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""" test the label propagation module """
import warnings
import numpy as np
import pytest
from scipy.sparse import issparse
from sklearn.datasets import make_classification
from sklearn.exceptions import ConvergenceWarning
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from sklearn.semi_supervised import _label_propagation as label_propagation
from sklearn.utils._testing import (
_convert_container,
assert_allclose,
assert_array_equal,
)
CONSTRUCTOR_TYPES = ("array", "sparse_csr", "sparse_csc")
ESTIMATORS = [
(label_propagation.LabelPropagation, {"kernel": "rbf"}),
(label_propagation.LabelPropagation, {"kernel": "knn", "n_neighbors": 2}),
(
label_propagation.LabelPropagation,
{"kernel": lambda x, y: rbf_kernel(x, y, gamma=20)},
),
(label_propagation.LabelSpreading, {"kernel": "rbf"}),
(label_propagation.LabelSpreading, {"kernel": "knn", "n_neighbors": 2}),
(
label_propagation.LabelSpreading,
{"kernel": lambda x, y: rbf_kernel(x, y, gamma=20)},
),
]
@pytest.mark.parametrize("Estimator, parameters", ESTIMATORS)
def test_fit_transduction(global_dtype, Estimator, parameters):
samples = np.asarray([[1.0, 0.0], [0.0, 2.0], [1.0, 3.0]], dtype=global_dtype)
labels = [0, 1, -1]
clf = Estimator(**parameters).fit(samples, labels)
assert clf.transduction_[2] == 1
@pytest.mark.parametrize("Estimator, parameters", ESTIMATORS)
def test_distribution(global_dtype, Estimator, parameters):
if parameters["kernel"] == "knn":
pytest.skip(
"Unstable test for this configuration: changes in k-NN ordering break it."
)
samples = np.asarray([[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]], dtype=global_dtype)
labels = [0, 1, -1]
clf = Estimator(**parameters).fit(samples, labels)
assert_allclose(clf.label_distributions_[2], [0.5, 0.5], atol=1e-2)
@pytest.mark.parametrize("Estimator, parameters", ESTIMATORS)
def test_predict(global_dtype, Estimator, parameters):
samples = np.asarray([[1.0, 0.0], [0.0, 2.0], [1.0, 3.0]], dtype=global_dtype)
labels = [0, 1, -1]
clf = Estimator(**parameters).fit(samples, labels)
assert_array_equal(clf.predict([[0.5, 2.5]]), np.array([1]))
@pytest.mark.parametrize("Estimator, parameters", ESTIMATORS)
def test_predict_proba(global_dtype, Estimator, parameters):
samples = np.asarray([[1.0, 0.0], [0.0, 1.0], [1.0, 2.5]], dtype=global_dtype)
labels = [0, 1, -1]
clf = Estimator(**parameters).fit(samples, labels)
assert_allclose(clf.predict_proba([[1.0, 1.0]]), np.array([[0.5, 0.5]]))
@pytest.mark.parametrize("alpha", [0.1, 0.3, 0.5, 0.7, 0.9])
@pytest.mark.parametrize("Estimator, parameters", ESTIMATORS)
def test_label_spreading_closed_form(global_dtype, Estimator, parameters, alpha):
n_classes = 2
X, y = make_classification(n_classes=n_classes, n_samples=200, random_state=0)
X = X.astype(global_dtype, copy=False)
y[::3] = -1
gamma = 0.1
clf = label_propagation.LabelSpreading(gamma=gamma).fit(X, y)
# adopting notation from Zhou et al (2004):
S = clf._build_graph()
Y = np.zeros((len(y), n_classes + 1), dtype=X.dtype)
Y[np.arange(len(y)), y] = 1
Y = Y[:, :-1]
expected = np.dot(np.linalg.inv(np.eye(len(S), dtype=S.dtype) - alpha * S), Y)
expected /= expected.sum(axis=1)[:, np.newaxis]
clf = label_propagation.LabelSpreading(
max_iter=100, alpha=alpha, tol=1e-10, gamma=gamma
)
clf.fit(X, y)
assert_allclose(expected, clf.label_distributions_)
def test_label_propagation_closed_form(global_dtype):
n_classes = 2
X, y = make_classification(n_classes=n_classes, n_samples=200, random_state=0)
X = X.astype(global_dtype, copy=False)
y[::3] = -1
Y = np.zeros((len(y), n_classes + 1))
Y[np.arange(len(y)), y] = 1
unlabelled_idx = Y[:, (-1,)].nonzero()[0]
labelled_idx = (Y[:, (-1,)] == 0).nonzero()[0]
clf = label_propagation.LabelPropagation(max_iter=100, tol=1e-10, gamma=0.1)
clf.fit(X, y)
# adopting notation from Zhu et al 2002
T_bar = clf._build_graph()
Tuu = T_bar[tuple(np.meshgrid(unlabelled_idx, unlabelled_idx, indexing="ij"))]
Tul = T_bar[tuple(np.meshgrid(unlabelled_idx, labelled_idx, indexing="ij"))]
Y = Y[:, :-1]
Y_l = Y[labelled_idx, :]
Y_u = np.dot(np.dot(np.linalg.inv(np.eye(Tuu.shape[0]) - Tuu), Tul), Y_l)
expected = Y.copy()
expected[unlabelled_idx, :] = Y_u
expected /= expected.sum(axis=1)[:, np.newaxis]
assert_allclose(expected, clf.label_distributions_, atol=1e-4)
@pytest.mark.parametrize("accepted_sparse_type", ["sparse_csr", "sparse_csc"])
@pytest.mark.parametrize("index_dtype", [np.int32, np.int64])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("Estimator, parameters", ESTIMATORS)
def test_sparse_input_types(
accepted_sparse_type, index_dtype, dtype, Estimator, parameters
):
# This is non-regression test for #17085
X = _convert_container([[1.0, 0.0], [0.0, 2.0], [1.0, 3.0]], accepted_sparse_type)
X.data = X.data.astype(dtype, copy=False)
X.indices = X.indices.astype(index_dtype, copy=False)
X.indptr = X.indptr.astype(index_dtype, copy=False)
labels = [0, 1, -1]
clf = Estimator(**parameters).fit(X, labels)
assert_array_equal(clf.predict([[0.5, 2.5]]), np.array([1]))
@pytest.mark.parametrize("constructor_type", CONSTRUCTOR_TYPES)
def test_convergence_speed(constructor_type):
# This is a non-regression test for #5774
X = _convert_container([[1.0, 0.0], [0.0, 1.0], [1.0, 2.5]], constructor_type)
y = np.array([0, 1, -1])
mdl = label_propagation.LabelSpreading(kernel="rbf", max_iter=5000)
mdl.fit(X, y)
# this should converge quickly:
assert mdl.n_iter_ < 10
assert_array_equal(mdl.predict(X), [0, 1, 1])
def test_convergence_warning():
# This is a non-regression test for #5774
X = np.array([[1.0, 0.0], [0.0, 1.0], [1.0, 2.5]])
y = np.array([0, 1, -1])
mdl = label_propagation.LabelSpreading(kernel="rbf", max_iter=1)
warn_msg = "max_iter=1 was reached without convergence."
with pytest.warns(ConvergenceWarning, match=warn_msg):
mdl.fit(X, y)
assert mdl.n_iter_ == mdl.max_iter
mdl = label_propagation.LabelPropagation(kernel="rbf", max_iter=1)
with pytest.warns(ConvergenceWarning, match=warn_msg):
mdl.fit(X, y)
assert mdl.n_iter_ == mdl.max_iter
mdl = label_propagation.LabelSpreading(kernel="rbf", max_iter=500)
with warnings.catch_warnings():
warnings.simplefilter("error", ConvergenceWarning)
mdl.fit(X, y)
mdl = label_propagation.LabelPropagation(kernel="rbf", max_iter=500)
with warnings.catch_warnings():
warnings.simplefilter("error", ConvergenceWarning)
mdl.fit(X, y)
@pytest.mark.parametrize(
"LabelPropagationCls",
[label_propagation.LabelSpreading, label_propagation.LabelPropagation],
)
def test_label_propagation_non_zero_normalizer(LabelPropagationCls):
# check that we don't divide by zero in case of null normalizer
# non-regression test for
# https://github.com/scikit-learn/scikit-learn/pull/15946
# https://github.com/scikit-learn/scikit-learn/issues/9292
X = np.array([[100.0, 100.0], [100.0, 100.0], [0.0, 0.0], [0.0, 0.0]])
y = np.array([0, 1, -1, -1])
mdl = LabelPropagationCls(kernel="knn", max_iter=100, n_neighbors=1)
with warnings.catch_warnings():
warnings.simplefilter("error", RuntimeWarning)
mdl.fit(X, y)
def test_predict_sparse_callable_kernel(global_dtype):
# This is a non-regression test for #15866
# Custom sparse kernel (top-K RBF)
def topk_rbf(X, Y=None, n_neighbors=10, gamma=1e-5):
nn = NearestNeighbors(n_neighbors=10, metric="euclidean", n_jobs=2)
nn.fit(X)
W = -1 * nn.kneighbors_graph(Y, mode="distance").power(2) * gamma
np.exp(W.data, out=W.data)
assert issparse(W)
return W.T
n_classes = 4
n_samples = 500
n_test = 10
X, y = make_classification(
n_classes=n_classes,
n_samples=n_samples,
n_features=20,
n_informative=20,
n_redundant=0,
n_repeated=0,
random_state=0,
)
X = X.astype(global_dtype)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=n_test, random_state=0
)
model = label_propagation.LabelSpreading(kernel=topk_rbf)
model.fit(X_train, y_train)
assert model.score(X_test, y_test) >= 0.9
model = label_propagation.LabelPropagation(kernel=topk_rbf)
model.fit(X_train, y_train)
assert model.score(X_test, y_test) >= 0.9
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