def test_one_hot_encoder_handle_unknown():
X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
X2 = np.array([[4, 1, 1]])
# Test that one hot encoder raises error for unknown features
# present during transform.
oh = OneHotEncoder(handle_unknown='error')
oh.fit(X)
with pytest.raises(ValueError, match='Found unknown categories'):
oh.transform(X2)
# Test the ignore option, ignores unknown features (giving all 0's)
oh = OneHotEncoder(handle_unknown='ignore')
oh.fit(X)
X2_passed = X2.copy()
assert_array_equal(
oh.transform(X2_passed).toarray(),
np.array([[0., 0., 0., 0., 1., 0., 0.]]))
# ensure transformed data was not modified in place
assert_allclose(X2, X2_passed)
# Raise error if handle_unknown is neither ignore or error.
oh = OneHotEncoder(handle_unknown='42')
with pytest.raises(ValueError, match='handle_unknown should be either'):
oh.fit(X)
def test_ridge_loo_cv_asym_scoring():
# checking on asymmetric scoring
scoring = 'explained_variance'
n_samples, n_features = 10, 5
n_targets = 1
X, y = _make_sparse_offset_regression(n_samples=n_samples,
n_features=n_features,
n_targets=n_targets,
random_state=0,
shuffle=False,
noise=1,
n_informative=5)
alphas = [1e-3, .1, 1., 10., 1e3]
loo_ridge = RidgeCV(cv=n_samples,
fit_intercept=True,
alphas=alphas,
scoring=scoring,
normalize=True)
gcv_ridge = RidgeCV(fit_intercept=True,
alphas=alphas,
scoring=scoring,
normalize=True)
loo_ridge.fit(X, y)
gcv_ridge.fit(X, y)
assert gcv_ridge.alpha_ == pytest.approx(loo_ridge.alpha_)
assert_allclose(gcv_ridge.coef_, loo_ridge.coef_, rtol=1e-3)
assert_allclose(gcv_ridge.intercept_, loo_ridge.intercept_, rtol=1e-3)
def test_one_hot_encoder_pandas():
pd = pytest.importorskip('pandas')
X_df = pd.DataFrame({'A': ['a', 'b'], 'B': [1, 2]})
Xtr = check_categorical_onehot(X_df)
assert_allclose(Xtr, [[1, 0, 1, 0], [0, 1, 0, 1]])
def test_ridge_gcv_vs_ridge_loo_cv(gcv_mode, X_constructor, X_shape, y_shape,
fit_intercept, normalize, noise):
n_samples, n_features = X_shape
n_targets = y_shape[-1] if len(y_shape) == 2 else 1
X, y = _make_sparse_offset_regression(n_samples=n_samples,
n_features=n_features,
n_targets=n_targets,
random_state=0,
shuffle=False,
noise=noise,
n_informative=5)
y = y.reshape(y_shape)
alphas = [1e-3, .1, 1., 10., 1e3]
loo_ridge = RidgeCV(cv=n_samples,
fit_intercept=fit_intercept,
alphas=alphas,
scoring='neg_mean_squared_error',
normalize=normalize)
gcv_ridge = RidgeCV(gcv_mode=gcv_mode,
fit_intercept=fit_intercept,
alphas=alphas,
normalize=normalize)
loo_ridge.fit(X, y)
X_gcv = X_constructor(X)
gcv_ridge.fit(X_gcv, y)
assert gcv_ridge.alpha_ == pytest.approx(loo_ridge.alpha_)
assert_allclose(gcv_ridge.coef_, loo_ridge.coef_, rtol=1e-3)
assert_allclose(gcv_ridge.intercept_, loo_ridge.intercept_, rtol=1e-3)
def test_solver_consistency(solver, proportion_nonzero, n_samples, dtype,
sparse_X, seed):
alpha = 1.
noise = 50. if proportion_nonzero > .9 else 500.
X, y = _make_sparse_offset_regression(
bias=10,
n_features=30,
proportion_nonzero=proportion_nonzero,
noise=noise,
random_state=seed,
n_samples=n_samples)
svd_ridge = Ridge(solver='svd', normalize=True, alpha=alpha).fit(X, y)
X = X.astype(dtype, copy=False)
y = y.astype(dtype, copy=False)
if sparse_X:
X = sp.csr_matrix(X)
if solver == 'ridgecv':
ridge = RidgeCV(alphas=[alpha], normalize=True)
else:
ridge = Ridge(solver=solver, tol=1e-10, normalize=True, alpha=alpha)
ridge.fit(X, y)
assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3)
assert_allclose(ridge.intercept_,
svd_ridge.intercept_,
atol=1e-3,
rtol=1e-3)
def test_ridge_regression_dtype_stability(solver, seed):
random_state = np.random.RandomState(seed)
n_samples, n_features = 6, 5
X = random_state.randn(n_samples, n_features)
coef = random_state.randn(n_features)
y = np.dot(X, coef) + 0.01 * random_state.randn(n_samples)
alpha = 1.0
results = dict()
# XXX: Sparse CG seems to be far less numerically stable than the
# others, maybe we should not enable float32 for this one.
atol = 1e-3 if solver == "sparse_cg" else 1e-5
for current_dtype in (np.float32, np.float64):
results[current_dtype] = ridge_regression(X.astype(current_dtype),
y.astype(current_dtype),
alpha=alpha,
solver=solver,
random_state=random_state,
sample_weight=None,
max_iter=500,
tol=1e-10,
return_n_iter=False,
return_intercept=False)
assert results[np.float32].dtype == np.float32
assert results[np.float64].dtype == np.float64
assert_allclose(results[np.float32], results[np.float64], atol=atol)
def test_knn_imputer_zero_nan_imputes_the_same(na):
# Test with an imputable matrix and compare with different missing_values
X_zero = np.array([
[1, 0, 1, 1, 1.],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 0],
[6, 6, 0, 6, 6],
])
X_nan = np.array([
[1, na, 1, 1, 1.],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, na],
[6, 6, na, 6, 6],
])
X_imputed = np.array([
[1, 2.5, 1, 1, 1.],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 1.5],
[6, 6, 2.5, 6, 6],
])
imputer_zero = KNNImputer(missing_values=0,
n_neighbors=2,
weights="uniform")
imputer_nan = KNNImputer(missing_values=na,
n_neighbors=2,
weights="uniform")
assert_allclose(imputer_zero.fit_transform(X_zero), X_imputed)
assert_allclose(imputer_zero.fit_transform(X_zero),
imputer_nan.fit_transform(X_nan))
def test_stacking_regressor_diabetes(cv, final_estimator, predict_params,
passthrough):
# prescale the data to avoid convergence warning without using a pipeline
# for later assert
X_train, X_test, y_train, _ = train_test_split(scale(X_diabetes),
y_diabetes,
random_state=42)
estimators = [('lr', LinearRegression()), ('svr', LinearSVR())]
reg = StackingRegressor(estimators=estimators,
final_estimator=final_estimator,
cv=cv,
passthrough=passthrough)
reg.fit(X_train, y_train)
result = reg.predict(X_test, **predict_params)
expected_result_length = 2 if predict_params else 1
if predict_params:
assert len(result) == expected_result_length
X_trans = reg.transform(X_test)
expected_column_count = 12 if passthrough else 2
assert X_trans.shape[1] == expected_column_count
if passthrough:
assert_allclose(X_test, X_trans[:, -10:])
reg.set_params(lr='drop')
reg.fit(X_train, y_train)
reg.predict(X_test)
X_trans = reg.transform(X_test)
expected_column_count_drop = 11 if passthrough else 1
assert X_trans.shape[1] == expected_column_count_drop
if passthrough:
assert_allclose(X_test, X_trans[:, -10:])
def test_ovr_decision_function():
# test properties for ovr decision function
predictions = np.array([[0, 1, 1], [0, 1, 0], [0, 1, 1], [0, 1, 1]])
confidences = np.array([[-1e16, 0, -1e16], [1., 2., -3.], [-5., 2., 5.],
[-0.5, 0.2, 0.5]])
n_classes = 3
dec_values = _ovr_decision_function(predictions, confidences, n_classes)
# check that the decision values are within 0.5 range of the votes
votes = np.array([[1, 0, 2], [1, 1, 1], [1, 0, 2], [1, 0, 2]])
assert_allclose(votes, dec_values, atol=0.5)
# check that the prediction are what we expect
# highest vote or highest confidence if there is a tie.
# for the second sample we have a tie (should be won by 1)
expected_prediction = np.array([2, 1, 2, 2])
assert_array_equal(np.argmax(dec_values, axis=1), expected_prediction)
# third and fourth sample have the same vote but third sample
# has higher confidence, this should reflect on the decision values
assert (dec_values[2, 2] > dec_values[3, 2])
# assert subset invariance.
dec_values_one = [
_ovr_decision_function(np.array([predictions[i]]),
np.array([confidences[i]]), n_classes)[0]
for i in range(4)
]
assert_allclose(dec_values, dec_values_one, atol=1e-6)
def test_iterative_imputer_additive_matrix():
rng = np.random.RandomState(0)
n = 100
d = 10
A = rng.randn(n, d)
B = rng.randn(n, d)
X_filled = np.zeros(A.shape)
for i in range(d):
for j in range(d):
X_filled[:, (i+j) % d] += (A[:, i] + B[:, j]) / 2
# a quarter is randomly missing
nan_mask = rng.rand(n, d) < 0.25
X_missing = X_filled.copy()
X_missing[nan_mask] = np.nan
# split up data
n = n // 2
X_train = X_missing[:n]
X_test_filled = X_filled[n:]
X_test = X_missing[n:]
imputer = IterativeImputer(max_iter=10,
verbose=1,
random_state=rng).fit(X_train)
X_test_est = imputer.transform(X_test)
assert_allclose(X_test_filled, X_test_est, rtol=1e-3, atol=0.01)
def test_stacking_with_sample_weight(stacker, X, y):
# check that sample weights has an influence on the fitting
# note: ConvergenceWarning are catch since we are not worrying about the
# convergence here
n_half_samples = len(y) // 2
total_sample_weight = np.array([0.1] * n_half_samples + [0.9] *
(len(y) - n_half_samples))
X_train, X_test, y_train, _, sample_weight_train, _ = train_test_split(
X, y, total_sample_weight, random_state=42)
with ignore_warnings(category=ConvergenceWarning):
stacker.fit(X_train, y_train)
y_pred_no_weight = stacker.predict(X_test)
with ignore_warnings(category=ConvergenceWarning):
stacker.fit(X_train, y_train, sample_weight=np.ones(y_train.shape))
y_pred_unit_weight = stacker.predict(X_test)
assert_allclose(y_pred_no_weight, y_pred_unit_weight)
with ignore_warnings(category=ConvergenceWarning):
stacker.fit(X_train, y_train, sample_weight=sample_weight_train)
y_pred_biased = stacker.predict(X_test)
assert np.abs(y_pred_no_weight - y_pred_biased).sum() > 0
def test_iterative_imputer_early_stopping():
rng = np.random.RandomState(0)
n = 50
d = 5
A = rng.rand(n, 1)
B = rng.rand(1, d)
X = np.dot(A, B)
nan_mask = rng.rand(n, d) < 0.5
X_missing = X.copy()
X_missing[nan_mask] = np.nan
imputer = IterativeImputer(max_iter=100,
tol=1e-2,
sample_posterior=False,
verbose=1,
random_state=rng)
X_filled_100 = imputer.fit_transform(X_missing)
assert len(imputer.imputation_sequence_) == d * imputer.n_iter_
imputer = IterativeImputer(max_iter=imputer.n_iter_,
sample_posterior=False,
verbose=1,
random_state=rng)
X_filled_early = imputer.fit_transform(X_missing)
assert_allclose(X_filled_100, X_filled_early, atol=1e-7)
imputer = IterativeImputer(max_iter=100,
tol=0,
sample_posterior=False,
verbose=1,
random_state=rng)
imputer.fit(X_missing)
assert imputer.n_iter_ == imputer.max_iter
def test_iterative_imputer_skip_non_missing(skip_complete):
# check the imputing strategy when missing data are present in the
# testing set only.
# taken from: https://github.com/scikit-learn/scikit-learn/issues/14383
rng = np.random.RandomState(0)
X_train = np.array([
[5, 2, 2, 1],
[10, 1, 2, 7],
[3, 1, 1, 1],
[8, 4, 2, 2]
])
X_test = np.array([
[np.nan, 2, 4, 5],
[np.nan, 4, 1, 2],
[np.nan, 1, 10, 1]
])
imputer = IterativeImputer(
initial_strategy='mean', skip_complete=skip_complete, random_state=rng
)
X_test_est = imputer.fit(X_train).transform(X_test)
if skip_complete:
# impute with the initial strategy: 'mean'
assert_allclose(X_test_est[:, 0], np.mean(X_train[:, 0]))
else:
assert_allclose(X_test_est[:, 0], [11, 7, 12], rtol=1e-4)
def test_check_sample_weight():
# check array order
sample_weight = np.ones(10)[::2]
assert not sample_weight.flags["C_CONTIGUOUS"]
sample_weight = _check_sample_weight(sample_weight, X=np.ones((5, 1)))
assert sample_weight.flags["C_CONTIGUOUS"]
# check None input
sample_weight = _check_sample_weight(None, X=np.ones((5, 2)))
assert_allclose(sample_weight, np.ones(5))
# check numbers input
sample_weight = _check_sample_weight(2.0, X=np.ones((5, 2)))
assert_allclose(sample_weight, 2 * np.ones(5))
# check wrong number of dimensions
with pytest.raises(ValueError,
match="Sample weights must be 1D array or scalar"):
_check_sample_weight(np.ones((2, 4)), X=np.ones((2, 2)))
# check incorrect n_samples
msg = r"sample_weight.shape == \(4,\), expected \(2,\)!"
with pytest.raises(ValueError, match=msg):
_check_sample_weight(np.ones(4), X=np.ones((2, 2)))
# float32 dtype is preserved
X = np.ones((5, 2))
sample_weight = np.ones(5, dtype=np.float32)
sample_weight = _check_sample_weight(sample_weight, X)
assert sample_weight.dtype == np.float32
# int dtype will be converted to float64 instead
X = np.ones((5, 2), dtype=np.int)
sample_weight = _check_sample_weight(None, X, dtype=X.dtype)
assert sample_weight.dtype == np.float64
def test_dtype_match(solver):
rng = np.random.RandomState(0)
alpha = 1.0
n_samples, n_features = 6, 5
X_64 = rng.randn(n_samples, n_features)
y_64 = rng.randn(n_samples)
X_32 = X_64.astype(np.float32)
y_32 = y_64.astype(np.float32)
tol = 2 * np.finfo(np.float32).resolution
# Check type consistency 32bits
ridge_32 = Ridge(alpha=alpha, solver=solver, max_iter=500, tol=tol)
ridge_32.fit(X_32, y_32)
coef_32 = ridge_32.coef_
# Check type consistency 64 bits
ridge_64 = Ridge(alpha=alpha, solver=solver, max_iter=500, tol=tol)
ridge_64.fit(X_64, y_64)
coef_64 = ridge_64.coef_
# Do the actual checks at once for easier debug
assert coef_32.dtype == X_32.dtype
assert coef_64.dtype == X_64.dtype
assert ridge_32.predict(X_32).dtype == X_32.dtype
assert ridge_64.predict(X_64).dtype == X_64.dtype
assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4, atol=5e-4)
def test_inverse_transform(algo, X_sparse):
# We need a lot of components for the reconstruction to be "almost
# equal" in all positions. XXX Test means or sums instead?
tsvd = TruncatedSVD(n_components=52, random_state=42, algorithm=algo)
Xt = tsvd.fit_transform(X_sparse)
Xinv = tsvd.inverse_transform(Xt)
assert_allclose(Xinv, X_sparse.toarray(), rtol=1e-1, atol=2e-1)
def test_threshold_and_max_features():
X, y = datasets.make_classification(n_samples=1000,
n_features=10,
n_informative=3,
n_redundant=0,
n_repeated=0,
shuffle=False,
random_state=0)
est = RandomForestClassifier(n_estimators=50, random_state=0)
transformer1 = SelectFromModel(estimator=est,
max_features=3,
threshold=-np.inf)
X_new1 = transformer1.fit_transform(X, y)
transformer2 = SelectFromModel(estimator=est, threshold=0.04)
X_new2 = transformer2.fit_transform(X, y)
transformer3 = SelectFromModel(estimator=est,
max_features=3,
threshold=0.04)
X_new3 = transformer3.fit_transform(X, y)
assert X_new3.shape[1] == min(X_new1.shape[1], X_new2.shape[1])
selected_indices = transformer3.transform(
np.arange(X.shape[1])[np.newaxis, :])
assert_allclose(X_new3, X[:, selected_indices[0]])
def test_knn_imputer_distance_weighted_not_enough_neighbors(
na, working_memory):
X = np.array([[3, na], [2, na], [na, 4], [5, 6], [6, 8], [na, 5]])
dist = pairwise_distances(X,
metric="nan_euclidean",
squared=False,
missing_values=na)
X_01 = np.average(X[3:5, 1], weights=1 / dist[0, 3:5])
X_11 = np.average(X[3:5, 1], weights=1 / dist[1, 3:5])
X_20 = np.average(X[3:5, 0], weights=1 / dist[2, 3:5])
X_50 = np.average(X[3:5, 0], weights=1 / dist[5, 3:5])
X_expected = np.array([[3, X_01], [2, X_11], [X_20, 4], [5, 6], [6, 8],
[X_50, 5]])
with config_context(working_memory=working_memory):
knn_3 = KNNImputer(missing_values=na,
n_neighbors=3,
weights='distance')
assert_allclose(knn_3.fit_transform(X), X_expected)
knn_4 = KNNImputer(missing_values=na,
n_neighbors=4,
weights='distance')
assert_allclose(knn_4.fit_transform(X), X_expected)
def test_iterative_imputer_all_missing():
n = 100
d = 3
X = np.zeros((n, d))
imputer = IterativeImputer(missing_values=0, max_iter=1)
X_imputed = imputer.fit_transform(X)
assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))
def test_transform_target_regressor_multi_to_single():
X = friedman[0]
y = np.transpose([friedman[1], (friedman[1]**2 + 1)])
def func(y):
out = np.sqrt(y[:, 0]**2 + y[:, 1]**2)
return out[:, np.newaxis]
def inverse_func(y):
return y
tt = TransformedTargetRegressor(func=func,
inverse_func=inverse_func,
check_inverse=False)
tt.fit(X, y)
y_pred_2d_func = tt.predict(X)
assert y_pred_2d_func.shape == (100, 1)
# force that the function only return a 1D array
def func(y):
return np.sqrt(y[:, 0]**2 + y[:, 1]**2)
tt = TransformedTargetRegressor(func=func,
inverse_func=inverse_func,
check_inverse=False)
tt.fit(X, y)
y_pred_1d_func = tt.predict(X)
assert y_pred_1d_func.shape == (100, 1)
assert_allclose(y_pred_1d_func, y_pred_2d_func)
def test_pca_svd_solver_auto(data, n_components, expected_solver):
pca_auto = PCA(n_components=n_components, random_state=0)
pca_test = PCA(n_components=n_components,
svd_solver=expected_solver,
random_state=0)
pca_auto.fit(data)
pca_test.fit(data)
assert_allclose(pca_auto.components_, pca_test.components_)
def test_pca_check_projection_list(svd_solver):
# Test that the projection of data is correct
X = [[1.0, 0.0], [0.0, 1.0]]
pca = PCA(n_components=1, svd_solver=svd_solver, random_state=0)
X_trans = pca.fit_transform(X)
assert X_trans.shape, (2, 1)
assert_allclose(X_trans.mean(), 0.00, atol=1e-12)
assert_allclose(X_trans.std(), 0.71, rtol=5e-3)
def test_pca_explained_variance_empirical(X, svd_solver):
pca = PCA(n_components=2, svd_solver=svd_solver, random_state=0)
X_pca = pca.fit_transform(X)
assert_allclose(pca.explained_variance_, np.var(X_pca, ddof=1, axis=0))
expected_result = np.linalg.eig(np.cov(X, rowvar=False))[0]
expected_result = sorted(expected_result, reverse=True)[:2]
assert_allclose(pca.explained_variance_, expected_result, rtol=5e-3)
def test_pca_score_consistency_solvers(svd_solver):
# Check the consistency of score between solvers
X, _ = datasets.load_digits(return_X_y=True)
pca_full = PCA(n_components=30, svd_solver='full', random_state=0)
pca_other = PCA(n_components=30, svd_solver=svd_solver, random_state=0)
pca_full.fit(X)
pca_other.fit(X)
assert_allclose(pca_full.score(X), pca_other.score(X), rtol=5e-6)
def test_knn_imputer_drops_all_nan_features(na):
X1 = np.array([[na, 1], [na, 2]])
knn = KNNImputer(missing_values=na, n_neighbors=1)
X1_expected = np.array([[1], [2]])
assert_allclose(knn.fit_transform(X1), X1_expected)
X2 = np.array([[1, 2], [3, na]])
X2_expected = np.array([[2], [1.5]])
assert_allclose(knn.transform(X2), X2_expected)
def test_knn_imputer_one_n_neighbors(na):
X = np.array([[0, 0], [na, 2], [4, 3], [5, na], [7, 7], [na, 8], [14, 13]])
X_imputed = np.array([[0, 0], [4, 2], [4, 3], [5, 3], [7, 7], [7, 8],
[14, 13]])
imputer = KNNImputer(n_neighbors=1, missing_values=na)
assert_allclose(imputer.fit_transform(X), X_imputed)
def test_nrm2(dtype):
nrm2 = _nrm2_memview[NUMPY_TO_CYTHON[dtype]]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
expected = np.linalg.norm(x)
actual = nrm2(x)
assert_allclose(actual, expected, rtol=RTOL[dtype])
def test_asum(dtype):
asum = _asum_memview[NUMPY_TO_CYTHON[dtype]]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
expected = np.abs(x).sum()
actual = asum(x)
assert_allclose(actual, expected, rtol=RTOL[dtype])
def test_pca_deterministic_output(svd_solver):
rng = np.random.RandomState(0)
X = rng.rand(10, 10)
transformed_X = np.zeros((20, 2))
for i in range(20):
pca = PCA(n_components=2, svd_solver=svd_solver, random_state=rng)
transformed_X[i, :] = pca.fit_transform(X)[0]
assert_allclose(transformed_X,
np.tile(transformed_X[0, :], 20).reshape(20, 2))
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_)
