cv tests to pytest

tests/test_cv.py

@@ -1,50 +1,47 @@
-import unittest
+import pytest
 
 import pandas as pd
-from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 
 from explainerdashboard.explainers import ClassifierExplainer, RegressionExplainer
 from explainerdashboard.datasets import titanic_survive, titanic_fare
 
 
-class ClassifierCVTests(unittest.TestCase):
-    def setUp(self):
-        X_train, y_train, X_test, y_test = titanic_survive()
+@pytest.fixture(scope="module")
+def classifier_explainer_with_cv(fitted_rf_classifier_model):
+    _, _, X_test, y_test = titanic_survive()
+    return ClassifierExplainer(
+        fitted_rf_classifier_model, 
+        X_test, y_test, 
+        cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']}, 'Deck', 'Embarked'],
+        cv=3
+    )
 
-        model = RandomForestClassifier(n_estimators=5, max_depth=2)
-        model.fit(X_train, y_train)
+@pytest.fixture(scope="module")
+def regression_explainer_with_cv(fitted_rf_regression_model):
+    _, _, X_test, y_test = titanic_fare()
+    return RegressionExplainer(
+        fitted_rf_regression_model, 
+        X_test, y_test, 
+        cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']}, 'Deck', 'Embarked'],
+        cv=3
+    )
 
-        self.explainer = ClassifierExplainer(
-                            model, X_train.iloc[:50], y_train.iloc[:50], 
-                            cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']}, 
-                                                'Deck', 'Embarked'],
-                            cv=3)
 
-    def test_cv_permutation_importances(self):
-        self.assertIsInstance(self.explainer.permutation_importances(), pd.DataFrame)
-        self.assertIsInstance(self.explainer.permutation_importances(pos_label=0), pd.DataFrame)
 
-    def test_cv_metrics(self):
-        self.assertIsInstance(self.explainer.metrics(), dict)
-        self.assertIsInstance(self.explainer.metrics(pos_label=0), dict)
+def test_clas_cv_permutation_importances(classifier_explainer_with_cv):
+    assert isinstance(classifier_explainer_with_cv.permutation_importances(), pd.DataFrame)
+    assert isinstance(classifier_explainer_with_cv.permutation_importances(pos_label=0), pd.DataFrame)
 
+def test_clas_cv_metrics(classifier_explainer_with_cv):
+    assert isinstance(classifier_explainer_with_cv.metrics(), dict)
+    assert isinstance(classifier_explainer_with_cv.metrics(pos_label=0), dict)
 
-class RegressionCVTests(unittest.TestCase):
-    def setUp(self):
-        X_train, y_train, X_test, y_test = titanic_fare()
-        model = RandomForestRegressor(n_estimators=5, max_depth=2).fit(X_train, y_train)
 
-        self.explainer = RegressionExplainer(
-                            model, X_test, y_test, 
-                            cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']}, 
-                                                'Deck', 'Embarked'],
-                            cv=3)
-
-    def test_cv_permutation_importances(self):
-        self.assertIsInstance(self.explainer.permutation_importances(), pd.DataFrame)
-
-    def test_cv_metrics(self):
-        self.assertIsInstance(self.explainer.metrics(), dict)
+def test_reg_cv_permutation_importances(regression_explainer_with_cv):
+    assert isinstance(regression_explainer_with_cv.permutation_importances(), pd.DataFrame)
+    assert isinstance(regression_explainer_with_cv.permutation_importances(pos_label=0), pd.DataFrame)
 
+def test_reg_cv_metrics(regression_explainer_with_cv):
+    assert isinstance(regression_explainer_with_cv.metrics(), dict)