def test_parameter_checking():
A = np.ones((2, 2))
name = 'spam'
msg = "Invalid solver parameter: got 'spam' instead of one of"
assert_raise_message(ValueError, msg, NMF(solver=name).fit, A)
msg = "Invalid init parameter: got 'spam' instead of one of"
assert_raise_message(ValueError, msg, NMF(init=name).fit, A)
msg = "Invalid beta_loss parameter: got 'spam' instead of one"
assert_raise_message(ValueError, msg,
NMF(solver='mu', beta_loss=name).fit, A)
msg = "Invalid beta_loss parameter: solver 'cd' does not handle "
msg += "beta_loss = 1.0"
assert_raise_message(ValueError, msg,
NMF(solver='cd', beta_loss=1.0).fit, A)
msg = "Negative values in data passed to"
assert_raise_message(ValueError, msg, NMF().fit, -A)
assert_raise_message(ValueError, msg, nmf._initialize_nmf, -A, 2, 'nndsvd')
clf = NMF(2, tol=0.1).fit(A)
assert_raise_message(ValueError, msg, clf.transform, -A)
for init in ['nndsvd', 'nndsvda', 'nndsvdar']:
msg = ("init = '{}' can only be used when "
"n_components <= min(n_samples, n_features)".format(init))
assert_raise_message(ValueError, msg, NMF(3, init).fit, A)
assert_raise_message(ValueError, msg, nmf._initialize_nmf, A, 3, init)
def test_max_iter_error():
km = KMeans(max_iter=-1)
assert_raise_message(ValueError, 'Number of iterations should be', km.fit,
X)
def test_non_negative_factorization_checking():
A = np.ones((2, 2))
# Test parameters checking is public function
nnmf = non_negative_factorization
msg = ("Number of components must be a positive integer; "
"got (n_components=1.5)")
assert_raise_message(ValueError, msg, nnmf, A, A, A, 1.5, 'random')
msg = ("Number of components must be a positive integer; "
"got (n_components='2')")
assert_raise_message(ValueError, msg, nnmf, A, A, A, '2', 'random')
msg = "Negative values in data passed to NMF (input H)"
assert_raise_message(ValueError, msg, nnmf, A, A, -A, 2, 'custom')
msg = "Negative values in data passed to NMF (input W)"
assert_raise_message(ValueError, msg, nnmf, A, -A, A, 2, 'custom')
msg = "Array passed to NMF (input H) is full of zeros"
assert_raise_message(ValueError, msg, nnmf, A, A, 0 * A, 2, 'custom')
msg = "Invalid regularization parameter: got 'spam' instead of one of"
assert_raise_message(ValueError, msg, nnmf, A, A, 0 * A, 2, 'custom', True,
'cd', 2., 1e-4, 200, 0., 0., 'spam')
def test_non_negative_factorization_checking():
A = np.ones((2, 2))
# Test parameters checking is public function
nnmf = non_negative_factorization
msg = ("The default value of init will change from "
"random to None in 0.23 to make it consistent "
"with decomposition.NMF.")
assert_warns_message(FutureWarning, msg, nnmf, A, A, A, np.int64(1))
msg = ("Number of components must be a positive integer; "
"got (n_components=1.5)")
assert_raise_message(ValueError, msg, nnmf, A, A, A, 1.5, 'random')
msg = ("Number of components must be a positive integer; "
"got (n_components='2')")
assert_raise_message(ValueError, msg, nnmf, A, A, A, '2', 'random')
msg = "Negative values in data passed to NMF (input H)"
assert_raise_message(ValueError, msg, nnmf, A, A, -A, 2, 'custom')
msg = "Negative values in data passed to NMF (input W)"
assert_raise_message(ValueError, msg, nnmf, A, -A, A, 2, 'custom')
msg = "Array passed to NMF (input H) is full of zeros"
assert_raise_message(ValueError, msg, nnmf, A, A, 0 * A, 2, 'custom')
msg = "Invalid regularization parameter: got 'spam' instead of one of"
assert_raise_message(ValueError, msg, nnmf, A, A, 0 * A, 2, 'custom', True,
'cd', 2., 1e-4, 200, 0., 0., 'spam')
def test_meanshift_all_orphans():
# init away from the data, crash with a sensible warning
ms = MeanShift(bandwidth=0.1, seeds=[[-9, -9], [-10, -10]])
msg = "No point was within bandwidth=0.1"
assert_raise_message(ValueError, msg, ms.fit, X,)
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_check_array_min_samples_and_features_messages():
# empty list is considered 2D by default:
msg = "0 feature(s) (shape=(1, 0)) while a minimum of 1 is required."
assert_raise_message(ValueError, msg, check_array, [[]])
# If considered a 1D collection when ensure_2d=False, then the minimum
# number of samples will break:
msg = "0 sample(s) (shape=(0,)) while a minimum of 1 is required."
assert_raise_message(ValueError, msg, check_array, [], ensure_2d=False)
# Invalid edge case when checking the default minimum sample of a scalar
msg = "Singleton array array(42) cannot be considered a valid collection."
assert_raise_message(TypeError, msg, check_array, 42, ensure_2d=False)
# Simulate a model that would need at least 2 samples to be well defined
X = np.ones((1, 10))
y = np.ones(1)
msg = "1 sample(s) (shape=(1, 10)) while a minimum of 2 is required."
assert_raise_message(ValueError,
msg,
check_X_y,
X,
y,
ensure_min_samples=2)
# The same message is raised if the data has 2 dimensions even if this is
# not mandatory
assert_raise_message(ValueError,
msg,
check_X_y,
X,
y,
ensure_min_samples=2,
ensure_2d=False)
# Simulate a model that would require at least 3 features (e.g. SelectKBest
# with k=3)
X = np.ones((10, 2))
y = np.ones(2)
msg = "2 feature(s) (shape=(10, 2)) while a minimum of 3 is required."
assert_raise_message(ValueError,
msg,
check_X_y,
X,
y,
ensure_min_features=3)
# Only the feature check is enabled whenever the number of dimensions is 2
# even if allow_nd is enabled:
assert_raise_message(ValueError,
msg,
check_X_y,
X,
y,
ensure_min_features=3,
allow_nd=True)
# Simulate a case where a pipeline stage as trimmed all the features of a
# 2D dataset.
X = np.empty(0).reshape(10, 0)
y = np.ones(10)
msg = "0 feature(s) (shape=(10, 0)) while a minimum of 1 is required."
assert_raise_message(ValueError, msg, check_X_y, X, y)
# nd-data is not checked for any minimum number of features by default:
X = np.ones((10, 0, 28, 28))
y = np.ones(10)
X_checked, y_checked = check_X_y(X, y, allow_nd=True)
assert_array_equal(X, X_checked)
assert_array_equal(y, y_checked)
def test_zero_cosine_linkage_tree():
# Check that zero vectors in X produce an error when
# 'cosine' affinity is used
X = np.array([[0, 1], [0, 0]])
msg = 'Cosine affinity cannot be used when X contains zero vectors'
assert_raise_message(ValueError, msg, linkage_tree, X, affinity='cosine')
def test_l1_min_c_l2_loss():
# loss='l2' should raise ValueError
assert_raise_message(ValueError, "loss type not in", l1_min_c, dense_X, Y1,
"l2")
def test_rational_quadratic_kernel():
kernel = RationalQuadratic(length_scale=[1., 1.])
assert_raise_message(AttributeError,
"RationalQuadratic kernel only supports isotropic "
"version, please use a single "
"scalar for length_scale", kernel, X)
def test_bayesian_mixture_precisions_prior_initialisation():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
X = rng.rand(n_samples, n_features)
# Check raise message for a bad value of degrees_of_freedom_prior
bad_degrees_of_freedom_prior_ = n_features - 1.
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=bad_degrees_of_freedom_prior_,
random_state=rng)
assert_raise_message(
ValueError, "The parameter 'degrees_of_freedom_prior' should be "
"greater than %d, but got %.3f." %
(n_features - 1, bad_degrees_of_freedom_prior_), bgmm.fit, X)
# Check correct init for a given value of degrees_of_freedom_prior
degrees_of_freedom_prior = rng.rand() + n_features - 1.
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=degrees_of_freedom_prior,
random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior,
bgmm.degrees_of_freedom_prior_)
# Check correct init for the default value of degrees_of_freedom_prior
degrees_of_freedom_prior_default = n_features
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=degrees_of_freedom_prior_default,
random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior_default,
bgmm.degrees_of_freedom_prior_)
# Check correct init for a given value of covariance_prior
covariance_prior = {
'full': np.cov(X.T, bias=1) + 10,
'tied': np.cov(X.T, bias=1) + 5,
'diag': np.diag(np.atleast_2d(np.cov(X.T, bias=1))) + 3,
'spherical': rng.rand()
}
bgmm = BayesianGaussianMixture(random_state=rng)
for cov_type in ['full', 'tied', 'diag', 'spherical']:
bgmm.covariance_type = cov_type
bgmm.covariance_prior = covariance_prior[cov_type]
bgmm.fit(X)
assert_almost_equal(covariance_prior[cov_type], bgmm.covariance_prior_)
# Check raise message for a bad spherical value of covariance_prior
bad_covariance_prior_ = -1.
bgmm = BayesianGaussianMixture(covariance_type='spherical',
covariance_prior=bad_covariance_prior_,
random_state=rng)
assert_raise_message(
ValueError, "The parameter 'spherical covariance_prior' "
"should be greater than 0., but got %.3f." % bad_covariance_prior_,
bgmm.fit, X)
# Check correct init for the default value of covariance_prior
covariance_prior_default = {
'full': np.atleast_2d(np.cov(X.T)),
'tied': np.atleast_2d(np.cov(X.T)),
'diag': np.var(X, axis=0, ddof=1),
'spherical': np.var(X, axis=0, ddof=1).mean()
}
bgmm = BayesianGaussianMixture(random_state=0)
for cov_type in ['full', 'tied', 'diag', 'spherical']:
bgmm.covariance_type = cov_type
bgmm.fit(X)
assert_almost_equal(covariance_prior_default[cov_type],
bgmm.covariance_prior_)
def test_check_array(setup):
# accept_sparse == False
# raise error on sparse inputs
X = [[1, 2], [3, 4]]
X_csr = sp.csr_matrix(X)
with pytest.raises(TypeError):
check_array(X_csr)
X_csr = mt.tensor(sp.csr_matrix(X))
with pytest.raises(TypeError):
check_array(X_csr)
# ensure_2d=False
X_array = check_array([0, 1, 2], ensure_2d=False)
assert X_array.ndim == 1
# ensure_2d=True with 1d array
assert_raise_message(ValueError,
'Expected 2D array, got 1D array instead',
check_array, [0, 1, 2],
ensure_2d=True)
assert_raise_message(ValueError,
'Expected 2D array, got 1D array instead',
check_array,
mt.tensor([0, 1, 2]),
ensure_2d=True)
# ensure_2d=True with scalar array
assert_raise_message(ValueError,
'Expected 2D array, got scalar array instead',
check_array,
10,
ensure_2d=True)
# don't allow ndim > 3
X_ndim = mt.arange(8).reshape(2, 2, 2)
with pytest.raises(ValueError):
check_array(X_ndim)
check_array(X_ndim, allow_nd=True) # doesn't raise
# dtype and order enforcement.
X_C = mt.arange(4).reshape(2, 2).copy("C")
X_F = X_C.copy("F")
X_int = X_C.astype(mt.int)
X_float = X_C.astype(mt.float)
Xs = [X_C, X_F, X_int, X_float]
dtypes = [mt.int32, mt.int, mt.float, mt.float32, None, mt.bool, object]
orders = ['C', 'F', None]
copys = [True, False]
for X, dtype, order, copy in product(Xs, dtypes, orders, copys):
X_checked = check_array(X,
dtype=dtype,
order=order,
copy=copy,
force_all_finite=False)
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if order == 'C':
assert X_checked.flags['C_CONTIGUOUS']
assert not X_checked.flags['F_CONTIGUOUS']
elif order == 'F':
assert X_checked.flags['F_CONTIGUOUS']
assert not X_checked.flags['C_CONTIGUOUS']
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if (X.dtype == X_checked.dtype and X_checked.flags['C_CONTIGUOUS']
== X.flags['C_CONTIGUOUS']
and X_checked.flags['F_CONTIGUOUS']
== X.flags['F_CONTIGUOUS']):
assert X is X_checked
# other input formats
# convert lists to arrays
X_dense = check_array([[1, 2], [3, 4]])
assert isinstance(X_dense, Tensor)
# raise on too deep lists
with pytest.raises(ValueError):
check_array(X_ndim.to_numpy().tolist())
check_array(X_ndim.to_numpy().tolist(), allow_nd=True) # doesn't raise
# convert weird stuff to arrays
X_no_array = NotAnArray(X_dense.to_numpy())
result = check_array(X_no_array)
assert isinstance(result, Tensor)
# deprecation warning if string-like array with dtype="numeric"
expected_warn_regex = r"converted to decimal numbers if dtype='numeric'"
X_str = [['11', '12'], ['13', 'xx']]
for X in [X_str, mt.array(X_str, dtype='U'), mt.array(X_str, dtype='S')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
# deprecation warning if byte-like array with dtype="numeric"
X_bytes = [[b'a', b'b'], [b'c', b'd']]
for X in [X_bytes, mt.array(X_bytes, dtype='V1')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
# test finite
X = [[1.0, np.nan], [2.0, 3.0]]
with pytest.raises(ValueError):
_ = check_array(X).execute()
def test_mcd_class_on_invalid_input():
X = np.arange(100)
mcd = MinCovDet()
assert_raise_message(ValueError, 'Expected 2D array, got 1D array instead',
mcd.fit, X)
def test_fast_mcd_on_invalid_input():
X = np.arange(100)
assert_raise_message(ValueError, 'Expected 2D array, got 1D array instead',
fast_mcd, X)
def test_check_docstring_parameters():
try:
import numpydoc # noqa
except ImportError:
raise SkipTest("numpydoc is required to test the docstrings")
incorrect = check_docstring_parameters(f_ok)
assert incorrect == []
incorrect = check_docstring_parameters(f_ok, ignore=['b'])
assert incorrect == []
incorrect = check_docstring_parameters(f_missing, ignore=['b'])
assert incorrect == []
assert_raise_message(RuntimeError, 'Unknown section Results',
check_docstring_parameters, f_bad_sections)
assert_raise_message(RuntimeError, 'Unknown section Parameter',
check_docstring_parameters, Klass.f_bad_sections)
incorrect = check_docstring_parameters(f_check_param_definition)
assert (incorrect == [
"sklearn.utils.tests.test_testing.f_check_param_definition There "
"was no space between the param name and colon ('a: int')",
"sklearn.utils.tests.test_testing.f_check_param_definition There "
"was no space between the param name and colon ('b:')",
"sklearn.utils.tests.test_testing.f_check_param_definition "
"Parameter 'c :' has an empty type spec. Remove the colon",
"sklearn.utils.tests.test_testing.f_check_param_definition There "
"was no space between the param name and colon ('d:int')",
])
messages = [
[
"In function: sklearn.utils.tests.test_testing.f_bad_order",
"There's a parameter name mismatch in function docstring w.r.t."
" function signature, at index 0 diff: 'b' != 'a'", "Full diff:",
"- ['b', 'a']", "+ ['a', 'b']"
],
[
"In function: " +
"sklearn.utils.tests.test_testing.f_too_many_param_docstring",
"Parameters in function docstring have more items w.r.t. function"
" signature, first extra item: c", "Full diff:", "- ['a', 'b']",
"+ ['a', 'b', 'c']", "? +++++"
],
[
"In function: sklearn.utils.tests.test_testing.f_missing",
"Parameters in function docstring have less items w.r.t. function"
" signature, first missing item: b", "Full diff:", "- ['a', 'b']",
"+ ['a']"
],
[
"In function: sklearn.utils.tests.test_testing.Klass.f_missing",
"Parameters in function docstring have less items w.r.t. function"
" signature, first missing item: X", "Full diff:", "- ['X', 'y']",
"+ []"
],
[
"In function: " +
"sklearn.utils.tests.test_testing.MockMetaEstimator.predict",
"There's a parameter name mismatch in function docstring w.r.t."
" function signature, at index 0 diff: 'X' != 'y'", "Full diff:",
"- ['X']", "? ^", "+ ['y']", "? ^"
],
[
"In function: " +
"sklearn.utils.tests.test_testing.MockMetaEstimator." +
"predict_proba",
"Parameters in function docstring have less items w.r.t. function"
" signature, first missing item: X", "Full diff:", "- ['X']",
"+ []"
],
[
"In function: " +
"sklearn.utils.tests.test_testing.MockMetaEstimator.score",
"Parameters in function docstring have less items w.r.t. function"
" signature, first missing item: X", "Full diff:", "- ['X']",
"+ []"
],
[
"In function: " +
"sklearn.utils.tests.test_testing.MockMetaEstimator.fit",
"Parameters in function docstring have less items w.r.t. function"
" signature, first missing item: X", "Full diff:", "- ['X', 'y']",
"+ []"
],
]
mock_meta = MockMetaEstimator(delegate=MockEst())
for msg, f in zip(messages, [
f_bad_order, f_too_many_param_docstring, f_missing,
Klass.f_missing, mock_meta.predict, mock_meta.predict_proba,
mock_meta.score, mock_meta.fit
]):
incorrect = check_docstring_parameters(f)
assert msg == incorrect, ('\n"%s"\n not in \n"%s"' % (msg, incorrect))
def test_gaussian_mixture_attributes():
# test bad parameters
rng = np.random.RandomState(0)
X = rng.rand(10, 2)
n_components_bad = 0
gmm = GaussianMixture(n_components=n_components_bad)
assert_raise_message(
ValueError, "Invalid value for 'n_components': %d "
"Estimation requires at least one component" % n_components_bad,
gmm.fit, X)
# covariance_type should be in [spherical, diag, tied, full]
covariance_type_bad = 'bad_covariance_type'
gmm = GaussianMixture(covariance_type=covariance_type_bad)
assert_raise_message(
ValueError, "Invalid value for 'covariance_type': %s "
"'covariance_type' should be in "
"['spherical', 'tied', 'diag', 'full']" % covariance_type_bad, gmm.fit,
X)
tol_bad = -1
gmm = GaussianMixture(tol=tol_bad)
assert_raise_message(
ValueError, "Invalid value for 'tol': %.5f "
"Tolerance used by the EM must be non-negative" % tol_bad, gmm.fit, X)
reg_covar_bad = -1
gmm = GaussianMixture(reg_covar=reg_covar_bad)
assert_raise_message(
ValueError, "Invalid value for 'reg_covar': %.5f "
"regularization on covariance must be "
"non-negative" % reg_covar_bad, gmm.fit, X)
max_iter_bad = 0
gmm = GaussianMixture(max_iter=max_iter_bad)
assert_raise_message(
ValueError, "Invalid value for 'max_iter': %d "
"Estimation requires at least one iteration" % max_iter_bad, gmm.fit,
X)
n_init_bad = 0
gmm = GaussianMixture(n_init=n_init_bad)
assert_raise_message(
ValueError, "Invalid value for 'n_init': %d "
"Estimation requires at least one run" % n_init_bad, gmm.fit, X)
init_params_bad = 'bad_method'
gmm = GaussianMixture(init_params=init_params_bad)
assert_raise_message(
ValueError,
"Unimplemented initialization method '%s'" % init_params_bad, gmm.fit,
X)
# test good parameters
n_components, tol, n_init, max_iter, reg_covar = 2, 1e-4, 3, 30, 1e-1
covariance_type, init_params = 'full', 'random'
gmm = GaussianMixture(n_components=n_components,
tol=tol,
n_init=n_init,
max_iter=max_iter,
reg_covar=reg_covar,
covariance_type=covariance_type,
init_params=init_params).fit(X)
assert gmm.n_components == n_components
assert gmm.covariance_type == covariance_type
assert gmm.tol == tol
assert gmm.reg_covar == reg_covar
assert gmm.max_iter == max_iter
assert gmm.n_init == n_init
assert gmm.init_params == init_params
def test_check_classification_targets():
# Test that check_classification_target return correct type. #5782
y = np.array([0.0, 1.1, 2.0, 3.0])
msg = type_of_target(y)
assert_raise_message(ValueError, msg, check_classification_targets, y)
def test_fetch_openml_raises_missing_values_target(monkeypatch, gzip_response):
data_id = 2
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response)
assert_raise_message(ValueError, "Target column ",
fetch_openml, data_id=data_id, target_column='family')
def test_check_array():
# accept_sparse == False
# raise error on sparse inputs
X = [[1, 2], [3, 4]]
X_csr = sp.csr_matrix(X)
assert_raises(TypeError, check_array, X_csr)
# ensure_2d=False
X_array = check_array([0, 1, 2], ensure_2d=False)
assert X_array.ndim == 1
# ensure_2d=True with 1d array
assert_raise_message(ValueError,
'Expected 2D array, got 1D array instead',
check_array, [0, 1, 2],
ensure_2d=True)
# ensure_2d=True with scalar array
assert_raise_message(ValueError,
'Expected 2D array, got scalar array instead',
check_array,
10,
ensure_2d=True)
# don't allow ndim > 3
X_ndim = np.arange(8).reshape(2, 2, 2)
assert_raises(ValueError, check_array, X_ndim)
check_array(X_ndim, allow_nd=True) # doesn't raise
# dtype and order enforcement.
X_C = np.arange(4).reshape(2, 2).copy("C")
X_F = X_C.copy("F")
X_int = X_C.astype(np.int)
X_float = X_C.astype(np.float)
Xs = [X_C, X_F, X_int, X_float]
dtypes = [np.int32, np.int, np.float, np.float32, None, np.bool, object]
orders = ['C', 'F', None]
copys = [True, False]
for X, dtype, order, copy in product(Xs, dtypes, orders, copys):
X_checked = check_array(X, dtype=dtype, order=order, copy=copy)
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if order == 'C':
assert X_checked.flags['C_CONTIGUOUS']
assert not X_checked.flags['F_CONTIGUOUS']
elif order == 'F':
assert X_checked.flags['F_CONTIGUOUS']
assert not X_checked.flags['C_CONTIGUOUS']
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if (X.dtype == X_checked.dtype and X_checked.flags['C_CONTIGUOUS']
== X.flags['C_CONTIGUOUS']
and X_checked.flags['F_CONTIGUOUS']
== X.flags['F_CONTIGUOUS']):
assert X is X_checked
# allowed sparse != None
X_csc = sp.csc_matrix(X_C)
X_coo = X_csc.tocoo()
X_dok = X_csc.todok()
X_int = X_csc.astype(np.int)
X_float = X_csc.astype(np.float)
Xs = [X_csc, X_coo, X_dok, X_int, X_float]
accept_sparses = [['csr', 'coo'], ['coo', 'dok']]
for X, dtype, accept_sparse, copy in product(Xs, dtypes, accept_sparses,
copys):
with warnings.catch_warnings(record=True) as w:
X_checked = check_array(X,
dtype=dtype,
accept_sparse=accept_sparse,
copy=copy)
if (dtype is object or sp.isspmatrix_dok(X)) and len(w):
message = str(w[0].message)
messages = [
"object dtype is not supported by sparse matrices",
"Can't check dok sparse matrix for nan or inf."
]
assert message in messages
else:
assert len(w) == 0
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if X.format in accept_sparse:
# no change if allowed
assert X.format == X_checked.format
else:
# got converted
assert X_checked.format == accept_sparse[0]
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if X.dtype == X_checked.dtype and X.format == X_checked.format:
assert X is X_checked
# other input formats
# convert lists to arrays
X_dense = check_array([[1, 2], [3, 4]])
assert isinstance(X_dense, np.ndarray)
# raise on too deep lists
assert_raises(ValueError, check_array, X_ndim.tolist())
check_array(X_ndim.tolist(), allow_nd=True) # doesn't raise
# convert weird stuff to arrays
X_no_array = _NotAnArray(X_dense)
result = check_array(X_no_array)
assert isinstance(result, np.ndarray)
# deprecation warning if string-like array with dtype="numeric"
expected_warn_regex = r"converted to decimal numbers if dtype='numeric'"
X_str = [['11', '12'], ['13', 'xx']]
for X in [X_str, np.array(X_str, dtype='U'), np.array(X_str, dtype='S')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
# deprecation warning if byte-like array with dtype="numeric"
X_bytes = [[b'a', b'b'], [b'c', b'd']]
for X in [X_bytes, np.array(X_bytes, dtype='V1')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
def test_estimate_bandwidth_with_sparse_matrix():
# Test estimate_bandwidth with sparse matrix
X = sparse.lil_matrix((1000, 1000))
msg = "A sparse matrix was passed, but dense data is required."
assert_raise_message(TypeError, msg, estimate_bandwidth, X, 200)
def test_check_X_y_informative_error():
X = np.ones((2, 2))
y = None
assert_raise_message(ValueError, "y cannot be None", check_X_y, X, y)
def test_estimator_init():
eclf = VotingClassifier(estimators=[])
msg = ("Invalid 'estimators' attribute, 'estimators' should be"
" a list of (string, estimator) tuples.")
assert_raise_message(ValueError, msg, eclf.fit, X, y)
clf = LogisticRegression(random_state=1)
eclf = VotingClassifier(estimators=[('lr', clf)], voting='error')
msg = ('Voting must be \'soft\' or \'hard\'; got (voting=\'error\')')
assert_raise_message(ValueError, msg, eclf.fit, X, y)
eclf = VotingClassifier(estimators=[('lr', clf)], weights=[1, 2])
msg = ('Number of `estimators` and weights must be equal'
'; got 2 weights, 1 estimators')
assert_raise_message(ValueError, msg, eclf.fit, X, y)
eclf = VotingClassifier(estimators=[('lr', clf), ('lr', clf)],
weights=[1, 2])
msg = "Names provided are not unique: ['lr', 'lr']"
assert_raise_message(ValueError, msg, eclf.fit, X, y)
eclf = VotingClassifier(estimators=[('lr__', clf)])
msg = "Estimator names must not contain __: got ['lr__']"
assert_raise_message(ValueError, msg, eclf.fit, X, y)
eclf = VotingClassifier(estimators=[('estimators', clf)])
msg = "Estimator names conflict with constructor arguments: ['estimators']"
assert_raise_message(ValueError, msg, eclf.fit, X, y)