model, + x_explain = x_explain, + x_train = x_train, approach = "empirical", - explainer = explainer, - prediction_zero = p + prediction_zero = p0 ) # Printing the Shapley values for the test data. # For more information about the interpretation of the values in the table, see ?shapr::explain. -print(explanation$dt) +print(explanation$shapley_values) # Plot the resulting explanations for observations 1 and 6 -plot(explanation, plot_phi0 = FALSE, index_x_test = c(1, 6)) -``` +plot(explanation, bar_plot_phi0 = FALSE, index_x_explain = c(1, 6)) +``` -The Gaussian approach is used as follows: +There are multiple plot options specified by the `plot_type` argument in +`plot`. The `waterfall` option shows the changes in the prediction score +due to each features contribution (their Shapley values): ```{r} -# Use the Gaussian approach -explanation_gaussian <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - prediction_zero = p -) - -# Plot the resulting explanations for observations 1 and 6 -plot(explanation_gaussian, plot_phi0 = FALSE, index_x_test = c(1, 6)) +plot(explanation, plot_type = "waterfall", index_x_explain = c(1, 6)) ``` -The Gaussian copula approach is used as follows: +The other two plot options, `"beeswarm"` and `"scatter"`, can be useful +when you have many observations that you want to explain. For the +purpose of illustration, we explain the whole `airquality` dataset +(including the training data) for these plot types. The +`plot_type = "beeswarm"` summarises the distribution of the Shapley +values along the x-axis across all features. Each point gives the +Shapley value of a given instance, where the points are colored by the +feature value of that instance: ```{r} -# Use the Gaussian copula approach -explanation_copula <- explain( - x_test, - approach = "copula", - explainer = explainer, - prediction_zero = p +x_explain_many <- data[, ..x_var] +explanation_plot <- explain( + model = model, + x_explain = x_explain_many, + x_train = x_train, + approach = "empirical", + prediction_zero = p0 ) - -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_copula, plot_phi0 = FALSE, index_x_test = c(1, 6)) +plot(explanation_plot, plot_type = "beeswarm") ``` -The conditional inference tree approach is used as follows: +The `plot_type = "scatter"` plots the feature values on the x-axis and +Shapley values on the y-axis, as well as (optionally) a background +scatter_hist showing the distribution of the feature data: ```{r} -# Use the conditional inference tree approach -explanation_ctree <- explain( - x_test, - approach = "ctree", - explainer = explainer, - prediction_zero = p -) - -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_ctree, plot_phi0 = FALSE, index_x_test = c(1, 6)) +plot(explanation_plot, plot_type = "scatter", scatter_hist = TRUE) ``` - -We can use mixed (i.e continuous, categorical, ordinal) data with ctree. Use ctree with categorical data in the following manner: +We can use mixed (i.e continuous, categorical, ordinal) data with ctree. +Use ctree with mixed data in the following manner: ```{r} -x_var_cat <- c("lstat", "chas", "rad", "indus") -y_var <- "medv" - -# convert to factors -Boston$rad = as.factor(Boston$rad) -Boston$chas = as.factor(Boston$chas) +# convert the month variable to a factor +data[, Month_factor := as.factor(Month)] -x_train_cat <- Boston[-1:-6, x_var_cat] -y_train <- Boston[-1:-6, y_var] -x_test_cat <- Boston[1:6, x_var_cat] +data_train_cat <- data[-ind_x_explain, ] +data_explain_cat <- data[ind_x_explain, ] -# -- special function when using categorical data + xgboost -dummylist <- make_dummies(traindata = x_train_cat, testdata = x_test_cat) +x_var_cat <- c("Solar.R", "Wind", "Temp", "Month_factor") -x_train_dummy <- dummylist$train_dummies -x_test_dummy <- dummylist$test_dummies - -# Fitting a basic xgboost model to the training data -model_cat <- xgboost::xgboost( - data = x_train_dummy, - label = y_train, - nround = 20, - verbose = FALSE -) -model_cat$feature_list <- dummylist$feature_list +x_train_cat <- data_train_cat[, ..x_var_cat] +x_explain_cat <- data_explain_cat[, ..x_var_cat] -explainer_cat <- shapr(dummylist$traindata_new, model_cat) +# Fitting an lm model here as xgboost does not handle categorical features directly +# (work around in example below) +lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + "))) -p <- mean(y_train) +model_lm_cat <- lm(lm_formula, data_train_cat) -explanation_cat <- explain( - dummylist$testdata_new, +p0 <- mean(y_train) +explanation_lm_cat <- explain( + model = model_lm_cat, + x_explain = x_explain_cat, + x_train = x_train_cat, approach = "ctree", - explainer = explainer_cat, - prediction_zero = p + prediction_zero = p0 ) # Plot the resulting explanations for observations 1 and 6, excluding # the no-covariate effect -plot(explanation_cat, plot_phi0 = FALSE, index_x_test = c(1, 6)) - +plot(explanation_lm_cat, bar_plot_phi0 = FALSE, index_x_explain = c(1, 6)) ``` -We can specify parameters used to build the conditional inference trees using the following manner. Default values are based on @hothorn2006unbiased. +We can specify parameters used to build the conditional inference trees +in the following manner. Default values are based on +@hothorn2006unbiased. ```{r} # Use the conditional inference tree approach -# We can specify parameters used to building trees by specifying mincriterion, +# We can specify parameters used to building trees by specifying mincriterion, # minsplit, minbucket - explanation_ctree <- explain( - x_test, + model = model_lm_cat, + x_explain = x_explain_cat, + x_train = x_train_cat, approach = "ctree", - explainer = explainer, - prediction_zero = p, - mincriterion = 0.80, - minsplit = 20, - minbucket = 20 + prediction_zero = p0, + ctree.mincriterion = 0.80, + ctree.minsplit = 20, + ctree.minbucket = 20 ) - # Default parameters (based on (Hothorn, 2006)) are: # mincriterion = 0.95 # minsplit = 20 # minbucket = 7 ``` -We can also specify multiple different mincriterion (1 minus the boundary for when to stop splitting nodes) parameters to use when conditioning on different numbers of features. In this case, a vector of length = number of features must be provided. +If **all** features are categorical, one may use the categorical +approach as follows: ```{r} -# Use the conditional inference tree approach -# Specify a vector of mincriterions instead of just one -# In this case, when conditioning on 1 or 2 features, use mincriterion = 0.25 -# When conditioning on 3 or 4 features, use mincriterion = 0.95 +# For the sake of illustration, convert ALL features to factors +data[, Solar.R_factor := as.factor(cut(Solar.R, 10))] +data[, Wind_factor := as.factor(cut(Wind, 3))] +data[, Temp_factor := as.factor(cut(Temp, 2))] +data[, Month_factor := as.factor(Month)] -explanation_ctree <- explain( - x_test, - approach = "ctree", - explainer = explainer, - prediction_zero = p, - mincriterion = c(0.25, 0.25, 0.95, 0.95) +data_train_all_cat <- data[-ind_x_explain, ] +data_explain_all_cat <- data[ind_x_explain, ] + + +x_var_all_cat <- c("Solar.R_factor", "Wind_factor", "Temp_factor", "Month_factor") + +x_train_all_cat <- data_train_all_cat[, ..x_var_all_cat] +x_explain_all_cat <- data_explain_all_cat[, ..x_var_all_cat] + +# Fit an lm model here +lm_formula_all_cat <- as.formula(paste0(y_var, " ~ ", paste0(x_var_all_cat, collapse = " + "))) + +model_lm_all_cat <- lm(lm_formula_all_cat, data_train_all_cat) + +explanation_cat_method <- explain( + model = model_lm_all_cat, + x_explain = x_explain_all_cat, + x_train = x_train_all_cat, + approach = "categorical", + prediction_zero = p0 ) ``` +Shapley values can be used to explain any predictive model. For +predictive models taking time series as input, `approach='timeseries'` +can be used. In such models, joint behavior of consecutive time points +is often more important for the outcome than the single time points. +Therefore, it makes sense to derive Shapley value segments of the time +series instead of for each single time point. In `shapr` this can be +achieved through the `group` attribute. Other optional parameters of +`approach='timeseries'` are `timeseries.fixed_sigma_vec` and +`timeseries.bounds` (a vector indicating upper and lower bounds of the +time series if necessary). -## Main arguments in `shapr` +```{r} +# Simulate time series data with AR(1)-structure +set.seed(1) +data_ts <- data.frame(matrix(NA, ncol = 41, nrow = 4)) +for (n in 1:100) { + set.seed(n) + e <- rnorm(42, mean = 0, sd = 1) + + m_1 <- 0 + for (i in 2:length(e)) { + m_1[i] <- 1 + 0.8 * m_1[i - 1] + e[i] + } + data_ts[n, ] <- m_1[-1] +} +data_ts <- data.table::as.data.table(data_ts) -When using `shapr`, the default behavior is to use all feature combinations in the Shapley -formula. Kernel SHAP's sampling based approach may be used by specifying `n_combinations`, which -is the number of feature combinations to sample. If not specified, the exact method is used. -The computation time grows approximately exponentially with the number of samples. The training data -and the model whose predictions we wish to explain must be provided through -the arguments `x` and `model`. Note that `x` must be a `data.frame` or a `matrix`, and all -elements must be finite numerical values. Currently we do not support categorical features or -missing values. - +x_var_ts <- paste0("X", 1:40) +y_var_ts <- "X41" -## Main arguments in `explain` +ind_x_explain <- 1:6 +data_ts_train <- data_ts[-ind_x_explain] -The test data given by `x`, whose predicted values we wish to explain, must be provided. Note that -`x` must be a `data.frame` or a `matrix,` where all elements are finite numerical values. -One must also provide the object returned by `shapr` through the argument `explainer`. -The default approach when computing the Shapley values is the empirical approach (i.e. `approach = "empirical"`). -If you'd like to use a different approach you'll need to set `approach` equal to either `copula` or `gaussian`, or a -vector of them, with length equal to the number of features. If a vector, a combined approach is used, and element `i` -indicates the approach to use when conditioning on `i` variables. For more details see [Combined approach](#combined) below. - -When computing the kernel SHAP values by `explain`, the maximum number of samples to use in the -Monte Carlo integration for every conditional expectation is controlled by the argument `n_samples` -(default equals `1000`). The computation time grows approximately linear with this number. You will also -need to pass a numeric value for the argument `prediction_zero`, which represents the prediction value when not -conditioning on any features. We recommend setting this equal to the mean of the response, but other values, like the -mean prediction of a large test data set is also a possibility. If the empirical method is used, specific settings for -that approach, like a vector of fixed $\sigma$ values can be specified through the argument `sigma_vec`. See `?explain` -for more information. If `approach = "gaussian"`, you may specify the mean vector and covariance matrix of the data -generating distribution by the arguments `mu` and `cov_mat`. If not specified, they are estimated from the training data. +# Creating a predictive model (for illustration just predicting the next point in the time series with a linear model) +lm_ts_formula <- as.formula(X41 ~ .) +model_lm_ts <- lm(lm_ts_formula, data_ts_train) - +x_explain_ts <- data_ts[ind_x_explain, ..x_var_ts] +x_train_ts <- data_ts[-ind_x_explain, ..x_var_ts] -
+# Spitting the time series into 4 segments +group_ts <- list( + S1 = paste0("X", 1:10), + S2 = paste0("X", 11:20), + S3 = paste0("X", 21:30), + S4 = paste0("X", 31:40) +) -# Advanced usage +p0_ts <- mean(unlist(data_ts_train[, ..y_var_ts])) - +explanation_timeseries <- explain( + model = model_lm_ts, + x_explain = x_explain_ts, + x_train = x_train_ts, + approach = "timeseries", + prediction_zero = p0_ts, + group = group_ts +) +``` -## Combined approach +## Main arguments in `explain` -In addition to letting the user select one of the three aforementioned approaches for estimating the conditional -distribution of the data (i.e. `approach` equals either [`"gaussian"`](#gaussian), [`"copula"`](#copula), -[`"empirical"`](#empirical) or -[`"ctree"`](#ctree)) the package allows the user to combine the four approaches. To simplify the usage, -the flexibility is restricted such that the same approach is used when conditioning on the same number of features. -This is also in line @aas2019explaining[, Section 3.4]. - -This can be done by setting `approach` equal to a character vector, where the length of the vector is equal to the -number of features in the model. Consider a situation where you have trained a model that consists of 10 features, and -you would like to use the `"empirical"` approach when you condition on 1-3 features, the `"copula"` approach when you -condition on 4-5 features, and the `"gaussian"` approach when conditioning on 6 or more features. This can be applied -by simply passing `approach = c(rep("empirical", 3), rep("copula", 2), rep("gaussian", 5))`, i.e. `approach[i]` determines -which method to use when conditioning on `i` features. - -The code below exemplifies this approach for a case where there are four features, -using `"empirical", "copula"` and `"gaussian"` when conditioning on respectively 1, 2 and 3-4 features. Note that it -does not matter what method that is specified when conditioning on all features, as that equals the actual prediction -regardless of the specified approach. +When using `explain`, the default behavior is to use all feature +combinations in the Shapley formula. Kernel SHAP's sampling based +approach may be used by specifying `n_combinations`, which is the number +of unique feature combinations to sample. If not specified, the exact +method is used. The computation time grows approximately exponentially +with the number of features. The training data and the model whose +predictions we wish to explain must be provided through the arguments +`x_train` and `model`. The data whose predicted values we wish to +explain must be given by the argument `x_explain`. Note that both +`x_train` and `x_explain` must be a `data.frame` or a `matrix`, and all +elements must be finite numerical values. Currently we do not support +missing values. The default approach when computing the Shapley values +is the empirical approach (i.e. `approach = "empirical"`). If you'd like +to use a different approach you'll need to set `approach` equal to +either `copula` or `gaussian`, or a vector of them, with length equal to +the number of features. If a vector, a combined approach is used, and +element `i` indicates the approach to use when conditioning on `i` +variables. For more details see [Combined approach](#combined) below. + +When computing the kernel SHAP values by `explain`, the maximum number +of samples to use in the Monte Carlo integration for every conditional +expectation is controlled by the argument `n_samples` (default equals +`1000`). The computation time grows approximately linear with this +number. You will also need to pass a numeric value for the argument +`prediction_zero`, which represents the prediction value when not +conditioning on any features. We recommend setting this equal to the +mean of the response, but other values, like the mean prediction of a +large test data set is also a possibility. If the empirical method is +used, specific settings for that approach, like a vector of fixed +$\sigma$ values can be specified through the argument +`empirical.fixed_sigma`. See `?explain` for more information. If +`approach = "gaussian"`, you may specify the mean vector and covariance +matrix of the data generating distribution by the arguments +`gaussian.mu` and `gaussian.cov_mat`. If not specified, they are +estimated from the training data. + +## Explaining a forecasting model using `explain_forecast` + +`shapr` provides a specific function, `explain_forecast`, to explain +forecasts from time series models, at one or more steps into the future. +The main difference compared to `explain` is that the data is supplied +as (set of) time series, in addition to index arguments (`train_idx` and +`explain_idx`) specifying which time points that represents the train +and explain parts of the data. See `?explain_forecast` for more +information. + +To demonstrate how to use the function, 500 observations are generated +which follow an AR(1) structure, i.e. +$y_t = 0.5 y_{t-1} + \varepsilon_t$. To this data an arima model of +order (2, 0, 0) is fitted, and we therefore would like to explain the +forecasts in terms of the two previous lags of the time series. This is +is specified through the argument `explain_y_lags = 2`. Note that some +models may also put restrictions on the amount of data required to make +a forecast. The AR(2) model we used there, for instance, requires two +previous time point to make a forecast. + +In the example, two separate forecasts, each three steps ahead, are +explained. To set the starting points of the two forecasts, +`explain_idx` is set to `499:500`. This means that one forecast of +$t = (500, 501, 502)$ and another of $t = (501, 502, 503)$, will be +explained. In other words, `explain_idx` tells `shapr` at which points +in time data was available up until, when making the forecast to +explain. + +In the same way, `train_idx` denotes the points in time used to estimate +the conditional expectations used to explain the different forecasts. +Note that since we want to explain the forecasts in terms of the two +previous lags (`explain_y_lags = 2`), the smallest value of `train_idx` +must also be 2, because at time $t = 1$ there was only a single +observation available. + +Since the data is stationary, the mean of the data is used as value of +`prediction_zero` (i.e. $\phi_0$). This can however be chosen +differently depending on the data and application. + +For a multivariate model such as a VAR (Vector AutoRegressive model), it +may be of more interesting to explain the impact of each variable, +rather than each lag of each variable. This can be done by setting +`group_lags = TRUE`. ```{r} -# Use the combined approach -explanation_combined <- explain( - x_test, - approach = c("empirical", "copula", "gaussian", "gaussian"), - explainer = explainer, - prediction_zero = p +# Simulate time series data with AR(1)-structure. +set.seed(1) +data_ts <- data.frame(Y = arima.sim(list(order = c(1, 0, 0), ar = .5), n = 500)) +data_ts <- data.table::as.data.table(data_ts) + +# Fit an ARIMA(2, 0, 0) model. +arima_model <- arima(data_ts, order = c(2, 0, 0)) + +# Set prediction zero as the mean of the data for each forecast point. +p0_ar <- rep(mean(data_ts$Y), 3) + +# Explain forecasts from points t = 499 and t = 500. +explain_idx <- 499:500 + +explanation_forecast <- explain_forecast( + model = arima_model, + y = data_ts, + train_idx = 2:498, + explain_idx = 499:500, + explain_y_lags = 2, + horizon = 3, + approach = "empirical", + prediction_zero = p0_ar, + group_lags = FALSE ) +explanation_forecast +``` -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_combined, plot_phi0 = FALSE, index_x_test = c(1, 6)) +Note that for a multivariate model such as a VAR (Vector AutoRegressive +model), or for models also including several exogenous variables, it may +be of more informative to explain the impact of each variable, rather +than each lag of each variable. This can be done by setting +`group_lags = TRUE`. This does not make sense for this model, however, +as that would result in decomposing the forecast into a single group. + +We now give a more hands on example of how to use the `explain_forecast` +function. Say that we have an AR(2) model which describes the change +over time of the variable `Temp` in the dataset `airquality`. It seems +reasonable to assume that the temperature today should affect the +temperature tomorrow. To a lesser extent, we may also suggest that the +temperature today should also have an impact on that of the day after +tomorrow. + +We start by building our AR(2) model, naming it `model_ar_temp`. This +model is then used to make a forecast of the temperature of the day that +comes after the last day in the data, this forecast starts from index +153. + +```{r} +data <- data.table::as.data.table(airquality) + +model_ar_temp <- ar(data$Temp, order = 2) + +predict(model_ar_temp, n.ahead = 2)$pred ``` -As a second example using `"ctree"` for the first 3 features and `"empirical"` for the last: +First, we pass the model and the data as `model` and `y`. Since we have +an AR(2) model, we want to explain the forecasts in terms of the two +previous lags, whihc we specify with `explain_y_lags = 2`. Then, we let +`shapr` know which time indices to use as training data through the +argument `train_idx`. We use `2:152`, meaning that we skip the first +index, as we want to explain the two previous lags. Letting the training +indices go up until 152 means that every point in time except the first +and last will be used as training data. + +The last index, 153 is passed as the argument `explain_idx`, which means +that we want to explain a forecast made from time point 153 in the data. +The argument `horizon` is set to 2 in order to explain a forecast of +length 2. + +The argument `prediction_zero` is set to the mean of the time series, +and is repeated two times. Each value of `prediction_zero` is the +baseline for each forecast horizon. In our example, we assume that given +no effect from the two lags, the temperature would just be the average +during the observed period. Finally, we opt to not group the lags by +setting `group_lags` to `FALSE`. This means that lag 1 and 2 will be +explained separately. Grouping lags may be more interesting to do in a +model with multiple variables, as it is then possible to explain each +variable separately. ```{r} -# Use the combined approach -explanation_combined <- explain( - x_test, - approach = c("ctree", "ctree", "ctree", "empirical"), - explainer = explainer, - prediction_zero = p +explanation <- explain_forecast( + model = model_ar_temp, + y = data[, "Temp"], + train_idx = 2:152, + explain_idx = 153, + explain_y_lags = 2, + horizon = 2, + approach = "empirical", + prediction_zero = rep(mean(data$Temp), 2), + group_lags = FALSE, + n_batches = 1, + timing = FALSE ) + +print(explanation) ``` -## Using ctree when features are mixed numerical and categorical +The results are presented per value of `explain_idx` and forecast +horizon. We can see that the mean temperature was around 77.9 degrees. +At horizon 1, the first lag in the model caused it to be 6.6 degrees +lower, and the second lag had just a minor effect. At horizon 2, the +first lag has a slightly smaller negative impact, and the second lag has +a slightly larger impact. -```{r} -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" +It is also possible to explain a forecasting model which uses exogenous +regressors. The previous example is expanded to use an ARIMA(2,0,0) +model with `Wind` as an exogenous regressor. Since the exogenous +regressor must be available for the predicted time points, the model is +just fit on the 151 first observations, leaving two observations of +`Wind` to be used as exogenous values during the prediction phase. -# Convert two features as factors -dt <- Boston[, c(x_var, y_var)] -dt$rm <- as.factor(round(dt$rm/3)) -dt$dis <- as.factor(round(dt$dis/4)) +```{r} +data <- data.table::as.data.table(airquality) -xy_train_cat <- dt[-1:-6, ] -y_train_cat <- dt[-1:-6, y_var] -x_train_cat <- dt[-1:-6, x_var] -x_test_cat <- dt[1:6, x_var] +data_fit <- data[seq_len(151), ] +model_arimax_temp <- arima(data_fit$Temp, order = c(2, 0, 0), xreg = data_fit$Wind) -# Fit a basic linear regression model to the training data -model_mixed <- lm(medv ~ lstat + rm + dis + indus, data = xy_train_cat) +newxreg <- data[-seq_len(151), "Wind", drop = FALSE] -# Prepare the data for explanation -explainer <- shapr(x_train_cat, model_mixed) +predict(model_arimax_temp, n.ahead = 2, newxreg = newxreg)$pred +``` -# Specifying the phi_0, i.e. the expected prediction without any features -p <- mean(y_train_cat) +The `shapr` package can then explain not only the two autoregressive +lags, but also the single lag of the exogenous regressor. In order to do +so, the `Wind` variable is passed as the argument `xreg`, and +`explain_xreg_lags` is set to 1. Notice how only the first 151 +observations are used for `y` and all 153 are used for `xreg`. This +makes it possible for `shapr` to not only explain the effect of the +first lag of the exogenous variable, but also the contemporary effect +during the forecasting period. -# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using -explanation_categorical <- explain( - x_test_cat, - approach = "ctree", - explainer = explainer, - prediction_zero = p +```{r} +explanation <- explain_forecast( + model = model_ar_temp, + y = data_fit[, "Temp"], + xreg = data[, "Wind"], + train_idx = 2:150, + explain_idx = 151, + explain_y_lags = 2, + explain_xreg_lags = 1, + horizon = 2, + approach = "empirical", + prediction_zero = rep(mean(data_fit$Temp), 2), + group_lags = FALSE, + n_batches = 1, + timing = FALSE ) -# Note that nothing has to be specified to tell "ctree" that two of the features are -# cateogrical and two are numerical - -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_categorical, plot_phi0 = FALSE, index_x_test = c(1, 6)) +print(explanation$shapley_values) ``` -## Explain groups of features + -In some cases, especially when the number of features is very large, it may be more appropriate to explain predictions -in terms of groups of features instead of single features, see (@jullum2021efficient) for intuition and real world -examples. Explaining prediction in terms of groups of features is very easy using `shapr`: +
-```{r} +# Advanced usage -# Define the feature groups + -group_list <- list(A = c("lstat","rm"), - B = c("dis","indus")) +## Combined approach -# Set up the explainer using the groups -explainer_group <- shapr(x_train,model,group = group_list) +In addition to letting the user select one of the five aforementioned +approaches for estimating the conditional distribution of the data (i.e. +`approach` equals either [`"gaussian"`](#gaussian), +[`"copula"`](#copula), [`"empirical"`](#empirical) or +[`"ctree"`](#ctree) or [`"categorical"`](#categorical)), the package +allows the user to combine the given approaches. To simplify the usage, +the flexibility is restricted such that the same approach is used when +conditioning on the same number of features. This is also in line +@aas2019explaining [, Section 3.4]. + +This can be done by setting `approach` equal to a character vector, +where the length of the vector is equal to the number of features in the +model. Consider a situation where you have trained a model that consists +of 10 features, and you would like to use the `"empirical"` approach +when you condition on 1-3 features, the `"copula"` approach when you +condition on 4-5 features, and the `"gaussian"` approach when +conditioning on 6 or more features. This can be applied by simply +passing +`approach = c(rep("empirical", 3), rep("copula", 2), rep("gaussian", 5))`, +i.e. `approach[i]` determines which method to use when conditioning on +`i` features. + +The code below exemplifies this approach for a case where there are four +features, using `"empirical", "copula"` and `"gaussian"` when +conditioning on respectively 1, 2 and 3-4 features. Note that it does +not matter what method that is specified when conditioning on all +features, as that equals the actual prediction regardless of the +specified approach. +```{r} +# Use the combined approach +explanation_combined <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = c("empirical", "copula", "gaussian", "gaussian"), + prediction_zero = p0 +) +# Plot the resulting explanations for observations 1 and 6, excluding +# the no-covariate effect +plot(explanation_combined, bar_plot_phi0 = FALSE, index_x_explain = c(1, 6)) +``` + +As a second example using `"ctree"` for the first 3 features and +`"empirical"` for the last: +```{r} # Use the combined approach +explanation_combined <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = c("ctree", "ctree", "ctree", "empirical"), + prediction_zero = p0 +) +``` + +## Explain groups of features + +In some cases, especially when the number of features is very large, it +may be more appropriate to explain predictions in terms of groups of +features instead of single features, see (@jullum2021efficient) for +intuition and real world examples. Explaining prediction in terms of +groups of features is very easy using `shapr`: + +```{r} +# Define the feature groups +group_list <- list( + A = c("Temp", "Month"), + B = c("Wind", "Solar.R") +) + +# Use the empirical approach explanation_group <- explain( - x_test, + model = model, + x_explain = x_explain, + x_train = x_train, approach = "empirical", - explainer = explainer_group, - prediction_zero = p + prediction_zero = p0, + group = group_list ) - # Prints the group-wise explanations explanation_group - # Plots the group-wise explanations -plot(explanation_group, plot_phi0 = T, index_x_test = c(1, 6)) - +plot(explanation_group, bar_plot_phi0 = TRUE, index_x_explain = c(1, 6)) ``` - ## Explain custom models -`shapr` currently natively supports explanation of predictions from models fitted with the following functions: - -* `stats::lm` -* `stats::glm` -* `ranger::ranger` -* `mgcv::gam` -* `xgboost::xgboost`/`xgboost::xgb.train` - -Any continuous response regression model or binary classification model of these model classes, can be explained with -the package directly as exemplified above. -Moreover, essentially any feature dependent prediction model can be explained by the package by specifying two (or one) -simple additional functions to the class your model belongs to. - -*Note: The below procedure for specifying custom models was changed in shapr v0.2.0* -The first class function is `predict_model`, taking the model and data (as a `matrix` or `data.frame/data.table`) as -input and outputting the corresponding prediction as a numeric vector. -The second (optional, but highly recommended) class function is `get_model_specs`, taking the model as input and -outputting a list with the following elements: -*labels* (vector with the feature names to compute Shapley values for), -*classes* (a named vector with the labels as names and the class type as elements), -*factor_levels* (a named list with the labels as names and vectors with the factor levels as elements (NULL if the -feature is not a factor)). -The `get_model_specs` function is used to check that the format of the data passed to `shapr` and `explain` have the -correct format in terms of the necessary feature columns being available and having the correct class/attributes. -It is highly recommended to do such checks in order to ensure correct usage of `shapr` and `explain`. -If, for some reason, such checking is not desirable, one does not have to provide the `get_model_specs` function class. -This will, however, throw a warning that all feature consistency checking against the model is disabled. - -Once the above class functions are created, one can explain predictions from this model class as before. -These functions **can** be made general enough to handle all supported model types of that class, or they can be made -minimal, possibly only allowing explanation of the specific version of the model class at hand. -Below we give examples of both full support versions of these functions and a minimal version which skips the -`get_model_specs` function. -We do this for the `gbm` model class from the `gbm` package, fitted to the same Boston data set as used above. - - -```{r} +`shapr` currently natively supports explanation of predictions from +models fitted with the following functions: + +- `stats::lm` +- `stats::glm` +- `ranger::ranger` +- `mgcv::gam` +- `xgboost::xgboost`/`xgboost::xgb.train` + +Any continuous response regression model or binary classification model +of these model classes, can be explained with the package directly as +exemplified above. Moreover, essentially any feature dependent +prediction model can be explained by the package by specifying two (or +one) simple additional functions for your model. + +*Note: The below procedure for specifying custom models was changed in +shapr v0.3.0* The first function is `predict_model`, taking the model +and data (as a `matrix` or `data.frame/data.table`) as input and +outputting the corresponding prediction as a numeric vector. The second +(optional, but highly recommended) function is `get_model_specs`, taking +the model as input and outputting a list with the following elements: +*labels* (vector with the feature names to compute Shapley values for), +*classes* (a named vector with the labels as names and the class type as +elements), *factor_levels* (a named list with the labels as names and +vectors with the factor levels as elements (NULL if the feature is not a +factor)). The `get_model_specs` function is used to check that the +format of the data passed to `explain` have the correct format in terms +of the necessary feature columns being available and having the correct +class/attributes. It is highly recommended to do such checks in order to +ensure correct usage of `explain`. If, for some reason, such checking is +not desirable, one does not have to provide the `get_model_specs` +function. This will, however, throw a warning that all feature +consistency checking against the model is disabled. + +Once the above functions are created, you can explain predictions from +this model as before by passing the functions through the input +arguments `predict_model` and `get_model_specs` of `explain()`. + +These functions **can** be made general enough to handle all supported +model types of that class, or they can be made minimal, possibly only +allowing explanation of the specific version of the model class at hand. +Below we give examples of both full support versions of these functions +and a minimal version which skips the `get_model_specs` function. We do +this for the `gbm` model class from the `gbm` package, fitted to the +same airquality data set as used above. + +```{r,warning=TRUE, message=TRUE} library(gbm) -xy_train <- data.frame(x_train,medv = y_train) - -form <- as.formula(paste0(y_var,"~",paste0(x_var,collapse="+"))) - +formula_gbm <- as.formula(paste0(y_var, "~", paste0(x_var, collapse = "+"))) # Fitting a gbm model set.seed(825) -model <- gbm::gbm( - form, - data = xy_train, +model_gbm <- gbm::gbm( + formula_gbm, + data = cbind(x_train, Ozone = y_train), distribution = "gaussian" ) #### Full feature versions of the three required model functions #### - -predict_model.gbm <- function(x, newdata) { - - if (!requireNamespace('gbm', quietly = TRUE)) { - stop('The gbm package is required for predicting train models') +MY_predict_model <- function(x, newdata) { + if (!requireNamespace("gbm", quietly = TRUE)) { + stop("The gbm package is required for predicting train models") } - model_type <- ifelse( - x$distribution$name %in% c("bernoulli","adaboost"), + x$distribution$name %in% c("bernoulli", "adaboost"), "classification", "regression" ) if (model_type == "classification") { - - predict(x, as.data.frame(newdata), type = "response",n.trees = x$n.trees) + predict(x, as.data.frame(newdata), type = "response", n.trees = x$n.trees) } else { - - predict(x, as.data.frame(newdata),n.trees = x$n.trees) + predict(x, as.data.frame(newdata), n.trees = x$n.trees) } } - -get_model_specs.gbm <- function(x){ - feature_list = list() - feature_list$labels <- labels(x$Terms) - m <- length(feature_list$labels) - - feature_list$classes <- attr(x$Terms,"dataClasses")[-1] - feature_list$factor_levels <- setNames(vector("list", m), feature_list$labels) - feature_list$factor_levels[feature_list$classes=="factor"] <- NA # the model object doesn't contain factor levels info - - return(feature_list) +MY_get_model_specs <- function(x) { + feature_specs <- list() + feature_specs$labels <- labels(x$Terms) + m <- length(feature_specs$labels) + feature_specs$classes <- attr(x$Terms, "dataClasses")[-1] + feature_specs$factor_levels <- setNames(vector("list", m), feature_specs$labels) + feature_specs$factor_levels[feature_specs$classes == "factor"] <- NA # model object doesn't contain factor levels info + return(feature_specs) } -# Prepare the data for explanation +# Compute the Shapley values set.seed(123) -explainer <- shapr(xy_train, model) -p0 <- mean(xy_train[,y_var]) -explanation <- explain(x_test, explainer, approach = "empirical", prediction_zero = p0) -# Plot results -plot(explanation) +p0 <- mean(y_train) +explanation_custom <- explain( + model = model_gbm, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + predict_model = MY_predict_model, + get_model_specs = MY_get_model_specs +) -#### Minimal version of the three required model functions #### -# Note: Working only for this exact version of the model class -# Avoiding to define get_model_specs skips all feature -# consistency checking between your data and model +# Plot results +plot(explanation_custom, index_x_explain = c(1, 6)) -# Removing the previously defined functions to simulate a fresh start -rm(predict_model.gbm) -rm(get_model_specs.gbm) -predict_model.gbm <- function(x, newdata) { - predict(x, as.data.frame(newdata),n.trees = x$n.trees) +#### Minimal version of the three required model functions #### +# Note: Working only for this exact version of the model class +# Avoiding to define get_model_specs skips all feature +# consistency checking between your data and model +MY_MINIMAL_predict_model <- function(x, newdata) { + predict(x, as.data.frame(newdata), n.trees = x$n.trees) } -# Prepare the data for explanation +# Compute the Shapley values set.seed(123) -explainer <- shapr(x_train, model) -p0 <- mean(xy_train[,y_var]) -explanation <- explain(x_test, explainer, approach = "empirical", prediction_zero = p0) +explanation_custom_minimal <- explain( + model = model_gbm, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + predict_model = MY_MINIMAL_predict_model +) + # Plot results -plot(explanation) +plot(explanation_custom_minimal, index_x_explain = c(1, 6)) +``` + + +
+ +# Scalability and efficency + +## Batch computation + +The computational complexity of Shapley value based explanations grows +fast in the number of features, as the number of conditional +expectations one needs to estimate in the Shapley formula grows +exponentially. As outlined [above](#KSHAP), the estimating of each of +these conditional expectations is also computationally expensive, +typically requiring estimation of a conditional probability +distribution, followed by Monte Carlo integration. These computations +are not only heavy for the CPU, they also require a lot of memory (RAM), +which typically is a limited resource. By doing the most resource hungry +computations (the computation of v(S)) in sequential batches with +different feature subsets $S$, the memory usage can be significantly +reduces. Such batching comes at the cost of an increase in computation +time, which depends on the number of feature subsets (`n_combinations`), +the number of features, the estimation `approach` and so on. When +calling `shapr::explain()`, we allow the user to set the number of +batches with the argument `n_batches`. The default of this argument is +`NULL`, which uses a (hopefully) reasonable trade-off between +computation speed and memory consumption which depends on +`n_combinations` and `approach`. The memory/computation time trade-off +is most apparent for models with more than say 6-7 features. Below we a +basic example where `n_batches=10`: +```{r} +explanation_batch <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + n_batches = 10 +) ``` +## Parallelized computation + +In addition to reducing the memory consumption, the introduction of the +`n_batch` argument allows computation within each batch to be performed in parallel. +The parallelization in `shapr::explain()` is handled by the +`future_apply` which builds on the `future` environment. The `future` +package works on all OS, allows the user to decide the parallelization +backend (mutliple R procesess or forking), works directly with hpc +clusters, and also supports progress updates for the parallelized task +(see below). + +Note that, since it takes some time to duplicate data into different +processes/machines when running in parallel, it is not always +preferrable to run `shapr::explain()` in parallel, at least not with +many parallel sessions (hereby called **workers**). Parallelizatiob also +increases the memory consumption proportionally, so you want to limit +the number of workers for that reason too. In a future version of +`shapr` we will provide experienced based automatic selection of the +number of workers. In the meanwhile, this is all lef to the user, and we +advice that `n_batches` equals some positive integer multiplied by the +number of workers. Below is a basic example of a parallelization with +two workers. +```{r} +library(future) +future::plan(multisession, workers = 2) + +explanation_par <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + n_batches = 10 +) + +future::plan(sequential) # To return to non-parallel computation +``` +## Progress updates + +`shapr` provides progress updates of the computation of the Shapley +values through the R-package `progressr`. This gives the user full +control over the visual apperance of the progress updates, and also +intergrates seemlessly with the parallelization framework `future` used +by `shapr` (see above). Note that the progress is updated as the batches +are completed, meaning that if you have choosen `n_batches=1`, you will +not get intermediate updates, while if you set `n_batches=10` you will +get updates on every 10% of the computation. + +Progress updates are enabled for the current R-session by running the +command `progressr::handlers(local=TRUE)`, before calling +`shapr::explain()`. To use progress updates for only a single call to +`shapr::explain()`, one can wrap the call using +`progressr::with_progress` as follows: +`progressr::with_progress({ shapr::explain() })` The default appearence +of the progress updates is a basic ASCII-based horizontal progress bar. +Other variants can be chosen by passing different strings to +`progressr::handlers()`, some of which require additional packages. If +you are using Rstudio, the progress can be displayed directly in the gui +with `progressr::handlers('rstudio')` (requires the `rstudioapi` +package). If you are running Windows, you may use the pop-up gui +progress bar `progressr::handlers('handler_winprogressbar')`. A wrapper +for progressbar of the flexible `cli` package is also available +`progressr::handlers('cli')` (requires the `cli` package). + +For a full list of all progression handlers and the customization +options available with `progressr`, see the `progressr` +[vignette](https://cran.r-project.org/web/packages/progressr/vignettes/progressr-intro.html). +A full code example of using `progressr` with `shapr` is shown below: + +```{r,eval = FALSE} +library(progressr) +progressr::handlers(global = TRUE) +# If no progression handler is specified, the txtprogressbar is used +# Other progression handlers: +# progressr::handlers('rstudio') # requires the 'rstudioapi' package +# progressr::handlers('handler_winprogressbar') # Window only +# progressr::handlers('cli') # requires the 'cli' package +explanation <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + n_batches = 10 +) +#| [=================================>----------------------] 60% Estimating v(S) +``` @@ -709,27 +1154,33 @@ plot(explanation) # Comparison to Lundberg & Lee's implementation -As mentioned above, the original (independence assuming) Kernel SHAP implementation can be -approximated by setting a large $\sigma$ value using our empirical approach. If we specify that the -distances to *all* training observations should be used (i.e. setting `approach = "empirical"` and -`w_threshold = 1` when using `explain`, we can approximate the original method arbitrarily -well by increasing $\sigma$. For completeness of the `shapr`, we have also implemented -a version of the original method, which samples training observations independently with respect to -their distances to test observations (i.e. without the large-$\sigma$ approximation). This method is -available by using `approach = "independence"` in `explain`. - -We have compared the results using these two variants with the original implementation of -@lundberg2017unified, available through the Python library [`shap`](https://github.com/slundberg/shap). -As above, we used the Boston housing -data, trained via `xgboost`. We specify that *all* training observations should be used when -explaining all of the 6 test observations. To run the individual explanation method in the `shap` -Python library we use the `reticulate` `R`-package, allowing Python code to run within `R`. -As this requires installation of Python package, the comparison code and results is not included -in this vignette, but can be found +As mentioned above, the original (independence assuming) Kernel SHAP +implementation can be approximated by setting a large $\sigma$ value +using our empirical approach. If we specify that the distances to *all* +training observations should be used (i.e. setting +`approach = "empirical"` and `empirical.eta = 1` when using `explain`, +we can approximate the original method arbitrarily well by increasing +$\sigma$. For completeness of the `shapr` package, we have also +implemented a version of the original method, which samples training +observations independently with respect to their distances to test +observations (i.e. without the large-$\sigma$ approximation). This +method is available by using `approach = "independence"` in `explain`. + +We have compared the results using these two variants with the original +implementation of @lundberg2017unified, available through the Python +library [`shap`](https://github.com/slundberg/shap). As above, we used +the Boston housing data, trained via `xgboost`. We specify that *all* +training observations should be used when explaining all of the 6 test +observations. To run the individual explanation method in the `shap` +Python library we use the `reticulate` `R`-package, allowing Python code +to run within `R`. As this requires installation of Python package, the +comparison code and results is not included in this vignette, but can be +found [here](https://github.com/NorskRegnesentral/shapr/blob/master/inst/scripts/compare_shap_python.R). -As indicated by the (commented out) results in the file above both methods in our -`R`-package give (up to numerical approximation error) identical results to the original -implementation in the Python `shap` library. +As indicated by the (commented out) results in the file above both +methods in our `R`-package give (up to numerical approximation error) +identical results to the original implementation in the Python `shap` +library.
# The Kernel SHAP Method -Assume a predictive model $f(\boldsymbol{x})$ for a response value $y$ with features -$\boldsymbol{x}\in \mathbb{R}^M$, trained on a training set, and that we want to explain the -predictions for new sets of data. This may be done using ideas from cooperative game theory, -letting a single prediction take the place of the game being played and the features the place of -the players. Letting $N$ denote the set of all $M$ players, and $S \subseteq N$ be a subset -of $|S|$ players, the "contribution" function $v(S)$ describes the total expected sum of payoffs -the members of $S$ can obtain by cooperation. The Shapley value (@Shapley53) is one way to -distribute the total gains to the players, assuming that they all collaborate. The amount that -player $i$ gets is then +Assume a predictive model $f(\boldsymbol{x})$ for a response value $y$ +with features $\boldsymbol{x}\in \mathbb{R}^M$, trained on a training +set, and that we want to explain the predictions for new sets of data. +This may be done using ideas from cooperative game theory, letting a +single prediction take the place of the game being played and the +features the place of the players. Letting $N$ denote the set of all $M$ +players, and $S \subseteq N$ be a subset of $|S|$ players, the +"contribution" function $v(S)$ describes the total expected sum of +payoffs the members of $S$ can obtain by cooperation. The Shapley value +(@Shapley53) is one way to distribute the total gains to the players, +assuming that they all collaborate. The amount that player $i$ gets is +then $$\phi_i(v) = \phi_i = \sum_{S \subseteq N \setminus\{i\}} \frac{|S| ! (M-| S| - 1)!}{M!}(v(S\cup \{i\})-v(S)),$$ +that is, a weighted mean over all subsets $S$ of players not containing +player $i$. @lundberg2017unified define the contribution function for a +certain subset $S$ of these features $\boldsymbol{x}_S$ as +$v(S) = \mbox{E}[f(\boldsymbol{x})|\boldsymbol{x}_S]$, the expected +output of the predictive model conditional on the feature values of the +subset. @lundberg2017unified names this type of Shapley values SHAP +(SHapley Additive exPlanation) values. Since the conditional +expectations can be written as -that is, a weighted mean over all subsets $S$ of players not containing player $i$. -@lundberg2017unified define the contribution function for a certain subset $S$ of these features -$\boldsymbol{x}_S$ as $v(S) = \mbox{E}[f(\boldsymbol{x})|\boldsymbol{x}_S]$, the expected output -of the predictive model conditional on the feature values of the subset. @lundberg2017unified names -this type of Shapley values SHAP (SHapley Additive exPlanation) values. Since the conditional -expectations can be written as - +```{=tex} \begin{equation} \label{eq:CondExp} E[f(\boldsymbol{x})|\boldsymbol{x}_s=\boldsymbol{x}_S^*] = E[f(\boldsymbol{x}_{\bar{S}},\boldsymbol{x}_S)|\boldsymbol{x}_S=\boldsymbol{x}_S^*] = \int f(\boldsymbol{x}_{\bar{S}},\boldsymbol{x}_S^*)\,p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)d\boldsymbol{x}_{\bar{S}}, \end{equation} - -the conditional distributions $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ are -needed to compute the contributions. The Kernel SHAP method of @lundberg2017unified assumes feature -independence, so that $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)=p(\boldsymbol{x}_{\bar{S}})$. -If samples -$\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$, from $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ -are available, the conditional expectation in above can be approximated by - +``` +the conditional distributions +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ are +needed to compute the contributions. The Kernel SHAP method of +@lundberg2017unified assumes feature independence, so that +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)=p(\boldsymbol{x}_{\bar{S}})$. +If samples $\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$, from +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ are +available, the conditional expectation in above can be approximated by + +```{=tex} \begin{equation} v_{\text{KerSHAP}}(S) = \frac{1}{K}\sum_{k=1}^K f(\boldsymbol{x}_{\bar{S}}^{k},\boldsymbol{x}_S^*). \end{equation} - -In Kernel SHAP, $\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$ are sampled from the $\bar{S}$-part of -the training data, *independently* of $\boldsymbol{x}_{S}$. This is motivated by using the -training set as the empirical distribution of $\boldsymbol{x}_{\bar{S}}$, and assuming that -$\boldsymbol{x}_{\bar{S}}$ is independent of $\boldsymbol{x}_S=\boldsymbol{x}_S^*$. -Due to the independence assumption, if the features in a given model are highly dependent, the -Kernel SHAP method may give a completely wrong answer. This can be avoided by estimating the -conditional distribution $p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ directly -and generating samples from this distribution. With this small change, the contributions and -Shapley values may then be approximated as in the ordinary Kernel SHAP framework. @aas2019explaining -propose three different approaches for estimating the conditional probabilities. The methods may -also be combined, such that e.g. one method is used when conditioning on a small number of features, -while another method is used otherwise. +``` +In Kernel SHAP, $\boldsymbol{x}_{\bar{S}}^{k}, k=1,\ldots,K$ are sampled +from the $\bar{S}$-part of the training data, *independently* of +$\boldsymbol{x}_{S}$. This is motivated by using the training set as the +empirical distribution of $\boldsymbol{x}_{\bar{S}}$, and assuming that +$\boldsymbol{x}_{\bar{S}}$ is independent of +$\boldsymbol{x}_S=\boldsymbol{x}_S^*$. Due to the independence +assumption, if the features in a given model are highly dependent, the +Kernel SHAP method may give a completely wrong answer. This can be +avoided by estimating the conditional distribution +$p(\boldsymbol{x}_{\bar{S}}|\boldsymbol{x}_S=\boldsymbol{x}_S^*)$ +directly and generating samples from this distribution. With this small +change, the contributions and Shapley values may then be approximated as +in the ordinary Kernel SHAP framework. @aas2019explaining propose three +different approaches for estimating the conditional probabilities which +are implemented: `empirical`, `gaussian` and `copula`. The package also +implements the method `ctree` method from @redelmeier2020explaining. The +original `independence` approach of @lundberg2017unified is also +available. The methods may also be combined, such that e.g. one method +is used when conditioning on a small number of features, while another +method is used otherwise. ## Multivariate Gaussian Distribution Approach -The first approach arises from the assumption that the feature vector $\boldsymbol{x}$ stems from a -multivariate Gaussian distribution with some mean vector $\boldsymbol{\mu}$ and covariance matrix -$\boldsymbol{\Sigma}$. Under this assumption, the conditional distribution -$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$ -is also multivariate Gaussian -$\text{N}_{|\bar{\mathcal{S}}|}(\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}},\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}})$, -with analytical expressions for the conditional mean vector $\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ -and covariance matrix $\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$, see @aas2019explaining for details. -Hence, instead of sampling from the marginal empirical distribution of $\boldsymbol{x}_{\bar{\mathcal{S}}}$ -approximated by the training data, we can sample from the Gaussian conditional distribution, which is fitted -using the training data. Using the resulting samples -$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, the conditional expectations be approximated -as in the Kernel SHAP. +The first approach arises from the assumption that the feature vector +$\boldsymbol{x}$ stems from a multivariate Gaussian distribution with +some mean vector $\boldsymbol{\mu}$ and covariance matrix +$\boldsymbol{\Sigma}$. Under this assumption, the conditional +distribution +$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$ +is also multivariate Gaussian\ +$\text{N}_{|\bar{\mathcal{S}}|}(\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}},\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}})$, +with analytical expressions for the conditional mean vector +$\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ and covariance matrix +$\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$, see +@aas2019explaining for details. Hence, instead of sampling from the +marginal empirical distribution of $\boldsymbol{x}_{\bar{\mathcal{S}}}$ +approximated by the training data, we can sample from the Gaussian +conditional distribution, which is fitted using the training data. Using +the resulting samples +$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, the conditional +expectations be approximated as in the Kernel SHAP. ## Gaussian Copula Approach -If the features are far from multivariate Gaussian, an alternative approach is to instead represent -the marginals by their empirical distributions, and model the dependence structure by a Gaussian -copula. Assuming a Gaussian copula, we may convert the marginals of the training data to Gaussian -features using their empirical distributions, and then fit a multivariate Gaussian distribution to these. - -To produce samples from the conditional distribution -$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$, -we convert the marginals of $\boldsymbol{x}_{\mathcal{S}}$ to Gaussians, sample from the conditional -Gaussian distribution as above, and convert the marginals of the samples back to the original -distribution. Those samples are then used to approximate the sample from the resulting multivariate -Gaussian conditional distribution. While other copulas may be used, the Gaussian copula has the -benefit that we may use the analytical expressions for the conditionals -$\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ and $\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$. -Finally, we may convert the marginals back to their original distribution, and use the resulting -samples to approximate the conditional expectations as in the Kernel SHAP. +If the features are far from multivariate Gaussian, an alternative +approach is to instead represent the marginals by their empirical +distributions, and model the dependence structure by a Gaussian copula. +Assuming a Gaussian copula, we may convert the marginals of the training +data to Gaussian features using their empirical distributions, and then +fit a multivariate Gaussian distribution to these. + +To produce samples from the conditional distribution +$p(\boldsymbol{x}_{\bar{\mathcal{S}}} |\boldsymbol{x}_{\mathcal{S}}=\boldsymbol{x}_{\mathcal{S}}^*)$, +we convert the marginals of $\boldsymbol{x}_{\mathcal{S}}$ to Gaussians, +sample from the conditional Gaussian distribution as above, and convert +the marginals of the samples back to the original distribution. Those +samples are then used to approximate the sample from the resulting +multivariate Gaussian conditional distribution. While other copulas may +be used, the Gaussian copula has the benefit that we may use the +analytical expressions for the conditionals +$\boldsymbol{\mu}_{\bar{\mathcal{S}}|\mathcal{S}}$ and +$\boldsymbol{\Sigma}_{\bar{\mathcal{S}}|\mathcal{S}}$. Finally, we may +convert the marginals back to their original distribution, and use the +resulting samples to approximate the conditional expectations as in the +Kernel SHAP. ## Empirical Conditional Distribution Approach -If both the dependence structure and the marginal distributions of $\boldsymbol{x}$ are very far -from the Gaussian, neither of the two aforementioned methods will work very well. Few methods -exists for the non-parametric estimation of conditional densities, and the classic kernel -estimator (@rosenblatt1956) for non-parametric density estimation suffers greatly from the -curse of dimensionality and does not provide a way to generate samples from the estimated -distribution. For such situations, @aas2019explaining propose an empirical conditional approach -to sample approximately from $p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$. -The idea is to compute weights $w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^i),\ i=1,...,n_{\text{train}}$ -for all training instances based on their Mahalanobis distances (in the $S$ subset only) to the -instance $\boldsymbol{x}^*$ to be explained. Instead of sampling from this weighted (conditional) -empirical distribution, @aas2019explaining suggests a more efficient variant, using only the $K$ -instances with the largest weights: +If both the dependence structure and the marginal distributions of +$\boldsymbol{x}$ are very far from the Gaussian, neither of the two +aforementioned methods will work very well. Few methods exists for the +non-parametric estimation of conditional densities, and the classic +kernel estimator (@rosenblatt1956) for non-parametric density estimation +suffers greatly from the curse of dimensionality and does not provide a +way to generate samples from the estimated distribution. For such +situations, @aas2019explaining propose an empirical conditional approach +to sample approximately from +$p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$. +The idea is to compute weights +$w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^i),\ i=1,...,n_{\text{train}}$ +for all training instances based on their Mahalanobis distances (in the +$S$ subset only) to the instance $\boldsymbol{x}^*$ to be explained. +Instead of sampling from this weighted (conditional) empirical +distribution, @aas2019explaining suggests a more efficient variant, +using only the $K$ instances with the largest weights: $$v_{\text{condKerSHAP}}(\mathcal{S}) = \frac{\sum_{k=1}^K w_{\mathcal{S}}(\boldsymbol{x}^*, \boldsymbol{x}^{[k]}) f(\boldsymbol{x}_{\bar{\mathcal{S}}}^{[k]}, -\boldsymbol{x}_{\mathcal{S}}^*)}{\sum_{k=1}^K w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^{[k]})},$$ - -The number of samples $K$ to be used in the approximate prediction can for instance be chosen such -that the $K$ largest weights accounts for a fraction $\eta$, for example $0.9$, of the total weight. -If $K$ exceeds a certain limit, for instance $5,000$, it might be set to that limit. A bandwidth -parameter $\sigma$ used to scale the weights, must also be specified. This choice may be viewed as -a bias-variance trade-off. A small $\sigma$ puts most of the weight to a few of the closest -training observations and thereby gives low bias, but high variance. When $\sigma \rightarrow \infty$, -this method converges to the original Kernel SHAP assuming feature independence. Typically, when -the features are highly dependent, a small $\sigma$ is typically needed such that the bias does -not dominate. @aas2019explaining show that a proper criterion for selecting $\sigma$ is a -small-sample-size corrected version of the AIC known as AICc. As calculation of it is -computationally intensive, an approximate version of the selection criterion is also suggested. +\boldsymbol{x}_{\mathcal{S}}^*)}{\sum_{k=1}^K w_{\mathcal{S}}(\boldsymbol{x}^*,\boldsymbol{x}^{[k]})},$$ + +The number of samples $K$ to be used in the approximate prediction can +for instance be chosen such that the $K$ largest weights accounts for a +fraction $\eta$, for example $0.9$, of the total weight. If $K$ exceeds +a certain limit, for instance $5,000$, it might be set to that limit. A +bandwidth parameter $\sigma$ used to scale the weights, must also be +specified. This choice may be viewed as a bias-variance trade-off. A +small $\sigma$ puts most of the weight to a few of the closest training +observations and thereby gives low bias, but high variance. When +$\sigma \rightarrow \infty$, this method converges to the original +Kernel SHAP assuming feature independence. Typically, when the features +are highly dependent, a small $\sigma$ is typically needed such that the +bias does not dominate. @aas2019explaining show that a proper criterion +for selecting $\sigma$ is a small-sample-size corrected version of the +AIC known as AICc. As calculation of it is computationally intensive, an +approximate version of the selection criterion is also suggested. Details on this is found in @aas2019explaining. @@ -211,16 +249,58 @@ Details on this is found in @aas2019explaining. ## Conditional Inference Tree Approach -The previous three methods can only handle numerical data. This means that if the data contains categorical/discrete/ordinal features, the features first have to be one-hot encoded. When the number of levels/features is large, this is not feasible. An approach that handles mixed (i.e numerical, categorical, discrete, ordinal) features and both univariate and multivariate responses is conditional inference trees (@hothorn2006unbiased). - -Conditional inference trees is a special tree fitting procedure that relies on hypothesis tests to choose both the splitting feature and the splitting point. The tree fitting procedure is sequential: first a splitting feature is chosen (the feature that is least independent of the response), and then a splitting point is chosen for this feature. This decreases the chance of being biased towards features with many splits (@hothorn2006unbiased). - -We use conditional inference trees (*ctree*) to model the conditional distribution, $p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$, found in the Shapley methodology. -First, we fit a different conditional inference tree to each conditional distribution. -Once a tree is fit for given dependent features, the end node of $\boldsymbol{x}_{\mathcal{S}}^*$ is found. Then, we sample from this end node and use the resulting samples, -$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, when approximating the conditional expectations as in Kernel SHAP. See @Redelmeier2020ctree for more details. - -The conditional inference trees are fit using the *party* and *partykit* packages (@partykit_package). +The previous three methods can only handle numerical data. This means +that if the data contains categorical/discrete/ordinal features, the +features first have to be one-hot encoded. When the number of +levels/features is large, this is not feasible. An approach that handles +mixed (i.e numerical, categorical, discrete, ordinal) features and both +univariate and multivariate responses is conditional inference trees +(@hothorn2006unbiased). + +Conditional inference trees is a special tree fitting procedure that +relies on hypothesis tests to choose both the splitting feature and the +splitting point. The tree fitting procedure is sequential: first a +splitting feature is chosen (the feature that is least independent of +the response), and then a splitting point is chosen for this feature. +This decreases the chance of being biased towards features with many +splits (@hothorn2006unbiased). + +We use conditional inference trees (*ctree*) to model the conditional +distribution, +$p(\boldsymbol{x}_{\bar{\mathcal{S}}}|\boldsymbol{x}_{\mathcal{S}}^*)$, +found in the Shapley methodology. First, we fit a different conditional +inference tree to each conditional distribution. Once a tree is fit for +given dependent features, the end node of +$\boldsymbol{x}_{\mathcal{S}}^*$ is found. Then, we sample from this end +node and use the resulting samples, +$\boldsymbol{x}_{\bar{\mathcal{S}}}^k, k=1,\ldots,K$, when approximating +the conditional expectations as in Kernel SHAP. See +@redelmeier2020explaining for more details. + +The conditional inference trees are fit using the *party* and *partykit* +packages (@partykit_package). + +## Categorical Approach + +When the features are all categorical, we can estimate the conditional +expectations using basic statistical formulas. For example, if we have +three features, $x_1, x_2, x_3$ with three levels each (indicated as 1, +2, 3), and we are provided with a table of counts indicating how many +times each combination of feature values occurs, we can estimate the +marginal and conditional probabilities as follows. Marginal +probabilities are estimated by dividing the number of times a given +feature (or features) takes on a certain value in the data set with the +total number of observations in the data set. Condititional +probabilities (for example, $P(X_1 = 1 | X_2 = 1)$) are estimated by +first subsetting the data set to reflect the conditioning (i.e., +extracting all rows where $X_2 = 1$), and then dividing the number of +times the feature on the left hand side of $|$ takes the given value in +this subset by the total number of observations in this subset. Once the +marginal and conditional probabilities are estimated for all +combinations of feature values, each conditional expectation can be +calculated. For example, the expected value of $X_1$ given $X_2 = 1$ and +$X_3 = 2$ is +$$E(X_1|X_2, X_3) = \sum_{x}x P(X_1 = x | X_2=1, X_3=2) = \sum_{x} x \frac{P(X_1 = x, X_2 = 1, X_3 = 2)}{P(X_2=1, X_3=2)}.$$. @@ -228,480 +308,845 @@ The conditional inference trees are fit using the *party* and *partykit* package # Examples -`shapr` supports computation of Shapley values with any predictive model which takes a set of -numeric features and produces a numeric outcome. Note that the ctree method takes both numeric and categorical variables. Check under "Advanced usage" for an example of how this can be done. +`shapr` supports computation of Shapley values with any predictive model +which takes a set of numeric features and produces a numeric outcome. +Note that the ctree method takes both numeric and categorical variables. +Check under "Advanced usage" for an example of how this can be done. -The following example shows how a simple `xgboost` model is trained using the Boston Housing Data, -and how `shapr` can be used to explain the individual predictions. Note that the empirical conditional -distribution approach is the default (i.e. `approach = "empirical"`), and that the Gaussian, -Gaussian copula, and ctree approaches can be used instead by setting the argument `approach` to either -`"gaussian"`, `"copula"`, or `"ctree`". +The following example shows how a simple `xgboost` model is trained +using the `airquality` dataset, and how `shapr` can be used to explain +the individual predictions. Note that the empirical conditional +distribution approach is the default (i.e. `approach = "empirical"`). +The Gaussian, Gaussian copula, ctree or independence approaches can be +used instead by setting the argument `approach` to either `"gaussian"`, +`"copula"`, `"ctree"`, `"categorical"` or `"independence"` in the code +below. ```{r, warning=FALSE} library(xgboost) -library(shapr) +library(data.table) -data("Boston", package = "MASS") +data("airquality") +data <- data.table::as.data.table(airquality) +data <- data[complete.cases(data), ] -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" +x_var <- c("Solar.R", "Wind", "Temp", "Month") +y_var <- "Ozone" -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) +ind_x_explain <- 1:6 +x_train <- data[-ind_x_explain, ..x_var] +y_train <- data[-ind_x_explain, get(y_var)] +x_explain <- data[ind_x_explain, ..x_var] # Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, +model <- xgboost::xgboost( + data = as.matrix(x_train), label = y_train, nround = 20, verbose = FALSE ) -# Prepare the data for explanation -explainer <- shapr(x_train, model) - # Specifying the phi_0, i.e. the expected prediction without any features -p <- mean(y_train) +p0 <- mean(y_train) # Computing the actual Shapley values with kernelSHAP accounting for feature dependence using # the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default) explanation <- explain( - x_test, + model = model, + x_explain = x_explain, + x_train = x_train, approach = "empirical", - explainer = explainer, - prediction_zero = p + prediction_zero = p0 ) # Printing the Shapley values for the test data. # For more information about the interpretation of the values in the table, see ?shapr::explain. -print(explanation$dt) +print(explanation$shapley_values) # Plot the resulting explanations for observations 1 and 6 -plot(explanation, plot_phi0 = FALSE, index_x_test = c(1, 6)) -``` +plot(explanation, bar_plot_phi0 = FALSE, index_x_explain = c(1, 6)) +``` -The Gaussian approach is used as follows: +There are multiple plot options specified by the `plot_type` argument in +`plot`. The `waterfall` option shows the changes in the prediction score +due to each features contribution (their Shapley values): ```{r} -# Use the Gaussian approach -explanation_gaussian <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - prediction_zero = p -) - -# Plot the resulting explanations for observations 1 and 6 -plot(explanation_gaussian, plot_phi0 = FALSE, index_x_test = c(1, 6)) +plot(explanation, plot_type = "waterfall", index_x_explain = c(1, 6)) ``` -The Gaussian copula approach is used as follows: +The other two plot options, `"beeswarm"` and `"scatter"`, can be useful +when you have many observations that you want to explain. For the +purpose of illustration, we explain the whole `airquality` dataset +(including the training data) for these plot types. The +`plot_type = "beeswarm"` summarises the distribution of the Shapley +values along the x-axis across all features. Each point gives the +Shapley value of a given instance, where the points are colored by the +feature value of that instance: ```{r} -# Use the Gaussian copula approach -explanation_copula <- explain( - x_test, - approach = "copula", - explainer = explainer, - prediction_zero = p +x_explain_many <- data[, ..x_var] +explanation_plot <- explain( + model = model, + x_explain = x_explain_many, + x_train = x_train, + approach = "empirical", + prediction_zero = p0 ) - -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_copula, plot_phi0 = FALSE, index_x_test = c(1, 6)) +plot(explanation_plot, plot_type = "beeswarm") ``` -The conditional inference tree approach is used as follows: +The `plot_type = "scatter"` plots the feature values on the x-axis and +Shapley values on the y-axis, as well as (optionally) a background +scatter_hist showing the distribution of the feature data: ```{r} -# Use the conditional inference tree approach -explanation_ctree <- explain( - x_test, - approach = "ctree", - explainer = explainer, - prediction_zero = p -) - -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_ctree, plot_phi0 = FALSE, index_x_test = c(1, 6)) +plot(explanation_plot, plot_type = "scatter", scatter_hist = TRUE) ``` - -We can use mixed (i.e continuous, categorical, ordinal) data with ctree. Use ctree with categorical data in the following manner: +We can use mixed (i.e continuous, categorical, ordinal) data with ctree. +Use ctree with mixed data in the following manner: ```{r} -x_var_cat <- c("lstat", "chas", "rad", "indus") -y_var <- "medv" - -# convert to factors -Boston$rad = as.factor(Boston$rad) -Boston$chas = as.factor(Boston$chas) +# convert the month variable to a factor +data[, Month_factor := as.factor(Month)] -x_train_cat <- Boston[-1:-6, x_var_cat] -y_train <- Boston[-1:-6, y_var] -x_test_cat <- Boston[1:6, x_var_cat] +data_train_cat <- data[-ind_x_explain, ] +data_explain_cat <- data[ind_x_explain, ] -# -- special function when using categorical data + xgboost -dummylist <- make_dummies(traindata = x_train_cat, testdata = x_test_cat) +x_var_cat <- c("Solar.R", "Wind", "Temp", "Month_factor") -x_train_dummy <- dummylist$train_dummies -x_test_dummy <- dummylist$test_dummies - -# Fitting a basic xgboost model to the training data -model_cat <- xgboost::xgboost( - data = x_train_dummy, - label = y_train, - nround = 20, - verbose = FALSE -) -model_cat$feature_list <- dummylist$feature_list +x_train_cat <- data_train_cat[, ..x_var_cat] +x_explain_cat <- data_explain_cat[, ..x_var_cat] -explainer_cat <- shapr(dummylist$traindata_new, model_cat) +# Fitting an lm model here as xgboost does not handle categorical features directly +# (work around in example below) +lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + "))) -p <- mean(y_train) +model_lm_cat <- lm(lm_formula, data_train_cat) -explanation_cat <- explain( - dummylist$testdata_new, +p0 <- mean(y_train) +explanation_lm_cat <- explain( + model = model_lm_cat, + x_explain = x_explain_cat, + x_train = x_train_cat, approach = "ctree", - explainer = explainer_cat, - prediction_zero = p + prediction_zero = p0 ) # Plot the resulting explanations for observations 1 and 6, excluding # the no-covariate effect -plot(explanation_cat, plot_phi0 = FALSE, index_x_test = c(1, 6)) - +plot(explanation_lm_cat, bar_plot_phi0 = FALSE, index_x_explain = c(1, 6)) ``` -We can specify parameters used to build the conditional inference trees using the following manner. Default values are based on @hothorn2006unbiased. +We can specify parameters used to build the conditional inference trees +in the following manner. Default values are based on +@hothorn2006unbiased. ```{r} # Use the conditional inference tree approach -# We can specify parameters used to building trees by specifying mincriterion, +# We can specify parameters used to building trees by specifying mincriterion, # minsplit, minbucket - explanation_ctree <- explain( - x_test, + model = model_lm_cat, + x_explain = x_explain_cat, + x_train = x_train_cat, approach = "ctree", - explainer = explainer, - prediction_zero = p, - mincriterion = 0.80, - minsplit = 20, - minbucket = 20 + prediction_zero = p0, + ctree.mincriterion = 0.80, + ctree.minsplit = 20, + ctree.minbucket = 20 ) - # Default parameters (based on (Hothorn, 2006)) are: # mincriterion = 0.95 # minsplit = 20 # minbucket = 7 ``` -We can also specify multiple different mincriterion (1 minus the boundary for when to stop splitting nodes) parameters to use when conditioning on different numbers of features. In this case, a vector of length = number of features must be provided. +If **all** features are categorical, one may use the categorical +approach as follows: ```{r} -# Use the conditional inference tree approach -# Specify a vector of mincriterions instead of just one -# In this case, when conditioning on 1 or 2 features, use mincriterion = 0.25 -# When conditioning on 3 or 4 features, use mincriterion = 0.95 +# For the sake of illustration, convert ALL features to factors +data[, Solar.R_factor := as.factor(cut(Solar.R, 10))] +data[, Wind_factor := as.factor(cut(Wind, 3))] +data[, Temp_factor := as.factor(cut(Temp, 2))] +data[, Month_factor := as.factor(Month)] -explanation_ctree <- explain( - x_test, - approach = "ctree", - explainer = explainer, - prediction_zero = p, - mincriterion = c(0.25, 0.25, 0.95, 0.95) +data_train_all_cat <- data[-ind_x_explain, ] +data_explain_all_cat <- data[ind_x_explain, ] + + +x_var_all_cat <- c("Solar.R_factor", "Wind_factor", "Temp_factor", "Month_factor") + +x_train_all_cat <- data_train_all_cat[, ..x_var_all_cat] +x_explain_all_cat <- data_explain_all_cat[, ..x_var_all_cat] + +# Fit an lm model here +lm_formula_all_cat <- as.formula(paste0(y_var, " ~ ", paste0(x_var_all_cat, collapse = " + "))) + +model_lm_all_cat <- lm(lm_formula_all_cat, data_train_all_cat) + +explanation_cat_method <- explain( + model = model_lm_all_cat, + x_explain = x_explain_all_cat, + x_train = x_train_all_cat, + approach = "categorical", + prediction_zero = p0 ) ``` +Shapley values can be used to explain any predictive model. For +predictive models taking time series as input, `approach='timeseries'` +can be used. In such models, joint behavior of consecutive time points +is often more important for the outcome than the single time points. +Therefore, it makes sense to derive Shapley value segments of the time +series instead of for each single time point. In `shapr` this can be +achieved through the `group` attribute. Other optional parameters of +`approach='timeseries'` are `timeseries.fixed_sigma_vec` and +`timeseries.bounds` (a vector indicating upper and lower bounds of the +time series if necessary). -## Main arguments in `shapr` +```{r} +# Simulate time series data with AR(1)-structure +set.seed(1) +data_ts <- data.frame(matrix(NA, ncol = 41, nrow = 4)) +for (n in 1:100) { + set.seed(n) + e <- rnorm(42, mean = 0, sd = 1) + + m_1 <- 0 + for (i in 2:length(e)) { + m_1[i] <- 1 + 0.8 * m_1[i - 1] + e[i] + } + data_ts[n, ] <- m_1[-1] +} +data_ts <- data.table::as.data.table(data_ts) -When using `shapr`, the default behavior is to use all feature combinations in the Shapley -formula. Kernel SHAP's sampling based approach may be used by specifying `n_combinations`, which -is the number of feature combinations to sample. If not specified, the exact method is used. -The computation time grows approximately exponentially with the number of samples. The training data -and the model whose predictions we wish to explain must be provided through -the arguments `x` and `model`. Note that `x` must be a `data.frame` or a `matrix`, and all -elements must be finite numerical values. Currently we do not support categorical features or -missing values. - +x_var_ts <- paste0("X", 1:40) +y_var_ts <- "X41" -## Main arguments in `explain` +ind_x_explain <- 1:6 +data_ts_train <- data_ts[-ind_x_explain] -The test data given by `x`, whose predicted values we wish to explain, must be provided. Note that -`x` must be a `data.frame` or a `matrix,` where all elements are finite numerical values. -One must also provide the object returned by `shapr` through the argument `explainer`. -The default approach when computing the Shapley values is the empirical approach (i.e. `approach = "empirical"`). -If you'd like to use a different approach you'll need to set `approach` equal to either `copula` or `gaussian`, or a -vector of them, with length equal to the number of features. If a vector, a combined approach is used, and element `i` -indicates the approach to use when conditioning on `i` variables. For more details see [Combined approach](#combined) below. - -When computing the kernel SHAP values by `explain`, the maximum number of samples to use in the -Monte Carlo integration for every conditional expectation is controlled by the argument `n_samples` -(default equals `1000`). The computation time grows approximately linear with this number. You will also -need to pass a numeric value for the argument `prediction_zero`, which represents the prediction value when not -conditioning on any features. We recommend setting this equal to the mean of the response, but other values, like the -mean prediction of a large test data set is also a possibility. If the empirical method is used, specific settings for -that approach, like a vector of fixed $\sigma$ values can be specified through the argument `sigma_vec`. See `?explain` -for more information. If `approach = "gaussian"`, you may specify the mean vector and covariance matrix of the data -generating distribution by the arguments `mu` and `cov_mat`. If not specified, they are estimated from the training data. +# Creating a predictive model (for illustration just predicting the next point in the time series with a linear model) +lm_ts_formula <- as.formula(X41 ~ .) +model_lm_ts <- lm(lm_ts_formula, data_ts_train) - +x_explain_ts <- data_ts[ind_x_explain, ..x_var_ts] +x_train_ts <- data_ts[-ind_x_explain, ..x_var_ts] -
+# Spitting the time series into 4 segments +group_ts <- list( + S1 = paste0("X", 1:10), + S2 = paste0("X", 11:20), + S3 = paste0("X", 21:30), + S4 = paste0("X", 31:40) +) -# Advanced usage +p0_ts <- mean(unlist(data_ts_train[, ..y_var_ts])) - +explanation_timeseries <- explain( + model = model_lm_ts, + x_explain = x_explain_ts, + x_train = x_train_ts, + approach = "timeseries", + prediction_zero = p0_ts, + group = group_ts +) +``` -## Combined approach +## Main arguments in `explain` -In addition to letting the user select one of the three aforementioned approaches for estimating the conditional -distribution of the data (i.e. `approach` equals either [`"gaussian"`](#gaussian), [`"copula"`](#copula), -[`"empirical"`](#empirical) or -[`"ctree"`](#ctree)) the package allows the user to combine the four approaches. To simplify the usage, -the flexibility is restricted such that the same approach is used when conditioning on the same number of features. -This is also in line @aas2019explaining[, Section 3.4]. - -This can be done by setting `approach` equal to a character vector, where the length of the vector is equal to the -number of features in the model. Consider a situation where you have trained a model that consists of 10 features, and -you would like to use the `"empirical"` approach when you condition on 1-3 features, the `"copula"` approach when you -condition on 4-5 features, and the `"gaussian"` approach when conditioning on 6 or more features. This can be applied -by simply passing `approach = c(rep("empirical", 3), rep("copula", 2), rep("gaussian", 5))`, i.e. `approach[i]` determines -which method to use when conditioning on `i` features. - -The code below exemplifies this approach for a case where there are four features, -using `"empirical", "copula"` and `"gaussian"` when conditioning on respectively 1, 2 and 3-4 features. Note that it -does not matter what method that is specified when conditioning on all features, as that equals the actual prediction -regardless of the specified approach. +When using `explain`, the default behavior is to use all feature +combinations in the Shapley formula. Kernel SHAP's sampling based +approach may be used by specifying `n_combinations`, which is the number +of unique feature combinations to sample. If not specified, the exact +method is used. The computation time grows approximately exponentially +with the number of features. The training data and the model whose +predictions we wish to explain must be provided through the arguments +`x_train` and `model`. The data whose predicted values we wish to +explain must be given by the argument `x_explain`. Note that both +`x_train` and `x_explain` must be a `data.frame` or a `matrix`, and all +elements must be finite numerical values. Currently we do not support +missing values. The default approach when computing the Shapley values +is the empirical approach (i.e. `approach = "empirical"`). If you'd like +to use a different approach you'll need to set `approach` equal to +either `copula` or `gaussian`, or a vector of them, with length equal to +the number of features. If a vector, a combined approach is used, and +element `i` indicates the approach to use when conditioning on `i` +variables. For more details see [Combined approach](#combined) below. + +When computing the kernel SHAP values by `explain`, the maximum number +of samples to use in the Monte Carlo integration for every conditional +expectation is controlled by the argument `n_samples` (default equals +`1000`). The computation time grows approximately linear with this +number. You will also need to pass a numeric value for the argument +`prediction_zero`, which represents the prediction value when not +conditioning on any features. We recommend setting this equal to the +mean of the response, but other values, like the mean prediction of a +large test data set is also a possibility. If the empirical method is +used, specific settings for that approach, like a vector of fixed +$\sigma$ values can be specified through the argument +`empirical.fixed_sigma`. See `?explain` for more information. If +`approach = "gaussian"`, you may specify the mean vector and covariance +matrix of the data generating distribution by the arguments +`gaussian.mu` and `gaussian.cov_mat`. If not specified, they are +estimated from the training data. + +## Explaining a forecasting model using `explain_forecast` + +`shapr` provides a specific function, `explain_forecast`, to explain +forecasts from time series models, at one or more steps into the future. +The main difference compared to `explain` is that the data is supplied +as (set of) time series, in addition to index arguments (`train_idx` and +`explain_idx`) specifying which time points that represents the train +and explain parts of the data. See `?explain_forecast` for more +information. + +To demonstrate how to use the function, 500 observations are generated +which follow an AR(1) structure, i.e. +$y_t = 0.5 y_{t-1} + \varepsilon_t$. To this data an arima model of +order (2, 0, 0) is fitted, and we therefore would like to explain the +forecasts in terms of the two previous lags of the time series. This is +is specified through the argument `explain_y_lags = 2`. Note that some +models may also put restrictions on the amount of data required to make +a forecast. The AR(2) model we used there, for instance, requires two +previous time point to make a forecast. + +In the example, two separate forecasts, each three steps ahead, are +explained. To set the starting points of the two forecasts, +`explain_idx` is set to `499:500`. This means that one forecast of +$t = (500, 501, 502)$ and another of $t = (501, 502, 503)$, will be +explained. In other words, `explain_idx` tells `shapr` at which points +in time data was available up until, when making the forecast to +explain. + +In the same way, `train_idx` denotes the points in time used to estimate +the conditional expectations used to explain the different forecasts. +Note that since we want to explain the forecasts in terms of the two +previous lags (`explain_y_lags = 2`), the smallest value of `train_idx` +must also be 2, because at time $t = 1$ there was only a single +observation available. + +Since the data is stationary, the mean of the data is used as value of +`prediction_zero` (i.e. $\phi_0$). This can however be chosen +differently depending on the data and application. + +For a multivariate model such as a VAR (Vector AutoRegressive model), it +may be of more interesting to explain the impact of each variable, +rather than each lag of each variable. This can be done by setting +`group_lags = TRUE`. ```{r} -# Use the combined approach -explanation_combined <- explain( - x_test, - approach = c("empirical", "copula", "gaussian", "gaussian"), - explainer = explainer, - prediction_zero = p +# Simulate time series data with AR(1)-structure. +set.seed(1) +data_ts <- data.frame(Y = arima.sim(list(order = c(1, 0, 0), ar = .5), n = 500)) +data_ts <- data.table::as.data.table(data_ts) + +# Fit an ARIMA(2, 0, 0) model. +arima_model <- arima(data_ts, order = c(2, 0, 0)) + +# Set prediction zero as the mean of the data for each forecast point. +p0_ar <- rep(mean(data_ts$Y), 3) + +# Explain forecasts from points t = 499 and t = 500. +explain_idx <- 499:500 + +explanation_forecast <- explain_forecast( + model = arima_model, + y = data_ts, + train_idx = 2:498, + explain_idx = 499:500, + explain_y_lags = 2, + horizon = 3, + approach = "empirical", + prediction_zero = p0_ar, + group_lags = FALSE ) +explanation_forecast +``` -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_combined, plot_phi0 = FALSE, index_x_test = c(1, 6)) +Note that for a multivariate model such as a VAR (Vector AutoRegressive +model), or for models also including several exogenous variables, it may +be of more informative to explain the impact of each variable, rather +than each lag of each variable. This can be done by setting +`group_lags = TRUE`. This does not make sense for this model, however, +as that would result in decomposing the forecast into a single group. + +We now give a more hands on example of how to use the `explain_forecast` +function. Say that we have an AR(2) model which describes the change +over time of the variable `Temp` in the dataset `airquality`. It seems +reasonable to assume that the temperature today should affect the +temperature tomorrow. To a lesser extent, we may also suggest that the +temperature today should also have an impact on that of the day after +tomorrow. + +We start by building our AR(2) model, naming it `model_ar_temp`. This +model is then used to make a forecast of the temperature of the day that +comes after the last day in the data, this forecast starts from index +153. + +```{r} +data <- data.table::as.data.table(airquality) + +model_ar_temp <- ar(data$Temp, order = 2) + +predict(model_ar_temp, n.ahead = 2)$pred ``` -As a second example using `"ctree"` for the first 3 features and `"empirical"` for the last: +First, we pass the model and the data as `model` and `y`. Since we have +an AR(2) model, we want to explain the forecasts in terms of the two +previous lags, whihc we specify with `explain_y_lags = 2`. Then, we let +`shapr` know which time indices to use as training data through the +argument `train_idx`. We use `2:152`, meaning that we skip the first +index, as we want to explain the two previous lags. Letting the training +indices go up until 152 means that every point in time except the first +and last will be used as training data. + +The last index, 153 is passed as the argument `explain_idx`, which means +that we want to explain a forecast made from time point 153 in the data. +The argument `horizon` is set to 2 in order to explain a forecast of +length 2. + +The argument `prediction_zero` is set to the mean of the time series, +and is repeated two times. Each value of `prediction_zero` is the +baseline for each forecast horizon. In our example, we assume that given +no effect from the two lags, the temperature would just be the average +during the observed period. Finally, we opt to not group the lags by +setting `group_lags` to `FALSE`. This means that lag 1 and 2 will be +explained separately. Grouping lags may be more interesting to do in a +model with multiple variables, as it is then possible to explain each +variable separately. ```{r} -# Use the combined approach -explanation_combined <- explain( - x_test, - approach = c("ctree", "ctree", "ctree", "empirical"), - explainer = explainer, - prediction_zero = p +explanation <- explain_forecast( + model = model_ar_temp, + y = data[, "Temp"], + train_idx = 2:152, + explain_idx = 153, + explain_y_lags = 2, + horizon = 2, + approach = "empirical", + prediction_zero = rep(mean(data$Temp), 2), + group_lags = FALSE, + n_batches = 1, + timing = FALSE ) + +print(explanation) ``` -## Using ctree when features are mixed numerical and categorical +The results are presented per value of `explain_idx` and forecast +horizon. We can see that the mean temperature was around 77.9 degrees. +At horizon 1, the first lag in the model caused it to be 6.6 degrees +lower, and the second lag had just a minor effect. At horizon 2, the +first lag has a slightly smaller negative impact, and the second lag has +a slightly larger impact. -```{r} -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" +It is also possible to explain a forecasting model which uses exogenous +regressors. The previous example is expanded to use an ARIMA(2,0,0) +model with `Wind` as an exogenous regressor. Since the exogenous +regressor must be available for the predicted time points, the model is +just fit on the 151 first observations, leaving two observations of +`Wind` to be used as exogenous values during the prediction phase. -# Convert two features as factors -dt <- Boston[, c(x_var, y_var)] -dt$rm <- as.factor(round(dt$rm/3)) -dt$dis <- as.factor(round(dt$dis/4)) +```{r} +data <- data.table::as.data.table(airquality) -xy_train_cat <- dt[-1:-6, ] -y_train_cat <- dt[-1:-6, y_var] -x_train_cat <- dt[-1:-6, x_var] -x_test_cat <- dt[1:6, x_var] +data_fit <- data[seq_len(151), ] +model_arimax_temp <- arima(data_fit$Temp, order = c(2, 0, 0), xreg = data_fit$Wind) -# Fit a basic linear regression model to the training data -model_mixed <- lm(medv ~ lstat + rm + dis + indus, data = xy_train_cat) +newxreg <- data[-seq_len(151), "Wind", drop = FALSE] -# Prepare the data for explanation -explainer <- shapr(x_train_cat, model_mixed) +predict(model_arimax_temp, n.ahead = 2, newxreg = newxreg)$pred +``` -# Specifying the phi_0, i.e. the expected prediction without any features -p <- mean(y_train_cat) +The `shapr` package can then explain not only the two autoregressive +lags, but also the single lag of the exogenous regressor. In order to do +so, the `Wind` variable is passed as the argument `xreg`, and +`explain_xreg_lags` is set to 1. Notice how only the first 151 +observations are used for `y` and all 153 are used for `xreg`. This +makes it possible for `shapr` to not only explain the effect of the +first lag of the exogenous variable, but also the contemporary effect +during the forecasting period. -# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using -explanation_categorical <- explain( - x_test_cat, - approach = "ctree", - explainer = explainer, - prediction_zero = p +```{r} +explanation <- explain_forecast( + model = model_ar_temp, + y = data_fit[, "Temp"], + xreg = data[, "Wind"], + train_idx = 2:150, + explain_idx = 151, + explain_y_lags = 2, + explain_xreg_lags = 1, + horizon = 2, + approach = "empirical", + prediction_zero = rep(mean(data_fit$Temp), 2), + group_lags = FALSE, + n_batches = 1, + timing = FALSE ) -# Note that nothing has to be specified to tell "ctree" that two of the features are -# cateogrical and two are numerical - -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_categorical, plot_phi0 = FALSE, index_x_test = c(1, 6)) +print(explanation$shapley_values) ``` -## Explain groups of features + -In some cases, especially when the number of features is very large, it may be more appropriate to explain predictions -in terms of groups of features instead of single features, see (@jullum2021efficient) for intuition and real world -examples. Explaining prediction in terms of groups of features is very easy using `shapr`: +
-```{r} +# Advanced usage -# Define the feature groups + -group_list <- list(A = c("lstat","rm"), - B = c("dis","indus")) +## Combined approach -# Set up the explainer using the groups -explainer_group <- shapr(x_train,model,group = group_list) +In addition to letting the user select one of the five aforementioned +approaches for estimating the conditional distribution of the data (i.e. +`approach` equals either [`"gaussian"`](#gaussian), +[`"copula"`](#copula), [`"empirical"`](#empirical) or +[`"ctree"`](#ctree) or [`"categorical"`](#categorical)), the package +allows the user to combine the given approaches. To simplify the usage, +the flexibility is restricted such that the same approach is used when +conditioning on the same number of features. This is also in line +@aas2019explaining [, Section 3.4]. + +This can be done by setting `approach` equal to a character vector, +where the length of the vector is equal to the number of features in the +model. Consider a situation where you have trained a model that consists +of 10 features, and you would like to use the `"empirical"` approach +when you condition on 1-3 features, the `"copula"` approach when you +condition on 4-5 features, and the `"gaussian"` approach when +conditioning on 6 or more features. This can be applied by simply +passing +`approach = c(rep("empirical", 3), rep("copula", 2), rep("gaussian", 5))`, +i.e. `approach[i]` determines which method to use when conditioning on +`i` features. + +The code below exemplifies this approach for a case where there are four +features, using `"empirical", "copula"` and `"gaussian"` when +conditioning on respectively 1, 2 and 3-4 features. Note that it does +not matter what method that is specified when conditioning on all +features, as that equals the actual prediction regardless of the +specified approach. +```{r} +# Use the combined approach +explanation_combined <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = c("empirical", "copula", "gaussian", "gaussian"), + prediction_zero = p0 +) +# Plot the resulting explanations for observations 1 and 6, excluding +# the no-covariate effect +plot(explanation_combined, bar_plot_phi0 = FALSE, index_x_explain = c(1, 6)) +``` + +As a second example using `"ctree"` for the first 3 features and +`"empirical"` for the last: +```{r} # Use the combined approach +explanation_combined <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = c("ctree", "ctree", "ctree", "empirical"), + prediction_zero = p0 +) +``` + +## Explain groups of features + +In some cases, especially when the number of features is very large, it +may be more appropriate to explain predictions in terms of groups of +features instead of single features, see (@jullum2021efficient) for +intuition and real world examples. Explaining prediction in terms of +groups of features is very easy using `shapr`: + +```{r} +# Define the feature groups +group_list <- list( + A = c("Temp", "Month"), + B = c("Wind", "Solar.R") +) + +# Use the empirical approach explanation_group <- explain( - x_test, + model = model, + x_explain = x_explain, + x_train = x_train, approach = "empirical", - explainer = explainer_group, - prediction_zero = p + prediction_zero = p0, + group = group_list ) - # Prints the group-wise explanations explanation_group - # Plots the group-wise explanations -plot(explanation_group, plot_phi0 = T, index_x_test = c(1, 6)) - +plot(explanation_group, bar_plot_phi0 = TRUE, index_x_explain = c(1, 6)) ``` - ## Explain custom models -`shapr` currently natively supports explanation of predictions from models fitted with the following functions: - -* `stats::lm` -* `stats::glm` -* `ranger::ranger` -* `mgcv::gam` -* `xgboost::xgboost`/`xgboost::xgb.train` - -Any continuous response regression model or binary classification model of these model classes, can be explained with -the package directly as exemplified above. -Moreover, essentially any feature dependent prediction model can be explained by the package by specifying two (or one) -simple additional functions to the class your model belongs to. - -*Note: The below procedure for specifying custom models was changed in shapr v0.2.0* -The first class function is `predict_model`, taking the model and data (as a `matrix` or `data.frame/data.table`) as -input and outputting the corresponding prediction as a numeric vector. -The second (optional, but highly recommended) class function is `get_model_specs`, taking the model as input and -outputting a list with the following elements: -*labels* (vector with the feature names to compute Shapley values for), -*classes* (a named vector with the labels as names and the class type as elements), -*factor_levels* (a named list with the labels as names and vectors with the factor levels as elements (NULL if the -feature is not a factor)). -The `get_model_specs` function is used to check that the format of the data passed to `shapr` and `explain` have the -correct format in terms of the necessary feature columns being available and having the correct class/attributes. -It is highly recommended to do such checks in order to ensure correct usage of `shapr` and `explain`. -If, for some reason, such checking is not desirable, one does not have to provide the `get_model_specs` function class. -This will, however, throw a warning that all feature consistency checking against the model is disabled. - -Once the above class functions are created, one can explain predictions from this model class as before. -These functions **can** be made general enough to handle all supported model types of that class, or they can be made -minimal, possibly only allowing explanation of the specific version of the model class at hand. -Below we give examples of both full support versions of these functions and a minimal version which skips the -`get_model_specs` function. -We do this for the `gbm` model class from the `gbm` package, fitted to the same Boston data set as used above. - - -```{r} +`shapr` currently natively supports explanation of predictions from +models fitted with the following functions: + +- `stats::lm` +- `stats::glm` +- `ranger::ranger` +- `mgcv::gam` +- `xgboost::xgboost`/`xgboost::xgb.train` + +Any continuous response regression model or binary classification model +of these model classes, can be explained with the package directly as +exemplified above. Moreover, essentially any feature dependent +prediction model can be explained by the package by specifying two (or +one) simple additional functions for your model. + +*Note: The below procedure for specifying custom models was changed in +shapr v0.3.0* The first function is `predict_model`, taking the model +and data (as a `matrix` or `data.frame/data.table`) as input and +outputting the corresponding prediction as a numeric vector. The second +(optional, but highly recommended) function is `get_model_specs`, taking +the model as input and outputting a list with the following elements: +*labels* (vector with the feature names to compute Shapley values for), +*classes* (a named vector with the labels as names and the class type as +elements), *factor_levels* (a named list with the labels as names and +vectors with the factor levels as elements (NULL if the feature is not a +factor)). The `get_model_specs` function is used to check that the +format of the data passed to `explain` have the correct format in terms +of the necessary feature columns being available and having the correct +class/attributes. It is highly recommended to do such checks in order to +ensure correct usage of `explain`. If, for some reason, such checking is +not desirable, one does not have to provide the `get_model_specs` +function. This will, however, throw a warning that all feature +consistency checking against the model is disabled. + +Once the above functions are created, you can explain predictions from +this model as before by passing the functions through the input +arguments `predict_model` and `get_model_specs` of `explain()`. + +These functions **can** be made general enough to handle all supported +model types of that class, or they can be made minimal, possibly only +allowing explanation of the specific version of the model class at hand. +Below we give examples of both full support versions of these functions +and a minimal version which skips the `get_model_specs` function. We do +this for the `gbm` model class from the `gbm` package, fitted to the +same airquality data set as used above. + +```{r,warning=TRUE, message=TRUE} library(gbm) -xy_train <- data.frame(x_train,medv = y_train) - -form <- as.formula(paste0(y_var,"~",paste0(x_var,collapse="+"))) - +formula_gbm <- as.formula(paste0(y_var, "~", paste0(x_var, collapse = "+"))) # Fitting a gbm model set.seed(825) -model <- gbm::gbm( - form, - data = xy_train, +model_gbm <- gbm::gbm( + formula_gbm, + data = cbind(x_train, Ozone = y_train), distribution = "gaussian" ) #### Full feature versions of the three required model functions #### - -predict_model.gbm <- function(x, newdata) { - - if (!requireNamespace('gbm', quietly = TRUE)) { - stop('The gbm package is required for predicting train models') +MY_predict_model <- function(x, newdata) { + if (!requireNamespace("gbm", quietly = TRUE)) { + stop("The gbm package is required for predicting train models") } - model_type <- ifelse( - x$distribution$name %in% c("bernoulli","adaboost"), + x$distribution$name %in% c("bernoulli", "adaboost"), "classification", "regression" ) if (model_type == "classification") { - - predict(x, as.data.frame(newdata), type = "response",n.trees = x$n.trees) + predict(x, as.data.frame(newdata), type = "response", n.trees = x$n.trees) } else { - - predict(x, as.data.frame(newdata),n.trees = x$n.trees) + predict(x, as.data.frame(newdata), n.trees = x$n.trees) } } - -get_model_specs.gbm <- function(x){ - feature_list = list() - feature_list$labels <- labels(x$Terms) - m <- length(feature_list$labels) - - feature_list$classes <- attr(x$Terms,"dataClasses")[-1] - feature_list$factor_levels <- setNames(vector("list", m), feature_list$labels) - feature_list$factor_levels[feature_list$classes=="factor"] <- NA # the model object doesn't contain factor levels info - - return(feature_list) +MY_get_model_specs <- function(x) { + feature_specs <- list() + feature_specs$labels <- labels(x$Terms) + m <- length(feature_specs$labels) + feature_specs$classes <- attr(x$Terms, "dataClasses")[-1] + feature_specs$factor_levels <- setNames(vector("list", m), feature_specs$labels) + feature_specs$factor_levels[feature_specs$classes == "factor"] <- NA # model object doesn't contain factor levels info + return(feature_specs) } -# Prepare the data for explanation +# Compute the Shapley values set.seed(123) -explainer <- shapr(xy_train, model) -p0 <- mean(xy_train[,y_var]) -explanation <- explain(x_test, explainer, approach = "empirical", prediction_zero = p0) -# Plot results -plot(explanation) +p0 <- mean(y_train) +explanation_custom <- explain( + model = model_gbm, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + predict_model = MY_predict_model, + get_model_specs = MY_get_model_specs +) -#### Minimal version of the three required model functions #### -# Note: Working only for this exact version of the model class -# Avoiding to define get_model_specs skips all feature -# consistency checking between your data and model +# Plot results +plot(explanation_custom, index_x_explain = c(1, 6)) -# Removing the previously defined functions to simulate a fresh start -rm(predict_model.gbm) -rm(get_model_specs.gbm) -predict_model.gbm <- function(x, newdata) { - predict(x, as.data.frame(newdata),n.trees = x$n.trees) +#### Minimal version of the three required model functions #### +# Note: Working only for this exact version of the model class +# Avoiding to define get_model_specs skips all feature +# consistency checking between your data and model +MY_MINIMAL_predict_model <- function(x, newdata) { + predict(x, as.data.frame(newdata), n.trees = x$n.trees) } -# Prepare the data for explanation +# Compute the Shapley values set.seed(123) -explainer <- shapr(x_train, model) -p0 <- mean(xy_train[,y_var]) -explanation <- explain(x_test, explainer, approach = "empirical", prediction_zero = p0) +explanation_custom_minimal <- explain( + model = model_gbm, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + predict_model = MY_MINIMAL_predict_model +) + # Plot results -plot(explanation) +plot(explanation_custom_minimal, index_x_explain = c(1, 6)) +``` + + +
+ +# Scalability and efficency + +## Batch computation + +The computational complexity of Shapley value based explanations grows +fast in the number of features, as the number of conditional +expectations one needs to estimate in the Shapley formula grows +exponentially. As outlined [above](#KSHAP), the estimating of each of +these conditional expectations is also computationally expensive, +typically requiring estimation of a conditional probability +distribution, followed by Monte Carlo integration. These computations +are not only heavy for the CPU, they also require a lot of memory (RAM), +which typically is a limited resource. By doing the most resource hungry +computations (the computation of v(S)) in sequential batches with +different feature subsets $S$, the memory usage can be significantly +reduces. Such batching comes at the cost of an increase in computation +time, which depends on the number of feature subsets (`n_combinations`), +the number of features, the estimation `approach` and so on. When +calling `shapr::explain()`, we allow the user to set the number of +batches with the argument `n_batches`. The default of this argument is +`NULL`, which uses a (hopefully) reasonable trade-off between +computation speed and memory consumption which depends on +`n_combinations` and `approach`. The memory/computation time trade-off +is most apparent for models with more than say 6-7 features. Below we a +basic example where `n_batches=10`: +```{r} +explanation_batch <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + n_batches = 10 +) ``` +## Parallelized computation + +In addition to reducing the memory consumption, the introduction of the +`n_batch` argument allows computation within each batch to be performed in parallel. +The parallelization in `shapr::explain()` is handled by the +`future_apply` which builds on the `future` environment. The `future` +package works on all OS, allows the user to decide the parallelization +backend (mutliple R procesess or forking), works directly with hpc +clusters, and also supports progress updates for the parallelized task +(see below). + +Note that, since it takes some time to duplicate data into different +processes/machines when running in parallel, it is not always +preferrable to run `shapr::explain()` in parallel, at least not with +many parallel sessions (hereby called **workers**). Parallelizatiob also +increases the memory consumption proportionally, so you want to limit +the number of workers for that reason too. In a future version of +`shapr` we will provide experienced based automatic selection of the +number of workers. In the meanwhile, this is all lef to the user, and we +advice that `n_batches` equals some positive integer multiplied by the +number of workers. Below is a basic example of a parallelization with +two workers. +```{r} +library(future) +future::plan(multisession, workers = 2) + +explanation_par <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + n_batches = 10 +) + +future::plan(sequential) # To return to non-parallel computation +``` +## Progress updates + +`shapr` provides progress updates of the computation of the Shapley +values through the R-package `progressr`. This gives the user full +control over the visual apperance of the progress updates, and also +intergrates seemlessly with the parallelization framework `future` used +by `shapr` (see above). Note that the progress is updated as the batches +are completed, meaning that if you have choosen `n_batches=1`, you will +not get intermediate updates, while if you set `n_batches=10` you will +get updates on every 10% of the computation. + +Progress updates are enabled for the current R-session by running the +command `progressr::handlers(local=TRUE)`, before calling +`shapr::explain()`. To use progress updates for only a single call to +`shapr::explain()`, one can wrap the call using +`progressr::with_progress` as follows: +`progressr::with_progress({ shapr::explain() })` The default appearence +of the progress updates is a basic ASCII-based horizontal progress bar. +Other variants can be chosen by passing different strings to +`progressr::handlers()`, some of which require additional packages. If +you are using Rstudio, the progress can be displayed directly in the gui +with `progressr::handlers('rstudio')` (requires the `rstudioapi` +package). If you are running Windows, you may use the pop-up gui +progress bar `progressr::handlers('handler_winprogressbar')`. A wrapper +for progressbar of the flexible `cli` package is also available +`progressr::handlers('cli')` (requires the `cli` package). + +For a full list of all progression handlers and the customization +options available with `progressr`, see the `progressr` +[vignette](https://cran.r-project.org/web/packages/progressr/vignettes/progressr-intro.html). +A full code example of using `progressr` with `shapr` is shown below: + +```{r,eval = FALSE} +library(progressr) +progressr::handlers(global = TRUE) +# If no progression handler is specified, the txtprogressbar is used +# Other progression handlers: +# progressr::handlers('rstudio') # requires the 'rstudioapi' package +# progressr::handlers('handler_winprogressbar') # Window only +# progressr::handlers('cli') # requires the 'cli' package +explanation <- explain( + model = model, + x_explain = x_explain, + x_train = x_train, + approach = "empirical", + prediction_zero = p0, + n_batches = 10 +) +#| [=================================>----------------------] 60% Estimating v(S) +``` @@ -709,27 +1154,33 @@ plot(explanation) # Comparison to Lundberg & Lee's implementation -As mentioned above, the original (independence assuming) Kernel SHAP implementation can be -approximated by setting a large $\sigma$ value using our empirical approach. If we specify that the -distances to *all* training observations should be used (i.e. setting `approach = "empirical"` and -`w_threshold = 1` when using `explain`, we can approximate the original method arbitrarily -well by increasing $\sigma$. For completeness of the `shapr`, we have also implemented -a version of the original method, which samples training observations independently with respect to -their distances to test observations (i.e. without the large-$\sigma$ approximation). This method is -available by using `approach = "independence"` in `explain`. - -We have compared the results using these two variants with the original implementation of -@lundberg2017unified, available through the Python library [`shap`](https://github.com/slundberg/shap). -As above, we used the Boston housing -data, trained via `xgboost`. We specify that *all* training observations should be used when -explaining all of the 6 test observations. To run the individual explanation method in the `shap` -Python library we use the `reticulate` `R`-package, allowing Python code to run within `R`. -As this requires installation of Python package, the comparison code and results is not included -in this vignette, but can be found +As mentioned above, the original (independence assuming) Kernel SHAP +implementation can be approximated by setting a large $\sigma$ value +using our empirical approach. If we specify that the distances to *all* +training observations should be used (i.e. setting +`approach = "empirical"` and `empirical.eta = 1` when using `explain`, +we can approximate the original method arbitrarily well by increasing +$\sigma$. For completeness of the `shapr` package, we have also +implemented a version of the original method, which samples training +observations independently with respect to their distances to test +observations (i.e. without the large-$\sigma$ approximation). This +method is available by using `approach = "independence"` in `explain`. + +We have compared the results using these two variants with the original +implementation of @lundberg2017unified, available through the Python +library [`shap`](https://github.com/slundberg/shap). As above, we used +the Boston housing data, trained via `xgboost`. We specify that *all* +training observations should be used when explaining all of the 6 test +observations. To run the individual explanation method in the `shap` +Python library we use the `reticulate` `R`-package, allowing Python code +to run within `R`. As this requires installation of Python package, the +comparison code and results is not included in this vignette, but can be +found [here](https://github.com/NorskRegnesentral/shapr/blob/master/inst/scripts/compare_shap_python.R). -As indicated by the (commented out) results in the file above both methods in our -`R`-package give (up to numerical approximation error) identical results to the original -implementation in the Python `shap` library. +As indicated by the (commented out) results in the file above both +methods in our `R`-package give (up to numerical approximation error) +identical results to the original implementation in the Python `shap` +library.