def test_partial_dependence_baseline():
X = pd.DataFrame([[1, 0], [0, 1]])
y = pd.Series([0, 1])
pipeline = BinaryClassificationPipeline(component_graph=["Baseline Classifier"], parameters={})
pipeline.fit(X, y)
with pytest.raises(ValueError, match="Partial dependence plots are not supported for Baseline pipelines"):
partial_dependence(pipeline, X, features=0, grid_resolution=20)
def test_partial_dependence_respect_grid_resolution():
X, y = load_fraud(1000)
pl = BinaryClassificationPipeline(component_graph=["DateTime Featurization Component", "One Hot Encoder", "Random Forest Classifier"])
pl.fit(X, y)
dep = partial_dependence(pl, X, features="amount", grid_resolution=20)
assert dep.shape[0] == 20
assert dep.shape[0] != max(X.select('categorical').describe().loc["nunique"]) + 1
dep = partial_dependence(pl, X, features="provider", grid_resolution=20)
assert dep.shape[0] == X['provider'].to_series().nunique()
assert dep.shape[0] != max(X.select('categorical').describe().loc["nunique"]) + 1
def test_graph_partial_dependence_regression_date_order(X_y_binary):
pytest.importorskip('plotly.graph_objects', reason='Skipping plotting test because plotly not installed')
X, y = X_y_binary
pipeline = BinaryClassificationPipeline(component_graph=['Imputer', 'One Hot Encoder', 'DateTime Featurization Component', 'Standard Scaler', 'Logistic Regression Classifier'])
X = pd.DataFrame(X, columns=[str(i) for i in range(X.shape[1])])
y = pd.Series(y)
dt_series = pd.Series(pd.date_range('20200101', periods=X.shape[0])).sample(frac=1).reset_index(drop=True)
X['dt_column'] = pd.to_datetime(dt_series, errors='coerce')
pipeline.fit(X, y)
fig = graph_partial_dependence(pipeline, X, features='dt_column', grid_resolution=5)
plot_data = fig.to_dict()['data'][0]
assert plot_data['type'] == 'scatter'
assert plot_data['x'].tolist() == list(pd.date_range('20200101', periods=X.shape[0]))
def test_partial_dependence_datetime(problem_type, X_y_regression, X_y_binary, X_y_multi):
if problem_type == 'binary':
X, y = X_y_binary
pipeline = BinaryClassificationPipeline(component_graph=['Imputer', 'One Hot Encoder', 'DateTime Featurization Component', 'Standard Scaler', 'Logistic Regression Classifier'])
elif problem_type == 'multiclass':
X, y = X_y_multi
pipeline = MulticlassClassificationPipeline(component_graph=['Imputer', 'One Hot Encoder', 'DateTime Featurization Component', 'Standard Scaler', 'Logistic Regression Classifier'])
else:
X, y = X_y_regression
pipeline = RegressionPipeline(component_graph=['Imputer', 'One Hot Encoder', 'DateTime Featurization Component', 'Standard Scaler', 'Linear Regressor'])
X = pd.DataFrame(X, columns=[str(i) for i in range(X.shape[1])])
y = pd.Series(y)
X['dt_column'] = pd.Series(pd.date_range('20200101', periods=X.shape[0]))
pipeline.fit(X, y)
part_dep = partial_dependence(pipeline, X, features='dt_column')
if problem_type == 'multiclass':
assert len(part_dep["partial_dependence"]) == 300 # 100 rows * 3 classes
assert len(part_dep["feature_values"]) == 300
else:
assert len(part_dep["partial_dependence"]) == 100
assert len(part_dep["feature_values"]) == 100
assert not part_dep.isnull().any(axis=None)
part_dep = partial_dependence(pipeline, X, features=20)
if problem_type == 'multiclass':
assert len(part_dep["partial_dependence"]) == 300 # 100 rows * 3 classes
assert len(part_dep["feature_values"]) == 300
else:
assert len(part_dep["partial_dependence"]) == 100
assert len(part_dep["feature_values"]) == 100
assert not part_dep.isnull().any(axis=None)
with pytest.raises(ValueError, match='Two-way partial dependence is not supported for datetime columns.'):
part_dep = partial_dependence(pipeline, X, features=('0', 'dt_column'))
with pytest.raises(ValueError, match='Two-way partial dependence is not supported for datetime columns.'):
part_dep = partial_dependence(pipeline, X, features=(0, 20))
def decision_tree_classification_pipeline_class(X_y_categorical_classification):
pipeline = BinaryClassificationPipeline(['Simple Imputer', 'One Hot Encoder', 'Standard Scaler', 'Decision Tree Classifier'])
X, y = X_y_categorical_classification
pipeline.fit(X, y)
return pipeline