def test_partial_fit():
est = PassiveAggressiveClassifier(random_state=0, shuffle=False)
transformer = SelectFromModel(estimator=est)
transformer.partial_fit(data, y, classes=np.unique(y))
old_model = transformer.estimator_
transformer.partial_fit(data, y, classes=np.unique(y))
new_model = transformer.estimator_
assert_true(old_model is new_model)
X_transform = transformer.transform(data)
transformer.fit(np.vstack((data, data)), np.concatenate((y, y)))
assert_array_equal(X_transform, transformer.transform(data))
def test_partial_fit():
est = PassiveAggressiveClassifier(random_state=0, shuffle=False)
transformer = SelectFromModel(estimator=est)
transformer.partial_fit(data, y, classes=np.unique(y))
old_model = transformer.estimator_
transformer.partial_fit(data, y, classes=np.unique(y))
new_model = transformer.estimator_
assert_true(old_model is new_model)
X_transform = transformer.transform(data)
transformer.fit(np.vstack((data, data)), np.concatenate((y, y)))
assert_array_equal(X_transform, transformer.transform(data))
def test_prefit():
# Test all possible combinations of the prefit parameter.
# Passing a prefit parameter with the selected model
# and fitting a unfit model with prefit=False should give same results.
clf = SGDClassifier(alpha=0.1,
max_iter=10,
shuffle=True,
random_state=0,
tol=None)
model = SelectFromModel(clf)
model.fit(data, y)
X_transform = model.transform(data)
clf.fit(data, y)
model = SelectFromModel(clf, prefit=True)
assert_array_almost_equal(model.transform(data), X_transform)
model.fit(data, y)
assert model.estimator_ is not clf
# Check that the model is rewritten if prefit=False and a fitted model is
# passed
model = SelectFromModel(clf, prefit=False)
model.fit(data, y)
assert_array_almost_equal(model.transform(data), X_transform)
# Check that passing an unfitted estimator with `prefit=True` raises a
# `ValueError`
clf = SGDClassifier(alpha=0.1,
max_iter=10,
shuffle=True,
random_state=0,
tol=None)
model = SelectFromModel(clf, prefit=True)
err_msg = "When `prefit=True`, `estimator` is expected to be a fitted estimator."
with pytest.raises(NotFittedError, match=err_msg):
model.fit(data, y)
with pytest.raises(NotFittedError, match=err_msg):
model.partial_fit(data, y)
with pytest.raises(NotFittedError, match=err_msg):
model.transform(data)
# Check that the internal parameters of prefitted model are not changed
# when calling `fit` or `partial_fit` with `prefit=True`
clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True,
tol=None).fit(data, y)
model = SelectFromModel(clf, prefit=True)
model.fit(data, y)
assert_allclose(model.estimator_.coef_, clf.coef_)
model.partial_fit(data, y)
assert_allclose(model.estimator_.coef_, clf.coef_)
def test_partial_fit():
est = PassiveAggressiveClassifier(random_state=0, shuffle=False)
transformer = SelectFromModel(estimator=est)
transformer.partial_fit(data, y, classes=np.unique(y))
old_model = transformer.estimator_
transformer.partial_fit(data, y, classes=np.unique(y))
new_model = transformer.estimator_
assert_true(old_model is new_model)
X_transform = transformer.transform(data)
transformer.fit(np.vstack((data, data)), np.concatenate((y, y)))
assert_array_equal(X_transform, transformer.transform(data))
# check that if est doesn't have partial_fit, neither does SelectFromModel
transformer = SelectFromModel(estimator=RandomForestClassifier())
assert_false(hasattr(transformer, "partial_fit"))
def test_partial_fit():
est = PassiveAggressiveClassifier(random_state=0, shuffle=False)
transformer = SelectFromModel(estimator=est)
transformer.partial_fit(data, y,
classes=np.unique(y))
old_model = transformer.estimator_
transformer.partial_fit(data, y,
classes=np.unique(y))
new_model = transformer.estimator_
assert_true(old_model is new_model)
X_transform = transformer.transform(data)
transformer.fit(np.vstack((data, data)), np.concatenate((y, y)))
assert_array_equal(X_transform, transformer.transform(data))
# check that if est doesn't have partial_fit, neither does SelectFromModel
transformer = SelectFromModel(estimator=RandomForestClassifier())
assert_false(hasattr(transformer, "partial_fit"))
