class RegressionBunchTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestRegressor(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(
model,
X_test,
y_test,
r2_score,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
idxs=test_names)
def test_residuals(self):
self.assertIsInstance(self.explainer.residuals, pd.Series)
def test_prediction_result_markdown(self):
result_index = self.explainer.prediction_result_markdown(0)
self.assertIsInstance(result_index, str)
result_name = self.explainer.prediction_result_markdown(self.names[0])
self.assertIsInstance(result_name, str)
def test_metrics(self):
metrics_dict = self.explainer.metrics()
self.assertIsInstance(metrics_dict, dict)
self.assertTrue('root_mean_squared_error' in metrics_dict)
self.assertTrue('mean_absolute_error' in metrics_dict)
self.assertTrue('R-squared' in metrics_dict)
self.assertIsInstance(self.explainer.metrics_descriptions(), dict)
def test_plot_predicted_vs_actual(self):
fig = self.explainer.plot_predicted_vs_actual(logs=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_predicted_vs_actual(logs=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_predicted_vs_actual(log_x=True, log_y=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_residuals(self):
fig = self.explainer.plot_residuals()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals(vs_actual=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals(residuals='ratio')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals(residuals='log-ratio')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals(residuals='log-ratio',
vs_actual=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_residuals_vs_feature(self):
fig = self.explainer.plot_residuals_vs_feature("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals_vs_feature("Age",
residuals='log-ratio')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals_vs_feature("Age", dropna=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals_vs_feature("Gender", points=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals_vs_feature("Gender", winsor=10)
self.assertIsInstance(fig, go.Figure)
def test_plot_y_vs_feature(self):
fig = self.explainer.plot_y_vs_feature("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_y_vs_feature("Age", dropna=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_y_vs_feature("Gender", points=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_y_vs_feature("Gender", winsor=10)
self.assertIsInstance(fig, go.Figure)
def test_plot_preds_vs_feature(self):
fig = self.explainer.plot_preds_vs_feature("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_preds_vs_feature("Age", dropna=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_preds_vs_feature("Gender", points=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_preds_vs_feature("Gender", winsor=10)
self.assertIsInstance(fig, go.Figure)
class RegressionBunchTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestRegressor(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model,
X_test,
y_test,
r2_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
idxs=test_names)
def test_residuals(self):
self.assertIsInstance(self.explainer.residuals, pd.Series)
def test_prediction_result_markdown(self):
result_index = self.explainer.prediction_result_markdown(0)
self.assertIsInstance(result_index, str)
result_name = self.explainer.prediction_result_markdown(self.names[0])
self.assertIsInstance(result_name, str)
def test_metrics(self):
metrics_dict = self.explainer.metrics()
self.assertIsInstance(metrics_dict, dict)
self.assertTrue('rmse' in metrics_dict)
self.assertTrue('mae' in metrics_dict)
self.assertTrue('R2' in metrics_dict)
def test_plot_predicted_vs_actual(self):
fig = self.explainer.plot_predicted_vs_actual(logs=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_predicted_vs_actual(logs=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_residuals(self):
fig = self.explainer.plot_residuals()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals(vs_actual=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals(ratio=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals(ratio=True, vs_actual=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_residuals_vs_feature(self):
fig = self.explainer.plot_residuals_vs_feature("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals_vs_feature("Age", ratio=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_residuals_vs_feature("Age", dropna=True)
self.assertIsInstance(fig, go.Figure)
