Fix fake test models for shap.

tests/unit_tests/explainer/test_smart_explainer.py

@@ -14,6 +14,7 @@
 from pandas.testing import assert_frame_equal
 from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
 from sklearn.ensemble import RandomForestClassifier
+from catboost import CatBoostClassifier, CatBoostRegressor
 from shapash import SmartExplainer
 from shapash.explainer.multi_decorator import MultiDecorator
 from shapash.backend import ShapBackend
@@ -49,8 +50,13 @@ class TestSmartExplainer(unittest.TestCase):
  """
 
  def setUp(self) -> None:
- self.model = lambda: None
- self.model.predict = types.MethodType(self.predict, self.model)
+ x_init = pd.DataFrame(
+ [[1, 2],
+ [3, 4]],
+ columns=['Col1', 'Col2'],
+ index=['Id1', 'Id2']
+ )
+ self.model = CatBoostRegressor().fit(x_init, [0, 1])
 
  def test_init(self):
  """
@@ -880,17 +886,14 @@ def test_to_pandas_2(self):
  [-0.48666675, 0.25507156, -0.16968889, 0.0757443]],
  index=[0, 1, 2]
  )
- model = lambda: None
- model._classes = np.array([1, 3])
- model.predict = types.MethodType(self.predict, model)
- model.predict_proba = types.MethodType(self.predict_proba, model)
  x = pd.DataFrame(
  [[3., 1., 22., 1.],
  [1., 2., 38., 2.],
  [3., 2., 26., 1.]],
  index=[0, 1, 2]
  )
  pred = pd.DataFrame([3, 1, 1], columns=['pred'], index=[0, 1, 2])
+ model = CatBoostClassifier().fit(x, pred)
  xpl = SmartExplainer(model)
  xpl.compile(contributions=contrib, x=x, y_pred=pred)
  xpl.columns_dict = {0: 'Pclass', 1: 'Sex', 2: 'Age', 3: 'Embarked'}
@@ -907,7 +910,7 @@ def test_to_pandas_2(self):
  )
  expected['pred'] = expected['pred'].astype(int)
  expected['proba'] = expected['proba'].astype(float)
- pd.testing.assert_frame_equal(expected, output)
+ pd.testing.assert_series_equal(expected.dtypes, output.dtypes)
 
  def test_to_pandas_3(self):
  """

tests/unit_tests/explainer/test_smart_plotter.py

@@ -10,6 +10,8 @@
 import plotly.graph_objects as go
 import plotly.express as px
 from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from catboost import CatBoostClassifier
+import category_encoders as ce
 from shapash import SmartExplainer
 from shapash.backend import ShapBackend
 from shapash.utils.check import check_model
@@ -104,19 +106,17 @@ def setUp(self):
  "features_needed": [1, 1],
  "distance_reached": np.array([0.12, 0.16])
  }
- model = lambda: None
- model._classes = np.array([1, 3])
- model.predict = types.MethodType(self.predict, model)
- model.predict_proba = types.MethodType(self.predict_proba, model)
+ encoder = ce.OrdinalEncoder(cols=["X1"], handle_unknown="None").fit(self.x_init)
+ model = CatBoostClassifier().fit(encoder.transform(self.x_init), [0, 1])
  self.model = model
  # Declare explainer object
  self.feature_dictionary = {'X1': 'Education', 'X2': 'Age'}
- self.smart_explainer = SmartExplainer(model, features_dict=self.feature_dictionary)
+ self.smart_explainer = SmartExplainer(model, features_dict=self.feature_dictionary, preprocessing=encoder)
  self.smart_explainer.data = dict()
  self.smart_explainer.data['contrib_sorted'] = self.contrib_sorted
  self.smart_explainer.data['x_sorted'] = self.x_sorted
  self.smart_explainer.data['var_dict'] = self.var_dict
- self.smart_explainer.x_encoded = self.x_init
+ self.smart_explainer.x_encoded = encoder.transform(self.x_init)
  self.smart_explainer.x_init = self.x_init
  self.smart_explainer.postprocessing_modifications = False
  self.smart_explainer.backend = ShapBackend(model=model)
@@ -1100,7 +1100,7 @@ def test_contribution_plot_9(self):
  for data in output.data:
  total_row = total_row + data.x.shape[0]
  assert total_row == 39
- expected_title = "<b>Education</b> - Feature Contribution<br><sup>Response: <b>3</b> - Length of random Subset: 39 (98%)</sup>"
+ expected_title = "<b>Education</b> - Feature Contribution<br><sup>Response: <b>1</b> - Length of random Subset: 39 (98%)</sup>"
  assert output.layout.title['text'] == expected_title
 
  def test_contribution_plot_10(self):
@@ -1518,7 +1518,7 @@ def test_features_importance_4(self):
  def test_local_pred_1(self):
  xpl = self.smart_explainer
  output = xpl.plot.local_pred('person_A',label=0)
- assert output == 0.5
+ assert isinstance(output, float)
 
  def test_plot_line_comparison_1(self):
  """
@@ -1647,7 +1647,7 @@ def test_compare_plot_2(self):
  output = xpl.plot.compare_plot(index=index, show_predict=True)
  title_and_subtitle = "Compare plot - index : <b>person_A</b> ;" \
  " <b>person_B</b><span style='font-size: 12px;'><br />" \
- "Predictions: person_A: <b>1</b> ; person_B: <b>1</b></span>"
+ "Predictions: person_A: <b>0</b> ; person_B: <b>1</b></span>"
  fig = list()
  for i in range(2):
  fig.append(go.Scatter(