Add UCI tutorial

README.md

@@ -2,7 +2,11 @@
 <a href="#"><img width="380" img src=".assets/kronfluence.svg" alt="Kronfluence"/></a>
 </p>
 
+
 <p align="center">
+    <a href="https://pypi.org/project/kronfluence">
+        <img alt="License" src="https://img.shields.io/pypi/v/kronfluence.svg?style=flat-square">
+    </a>
     <a href="https://github.com/pomonam/kronfluence/blob/main/LICENSE">
         <img alt="License" src="https://img.shields.io/badge/License-Apache_2.0-blue.svg">
     </a>

examples/cifar/README.md

@@ -0,0 +1,32 @@
+# CIFAR-10 & ResNet-9 Example
+
+This directory contains scripts designed for training ResNet-9 on CIFAR-10. 
+
+## Training
+
+To train a regression model on the Concrete dataset, run the following command:
+```bash
+python train.py --dataset_name concrete \ 
+    --dataset_dir ./data \
+    --output_dir ./checkpoints \
+    --train_batch_size 32 \
+    --eval_batch_size 1024 \
+    --learning_rate 0.03 \
+    --weight_decay 1e-5 \
+    --num_train_epochs 20 \
+    --seed 1004
+```
+
+# Computing Pairwise Influence Scores
+
+To obtain a pairwise influence scores using EKFAC, run the following command:
+```bash
+python analyze.py --dataset_name concrete \ 
+    --dataset_dir ./data \
+    --checkpoint_dir ./checkpoints \
+    --factor_strategy ekfac
+```
+
+# Counterfactual Evaluation
+
+You can check the notebook `tutorial.ipynb` for running the counterfactual evaluation.

examples/uci/requirements.txt

@@ -1,2 +1,3 @@
 scikit-learn
-jupyter
+jupyter
+tueplots

tests/factors/test_covariances.py

@@ -8,13 +8,13 @@
 
 from kronfluence.analyzer import Analyzer, prepare_model
 from kronfluence.arguments import FactorArguments
+from kronfluence.task import Task
 from kronfluence.utils.constants import (
     ACTIVATION_COVARIANCE_MATRIX_NAME,
     COVARIANCE_FACTOR_NAMES,
     GRADIENT_COVARIANCE_MATRIX_NAME,
     NUM_COVARIANCE_PROCESSED,
 )
-from kronfluence.task import Task
 from kronfluence.utils.dataset import DataLoaderKwargs
 from tests.utils import ATOL, RTOL, check_tensor_dict_equivalence, prepare_test
 

tests/factors/test_eigens.py

@@ -8,6 +8,7 @@
 
 from kronfluence.analyzer import Analyzer, prepare_model
 from kronfluence.arguments import FactorArguments
+from kronfluence.task import Task
 from kronfluence.utils.constants import (
     ACTIVATION_EIGENVECTORS_NAME,
     EIGENDECOMPOSITION_FACTOR_NAMES,
@@ -16,7 +17,6 @@
     LAMBDA_MATRIX_NAME,
     NUM_LAMBDA_PROCESSED,
 )
-from kronfluence.task import Task
 from kronfluence.utils.dataset import DataLoaderKwargs
 from tests.utils import ATOL, RTOL, check_tensor_dict_equivalence, prepare_test
 

tests/scores/test_pairwise_scores.py

@@ -8,8 +8,8 @@
 
 from kronfluence.analyzer import Analyzer, prepare_model
 from kronfluence.arguments import FactorArguments, ScoreArguments
-from kronfluence.utils.constants import ALL_MODULE_NAME
 from kronfluence.task import Task
+from kronfluence.utils.constants import ALL_MODULE_NAME
 from kronfluence.utils.dataset import DataLoaderKwargs
 from tests.utils import ATOL, RTOL, check_tensor_dict_equivalence, prepare_test
 

tests/scores/test_self_scores.py

@@ -8,8 +8,8 @@
 
 from kronfluence.analyzer import Analyzer, prepare_model
 from kronfluence.arguments import FactorArguments, ScoreArguments
-from kronfluence.utils.constants import ALL_MODULE_NAME
 from kronfluence.task import Task
+from kronfluence.utils.constants import ALL_MODULE_NAME
 from kronfluence.utils.dataset import DataLoaderKwargs
 from tests.utils import ATOL, RTOL, check_tensor_dict_equivalence, prepare_test
 

tests/test_per_sample_gradients.py

@@ -13,13 +13,10 @@
 
 from kronfluence.analyzer import Analyzer, prepare_model
 from kronfluence.arguments import FactorArguments
-from kronfluence.utils.constants import (
-    LAMBDA_MATRIX_NAME,
-    PRECONDITIONED_GRADIENT_NAME,
-)
 from kronfluence.module.tracked_module import ModuleMode, TrackedModule
 from kronfluence.module.utils import set_mode, update_factor_args
 from kronfluence.task import Task
+from kronfluence.utils.constants import LAMBDA_MATRIX_NAME, PRECONDITIONED_GRADIENT_NAME
 from kronfluence.utils.dataset import DataLoaderKwargs
 from tests.utils import (
     ATOL,

tests/testable_tasks/classification.py

@@ -6,10 +6,11 @@
 import torch.nn.functional as F
 import torchvision
 from accelerate.utils import set_seed
-from kronfluence.task import Task
 from torch import nn
 from torch.utils import data
 
+from kronfluence.task import Task
+
 BATCH_TYPE = Tuple[torch.Tensor, torch.Tensor]
 
 

tests/testable_tasks/language_modeling.py

@@ -6,11 +6,12 @@
 import torch
 import torch.nn.functional as F
 from datasets import load_dataset
-from kronfluence.task import Task
 from torch import nn
 from torch.utils import data
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, Conv1D
 
+from kronfluence.task import Task
+
 BATCH_TYPE = Dict[str, torch.Tensor]
 
 

tests/testable_tasks/text_classification.py

@@ -5,11 +5,12 @@
 import torch
 import torch.nn.functional as F
 from datasets import load_dataset
-from kronfluence.task import Task
 from torch import nn
 from torch.utils import data
 from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
 
+from kronfluence.task import Task
+
 BATCH_TYPE = Dict[str, torch.Tensor]