Merge branch 'charmlab:main' into main

Author: Chenghao-Tan

.github/workflows/pre-commit.yaml

@@ -4,21 +4,24 @@ on: [pull_request]
 
 jobs:
   pre-commit:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
 
     steps:
       - name: Check out the code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
-      - name: Set up Python
-        uses: actions/setup-python@v4
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v5
         with:
           python-version: '3.7'
+          cache: 'pip'
 
-      - name: Install dependencies
+      - name: Install project deps (same as local)
         run: |
+          python -m pip install -U pip setuptools wheel
+          pip install -r requirements-dev.txt
+          pip install -e .
           pip install pre-commit
-          pre-commit install-hooks
 
       - name: Run pre-commit hooks
-        run: pre-commit run --all-files
+        run: pre-commit run --all-files --show-diff-on-failure

.pre-commit-config.yaml

@@ -25,30 +25,32 @@ repos:
         entry: python tools/clean_pytest_cache.py
         language: system
         types: [python]
-  - repo: local
-    hooks:
-      - id: check-new-methods-structure
-        name: Check new_methods Folder Structure and Run Unit Tests
-        entry: python tools/folder_structure_check.py
-        language: python
-        types: [python]
-        additional_dependencies:
-          - causalgraphicalmodels==0.0.4
-          - dice-ml==0.5
-          - ipython==7.16.0
-          - h5py==2.10.0
-          - keras==2.3.0
-          - lime==0.2.0.1
-          - matplotlib
-          - mip==1.12.0
-          - pandas
-          - protobuf<=3.21
-          - PySMT==0.9.5
-          - pytest==6.1.2
-          - pyyaml
-          - recourse==1.0.0
-          - scikit-learn==0.23.2
-          - tensorflow==1.14.0
-          - torch
-          - torchvision
-          - xgboost==1.4.2
+  # - repo: local
+  #   hooks:
+  #     - id: check-new-methods-structure
+  #       name: Check new_methods Folder Structure and Run Unit Tests
+  #       entry: python tools/folder_structure_check.py
+  #       pass_filenames: false
+  #       language: python
+  #       types: [python]
+  #       additional_dependencies:
+  #         - .
+  #         - causalgraphicalmodels==0.0.4
+  #         - dice-ml==0.5
+  #         - ipython==7.16.0
+  #         - h5py==2.10.0
+  #         - keras==2.3.0
+  #         - lime==0.2.0.1
+  #         - matplotlib
+  #         - mip==1.12.0
+  #         - pandas
+  #         - protobuf<=3.21
+  #         - PySMT==0.9.5
+  #         - pytest==6.1.2
+  #         - pyyaml
+  #         - recourse==1.0.0
+  #         - scikit-learn==0.23.2
+  #         - tensorflow==1.14.0
+  #         - torch
+  #         - torchvision
+  #         - xgboost==1.4.2

assignment/assignment.py

@@ -25,7 +25,7 @@
 from methods.processing import create_hash_dataframe
 from models.catalog import ModelCatalog
 from models.negative_instances import predict_negative_instances
-from tools.logging import log
+from tools.log import log
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 warnings.simplefilter(action="ignore", category=FutureWarning)

experiments/run_experiment.py

@@ -22,7 +22,7 @@
 from models.api import MLModel
 from models.catalog import ModelCatalog
 from models.negative_instances import predict_negative_instances
-from tools.logging import log
+from tools.log import log
 
 RANDOM_SEED = 54321
 

methods/autoencoder/models/csvae.py

@@ -12,7 +12,7 @@
 
 from methods.autoencoder.losses import csvae_loss
 from methods.autoencoder.save_load import get_home
-from tools.logging import log
+from tools.log import log
 
 tf.compat.v1.disable_eager_execution()
 

methods/autoencoder/models/vae.py

@@ -8,7 +8,7 @@
 import torch.nn as nn
 
 from methods.autoencoder.save_load import get_home
-from tools.logging import log
+from tools.log import log
 
 tf.compat.v1.disable_eager_execution()
 

methods/catalog/actionable_recourse/model.py

@@ -6,7 +6,7 @@
 from lime.lime_tabular import LimeTabularExplainer
 
 from methods.processing import check_counterfactuals, encode_feature_names
-from tools.logging import log
+from tools.log import log
 
 from ...api import RecourseMethod
 from ...processing.counterfactuals import merge_default_parameters

methods/catalog/cchvae/model.py

@@ -13,7 +13,7 @@
     reconstruct_encoding_constraints,
 )
 from models.api import MLModel
-from tools.logging import log
+from tools.log import log
 
 
 class CCHVAE(RecourseMethod):

…hods/catalog/claproar/flaky_reproduce.py methods/catalog/claproar/reproduce.pymethods/catalog/claproar/flaky_reproduce.py renamed to methods/catalog/claproar/reproduce.py methods/catalog/claproar/flaky_reproduce.py renamed to methods/catalog/claproar/reproduce.py

@@ -58,31 +58,32 @@ def test_claproar_counterfactuals_standard_deviation(dataset_name):
 Patrick Altmeyer, Giovan Angela, Karol Dobiczek, Arie van Deursen, Cynthia C. S. Liem
 """
 
+# flaky test
 
-@pytest.mark.parametrize("dataset_name", [("credit")])
-def test_claproar_distribution_shift(dataset_name):
-    data = DataCatalog(dataset_name, "linear", 0.7)
-    model = ModelCatalog(data, "linear", backend="pytorch")
+# @pytest.mark.parametrize("dataset_name", [("credit")])
+# def test_claproar_distribution_shift(dataset_name):
+#     data = DataCatalog(dataset_name, "linear", 0.7)
+#     model = ModelCatalog(data, "linear", backend="pytorch")
 
-    claproar = ClaPROAR(mlmodel=model)
+#     claproar = ClaPROAR(mlmodel=model)
 
-    total_factuals = predict_negative_instances(model, data)
+#     total_factuals = predict_negative_instances(model, data)
 
-    factuals = total_factuals.iloc[:5]
+#     factuals = total_factuals.iloc[:5]
 
-    counterfactuals = claproar.get_counterfactuals(factuals)
+#     counterfactuals = claproar.get_counterfactuals(factuals)
 
-    negative_instances = predict_negative_instances(model, data).iloc[:5]
+#     negative_instances = predict_negative_instances(model, data).iloc[:5]
 
-    original_np = negative_instances.drop("y", axis=1).to_numpy()
-    counterfactual_np = counterfactuals.to_numpy()
-    mmd_value = compute_mmd(original_np, counterfactual_np)
+#     original_np = negative_instances.drop("y", axis=1).to_numpy()
+#     counterfactual_np = counterfactuals.to_numpy()
+#     mmd_value = compute_mmd(original_np, counterfactual_np)
 
-    expected_mmd_value = 0.03
+#     expected_mmd_value = 0.03
 
-    tolerance = 0.03
+#     tolerance = 0.03
 
-    assert abs(mmd_value - expected_mmd_value) <= tolerance, "MMD value mismatch."
+#     assert abs(mmd_value - expected_mmd_value) <= tolerance, "MMD value mismatch."
 
 
 """

methods/catalog/clue/library/clue_ml/AE_models/AE/fc_gauss_cat.py

@@ -16,7 +16,7 @@
 from methods.catalog.clue.library.clue_ml.src.probability import normal_parse_params
 from methods.catalog.clue.library.clue_ml.src.radam import RAdam
 from methods.catalog.clue.library.clue_ml.src.utils import BaseNet, to_variable
-from tools.logging import log
+from tools.log import log
 
 from .models import MLP_preact_generator_net, MLP_preact_recognition_net