Merge pull request #113 from ing-bank/cronjob_test_dependencies

Add github action that will run unit tests everyday, closes #42

.github/workflows/cronjob_unit_tests.yml

@@ -0,0 +1,44 @@
+name: Cron Test Dependencies
+
+# Controls when the action will run.
+# Everyday at 4:05
+# See https://crontab.guru/#5_4_*_*_*
+on:
+  schedule:
+    - cron: "5 4 * * *"
+
+jobs:
+  run:
+    name: Run unit tests
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        build: [macos, ubuntu, windows]
+        include:
+          - build: macos
+            os: macos-latest
+            SKIP_LIGHTGBM: True
+          - build: ubuntu
+            os: ubuntu-latest
+            SKIP_LIGHTGBM: False
+          - build: windows
+            os: windows-latest
+            SKIP_LIGHTGBM: False
+        python-version: [3.6, 3.7, 3.8]
+    steps:
+    - uses: actions/checkout@master
+    - name: Setup Python  
+      uses: actions/setup-python@master
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      env:
+        SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }}
+      run: |
+        pip3 install --upgrade setuptools pip
+        pip3 install ".[all]"
+    - name: Run unit tests
+      env:
+        SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }}
+      run: |
+        pytest

tests/interpret/test_model_interpret.py

@@ -4,7 +4,6 @@
 import numpy as np
 import pandas as pd
 from probatus.interpret import ShapModelInterpreter
-from unittest.mock import patch
 import os
 
 
@@ -116,16 +115,14 @@ def test_shap_interpret(fitted_tree, X_train, y_train, X_test, y_test, expected_
     assert test_auc == pytest.approx(0.833, 0.01)
 
     # Check if plots work for such dataset
-    with patch("matplotlib.pyplot.figure") as _:
-        with patch("shap.plots._waterfall.waterfall_legacy"):
-            ax1 = shap_interpret.plot("importance", target_set="test", show=False)
-            ax2 = shap_interpret.plot("summary", target_set="test", show=False)
-            ax3 = shap_interpret.plot("dependence", target_columns="col_3", target_set="test", show=False)
-            ax4 = shap_interpret.plot("sample", samples_index=X_test.index.tolist()[0:2], target_set="test", show=False)
-            ax5 = shap_interpret.plot("importance", target_set="train")
-            ax6 = shap_interpret.plot("summary", target_set="train")
-            ax7 = shap_interpret.plot("dependence", target_columns="col_3", target_set="train")
-            ax8 = shap_interpret.plot("sample", samples_index=X_train.index.tolist()[0:2], target_set="train")
+    ax1 = shap_interpret.plot("importance", target_set="test", show=False)
+    ax2 = shap_interpret.plot("summary", target_set="test", show=False)
+    ax3 = shap_interpret.plot("dependence", target_columns="col_3", target_set="test", show=False)
+    ax4 = shap_interpret.plot("sample", samples_index=X_test.index.tolist()[0:2], target_set="test", show=False)
+    ax5 = shap_interpret.plot("importance", target_set="train", show=False)
+    ax6 = shap_interpret.plot("summary", target_set="train", show=False)
+    ax7 = shap_interpret.plot("dependence", target_columns="col_3", target_set="train", show=False)
+    ax8 = shap_interpret.plot("sample", samples_index=X_train.index.tolist()[0:2], target_set="train", show=False)
     assert not (isinstance(ax1, list))
     assert not (isinstance(ax2, list))
     assert isinstance(ax3, list) and len(ax4) == 2
@@ -167,16 +164,14 @@ def test_shap_interpret_lin_models(
     assert test_auc == pytest.approx(0.833, 0.01)
 
     # Check if plots work for such dataset
-    with patch("matplotlib.pyplot.figure") as _:
-        with patch("shap.plots._waterfall.waterfall_legacy"):
-            ax1 = shap_interpret.plot("importance", target_set="test", show=False)
-            ax2 = shap_interpret.plot("summary", target_set="test", show=False)
-            ax3 = shap_interpret.plot("dependence", target_columns="col_3", target_set="test", show=False)
-            ax4 = shap_interpret.plot("sample", samples_index=X_test.index.tolist()[0:2], target_set="test", show=False)
-            ax5 = shap_interpret.plot("importance", target_set="train")
-            ax6 = shap_interpret.plot("summary", target_set="train")
-            ax7 = shap_interpret.plot("dependence", target_columns="col_3", target_set="train")
-            ax8 = shap_interpret.plot("sample", samples_index=X_train.index.tolist()[0:2], target_set="train")
+    ax1 = shap_interpret.plot("importance", target_set="test", show=False)
+    ax2 = shap_interpret.plot("summary", target_set="test", show=False)
+    ax3 = shap_interpret.plot("dependence", target_columns="col_3", target_set="test", show=False)
+    ax4 = shap_interpret.plot("sample", samples_index=X_test.index.tolist()[0:2], target_set="test", show=False)
+    ax5 = shap_interpret.plot("importance", target_set="train", show=False)
+    ax6 = shap_interpret.plot("summary", target_set="train", show=False)
+    ax7 = shap_interpret.plot("dependence", target_columns="col_3", target_set="train", show=False)
+    ax8 = shap_interpret.plot("sample", samples_index=X_train.index.tolist()[0:2], target_set="train", show=False)
     assert not (isinstance(ax1, list))
     assert not (isinstance(ax2, list))
     assert isinstance(ax3, list) and len(ax4) == 2
@@ -263,16 +258,14 @@ def test_shap_interpret_complex_data(complex_data_split, complex_fitted_lightgbm
     assert importance_df.shape[0] == X_train.shape[1]
 
     # Check if plots work for such dataset
-    with patch("matplotlib.pyplot.figure") as _:
-        with patch("shap.plots._waterfall.waterfall_legacy"):
-            ax1 = shap_interpret.plot("importance", target_set="test")
-            ax2 = shap_interpret.plot("summary", target_set="test")
-            ax3 = shap_interpret.plot("dependence", target_columns="f2_missing", target_set="test")
-            ax4 = shap_interpret.plot("sample", samples_index=X_test.index.tolist()[0:2], target_set="test")
-            ax5 = shap_interpret.plot("importance", target_set="train")
-            ax6 = shap_interpret.plot("summary", target_set="train")
-            ax7 = shap_interpret.plot("dependence", target_columns="f2_missing", target_set="train")
-            ax8 = shap_interpret.plot("sample", samples_index=X_train.index.tolist()[0:2], target_set="train")
+    ax1 = shap_interpret.plot("importance", target_set="test", show=False)
+    ax2 = shap_interpret.plot("summary", target_set="test", show=False)
+    ax3 = shap_interpret.plot("dependence", target_columns="f2_missing", target_set="test", show=False)
+    ax4 = shap_interpret.plot("sample", samples_index=X_test.index.tolist()[0:2], target_set="test", show=False)
+    ax5 = shap_interpret.plot("importance", target_set="train", show=False)
+    ax6 = shap_interpret.plot("summary", target_set="train", show=False)
+    ax7 = shap_interpret.plot("dependence", target_columns="f2_missing", target_set="train", show=False)
+    ax8 = shap_interpret.plot("sample", samples_index=X_train.index.tolist()[0:2], target_set="train", show=False)
     assert not (isinstance(ax1, list))
     assert not (isinstance(ax2, list))
     assert isinstance(ax3, list) and len(ax4) == 2

tests/interpret/test_shap_dependence.py

@@ -3,7 +3,6 @@
 import numpy as np
 import pandas as pd
 from sklearn.ensemble import RandomForestClassifier
-from unittest.mock import patch
 from probatus.interpret.shap_dependence import DependencePlotter
 from probatus.utils.exceptions import NotFittedError
 import os
@@ -121,9 +120,8 @@ def test_fit_complex(complex_data_split, complex_fitted_lightgbm):
     assert plotter.fitted is True
 
     # Check if plotting doesnt cause errors
-    with patch("matplotlib.pyplot.figure") as _:
-        for binning in ["simple", "agglomerative", "quantile"]:
-            _ = plotter.plot(feature="f2_missing", type_binning=binning)
+    for binning in ["simple", "agglomerative", "quantile"]:
+        _ = plotter.plot(feature="f2_missing", type_binning=binning, show=False)
 
 
 def test_get_X_y_shap_with_q_cut_normal(X_y, clf):

tests/metric_volatility/test_metric_volatility.py

@@ -397,8 +397,7 @@ def test_fit_compute_full_process(X_df, y_series):
     assert report.shape == (2, 6)
 
     # Check if plot runs
-    with patch("matplotlib.pyplot.figure") as _:
-        vol.plot()
+    vol.plot(show=False)
 
 
 @pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
@@ -420,5 +419,4 @@ def test_fit_compute_complex(complex_data, complex_lightgbm):
     assert report.shape == (1, 6)
 
     # Check if plot runs
-    with patch("matplotlib.pyplot.figure") as _:
-        vol.plot(show=False)
+    vol.plot(show=False)