diff --git a/.github/workflows/gatk-tests.yml b/.github/workflows/gatk-tests.yml index 76fa7a9d07e..5d2e7f5f497 100644 --- a/.github/workflows/gatk-tests.yml +++ b/.github/workflows/gatk-tests.yml @@ -291,7 +291,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - wdlTest: [ 'RUN_CNV_GERMLINE_COHORT_WDL', 'RUN_CNV_GERMLINE_CASE_WDL', 'RUN_CNV_SOMATIC_WDL', 'RUN_M2_WDL', 'RUN_CNN_WDL', 'RUN_VCF_SITE_LEVEL_FILTERING_WDL' ] + wdlTest: [ 'RUN_CNV_GERMLINE_COHORT_WDL', 'RUN_CNV_GERMLINE_CASE_WDL', 'RUN_CNV_SOMATIC_WDL', 'RUN_M2_WDL', 'RUN_VCF_SITE_LEVEL_FILTERING_WDL' ] continue-on-error: true name: WDL test ${{ matrix.wdlTest }} on cromwell steps: diff --git a/Dockerfile b/Dockerfile index ceec398a9d3..6349f4088fa 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -ARG BASE_DOCKER=broadinstitute/gatk:gatkbase-3.2.0 +ARG BASE_DOCKER=broadinstitute/gatk:gatkbase-3.3.0 # stage 1 for constructing the GATK zip FROM ${BASE_DOCKER} AS gradleBuild @@ -85,7 +85,7 @@ ENV CLASSPATH=/gatk/gatk.jar:$CLASSPATH PATH=$CONDA_PATH/envs/gatk/bin:$CONDA_PA # Start GATK Python environment -RUN conda env create -n gatk -f /gatk/gatkcondaenv.yml && \ +RUN conda env create -vv -n gatk -f /gatk/gatkcondaenv.yml && \ echo "source activate gatk" >> /gatk/gatkenv.rc && \ echo "source /gatk/gatk-completion.sh" >> /gatk/gatkenv.rc && \ conda clean -afy && \ diff --git a/build.gradle b/build.gradle index 9e82637cdfe..1c31efa500b 100644 --- a/build.gradle +++ b/build.gradle @@ -684,7 +684,7 @@ task localDevCondaEnv(type: Exec) { dependsOn 'condaEnvironmentDefinition' inputs.file("$buildDir/$pythonPackageArchiveName") workingDir "$buildDir" - commandLine "conda", "env", "create", "--force", "-f", gatkCondaYML + commandLine "conda", "env", "create", "--yes", "-f", gatkCondaYML } task javadocJar(type: Jar, dependsOn: javadoc) { diff --git a/scripts/cnv_cromwell_tests/germline/cnv_germline_case_scattered_workflow.json b/scripts/cnv_cromwell_tests/germline/cnv_germline_case_scattered_workflow.json index aeb0bb8796d..ab85fe78e83 100644 --- a/scripts/cnv_cromwell_tests/germline/cnv_germline_case_scattered_workflow.json +++ b/scripts/cnv_cromwell_tests/germline/cnv_germline_case_scattered_workflow.json @@ -17,8 +17,8 @@ "CNVGermlineCaseScatteredWorkflow.gcnv_max_training_epochs": 1, "CNVGermlineCaseScatteredWorkflow.gcnv_min_training_epochs": 1, "CNVGermlineCaseScatteredWorkflow.gcnv_model_tars": [ - "/home/runner/work/gatk/gatk/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-gcnv-model-0.tar.gz", - "/home/runner/work/gatk/gatk/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-gcnv-model-1.tar.gz"], + "/home/runner/work/gatk/gatk/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-gcnv-model-shard-0.tar.gz", + "/home/runner/work/gatk/gatk/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-gcnv-model-shard-1.tar.gz"], "CNVGermlineCaseScatteredWorkflow.gcnv_num_thermal_advi_iters": 1, "CNVGermlineCaseScatteredWorkflow.intervals": "/home/runner/work/gatk/gatk/src/test/resources/large/cnv_germline_workflows_test_files/ice_targets_chr20xy.interval_list", "CNVGermlineCaseScatteredWorkflow.filtered_intervals": "/home/runner/work/gatk/gatk/src/test/resources/large/cnv_germline_workflows_test_files/ice_targets_chr20xy.preprocessed.filtered.interval_list", diff --git a/scripts/docker/gatkbase/Dockerfile b/scripts/docker/gatkbase/Dockerfile index a4ba3ac557a..7f405744c85 100644 --- a/scripts/docker/gatkbase/Dockerfile +++ b/scripts/docker/gatkbase/Dockerfile @@ -30,6 +30,7 @@ RUN apt update && \ git \ gpg-agent \ build-essential \ + libblas-dev \ openjdk-17-jdk \ vim \ software-properties-common && \ diff --git a/scripts/gatkcondaenv.yml.template b/scripts/gatkcondaenv.yml.template index 75284b829e8..4fa5ae1d805 100644 --- a/scripts/gatkcondaenv.yml.template +++ b/scripts/gatkcondaenv.yml.template @@ -15,53 +15,41 @@ name: $condaEnvName channels: # if channels other than conda-forge are added and the channel order is changed (note that conda channel_priority is currently set to flexible), -# verify that key dependencies are installed from the correct channel and compiled against MKL +# verify that key dependencies are installed from the correct channel - conda-forge -- defaults + dependencies: # core python dependencies -- conda-forge::python=3.6.10 # do not update -- conda-forge::pip=21.3.1 -- conda-forge::mkl=2019.5 # MKL typically provides dramatic performance increases for theano, tensorflow, and other key dependencies -- conda-forge::mkl-service=2.3.0 -- conda-forge::joblib=1.1.1 # must pin joblib - versions after 1.1.1 no longer support python 3.6 -- conda-forge::numpy=1.17.5 # do not update, this will break scipy=1.0.0 - # verify that numpy is compiled against MKL (e.g., by checking *_mkl_info using numpy.show_config()) - # and that it is used in tensorflow, theano, and other key dependencies -- conda-forge::theano=1.0.4 # it is unlikely that new versions of theano will be released - # verify that this is using numpy compiled against MKL (e.g., by the presence of -lmkl_rt in theano.config.blas.ldflags) -- defaults::tensorflow=1.15.0 # update only if absolutely necessary, as this may cause conflicts with other core dependencies - # verify that this is using numpy compiled against MKL (e.g., by checking tensorflow.pywrap_tensorflow.IsMklEnabled()) -- conda-forge::scipy=1.0.0 # do not update, this will break a scipy.misc.logsumexp import (deprecated in scipy=1.0.0) in pymc3=3.1 -- conda-forge::pymc3=3.1 # do not update, this will break gcnvkernel -- conda-forge::h5py=2.10.0 # required by keras 2.2.4 -- conda-forge::keras=2.2.4 # updated from pip-installed 2.2.0, which caused various conflicts/clobbers of conda-installed packages - # conda-installed 2.2.4 appears to be the most recent version with a consistent API and without conflicts/clobbers - # if you wish to update, note that versions of conda-forge::keras after 2.2.5 - # undesirably set the environment variable KERAS_BACKEND = theano by default -- defaults::intel-openmp=2019.4 -- conda-forge::scikit-learn=0.23.1 -- conda-forge::matplotlib=3.2.1 -- conda-forge::pandas=1.0.3 -- conda-forge::typing_extensions=4.1.1 # see https://github.com/broadinstitute/gatk/issues/7800 and linked PRs -- conda-forge::dill=0.3.4 # used for pickling lambdas in TrainVariantAnnotationsModel +- conda-forge::python=3.10.13 # do not update without good reason +- conda-forge:pip=23.3.1 +- conda-forge:blas=1.0=mkl # our official environment uses MKL versions of various packages; if other versions are desired, users should edit this YML accordingly +- conda-forge::numpy=1.26.2 +- conda-forge::pymc=5.10.1 +- conda-forge::pytensor=2.18.3 +- conda-forge::scipy=1.11.4 +- conda-forge::h5py=3.10.0 +- conda-forge::pytorch=2.1.0=*mkl*100 +- conda-forge::scikit-learn=1.3.2 +- conda-forge::matplotlib=3.8.2 +- conda-forge::pandas=2.1.3 +- conda-forge::tqdm=4.66.1 +- conda-forge::dill=0.3.7 # used for pickling lambdas in TrainVariantAnnotationsModel # core R dependencies; these should only be used for plotting and do not take precedence over core python dependencies! -- r-base=3.6.2 -- r-data.table=1.12.8 -- r-dplyr=0.8.5 -- r-getopt=1.20.3 -- r-ggplot2=3.3.0 -- r-gplots=3.0.3 -- r-gsalib=2.1 -- r-optparse=1.6.4 -- r-backports=1.1.10 +- r-base=4.3.1 +- r-data.table=1.14.8 +- r-dplyr=1.1.3 +- r-getopt=1.20.4 +- r-ggplot2=3.4.4 +- r-gplots=3.1.3 +- r-gsalib=2.2.1 +- r-optparse=1.7.3 +- r-backports=1.4.1 # other python dependencies; these should be removed after functionality is moved into Java code -- biopython=1.76 -- pyvcf=0.6.8 -- bioconda::pysam=0.15.3 # using older conda-installed versions may result in libcrypto / openssl bugs +- bioconda::pysam=0.22.0 +- conda-forge::pyvcf=0.6.8 # pip installs should be avoided, as pip may not respect the dependencies found by the conda solver - pip: diff --git a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/DetermineGermlineContigPloidy.java b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/DetermineGermlineContigPloidy.java index fcb93e202b9..bb2f7f1f0e9 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/DetermineGermlineContigPloidy.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/DetermineGermlineContigPloidy.java @@ -67,12 +67,12 @@ *

OpenMP and MKL parallelism can be controlled by setting the OMP_NUM_THREADS and MKL_NUM_THREADS * environment variables, respectively.

* - *

Advanced users may wish to set the THEANO_FLAGS environment variable to override the GATK theano + *

Advanced users may wish to set the PYTENSOR_FLAGS environment variable to override the GATK PyTensor * configuration. For example, by running - * THEANO_FLAGS="base_compiledir=PATH/TO/BASE_COMPILEDIR" gatk DetermineGermlineContigPloidy ..., users can specify - * the theano compilation directory (which is set to $HOME/.theano by default). See theano documentation - * at - * https://theano-pymc.readthedocs.io/en/latest/library/config.html. + * PYTENSOR_FLAGS="base_compiledir=PATH/TO/BASE_COMPILEDIR" gatk DetermineGermlineContigPloidy ..., users can specify + * the PyTensor compilation directory (which is set to $HOME/.pytensor by default). See PyTensor documentation + * at + * https://pytensor.readthedocs.io/en/latest/library/config.html. *

* *

Tool run modes

diff --git a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCaller.java b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCaller.java index 095ffb76850..aac440912bd 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCaller.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCaller.java @@ -92,12 +92,12 @@ *

OpenMP and MKL parallelism can be controlled by setting the OMP_NUM_THREADS and MKL_NUM_THREADS * environment variables, respectively.

* - *

Advanced users may wish to set the THEANO_FLAGS environment variable to override the GATK theano + *

Advanced users may wish to set the PYTENSOR_FLAGS environment variable to override the GATK PyTensor * configuration. For example, by running - * THEANO_FLAGS="base_compiledir=PATH/TO/BASE_COMPILEDIR" gatk GermlineCNVCaller ..., users can specify - * the theano compilation directory (which is set to $HOME/.theano by default). See theano documentation - * at - * https://theano-pymc.readthedocs.io/en/latest/library/config.html. + * PYTENSOR_FLAGS="base_compiledir=PATH/TO/BASE_COMPILEDIR" gatk DetermineGermlineContigPloidy ..., users can specify + * the PyTensor compilation directory (which is set to $HOME/.pytensor by default). See PyTensor documentation + * at + * https://pytensor.readthedocs.io/en/latest/library/config.html. *

* *

Resource usage

diff --git a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/PostprocessGermlineCNVCalls.java b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/PostprocessGermlineCNVCalls.java index fa68bb4f34d..b9ac8561e59 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/PostprocessGermlineCNVCalls.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/PostprocessGermlineCNVCalls.java @@ -89,12 +89,12 @@ * the python environment is already set up. Otherwise, the environment must be created and activated as described in the * main GATK README.md file.

* - *

Advanced users may wish to set the THEANO_FLAGS environment variable to override the GATK theano + *

Advanced users may wish to set the PYTENSOR_FLAGS environment variable to override the GATK PyTensor * configuration. For example, by running - * THEANO_FLAGS="base_compiledir=PATH/TO/BASE_COMPILEDIR" gatk PostprocessGermlineCNVCalls ..., users can specify - * the theano compilation directory (which is set to $HOME/.theano by default). See theano documentation - * at - * https://theano-pymc.readthedocs.io/en/latest/library/config.html. + * PYTENSOR_FLAGS="base_compiledir=PATH/TO/BASE_COMPILEDIR" gatk DetermineGermlineContigPloidy ..., users can specify + * the PyTensor compilation directory (which is set to $HOME/.pytensor by default). See PyTensor documentation + * at + * https://pytensor.readthedocs.io/en/latest/library/config.html. *

* *

Required inputs:

diff --git a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotDenoisedCopyRatios.java b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotDenoisedCopyRatios.java index 33672cac84f..215b03154fa 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotDenoisedCopyRatios.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotDenoisedCopyRatios.java @@ -204,8 +204,7 @@ private void writeDenoisingPlots(final String sampleName, //this runs the R statement "source("CNVPlottingLibrary.R")" before the main script runs executor.addScript(new Resource(PlottingUtils.CNV_PLOTTING_R_LIBRARY, PlotDenoisedCopyRatios.class)); executor.addScript(new Resource(PLOT_DENOISED_COPY_RATIOS_R_SCRIPT, PlotDenoisedCopyRatios.class)); - //--args is needed for Rscript to recognize other arguments properly - executor.addArgs("--args", + executor.addArgs( "--sample_name=" + sampleName, "--standardized_copy_ratios_file=" + CopyNumberArgumentValidationUtils.getCanonicalPath(inputStandardizedCopyRatiosFile), "--denoised_copy_ratios_file=" + CopyNumberArgumentValidationUtils.getCanonicalPath(inputDenoisedCopyRatiosFile), diff --git a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotModeledSegments.java b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotModeledSegments.java index 89a211cfdd9..424c15880b2 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotModeledSegments.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/copynumber/plotting/PlotModeledSegments.java @@ -285,8 +285,7 @@ private void writeModeledSegmentsPlot(final String sampleName, //this runs the R statement "source("CNVPlottingLibrary.R")" before the main script runs executor.addScript(new Resource(PlottingUtils.CNV_PLOTTING_R_LIBRARY, PlotModeledSegments.class)); executor.addScript(new Resource(PLOT_MODELED_SEGMENTS_R_SCRIPT, PlotModeledSegments.class)); - //--args is needed for Rscript to recognize other arguments properly - executor.addArgs("--args", + executor.addArgs( "--sample_name=" + sampleName, "--denoised_copy_ratios_file=" + (inputDenoisedCopyRatiosFile == null ? null : CopyNumberArgumentValidationUtils.getCanonicalPath(inputDenoisedCopyRatiosFile)), "--allelic_counts_file=" + (inputAllelicCountsFile == null ? null : CopyNumberArgumentValidationUtils.getCanonicalPath(inputAllelicCountsFile)), diff --git a/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariants.java b/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariants.java index a17da13b0cf..88e71d7df40 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariants.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariants.java @@ -103,6 +103,7 @@ * -weights path/to/my_model_folder/2dmodel.hd5 * */ +@DeprecatedFeature @DocumentedFeature @CommandLineProgramProperties( summary = CNNScoreVariants.USAGE_SUMMARY, diff --git a/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantTrain.java b/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantTrain.java index 32ea3568b10..8fe917a2cff 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantTrain.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantTrain.java @@ -57,8 +57,8 @@ oneLineSummary = "Train a CNN model for filtering variants", programGroup = VariantFilteringProgramGroup.class ) +@DeprecatedFeature @DocumentedFeature -@ExperimentalFeature public class CNNVariantTrain extends CommandLineProgram { @Argument(fullName = "input-tensor-dir", shortName = "input-tensor-dir", doc = "Directory of training tensors to create.") diff --git a/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantWriteTensors.java b/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantWriteTensors.java index 0fd0ec79e17..49ce59bdd0c 100644 --- a/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantWriteTensors.java +++ b/src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantWriteTensors.java @@ -71,8 +71,8 @@ oneLineSummary = "Write variant tensors for training a CNN to filter variants", programGroup = VariantFilteringProgramGroup.class ) +@DeprecatedFeature @DocumentedFeature -@ExperimentalFeature public class CNNVariantWriteTensors extends CommandLineProgram { @Argument(fullName = StandardArgumentDefinitions.REFERENCE_LONG_NAME, diff --git a/src/main/python/org/broadinstitute/hellbender/README.md b/src/main/python/org/broadinstitute/hellbender/README.md index 1c57defd1a3..9780f6a7b3d 100644 --- a/src/main/python/org/broadinstitute/hellbender/README.md +++ b/src/main/python/org/broadinstitute/hellbender/README.md @@ -4,3 +4,9 @@ can be installed as a standalone package, a corresponding `setup_. file may be placed in this directory. However, during creation of the common GATK conda environment, all packages will be combined and pip-installed as a single package named ``gatkpythonpackages`` by `setup.py`. + +However, note that it is easier to do development by installing live/editable versions of these packages +(i.e., running `pip install --editable .` in this directory), so that any code changes are immediately reflected in +the underlying environment. To do this, 1) remove the pip install of the `gatkpythonpackages.zip` archive in the +conda environment file, 2) create and activate the corresponding conda environment, then +3) run the editable pip install. diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/README.txt b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/README.txt index da8aa9c131a..92e9c9e3c3d 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/README.txt +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/README.txt @@ -8,7 +8,7 @@ germline copy number variant (gCNV) tools and workflows. This module implements inference schemes for read-depth profile denoising, germline integer copy number variation discovery, germline contig ploidy determination, associated I/O methods, and helper CLI scripts. `gcnvkernel` additionally provides general-purpose -inference schemas built on the top of `PyMC3` and `theano`. +inference schemas built on the top of `PyMC` and `pytensor`. The module is organized as follows:: @@ -20,7 +20,7 @@ The module is organized as follows:: genomic intervals, read count data, global and sample-specific posteriors, and sample metadata. - `gcnvkernel.models`: `PyMC3` model declarations, `theano` symbolic operations (e.g. + `gcnvkernel.models`: `PyMC` model declarations, `pytensor` symbolic operations (e.g. forward-backward algorithm for HMMs), and custom probability distributions. `gcnvkernel.preprocess`: Routines for filtering interval lists. diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/__init__.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/__init__.py index 1b76f1d4adf..1cf10633a44 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/__init__.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/__init__.py @@ -1,4 +1,4 @@ -from pymc3 import __version__ as pymc3_version +from pymc import __version__ as pymc_version from ._version import __version__ from .io import io_commons, io_consts, io_ploidy, io_denoising_calling, \ @@ -26,6 +26,6 @@ from .tasks.inference_task_base import ConvergenceError from .utils import cli_commons, math -assert pymc3_version == "3.1", "gcnvkernel currently only supports PyMC3 3.1; version found: {0}; " \ - "please upgrade or downgrade the PyMC3 module in your python environment " \ - "accordingly.".format(pymc3_version) +assert pymc_version == "5.10.1", "gcnvkernel currently only supports PyMC 5.10.1; version found: {0}; " \ + "please upgrade or downgrade the PyMC module in your python environment " \ + "accordingly.".format(pymc_version) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/_version.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/_version.py index 376df7ca596..e46aee1fb54 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/_version.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/_version.py @@ -1 +1 @@ -__version__ = '0.8' +__version__ = '0.9' diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/config.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/config.py index 2afda1e8975..3f1ab955e91 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/config.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/config.py @@ -1,4 +1,4 @@ -# let theano share memory workspace on large tensors with numpy +# let pytensor share memory workspace on large tensors with numpy borrow_numpy = True # if a normalized PMF violates total probability by the following threshold, it will diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/convergence_tracker.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/convergence_tracker.py index a5e94592ef3..6bd1663b945 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/convergence_tracker.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/convergence_tracker.py @@ -1,5 +1,5 @@ import numpy as np -from pymc3.variational.callbacks import Callback +from pymc.variational.callbacks import Callback from ..utils.rls import NonStationaryLinearRegression diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/deterministic_annealing.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/deterministic_annealing.py index e6524ac3dd2..8e7a901412d 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/deterministic_annealing.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/deterministic_annealing.py @@ -1,4 +1,4 @@ -import pymc3 as pm +import pymc as pm from .. import types Operator = pm.operators.Operator @@ -14,16 +14,15 @@ def __init__(self, """Initializer. Args: - approx: an instance of PyMC3 approximation - temperature: a scalar shared theano tensor variable + approx: an instance of PyMC approximation + temperature: a scalar shared pytensor tensor variable """ super().__init__(approx) assert temperature is not None self.temperature = temperature def apply(self, f): - z = self.input - return self.temperature * self.logq_norm(z) - self.logp_norm(z) + return (self.temperature * self.logq_norm - self.logp_norm)[0] class ADVIDeterministicAnnealing(Inference): @@ -32,7 +31,7 @@ class ADVIDeterministicAnnealing(Inference): Note: Temperature is not updated automatically by this class. This task is delegated to the ADVI step function. This can be done by including a temperature update in `more_updates`; refer to - `pymc3.opvi.ObjectiveFunction.step_function` for more information. + `pymc.opvi.ObjectiveFunction.step_function` for more information. """ def __init__(self, @@ -40,16 +39,16 @@ def __init__(self, model=None, cost_part_grad_scale=1, scale_cost_to_minibatch=False, - random_seed=None, start=None, + random_seed=None, + start=None, temperature=None): - assert temperature is not None, "Temperature (a scalar theano shared tensor) is not provided" + assert temperature is not None, "Temperature (a scalar pytensor shared tensor) is not provided" + approx = MeanField(local_rv=local_rv, + model=model, + cost_part_grad_scale=cost_part_grad_scale, + scale_cost_to_minibatch=scale_cost_to_minibatch, + random_seed=random_seed, + start=start) super().__init__( - KLThermal, MeanField, None, - local_rv=local_rv, - model=model, - cost_part_grad_scale=cost_part_grad_scale, - scale_cost_to_minibatch=scale_cost_to_minibatch, - random_seed=random_seed, - start=start, - op_kwargs={'temperature': temperature}) + KLThermal, approx, None, temperature=temperature) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/fancy_optimizers.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/fancy_optimizers.py index 5eb5b733694..9eff78cbc76 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/fancy_optimizers.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/fancy_optimizers.py @@ -4,10 +4,10 @@ from typing import List import numpy as np -import pymc3 as pm -import theano as th -import theano.tensor as tt -from pymc3.variational.updates import get_or_compute_grads +import pymc as pm +import pytensor +import pytensor.tensor as pt +from pymc.variational.updates import get_or_compute_grads from .. import types from ..io import io_commons @@ -28,12 +28,12 @@ def get_optimizer(self, """ Args: - model: a generalized continuous PyMC3 model - approx: an instance of PyMC3 mean-field approximation + model: a generalized continuous PyMC model + approx: an instance of PyMC mean-field approximation Returns: A callable function that upon providing `loss_or_grads` and `params`, returns an - `OrderedDict` of shared theano tensor updates (for example, see `FancyAdamax.get_optimizer`). + `OrderedDict` of shared pytensor tensor updates (for example, see `FancyAdamax.get_optimizer`). """ raise NotImplementedError @@ -139,7 +139,7 @@ def structured_adamax(loss_or_grads=None, Returns: returns the function itself if `loss_or_grads` and `params` are not given; - otherwise, returns an ordered dict of shared tensor updates (to be used in pymc3 for compiling + otherwise, returns an ordered dict of shared tensor updates (to be used in pymc for compiling the step function) """ if loss_or_grads is None and params is None: @@ -160,16 +160,16 @@ def structured_adamax(loss_or_grads=None, for vmap in vmap_list: if vmap.var in model.sample_specific_var_registry: sample_specific_indices += [idx for idx in range(vmap.slc.start, vmap.slc.stop)] - update_indices = th.shared(np.asarray(sample_specific_indices, dtype=np.int)) + update_indices = pytensor.shared(np.asarray(sample_specific_indices, dtype=int)) num_dof = len(sample_specific_indices) - # Using theano constant to prevent upcasting of float32 - one = tt.constant(1) + # Using pytensor constant to prevent upcasting of float32 + one = pt.constant(1) if disable_bias_correction: a_t = learning_rate else: - res_prev = th.shared(pm.theanof.floatX(beta1)) + res_prev = pytensor.shared(pm.pytensorf.floatX(beta1)) res = beta1 * res_prev a_t = learning_rate / (one - res) updates[res_prev] = res @@ -179,17 +179,17 @@ def structured_adamax(loss_or_grads=None, for param, g_t in zip(params, all_grads): if sample_specific_only: g_t_view = g_t[update_indices] - m_prev = th.shared(np.zeros((num_dof,), dtype=types.floatX), - broadcastable=(False,)) - u_prev = th.shared(np.zeros((num_dof,), dtype=types.floatX), - broadcastable=(False,)) + m_prev = pytensor.shared(np.zeros((num_dof,), dtype=types.floatX), + broadcastable=(False,)) + u_prev = pytensor.shared(np.zeros((num_dof,), dtype=types.floatX), + broadcastable=(False,)) else: g_t_view = g_t value = param.get_value(borrow=True) - m_prev = th.shared(np.zeros(value.shape, dtype=types.floatX), - broadcastable=(False,)) - u_prev = th.shared(np.zeros(value.shape, dtype=types.floatX), - broadcastable=(False,)) + m_prev = pytensor.shared(np.zeros(value.shape, dtype=types.floatX), + broadcastable=(False,)) + u_prev = pytensor.shared(np.zeros(value.shape, dtype=types.floatX), + broadcastable=(False,)) # save a reference to m and u in the base class if base_class is not None: @@ -197,11 +197,11 @@ def structured_adamax(loss_or_grads=None, base_class.u_tensors.append(u_prev) m_t = beta1 * m_prev + (one - beta1) * g_t_view - u_t = tt.maximum(beta2 * u_prev, abs(g_t_view)) + u_t = pt.maximum(beta2 * u_prev, abs(g_t_view)) step = a_t * m_t / (u_t + epsilon) if sample_specific_only: - new_param = tt.inc_subtensor(param[update_indices], -step) + new_param = pt.inc_subtensor(param[update_indices], -step) else: new_param = param - step diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/param_tracker.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/param_tracker.py index c3dc0b83a0e..594e8ea3ffb 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/param_tracker.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/inference/param_tracker.py @@ -1,5 +1,5 @@ import numpy as np -import pymc3 as pm +import pymc as pm from typing import Callable, Dict from ..io import io_commons diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_commons.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_commons.py index bb8a407a878..5ff7e975353 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_commons.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_commons.py @@ -6,9 +6,10 @@ import pandas as pd from ast import literal_eval as make_tuple from typing import List, Optional, Tuple, Set, Dict +import collections import numpy as np -import pymc3 as pm +import pymc as pm from . import io_consts from .._version import __version__ as gcnvkernel_version @@ -17,6 +18,10 @@ _logger = logging.getLogger(__name__) +# originally in pymc3.blocking in PyMC3 3.5 +VarMap = collections.namedtuple('VarMap', 'var, slc, shp, dtyp') + + def read_csv(input_file: str, dtypes_dict: Dict[str, object] = None, mandatory_columns_set: Set[str] = None, @@ -268,30 +273,36 @@ def read_ndarray_from_tsv(input_file: str, return df.values.reshape(shape) -def get_var_map_list_from_mean_field_approx(approx: pm.MeanField) -> List[pm.blocking.VarMap]: - """Extracts the variable-to-linear-array of a PyMC3 mean-field approximation. +def get_var_map_list_from_mean_field_approx(approx: pm.MeanField) -> List[VarMap]: + """Extracts the variable-to-linear-array of a PyMC mean-field approximation. Args: - approx: an instance of PyMC3 mean-field approximation + approx: an instance of PyMC mean-field approximation Returns: - A list of `pymc3.blocking.VarMap` + A list of VarMap """ - if pm.__version__ == "3.1": - return approx.gbij.ordering.vmap - elif pm.__version__ == "3.2": - return approx.bij.ordering.vmap - else: - raise Exception("Unsupported PyMC3 version") + # Originally, with PyMC3 3.5, this simply returned a List[pymc3.blocking.VarMap]: + # return approx.bij.ordering.vmap + # However, changes were made to the API and the VarMap class was obviated by the use of Xarray, see: + # https://discourse.pymc.io/t/how-to-get-named-means-and-sds-from-advi-fit/11073 + # Unfortunately, this new functionality appears to be somewhat brittle and yields an error in our use case. + # We instead bring the old VarMap class into this module and recreate the old functionality to + # preserve our preexisting interfaces. + var_map_list = [] + for var, slc, shp, dtyp in approx.ordering.values(): + var_map_list.append(VarMap(var, slc, shp, dtyp)) + return var_map_list + def extract_mean_field_posterior_parameters(approx: pm.MeanField) \ -> Tuple[Set[str], Dict[str, np.ndarray], Dict[str, np.ndarray]]: """Extracts mean-field posterior parameters in the right shape and dtype from an instance - of PyMC3 mean-field approximation. + of PyMC mean-field approximation. Args: - approx: an instance of PyMC3 mean-field approximation + approx: an instance of PyMC mean-field approximation Returns: A tuple (set of variable names, @@ -373,7 +384,7 @@ def write_mean_field_sample_specific_params(sample_index: int, approx_std_map: Dict[str, np.ndarray], model: GeneralizedContinuousModel, extra_comment_lines: Optional[List[str]] = None): - """Writes sample-specific parameters contained in an instance of PyMC3 mean-field approximation + """Writes sample-specific parameters contained in an instance of PyMC mean-field approximation to disk. Args: @@ -405,11 +416,11 @@ def write_mean_field_sample_specific_params(sample_index: int, def write_mean_field_global_params(output_path: str, approx: pm.MeanField, model: GeneralizedContinuousModel): - """Writes global parameters contained in an instance of PyMC3 mean-field approximation to disk. + """Writes global parameters contained in an instance of PyMC mean-field approximation to disk. Args: output_path: output path (must be writable) - approx: an instance of PyMC3 mean-field approximation + approx: an instance of PyMC mean-field approximation model: the generalized model corresponding to the provided mean-field approximation """ # parse mean-field posterior parameters @@ -437,7 +448,7 @@ def read_mean_field_global_params(input_model_path: str, Args: input_model_path: input model path - approx: an instance of PyMC3 mean-field approximation to be updated + approx: an instance of PyMC mean-field approximation to be updated model: the generalized model corresponding to the provided mean-field approximation and the saved instance """ @@ -460,7 +471,7 @@ def _update_param_inplace(param, slc, dtype, new_value): var_mu = read_ndarray_from_tsv(var_mu_input_file) var_std = read_ndarray_from_tsv(var_std_input_file) - # convert std to rho, see pymc3.dist_math.sd2rho + # convert std to rho, see pymc.dist_math.sd2rho var_rho = np.log(np.exp(var_std) - 1) del var_std @@ -491,7 +502,7 @@ def read_mean_field_sample_specific_params(input_sample_calls_path: str, sample_index: index of the sample in the current instance of model/approximation sample_name: name of the sample in the current instance of model/approximation (used to check whether `input_sample_calls_path` actually corresponds to the sample) - approx: an instance of PyMC3 mean-field approximation corresponding to the provided model + approx: an instance of PyMC mean-field approximation corresponding to the provided model model: the generalized model corresponding to the provided mean-field approximation Returns: @@ -548,7 +559,7 @@ def _update_param_inplace(_param: np.ndarray, var_mu = read_ndarray_from_tsv(var_mu_input_file) var_std = read_ndarray_from_tsv(var_std_input_file) - # convert std to rho, see pymc3.dist_math.sd2rho + # convert std to rho, see pymc.dist_math.sd2rho var_rho = np.log(np.exp(var_std) - 1) del var_std diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_denoising_calling.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_denoising_calling.py index d0f9d24f59c..416c0c9c93d 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_denoising_calling.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_denoising_calling.py @@ -3,7 +3,7 @@ from typing import List, Optional import numpy as np -import pymc3 as pm +import pymc as pm from . import io_commons from . import io_consts @@ -155,9 +155,9 @@ def __call__(self): # compute approximate denoised copy ratios _logger.info("Sampling and approximating posteriors for denoised copy ratios...") denoising_copy_ratios_st_approx_generator = commons.get_sampling_generator_for_model_approximation( - model_approx=self.denoising_model_approx, node=self.denoising_model['denoised_copy_ratio_st'], + approx=self.denoising_model_approx, node=self.denoising_model['denoised_copy_ratio_st'], num_samples=self.denoising_config.num_samples_copy_ratio_approx) - mu_denoised_copy_ratio_st, var_denoised_copy_ratio_st =\ + mu_denoised_copy_ratio_st, var_denoised_copy_ratio_st = \ math.calculate_mean_and_variance_online(denoising_copy_ratios_st_approx_generator) std_denoised_copy_ratio_st = np.sqrt(var_denoised_copy_ratio_st) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_ploidy.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_ploidy.py index 1acc4a43c61..49a850733d5 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_ploidy.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/io/io_ploidy.py @@ -3,7 +3,7 @@ from typing import List, Dict import numpy as np -import pymc3 as pm +import pymc as pm from . import io_commons from . import io_consts diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/commons.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/commons.py index 01f91a3fc36..5506cd3b107 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/commons.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/commons.py @@ -1,9 +1,12 @@ import numpy as np import logging -import theano.tensor as tt -import pymc3 as pm -import theano as th -import pymc3.distributions.dist_math as pm_dist_math +import pytensor +import pytensor.tensor as pt +from pytensor.graph.replace import graph_replace +import pymc as pm +import pymc.distributions.dist_math as pm_dist_math +from pymc.util import makeiter +from pymc.variational.opvi import _known_scan_ignored_inputs from typing import Tuple, Generator _logger = logging.getLogger(__name__) @@ -51,7 +54,7 @@ def negative_binomial_logp(mu, alpha, value): Returns: symbolic negative binomial logp """ - return pm_dist_math.bound(pm_dist_math.binomln(value + alpha - 1, value) + return pm_dist_math.check_parameters(pm_dist_math.binomln(value + alpha - 1, value) + pm_dist_math.logpow(mu / (mu + alpha), value) + pm_dist_math.logpow(alpha / (mu + alpha), alpha), mu > 0, value >= 0, alpha > 0) @@ -72,7 +75,7 @@ def negative_binomial_gaussian_approx_logp(mu, alpha, value): symbolic approximate negative binomial logp """ tau = alpha / (mu * (alpha + mu)) # precision - return pm_dist_math.bound(0.5 * (tt.log(tau) - _log_2_pi - tau * tt.square(value - mu)), + return pm_dist_math.check_parameters(0.5 * (pt.log(tau) - _log_2_pi - tau * pt.square(value - mu)), mu > 0, value >= 0, alpha > 0) @@ -107,8 +110,8 @@ def centered_heavy_tail_logp(mu, value): Returns: symbolic logp """ - return pm_dist_math.bound(tt.log(1.0 + 2.0 * mu) + 2.0 * mu * tt.log(value) - - 2.0 * (1.0 + mu) * tt.log(1.0 + value), + return pm_dist_math.check_parameters(pt.log(1.0 + 2.0 * mu) + 2.0 * mu * pt.log(value) + - 2.0 * (1.0 + mu) * pt.log(1.0 + value), mu >= 0, value > 0) @@ -120,10 +123,10 @@ def safe_logaddexp(a, b): symbolic log(exp(a) + exp(b)) """ diff = b - a - safe_diff = tt.switch(tt.isnan(diff), 0, diff) - return tt.switch(safe_diff >= 0, - b + tt.log1p(tt.exp(-safe_diff)), - a + tt.log1p(tt.exp(safe_diff))) + safe_diff = pt.switch(pt.isnan(diff), 0, diff) + return pt.switch(safe_diff >= 0, + b + pt.log1p(pt.exp(-safe_diff)), + a + pt.log1p(pt.exp(safe_diff))) def get_jensen_shannon_divergence(log_p_1, log_p_2): @@ -136,12 +139,12 @@ def get_jensen_shannon_divergence(log_p_1, log_p_2): Returns: Symbolic Jensen-Shannon distance """ - p_1 = tt.exp(log_p_1) - p_2 = tt.exp(log_p_2) + p_1 = pt.exp(log_p_1) + p_2 = pt.exp(log_p_2) diff_12 = p_1 - p_2 log_diff_12 = log_p_1 - log_p_2 - safe_log_diff_12 = tt.switch(tt.isnan(log_diff_12), 0, log_diff_12) - return 0.5 * tt.sum(diff_12 * safe_log_diff_12, axis=-1) + safe_log_diff_12 = pt.switch(pt.isnan(log_diff_12), 0, log_diff_12) + return 0.5 * pt.sum(diff_12 * safe_log_diff_12, axis=-1) def get_hellinger_distance(log_p_1, log_p_2): @@ -154,9 +157,9 @@ def get_hellinger_distance(log_p_1, log_p_2): Returns: Symbolic Hellinger distance """ - p_1 = tt.exp(log_p_1) - p_2 = tt.exp(log_p_2) - return tt.sqrt(tt.sum(tt.square(tt.sqrt(p_1) - tt.sqrt(p_2)), axis=-1)) / tt.sqrt(2) + p_1 = pt.exp(log_p_1) + p_2 = pt.exp(log_p_2) + return pt.sqrt(pt.sum(pt.square(pt.sqrt(p_1) - pt.sqrt(p_2)), axis=-1)) / pt.sqrt(2) def perform_genotyping(log_p: np.ndarray) -> Tuple[int, float]: @@ -179,67 +182,66 @@ def perform_genotyping(log_p: np.ndarray) -> Tuple[int, float]: phred_genotype_quality = _10_inv_log_10 * (sorted_log_p[0][1] - sorted_log_p[1][1]) return max_likely_genotype_idx, phred_genotype_quality +# PyMC/pytensor logsumexp doesn't include the stability trick, so we port the PyMC3/theano version here for consistency +def logsumexp(x, axis=None): + # Adapted from https://github.com/Theano/Theano/issues/1563 + x_max = pt.max(x, axis=axis, keepdims=True) + return pt.log(pt.sum(pt.exp(x - x_max), axis=axis, keepdims=True)) + x_max -def stochastic_node_mean_symbolic(approx: pm.MeanField, node, size=100, - more_replacements=None, shape=None, dtype=None): +def stochastic_node_mean_symbolic(approx: pm.MeanField, node, size=100): """Symbolic mean of a given PyMC3 stochastic node with respect to a given variational posterior approximation. - Args: approx: an instance of PyMC3 approximation node: stochastic node size: the number of samples to use for calculating the mean - more_replacements: (optional) an ordered dictionary of node replacements to be - applied to the computational graph before sampling - shape: (optional) shape of the node - dtype: (optional) dtype of the node - - Raises: - ValueError: If `node.tag.test_value` is not present and `shape` and `dtype` are - not provided - Returns: Symbolic approximate mean of the stochastic node """ assert size > 0 - if shape is not None and dtype is not None: - cum_sum = tt.zeros(shape, dtype) - elif hasattr(node.tag, 'test_value') and node.tag.test_value is not None: - cum_sum = tt.zeros(node.tag.test_value.shape, node.tag.test_value.dtype) - else: - raise ValueError("Can not determine the shape of the node to be sampled") - - if more_replacements is not None: - node = th.clone(node, more_replacements, strict=False) - posterior_samples = approx.random(size) + cum_sum = pt.zeros(node.shape, node.dtype) + # see Approximation.sample_node() + node = approx.model.replace_rvs_by_values([node]) + node = node[0] node = approx.to_flat_input(node) + posterior_samples = approx.symbolic_random.astype(approx.symbolic_initial.dtype) + posterior_samples = pt.specify_shape(posterior_samples, approx.symbolic_initial.type.shape) + posterior_samples = approx.set_size_and_deterministic(posterior_samples, s=size, d=False) def add_sample_to_cum_sum(posterior_sample, _cum_sum): - new_sample = th.clone(node, {approx.input: posterior_sample}, strict=False) - return _cum_sum + tt.patternbroadcast(new_sample, _cum_sum.broadcastable) + new_sample = graph_replace(node, {approx.input: posterior_sample}, strict=False) + return _cum_sum + new_sample - outputs, _ = th.scan(add_sample_to_cum_sum, - sequences=[posterior_samples], - outputs_info=[cum_sum], - n_steps=size) + outputs, _ = pytensor.scan(add_sample_to_cum_sum, + sequences=posterior_samples, + non_sequences=_known_scan_ignored_inputs(makeiter(posterior_samples)), + outputs_info=cum_sum, + n_steps=size) return outputs[-1] / size - -def get_sampling_generator_for_model_approximation(model_approx: pm.MeanField, node, +def get_sampling_generator_for_model_approximation(approx: pm.MeanField, node, num_samples: int = 20) -> Generator: """Get a generator that returns samples of a precomputed model approximation for a specific variable in that model - Args: - model_approx: an instance of PyMC3 mean-field approximation + approx: an instance of PyMC3 mean-field approximation node: a stochastic node in the model num_samples: number of samples to draw - Returns: A generator that will yield `num_samples` samples from an approximation to a posterior """ - sample = model_approx.sample_node(node, size=1)[0] - return (sample.eval() for _ in range(num_samples)) + assert num_samples > 0 + + # see Approximation.sample_node() + node = approx.model.replace_rvs_by_values([node]) + node = node[0] + node_sample = approx.symbolic_sample_over_posterior(node) + node_sample = approx.set_size_and_deterministic(node_sample, s=1, d=False) + # must use compile_pymc to pass random_seed for reproducible sampling + node_sample_func = pm.pytensorf.compile_pymc(inputs=[], outputs=node_sample, + random_seed=approx.rng.randint(2**30, dtype=np.int64)) + + return (node_sample_func()[0] for _ in range(num_samples)) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/dists.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/dists.py deleted file mode 100644 index f8db4241d59..00000000000 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/dists.py +++ /dev/null @@ -1,63 +0,0 @@ -from pymc3.distributions.continuous import PositiveContinuous,\ - assert_negative_support, bound, gammaln, get_variable_name -import theano.tensor as tt - - -class PositiveFlatTop(PositiveContinuous): - """A positive continuous two-parameter flat-top distribution with super-exponential light tail. - The pdf of this distribution is given as: - - p(x) \propto \exp(-(x/u)^k) - - Here, `u` is the soft upper bound of the distribution and `k` is the decay exponent. - In the limit `k` \rightarrow \infty, p(x) converges to the uniform distribution in [0, u]. - The case `k` = 2 corresponds to the half-normal distribution. - - """ - def __init__(self, u, k, *args, **kwargs): - """Initializer. - - Args: - u: soft upper bound of the distribution (positive) - k: decay exponent (positive) - """ - super(PositiveFlatTop, self).__init__(*args, **kwargs) - self.u = u = tt.as_tensor_variable(u) - self.k = k = tt.as_tensor_variable(k) - - assert_negative_support(u, 'u', 'PositiveFlatTop') - assert_negative_support(k, 'k', 'PositiveFlatTop') - - def random(self, point=None, size=None, repeat=None): - raise NotImplementedError - - def logp(self, value): - u = self.u - k = self.k - logp = -tt.pow(value / u, k) - tt.log(u) - gammaln(1 + tt.inv(k)) - return bound(logp, value > 0, u > 0, k > 0) - - def _repr_latex_(self, name=None, dist=None): - if dist is None: - dist = self - u = dist.u - k = dist.k - return r'${} \sim \text{{FlatTop}}(\mathit{{u}}={0}, \mathit{{k}}={1})$'.format( - name, get_variable_name(u), get_variable_name(k)) - - -class HalfFlat(PositiveContinuous): - """Improper flat prior over the positive reals.""" - - def __init__(self, *args, **kwargs): - self._default = 1 - super(HalfFlat, self).__init__(defaults=('_default',), *args, **kwargs) - - def random(self, point=None, size=None, repeat=None): - raise ValueError('Cannot sample from HalfFlat distribution') - - def logp(self, value): - return bound(tt.zeros_like(value), value > 0) - - def _repr_latex_(self, name=None, dist=None): - return r'${} \sim \text{{HalfFlat}}()$'.format(name) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/fancy_model.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/fancy_model.py index fe5bf80c006..ec6d1a86829 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/fancy_model.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/fancy_model.py @@ -1,14 +1,14 @@ -import pymc3 as pm +import pymc as pm import logging -from pymc3 import Model +from pymc import Model from typing import Optional, Set, Dict _logger = logging.getLogger(__name__) class GeneralizedContinuousModel(Model): - """An extension of PyMC3 `Model` class with the added functionality of labeling RVs + """An extension of PyMC `Model` class with the added functionality of labeling RVs as either global or sample-specific (for the purpose of I/O, and structured optimization). """ def __init__(self): @@ -17,12 +17,9 @@ def __init__(self): self.sample_specific_var_registry: Dict[str, int] = dict() super().__init__() - @staticmethod - def _get_var_name(var) -> str: + def _get_var_name(self, var) -> str: assert hasattr(var, 'name') - name = var.name - if hasattr(var, 'transformed'): - name = var.transformed.name + name = self.rvs_to_values[var].name return name def _assert_var_is_unannotated(self, var): @@ -36,7 +33,7 @@ def register_as_global(self, var): """Register a variable as global. Args: - var: a PyMC3 free variable + var: a PyMC free variable Returns: None @@ -49,7 +46,7 @@ def register_as_sample_specific(self, var, sample_axis: int): """Register a variable as sample-specific. Args: - var: a PyMC3 free variable + var: a PyMC free variable sample_axis: axis corresponding to sample index (it is used for slicing `var` to obtain single-sample parameters) @@ -64,7 +61,7 @@ def verify_var_registry(self): """Verifies that all variables are registered as either as global or sample-specific.""" _logger.info("Global model variables: " + str(self.global_var_registry)) _logger.info("Sample-specific model variables: " + str(set(self.sample_specific_var_registry.keys()))) - model_var_set = {self._get_var_name(var) for var in self.vars} + model_var_set = {self._get_var_name(var) for var in self.free_RVs} unannotated_vars = model_var_set \ .difference(self.global_var_registry) \ .difference(set(self.sample_specific_var_registry.keys())) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_denoising_calling.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_denoising_calling.py index 63ef4f9e86f..6aa7d127575 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_denoising_calling.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_denoising_calling.py @@ -7,17 +7,17 @@ from typing import List, Tuple, Set, Dict, Optional import numpy as np -import pymc3 as pm +import pymc as pm import scipy.sparse as sp -import theano as th -import theano.sparse as tst -import theano.tensor as tt -from pymc3 import Normal, Deterministic, DensityDist, Lognormal, Exponential +import pytensor +import pytensor.sparse as pst +import pytensor.tensor as pt +from pytensor.tensor.shape import unbroadcast +from pymc import Normal, Deterministic, Potential, Lognormal, Exponential, TruncatedNormal, Uniform from . import commons -from .dists import HalfFlat from .fancy_model import GeneralizedContinuousModel -from .theano_hmm import TheanoForwardBackward +from .pytensor_hmm import PytensorForwardBackward from .. import config, types from ..structs.interval import Interval, GCContentAnnotation from ..structs.metadata import SampleMetadataCollection @@ -26,6 +26,7 @@ _logger = logging.getLogger(__name__) _eps = commons.eps +_ard_upper_bound = 1E10 class DenoisingModelConfig: @@ -330,7 +331,7 @@ def initialize_posterior(denoising_config: DenoisingModelConfig, # interval class log posterior probs class_probs_k = np.asarray([1.0 - calling_config.p_active, calling_config.p_active], dtype=types.floatX) log_q_tau_tk = np.tile(np.log(class_probs_k), (shared_workspace.num_intervals, 1)) - shared_workspace.log_q_tau_tk = th.shared(log_q_tau_tk, name="log_q_tau_tk", borrow=config.borrow_numpy) + shared_workspace.log_q_tau_tk = pytensor.shared(log_q_tau_tk, name="log_q_tau_tk", borrow=config.borrow_numpy) # copy number log posterior probs log_q_c_stc = np.zeros((shared_workspace.num_samples, shared_workspace.num_intervals, @@ -345,11 +346,11 @@ def initialize_posterior(denoising_config: DenoisingModelConfig, sample_log_pi_jc = np.log(np.sum(sample_pi_jkc * class_probs_k[np.newaxis, :, np.newaxis], axis=1)) for ti in range(shared_workspace.num_intervals): log_q_c_stc[si, ti, :] = sample_log_pi_jc[t_to_j_map[ti], :] - shared_workspace.log_q_c_stc = th.shared(log_q_c_stc, name="log_q_c_stc", borrow=config.borrow_numpy) + shared_workspace.log_q_c_stc = pytensor.shared(log_q_c_stc, name="log_q_c_stc", borrow=config.borrow_numpy) class DenoisingCallingWorkspace: - """This class contains objects (numpy arrays, theano tensors, etc) shared between the denoising model + """This class contains objects (numpy arrays, pytensor tensors, etc) shared between the denoising model and the copy number caller.""" def __init__(self, denoising_config: DenoisingModelConfig, @@ -381,7 +382,7 @@ def __init__(self, contig_to_j_map = {contig: self.contig_list.index(contig) for contig in self.contig_list} t_to_j_map = np.asarray([contig_to_j_map[interval.contig] for interval in interval_list], dtype=types.small_uint) - self.t_to_j_map: types.TensorSharedVariable = th.shared( + self.t_to_j_map: types.TensorSharedVariable = pytensor.shared( t_to_j_map, name="t_to_j_map", borrow=config.borrow_numpy) self.global_read_depth_s, average_ploidy_s, self.baseline_copy_number_sj = \ @@ -393,22 +394,23 @@ def __init__(self, "The highest contig ploidy ({0}) must be smaller or equal to the highest copy number state ({1})".format( max_baseline_copy_number, calling_config.max_copy_number) - # shared theano tensors from the input data - self.n_st: types.TensorSharedVariable = th.shared( + # shared pytensor tensors from the input data + self.n_st: types.TensorSharedVariable = pytensor.shared( n_st.astype(types.med_uint), name="n_st", borrow=config.borrow_numpy) - self.average_ploidy_s: types.TensorSharedVariable = th.shared( + self.average_ploidy_s: types.TensorSharedVariable = pytensor.shared( average_ploidy_s.astype(types.floatX), name="average_ploidy_s", borrow=config.borrow_numpy) # copy-number event stay probability self.dist_t = np.asarray([self.interval_list[ti + 1].distance(self.interval_list[ti]) for ti in range(self.num_intervals - 1)]) cnv_stay_prob_t = np.exp(-self.dist_t / calling_config.cnv_coherence_length) - self.cnv_stay_prob_t = th.shared(cnv_stay_prob_t, name='cnv_stay_prob_t', borrow=config.borrow_numpy) + self.cnv_stay_prob_t = pytensor.shared(cnv_stay_prob_t, name='cnv_stay_prob_t', borrow=config.borrow_numpy) # copy number values for each copy number state copy_number_values_c = np.arange(0, calling_config.num_copy_number_states, dtype=types.small_uint) - self.copy_number_values_c = th.shared(copy_number_values_c, name='copy_number_values_c', - borrow=config.borrow_numpy) + self.copy_number_values_c = pytensor.shared(copy_number_values_c, name='copy_number_values_c', + borrow=config.borrow_numpy) + # copy number log posterior and derived quantities (to be initialized by `PosteriorInitializer`) self.log_q_c_stc: Optional[types.TensorSharedVariable] = None @@ -424,7 +426,7 @@ def __init__(self, # copy number emission log posterior log_copy_number_emission_stc = np.zeros( (self.num_samples, self.num_intervals, calling_config.num_copy_number_states), dtype=types.floatX) - self.log_copy_number_emission_stc: types.TensorSharedVariable = th.shared( + self.log_copy_number_emission_stc: types.TensorSharedVariable = pytensor.shared( log_copy_number_emission_stc, name="log_copy_number_emission_stc", borrow=config.borrow_numpy) # class log posterior (to be initialized by `PosteriorInitializer`) @@ -448,7 +450,7 @@ def __init__(self, # GC bias factors # (to be initialized by calling `initialize_bias_inference_vars`) - self.W_gc_tg: Optional[tst.SparseConstant] = None + self.W_gc_tg: Optional[pst.SparseConstant] = None # auxiliary data structures for hybrid q_c_expectation_mode calculation # (to be initialized by calling `initialize_bias_inference_vars`) @@ -456,7 +458,7 @@ def __init__(self, # denoised copy ratios denoised_copy_ratio_st = np.zeros((self.num_samples, self.num_intervals), dtype=types.floatX) - self.denoised_copy_ratio_st: types.TensorSharedVariable = th.shared( + self.denoised_copy_ratio_st: types.TensorSharedVariable = pytensor.shared( denoised_copy_ratio_st, name="denoised_copy_ratio_st", borrow=config.borrow_numpy) # initialize posterior @@ -475,7 +477,7 @@ def initialize_copy_number_class_inference_vars(self): # class emission log posterior log_class_emission_tk = np.zeros( (self.num_intervals, self.calling_config.num_copy_number_classes), dtype=types.floatX) - self.log_class_emission_tk: types.TensorSharedVariable = th.shared( + self.log_class_emission_tk: types.TensorSharedVariable = pytensor.shared( log_class_emission_tk, name="log_class_emission_tk", borrow=True) # class assignment prior probabilities @@ -484,7 +486,7 @@ def initialize_copy_number_class_inference_vars(self): # The second class is a CNV-active class (all copy number states are equally probable) class_probs_k = np.asarray([1.0 - self.calling_config.p_active, self.calling_config.p_active], dtype=types.floatX) - self.class_probs_k: types.TensorSharedVariable = th.shared( + self.class_probs_k: types.TensorSharedVariable = pytensor.shared( class_probs_k, name='class_probs_k', borrow=config.borrow_numpy) # class Markov chain log prior (initialized here and remains constant throughout) @@ -515,7 +517,7 @@ def update_auxiliary_vars(self): # MAP copy number call if self.c_map_st is None: c_map_st = np.zeros((self.num_samples, self.num_intervals), dtype=types.small_uint) - self.c_map_st = th.shared(c_map_st, name="c_map_st", borrow=config.borrow_numpy) + self.c_map_st = pytensor.shared(c_map_st, name="c_map_st", borrow=config.borrow_numpy) self.c_map_st.set_value( np.argmax(self.log_q_c_stc.get_value(borrow=True), axis=2).astype(types.small_uint), borrow=config.borrow_numpy) @@ -524,7 +526,7 @@ def update_auxiliary_vars(self): _logger.debug("Updating CNV-active class bitmask...") if self.active_class_bitmask_t is None: active_class_bitmask_t = np.zeros((self.num_intervals,), dtype=bool) - self.active_class_bitmask_t = th.shared( + self.active_class_bitmask_t = pytensor.shared( active_class_bitmask_t, name="active_class_bitmask_t", borrow=config.borrow_numpy) # bitmask for intervals of which the probability of being in the silent class is below 0.5 @@ -590,8 +592,8 @@ def _get_log_trans_tkk(dist_t: np.ndarray, return np.log(trans_tkl) @staticmethod - def _create_sparse_gc_bin_tensor_tg(interval_list: List[Interval], num_gc_bins: int) -> tst.SparseConstant: - """Creates a sparse 2d theano tensor with shape (num_intervals, gc_bin). The sparse + def _create_sparse_gc_bin_tensor_tg(interval_list: List[Interval], num_gc_bins: int) -> pst.SparseConstant: + """Creates a sparse 2d pytensor tensor with shape (num_intervals, gc_bin). The sparse tensor represents a 1-hot mapping of each interval to its GC bin index. The range [0, 1] is uniformly divided into num_gc_bins. """ @@ -609,8 +611,8 @@ def get_gc_bin_idx(gc_content): indptr = np.arange(0, num_intervals + 1) scipy_gc_matrix = sp.csr_matrix((data, indices, indptr), shape=(num_intervals, num_gc_bins), dtype=types.small_uint) - theano_gc_matrix: tst.SparseConstant = tst.as_sparse(scipy_gc_matrix) - return theano_gc_matrix + pytensor_gc_matrix: pst.SparseConstant = pst.as_sparse(scipy_gc_matrix) + return pytensor_gc_matrix @staticmethod def _get_baseline_copy_number_and_read_depth(sample_metadata_collection: SampleMetadataCollection, @@ -700,8 +702,10 @@ def get_init_ard_u(self) -> np.ndarray: class DenoisingModel(GeneralizedContinuousModel): + # PR #8561 some broadcastable arguments in the distributions below in PyMC3 were removed """The gCNV coverage denoising model declaration (continuous RVs only; discrete posteriors are assumed to be given).""" + @pytensor.config.change_flags(compute_test_value="off") def __init__(self, denoising_model_config: DenoisingModelConfig, shared_workspace: DenoisingCallingWorkspace, @@ -715,25 +719,22 @@ def __init__(self, # interval-specific unexplained variance psi_t = Exponential(name='psi_t', lam=1.0 / denoising_model_config.psi_t_scale, - shape=(shared_workspace.num_intervals,), - broadcastable=(False,)) + shape=(shared_workspace.num_intervals,)) register_as_global(psi_t) # sample-specific unexplained variance psi_s = Exponential(name='psi_s', lam=1.0 / denoising_model_config.psi_s_scale, - shape=(shared_workspace.num_samples,), - broadcastable=(False,)) + shape=(shared_workspace.num_samples,)) register_as_sample_specific(psi_s, sample_axis=0) # convert "unexplained variance" to negative binomial over-dispersion - alpha_st = tt.maximum(tt.inv(tt.exp(psi_t.dimshuffle('x', 0) + psi_s.dimshuffle(0, 'x')) - 1.0), + alpha_st = pt.maximum(pt.reciprocal(pt.exp(psi_t.dimshuffle('x', 0) + psi_s.dimshuffle(0, 'x')) - 1.0), _eps) # interval-specific mean log bias - log_mean_bias_t = Normal(name='log_mean_bias_t', mu=0.0, sd=denoising_model_config.log_mean_bias_std, + log_mean_bias_t = Normal(name='log_mean_bias_t', mu=0.0, sigma=denoising_model_config.log_mean_bias_std, shape=(shared_workspace.num_intervals,), - broadcastable=(False,), - testval=initial_model_parameters_supplier.get_init_log_mean_bias_t()) + initval=initial_model_parameters_supplier.get_init_log_mean_bias_t()) register_as_global(log_mean_bias_t) # log-normal read depth centered at the global read depth @@ -743,53 +744,51 @@ def __init__(self, mu=read_depth_mu_s, tau=denoising_model_config.depth_correction_tau, shape=(shared_workspace.num_samples,), - broadcastable=(False,), - testval=shared_workspace.global_read_depth_s) + initval=shared_workspace.global_read_depth_s) register_as_sample_specific(read_depth_s, sample_axis=0) # log bias modelling, starting with the log mean bias - log_bias_st = tt.tile(log_mean_bias_t, (shared_workspace.num_samples, 1)) + log_bias_st = pt.tile(log_mean_bias_t, (shared_workspace.num_samples, 1)) if denoising_model_config.enable_bias_factors: + # PR #8651 NOTE: originally used HalfFlat in PyMC3, but this now raises + # NotImplementedError: Cannot sample from half_flat variable. + # we instead use a Uniform with a very large, unexposed upper bound # ARD prior precisions - ard_u = HalfFlat(name='ard_u', - shape=(denoising_model_config.max_bias_factors,), - broadcastable=(False,), - testval=initial_model_parameters_supplier.get_init_ard_u()) + ard_u = Uniform(name='ard_u', lower=0., upper=_ard_upper_bound, + shape=(denoising_model_config.max_bias_factors,), + initval=initial_model_parameters_supplier.get_init_ard_u()) register_as_global(ard_u) # bias factors W_tu = Normal(name='W_tu', mu=0.0, tau=ard_u.dimshuffle('x', 0), - shape=(shared_workspace.num_intervals, denoising_model_config.max_bias_factors), - broadcastable=(False, False)) + shape=(shared_workspace.num_intervals, denoising_model_config.max_bias_factors)) register_as_global(W_tu) # sample-specific bias factor loadings - z_su = Normal(name='z_su', mu=0.0, sd=1.0, - shape=(shared_workspace.num_samples, denoising_model_config.max_bias_factors), - broadcastable=(False, False)) + z_su = Normal(name='z_su', mu=0.0, sigma=1.0, + shape=(shared_workspace.num_samples, denoising_model_config.max_bias_factors)) register_as_sample_specific(z_su, sample_axis=0) # add contribution to total log bias if denoising_model_config.disable_bias_factors_in_active_class: - prob_silent_class_t = tt.exp(shared_workspace.log_q_tau_tk[:, 0]) - log_bias_st += (prob_silent_class_t.dimshuffle('x', 0) * tt.dot(W_tu, z_su.T).T) + prob_silent_class_t = pt.exp(shared_workspace.log_q_tau_tk[:, 0]) + log_bias_st += (prob_silent_class_t.dimshuffle('x', 0) * pt.dot(W_tu, z_su.T).T) else: - log_bias_st += tt.dot(W_tu, z_su.T).T + log_bias_st += pt.dot(W_tu, z_su.T).T # GC bias if denoising_model_config.enable_explicit_gc_bias_modeling: # sample-specific GC bias factor loadings - z_sg = Normal(name='z_sg', mu=0.0, sd=denoising_model_config.gc_curve_sd, - shape=(shared_workspace.num_samples, denoising_model_config.num_gc_bins), - broadcastable=(False, False)) + z_sg = Normal(name='z_sg', mu=0.0, sigma=denoising_model_config.gc_curve_sd, + shape=(shared_workspace.num_samples, denoising_model_config.num_gc_bins)) register_as_sample_specific(z_sg, sample_axis=0) # add contribution to total log bias - log_bias_st += tst.dot(shared_workspace.W_gc_tg, z_sg.T).T + log_bias_st += pst.dot(shared_workspace.W_gc_tg, z_sg.T).T # useful expressions - bias_st = tt.exp(log_bias_st) + bias_st = pt.exp(log_bias_st) # the expected number of erroneously mapped reads mean_mapping_error_correction_s = eps_mapping * read_depth_s * shared_workspace.average_ploidy_s @@ -823,8 +822,8 @@ def _copy_number_emission_logp(_n_st): alpha_st.dimshuffle(0, 1, 'x'), _n_st.dimshuffle(0, 1, 'x')) log_q_c_stc = shared_workspace.log_q_c_stc - q_c_stc = tt.exp(log_q_c_stc) - return tt.sum(q_c_stc * (_log_copy_number_emission_stc - log_q_c_stc), axis=2) + q_c_stc = pt.exp(log_q_c_stc) + return pt.sum(q_c_stc * (_log_copy_number_emission_stc - log_q_c_stc), axis=2) elif denoising_model_config.q_c_expectation_mode == 'hybrid': def _copy_number_emission_logp(_n_st): @@ -834,17 +833,17 @@ def _copy_number_emission_logp(_n_st): # for CNV-active classes, calculate exact posterior expectation mu_active_stc = ((1.0 - eps_mapping) * read_depth_s.dimshuffle(0, 'x', 'x') - * bias_st.dimshuffle(0, 1, 'x')[:, active_class_indices, :] + * bias_st[:, active_class_indices].dimshuffle(0, 1, 'x') # PR #8561 switched indexing and dimshuffle to account for case when active_class_indices has a single element * shared_workspace.copy_number_values_c.dimshuffle('x', 'x', 0) + mean_mapping_error_correction_s.dimshuffle(0, 'x', 'x')) - alpha_active_stc = tt.maximum(tt.inv((tt.exp(psi_t.dimshuffle('x', 0)[:, active_class_indices] - + psi_s.dimshuffle(0, 'x')) - 1.0)).dimshuffle(0, 1, 'x'), + alpha_active_stc = pt.maximum(pt.reciprocal((pt.exp(psi_t[active_class_indices].dimshuffle('x', 0) # PR #8561 switched indexing and dimshuffle to account for case when active_class_indices has a single element, inv to reciprocal + + psi_s.dimshuffle(0, 'x')) - 1.0)).dimshuffle(0, 1, 'x'), _eps) - n_active_stc = _n_st.dimshuffle(0, 1, 'x')[:, active_class_indices, :] + n_active_stc = _n_st[:, active_class_indices].dimshuffle(0, 1, 'x') # PR #8561 switched indexing and dimshuffle to account for case when active_class_indices has a single element active_class_logp_stc = commons.negative_binomial_logp(mu_active_stc, alpha_active_stc, n_active_stc) log_q_c_active_stc = shared_workspace.log_q_c_stc[:, active_class_indices, :] - q_c_active_stc = tt.exp(log_q_c_active_stc) - active_class_logp = tt.sum(q_c_active_stc * (active_class_logp_stc - log_q_c_active_stc)) + q_c_active_stc = pt.exp(log_q_c_active_stc) + active_class_logp = pt.sum(q_c_active_stc * (active_class_logp_stc - log_q_c_active_stc)) # for CNV-silent classes, use MAP copy number state mu_silent_st = ((1.0 - eps_mapping) * read_depth_s.dimshuffle(0, 'x') * bias_st[:, silent_class_indices] @@ -852,7 +851,7 @@ def _copy_number_emission_logp(_n_st): + mean_mapping_error_correction_s.dimshuffle(0, 'x')) alpha_silent_st = alpha_st[:, silent_class_indices] n_silent_st = _n_st[:, silent_class_indices] - silent_class_logp = tt.sum(commons.negative_binomial_logp(mu_silent_st, alpha_silent_st, n_silent_st)) + silent_class_logp = pt.sum(commons.negative_binomial_logp(mu_silent_st, alpha_silent_st, n_silent_st)) return active_class_logp + silent_class_logp @@ -862,14 +861,15 @@ def _copy_number_emission_logp(_n_st): mu_stc, alpha_st.dimshuffle(0, 1, 'x'), _n_st.dimshuffle(0, 1, 'x')) - return pm.math.logsumexp(shared_workspace.log_q_c_stc + _log_copy_number_emission_stc, axis=2) + return commons.logsumexp(shared_workspace.log_q_c_stc + _log_copy_number_emission_stc, axis=2) else: raise Exception("Unknown q_c expectation mode; an exception should have been raised earlier") - DensityDist(name='n_st_obs', - logp=_copy_number_emission_logp, - observed=shared_workspace.n_st) + # originally DensityDist in PyMC3, but this now raises an error about sampling; + # changed in https://github.com/broadinstitute/gatk/pull/8561 + Potential(name='n_st_obs', + var=_copy_number_emission_logp(shared_workspace.n_st)) class CopyNumberEmissionBasicSampler: @@ -891,7 +891,7 @@ def __init__(self, def update_approximation(self, approx: pm.approximations.MeanField): """Generates a new compiled sampler based on a given approximation. Args: - approx: an instance of PyMC3 mean-field approximation + approx: an instance of PyMC mean-field approximation Returns: None @@ -907,14 +907,16 @@ def draw(self) -> np.ndarray: assert self.is_sampler_initialized, "Posterior approximation is not provided yet" return self._simultaneous_log_copy_number_emission_sampler() - @th.configparser.change_flags(compute_test_value="off") + @pytensor.config.change_flags(compute_test_value="off") def _get_compiled_simultaneous_log_copy_number_emission_sampler(self, approx: pm.approximations.MeanField): - """For a given variational approximation, returns a compiled theano function that draws posterior samples + """For a given variational approximation, returns a compiled pytensor function that draws posterior samples from log copy number emission probabilities.""" log_copy_number_emission_stc = commons.stochastic_node_mean_symbolic( approx, self.denoising_model['log_copy_number_emission_stc'], size=self.inference_params.log_emission_samples_per_round) - return th.function(inputs=[], outputs=log_copy_number_emission_stc) + # must use compile_pymc to pass random_seed for reproducible sampling + return pm.pytensorf.compile_pymc(inputs=[], outputs=log_copy_number_emission_stc, + random_seed=approx.rng.randint(2**30, dtype=np.int64)) class HHMMClassAndCopyNumberBasicCaller: @@ -947,7 +949,7 @@ class HHMMClassAndCopyNumberBasicCaller: forward-backward algorithm for each sample (see _update_copy_number_log_posterior) - Given q(c_s), the emission probability of each copy number class (tau) is determined - (see _get_update_log_class_emission_tk_theano_func). The class prior and transition probabilities + (see _get_update_log_class_emission_tk_pytensor_func). The class prior and transition probabilities are fixed hyperparameters. Therefore, q(tau) can be updated immediately using a single run of forward-backward algorithm (see _update_class_log_posterior). """ @@ -978,10 +980,10 @@ def __init__(self, calling_config.num_copy_number_states, calling_config.p_alt, shared_workspace.baseline_copy_number_sj[si, :])[:, :, :] - self.pi_sjkc: types.TensorSharedVariable = th.shared(pi_sjkc, name='pi_sjkc', borrow=config.borrow_numpy) + self.pi_sjkc: types.TensorSharedVariable = pytensor.shared(pi_sjkc, name='pi_sjkc', borrow=config.borrow_numpy) # compiled function for forward-backward updates of copy number posterior - self._hmm_q_copy_number = TheanoForwardBackward( + self._hmm_q_copy_number = PytensorForwardBackward( log_posterior_probs_output_tc=None, resolve_nans=False, do_thermalization=True, @@ -994,7 +996,7 @@ def __init__(self, # Note: # if p_active == 0, we have to deal with inf - inf expressions properly. # setting resolve_nans = True takes care of such ambiguities. - self._hmm_q_class = TheanoForwardBackward( + self._hmm_q_class = PytensorForwardBackward( log_posterior_probs_output_tc=shared_workspace.log_q_tau_tk, resolve_nans=(calling_config.p_active == 0), do_thermalization=True, @@ -1003,13 +1005,13 @@ def __init__(self, include_alpha_beta_output=False) # compiled function for update of class log emission - self._update_log_class_emission_tk_theano_func = self._get_update_log_class_emission_tk_theano_func() + self._update_log_class_emission_tk_pytensor_func = self._get_update_log_class_emission_tk_pytensor_func() else: - self._hmm_q_class: Optional[TheanoForwardBackward] = None - self._update_log_class_emission_tk_theano_func = None + self._hmm_q_class: Optional[PytensorForwardBackward] = None + self._update_log_class_emission_tk_pytensor_func = None # compiled function for variational update of copy number HMM specs - self._get_copy_number_hmm_specs_theano_func = self.get_compiled_copy_number_hmm_specs_theano_func() + self._get_copy_number_hmm_specs_pytensor_func = self.get_compiled_copy_number_hmm_specs_pytensor_func() @staticmethod def get_copy_number_prior_for_sample_jkc(num_copy_number_states: int, @@ -1084,7 +1086,7 @@ def _run_single_sample_fb(_sample_index: int): cnv_stay_prob_t = self.shared_workspace.cnv_stay_prob_t.get_value(borrow=True) log_q_tau_tk = self.shared_workspace.log_q_tau_tk.get_value(borrow=True) t_to_j_map = self.shared_workspace.t_to_j_map.get_value(borrow=True) - hmm_spec = self._get_copy_number_hmm_specs_theano_func(pi_jkc, cnv_stay_prob_t, log_q_tau_tk, t_to_j_map) + hmm_spec = self._get_copy_number_hmm_specs_pytensor_func(pi_jkc, cnv_stay_prob_t, log_q_tau_tk, t_to_j_map) log_prior_c = hmm_spec[0] log_trans_tcc = hmm_spec[1] @@ -1132,7 +1134,7 @@ def _update_log_q_c_stc_inplace(log_q_c_stc, _sample_index, new_log_posterior_tc return copy_number_update_s, copy_number_log_likelihoods_s def _update_log_class_emission_tk(self): - self._update_log_class_emission_tk_theano_func() + self._update_log_class_emission_tk_pytensor_func() def _update_class_log_posterior(self, class_update_summary_statistic_reducer) -> Tuple[float, float]: fb_result = self._hmm_q_class.perform_forward_backward( @@ -1150,19 +1152,19 @@ def update_auxiliary_vars(self): self.shared_workspace.update_auxiliary_vars() @staticmethod - @th.configparser.change_flags(compute_test_value="off") - def get_compiled_copy_number_hmm_specs_theano_func() -> th.compile.function_module.Function: + @pytensor.config.change_flags(compute_test_value="off") + def get_compiled_copy_number_hmm_specs_pytensor_func() -> pytensor.compile.Function: """Returns a compiled function that calculates the interval-class-averaged and probability-sum-normalized log copy number transition matrix and log copy number prior for the first interval - Returned theano function inputs: + Returned pytensor function inputs: pi_jkc: a 3d tensor containing copy-number priors for each contig (j) and each class (k) cnv_stay_prob_t: probability of staying on the same copy-number state at interval `t` log_q_tau_tk: log probability of copy-number classes at interval `t` t_to_j_map: a mapping from interval indices (t) to contig indices (j); it is used to unpack `pi_jkc` to `pi_tkc` (see below) - Returned theano function outputs: + Returned pytensor function outputs: log_prior_c_first_interval: log probability of copy-number states for the first interval log_trans_tab: log transition probability matrix from interval `t` to interval `t+1` @@ -1172,38 +1174,40 @@ def get_compiled_copy_number_hmm_specs_theano_func() -> th.compile.function_modu refers to contig index. """ # shorthands - pi_jkc = tt.tensor3(name='pi_jkc') - cnv_stay_prob_t = tt.vector(name='cnv_stay_prob_t') - log_q_tau_tk = tt.matrix(name='log_q_tau_tk') - t_to_j_map = tt.vector(name='t_to_j_map', dtype=tt.scal.uint32) + pi_jkc = pt.tensor3(name='pi_jkc') + cnv_stay_prob_t = pt.vector(name='cnv_stay_prob_t') + log_q_tau_tk = pt.matrix(name='log_q_tau_tk') + t_to_j_map = pt.vector(name='t_to_j_map', dtype=pytensor.scalar.uint32) + + # log prior probability for the first interval - log_prior_c_first_interval = tt.dot(tt.log(pi_jkc[t_to_j_map[0], :, :].T), tt.exp(log_q_tau_tk[0, :])) - log_prior_c_first_interval -= pm.logsumexp(log_prior_c_first_interval) + log_prior_c_first_interval = pt.dot(pt.log(pi_jkc[t_to_j_map[0], :, :].T), pt.exp(log_q_tau_tk[0, :])) + log_prior_c_first_interval -= commons.logsumexp(log_prior_c_first_interval) # log transition matrix - cnv_not_stay_prob_t = tt.ones_like(cnv_stay_prob_t) - cnv_stay_prob_t + cnv_not_stay_prob_t = pt.ones_like(cnv_stay_prob_t) - cnv_stay_prob_t num_copy_number_states = pi_jkc.shape[2] - delta_ab = tt.eye(num_copy_number_states) + delta_ab = pt.eye(num_copy_number_states) # map contig to interval and obtain pi_tkc for the rest of the targets pi_tkc = pi_jkc[t_to_j_map[1:], :, :] # calculate normalized log transition matrix # todo use logaddexp - log_trans_tkab = tt.log(cnv_not_stay_prob_t.dimshuffle(0, 'x', 'x', 'x') * pi_tkc.dimshuffle(0, 1, 'x', 2) + log_trans_tkab = pt.log(cnv_not_stay_prob_t.dimshuffle(0, 'x', 'x', 'x') * pi_tkc.dimshuffle(0, 1, 'x', 2) + cnv_stay_prob_t.dimshuffle(0, 'x', 'x', 'x') * delta_ab.dimshuffle('x', 'x', 0, 1)) - q_tau_tkab = tt.exp(log_q_tau_tk[1:, :]).dimshuffle(0, 1, 'x', 'x') - log_trans_tab = tt.sum(q_tau_tkab * log_trans_tkab, axis=1) - log_trans_tab -= pm.logsumexp(log_trans_tab, axis=2) + q_tau_tkab = pt.exp(log_q_tau_tk[1:, :]).dimshuffle(0, 1, 'x', 'x') + log_trans_tab = pt.sum(q_tau_tkab * log_trans_tkab, axis=1) + log_trans_tab -= commons.logsumexp(log_trans_tab, axis=2) inputs = [pi_jkc, cnv_stay_prob_t, log_q_tau_tk, t_to_j_map] outputs = [log_prior_c_first_interval, log_trans_tab] - return th.function(inputs=inputs, outputs=outputs) + return pytensor.function(inputs=inputs, outputs=outputs) - @th.configparser.change_flags(compute_test_value="off") - def _get_update_log_class_emission_tk_theano_func(self) -> th.compile.function_module.Function: + @pytensor.config.change_flags(compute_test_value="off") + def _get_update_log_class_emission_tk_pytensor_func(self) -> pytensor.compile.Function: """Returns a compiled function that calculates the log interval class emission probability and directly updates `log_class_emission_tk` in the workspace. @@ -1221,23 +1225,23 @@ def _get_update_log_class_emission_tk_theano_func(self) -> th.compile.function_m """ # shorthands cnv_stay_prob_t = self.shared_workspace.cnv_stay_prob_t - q_c_stc = tt.exp(self.shared_workspace.log_q_c_stc) + q_c_stc = pt.exp(self.shared_workspace.log_q_c_stc) pi_sjkc = self.pi_sjkc t_to_j_map = self.shared_workspace.t_to_j_map num_copy_number_states = self.calling_config.num_copy_number_states # log copy number transition matrix for each class - cnv_not_stay_prob_t = tt.ones_like(cnv_stay_prob_t) - cnv_stay_prob_t - delta_ab = tt.eye(num_copy_number_states) + cnv_not_stay_prob_t = pt.ones_like(cnv_stay_prob_t) - cnv_stay_prob_t + delta_ab = pt.eye(num_copy_number_states) # calculate log class emission by reducing over samples; see below - log_class_emission_cum_sum_tk = tt.zeros((self.shared_workspace.num_intervals - 1, + log_class_emission_cum_sum_tk = pt.zeros((self.shared_workspace.num_intervals - 1, self.calling_config.num_copy_number_classes), dtype=types.floatX) - # this converts TensorType from row to matrix when number in the edge case when number of intervals is equal to 2 + # this converts TensorType from row to matrix in the edge case when number of intervals is equal to 2 # (to avoid type mismatch later on) - log_class_emission_cum_sum_tk = tt.unbroadcast(log_class_emission_cum_sum_tk, 0) + log_class_emission_cum_sum_tk = unbroadcast(log_class_emission_cum_sum_tk, 0) def inc_log_class_emission_tk_except_for_first_interval(pi_jkc, q_c_tc, cum_sum_tk): """Adds the contribution of a given sample to the log class emission (symbolically). @@ -1254,15 +1258,15 @@ def inc_log_class_emission_tk_except_for_first_interval(pi_jkc, q_c_tc, cum_sum_ pi_tkc = pi_jkc[t_to_j_map[1:], :, :] # todo use logaddexp - log_trans_tkab = tt.log( + log_trans_tkab = pt.log( cnv_not_stay_prob_t.dimshuffle(0, 'x', 'x', 'x') * pi_tkc.dimshuffle(0, 1, 'x', 2) + cnv_stay_prob_t.dimshuffle(0, 'x', 'x', 'x') * delta_ab.dimshuffle('x', 'x', 0, 1)) xi_tab = q_c_tc[:-1, :].dimshuffle(0, 1, 'x') * q_c_tc[1:, :].dimshuffle(0, 'x', 1) - current_log_class_emission_tk = tt.sum(tt.sum( + current_log_class_emission_tk = pt.sum(pt.sum( xi_tab.dimshuffle(0, 'x', 1, 2) * log_trans_tkab, axis=-1), axis=-1) return cum_sum_tk + current_log_class_emission_tk - reduce_output = th.reduce(inc_log_class_emission_tk_except_for_first_interval, + reduce_output = pytensor.reduce(inc_log_class_emission_tk_except_for_first_interval, sequences=[pi_sjkc, q_c_stc], outputs_info=[log_class_emission_cum_sum_tk]) log_class_emission_tk_except_for_first_interval = reduce_output[0] @@ -1270,11 +1274,11 @@ def inc_log_class_emission_tk_except_for_first_interval(pi_jkc, q_c_tc, cum_sum_ # the first interval pi_skc_first = pi_sjkc[:, t_to_j_map[0], :, :] q_skc_first = q_c_stc[:, 0, :].dimshuffle(0, 'x', 1) - log_class_emission_k_first = tt.sum(tt.sum(tt.log(pi_skc_first) * q_skc_first, axis=0), axis=-1) + log_class_emission_k_first = pt.sum(pt.sum(pt.log(pi_skc_first) * q_skc_first, axis=0), axis=-1) # concatenate first and rest - log_class_emission_tk = tt.concatenate((log_class_emission_k_first.dimshuffle('x', 0), + log_class_emission_tk = pt.concatenate((log_class_emission_k_first.dimshuffle('x', 0), log_class_emission_tk_except_for_first_interval)) - return th.function(inputs=[], outputs=[], updates=[ + return pytensor.function(inputs=[], outputs=[], updates=[ (self.shared_workspace.log_class_emission_tk, log_class_emission_tk)]) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_ploidy.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_ploidy.py index fd50776420f..931ded52bed 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_ploidy.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/model_ploidy.py @@ -4,10 +4,10 @@ from typing import List, Dict, Set, Tuple import numpy as np -import pymc3 as pm -import theano as th -import theano.tensor as tt -from pymc3 import Normal, Deterministic, DensityDist, Bound, Exponential +import pymc as pm +import pytensor +import pytensor.tensor as pt +from pymc import Normal, Deterministic, Potential, Exponential, TruncatedNormal, Mixture, NegativeBinomial from . import commons from .fancy_model import GeneralizedContinuousModel @@ -147,56 +147,55 @@ def __init__(self, assert sample_metadata_collection.all_samples_have_coverage_metadata(sample_names), \ "Some samples do not have coverage metadata" - # number of intervals per contig as a shared theano tensor - self.t_j: types.TensorSharedVariable = th.shared( + # number of intervals per contig as a shared pytensor tensor + self.t_j: types.TensorSharedVariable = pytensor.shared( interval_list_metadata.t_j.astype(types.floatX), name='t_j', borrow=config.borrow_numpy) - # count per contig and total count as shared theano tensors + # count per contig and total count as shared pytensor tensors n_sj = np.zeros((self.num_samples, self.num_contigs), dtype=types.floatX) n_s = np.zeros((self.num_samples,), dtype=types.floatX) for si, sample_name in enumerate(self.sample_names): sample_metadata = sample_metadata_collection.get_sample_coverage_metadata(sample_name) n_sj[si, :] = sample_metadata.n_j[:] n_s[si] = sample_metadata.n_total - self.n_sj: types.TensorSharedVariable = th.shared(n_sj, name='n_sj', borrow=config.borrow_numpy) - self.n_s: types.TensorSharedVariable = th.shared(n_s, name='n_s', borrow=config.borrow_numpy) + self.n_sj: types.TensorSharedVariable = pytensor.shared(n_sj, name='n_sj', borrow=config.borrow_numpy) + self.n_s: types.TensorSharedVariable = pytensor.shared(n_s, name='n_s', borrow=config.borrow_numpy) # integer ploidy values int_ploidy_values_k = np.arange(0, ploidy_config.num_ploidy_states, dtype=types.small_uint) - self.int_ploidy_values_k = th.shared(int_ploidy_values_k, name='int_ploidy_values_k', + self.int_ploidy_values_k = pytensor.shared(int_ploidy_values_k, name='int_ploidy_values_k', borrow=config.borrow_numpy) # ploidy priors p_ploidy_jk = np.zeros((self.num_contigs, self.ploidy_config.num_ploidy_states), dtype=types.floatX) for j, contig in enumerate(interval_list_metadata.ordered_contig_list): p_ploidy_jk[j, :] = ploidy_config.contig_ploidy_prior_map[contig][:] - log_p_ploidy_jk = np.log(p_ploidy_jk) - self.log_p_ploidy_jk: types.TensorSharedVariable = th.shared(log_p_ploidy_jk, name='log_p_ploidy_jk', + log_p_ploidy_jk = np.log(np.maximum(p_ploidy_jk, _eps)) + self.log_p_ploidy_jk: types.TensorSharedVariable = pytensor.shared(log_p_ploidy_jk, name='log_p_ploidy_jk', borrow=config.borrow_numpy) # ploidy log posteriors (initial value is immaterial) log_q_ploidy_sjk = np.tile(log_p_ploidy_jk, (self.num_samples, 1, 1)) - self.log_q_ploidy_sjk: types.TensorSharedVariable = th.shared( + self.log_q_ploidy_sjk: types.TensorSharedVariable = pytensor.shared( log_q_ploidy_sjk, name='log_q_ploidy_sjk', borrow=config.borrow_numpy) # ploidy log emission (initial value is immaterial) log_ploidy_emission_sjk = np.zeros( (self.num_samples, self.num_contigs, ploidy_config.num_ploidy_states), dtype=types.floatX) - self.log_ploidy_emission_sjk: types.TensorSharedVariable = th.shared( + self.log_ploidy_emission_sjk: types.TensorSharedVariable = pytensor.shared( log_ploidy_emission_sjk, name="log_ploidy_emission_sjk", borrow=config.borrow_numpy) # exclusion mask; mask(j, k) = 1 - delta(j, k) contig_exclusion_mask_jj = (np.ones((self.num_contigs, self.num_contigs), dtype=types.small_uint) - np.eye(self.num_contigs, dtype=types.small_uint)) - self.contig_exclusion_mask_jj = th.shared(contig_exclusion_mask_jj, name='contig_exclusion_mask_jj') + self.contig_exclusion_mask_jj = pytensor.shared(contig_exclusion_mask_jj, name='contig_exclusion_mask_jj') class PloidyModel(GeneralizedContinuousModel): """Declaration of the germline contig ploidy model (continuous variables only; posterior of discrete variables are assumed to be known).""" - PositiveNormal = Bound(Normal, lower=0) # how cool is this? - + @pytensor.config.change_flags(compute_test_value="off") def __init__(self, ploidy_config: PloidyModelConfig, ploidy_workspace: PloidyWorkspace): @@ -208,17 +207,18 @@ def __init__(self, n_s = ploidy_workspace.n_s n_sj = ploidy_workspace.n_sj ploidy_k = ploidy_workspace.int_ploidy_values_k - q_ploidy_sjk = tt.exp(ploidy_workspace.log_q_ploidy_sjk) + q_ploidy_sjk = pt.exp(ploidy_workspace.log_q_ploidy_sjk) eps_mapping = ploidy_config.mapping_error_rate register_as_global = self.register_as_global register_as_sample_specific = self.register_as_sample_specific # mean per-contig bias - mean_bias_j = self.PositiveNormal('mean_bias_j', - mu=1.0, - sd=ploidy_config.mean_bias_sd, - shape=(ploidy_workspace.num_contigs,)) + mean_bias_j = TruncatedNormal(name='mean_bias_j', + mu=1.0, + sigma=ploidy_config.mean_bias_sd, + lower=0.0, + shape=(ploidy_workspace.num_contigs,)) register_as_global(mean_bias_j) # contig coverage unexplained variance @@ -234,27 +234,27 @@ def __init__(self, register_as_sample_specific(psi_s, sample_axis=0) # convert "unexplained variance" to negative binomial over-dispersion - alpha_sj = tt.maximum(tt.inv((tt.exp(psi_j.dimshuffle('x', 0) + psi_s.dimshuffle(0, 'x')) - 1.0)), + alpha_sj = pt.maximum(pt.reciprocal((pt.exp(psi_j.dimshuffle('x', 0) + psi_s.dimshuffle(0, 'x')) - 1.0)), _eps) # mean ploidy per contig per sample - mean_ploidy_sj = tt.sum(tt.exp(ploidy_workspace.log_q_ploidy_sjk) + mean_ploidy_sj = pt.sum(pt.exp(ploidy_workspace.log_q_ploidy_sjk) * ploidy_workspace.int_ploidy_values_k.dimshuffle('x', 'x', 0), axis=2) # mean-field amplification coefficient per contig gamma_sj = mean_ploidy_sj * t_j.dimshuffle('x', 0) * mean_bias_j.dimshuffle('x', 0) # gamma_rest_sj \equiv sum_{j' \neq j} gamma_sj - gamma_rest_sj = tt.dot(gamma_sj, contig_exclusion_mask_jj) + gamma_rest_sj = pt.dot(gamma_sj, contig_exclusion_mask_jj) # NB per-contig counts mu_num_sjk = (t_j.dimshuffle('x', 0, 'x') * mean_bias_j.dimshuffle('x', 0, 'x') * ploidy_k.dimshuffle('x', 'x', 0)) mu_den_sjk = gamma_rest_sj.dimshuffle(0, 1, 'x') + mu_num_sjk - eps_mapping_j = eps_mapping * t_j / tt.sum(t_j) # average number of reads erroneously mapped to contig j + eps_mapping_j = eps_mapping * t_j / pt.sum(t_j) # average number of reads erroneously mapped to contig j # the switch is required for a single contig edge case - mu_ratio_sjk = tt.switch(tt.eq(mu_den_sjk, 0.0), 0.0, mu_num_sjk / mu_den_sjk) + mu_ratio_sjk = pt.switch(pt.eq(mu_den_sjk, 0.0), 0.0, mu_num_sjk / mu_den_sjk) mu_sjk = ((1.0 - eps_mapping) * mu_ratio_sjk + eps_mapping_j.dimshuffle('x', 0, 'x')) * n_s.dimshuffle(0, 'x', 'x') @@ -265,9 +265,11 @@ def _get_logp_sjk(_n_sj): _n_sj.dimshuffle(0, 1, 'x')) # contig counts return _logp_sjk - DensityDist(name='n_sj_obs', - logp=lambda _n_sj: tt.sum(q_ploidy_sjk * _get_logp_sjk(_n_sj)), - observed=n_sj) + + # originally DensityDist, but this raised an error about random; + # changed in https://github.com/broadinstitute/gatk/pull/8561 + Potential(name='n_sj_obs', + var=pt.sum(q_ploidy_sjk * _get_logp_sjk(n_sj))) # for log ploidy emission sampling Deterministic(name='logp_sjk', var=_get_logp_sjk(n_sj)) @@ -284,7 +286,7 @@ def __init__(self, ploidy_model: PloidyModel, samples_per_round: int): def update_approximation(self, approx: pm.approximations.MeanField): """Generates a new compiled sampler based on a given approximation. Args: - approx: an instance of PyMC3 mean-field approximation + approx: an instance of PyMC mean-field approximation Returns: None @@ -298,13 +300,14 @@ def is_sampler_initialized(self): def draw(self) -> np.ndarray: return self._simultaneous_log_ploidy_emission_sampler() - @th.configparser.change_flags(compute_test_value="off") + @pytensor.config.change_flags(compute_test_value="off") def _get_compiled_simultaneous_log_ploidy_emission_sampler(self, approx: pm.approximations.MeanField): - """For a given variational approximation, returns a compiled theano function that draws posterior samples + """For a given variational approximation, returns a compiled pytensor function that draws posterior samples from the log ploidy emission.""" log_ploidy_emission_sjk = commons.stochastic_node_mean_symbolic( approx, self.ploidy_model['logp_sjk'], size=self.samples_per_round) - return th.function(inputs=[], outputs=log_ploidy_emission_sjk) + return pm.pytensorf.compile_pymc(inputs=[], outputs=log_ploidy_emission_sjk, + random_seed=approx.rng.randint(2**30, dtype=np.int64)) class PloidyBasicCaller: @@ -314,21 +317,21 @@ def __init__(self, ploidy_workspace: PloidyWorkspace): self.ploidy_workspace = ploidy_workspace self.inference_params = inference_params - self._update_log_q_ploidy_sjk_theano_func = self._get_update_log_q_ploidy_sjk_theano_func() + self._update_log_q_ploidy_sjk_pytensor_func = self._get_update_log_q_ploidy_sjk_pytensor_func() - @th.configparser.change_flags(compute_test_value="off") - def _get_update_log_q_ploidy_sjk_theano_func(self) -> th.compile.function_module.Function: + @pytensor.config.change_flags(compute_test_value="off") + def _get_update_log_q_ploidy_sjk_pytensor_func(self) -> pytensor.compile.Function: new_log_q_ploidy_sjk = (self.ploidy_workspace.log_p_ploidy_jk.dimshuffle('x', 0, 1) + self.ploidy_workspace.log_ploidy_emission_sjk) - new_log_q_ploidy_sjk -= pm.logsumexp(new_log_q_ploidy_sjk, axis=2) + new_log_q_ploidy_sjk -= commons.logsumexp(new_log_q_ploidy_sjk, axis=2) old_log_q_ploidy_sjk = self.ploidy_workspace.log_q_ploidy_sjk admixed_new_log_q_ploidy_sjk = commons.safe_logaddexp( new_log_q_ploidy_sjk + np.log(self.inference_params.caller_external_admixing_rate), old_log_q_ploidy_sjk + np.log(1.0 - self.inference_params.caller_external_admixing_rate)) update_norm_sj = commons.get_hellinger_distance(admixed_new_log_q_ploidy_sjk, old_log_q_ploidy_sjk) - return th.function(inputs=[], + return pytensor.function(inputs=[], outputs=[update_norm_sj], updates=[(self.ploidy_workspace.log_q_ploidy_sjk, admixed_new_log_q_ploidy_sjk)]) def call(self) -> np.ndarray: - return self._update_log_q_ploidy_sjk_theano_func() + return self._update_log_q_ploidy_sjk_pytensor_func() diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/theano_hmm.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/pytensor_hmm.py similarity index 76% rename from src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/theano_hmm.py rename to src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/pytensor_hmm.py index 8701e6487dd..d8dbf3c614a 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/theano_hmm.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/models/pytensor_hmm.py @@ -1,16 +1,16 @@ from typing import Optional, List, Tuple, Union import numpy as np -import pymc3 as pm -import theano as th -import theano.tensor as tt +import pytensor +import pytensor.tensor as pt -from . import commons +from ..models import commons +from ..models.commons import logsumexp from .. import types -class TheanoForwardBackward: - """Implementation of the forward-backward algorithm in theano.""" +class PytensorForwardBackward: + """Implementation of the forward-backward algorithm in pytensor.""" def __init__(self, log_posterior_probs_output_tc: Optional[types.TensorSharedVariable] = None, resolve_nans: bool = False, @@ -18,7 +18,7 @@ def __init__(self, do_admixing: bool = False, include_update_size_output: bool = False, include_alpha_beta_output: bool = False): - """Initializes the forward-backward algorithm by compiling a theano function according to the + """Initializes the forward-backward algorithm by compiling a pytensor function according to the boolean flags. Args: @@ -36,7 +36,7 @@ def __init__(self, self.do_admixing = do_admixing self.include_update_size_output = include_update_size_output self.include_alpha_beta_output = include_alpha_beta_output - self._forward_backward_theano_func = self._get_compiled_forward_backward_theano_func() + self._forward_backward_pytensor_func = self._get_compiled_forward_backward_pytensor_func() def perform_forward_backward(self, log_prior_c: np.ndarray, @@ -48,7 +48,7 @@ def perform_forward_backward(self, """Runs the forward-backward algorithm. Notes: - The inputs args must be compatible with the compiled theano function according to the + The inputs args must be compatible with the compiled pytensor function according to the class initializer flags. Args: @@ -82,12 +82,12 @@ class initializer flags. assert prev_log_posterior_tc is not None,\ "Update size output is enabled but `prev_log_posterior_tc` is not specified." - return self._decompose_theano_forward_backward_outputs( - self._forward_backward_theano_func(*self._compose_theano_forward_backward_inputs( + return self._decompose_pytensor_forward_backward_outputs( + self._forward_backward_pytensor_func(*self._compose_pytensor_forward_backward_inputs( log_prior_c, log_trans_tcc, log_emission_tc, prev_log_posterior_tc, admixing_rate, temperature))) - def _compose_theano_forward_backward_inputs(self, + def _compose_pytensor_forward_backward_inputs(self, log_prior_c: np.ndarray, log_trans_tcc: np.ndarray, log_emission_tc: np.ndarray, @@ -103,7 +103,7 @@ def _compose_theano_forward_backward_inputs(self, inputs += (temperature,) return inputs - def _decompose_theano_forward_backward_outputs(self, outputs: List[Union[np.ndarray, float]])\ + def _decompose_pytensor_forward_backward_outputs(self, outputs: List[Union[np.ndarray, float]])\ -> 'ForwardBackwardResult': result = ForwardBackwardResult() arg_idx = 0 @@ -122,13 +122,13 @@ def _decompose_theano_forward_backward_outputs(self, outputs: List[Union[np.ndar arg_idx += 1 return result - @th.configparser.change_flags(compute_test_value="ignore") - def _get_compiled_forward_backward_theano_func(self) -> th.compile.function_module.Function: - """Returns a compiled theano function that computes the posterior probabilities of hidden states using + @pytensor.config.change_flags(compute_test_value="ignore") + def _get_compiled_forward_backward_pytensor_func(self) -> pytensor.compile.Function: + """Returns a compiled pytensor function that computes the posterior probabilities of hidden states using the forward-backward algorithm. Note: - The input arguments and the output of the compiled theano function is determined by the initializer flags + The input arguments and the output of the compiled pytensor function is determined by the initializer flags as follows: There are 3 basic input arguments: @@ -162,17 +162,17 @@ def _get_compiled_forward_backward_theano_func(self) -> th.compile.function_modu and `beta_tc` (float vector). Returns: - A compiled theano function + A compiled pytensor function """ # basic inputs - log_prior_c = tt.vector('log_prior_c') - log_trans_tcc = tt.tensor3('log_trans_tcc') - log_emission_tc = tt.matrix('log_emission_tc') + log_prior_c = pt.vector('log_prior_c') + log_trans_tcc = pt.tensor3('log_trans_tcc') + log_emission_tc = pt.matrix('log_emission_tc') # optional inputs - prev_log_posterior_tc = tt.matrix('prev_log_posterior_tc') - admixing_rate = tt.scalar('admixing_rate') - temperature = tt.scalar('temperature') + prev_log_posterior_tc = pt.matrix('prev_log_posterior_tc') + admixing_rate = pt.scalar('admixing_rate') + temperature = pt.scalar('temperature') if self.do_thermalization: processed_log_prior_c, processed_log_trans_tcc, processed_log_emission_tc =\ @@ -187,8 +187,8 @@ def _get_compiled_forward_backward_theano_func(self) -> th.compile.function_modu if self.do_admixing: processed_log_posterior_tc = commons.safe_logaddexp( - new_log_posterior_tc + tt.log(admixing_rate), - prev_log_posterior_tc + tt.log(1.0 - admixing_rate)) + new_log_posterior_tc + pt.log(admixing_rate), + prev_log_posterior_tc + pt.log(1.0 - admixing_rate)) else: processed_log_posterior_tc = new_log_posterior_tc @@ -223,12 +223,12 @@ def _get_compiled_forward_backward_theano_func(self) -> th.compile.function_modu if self.include_alpha_beta_output: outputs += [alpha_tc, beta_tc] - return th.function(inputs=inputs, outputs=outputs, updates=updates) + return pytensor.function(inputs=inputs, outputs=outputs, updates=updates) @staticmethod - def get_symbolic_log_posterior(log_prior_c: types.TheanoVector, - log_trans_tcc: types.TheanoTensor3, - log_emission_tc: types.TheanoMatrix, + def get_symbolic_log_posterior(log_prior_c: types.PytensorVector, + log_trans_tcc: types.PytensorTensor3, + log_emission_tc: types.PytensorMatrix, resolve_nans: bool): """Generates symbolic tensors representing hidden-state log posterior, log data likelihood, forward table (alpha), and backward table (beta). @@ -238,9 +238,9 @@ def get_symbolic_log_posterior(log_prior_c: types.TheanoVector, """ num_states = log_prior_c.shape[0] - def calculate_next_alpha(c_log_trans_ab: types.TheanoMatrix, - c_log_emission_b: types.TheanoVector, - p_alpha_a: types.TheanoVector): + def calculate_next_alpha(c_log_trans_ab: types.PytensorMatrix, + c_log_emission_b: types.PytensorVector, + p_alpha_a: types.PytensorVector): """Calculates the next entry on the forward table, alpha_{t}, from alpha_{t-1}. Args: @@ -252,16 +252,16 @@ def calculate_next_alpha(c_log_trans_ab: types.TheanoMatrix, Returns: symbolic 1d tensor of alpha_{t} """ - mu_ba = tt.tile(p_alpha_a, (num_states, 1)) + c_log_trans_ab.T - n_alpha_b = c_log_emission_b + pm.math.logsumexp(mu_ba, axis=1).dimshuffle(0) + mu_ba = pt.tile(p_alpha_a, (num_states, 1)) + c_log_trans_ab.T + n_alpha_b = c_log_emission_b + logsumexp(mu_ba, axis=1).dimshuffle(0) if resolve_nans: - return tt.switch(tt.isnan(n_alpha_b), -np.inf, n_alpha_b) + return pt.switch(pt.isnan(n_alpha_b), -np.inf, n_alpha_b) else: return n_alpha_b - def calculate_prev_beta(n_log_trans_ab: types.TheanoMatrix, - n_log_emission_b: types.TheanoVector, - n_beta_b: types.TheanoVector): + def calculate_prev_beta(n_log_trans_ab: types.PytensorMatrix, + n_log_emission_b: types.PytensorVector, + n_beta_b: types.PytensorVector): """Calculates the previous entry on the backward table, beta_{t-1}, from beta_{t}. Args: @@ -273,10 +273,10 @@ def calculate_prev_beta(n_log_trans_ab: types.TheanoMatrix, Returns: symbolic 1d tensor of beta_{t-1} """ - nu_ab = tt.tile(n_beta_b + n_log_emission_b, (num_states, 1)) + n_log_trans_ab - p_beta_a = pm.math.logsumexp(nu_ab, axis=1).dimshuffle(0) + nu_ab = pt.tile(n_beta_b + n_log_emission_b, (num_states, 1)) + n_log_trans_ab + p_beta_a = logsumexp(nu_ab, axis=1).dimshuffle(0) if resolve_nans: - return tt.switch(tt.isnan(p_beta_a), -np.inf, p_beta_a) + return pt.switch(pt.isnan(p_beta_a), -np.inf, p_beta_a) else: return p_beta_a @@ -284,45 +284,45 @@ def calculate_prev_beta(n_log_trans_ab: types.TheanoMatrix, first_alpha_c = log_prior_c + log_emission_tc[0, :] # the rest of the forward table - rest_alpha_tc, alpha_updates = th.scan( + rest_alpha_tc, alpha_updates = pytensor.scan( fn=calculate_next_alpha, sequences=[log_trans_tcc, log_emission_tc[1:, :]], outputs_info=[first_alpha_c]) # concatenate with the first alpha - alpha_tc = tt.concatenate((first_alpha_c.dimshuffle('x', 0), rest_alpha_tc)) + alpha_tc = pt.concatenate((first_alpha_c.dimshuffle('x', 0), rest_alpha_tc)) # last entry of the backward table (zero for all states) - last_beta_c = tt.zeros_like(log_prior_c) + last_beta_c = pt.zeros_like(log_prior_c) # the rest of the backward table - rest_beta_tc, beta_updates = th.scan( + rest_beta_tc, beta_updates = pytensor.scan( fn=calculate_prev_beta, sequences=[log_trans_tcc, log_emission_tc[1:, :]], go_backwards=True, outputs_info=[last_beta_c]) # concatenate with the last beta and reverse - beta_tc = tt.concatenate((last_beta_c.dimshuffle('x', 0), rest_beta_tc))[::-1, :] + beta_tc = pt.concatenate((last_beta_c.dimshuffle('x', 0), rest_beta_tc))[::-1, :] # calculate normalized log posterior log_unnormalized_posterior_tc = alpha_tc + beta_tc - log_data_likelihood_t = pm.math.logsumexp(log_unnormalized_posterior_tc, axis=1) + log_data_likelihood_t = logsumexp(log_unnormalized_posterior_tc, axis=1) log_posterior_probs_tc = log_unnormalized_posterior_tc - log_data_likelihood_t return log_posterior_probs_tc, log_data_likelihood_t.dimshuffle(0), alpha_tc, beta_tc @staticmethod - def get_symbolic_thermal_hmm_params(log_prior_c: types.TheanoVector, - log_trans_tcc: types.TheanoTensor3, - log_emission_tc: types.TheanoMatrix, - temperature: tt.scalar): - inv_temperature = tt.inv(temperature) + def get_symbolic_thermal_hmm_params(log_prior_c: types.PytensorVector, + log_trans_tcc: types.PytensorTensor3, + log_emission_tc: types.PytensorMatrix, + temperature: pt.scalar): + inv_temperature = pt.reciprocal(temperature) thermal_log_prior_c = inv_temperature * log_prior_c - thermal_log_prior_c -= pm.math.logsumexp(thermal_log_prior_c) + thermal_log_prior_c -= logsumexp(thermal_log_prior_c) thermal_log_trans_tcc = inv_temperature * log_trans_tcc - thermal_log_trans_tcc -= pm.math.logsumexp(thermal_log_trans_tcc, axis=-1) + thermal_log_trans_tcc -= logsumexp(thermal_log_trans_tcc, axis=-1) thermal_log_emission_tc = inv_temperature * log_emission_tc return thermal_log_prior_c, thermal_log_trans_tcc, thermal_log_emission_tc @@ -343,31 +343,31 @@ def __init__(self, self.update_norm_t = update_norm_t -class TheanoViterbi: - """Implementation of the Viterbi algorithm in theano.""" +class PytensorViterbi: + """Implementation of the Viterbi algorithm in pytensor.""" def __init__(self): - self._viterbi_theano_func = self._get_compiled_viterbi_theano_func() + self._viterbi_pytensor_func = self._get_compiled_viterbi_pytensor_func() def get_viterbi_path(self, log_prior_c: np.ndarray, log_trans_tcc: np.ndarray, log_emission_tc: np.ndarray) -> List[int]: - return self._viterbi_theano_func(log_prior_c, log_trans_tcc, log_emission_tc).tolist() + return self._viterbi_pytensor_func(log_prior_c, log_trans_tcc, log_emission_tc).tolist() - @th.configparser.change_flags(compute_test_value="ignore") - def _get_compiled_viterbi_theano_func(self) -> th.compile.function_module.Function: - """Returns a theano function that calculates the Viterbi path.""" - log_prior_c = tt.vector('log_prior_c') - log_trans_tcc = tt.tensor3('log_trans_tcc') - log_emission_tc = tt.matrix('log_emission_tc') + @pytensor.config.change_flags(compute_test_value="ignore") + def _get_compiled_viterbi_pytensor_func(self) -> pytensor.compile.Function: + """Returns a pytensor function that calculates the Viterbi path.""" + log_prior_c = pt.vector('log_prior_c') + log_trans_tcc = pt.tensor3('log_trans_tcc') + log_emission_tc = pt.matrix('log_emission_tc') - return th.function(inputs=[log_prior_c, log_trans_tcc, log_emission_tc], + return pytensor.function(inputs=[log_prior_c, log_trans_tcc, log_emission_tc], outputs=self._get_symbolic_viterbi_path(log_prior_c, log_trans_tcc, log_emission_tc)) @staticmethod - def _get_symbolic_viterbi_path(log_prior_c: types.TheanoVector, - log_trans_tcc: types.TheanoTensor3, - log_emission_tc: types.TheanoMatrix): + def _get_symbolic_viterbi_path(log_prior_c: types.PytensorVector, + log_trans_tcc: types.PytensorTensor3, + log_emission_tc: types.PytensorMatrix): """Generates a symbolic 1d integer tensor representing the most-likely chain of hidden states (Viterbi algorithm). @@ -395,7 +395,7 @@ def calculate_next_omega_psi(p_log_trans_ab, c_log_emission_b, p_omega_a): next omega, next psi """ tau_ab = p_log_trans_ab + p_omega_a.dimshuffle(0, 'x') - max_tau_b, psi_b = tt.max_and_argmax(tau_ab, axis=0) + max_tau_b, psi_b = pt.max_and_argmax(tau_ab, axis=0) n_omega_b = c_log_emission_b + max_tau_b return n_omega_b, psi_b @@ -416,7 +416,7 @@ def calculate_previous_best_state(c_psi_c, c_best_state): # calculate the log data likelihood of the partial max sum-product paths (omega) # and the backtracking table (psi) - omega_psi_list, _ = th.scan( + omega_psi_list, _ = pytensor.scan( fn=calculate_next_omega_psi, sequences=[log_trans_tcc, log_emission_tc[1:, :]], outputs_info=[omega_first_a, None]) @@ -424,16 +424,16 @@ def calculate_previous_best_state(c_psi_c, c_best_state): psi_tc = omega_psi_list[1] # the best terminal state - last_best_state = tt.argmax(omega_tc[-1, :]) + last_best_state = pt.argmax(omega_tc[-1, :]) # backtrack to obtain the previous states of the max sum-product path - rest_best_states_t, _ = th.scan( + rest_best_states_t, _ = pytensor.scan( fn=calculate_previous_best_state, sequences=[psi_tc], outputs_info=[last_best_state], go_backwards=True) # concatenate with the terminal state - viterbi_path_t = tt.concatenate([tt.stack(last_best_state), rest_best_states_t])[::-1] + viterbi_path_t = pt.concatenate([last_best_state.dimshuffle('x'), rest_best_states_t])[::-1] return viterbi_path_t diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/segment_quality_utils.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/segment_quality_utils.py index 8ee677b909e..58dd81e835d 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/segment_quality_utils.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/segment_quality_utils.py @@ -2,10 +2,10 @@ from typing import Dict, List import numpy as np -import pymc3 as pm -import theano as th -import theano.tensor as tt -from scipy.misc import logsumexp +import pytensor +import pytensor.tensor as pt +from ..models import commons +from scipy.special import logsumexp from ..utils.math import logsumexp_double_complement, logp_to_phred @@ -67,9 +67,9 @@ def __init__(self, for left_out_state in range(self.num_states)} @staticmethod - @th.configparser.change_flags(compute_test_value="ignore") - def _get_compiled_constrained_path_logp_theano_func() -> th.compile.function_module.Function: - """Returns a theano function that calculates the log posterior probability of hidden state paths composed + @pytensor.config.change_flags(compute_test_value="ignore") + def _get_compiled_constrained_path_logp_pytensor_func() -> pytensor.compile.Function: + """Returns a pytensor function that calculates the log posterior probability of hidden state paths composed from a subset X of all hidden states S. More explicitly, this function calculates the logp of all paths constrained to the hidden-state set @@ -79,7 +79,7 @@ def _get_compiled_constrained_path_logp_theano_func() -> th.compile.function_mod t < t_0 | t_0 t_1 ... t_N | t > t_N c in S | c in X c in X c in X | c in S - The inputs for the returned theano function are as follows: + The inputs for the returned pytensor function are as follows: alpha_first_c: forward log likelihood (alpha) for t = t_0 and c in X beta_last_c: backward log likelihood (beta) for t = t_N and c in X @@ -101,32 +101,32 @@ def _get_compiled_constrained_path_logp_theano_func() -> th.compile.function_mod are strictly forbidden by the prior and/or the transition matrix) Returns: - a theano function + a pytensor function """ - alpha_first_c = tt.vector('alpha_first_c') - beta_last_c = tt.vector('beta_last_c') - log_emission_tc = tt.matrix('log_emission_tc') - log_trans_tcc = tt.tensor3('log_trans_tcc') - log_data_likelihood = tt.scalar('log_data_likelihood') - - def update_alpha(c_log_emission_c: tt.vector, - c_log_trans_cc: tt.matrix, - p_alpha_c: tt.vector): - return c_log_emission_c + pm.math.logsumexp( + alpha_first_c = pt.vector('alpha_first_c') + beta_last_c = pt.vector('beta_last_c') + log_emission_tc = pt.matrix('log_emission_tc') + log_trans_tcc = pt.tensor3('log_trans_tcc') + log_data_likelihood = pt.scalar('log_data_likelihood') + + def update_alpha(c_log_emission_c: pt.vector, + c_log_trans_cc: pt.matrix, + p_alpha_c: pt.vector): + return c_log_emission_c + commons.logsumexp( p_alpha_c.dimshuffle(0, 'x') + c_log_trans_cc, axis=0).dimshuffle(1) - alpha_seg_iters, _ = th.scan( + alpha_seg_iters, _ = pytensor.scan( fn=update_alpha, sequences=[log_emission_tc, log_trans_tcc], outputs_info=[alpha_first_c]) alpha_seg_end_c = alpha_seg_iters[-1, :] inputs = [alpha_first_c, beta_last_c, log_emission_tc, log_trans_tcc, log_data_likelihood] - output = pm.math.logsumexp(alpha_seg_end_c + beta_last_c) - log_data_likelihood - return th.function(inputs=inputs, outputs=output) + output = commons.logsumexp(alpha_seg_end_c + beta_last_c) - log_data_likelihood + return pytensor.function(inputs=inputs, outputs=output) # make a private static instance - _constrained_path_logp_theano_func = _get_compiled_constrained_path_logp_theano_func.__func__() + _constrained_path_logp_pytensor_func = _get_compiled_constrained_path_logp_pytensor_func.__func__() def get_log_constrained_posterior_prob(self, start_index: int, end_index: int, @@ -162,11 +162,11 @@ def get_log_constrained_posterior_prob(self, self.log_emission_tc[(start_index + 1):(end_index + 1), allowed_states] constrained_log_trans_tcc = \ self.log_trans_tcc[start_index:end_index, allowed_states, :][:, :, allowed_states] - logp = self._constrained_path_logp_theano_func( + logp = self._constrained_path_logp_pytensor_func( constrained_alpha_first_c, constrained_beta_last_c, constrained_log_emission_tc, constrained_log_trans_tcc, self.log_data_likelihood) - return np.asscalar(logp) + return logp.item() def get_segment_quality_some_called(self, start_index: int, end_index: int, call_state: int) -> float: """Calculates the phred-scaled posterior probability that one or more ("some") sites in a segment have @@ -224,7 +224,7 @@ def get_segment_quality_all_called(self, start_index: int, end_index: int, call_ if start_index == end_index: log_compl_prob = logsumexp( - self.log_posterior_prob_tc[start_index, self.leave_one_out_state_lists[call_state]]) + self.log_posterior_prob_tc[start_index, self.leave_one_out_state_lists[call_state]])[0] return logp_to_phred(log_compl_prob, complement=False) else: # calculate the uncorrelated log complementary probability diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/viterbi_segmentation.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/viterbi_segmentation.py index 9a256a9e50b..d1fb831cb6a 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/viterbi_segmentation.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/postprocess/viterbi_segmentation.py @@ -13,7 +13,7 @@ from ..io import io_consts, io_commons, io_denoising_calling, io_intervals_and_counts, io_vcf_parsing from ..models.model_denoising_calling import DenoisingModelConfig, CopyNumberCallingConfig, \ HHMMClassAndCopyNumberBasicCaller -from ..models.theano_hmm import TheanoForwardBackward, TheanoViterbi +from ..models.pytensor_hmm import PytensorForwardBackward, PytensorViterbi from ..structs.interval import Interval from ..structs.metadata import IntervalListMetadata from ..structs.metadata import SampleMetadataCollection @@ -97,8 +97,8 @@ def __init__(self, self.cnv_stay_prob_t_j[contig] = np.exp(-dist_t / self.calling_config.cnv_coherence_length) # forward-backward algorithm - _logger.info("Compiling theano forward-backward function...") - self.theano_forward_backward = TheanoForwardBackward( + _logger.info("Compiling pytensor forward-backward function...") + self.pytensor_forward_backward = PytensorForwardBackward( log_posterior_probs_output_tc=None, resolve_nans=False, do_thermalization=False, @@ -107,13 +107,13 @@ def __init__(self, include_alpha_beta_output=True) # viterbi algorithm - _logger.info("Compiling theano Viterbi function...") - self.theano_viterbi = TheanoViterbi() + _logger.info("Compiling pytensor Viterbi function...") + self.pytensor_viterbi = PytensorViterbi() # copy-number HMM specs generator - _logger.info("Compiling theano variational HHMM...") + _logger.info("Compiling pytensor variational HHMM...") self.get_copy_number_hmm_specs = HHMMClassAndCopyNumberBasicCaller\ - .get_compiled_copy_number_hmm_specs_theano_func() + .get_compiled_copy_number_hmm_specs_pytensor_func() def _viterbi_segments_generator(self) -> Generator[IntegerCopyNumberSegment, None, None]: """Performs Viterbi segmentation and segment quality calculation for a single sample in @@ -165,7 +165,7 @@ def _viterbi_segments_generator(self) -> Generator[IntegerCopyNumberSegment, Non log_trans_contig_tcc = hmm_specs[1] # run forward-back algorithm - fb_result = self.theano_forward_backward.perform_forward_backward( + fb_result = self.pytensor_forward_backward.perform_forward_backward( log_prior_c, log_trans_contig_tcc, copy_number_log_emission_contig_tc) log_posterior_prob_tc = fb_result.log_posterior_probs_tc log_data_likelihood = fb_result.log_data_likelihood @@ -182,7 +182,7 @@ def _viterbi_segments_generator(self) -> Generator[IntegerCopyNumberSegment, Non if bool(self.clustered_vcf is None) != bool(self.intervals_vcf is None): raise Exception("If clustered_vcf is provided, then intervals_vcf must be provided.") # run viterbi algorithm - viterbi_path_t_contig = self.theano_viterbi.get_viterbi_path( + viterbi_path_t_contig = self.pytensor_viterbi.get_viterbi_path( log_prior_c, log_trans_contig_tcc, copy_number_log_emission_contig_tc) # coalesce into piecewise constant copy-number segments diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/inference_task_base.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/inference_task_base.py index f650b1cfdaa..751b2b4cf63 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/inference_task_base.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/inference_task_base.py @@ -7,9 +7,9 @@ from typing import List, Callable, Optional, Set, Tuple, Any, Dict import numpy as np -import pymc3 as pm -import theano as th -import theano.tensor as tt +import pymc as pm +import pytensor +import pytensor.tensor as pt import tqdm from .. import types @@ -40,7 +40,7 @@ def update_approximation(self, approx: pm.approximations.MeanField) -> None: """Take a new mean-field approximation and update the sampler routine accordingly. Args: - approx: an instance of PyMC3 mean-field posterior approximation + approx: an instance of PyMC mean-field posterior approximation Returns: None @@ -198,9 +198,9 @@ class HybridInferenceTask(InferenceTask): The general implementation motif is: - (a) to store sufficient statistics from q(DRVs) as a shared theano tensor such that the the + (a) to store sufficient statistics from q(DRVs) as a shared pytensor tensor such that the the model can access it, - (b) to store log_emission(DRVs) as a shared theano tensor (or ndarray) such that the caller + (b) to store log_emission(DRVs) as a shared pytensor tensor (or ndarray) such that the caller can access it, and: (c) let the caller directly update the shared sufficient statistics. """ @@ -219,7 +219,7 @@ def __init__(self, Args: hybrid_inference_params: inference configuration - continuous_model: a PyMC3 model representing the continuous sector of the PGM + continuous_model: a PyMC model representing the continuous sector of the PGM sampler: log emission probability sampler caller: discrete RV posterior updater **kwargs: extra keywords @@ -262,7 +262,7 @@ def __init__(self, with self.continuous_model: if not hasattr(self, 'temperature'): initial_temperature = self.hybrid_inference_params.initial_temperature - self.temperature: types.TensorSharedVariable = th.shared( + self.temperature: types.TensorSharedVariable = pytensor.shared( np.asarray([initial_temperature], dtype=types.floatX)) initial_temperature = self.temperature.get_value()[0] if (np.abs(initial_temperature - 1.0) < self.temperature_tolerance or @@ -274,7 +274,7 @@ def __init__(self, temperature_drop_per_iter = ((initial_temperature - 1.0) / self.hybrid_inference_params.num_thermal_advi_iters) temperature_update = [(self.temperature, - tt.maximum(1.0, self.temperature - temperature_drop_per_iter))] + pt.maximum(1.0, self.temperature - temperature_drop_per_iter))] self.continuous_model_advi = ADVIDeterministicAnnealing( random_seed=self.hybrid_inference_params.random_seed, @@ -341,7 +341,7 @@ def disengage(self): def engage(self): try: all_converged = False - while self.i_epoch <= self.hybrid_inference_params.max_training_epochs: + while self.i_epoch <= self.hybrid_inference_params.max_training_epochs: _logger.debug("Starting epoch {0}...".format(self.i_epoch)) converged_continuous = self._update_continuous_posteriors() all_converged = converged_continuous diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_case_denoising_calling.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_case_denoising_calling.py index df9ddc38ada..a04d996831b 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_case_denoising_calling.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_case_denoising_calling.py @@ -2,7 +2,7 @@ from typing import Callable import numpy as np -import theano as th +import pytensor from .inference_task_base import Caller, CallerUpdateSummary, HybridInferenceTask, HybridInferenceParameters from .. import types @@ -104,7 +104,7 @@ def __init__(self, else: _logger.info("Instantiating the copy number caller...") initial_temperature = hybrid_inference_params.initial_temperature - self.temperature: types.TensorSharedVariable = th.shared( + self.temperature: types.TensorSharedVariable = pytensor.shared( np.asarray([initial_temperature], dtype=types.floatX)) copy_number_caller = HMMCopyNumberCaller( calling_config, hybrid_inference_params, shared_workspace, self.temperature) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_denoising_calling.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_denoising_calling.py index 91c5bac7a28..3cd7a4b3c5d 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_denoising_calling.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_denoising_calling.py @@ -2,9 +2,10 @@ from typing import Callable, Optional import numpy as np -import pymc3 as pm -import theano as th +import pymc as pm +import pytensor +from ..models import commons from .inference_task_base import Sampler, Caller, CallerUpdateSummary, HybridInferenceTask, HybridInferenceParameters from .. import config, types from ..models.model_denoising_calling import DenoisingModel, DenoisingModelConfig, \ @@ -14,6 +15,9 @@ _logger = logging.getLogger(__name__) +_eps = commons.eps + + class HHMMClassAndCopyNumberCaller(Caller): """This class is a wrapper around `HHMMClassAndCopyNumberBasicCaller` to be used in a cohort denoising and calling task.""" @@ -53,15 +57,15 @@ def finalize(self): # admix q_c_stc with the snapshot self.shared_workspace.log_q_c_stc.set_value( np.logaddexp( - log_q_c_stc_latest + np.log(self.hybrid_inference_params.caller_external_admixing_rate), - self.log_q_c_stc_snapshot + np.log(1 - self.hybrid_inference_params.caller_external_admixing_rate)), + log_q_c_stc_latest + np.log(np.maximum(self.hybrid_inference_params.caller_external_admixing_rate, _eps)), + self.log_q_c_stc_snapshot + np.log(np.maximum(1 - self.hybrid_inference_params.caller_external_admixing_rate, _eps))), borrow=True) # admix q_tau_tk with the snapshot self.shared_workspace.log_q_tau_tk.set_value( np.logaddexp( - log_q_tau_tk_latest + np.log(self.hybrid_inference_params.caller_external_admixing_rate), - self.log_q_tau_tk_snapshot + np.log(1 - self.hybrid_inference_params.caller_external_admixing_rate)), + log_q_tau_tk_latest + np.log(np.maximum(self.hybrid_inference_params.caller_external_admixing_rate, _eps)), + self.log_q_tau_tk_snapshot + np.log(np.maximum(1 - self.hybrid_inference_params.caller_external_admixing_rate, _eps))), borrow=True) def update_auxiliary_vars(self): @@ -173,7 +177,7 @@ def __init__(self, else: _logger.info("Instantiating the copy number caller...") initial_temperature = hybrid_inference_params.initial_temperature - self.temperature: types.TensorSharedVariable = th.shared( + self.temperature: types.TensorSharedVariable = pytensor.shared( np.asarray([initial_temperature], dtype=types.floatX)) copy_number_caller = HHMMClassAndCopyNumberCaller( calling_config, hybrid_inference_params, shared_workspace, self.temperature) diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_ploidy_determination.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_ploidy_determination.py index 7b949a68267..32630874b5c 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_ploidy_determination.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/tasks/task_cohort_ploidy_determination.py @@ -1,6 +1,6 @@ import logging import numpy as np -import pymc3 as pm +import pymc as pm from typing import Callable from .inference_task_base import Sampler, Caller, CallerUpdateSummary, \ diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/types.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/types.py index cf1ea24b562..1c30911f67b 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/types.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/types.py @@ -1,9 +1,9 @@ import numpy as np -import theano -import theano.tensor as tt +import pytensor +import pytensor.tensor as pt # the following dtype will be used for all float numpy ndarrays -floatX = theano.config.floatX +floatX = pytensor.config.floatX # big uint dtype (used for aggregated counts) big_uint = np.uint64 @@ -18,8 +18,8 @@ int_dtypes = [np.int8, np.int16, np.int32, np.int64] uint_dtypes = [np.uint8, np.uint16, np.uint32, np.uint64] -# theano tensor types -TheanoVector = tt.TensorType(floatX, (False,)) -TheanoMatrix = tt.TensorType(floatX, (False, False)) -TheanoTensor3 = tt.TensorType(floatX, (False, False, False)) -TensorSharedVariable = theano.tensor.sharedvar.TensorSharedVariable +# pytensor tensor types +PytensorVector = pt.TensorType(floatX, (False,)) +PytensorMatrix = pt.TensorType(floatX, (False, False)) +PytensorTensor3 = pt.TensorType(floatX, (False, False, False)) +TensorSharedVariable = pytensor.tensor.sharedvar.TensorSharedVariable diff --git a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/utils/math.py b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/utils/math.py index caa97432f7d..33a5e6755ea 100644 --- a/src/main/python/org/broadinstitute/hellbender/gcnvkernel/utils/math.py +++ b/src/main/python/org/broadinstitute/hellbender/gcnvkernel/utils/math.py @@ -80,10 +80,10 @@ def logsumexp_double_complement(a: np.ndarray, rel_tol: float = 1e-3) -> float: """ try: assert isinstance(a, np.ndarray) - a = np.asarray(a.copy(), dtype=np.float) + a = np.asarray(a.copy(), dtype=float) except AssertionError: try: - a = np.asarray(a, dtype=np.float) + a = np.asarray(a, dtype=float) except ValueError: raise ValueError("The input argument must be castable to a float ndarray.") assert len(a) > 0 @@ -93,7 +93,7 @@ def logsumexp_double_complement(a: np.ndarray, rel_tol: float = 1e-3) -> float: a[a > 0.] = 0. if len(a) == 1: - return np.asscalar(a) + return a.item() else: a = np.sort(a.flatten())[::-1] x = a[0] diff --git a/src/main/python/org/broadinstitute/hellbender/setup.py b/src/main/python/org/broadinstitute/hellbender/setup.py index d4717d85396..82387193e2a 100644 --- a/src/main/python/org/broadinstitute/hellbender/setup.py +++ b/src/main/python/org/broadinstitute/hellbender/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='gatkpythonpackages', - version='0.1', + version='0.2', description='GATK python packages', author='GATK Team', packages=find_packages(), diff --git a/src/main/python/org/broadinstitute/hellbender/setup_gcnvkernel.py b/src/main/python/org/broadinstitute/hellbender/setup_gcnvkernel.py index 02e70e47255..665812b3652 100644 --- a/src/main/python/org/broadinstitute/hellbender/setup_gcnvkernel.py +++ b/src/main/python/org/broadinstitute/hellbender/setup_gcnvkernel.py @@ -32,9 +32,9 @@ def get_version_string(): description='GATK gCNV computational kernel', long_description=open('gcnvkernel/README.txt').read(), install_requires=[ - "theano == 1.0.4", - "pymc3 == 3.1", - "numpy >= 1.13.1", - "scipy >= 0.19.1", - "tqdm >= 4.15.0" + "pymc == 5.10.0", + "pytensor == 2.18.1", + "numpy == 1.26.2", + "scipy == 1.11.4", + "tqdm == 4.66.1" ]) diff --git a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_denoising_calling.py b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_denoising_calling.py index 20c1cc42965..5fa2ba0b752 100644 --- a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_denoising_calling.py +++ b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_denoising_calling.py @@ -1,12 +1,12 @@ import os import sys -# set theano flags -user_theano_flags = os.environ.get("THEANO_FLAGS") -default_theano_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ - "openmp=true,blas.ldflags=-lmkl_rt,openmp_elemwise_minsize=10" -theano_flags = default_theano_flags + ("" if user_theano_flags is None else "," + user_theano_flags) -os.environ["THEANO_FLAGS"] = theano_flags +# set pytensor flags +user_pytensor_flags = os.environ.get("PYTENSOR_FLAGS") +default_pytensor_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ + "openmp=true,blas__ldflags=-lmkl_rt,openmp_elemwise_minsize=10,exception_verbosity=high" +pytensor_flags = default_pytensor_flags + ("" if user_pytensor_flags is None else "," + user_pytensor_flags) +os.environ["PYTENSOR_FLAGS"] = pytensor_flags import logging import argparse @@ -148,7 +148,7 @@ def update_args_dict_from_saved_model(input_model_path: str, args = parser.parse_args() gcnvkernel.cli_commons.set_logging_config_from_args(args) - logger.info("THEANO_FLAGS environment variable has been set to: {theano_flags}".format(theano_flags=theano_flags)) + logger.info("PYTENSOR_FLAGS environment variable has been set to: {pytensor_flags}".format(pytensor_flags=pytensor_flags)) # check gcnvkernel version in the input model path gcnvkernel.io_commons.check_gcnvkernel_version_from_path(args.input_model_path) diff --git a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_determine_ploidy_and_depth.py b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_determine_ploidy_and_depth.py index 880f0dd4180..da3da6a8c37 100644 --- a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_determine_ploidy_and_depth.py +++ b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/case_determine_ploidy_and_depth.py @@ -1,11 +1,11 @@ import os -# set theano flags -user_theano_flags = os.environ.get("THEANO_FLAGS") -default_theano_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ - "openmp=true,blas.ldflags=-lmkl_rt,openmp_elemwise_minsize=10" -theano_flags = default_theano_flags + ("" if user_theano_flags is None else "," + user_theano_flags) -os.environ["THEANO_FLAGS"] = theano_flags +# set pytensor flags +user_pytensor_flags = os.environ.get("PYTENSOR_FLAGS") +default_pytensor_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ + "openmp=true,blas__ldflags=-lmkl_rt,openmp_elemwise_minsize=10,exception_verbosity=high" +pytensor_flags = default_pytensor_flags + ("" if user_pytensor_flags is None else "," + user_pytensor_flags) +os.environ["PYTENSOR_FLAGS"] = pytensor_flags import logging import argparse @@ -80,7 +80,7 @@ args = parser.parse_args() gcnvkernel.cli_commons.set_logging_config_from_args(args) - logger.info("THEANO_FLAGS environment variable has been set to: {theano_flags}".format(theano_flags=theano_flags)) + logger.info("PYTENSOR_FLAGS environment variable has been set to: {pytensor_flags}".format(pytensor_flags=pytensor_flags)) # check gcnvkernel version in the input model path gcnvkernel.io_commons.check_gcnvkernel_version_from_path(args.input_model_path) diff --git a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_denoising_calling.py b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_denoising_calling.py index bc9de7c9bc7..c9bbf1d398d 100644 --- a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_denoising_calling.py +++ b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_denoising_calling.py @@ -1,11 +1,11 @@ import os -# set theano flags -user_theano_flags = os.environ.get("THEANO_FLAGS") -default_theano_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ - "openmp=true,blas.ldflags=-lmkl_rt,openmp_elemwise_minsize=10" -theano_flags = default_theano_flags + ("" if user_theano_flags is None else "," + user_theano_flags) -os.environ["THEANO_FLAGS"] = theano_flags +# set pytensor flags +user_pytensor_flags = os.environ.get("PYTENSOR_FLAGS") +default_pytensor_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ + "openmp=true,blas__ldflags=-lmkl_rt,openmp_elemwise_minsize=10,exception_verbosity=high" +pytensor_flags = default_pytensor_flags + ("" if user_pytensor_flags is None else "," + user_pytensor_flags) +os.environ["PYTENSOR_FLAGS"] = pytensor_flags import logging import argparse @@ -100,7 +100,7 @@ args = parser.parse_args() gcnvkernel.cli_commons.set_logging_config_from_args(args) - logger.info("THEANO_FLAGS environment variable has been set to: {theano_flags}".format(theano_flags=theano_flags)) + logger.info("PYTENSOR_FLAGS environment variable has been set to: {pytensor_flags}".format(pytensor_flags=pytensor_flags)) # copy the intervals to the model and calls paths # (we do this early to avoid inadvertent cleanup of temporary files) diff --git a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_determine_ploidy_and_depth.py b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_determine_ploidy_and_depth.py index 913c2ab8f21..57c8b1d4c04 100644 --- a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_determine_ploidy_and_depth.py +++ b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/cohort_determine_ploidy_and_depth.py @@ -1,11 +1,11 @@ import os -# set theano flags -user_theano_flags = os.environ.get("THEANO_FLAGS") -default_theano_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ - "openmp=true,blas.ldflags=-lmkl_rt,openmp_elemwise_minsize=10" -theano_flags = default_theano_flags + ("" if user_theano_flags is None else "," + user_theano_flags) -os.environ["THEANO_FLAGS"] = theano_flags +# set pytensor flags +user_pytensor_flags = os.environ.get("PYTENSOR_FLAGS") +default_pytensor_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ + "openmp=true,blas__ldflags=-lmkl_rt,openmp_elemwise_minsize=10,exception_verbosity=high" +pytensor_flags = default_pytensor_flags + ("" if user_pytensor_flags is None else "," + user_pytensor_flags) +os.environ["PYTENSOR_FLAGS"] = pytensor_flags import logging import argparse @@ -86,7 +86,7 @@ args = parser.parse_args() gcnvkernel.cli_commons.set_logging_config_from_args(args) - logger.info("THEANO_FLAGS environment variable has been set to: {theano_flags}".format(theano_flags=theano_flags)) + logger.info("PYTENSOR_FLAGS environment variable has been set to: {pytensor_flags}".format(pytensor_flags=pytensor_flags)) # copy the intervals and ploidy priors to the model path # (we do this early to avoid inadvertent cleanup of temporary files) diff --git a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/segment_gcnv_calls.py b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/segment_gcnv_calls.py index 2345ef83d6d..5fbb293b3ea 100644 --- a/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/segment_gcnv_calls.py +++ b/src/main/resources/org/broadinstitute/hellbender/tools/copynumber/segment_gcnv_calls.py @@ -1,11 +1,11 @@ import os -# set theano flags -user_theano_flags = os.environ.get("THEANO_FLAGS") -default_theano_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ - "openmp=true,blas.ldflags=-lmkl_rt,openmp_elemwise_minsize=10" -theano_flags = default_theano_flags + ("" if user_theano_flags is None else "," + user_theano_flags) -os.environ["THEANO_FLAGS"] = theano_flags +# set pytensor flags +user_pytensor_flags = os.environ.get("PYTENSOR_FLAGS") +default_pytensor_flags = "device=cpu,floatX=float64,optimizer=fast_run,compute_test_value=ignore," + \ + "openmp=true,blas__ldflags=-lmkl_rt,openmp_elemwise_minsize=10,exception_verbosity=high" +pytensor_flags = default_pytensor_flags + ("" if user_pytensor_flags is None else "," + user_pytensor_flags) +os.environ["PYTENSOR_FLAGS"] = pytensor_flags import logging import argparse @@ -73,7 +73,7 @@ args = parser.parse_args() gcnvkernel.cli_commons.set_logging_config_from_args(args) - logger.info("THEANO_FLAGS environment variable has been set to: {theano_flags}".format(theano_flags=theano_flags)) + logger.info("PYTENSOR_FLAGS environment variable has been set to: {pytensor_flags}".format(pytensor_flags=pytensor_flags)) # load read depth and ploidy metadata logger.info("Loading ploidy calls...") diff --git a/src/test/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCallerIntegrationTest.java b/src/test/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCallerIntegrationTest.java index 056e40a5f9a..174ecb604f1 100644 --- a/src/test/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCallerIntegrationTest.java +++ b/src/test/java/org/broadinstitute/hellbender/tools/copynumber/GermlineCNVCallerIntegrationTest.java @@ -46,8 +46,8 @@ public final class GermlineCNVCallerIntegrationTest extends CommandLineProgramTe private static final File SIM_INTERVAL_LIST_SHARD_0_ANNOTATED_FILE = new File(GCNV_SIM_DATA_DIR + "sim_intervals_shard_0.annotated.tsv"); private static final double ALLOWED_DELTA_FOR_DOUBLE_VALUES = 1E-6; - final List MODEL_FILES_TO_COMPARE = Arrays.asList("log_q_tau_tk.tsv", "mu_ard_u_log__.tsv", "mu_psi_t_log__.tsv", - "std_ard_u_log__.tsv", "std_psi_t_log__.tsv", "mu_W_tu.tsv", "mu_log_mean_bias_t.tsv", "std_W_tu.tsv", "std_log_mean_bias_t.tsv"); + final List MODEL_FILES_TO_COMPARE = Arrays.asList("log_q_tau_tk.tsv", "mu_ard_u_interval__.tsv", "mu_psi_t_log__.tsv", + "std_ard_u_interval__.tsv", "std_psi_t_log__.tsv", "mu_W_tu.tsv", "mu_log_mean_bias_t.tsv", "std_W_tu.tsv", "std_log_mean_bias_t.tsv"); final List CALLS_FILES_TO_COMPARE = Arrays.asList("baseline_copy_number_t.tsv", "log_c_emission_tc.tsv", "log_q_c_tc.tsv", "mu_psi_s_log__.tsv", "mu_read_depth_s_log__.tsv", "mu_z_su.tsv", "sample_name.txt", "std_psi_s_log__.tsv", @@ -182,7 +182,7 @@ public void testNumericalAccuracy() { } catch (final IOException ex) { throw new GATKException("Could not remove GermlineCNVCaller tracking files."); } - IntStream.range(1, 20).forEach( + IntStream.range(1, TEST_COUNT_FILES.length).forEach( s -> { try { FileUtils.deleteDirectory(new File(Paths.get(GCNV_TEST_OUTPUT_DIR, outputPrefix + "-calls", "SAMPLE_" + s).toString())); diff --git a/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariantsIntegrationTest.java b/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariantsIntegrationTest.java index 9ed10874a40..591a9db98e0 100644 --- a/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariantsIntegrationTest.java +++ b/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariantsIntegrationTest.java @@ -52,7 +52,7 @@ public void testRequirePythonEnvironment() throws IOException { /** * Run the tool on a small test VCF. */ - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testAllDefaultArgs() { final ArgumentsBuilder argsBuilder = new ArgumentsBuilder(); final File tempVcf = createTempFile("tester", ".vcf"); @@ -66,7 +66,7 @@ public void testAllDefaultArgs() { assertInfoFieldsAreClose(tempVcf, expectedVcf, GATKVCFConstants.CNN_1D_KEY); } - @Test(groups = {"python"}, expectedExceptions = PythonScriptExecutorException.class) + @Test(groups = {"python"}, expectedExceptions = PythonScriptExecutorException.class, enabled = false) public void testExceptionDuringAsyncBatch() { final ArgumentsBuilder argsBuilder = new ArgumentsBuilder(); final File tempVcf = createTempFile("tester", ".vcf"); @@ -82,7 +82,7 @@ public void testExceptionDuringAsyncBatch() { runCommandLine(argsBuilder); } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInferenceArchitecture() { final boolean newExpectations = false; final String expectedVCFName = largeFileTestDir + "VQSR/expected/cnn_1d_chr20_subset_expected.vcf"; @@ -104,7 +104,7 @@ public void testInferenceArchitecture() { } } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInferenceWeights() { final File tempVcf = createTempFile("tester", ".vcf"); final File expectedVcf = new File(largeFileTestDir + "VQSR/expected/cnn_1d_chr20_subset_expected.vcf"); @@ -119,7 +119,7 @@ public void testInferenceWeights() { assertInfoFieldsAreClose(tempVcf, expectedVcf, GATKVCFConstants.CNN_1D_KEY); } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInferenceArchitectureAndWeights() { final File tempVcf = createTempFile("tester", ".vcf"); final File expectedVcf = new File(largeFileTestDir + "VQSR/expected/cnn_1d_chr20_subset_expected.vcf"); @@ -135,7 +135,7 @@ public void testInferenceArchitectureAndWeights() { assertInfoFieldsAreClose(tempVcf, expectedVcf, GATKVCFConstants.CNN_1D_KEY); } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInferenceWithIntervals() { final boolean newExpectations = false; final String expectedVCFName = largeFileTestDir + "VQSR/expected/cnn_1d_contig20_1m_10m_expected.vcf"; @@ -157,7 +157,7 @@ public void testInferenceWithIntervals() { } } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testSmallBatchInference() { final File tempVcf = createTempFile("tester", ".vcf"); final File expectedVcf = new File(largeFileTestDir + "VQSR/expected/cnn_1d_chr20_subset_expected.vcf"); @@ -172,7 +172,7 @@ public void testSmallBatchInference() { assertInfoFieldsAreClose(tempVcf, expectedVcf, GATKVCFConstants.CNN_1D_KEY); } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testOnContigEdge() { final String edgeVcf = toolsTestDir + "walkers/VQSR/variantNearContigEdge.vcf"; final File tempVcf = createTempFile("tester", ".vcf"); @@ -190,7 +190,7 @@ public void testOnContigEdge() { /** * Run the 2D Model on a small test VCF with the resource loaded weights and architecture. */ - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInference2dResourceModel() { // We reset the random number generator at the beginning of each test so that the random down-sampling of reads // by the reservoir down-sampler does not cause slightly different scores. @@ -215,7 +215,7 @@ public void testInference2dResourceModel() { /** * Run the 2D Model on a small test VCF. */ - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInferenceArchitecture2d() { Utils.resetRandomGenerator(); final boolean newExpectations = false; @@ -243,7 +243,7 @@ public void testInferenceArchitecture2d() { } } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInferenceWeights2d() { Utils.resetRandomGenerator(); TensorType tt = TensorType.read_tensor; @@ -264,7 +264,7 @@ public void testInferenceWeights2d() { assertInfoFieldsAreClose(tempVcf, expectedVcf, GATKVCFConstants.CNN_2D_KEY); } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testInferenceArchitectureAndWeights2d() { Utils.resetRandomGenerator(); TensorType tt = TensorType.read_tensor; @@ -300,7 +300,4 @@ private void assertInfoFieldsAreClose(File actualVcf, File expectedVcf, String i } Assert.assertTrue(!expectedVi.hasNext() && !actualVi.hasNext()); } - - - } diff --git a/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantPipelineTest.java b/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantPipelineTest.java index b7b4834d0f1..c5504b9bbc0 100644 --- a/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantPipelineTest.java +++ b/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNVariantPipelineTest.java @@ -17,13 +17,13 @@ public class CNNVariantPipelineTest extends GATKBaseTest { private static File readTensorDir; private static File referenceTensorDir; - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public static void makeTempDirectories() { readTensorDir = createTempDir("readTensorDir"); referenceTensorDir = createTempDir("referenceTensorDir"); } - @Test(groups = {"python"}, dependsOnMethods = {"makeTempDirectories"}) + @Test(groups = {"python"}, dependsOnMethods = {"makeTempDirectories"}, enabled = false) public void testGenerateReferenceTensors() { final ArgumentsBuilder args = new ArgumentsBuilder(); args.addRaw("CNNVariantWriteTensors") @@ -37,7 +37,7 @@ public void testGenerateReferenceTensors() { new Main().instanceMain(args.getArgsArray()); } - @Test(groups = {"python"}, dependsOnMethods = {"makeTempDirectories"}) + @Test(groups = {"python"}, dependsOnMethods = {"makeTempDirectories"}, enabled = false) public void testGenerateReadTensors() { final String bamFile = largeFileTestDir + "VQSR/g94982_b37_chr20_1m_8m_bamout.bam"; final ArgumentsBuilder args = new ArgumentsBuilder(); @@ -55,7 +55,7 @@ public void testGenerateReadTensors() { new Main().instanceMain(args.getArgsArray()); } - @Test(groups = {"python"}, dependsOnMethods = {"testGenerateReferenceTensors"}) + @Test(groups = {"python"}, dependsOnMethods = {"testGenerateReferenceTensors"}, enabled = false) public void testTrainingReferenceModel() { final ArgumentsBuilder args = new ArgumentsBuilder(); args.addRaw("CNNVariantTrain") @@ -69,7 +69,7 @@ public void testTrainingReferenceModel() { new Main().instanceMain(args.getArgsArray()); } - @Test(groups = {"python"}, dependsOnMethods = {"testGenerateReadTensors"}) + @Test(groups = {"python"}, dependsOnMethods = {"testGenerateReadTensors"}, enabled = false) public void testTrainingReadModel() { final ArgumentsBuilder args = new ArgumentsBuilder(); args.addRaw("CNNVariantTrain") @@ -85,7 +85,7 @@ public void testTrainingReadModel() { new Main().instanceMain(args.getArgsArray()); } - @Test(groups = {"python"}) + @Test(groups = {"python"}, enabled = false) public void testTranches() { final String trancheVCF = largeFileTestDir + "VQSR/g94982_b37_chr20_1m_10m.vcf.gz"; final String snpTruthVCF = largeFileTestDir + "VQSR/giab_chr20_1m_10m.vcf.gz"; diff --git a/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/FilterVariantTranchesIntegrationTest.java b/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/FilterVariantTranchesIntegrationTest.java index f0f76f11e6e..6587a957dad 100644 --- a/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/FilterVariantTranchesIntegrationTest.java +++ b/src/test/java/org/broadinstitute/hellbender/tools/walkers/vqsr/FilterVariantTranchesIntegrationTest.java @@ -97,7 +97,7 @@ public Object[][] getGoodResourceCombos() { /** * Run the tool on a small test VCF. */ - @Test(dataProvider = "getFilteringArgs") + @Test(dataProvider = "getFilteringArgs", enabled = false) public void runTrancheFiltering(final List resources, final List extraArgs, final String expectedOutput) throws IOException { final ArgumentsBuilder argsBuilder = new ArgumentsBuilder(); @@ -113,7 +113,7 @@ public void runTrancheFiltering(final List resources, final List spec.executeTest("testTrancheFiltering", this); } - @Test(dataProvider = "getBadResourceCombos", expectedExceptions = UserException.BadInput.class) + @Test(dataProvider = "getBadResourceCombos", expectedExceptions = UserException.BadInput.class, enabled = false) public void runTrancheFilteringOnBadInputs(final String inputVcf, final String resourceVcf) { final ArgumentsBuilder argsBuilder = new ArgumentsBuilder(); argsBuilder.add(StandardArgumentDefinitions.VARIANT_LONG_NAME, inputVcf) @@ -125,7 +125,7 @@ public void runTrancheFilteringOnBadInputs(final String inputVcf, final String r runCommandLine(argsBuilder); } - @Test(dataProvider = "getGoodResourceCombos") + @Test(dataProvider = "getGoodResourceCombos", enabled = false) public void runTrancheFilteringOnGoodInputs(final String inputVcf, final String resourceVcf) { final ArgumentsBuilder argsBuilder = new ArgumentsBuilder(); argsBuilder.add(StandardArgumentDefinitions.VARIANT_LONG_NAME, inputVcf) diff --git a/src/test/java/org/broadinstitute/hellbender/utils/python/PythonEnvironmentIntegrationTest.java b/src/test/java/org/broadinstitute/hellbender/utils/python/PythonEnvironmentIntegrationTest.java index b055d8461cd..f7f1baf0f38 100644 --- a/src/test/java/org/broadinstitute/hellbender/utils/python/PythonEnvironmentIntegrationTest.java +++ b/src/test/java/org/broadinstitute/hellbender/utils/python/PythonEnvironmentIntegrationTest.java @@ -17,17 +17,16 @@ public Object[][] getDataPackagePresent() { return new Object[][] { // names of base packages that we should be able to import from within the GATK conda environment // NOTE: these must be kept in sync with the versions in gatkcondaenv.yml.template - { "mkl", "2.3.0" }, - { "numpy", "1.17.5" }, - { "theano", "1.0.4" }, - { "tensorflow", "1.15.0" }, - { "scipy", "1.0.0" }, - { "pymc3", "3.1" }, - { "keras", "2.2.4" }, - { "h5py", "2.10.0" }, - { "sklearn", "0.23.1" }, - { "matplotlib", "3.2.1" }, - { "pandas", "1.0.3" }, + { "mkl", "2.4.0" }, + { "numpy", "1.26.2" }, + { "pytensor", "2.18.1" }, + { "torch", "2.1.0.post100" }, + { "scipy", "1.11.4" }, + { "pymc", "5.10.0" }, + { "h5py", "3.10.0" }, + { "sklearn", "1.3.2" }, + { "matplotlib", "3.8.2" }, + { "pandas", "2.1.3" }, { "argparse", null }, { "gcnvkernel", null }, @@ -43,9 +42,9 @@ public Object[][] getDataPackagePresent() { @DataProvider(name="dataPackageMKLEnabled") public Object[][] getDataPackageMKLEnabled() { return new Object[][] { - { "numpy", "numpy.__config__.get_info('blas_mkl_info') != {} and numpy.__config__.get_info('lapack_mkl_info') != {}" }, - { "theano", "'-lmkl_rt' in theano.config.blas.ldflags" }, - { "tensorflow", "tensorflow.pywrap_tensorflow.IsMklEnabled()" } + // { "numpy", "numpy.__config__.get_info('blas_mkl_info') != {} and numpy.__config__.get_info('lapack_mkl_info') != {}" }, TODO this check was removed after the update to conda-forge numpy 1.26.2, see https://github.com/broadinstitute/gatk/pull/8561 + { "pytensor", "'-lmkl_rt' in pytensor.config.blas__ldflags" }, + { "torch", "'BLAS_INFO=mkl' in torch.__config__.show() and 'USE_MKL=ON' in torch.__config__.show()" } }; } diff --git a/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-contig-ploidy-model.tar.gz b/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-contig-ploidy-model.tar.gz index 3a899addca0..9bd909d1365 100644 --- a/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-contig-ploidy-model.tar.gz +++ b/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-contig-ploidy-model.tar.gz @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a667caea27776a91ae612f57769fd79fde2850fe7d98ce9903c787bccbad1a1e -size 1221 +oid sha256:6f7ca34ae9404a25deaaab2d52861f2f8d5ac5f3ad9091217eea13617b6e4b05 +size 1220 diff --git a/src/test/resources/large/cnv_germline_workflows_test_files/wes-do-gc-gcnv-model-0.tar.gz 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a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_denoised_copy_ratio_t.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_denoised_copy_ratio_t.tsv index d9245516214..48c7ca4faac 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_denoised_copy_ratio_t.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_denoised_copy_ratio_t.tsv @@ -2,173 +2,173 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 -1.938195794952936 -2.1048836194514284 -1.8405429122613182 -2.0997240647283677 -2.1553921704485353 -1.9228466416210988 -1.75524666077193 -1.8632760147242609 -1.9383100443355137 -1.8842552763179274 -3.6873758488813704 -2.077332105054551 -2.1401929818507304 -2.0978615092588044 -2.643543316661906 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b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_psi_s_log__.tsv @@ -2,4 +2,4 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 --3.538641261098718 +-4.115896030605331 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_read_depth_s_log__.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_read_depth_s_log__.tsv index c96a77a10a3..ccc7fb7cf62 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_read_depth_s_log__.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_read_depth_s_log__.tsv @@ -2,4 +2,4 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 -6.046959920320962 +6.040779074567946 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_z_su.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_z_su.tsv index 3d86b49fe40..37c432d9947 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_z_su.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/mu_z_su.tsv @@ -2,9 +2,9 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 --0.012115853878657032 --1.0135477723806217 -0.8780923681315713 -1.750551551168998 --0.6475298304403772 -0.13033304429958514 +-2.053186822108883 +-0.27155276025900343 +-0.33779550124699503 +0.0020682680646162436 +0.5308094576020672 +-0.7951313743446329 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_denoised_copy_ratio_t.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_denoised_copy_ratio_t.tsv index 702e2774363..276934a687e 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_denoised_copy_ratio_t.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_denoised_copy_ratio_t.tsv @@ -2,173 +2,173 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 -0.35676082648741175 -0.34402859005051317 -0.3470647885008761 -0.404464047463586 -0.35673302506773813 -0.28819109468380133 -0.2768214842534659 -0.3218643171948218 -0.4566132252978298 -0.34157224618667303 -0.8502589001099329 -0.4037841365683954 -0.3970514119661808 -0.4050291550657542 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+0.2906127525123466 +0.23557634427299473 +0.32871953244459856 +0.29851864822644264 +0.2921502390292671 +0.2643240052044306 +0.25973006587944575 +0.35716966405447775 +0.2690896237272922 +0.3101609100198319 +0.25210101772252 +0.31555800089777025 +0.33767907177649886 +0.24696819846452356 +0.24648591139891013 +0.2540669176573066 +0.4052443013522526 +0.2742808588564273 +0.3344883982781669 +0.2787913700146262 +0.2746187280605595 +0.3090697830503116 +0.25334489152548734 +0.25184011088245944 +0.34353619482192504 +0.2610800052925577 +0.22839912434367227 +0.23620921717484936 +0.23473000517846238 +0.3393417023786324 +0.3356630037770536 +0.2583396418721628 +0.6325770467065485 +0.36243332284044205 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_psi_s_log__.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_psi_s_log__.tsv index 0a3c37391d5..1cad5dc6bcf 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_psi_s_log__.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_psi_s_log__.tsv @@ -2,4 +2,4 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 -0.13269916055190087 +0.15238544640390225 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_read_depth_s_log__.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_read_depth_s_log__.tsv index 609a876003f..fc490df2b21 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_read_depth_s_log__.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_read_depth_s_log__.tsv @@ -2,4 +2,4 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 -0.00886525733916229 +0.007974686289217315 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_z_su.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_z_su.tsv index 9adbd00547b..c765eb30454 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_z_su.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/SAMPLE_0/std_z_su.tsv @@ -2,9 +2,9 @@ @CO dtype:float64 @RG ID:GATKCopyNumber SM:SAMPLE_000 VALUE_0 -0.08666609905783836 -0.019104038538349806 -0.019352207566272466 -0.024772482294391822 -0.0199147773041517 -0.029881461734426608 +0.016402742030811372 +0.014263295035367513 +0.023064620931183673 +0.06592055351603773 +0.01586964248482667 +0.015457815474999846 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/gcnvkernel_version.json b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/gcnvkernel_version.json index ee711a11f0d..50d31529539 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/gcnvkernel_version.json +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-calls/gcnvkernel_version.json @@ -1,3 +1,3 @@ { - "version": "0.8" + "version": "0.9" } diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/gcnvkernel_version.json b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/gcnvkernel_version.json index ee711a11f0d..50d31529539 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/gcnvkernel_version.json +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/gcnvkernel_version.json @@ -1,3 +1,3 @@ { - "version": "0.8" + "version": "0.9" } diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/log_q_tau_tk.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/log_q_tau_tk.tsv index 92faf78e653..0756b14aa1a 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/log_q_tau_tk.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/log_q_tau_tk.tsv @@ -1,173 +1,173 @@ @CO shape:(170, 2) @CO dtype:float64 VALUE_0 VALUE_1 --9.313222415485711e-11 -23.097000166001383 --9.313222415485711e-11 -23.097000166052595 --9.313222415485711e-11 -23.097000160963137 --9.313222415485711e-11 -23.097000428866302 --0.0005098506463667163 -7.581647639882051 --6.457488015809698e-06 -11.950273395233049 --0.0007316526827102487 -7.220570437808737 --5.3404393589029375e-06 -12.140205302403109 --1.8355094547783946e-05 -10.905612567747532 --0.00030351743841222634 -8.100223246265807 --8.874950082932154e-05 -9.329737128513111 --0.00014621621019650366 -8.830497247161098 --0.00022113797789918843 -8.416834283814076 --0.000272022189643395 -8.209762923449361 --0.0002982189970393567 -8.117831557755276 --0.00035376024641137205 -7.9470680191806276 --0.00037050422601914157 -7.900830953380472 --0.0004946810970354321 -7.611844581804981 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a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_W_tu.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_W_tu.tsv index e00baf31de2..31b343fc1d4 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_W_tu.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_W_tu.tsv @@ -1,173 +1,173 @@ @CO shape:(170, 6) @CO dtype:float64 VALUE_0 VALUE_1 VALUE_2 VALUE_3 VALUE_4 VALUE_5 -0.026753700256980092 -0.4006105617803221 1.5199961373482915 -0.779905267815817 -0.5153844356567848 -0.9451118147201304 -0.015150016011497324 -1.8268418099390387 0.07982225054380333 -0.045688588920964224 1.2604554764051241 0.30051602599626903 -0.027967928165796515 -2.090194777388886 -0.9994619077127309 0.28307251584941956 2.0761753596131234 -0.4084505317477433 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a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_ard_u_log__.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_ard_u_log__.tsv deleted file mode 100644 index d0e7f8ae2f2..00000000000 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_ard_u_log__.tsv +++ /dev/null @@ -1,9 +0,0 @@ -@CO shape:(6,) -@CO dtype:float64 -VALUE_0 -3.3157452315872797 -0.27700210936024544 -0.17155080231183484 -0.5966390764523473 -0.332508299459193 -1.124462475245523 diff --git a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_log_mean_bias_t.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_log_mean_bias_t.tsv index 1066841cfb5..9a357595684 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_log_mean_bias_t.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_log_mean_bias_t.tsv @@ -1,173 +1,173 @@ @CO shape:(170,) @CO dtype:float64 VALUE_0 --0.25849585646014905 -0.040225177787154114 --0.026185431993490965 -0.03725559498343827 --0.16538441997032297 --0.09228167661400453 --0.1256880156401002 -0.08633265467685908 -0.022113132827958033 -0.15391751538075946 --0.10095306370885793 --0.1687663961928414 --0.11488124990804598 --0.057855651527406245 -0.032467834693093015 --0.16051173145443026 --0.15188551009171883 -0.07234064881915624 --0.07072211886982924 -0.1104903927299972 -0.003183972228064411 --0.13415244390416362 --0.0173410831331658 -0.1547962836712629 --0.13336018556534004 --0.03743712782117777 -0.012381405865775224 --0.2114343671895864 --0.025791090747636716 --0.21293904382110193 -0.04149588027009048 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b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_psi_t_log__.tsv index de47549e796..e4e7496a864 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_psi_t_log__.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/mu_psi_t_log__.tsv @@ -1,173 +1,173 @@ @CO shape:(170,) @CO dtype:float64 VALUE_0 --5.831494684210253 --5.701904843345005 --5.949368974150768 --6.050755166785436 --6.585187809043013 --6.650864603574508 --6.544312589203743 --6.441139369710851 --4.146274741068249 --5.524828329319168 --5.390424770356458 --5.586802120322322 --5.468917126300418 --5.698363787812316 --6.5383625395148 --5.5039598371557155 --6.400893019488429 --5.466109918505184 --4.974765480420021 --4.309302019458549 --5.609005040676659 --6.180305162670144 --5.976934300597773 --6.691007187635419 --5.4558432293894334 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a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/std_psi_t_log__.tsv b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/std_psi_t_log__.tsv index ac7fdfe96d0..692a2975b32 100644 --- a/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/std_psi_t_log__.tsv +++ b/src/test/resources/org/broadinstitute/hellbender/tools/copynumber/gcnv-numerical-accuracy/shard_0-model/std_psi_t_log__.tsv @@ -1,173 +1,173 @@ @CO shape:(170,) @CO dtype:float64 VALUE_0 -0.7720230850327123 -0.697166728658097 -0.7847974243061422 -0.8253380007127161 -0.9334279288237071 -1.0321250671017763 -0.9791284394392797 -0.9252520231672539 -0.38169143443616627 -0.6705357014121687 -0.6266127063407134 -0.7011411300256046 -0.6851215322141866 -0.7080319594541422 -0.9025932014928516 -0.6658485136950354 -0.9440406064757607 -0.6093630424050356 -0.5199411334291777 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