def test_one_hot_encoder_specified_categories():
X = np.array([['a', 'b']], dtype=object).T
enc = OneHotEncoder(categories=[['a', 'b', 'c']])
exp = np.array([[1., 0., 0.],
[0., 1., 0.]])
assert_array_equal(enc.fit_transform(X).toarray(), exp)
assert enc.categories[0] == ['a', 'b', 'c']
assert enc.categories_[0].tolist() == ['a', 'b', 'c']
assert np.issubdtype(enc.categories_[0].dtype, np.str_)
# unsorted passed categories raises for now
enc = OneHotEncoder(categories=[['c', 'b', 'a']])
msg = re.escape('Unsorted categories are not yet supported')
assert_raises_regex(ValueError, msg, enc.fit_transform, X)
# multiple columns
X = np.array([['a', 'b'], [0, 2]], dtype=object).T
enc = OneHotEncoder(categories=[['a', 'b', 'c'], [0, 1, 2]])
exp = np.array([[1., 0., 0., 1., 0., 0.],
[0., 1., 0., 0., 0., 1.]])
assert_array_equal(enc.fit_transform(X).toarray(), exp)
assert enc.categories_[0].tolist() == ['a', 'b', 'c']
assert np.issubdtype(enc.categories_[0].dtype, np.str_)
assert enc.categories_[1].tolist() == [0, 1, 2]
assert np.issubdtype(enc.categories_[1].dtype, np.integer)
# when specifying categories manually, unknown categories should already
# raise when fitting
X = np.array([['a', 'b', 'c']]).T
enc = OneHotEncoder(categories=[['a', 'b']])
assert_raises(ValueError, enc.fit, X)
enc = OneHotEncoder(categories=[['a', 'b']], handle_unknown='ignore')
exp = np.array([[1., 0.], [0., 1.], [0., 0.]])
assert_array_equal(enc.fit(X).transform(X).toarray(), exp)
def test_one_hot_encoder_unsorted_categories():
X = np.array([['a', 'b']], dtype=object).T
# unsorted passed categories raises for now
enc = OneHotEncoder(categories=[['c', 'b', 'a']])
msg = re.escape('Unsorted categories are not yet supported')
assert_raises_regex(ValueError, msg, enc.fit_transform, X)
def test_ridgecv_store_cv_values():
rng = np.random.RandomState(42)
n_samples = 8
n_features = 5
x = rng.randn(n_samples, n_features)
alphas = [1e-1, 1e0, 1e1]
n_alphas = len(alphas)
r = RidgeCV(alphas=alphas, cv=None, store_cv_values=True)
# with len(y.shape) == 1
y = rng.randn(n_samples)
r.fit(x, y)
assert r.cv_values_.shape == (n_samples, n_alphas)
# with len(y.shape) == 2
n_targets = 3
y = rng.randn(n_samples, n_targets)
r.fit(x, y)
assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)
r = RidgeCV(cv=3, store_cv_values=True)
assert_raises_regex(ValueError, 'cv!=None and store_cv_values',
r.fit, x, y)
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 check_dtype_object(name, Estimator):
# check that estimators treat dtype object as numeric if possible
rng = np.random.RandomState(0)
X = rng.rand(40, 10).astype(object)
y = (X[:, 0] * 4).astype(np.int)
y = multioutput_estimator_convert_y_2d(name, y)
with warnings.catch_warnings():
estimator = Estimator()
set_fast_parameters(estimator)
estimator.fit(X, y)
if hasattr(estimator, "predict"):
estimator.predict(X)
if hasattr(estimator, "transform"):
estimator.transform(X)
try:
estimator.fit(X, y.astype(object))
except Exception as e:
if "Unknown label type" not in str(e):
raise
X[0, 0] = {'foo': 'bar'}
msg = "argument must be a string or a number"
assert_raises_regex(TypeError, msg, estimator.fit, X, y)
def test_one_hot_encoder_invalid_params():
enc = OneHotEncoder(drop='second')
assert_raises_regex(
ValueError,
"Wrong input for parameter `drop`.",
enc.fit, [["Male"], ["Female"]])
enc = OneHotEncoder(handle_unknown='ignore', drop='first')
assert_raises_regex(
ValueError,
"`handle_unknown` must be 'error'",
enc.fit, [["Male"], ["Female"]])
enc = OneHotEncoder(drop='first')
assert_raises_regex(
ValueError,
"The handling of integer data will change in version",
enc.fit, [[1], [2]])
enc = OneHotEncoder(drop='first', categories='auto')
assert_no_warnings(enc.fit_transform, [[1], [2]])
enc = OneHotEncoder(drop=np.asarray('b', dtype=object))
assert_raises_regex(
ValueError,
"Wrong input for parameter `drop`.",
enc.fit, [['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]])
enc = OneHotEncoder(drop=['ghi', 3, 59])
assert_raises_regex(
ValueError,
"The following categories were supposed",
enc.fit, [['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]])
def test_graphviz_errors():
# Check for errors of export_graphviz
clf = DecisionTreeClassifier(max_depth=3, min_samples_split=2)
# Check not-fitted decision tree error
out = StringIO()
assert_raises(NotFittedError, export_graphviz, clf, out)
clf.fit(X, y)
# Check if it errors when length of feature_names
# mismatches with number of features
message = ("Length of feature_names, "
"1 does not match number of features, 2")
assert_raise_message(ValueError, message, export_graphviz, clf, None,
feature_names=["a"])
message = ("Length of feature_names, "
"3 does not match number of features, 2")
assert_raise_message(ValueError, message, export_graphviz, clf, None,
feature_names=["a", "b", "c"])
# Check class_names error
out = StringIO()
assert_raises(IndexError, export_graphviz, clf, out, class_names=[])
# Check precision error
out = StringIO()
assert_raises_regex(ValueError, "should be greater or equal",
export_graphviz, clf, out, precision=-1)
assert_raises_regex(ValueError, "should be an integer",
export_graphviz, clf, out, precision="1")
def test_bad_pyfunc_metric():
def wrong_distance(x, y):
return "1"
X = np.ones((5, 2))
assert_raises_regex(TypeError,
"Custom distance function must accept two vectors",
BallTree, X, metric=wrong_distance)
def test_check_class_weight_balanced_linear_classifier():
# check that ill-computed balanced weights raises an exception
assert_raises_regex(AssertionError,
"Classifier estimator_name is not computing"
" class_weight=balanced properly.",
check_class_weight_balanced_linear_classifier,
'estimator_name',
BadBalancedWeightsClassifier)
def test_k_means_n_init():
rnd = np.random.RandomState(0)
X = rnd.normal(size=(40, 2))
# two regression tests on bad n_init argument
# previous bug: n_init <= 0 threw non-informative TypeError (#3858)
assert_raises_regex(ValueError, "n_init", KMeans(n_init=0).fit, X)
assert_raises_regex(ValueError, "n_init", KMeans(n_init=-1).fit, X)
def test_check_estimators_unfitted():
# check that a ValueError/AttributeError is raised when calling predict
# on an unfitted estimator
msg = "AttributeError or ValueError not raised by predict"
assert_raises_regex(AssertionError, msg, check_estimators_unfitted, "estimator", NoSparseClassifier)
# check that CorrectNotFittedError inherit from either ValueError
# or AttributeError
check_estimators_unfitted("estimator", CorrectNotFittedErrorClassifier)
def test_gen_even_slices():
# check that gen_even_slices contains all samples
some_range = range(10)
joined_range = list(chain(*[some_range[slice] for slice in gen_even_slices(10, 3)]))
assert_array_equal(some_range, joined_range)
# check that passing negative n_chunks raises an error
slices = gen_even_slices(10, -1)
assert_raises_regex(ValueError, "gen_even_slices got n_packs=-1, must be" " >=1", next, slices)
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()
pipe = Pipeline([('scaler', scaler), ('pca', pca)])
assert_raises_regex(AttributeError,
"'PCA' object has no attribute 'fit_predict'",
getattr, pipe, 'fit_predict')
def test_precompute_invalid_argument():
X, y, _, _ = build_dataset()
for clf in [ElasticNetCV(precompute="invalid"), LassoCV(precompute="invalid")]:
assert_raises_regex(ValueError, ".*should be.*True.*False.*auto.*" "array-like.*Got 'invalid'", clf.fit, X, y)
# Precompute = 'auto' is not supported for ElasticNet
assert_raises_regex(
ValueError, ".*should be.*True.*False.*array-like.*" "Got 'auto'", ElasticNet(precompute="auto").fit, X, y
)
def test_check_classification_targets():
for y_type in EXAMPLES.keys():
if y_type in ["unknown", "continuous", 'continuous-multioutput']:
for example in EXAMPLES[y_type]:
msg = 'Unknown label type: '
assert_raises_regex(ValueError, msg,
check_classification_targets, example)
else:
for example in EXAMPLES[y_type]:
check_classification_targets(example)
def test_regression_metrics_at_limits():
assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be "
"used when targets contain negative values.",
mean_squared_log_error, [-1.], [-1.])
def test__check_reg_targets_exception():
invalid_multioutput = 'this_value_is_not_valid'
expected_message = ("Allowed 'multioutput' string values are.+"
"You provided multioutput={!r}".format(
invalid_multioutput))
assert_raises_regex(ValueError, expected_message,
_check_reg_targets,
[1, 2, 3],
[[1], [2], [3]],
invalid_multioutput)
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_multi_target_sample_weights_api():
X = [[1, 2, 3], [4, 5, 6]]
y = [[3.141, 2.718], [2.718, 3.141]]
w = [0.8, 0.6]
rgr = MultiOutputRegressor(Lasso())
assert_raises_regex(ValueError, "does not support sample weights", rgr.fit, X, y, w)
# no exception should be raised if the base estimator supports weights
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
rgr.fit(X, y, w)
def test_ovr_partial_fit_exceptions():
ovr = OneVsRestClassifier(MultinomialNB())
X = np.abs(np.random.randn(14, 2))
y = [1, 1, 1, 1, 2, 3, 3, 0, 0, 2, 3, 1, 2, 3]
ovr.partial_fit(X[:7], y[:7], np.unique(y))
# A new class value which was not in the first call of partial_fit
# It should raise ValueError
y1 = [5] + y[7:-1]
assert_raises_regex(ValueError, "Mini-batch contains \[.+\] while classes"
" must be subset of \[.+\]",
ovr.partial_fit, X=X[7:], y=y1)
def test_randomized_lasso_error_memory():
scaling = 0.3
selection_threshold = 0.5
tempdir = 5
clf = RandomizedLasso(verbose=False, alpha=[1, 0.8], random_state=42,
scaling=scaling,
selection_threshold=selection_threshold,
memory=tempdir)
assert_raises_regex(ValueError, "'memory' should either be a string or"
" a sklearn.utils.Memory instance",
clf.fit, X, y)
def test_scale_input_finiteness_validation():
# Check if non finite inputs raise ValueError
X = [np.nan, 5, 6, 7, 8]
assert_raises_regex(ValueError,
"Input contains NaN, infinity or a value too large",
scale, X)
X = [np.inf, 5, 6, 7, 8]
assert_raises_regex(ValueError,
"Input contains NaN, infinity or a value too large",
scale, X)
def test_ordinal_encoder_inverse():
X = [['abc', 2, 55], ['def', 1, 55]]
enc = OrdinalEncoder()
X_tr = enc.fit_transform(X)
exp = np.array(X, dtype=object)
assert_array_equal(enc.inverse_transform(X_tr), exp)
# incorrect shape raises
X_tr = np.array([[0, 1, 1, 2], [1, 0, 1, 0]])
msg = re.escape('Shape of the passed X data is not correct')
assert_raises_regex(ValueError, msg, enc.inverse_transform, X_tr)
def test_pipeline_wrong_memory():
# Test that an error is raised when memory is not a string or a Memory
# instance
iris = load_iris()
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_check_non_negative(retype):
A = np.array([[1, 1, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
X = retype(A)
check_non_negative(X, "")
X = retype([[0, 0], [0, 0]])
check_non_negative(X, "")
A[0, 0] = -1
X = retype(A)
assert_raises_regex(ValueError, "Negative ", check_non_negative, X, "")
def test_pipeline_wrong_memory():
# Test that an error is raised when memory is not a string or a Memory
# instance
iris = load_iris()
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 either be a string or a"
" sklearn.externals.joblib.Memory instance, got",
cached_pipe.fit, X, y)
def test_check_no_fit_attributes_set_in_init():
class NonConformantEstimator(object):
def __init__(self):
self.you_should_not_set_this_ = None
msg = ("By convention, attributes ending with '_'.+"
'should not be initialized in the constructor.+'
"Attribute 'you_should_not_set_this_' was found.+"
'in estimator estimator_name')
assert_raises_regex(AssertionError, msg,
check_no_fit_attributes_set_in_init,
'estimator_name',
NonConformantEstimator)
def test_check_estimator():
# tests that the estimator actually fails on "bad" estimators.
# not a complete test of all checks, which are very extensive.
# check that we have a set_params and can clone
msg = "it does not implement a 'get_params' methods"
assert_raises_regex(TypeError, msg, check_estimator, object)
# check that we have a fit method
msg = "object has no attribute 'fit'"
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator)
# check that fit does input validation
msg = "TypeError not raised by fit"
assert_raises_regex(AssertionError, msg, check_estimator, BaseBadClassifier)
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
assert_raises_regex(AssertionError, msg, check_estimator, NoCheckinPredict)
# check for sparse matrix input handling
msg = "Estimator type doesn't seem to fail gracefully on sparse data"
# the check for sparse input handling prints to the stdout,
# instead of raising an error, so as not to remove the original traceback.
# that means we need to jump through some hoops to catch it.
old_stdout = sys.stdout
string_buffer = StringIO()
sys.stdout = string_buffer
try:
check_estimator(NoSparseClassifier)
except:
pass
finally:
sys.stdout = old_stdout
assert_true(msg in string_buffer.getvalue())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
def test_sparse_validate_centers():
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
# Get a local optimum
centers = KMeans(n_clusters=4).fit(X).cluster_centers_
# Test that a ValueError is raised for validate_center_shape
classifier = KMeans(n_clusters=3, init=centers, n_init=1)
msg = "The shape of the initial centers \(\(4L?, 4L?\)\) " \
"does not match the number of clusters 3"
assert_raises_regex(ValueError, msg, classifier.fit, X)
def test_novelty_errors():
X = iris.data
# check errors for novelty=False
clf = neighbors.LocalOutlierFactor()
clf.fit(X)
# predict, decision_function and score_samples raise ValueError
for method in ['predict', 'decision_function', 'score_samples']:
msg = ('{} is not available when novelty=False'.format(method))
assert_raises_regex(AttributeError, msg, getattr, clf, method)
# check errors for novelty=True
clf = neighbors.LocalOutlierFactor(novelty=True)
msg = 'fit_predict is not available when novelty=True'
assert_raises_regex(AttributeError, msg, getattr, clf, 'fit_predict')
def test_check_estimator_transformer_no_mixin():
# check that TransformerMixin is not required for transformer tests to run
assert_raises_regex(AttributeError, '.*fit_transform.*',
check_estimator, BadTransformerWithoutMixin())
def check_samplers_no_fit_error(name, Sampler):
sampler = Sampler()
X = np.random.random((20, 2))
y = np.array([1] * 5 + [0] * 15)
assert_raises_regex(NotFittedError, "instance is not fitted yet.",
sampler.sample, X, y)
def test_check_estimator():
# tests that the estimator actually fails on "bad" estimators.
# not a complete test of all checks, which are very extensive.
# check that we have a set_params and can clone
msg = "it does not implement a 'get_params' methods"
assert_raises_regex(TypeError, msg, check_estimator, object)
# check that we have a fit method
msg = "object has no attribute 'fit'"
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator)
# check that fit does input validation
msg = "TypeError not raised"
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier)
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
assert_raises_regex(AssertionError, msg, check_estimator, NoCheckinPredict)
# check that estimator state does not change
# at transform/predict/predict_proba time
msg = 'Estimator changes __dict__ during predict'
assert_raises_regex(AssertionError, msg, check_estimator, ChangesDict)
# check that `fit` only changes attributes that
# are private (start with an _ or end with a _).
msg = ('Estimator changes public attribute\(s\) during the fit method.'
' Estimators are only allowed to change attributes started'
' or ended with _, but wrong_attribute changed')
assert_raises_regex(AssertionError, msg, check_estimator,
ChangesWrongAttribute)
# check that `fit` doesn't add any public attribute
msg = ('Estimator adds public attribute\(s\) during the fit method.'
' Estimators are only allowed to add private attributes'
' either started with _ or ended'
' with _ but wrong_attribute added')
assert_raises_regex(AssertionError, msg, check_estimator,
SetsWrongAttribute)
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = ("Estimator " + name + " doesn't seem to fail gracefully on"
" sparse data")
# the check for sparse input handling prints to the stdout,
# instead of raising an error, so as not to remove the original traceback.
# that means we need to jump through some hoops to catch it.
old_stdout = sys.stdout
string_buffer = StringIO()
sys.stdout = string_buffer
try:
check_estimator(NoSparseClassifier)
except:
pass
finally:
sys.stdout = old_stdout
assert msg in string_buffer.getvalue()
def test_iht_fit_sample_wrong_class_obj():
from sklearn.cluster import KMeans
est = KMeans()
iht = InstanceHardnessThreshold(estimator=est, random_state=RND_SEED)
assert_raises_regex(ValueError, "Invalid parameter `estimator`",
iht.fit_sample, X, Y)
def test_ncr_wrong_nn_obj():
nn = 'rnd'
ncr = NeighbourhoodCleaningRule(return_indices=True,
random_state=RND_SEED,
n_neighbors=nn)
assert_raises_regex(ValueError, "has to be one of", ncr.fit_sample, X, Y)
def test_smote_wrong_kind():
kind = 'rnd'
smote = SMOTE(kind=kind, random_state=RND_SEED)
assert_raises_regex(ValueError, "Unknown kind for SMOTE",
smote.fit_sample, X, Y)
def test_invalid_dimension():
assert_raises_regex(ValueError, "has to be a list or tuple",
space_check_dimension, "23")
assert_raises_regex(ValueError, "Invalid dimension", space_check_dimension,
(23, ))
def test_check_estimator():
# tests that the estimator actually fails on "bad" estimators.
# not a complete test of all checks, which are very extensive.
# check that we have a set_params and can clone
msg = "it does not implement a 'get_params' methods"
assert_raises_regex(TypeError, msg, check_estimator, object)
# check that we have a fit method
msg = "object has no attribute 'fit'"
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator)
# check that fit does input validation
msg = "TypeError not raised"
assert_raises_regex(AssertionError, msg, check_estimator, BaseBadClassifier)
# check that sample_weights in fit accepts pandas.Series type
try:
from pandas import Series # noqa
msg = ("Estimator NoSampleWeightPandasSeriesType raises error if "
"'sample_weight' parameter is of type pandas.Series")
assert_raises_regex(
ValueError, msg, check_estimator, NoSampleWeightPandasSeriesType)
except ImportError:
pass
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
assert_raises_regex(AssertionError, msg, check_estimator, NoCheckinPredict)
# check that estimator state does not change
# at transform/predict/predict_proba time
msg = 'Estimator changes __dict__ during predict'
assert_raises_regex(AssertionError, msg, check_estimator, ChangesDict)
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = "Estimator " + name + " doesn't seem to fail gracefully on sparse data"
# the check for sparse input handling prints to the stdout,
# instead of raising an error, so as not to remove the original traceback.
# that means we need to jump through some hoops to catch it.
old_stdout = sys.stdout
string_buffer = StringIO()
sys.stdout = string_buffer
try:
check_estimator(NoSparseClassifier)
except:
pass
finally:
sys.stdout = old_stdout
assert_true(msg in string_buffer.getvalue())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
check_estimator(MultiTaskElasticNet)
def test_assert_raises_msg():
with assert_raises_regex(AssertionError, 'Hello world'):
with assert_raises(ValueError, msg='Hello world'):
pass
def test_mutli_output_classifiation_partial_fit_no_first_classes_exception():
sgd_linear_clf = SGDClassifier(loss='log', random_state=1)
multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
assert_raises_regex(
ValueError, "classes must be passed on the first call "
"to partial_fit.", multi_target_linear.partial_fit, X, y)
def test_check_array_complex_data_error():
X = np.array([[1 + 2j, 3 + 4j, 5 + 7j], [2 + 3j, 4 + 5j, 6 + 7j]])
assert_raises_regex(ValueError, "Complex data not supported", check_array,
X)
# list of lists
X = [[1 + 2j, 3 + 4j, 5 + 7j], [2 + 3j, 4 + 5j, 6 + 7j]]
assert_raises_regex(ValueError, "Complex data not supported", check_array,
X)
# tuple of tuples
X = ((1 + 2j, 3 + 4j, 5 + 7j), (2 + 3j, 4 + 5j, 6 + 7j))
assert_raises_regex(ValueError, "Complex data not supported", check_array,
X)
# list of np arrays
X = [
np.array([1 + 2j, 3 + 4j, 5 + 7j]),
np.array([2 + 3j, 4 + 5j, 6 + 7j])
]
assert_raises_regex(ValueError, "Complex data not supported", check_array,
X)
# tuple of np arrays
X = (np.array([1 + 2j, 3 + 4j, 5 + 7j]), np.array([2 + 3j, 4 + 5j,
6 + 7j]))
assert_raises_regex(ValueError, "Complex data not supported", check_array,
X)
# dataframe
X = MockDataFrame(
np.array([[1 + 2j, 3 + 4j, 5 + 7j], [2 + 3j, 4 + 5j, 6 + 7j]]))
assert_raises_regex(ValueError, "Complex data not supported", check_array,
X)
# sparse matrix
X = sp.coo_matrix([[0, 1 + 2j], [0, 0]])
assert_raises_regex(ValueError, "Complex data not supported", check_array,
X)
def test_real_distance_out_of_range():
ints = Real(1, 10)
assert_raises_regex(RuntimeError, "compute distance for values within",
ints.distance, 11, 10)
def test_invalid_dimension():
assert_raises_regex(ValueError, "has to be a list or tuple",
space_check_dimension, "23")
# single value fixes dimension of space
space_check_dimension((23,))
def check_valid_transformation(klass):
assert klass(2, 30, transform="normalize")
assert klass(2, 30, transform="identity")
assert_raises_regex(ValueError, "should be 'normalize' or 'identity'",
klass, 2, 30, transform='not a valid transform name')
def test_check_estimator():
# tests that the estimator actually fails on "bad" estimators.
# not a complete test of all checks, which are very extensive.
# check that we have a set_params and can clone
msg = "it does not implement a 'get_params' methods"
assert_raises_regex(TypeError, msg, check_estimator, object)
assert_raises_regex(TypeError, msg, check_estimator, object())
# check that we have a fit method
msg = "object has no attribute 'fit'"
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator)
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator())
# check that fit does input validation
msg = "TypeError not raised"
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier)
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier())
# check that sample_weights in fit accepts pandas.Series type
try:
from pandas import Series # noqa
msg = ("Estimator NoSampleWeightPandasSeriesType raises error if "
"'sample_weight' parameter is of type pandas.Series")
assert_raises_regex(ValueError, msg, check_estimator,
NoSampleWeightPandasSeriesType)
except ImportError:
pass
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
assert_raises_regex(AssertionError, msg, check_estimator, NoCheckinPredict)
assert_raises_regex(AssertionError, msg, check_estimator,
NoCheckinPredict())
# check that estimator state does not change
# at transform/predict/predict_proba time
msg = 'Estimator changes __dict__ during predict'
assert_raises_regex(AssertionError, msg, check_estimator, ChangesDict)
# check that `fit` only changes attribures that
# are private (start with an _ or end with a _).
msg = ('Estimator ChangesWrongAttribute should not change or mutate '
'the parameter wrong_attribute from 0 to 1 during fit.')
assert_raises_regex(AssertionError, msg, check_estimator,
ChangesWrongAttribute)
check_estimator(ChangesUnderscoreAttribute)
# check that `fit` doesn't add any public attribute
msg = ('Estimator adds public attribute\(s\) during the fit method.'
' Estimators are only allowed to add private attributes'
' either started with _ or ended'
' with _ but wrong_attribute added')
assert_raises_regex(AssertionError, msg, check_estimator,
SetsWrongAttribute)
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = "Estimator %s doesn't seem to fail gracefully on sparse data" % name
# the check for sparse input handling prints to the stdout,
# instead of raising an error, so as not to remove the original traceback.
# that means we need to jump through some hoops to catch it.
old_stdout = sys.stdout
string_buffer = StringIO()
sys.stdout = string_buffer
try:
check_estimator(NoSparseClassifier)
except:
pass
finally:
sys.stdout = old_stdout
assert_true(msg in string_buffer.getvalue())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
check_estimator(AdaBoostClassifier())
check_estimator(MultiTaskElasticNet)
check_estimator(MultiTaskElasticNet())
def test_one_hot_encoder_sparse():
# Test OneHotEncoder's fit and transform.
X = [[3, 2, 1], [0, 1, 1]]
enc = OneHotEncoder()
with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
# discover max values automatically
X_trans = enc.fit_transform(X).toarray()
assert_equal(X_trans.shape, (2, 5))
assert_array_equal(enc.active_features_,
np.where([1, 0, 0, 1, 0, 1, 1, 0, 1])[0])
assert_array_equal(enc.feature_indices_, [0, 4, 7, 9])
# check outcome
assert_array_equal(X_trans,
[[0., 1., 0., 1., 1.], [1., 0., 1., 0., 1.]])
# max value given as 3
# enc = assert_warns(DeprecationWarning, OneHotEncoder, n_values=4)
enc = OneHotEncoder(n_values=4)
with ignore_warnings(category=DeprecationWarning):
X_trans = enc.fit_transform(X)
assert_equal(X_trans.shape, (2, 4 * 3))
assert_array_equal(enc.feature_indices_, [0, 4, 8, 12])
# max value given per feature
# enc = assert_warns(DeprecationWarning, OneHotEncoder, n_values=[3, 2, 2])
enc = OneHotEncoder(n_values=[3, 2, 2])
with ignore_warnings(category=DeprecationWarning):
X = [[1, 0, 1], [0, 1, 1]]
X_trans = enc.fit_transform(X)
assert_equal(X_trans.shape, (2, 3 + 2 + 2))
assert_array_equal(enc.n_values_, [3, 2, 2])
# check that testing with larger feature works:
X = np.array([[2, 0, 1], [0, 1, 1]])
enc.transform(X)
# test that an error is raised when out of bounds:
X_too_large = [[0, 2, 1], [0, 1, 1]]
assert_raises(ValueError, enc.transform, X_too_large)
error_msg = r"unknown categorical feature present \[2\] during transform"
assert_raises_regex(ValueError, error_msg, enc.transform, X_too_large)
with ignore_warnings(category=DeprecationWarning):
assert_raises(ValueError, OneHotEncoder(n_values=2).fit_transform, X)
# test that error is raised when wrong number of features
assert_raises(ValueError, enc.transform, X[:, :-1])
# test that error is raised when wrong number of features in fit
# with prespecified n_values
with ignore_warnings(category=DeprecationWarning):
assert_raises(ValueError, enc.fit, X[:, :-1])
# test exception on wrong init param
with ignore_warnings(category=DeprecationWarning):
assert_raises(TypeError, OneHotEncoder(n_values=np.int).fit, X)
enc = OneHotEncoder()
# test negative input to fit
with ignore_warnings(category=FutureWarning):
assert_raises(ValueError, enc.fit, [[0], [-1]])
# test negative input to transform
with ignore_warnings(category=FutureWarning):
enc.fit([[0], [1]])
assert_raises(ValueError, enc.transform, [[0], [-1]])
def test_invalid_drop_length(drop):
enc = OneHotEncoder(drop=drop)
assert_raises_regex(ValueError,
"`drop` should have length equal to the number",
enc.fit,
[['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]])
def test_ratio_minority_under_sampling():
assert_raises_regex(
ValueError, "'ratio'='minority' cannot be used with"
" under-sampler.", check_ratio, 'minority', np.array([1, 2, 3]),
'under-sampling')
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. '
'.*NoFit.*', Pipeline, [('clf', NoFit())])
# Smoke test with only an estimator
clf = NoTrans()
pipe = Pipeline([('svc', clf)])
assert_equal(
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_equal(clf.a, 0.1)
assert_equal(clf.b, 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 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_equal(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 = clone(pipe)
assert_false(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_equal(params, params2)
def test_cnn_fit_sample_with_wrong_object():
knn = 'rnd'
cnn = CondensedNearestNeighbour(random_state=RND_SEED, n_neighbors=knn)
assert_raises_regex(ValueError, "has to be a int or an ", cnn.fit_sample,
X, Y)
def test_load_with_offsets_error():
assert_raises_regex(ValueError, "n_features is required",
load_svmlight_file, datafile, offset=3, length=3)
def test_init_parameters_validation(GradientBoosting, X, y):
assert_raises_regex(
ValueError,
"Loss blah is not supported for",
GradientBoosting(loss='blah').fit, X, y
)
for learning_rate in (-1, 0):
assert_raises_regex(
ValueError,
f"learning_rate={learning_rate} must be strictly positive",
GradientBoosting(learning_rate=learning_rate).fit, X, y
)
assert_raises_regex(
ValueError,
f"max_iter=0 must not be smaller than 1",
GradientBoosting(max_iter=0).fit, X, y
)
assert_raises_regex(
ValueError,
f"max_leaf_nodes=0 should not be smaller than 1",
GradientBoosting(max_leaf_nodes=0).fit, X, y
)
assert_raises_regex(
ValueError,
f"max_depth=0 should not be smaller than 1",
GradientBoosting(max_depth=0).fit, X, y
)
assert_raises_regex(
ValueError,
f"min_samples_leaf=0 should not be smaller than 1",
GradientBoosting(min_samples_leaf=0).fit, X, y
)
assert_raises_regex(
ValueError,
f"l2_regularization=-1 must be positive",
GradientBoosting(l2_regularization=-1).fit, X, y
)
for max_bins in (1, 257):
assert_raises_regex(
ValueError,
f"max_bins={max_bins} should be no smaller than 2 and no larger",
GradientBoosting(max_bins=max_bins).fit, X, y
)
assert_raises_regex(
ValueError,
f"max_bins is set to 4 but the data is pre-binned with 256 bins",
GradientBoosting(max_bins=4).fit, X.astype(np.uint8), y
)
assert_raises_regex(
ValueError,
f"n_iter_no_change=-1 must be positive",
GradientBoosting(n_iter_no_change=-1).fit, X, y
)
for validation_split in (-1, 0):
assert_raises_regex(
ValueError,
f"validation_split={validation_split} must be strictly positive",
GradientBoosting(validation_split=validation_split).fit, X, y
)
assert_raises_regex(
ValueError,
f"tol=-1 must not be smaller than 0",
GradientBoosting(tol=-1).fit, X, y
)
def test_nearmiss_wrong_version():
version = 1000
nm = NearMiss(version=version, random_state=RND_SEED)
assert_raises_regex(ValueError, "must be 1, 2 or 3", nm.fit_sample, X, Y)
def test_raise_isinstance_error():
var = 10.0
assert_raises_regex(ValueError, "has to be one of", raise_isinstance_error,
'var', [int], var)
def test_oss_with_wrong_object():
knn = 'rnd'
oss = OneSidedSelection(random_state=RND_SEED, n_neighbors=knn)
assert_raises_regex(ValueError, "has to be a int", oss.fit_sample, X, Y)
def test_enn_not_good_object():
nn = 'rnd'
enn = EditedNearestNeighbours(n_neighbors=nn,
random_state=RND_SEED,
kind_sel='mode')
assert_raises_regex(ValueError, "has to be one of", enn.fit_sample, X, Y)
def test_sample_weight_length():
# check that an error is raised when passing sample weights
# with an incompatible shape
km = KMeans(n_clusters=n_clusters, random_state=42)
assert_raises_regex(ValueError, 'len\(sample_weight\)', km.fit, X,
sample_weight=np.ones(2))
def test_check_estimator():
# tests that the estimator actually fails on "bad" estimators.
# not a complete test of all checks, which are very extensive.
# check that we have a set_params and can clone
msg = "it does not implement a 'get_params' methods"
assert_raises_regex(TypeError, msg, check_estimator, object)
assert_raises_regex(TypeError, msg, check_estimator, object())
# check that values returned by get_params match set_params
msg = "get_params result does not match what was passed to set_params"
assert_raises_regex(AssertionError, msg, check_estimator,
ModifiesValueInsteadOfRaisingError())
assert_warns(UserWarning, check_estimator, RaisesErrorInSetParams())
assert_raises_regex(AssertionError, msg, check_estimator,
ModifiesAnotherValue())
# check that we have a fit method
msg = "object has no attribute 'fit'"
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator)
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator())
# check that fit does input validation
msg = "ValueError not raised"
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier)
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier())
# check that sample_weights in fit accepts pandas.Series type
try:
from pandas import Series # noqa
msg = ("Estimator NoSampleWeightPandasSeriesType raises error if "
"'sample_weight' parameter is of type pandas.Series")
assert_raises_regex(
ValueError, msg, check_estimator, NoSampleWeightPandasSeriesType)
except ImportError:
pass
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
assert_raises_regex(AssertionError, msg, check_estimator, NoCheckinPredict)
assert_raises_regex(AssertionError, msg, check_estimator,
NoCheckinPredict())
# check that estimator state does not change
# at transform/predict/predict_proba time
msg = 'Estimator changes __dict__ during predict'
assert_raises_regex(AssertionError, msg, check_estimator, ChangesDict)
# check that `fit` only changes attribures that
# are private (start with an _ or end with a _).
msg = ('Estimator ChangesWrongAttribute should not change or mutate '
'the parameter wrong_attribute from 0 to 1 during fit.')
assert_raises_regex(AssertionError, msg,
check_estimator, ChangesWrongAttribute)
check_estimator(ChangesUnderscoreAttribute)
# check that `fit` doesn't add any public attribute
msg = (r'Estimator adds public attribute\(s\) during the fit method.'
' Estimators are only allowed to add private attributes'
' either started with _ or ended'
' with _ but wrong_attribute added')
assert_raises_regex(AssertionError, msg,
check_estimator, SetsWrongAttribute)
# check for invariant method
name = NotInvariantPredict.__name__
method = 'predict'
msg = ("{method} of {name} is not invariant when applied "
"to a subset.").format(method=method, name=name)
assert_raises_regex(AssertionError, msg,
check_estimator, NotInvariantPredict)
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = "Estimator %s doesn't seem to fail gracefully on sparse data" % name
# the check for sparse input handling prints to the stdout,
# instead of raising an error, so as not to remove the original traceback.
# that means we need to jump through some hoops to catch it.
old_stdout = sys.stdout
string_buffer = StringIO()
sys.stdout = string_buffer
try:
check_estimator(NoSparseClassifier)
except:
pass
finally:
sys.stdout = old_stdout
assert msg in string_buffer.getvalue()
# Large indices test on bad estimator
msg = ('Estimator LargeSparseNotSupportedClassifier doesn\'t seem to '
r'support \S{3}_64 matrix, and is not failing gracefully.*')
assert_raises_regex(AssertionError, msg, check_estimator,
LargeSparseNotSupportedClassifier)
# non-regression test for estimators transforming to sparse data
check_estimator(SparseTransformer())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
check_estimator(AdaBoostClassifier())
check_estimator(MultiTaskElasticNet)
check_estimator(MultiTaskElasticNet())
def test_ada_wrong_nn_obj():
nn = 'rnd'
ada = ADASYN(random_state=RND_SEED, n_neighbors=nn)
assert_raises_regex(ValueError, "has to be one of", ada.fit_sample, X, Y)
