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_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_fit_predict_with_intermediate_fit_params():
# tests that Pipeline passes fit_params to intermediate steps
# when fit_predict is invoked
pipe = Pipeline([('transf', TransfFitParams()), ('clf', FitParamT())])
pipe.fit_predict(X=None,
y=None,
transf__should_get_this=True,
clf__should_succeed=True)
assert pipe.named_steps['transf'].fit_params['should_get_this']
assert pipe.named_steps['clf'].successful
assert 'should_succeed' not in pipe.named_steps['transf'].fit_params
def test_pipeline_fit_transform():
# Test whether pipeline works with a transformer missing fit_transform
X = iris.data
y = iris.target
transf = Transf()
pipeline = Pipeline([('mock', transf)])
# test fit_transform:
X_trans = pipeline.fit_transform(X, y)
X_trans2 = transf.fit(X, y).transform(X)
assert_array_almost_equal(X_trans, X_trans2)
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_named_steps():
transf = Transf()
mult2 = Mult(mult=2)
pipeline = Pipeline([('mock', transf), ("mult", mult2)])
# Test access via named_steps bunch object
assert 'mock' in pipeline.named_steps
assert 'mock2' not in pipeline.named_steps
assert pipeline.named_steps.mock is transf
assert pipeline.named_steps.mult is mult2
# Test bunch with conflict attribute of dict
pipeline = Pipeline([('values', transf), ("mult", mult2)])
assert pipeline.named_steps.values is not transf
assert pipeline.named_steps.mult is mult2
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_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_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_pipeline_slice():
pipe = Pipeline([('transf1', Transf()), ('transf2', Transf()),
('clf', FitParamT())])
pipe2 = pipe[:-1]
assert isinstance(pipe2, Pipeline)
assert pipe2.steps == pipe.steps[:-1]
assert 2 == len(pipe2.named_steps)
assert_raises(ValueError, lambda: pipe[::-1])
def test_fit_predict_on_pipeline_without_fit_predict():
# tests that a pipeline does not have fit_predict method when final
# step of pipeline does not have fit_predict defined
scaler = StandardScaler()
pca = PCA(svd_solver='full')
pipe = Pipeline([('scaler', scaler), ('pca', pca)])
assert_raises_regex(AttributeError,
"'PCA' object has no attribute 'fit_predict'", getattr,
pipe, 'fit_predict')
def test_pipeline_index():
transf = Transf()
clf = FitParamT()
pipe = Pipeline([('transf', transf), ('clf', clf)])
assert pipe[0] == transf
assert pipe['transf'] == transf
assert pipe[-1] == clf
assert pipe['clf'] == clf
assert_raises(IndexError, lambda: pipe[3])
assert_raises(KeyError, lambda: pipe['foobar'])
def test_fit_predict_on_pipeline():
# test that the fit_predict method is implemented on a pipeline
# test that the fit_predict on pipeline yields same results as applying
# transform and clustering steps separately
scaler = StandardScaler()
km = KMeans(random_state=0)
# As pipeline doesn't clone estimators on construction,
# it must have its own estimators
scaler_for_pipeline = StandardScaler()
km_for_pipeline = KMeans(random_state=0)
# first compute the transform and clustering step separately
scaled = scaler.fit_transform(iris.data)
separate_pred = km.fit_predict(scaled)
# use a pipeline to do the transform and clustering in one step
pipe = Pipeline([('scaler', scaler_for_pipeline),
('Kmeans', km_for_pipeline)])
pipeline_pred = pipe.fit_predict(iris.data)
assert_array_almost_equal(pipeline_pred, separate_pred)
def test_pipe_grid():
params = {
'p__n_components': [2, 4],
'd__n_neighbors': [2, 5],
'c__threshold': [.3, .5],
'c__classifier__max_depth': [2, 4]
}
p = Pipeline([('a', CCNoise(lcm)), ('s', StandardScaler()), ('p', PCA()),
('d', KDN()), ('c', Filter(DecisionTreeClassifier()))])
g = GridSearchCV(p, params, cv=CV1, error_score='raise')
g.fit(X, y)
g.predict(X)
def test_pipeline_wrong_memory():
# Test that an error is raised when memory is not a string or a Memory
# instance
X = iris.data
y = iris.target
# Define memory as an integer
memory = 1
cached_pipe = Pipeline([('transf', DummyTransf()), ('svc', SVC())],
memory=memory)
assert_raises_regex(
ValueError, "'memory' should be None, a string or"
" have the same interface as joblib.Memory."
" Got memory='1' instead.", cached_pipe.fit, 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_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_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_set_pipeline_steps():
transf1 = Transf()
transf2 = Transf()
pipeline = Pipeline([('mock', transf1)])
assert pipeline.named_steps['mock'] is transf1
# Directly setting attr
pipeline.steps = [('mock2', transf2)]
assert 'mock' not in pipeline.named_steps
assert pipeline.named_steps['mock2'] is transf2
assert [('mock2', transf2)] == pipeline.steps
# Using set_params
pipeline.set_params(steps=[('mock', transf1)])
assert [('mock', transf1)] == pipeline.steps
# Using set_params to replace single step
pipeline.set_params(mock=transf2)
assert [('mock', transf2)] == pipeline.steps
# With invalid data
pipeline.set_params(steps=[('junk', ())])
assert_raises(TypeError, pipeline.fit, [[1]], [1])
assert_raises(TypeError, pipeline.fit_transform, [[1]], [1])
def test_pipeline_raise_set_params_error():
# Test pipeline raises set params error message for nested models.
pipe = Pipeline([('cls', LinearRegression())])
# expected error message
error_msg = ('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.')
assert_raise_message(ValueError,
error_msg % ('fake', pipe),
pipe.set_params,
fake='nope')
# nested model check
assert_raise_message(ValueError,
error_msg % ("fake", pipe),
pipe.set_params,
fake__estimator='nope')
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_example_two():
from skclean.simulate_noise import UniformNoise
from skclean.detectors import KDN
from skclean.handlers import Filter
from skclean.pipeline import Pipeline, make_pipeline # Importing from skclean, not sklearn
from skclean.utils import load_data
X, y = load_data('breast_cancer')
clf = Pipeline([
('scale', StandardScaler()), # Scale features
('feat_sel', VarianceThreshold(.2)), # Feature selection
('detector', KDN()), # Detect mislabeled samples
('handler', Filter(SVC())
), # Filter out likely mislabeled samples and then train a SVM
])
clf_g = GridSearchCV(clf, {'detector__n_neighbors': [2, 5, 10]})
n_clf_g = make_pipeline(UniformNoise(.3),
clf_g) # Create label noise at the very first step
print(cross_val_score(n_clf_g, X, y,
cv=5).mean()) # 5-fold cross validation
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_init():
# Test the various init parameters of the pipeline.
assert_raises(TypeError, Pipeline)
# Check that we can't instantiate pipelines with objects without fit
# method
assert_raises_regex(
TypeError, 'Last step of Pipeline should implement fit '
'or be the string \'passthrough\''
'.*NoFit.*', Pipeline, [('clf', NoFit())])
# Smoke test with only an estimator
clf = NoTrans()
pipe = Pipeline([('svc', clf)])
assert (pipe.get_params(deep=True) == dict(svc__a=None,
svc__b=None,
svc=clf,
**pipe.get_params(deep=False)))
# Check that params are set
pipe.set_params(svc__a=0.1)
assert clf.a == 0.1
assert clf.b is None
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that estimators are not cloned on pipeline construction
assert pipe.named_steps['anova'] is filter1
assert pipe.named_steps['svc'] is clf
# Check that we can't instantiate with non-transformers on the way
# Note that NoTrans implements fit, but not transform
assert_raises_regex(
TypeError, 'All intermediate steps should be transformers'
'.*\\bNoTrans\\b.*', Pipeline, [('t', NoTrans()), ('svc', clf)])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert clf.C == 0.1
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
assert_raises(ValueError, pipe.set_params, anova__C=0.1)
# Test clone
pipe2 = assert_no_warnings(clone, pipe)
assert not pipe.named_steps['svc'] is pipe2.named_steps['svc']
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert params == params2
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_set_params_nested_pipeline():
estimator = Pipeline([('a', Pipeline([('b', DummyRegressor())]))])
estimator.set_params(a__b__alpha=0.001, a__b=Lasso())
estimator.set_params(a__steps=[('b', LogisticRegression())], a__b__C=5)
def make():
return Pipeline([('m2', mult2), ('m3', mult3), ('last', mult5)])
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_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)
