def test_enet_copy_X_True(check_input):
X, y, _, _ = build_dataset()
X = X.copy(order='F')
original_X = X.copy()
enet = ElasticNet(copy_X=True)
enet.fit(X, y, check_input=check_input)
assert_array_equal(original_X, X)
def test_additive_chi2_sampler():
# test that AdditiveChi2Sampler approximates kernel on random data
# compute exact kernel
# abbreviations for easier formula
X_ = X[:, np.newaxis, :]
Y_ = Y[np.newaxis, :, :]
large_kernel = 2 * X_ * Y_ / (X_ + Y_)
# reduce to n_samples_x x n_samples_y by summing over features
kernel = (large_kernel.sum(axis=2))
# approximate kernel mapping
transform = AdditiveChi2Sampler(sample_steps=3)
X_trans = transform.fit_transform(X)
Y_trans = transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
assert_array_almost_equal(kernel, kernel_approx, 1)
X_sp_trans = transform.fit_transform(csr_matrix(X))
Y_sp_trans = transform.transform(csr_matrix(Y))
assert_array_equal(X_trans, X_sp_trans.A)
assert_array_equal(Y_trans, Y_sp_trans.A)
# test error is raised on negative input
Y_neg = Y.copy()
Y_neg[0, 0] = -1
assert_raises(ValueError, transform.transform, Y_neg)
# test error on invalid sample_steps
transform = AdditiveChi2Sampler(sample_steps=4)
assert_raises(ValueError, transform.fit, X)
# test that the sample interval is set correctly
sample_steps_available = [1, 2, 3]
for sample_steps in sample_steps_available:
# test that the sample_interval is initialized correctly
transform = AdditiveChi2Sampler(sample_steps=sample_steps)
assert transform.sample_interval is None
# test that the sample_interval is changed in the fit method
transform.fit(X)
assert transform.sample_interval_ is not None
# test that the sample_interval is set correctly
sample_interval = 0.3
transform = AdditiveChi2Sampler(sample_steps=4,
sample_interval=sample_interval)
assert transform.sample_interval == sample_interval
transform.fit(X)
assert transform.sample_interval_ == sample_interval
def test_check_array_series():
# regression test that check_array works on pandas Series
pd = importorskip("pandas")
res = check_array(pd.Series([1, 2, 3]), ensure_2d=False)
assert_array_equal(res, np.array([1, 2, 3]))
# with categorical dtype (not a numpy dtype) (GH12699)
s = pd.Series(['a', 'b', 'c']).astype('category')
res = check_array(s, dtype=None, ensure_2d=False)
assert_array_equal(res, np.array(['a', 'b', 'c'], dtype=object))
def test_huber_sparse():
X, y = make_regression_with_outliers()
huber = HuberRegressor(alpha=0.1)
huber.fit(X, y)
X_csr = sparse.csr_matrix(X)
huber_sparse = HuberRegressor(alpha=0.1)
huber_sparse.fit(X_csr, y)
assert_array_almost_equal(huber_sparse.coef_, huber.coef_)
assert_array_equal(huber.outliers_, huber_sparse.outliers_)
def test_make_spd_matrix():
X = make_spd_matrix(n_dim=5, random_state=0)
assert X.shape == (5, 5), "X shape mismatch"
assert_array_almost_equal(X, X.T)
from numpy.linalg import eig
eigenvalues, _ = eig(X)
assert_array_equal(eigenvalues > 0, np.array([True] * 5),
"X is not positive-definite")
def test_check_tuple_input():
# Ensures that checks return valid tuples.
rng = np.random.RandomState(0)
XA = rng.random_sample((5, 4))
XA_tuples = tuplify(XA)
XB = rng.random_sample((5, 4))
XB_tuples = tuplify(XB)
XA_checked, XB_checked = check_pairwise_arrays(XA_tuples, XB_tuples)
assert_array_equal(XA_tuples, XA_checked)
assert_array_equal(XB_tuples, XB_checked)
def test_connectivity_ignores_diagonal():
rng = np.random.RandomState(0)
X = rng.rand(20, 5)
connectivity = kneighbors_graph(X, 3, include_self=False)
connectivity_include_self = kneighbors_graph(X, 3, include_self=True)
aglc1 = AgglomerativeClustering(connectivity=connectivity)
aglc2 = AgglomerativeClustering(connectivity=connectivity_include_self)
aglc1.fit(X)
aglc2.fit(X)
assert_array_equal(aglc1.labels_, aglc2.labels_)
def test_cluster_intensity_tie():
X = np.array([[1, 1], [2, 1], [1, 0],
[4, 7], [3, 5], [3, 6]])
c1 = MeanShift(bandwidth=2).fit(X)
X = np.array([[4, 7], [3, 5], [3, 6],
[1, 1], [2, 1], [1, 0]])
c2 = MeanShift(bandwidth=2).fit(X)
assert_array_equal(c1.labels_, [1, 1, 1, 0, 0, 0])
assert_array_equal(c2.labels_, [0, 0, 0, 1, 1, 1])
def test_column_transformer_no_remaining_remainder_transformer():
X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T
ct = ColumnTransformer([('trans1', Trans(), [0, 1, 2])],
remainder=DoubleTrans())
assert_array_equal(ct.fit_transform(X_array), X_array)
assert_array_equal(ct.fit(X_array).transform(X_array), X_array)
assert len(ct.transformers_) == 1
assert ct.transformers_[-1][0] != 'remainder'
def test_dbscan_no_core_samples():
rng = np.random.RandomState(0)
X = rng.rand(40, 10)
X[X < .8] = 0
for X_ in [X, sparse.csr_matrix(X)]:
db = DBSCAN(min_samples=6).fit(X_)
assert_array_equal(db.components_, np.empty((0, X_.shape[1])))
assert_array_equal(db.labels_, -1)
assert db.core_sample_indices_.shape == (0, )
def test_select_kbest_all():
# Test whether k="all" correctly returns all features.
X, y = make_classification(n_samples=20,
n_features=10,
shuffle=False,
random_state=0)
univariate_filter = SelectKBest(f_classif, k='all')
X_r = univariate_filter.fit(X, y).transform(X)
assert_array_equal(X, X_r)
def test_precomputed_dists():
redX = X[::2]
dists = pairwise_distances(redX, metric='euclidean')
clust1 = OPTICS(min_samples=10, algorithm='brute',
metric='precomputed').fit(dists)
clust2 = OPTICS(min_samples=10, algorithm='brute',
metric='euclidean').fit(redX)
assert_allclose(clust1.reachability_, clust2.reachability_)
assert_array_equal(clust1.labels_, clust2.labels_)
def test_load_large_qid():
"""
load large libsvm / svmlight file with qid attribute. Tests 64-bit query ID
"""
data = b"\n".join(
("3 qid:{0} 1:0.53 2:0.12\n2 qid:{0} 1:0.13 2:0.1".format(i).encode()
for i in range(1, 40 * 1000 * 1000)))
X, y, qid = load_svmlight_file(BytesIO(data), query_id=True)
assert_array_equal(y[-4:], [3, 2, 3, 2])
assert_array_equal(np.unique(qid), np.arange(1, 40 * 1000 * 1000))
def test_multi_class(kernel):
# Test GPC for multi-class classification problems.
gpc = GaussianProcessClassifier(kernel=kernel)
gpc.fit(X, y_mc)
y_prob = gpc.predict_proba(X2)
assert_almost_equal(y_prob.sum(1), 1)
y_pred = gpc.predict(X2)
assert_array_equal(np.argmax(y_prob, 1), y_pred)
def test_ovr_pipeline():
# Test with pipeline of length one
# This test is needed because the multiclass estimators may fail to detect
# the presence of predict_proba or decision_function.
clf = Pipeline([("tree", DecisionTreeClassifier())])
ovr_pipe = OneVsRestClassifier(clf)
ovr_pipe.fit(iris.data, iris.target)
ovr = OneVsRestClassifier(DecisionTreeClassifier())
ovr.fit(iris.data, iris.target)
assert_array_equal(ovr.predict(iris.data), ovr_pipe.predict(iris.data))
def test_lasso_non_float_y(model):
X = [[0, 0], [1, 1], [-1, -1]]
y = [0, 1, 2]
y_float = [0.0, 1.0, 2.0]
clf = model(fit_intercept=False)
clf.fit(X, y)
clf_float = model(fit_intercept=False)
clf_float.fit(X, y_float)
assert_array_equal(clf.coef_, clf_float.coef_)
def test_connectivity_callable():
rng = np.random.RandomState(0)
X = rng.rand(20, 5)
connectivity = kneighbors_graph(X, 3, include_self=False)
aglc1 = AgglomerativeClustering(connectivity=connectivity)
aglc2 = AgglomerativeClustering(connectivity=partial(
kneighbors_graph, n_neighbors=3, include_self=False))
aglc1.fit(X)
aglc2.fit(X)
assert_array_equal(aglc1.labels_, aglc2.labels_)
def test_median_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="median")
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
def test_column_transformer_negative_column_indexes():
X = np.random.randn(2, 2)
X_categories = np.array([[1], [2]])
X = np.concatenate([X, X_categories], axis=1)
ohe = OneHotEncoder()
tf_1 = ColumnTransformer([('ohe', ohe, [-1])], remainder='passthrough')
tf_2 = ColumnTransformer([('ohe', ohe, [2])], remainder='passthrough')
assert_array_equal(tf_1.fit_transform(X), tf_2.fit_transform(X))
def test_check_precisions():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
n_components, n_features = rand_data.n_components, rand_data.n_features
# Define the bad precisions for each covariance_type
precisions_bad_shape = {
'full': np.ones((n_components + 1, n_features, n_features)),
'tied': np.ones((n_features + 1, n_features + 1)),
'diag': np.ones((n_components + 1, n_features)),
'spherical': np.ones((n_components + 1))
}
# Define not positive-definite precisions
precisions_not_pos = np.ones((n_components, n_features, n_features))
precisions_not_pos[0] = np.eye(n_features)
precisions_not_pos[0, 0, 0] = -1.
precisions_not_positive = {
'full': precisions_not_pos,
'tied': precisions_not_pos[0],
'diag': np.full((n_components, n_features), -1.),
'spherical': np.full(n_components, -1.)
}
not_positive_errors = {
'full': 'symmetric, positive-definite',
'tied': 'symmetric, positive-definite',
'diag': 'positive',
'spherical': 'positive'
}
for covar_type in COVARIANCE_TYPE:
X = RandomData(rng).X[covar_type]
g = GaussianMixture(n_components=n_components,
covariance_type=covar_type,
random_state=rng)
# Check precisions with bad shapes
g.precisions_init = precisions_bad_shape[covar_type]
assert_raise_message(
ValueError, "The parameter '%s precision' should have "
"the shape of" % covar_type, g.fit, X)
# Check not positive precisions
g.precisions_init = precisions_not_positive[covar_type]
assert_raise_message(
ValueError, "'%s precision' should be %s" %
(covar_type, not_positive_errors[covar_type]), g.fit, X)
# Check the correct init of precisions_init
g.precisions_init = rand_data.precisions[covar_type]
g.fit(X)
assert_array_equal(rand_data.precisions[covar_type], g.precisions_init)
def test_isotonic_regression_ties_max():
# Setup examples with ties on maximum
x = [1, 2, 3, 4, 5, 5]
y = [1, 2, 3, 4, 5, 6]
y_true = [1, 2, 3, 4, 5.5, 5.5]
# Check that we get identical results for fit/transform and fit_transform
ir = IsotonicRegression()
ir.fit(x, y)
assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
assert_array_equal(y_true, ir.fit_transform(x, y))
def test_min_cluster_size(min_cluster_size):
redX = X[::2] # reduce for speed
clust = OPTICS(min_samples=9, min_cluster_size=min_cluster_size).fit(redX)
cluster_sizes = np.bincount(clust.labels_[clust.labels_ != -1])
if cluster_sizes.size:
assert min(cluster_sizes) >= min_cluster_size
# check behaviour is the same when min_cluster_size is a fraction
clust_frac = OPTICS(min_samples=9,
min_cluster_size=min_cluster_size / redX.shape[0])
clust_frac.fit(redX)
assert_array_equal(clust.labels_, clust_frac.labels_)
def test_one_hot_encoder_specified_categories_mixed_columns():
# 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.object_)
assert enc.categories_[1].tolist() == [0, 1, 2]
# integer categories but from object dtype data
assert np.issubdtype(enc.categories_[1].dtype, np.object_)
def test_zero_variance():
# Test VarianceThreshold with default setting, zero variance.
for X in [data, csr_matrix(data), csc_matrix(data), bsr_matrix(data)]:
sel = VarianceThreshold().fit(X)
assert_array_equal([0, 1, 3, 4], sel.get_support(indices=True))
with pytest.raises(ValueError):
VarianceThreshold().fit([[0, 1, 2, 3]])
with pytest.raises(ValueError):
VarianceThreshold().fit([[0, 1], [0, 1]])
def test_dummy_classifier_on_3D_array():
X = np.array([[['foo']], [['bar']], [['baz']]])
y = [2, 2, 2]
y_expected = [2, 2, 2]
y_proba_expected = [[1], [1], [1]]
cls = DummyClassifier()
cls.fit(X, y)
y_pred = cls.predict(X)
y_pred_proba = cls.predict_proba(X)
assert_array_equal(y_pred, y_expected)
assert_array_equal(y_pred_proba, y_proba_expected)
def test_nans():
# Assert that SelectKBest and SelectPercentile can handle NaNs.
# First feature has zero variance to confuse f_classif (ANOVA) and
# make it return a NaN.
X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]]
y = [1, 0, 1]
for select in (SelectKBest(f_classif,
2), SelectPercentile(f_classif, percentile=67)):
ignore_warnings(select.fit)(X, y)
assert_array_equal(select.get_support(indices=True), np.array([1, 2]))
def test_sgd_optimizer_no_momentum():
params = [np.zeros(shape) for shape in shapes]
for lr in [10**i for i in range(-3, 4)]:
optimizer = SGDOptimizer(params, lr, momentum=0, nesterov=False)
grads = [np.random.random(shape) for shape in shapes]
expected = [param - lr * grad for param, grad in zip(params, grads)]
optimizer.update_params(grads)
for exp, param in zip(expected, optimizer.params):
assert_array_equal(exp, param)
def test_clone_sparse_matrices():
sparse_matrix_classes = [
getattr(sp, name) for name in dir(sp) if name.endswith('_matrix')
]
for cls in sparse_matrix_classes:
sparse_matrix = cls(np.eye(5))
clf = MyEstimator(empty=sparse_matrix)
clf_cloned = clone(clf)
assert clf.empty.__class__ is clf_cloned.empty.__class__
assert_array_equal(clf.empty.toarray(), clf_cloned.empty.toarray())
def test_dummy_regressor_return_std():
X = [[0]] * 3 # ignored
y = np.array([2, 2, 2])
y_std_expected = np.array([0, 0, 0])
cls = DummyRegressor()
cls.fit(X, y)
y_pred_list = cls.predict(X, return_std=True)
# there should be two elements when return_std is True
assert len(y_pred_list) == 2
# the second element should be all zeros
assert_array_equal(y_pred_list[1], y_std_expected)
def test_dbscan_input_not_modified(use_sparse, metric):
# test that the input is not modified by dbscan
X = np.random.RandomState(0).rand(10, 10)
X = sparse.csr_matrix(X) if use_sparse else X
X_copy = X.copy()
dbscan(X, metric=metric)
if use_sparse:
assert_array_equal(X.toarray(), X_copy.toarray())
else:
assert_array_equal(X, X_copy)