def test_easy_preprocessor_transform():
titanic = load_titanic()
titanic_clean = clean(titanic)
X, y = titanic_clean.drop("survived", axis=1), titanic_clean.survived
X_train, X_val, y_train, y_val = train_test_split(X, y, stratify=y,
random_state=42)
pipe = make_pipeline(EasyPreprocessor(), LogisticRegression(C=0.1))
pipe.fit(X_train, y_train)
pipe.predict(X_train)
pipe.predict(X_val)
def test_loading():
# smoke test
ames = load_ames()
assert ames.shape == (2930, 82)
titanic = load_titanic()
assert titanic.shape == (1309, 14)
adult = load_adult()
assert adult.shape == (32561, 15)
pd.read_csv(data_path("titanic.csv"))
def test_explain_smoke_titanic():
titanic = load_titanic()
titanic_clean = clean(titanic)
sc = SimpleClassifier().fit(titanic_clean, target_col='survived')
explain(sc)
X, y = titanic_clean.drop("survived", axis=1), titanic_clean.survived
ep = EasyPreprocessor()
preprocessed = ep.fit_transform(X)
tree = DecisionTreeClassifier().fit(preprocessed, y)
explain(tree, feature_names=ep.get_feature_names())
pipe = make_pipeline(EasyPreprocessor(), LogisticRegression())
pipe.fit(X, y)
explain(pipe, feature_names=pipe[0].get_feature_names())
def test_explain_titanic_val(model):
# add multi-class
# add regression
titanic = load_titanic()
titanic_clean = clean(titanic)
X, y = titanic_clean.drop("survived", axis=1), titanic_clean.survived
X_train, X_val, y_train, y_val = train_test_split(X, y, stratify=y,
random_state=42)
pipe = make_pipeline(EasyPreprocessor(), model)
pipe.fit(X_train, y_train)
# without validation set
explain(pipe, feature_names=X.columns)
# with validation set
explain(pipe, X_val, y_val, feature_names=X.columns)
def test_dirty_float_target_regression():
titanic_data = load_titanic()
data = pd.DataFrame({'one': np.repeat(np.arange(50), 2)})
dirty = make_dirty_float()
data['target'] = dirty
with pytest.warns(UserWarning, match="Discarding dirty_float targets that "
"cannot be converted to float."):
clean(data, target_col="target")
with pytest.warns(UserWarning, match="Discarding dirty_float targets that "
"cannot be converted to float."):
plot(data, target_col="target")
# check if works for non dirty_float targets
plot(titanic_data, 'survived')
def test_any_classifier_titanic(monkeypatch):
monkeypatch.setattr(AnyClassifier, '_get_estimators',
mock_get_estimators_logreg)
titanic = load_titanic()
ac = AnyClassifier()
ac.fit(titanic, target_col='survived')
def test_simple_classifier_titanic():
titanic = load_titanic()
ec = SimpleClassifier()
ec.fit(titanic, target_col='survived')
ec.predict(titanic.drop('survived', axis=1))
def test_simple_classifier_titanic():
titanic = load_titanic()[::10]
ec = SimpleClassifier()
ec.fit(titanic, target_col='survived')
""" Mosaic Plot Example ========================================== """ import matplotlib.pyplot as plt from dabl.datasets import load_titanic from dabl.plot import mosaic_plot data = load_titanic() # Mosaic plot for frequencies of Titanic passengers grouped # by gender and survival status mosaic_plot(data, 'sex', 'survived') plt.show()
