def test_simplie_classifier_digits():
# regression test for doing clean in fit
# which means predict can't work
digits = load_digits()
X, y = digits.data[::10], digits.target[::10]
sc = SimpleClassifier().fit(X, y)
assert sc.score(X, y) > .8
def test_classification_of_string_targets():
X = np.array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2])
y = np.array(['a', 'b', 'a', 'b', 'a', 'b', 'a', 'b', 'a', 'b'])
obj = SimpleClassifier()
fitted = obj.fit(X, y)
pred = fitted.predict(np.array([1, 2]).reshape(-1, 1))
np.testing.assert_array_equal(obj.classes_, np.array(['a', 'b']))
np.testing.assert_array_equal(pred, np.array(['a', 'b']))
def test_deletation_simple(monkeypatch, model):
def mock_get_estimators_linearsvc(self):
return [model]
monkeypatch.setattr(SimpleClassifier, '_get_estimators',
mock_get_estimators_linearsvc)
sc = SimpleClassifier(random_state=0)
sc.fit(X_blobs, y_blobs)
assert isinstance(sc.est_[1], type(model))
assert (hasattr(sc,
'decision_function') == hasattr(model,
'decision_function'))
assert hasattr(sc, 'predict_proba') == hasattr(model, 'predict_proba')
def test_basic(X, y, refit):
# test on iris
ec = SimpleClassifier(refit=refit)
ec.fit(X, y)
if refit:
# smoke test
ec.predict(X)
else:
with pytest.raises(ValueError, match="refit"):
ec.predict(X)
def test_delegation_simple(monkeypatch, model):
def mock_get_estimators(self):
return [model]
monkeypatch.setattr(SimpleClassifier, '_get_estimators',
mock_get_estimators)
sc = SimpleClassifier(random_state=0)
sc.fit(X_blobs, y_blobs)
assert isinstance(sc.est_[1], type(model))
assert (hasattr(sc,
'decision_function') == hasattr(model,
'decision_function'))
assert hasattr(sc, 'predict_proba') == hasattr(model, 'predict_proba')
if hasattr(sc, 'predict_proba'):
assert sc.predict_proba(X_blobs).shape == (X_blobs.shape[0], 2)
if hasattr(sc, 'decision_function'):
assert sc.decision_function(X_blobs).shape == (X_blobs.shape[0], )
def test_dataframe():
path = os.path.dirname(__file__)
titanic = pd.read_csv(os.path.join(path, '../datasets/titanic.csv'))[::10]
ec = SimpleClassifier()
ec.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))
""" Model Explanation ================= """ from dabl.models import SimpleClassifier from dabl.explain import explain from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split wine = load_wine() X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target) sc = SimpleClassifier() sc.fit(X_train, y_train) explain(sc, X_test, y_test)
def test_simple_classifier_titanic():
titanic = load_titanic()[::10]
ec = SimpleClassifier()
ec.fit(titanic, target_col='survived')