def test_pipeline_methods_preprocessing_svm():
# Test the various methods of the pipeline (preprocessing + svm).
X = iris.data
y = iris.target
n_samples = X.shape[0]
n_classes = len(np.unique(y))
scaler = StandardScaler()
pca = PCA(n_components=2, svd_solver='randomized', whiten=True)
clf = SVC(probability=True, random_state=0, decision_function_shape='ovr')
for preprocessing in [scaler, pca]:
pipe = Pipeline([('preprocess', preprocessing), ('svc', clf)])
pipe.fit(X, y)
# check shapes of various prediction functions
predict = pipe.predict(X)
assert predict.shape == (n_samples, )
proba = pipe.predict_proba(X)
assert proba.shape == (n_samples, n_classes)
log_proba = pipe.predict_log_proba(X)
assert log_proba.shape == (n_samples, n_classes)
decision_function = pipe.decision_function(X)
assert decision_function.shape == (n_samples, n_classes)
pipe.score(X, y)
def test_stack_sim():
p = Pipeline([
('a', UniformNoise(.3)),
('b', CCNoise(lcm)),
('c', DecisionTreeClassifier()),
])
p.fit(X, y)
p.predict(X)
def test_predict_with_predict_params():
# tests that Pipeline passes predict_params to the final estimator
# when predict is invoked
pipe = Pipeline([('transf', Transf()), ('clf', DummyEstimatorParams())])
pipe.fit(None, None)
pipe.predict(X=None, got_attribute=True)
assert pipe.named_steps['clf'].got_attribute
def test_noise_sim(sim):
p = Pipeline([('s', sim), ('c', DecisionTreeClassifier())])
p.fit(X, y)
p.predict(X)
p = make_pipeline(sim, DecisionTreeClassifier())
p.fit(X, y)
p.predict(X)
def test_pipeline_sample_weight_supported():
# Pipeline should pass sample_weight
X = np.array([[1, 2]])
pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())])
pipe.fit(X, y=None)
assert pipe.score(X) == 3
assert pipe.score(X, y=None) == 3
assert pipe.score(X, y=None, sample_weight=None) == 3
assert pipe.score(X, sample_weight=np.array([2, 3])) == 8
def test_pipeline_init_tuple():
# Pipeline accepts steps as tuple
X = np.array([[1, 2]])
pipe = Pipeline((('transf', Transf()), ('clf', FitParamT())))
pipe.fit(X, y=None)
pipe.score(X)
pipe.set_params(transf='passthrough')
pipe.fit(X, y=None)
pipe.score(X)
def test_pipeline_with_cache_attribute():
X = np.array([[1, 2]])
pipe = Pipeline([('transf', Transf()), ('clf', Mult())],
memory=DummyMemory())
pipe.fit(X, y=None)
dummy = WrongDummyMemory()
pipe = Pipeline([('transf', Transf()), ('clf', Mult())], memory=dummy)
assert_raises_regex(
ValueError, "'memory' should be None, a string or"
" have the same interface as joblib.Memory."
" Got memory='{}' instead.".format(dummy), pipe.fit, X)
def test_pipeline_methods_pca_svm():
# Test the various methods of the pipeline (pca + svm).
X = iris.data
y = iris.target
# Test with PCA + SVC
clf = SVC(probability=True, random_state=0)
pca = PCA(svd_solver='full', n_components='mle', whiten=True)
pipe = Pipeline([('pca', pca), ('svc', clf)])
pipe.fit(X, y)
pipe.predict(X)
pipe.predict_proba(X)
pipe.predict_log_proba(X)
pipe.score(X, y)
def test_pipeline_sample_weight_unsupported():
# When sample_weight is None it shouldn't be passed
X = np.array([[1, 2]])
pipe = Pipeline([('transf', Transf()), ('clf', Mult())])
pipe.fit(X, y=None)
assert pipe.score(X) == 3
assert pipe.score(X, sample_weight=None) == 3
assert_raise_message(
TypeError,
"score() got an unexpected keyword argument 'sample_weight'",
pipe.score,
X,
sample_weight=np.array([2, 3]))
def test_pipeline_methods_anova():
# Test the various methods of the pipeline (anova).
X = iris.data
y = iris.target
# Test with Anova + LogisticRegression
clf = LogisticRegression()
filter1 = SelectKBest(f_classif, k=2)
pipe = Pipeline([('anova', filter1), ('logistic', clf)])
pipe.fit(X, y)
pipe.predict(X)
pipe.predict_proba(X)
pipe.predict_log_proba(X)
pipe.score(X, y)
def test_pipeline_correctly_adjusts_steps(passthrough):
X = np.array([[1]])
y = np.array([1])
mult2 = Mult(mult=2)
mult3 = Mult(mult=3)
mult5 = Mult(mult=5)
pipeline = Pipeline([('m2', mult2), ('bad', passthrough), ('m3', mult3),
('m5', mult5)])
pipeline.fit(X, y)
expected_names = ['m2', 'bad', 'm3', 'm5']
actual_names = [name for name, _ in pipeline.steps]
assert expected_names == actual_names
def test_pipeline_score_samples_pca_lof():
X = iris.data
# Test that the score_samples method is implemented on a pipeline.
# Test that the score_samples method on pipeline yields same results as
# applying transform and score_samples steps separately.
pca = PCA(svd_solver='full', n_components='mle', whiten=True)
lof = LocalOutlierFactor(novelty=True)
pipe = Pipeline([('pca', pca), ('lof', lof)])
pipe.fit(X)
# Check the shapes
assert pipe.score_samples(X).shape == (X.shape[0], )
# Check the values
lof.fit(pca.fit_transform(X))
assert_allclose(pipe.score_samples(X), lof.score_samples(pca.transform(X)))
def test_pipeline_fit_params():
# Test that the pipeline can take fit parameters
pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())])
pipe.fit(X=None, y=None, clf__should_succeed=True)
# classifier should return True
assert pipe.predict(None)
# and transformer params should not be changed
assert pipe.named_steps['transf'].a is None
assert pipe.named_steps['transf'].b is None
# invalid parameters should raise an error message
assert_raise_message(TypeError,
"fit() got an unexpected keyword argument 'bad'",
pipe.fit,
None,
None,
clf__bad=True)
def test_pipeline_transform():
# Test whether pipeline works with a transformer at the end.
# Also test pipeline.transform and pipeline.inverse_transform
X = iris.data
pca = PCA(n_components=2, svd_solver='full')
pipeline = Pipeline([('pca', pca)])
# test transform and fit_transform:
X_trans = pipeline.fit(X).transform(X)
X_trans2 = pipeline.fit_transform(X)
X_trans3 = pca.fit_transform(X)
assert_array_almost_equal(X_trans, X_trans2)
assert_array_almost_equal(X_trans, X_trans3)
X_back = pipeline.inverse_transform(X_trans)
X_back2 = pca.inverse_transform(X_trans)
assert_array_almost_equal(X_back, X_back2)
def test_set_pipeline_step_passthrough(passthrough):
X = np.array([[1]])
y = np.array([1])
mult2 = Mult(mult=2)
mult3 = Mult(mult=3)
mult5 = Mult(mult=5)
def make():
return Pipeline([('m2', mult2), ('m3', mult3), ('last', mult5)])
pipeline = make()
exp = 2 * 3 * 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
pipeline.set_params(m3=passthrough)
exp = 2 * 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
assert (pipeline.get_params(deep=True) == {
'steps': pipeline.steps,
'm2': mult2,
'm3': passthrough,
'last': mult5,
'memory': None,
'm2__mult': 2,
'last__mult': 5,
'verbose': False
})
pipeline.set_params(m2=passthrough)
exp = 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
# for other methods, ensure no AttributeErrors on None:
other_methods = [
'predict_proba', 'predict_log_proba', 'decision_function', 'transform',
'score'
]
for method in other_methods:
getattr(pipeline, method)(X)
pipeline.set_params(m2=mult2)
exp = 2 * 5
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
pipeline = make()
pipeline.set_params(last=passthrough)
# mult2 and mult3 are active
exp = 6
assert_array_equal([[exp]], pipeline.fit(X, y).transform(X))
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
assert_raise_message(AttributeError,
"'str' object has no attribute 'predict'", getattr,
pipeline, 'predict')
# Check 'passthrough' step at construction time
exp = 2 * 5
pipeline = Pipeline([('m2', mult2), ('m3', passthrough), ('last', mult5)])
assert_array_equal([[exp]], pipeline.fit_transform(X, y))
assert_array_equal([exp], pipeline.fit(X).predict(X))
assert_array_equal(X, pipeline.inverse_transform([[exp]]))
def test_detector_init():
p = Pipeline([('a', UniformNoise(.3)), ('p', StandardScaler()),
('d', RandomForestDetector(n_estimators=50)),
('c', Filter(DecisionTreeClassifier()))])
p.fit(X, y)
p.predict(X)
def test_pipe_pickling(X, y):
p = Pipeline([('a', CCNoise(lcm)), ('s', StandardScaler()), ('p', PCA()),
('d', KDN()), ('c', Filter(DecisionTreeClassifier()))])
p.fit(X, y)
q = pickle.loads(pickle.dumps(p))
assert_allclose(p.predict(X), q.predict(X))
def test_det_wo_hand():
p = Pipeline([('a', CCNoise(lcm)), ('s', StandardScaler()), ('p', PCA()),
('d', KDN()), ('c', DecisionTreeClassifier())])
p.fit(X, y)
p.predict(X)
def test_pipeline_memory():
X = iris.data
y = iris.target
cachedir = mkdtemp()
try:
if LooseVersion(joblib.__version__) < LooseVersion('0.12'):
# Deal with change of API in joblib
memory = joblib.Memory(cachedir=cachedir, verbose=10)
else:
memory = joblib.Memory(location=cachedir, verbose=10)
# Test with Transformer + SVC
clf = SVC(probability=True, random_state=0)
transf = DummyTransf()
pipe = Pipeline([('transf', clone(transf)), ('svc', clf)])
cached_pipe = Pipeline([('transf', transf), ('svc', clf)],
memory=memory)
# Memoize the transformer at the first fit
cached_pipe.fit(X, y)
pipe.fit(X, y)
# Get the time stamp of the transformer in the cached pipeline
ts = cached_pipe.named_steps['transf'].timestamp_
# Check that cached_pipe and pipe yield identical results
assert_array_equal(pipe.predict(X), cached_pipe.predict(X))
assert_array_equal(pipe.predict_proba(X), cached_pipe.predict_proba(X))
assert_array_equal(pipe.predict_log_proba(X),
cached_pipe.predict_log_proba(X))
assert_array_equal(pipe.score(X, y), cached_pipe.score(X, y))
assert_array_equal(pipe.named_steps['transf'].means_,
cached_pipe.named_steps['transf'].means_)
assert not hasattr(transf, 'means_')
# Check that we are reading the cache while fitting
# a second time
cached_pipe.fit(X, y)
# Check that cached_pipe and pipe yield identical results
assert_array_equal(pipe.predict(X), cached_pipe.predict(X))
assert_array_equal(pipe.predict_proba(X), cached_pipe.predict_proba(X))
assert_array_equal(pipe.predict_log_proba(X),
cached_pipe.predict_log_proba(X))
assert_array_equal(pipe.score(X, y), cached_pipe.score(X, y))
assert_array_equal(pipe.named_steps['transf'].means_,
cached_pipe.named_steps['transf'].means_)
assert ts == cached_pipe.named_steps['transf'].timestamp_
# Create a new pipeline with cloned estimators
# Check that even changing the name step does not affect the cache hit
clf_2 = SVC(probability=True, random_state=0)
transf_2 = DummyTransf()
cached_pipe_2 = Pipeline([('transf_2', transf_2), ('svc', clf_2)],
memory=memory)
cached_pipe_2.fit(X, y)
# Check that cached_pipe and pipe yield identical results
assert_array_equal(pipe.predict(X), cached_pipe_2.predict(X))
assert_array_equal(pipe.predict_proba(X),
cached_pipe_2.predict_proba(X))
assert_array_equal(pipe.predict_log_proba(X),
cached_pipe_2.predict_log_proba(X))
assert_array_equal(pipe.score(X, y), cached_pipe_2.score(X, y))
assert_array_equal(pipe.named_steps['transf'].means_,
cached_pipe_2.named_steps['transf_2'].means_)
assert ts == cached_pipe_2.named_steps['transf_2'].timestamp_
finally:
shutil.rmtree(cachedir)
def test_hand_wo_det():
p = Pipeline([('a', CCNoise(lcm)), ('s', StandardScaler()), ('p', PCA()),
('c', Filter(DecisionTreeClassifier()))])
p.fit(X, y)
p.predict(X)