cleanup docstrings and remove np.random.state() -> replaced by seed s…

…et in explainer

probatus/feature_elimination/feature_elimination.py

@@ -332,10 +332,6 @@ def fit(
         Returns:
             (ShapRFECV): Fitted object.
         """
-        # Set seed for results reproducibility
-        if self.random_state is not None:
-            np.random.seed(self.random_state)
-
         # Initialise len_columns_to_keep based on columns_to_keep content validation
         len_columns_to_keep = 0
         if columns_to_keep:
@@ -398,10 +394,6 @@ def fit(
             # Current dataset
             current_X = self.X[remaining_removeable_features]
 
-            # Set seed for results reproducibility
-            if self.random_state is not None:
-                np.random.seed(self.random_state)
-
             # Optimize parameters
             if self.search_model:
                 current_search_model = clone(self.model).fit(current_X, self.y)

probatus/sample_similarity/resemblance_model.py

@@ -108,10 +108,6 @@ def fit(self, X1, X2, column_names=None, class_names=None):
             (BaseResemblanceModel):
                 Fitted object
         """
-        # Set seed for results reproducibility
-        if self.random_state is not None:
-            np.random.seed(self.random_state)
-
         # Set class names
         self.class_names = class_names
         if self.class_names is None:

probatus/utils/shap_helpers.py

@@ -73,7 +73,7 @@ def shap_calc(
         # https://github.com/slundberg/shap/issues/480
         if shap_kwargs.get("feature_perturbation") == "tree_path_dependent" or X.select_dtypes("category").shape[1] > 0:
             # Calculate Shap values.
-            explainer = Explainer(model, **shap_kwargs)
+            explainer = Explainer(model, seed=random_state, **shap_kwargs)
         else:
             # Create the background data,required for non tree based models.
             # A single datapoint can passed as mask
@@ -83,7 +83,7 @@ def shap_calc(
             else:
                 pass
             mask = sample(X, sample_size, random_state=random_state)
-            explainer = Explainer(model, masker=mask, **shap_kwargs)
+            explainer = Explainer(model, seed=random_state, masker=mask, **shap_kwargs)
 
         # For tree-explainers allow for using check_additivity and approximate arguments
         if isinstance(explainer, TreeExplainer):

tests/conftest.py

@@ -14,58 +14,39 @@
 
 @pytest.fixture(scope="function")
 def random_state():
-    """
-    Fixture to automatically provide a random state.
-    """
     RANDOM_STATE = 0
 
     return RANDOM_STATE
 
 
 @pytest.fixture(scope="function")
 def random_state_42():
-    """
-    Fixture to automatically provide a random state.
-    """
     RANDOM_STATE = 42
 
     return RANDOM_STATE
 
 
 @pytest.fixture(scope="function")
 def random_state_1234():
-    """
-    Fixture to automatically provide a random state.
-    """
     RANDOM_STATE = 1234
 
     return RANDOM_STATE
 
 
 @pytest.fixture(scope="function")
 def random_state_1():
-    """
-    Fixture to automatically provide a random state.
-    """
     RANDOM_STATE = 1
 
     return RANDOM_STATE
 
 
 @pytest.fixture(scope="function")
 def mock_model():
-    """
-    Fixture.
-    """
     return Mock()
 
 
 @pytest.fixture(scope="function")
 def complex_data(random_state):
-    """
-    Fixture.
-    """
-
     feature_names = ["f1_categorical", "f2_missing", "f3_static", "f4", "f5"]
 
     # Prepare two samples
@@ -93,9 +74,6 @@ def complex_data_with_categorical(complex_data):
 
 @pytest.fixture(scope="function")
 def complex_data_split(complex_data, random_state_42):
-    """
-    Fixture.
-    """
     X, y = complex_data
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state_42)
     return X_train, X_test, y_train, y_test
@@ -112,93 +90,67 @@ def complex_data_split_with_categorical(complex_data_split):
 
 @pytest.fixture(scope="function")
 def complex_lightgbm(random_state_42):
-    """This fixture allows to reuse the import of the LGBMClassifier class across different tests."""
     model = LGBMClassifier(max_depth=5, num_leaves=11, class_weight="balanced", random_state=random_state_42)
     return model
 
 
 @pytest.fixture(scope="function")
 def complex_fitted_lightgbm(complex_data_split_with_categorical, complex_lightgbm):
-    """
-    Fixture.
-    """
     X_train, _, y_train, _ = complex_data_split_with_categorical
 
     return complex_lightgbm.fit(X_train, y_train)
 
 
 @pytest.fixture(scope="function")
 def catboost_classifier(random_state):
-    """This fixture allows to reuse the import of the CatboostClassifier class across different tests."""
     model = CatBoostClassifier(random_seed=random_state)
     return model
 
 
 @pytest.fixture(scope="function")
 def decision_tree_classifier(random_state):
-    """This fixture allows to reuse the import of the DecisionTreeClassifier class across different tests."""
     model = DecisionTreeClassifier(max_depth=1, random_state=random_state)
     return model
 
 
 @pytest.fixture(scope="function")
 def randomized_search_decision_tree_classifier(decision_tree_classifier, random_state):
-    """This fixture allows to reuse the import of the DecisionTreeClassifier in combination with a new CV class across different tests."""
     param_grid = {"criterion": ["gini"], "min_samples_split": [1, 2]}
     cv = RandomizedSearchCV(decision_tree_classifier, param_grid, cv=2, n_iter=2, random_state=random_state)
     return cv
 
 
 @pytest.fixture(scope="function")
 def logistic_regression(random_state):
-    """This fixture allows to reuse the import of the DecisionTreeClassifier class across different tests."""
     model = LogisticRegression(random_state=random_state)
     return model
 
 
 @pytest.fixture(scope="function")
 def X_train():
-    """
-    Fixture.
-    """
     return pd.DataFrame({"col_1": [1, 1, 1, 1], "col_2": [0, 0, 0, 0], "col_3": [1, 0, 1, 0]}, index=[1, 2, 3, 4])
 
 
 @pytest.fixture(scope="function")
 def y_train():
-    """
-    Fixture.
-    """
     return pd.Series([1, 0, 1, 0], index=[1, 2, 3, 4])
 
 
 @pytest.fixture(scope="function")
 def X_test():
-    """
-    Fixture.
-    """
     return pd.DataFrame({"col_1": [1, 1, 1, 1], "col_2": [0, 0, 0, 0], "col_3": [1, 0, 1, 0]}, index=[5, 6, 7, 8])
 
 
 @pytest.fixture(scope="function")
 def y_test():
-    """
-    Fixture.
-    """
     return pd.Series([0, 0, 1, 0], index=[5, 6, 7, 8])
 
 
 @pytest.fixture(scope="function")
 def fitted_logistic_regression(X_train, y_train, logistic_regression):
-    """
-    Fixture.
-    """
     return logistic_regression.fit(X_train, y_train)
 
 
 @pytest.fixture(scope="function")
 def fitted_tree(X_train, y_train, decision_tree_classifier):
-    """
-    Fixture.
-    """
     return decision_tree_classifier.fit(X_train, y_train)