def test_early_stopping():
svc = SVC(gamma='scale', probability=True)
st = SelfTrainingClassifier(svc)
X_train_easy = [[1], [0], [1], [0.5]]
y_train_easy = [1, 0, -1, -1]
# X = [[0.5]] cannot be predicted on with a high confidence, so training
# stops early
st.fit(X_train_easy, y_train_easy)
assert st.n_iter_ == 1
assert st.termination_condition_ == 'no_change'
def test_verbose(capsys, verbose):
clf = SelfTrainingClassifier(KNeighborsClassifier(), verbose=verbose)
clf.fit(X_train, y_train_missing_labels)
captured = capsys.readouterr()
if verbose:
assert 'iteration' in captured.out
else:
assert 'iteration' not in captured.out
def test_none_iter():
# Check that the all samples were labeled after a 'reasonable' number of
# iterations.
st = SelfTrainingClassifier(KNeighborsClassifier(),
threshold=.55,
max_iter=None)
st.fit(X_train, y_train_missing_labels)
assert st.n_iter_ < 10
assert st.termination_condition_ == "all_labeled"
def test_labeled_iter(max_iter):
# Check that the amount of datapoints labeled in iteration 0 is equal to
# the amount of labeled datapoints we passed.
st = SelfTrainingClassifier(KNeighborsClassifier(), max_iter=max_iter)
st.fit(X_train, y_train_missing_labels)
amount_iter_0 = len(st.labeled_iter_[st.labeled_iter_ == 0])
assert amount_iter_0 == n_labeled_samples
# Check that the max of the iterations is less than the total amount of
# iterations
assert np.max(st.labeled_iter_) <= st.n_iter_ <= max_iter
def test_invalid_params(max_iter, threshold):
# Test negative iterations
base_estimator = SVC(gamma="scale", probability=True)
st = SelfTrainingClassifier(base_estimator, max_iter=max_iter)
with pytest.raises(ValueError, match="max_iter must be >= 0 or None"):
st.fit(X_train, y_train)
base_estimator = SVC(gamma="scale", probability=True)
st = SelfTrainingClassifier(base_estimator, threshold=threshold)
with pytest.raises(ValueError, match="threshold must be in"):
st.fit(X_train, y_train)
def test_no_unlabeled():
# Test that training on a fully labeled dataset produces the same results
# as training the classifier by itself.
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
st = SelfTrainingClassifier(knn)
with pytest.warns(UserWarning, match="y contains no unlabeled samples"):
st.fit(X_train, y_train)
assert_array_equal(knn.predict(X_test), st.predict(X_test))
# Assert that all samples were labeled in iteration 0 (since there were no
# unlabeled samples).
assert np.all(st.labeled_iter_ == 0)
assert st.termination_condition_ == "all_labeled"
def test_sanity_classification():
base_estimator = SVC(gamma="scale", probability=True)
base_estimator.fit(X_train[n_labeled_samples:], y_train[n_labeled_samples:])
st = SelfTrainingClassifier(base_estimator)
st.fit(X_train, y_train_missing_labels)
pred1, pred2 = base_estimator.predict(X_test), st.predict(X_test)
assert not np.array_equal(pred1, pred2)
score_supervised = accuracy_score(base_estimator.predict(X_test), y_test)
score_self_training = accuracy_score(st.predict(X_test), y_test)
assert score_self_training > score_supervised
def test_zero_iterations(base_estimator, y):
# Check classification for zero iterations.
# Fitting a SelfTrainingClassifier with zero iterations should give the
# same results as fitting a supervised classifier.
# This also asserts that string arrays work as expected.
clf1 = SelfTrainingClassifier(base_estimator, max_iter=0)
clf1.fit(X_train, y)
clf2 = base_estimator.fit(X_train[:n_labeled_samples], y[:n_labeled_samples])
assert_array_equal(clf1.predict(X_test), clf2.predict(X_test))
assert clf1.termination_condition_ == "max_iter"
def test_k_best_selects_best():
# Tests that the labels added by st really are the 10 best labels.
svc = SVC(gamma="scale", probability=True, random_state=0)
st = SelfTrainingClassifier(svc, criterion="k_best", max_iter=1, k_best=10)
has_label = y_train_missing_labels != -1
st.fit(X_train, y_train_missing_labels)
got_label = ~has_label & (st.transduction_ != -1)
svc.fit(X_train[has_label], y_train_missing_labels[has_label])
pred = svc.predict_proba(X_train[~has_label])
max_proba = np.max(pred, axis=1)
most_confident_svc = X_train[~has_label][np.argsort(max_proba)[-10:]]
added_by_st = X_train[np.where(got_label)].tolist()
for row in most_confident_svc.tolist():
assert row in added_by_st
def plot_varying_threshold(self, base_classifier, X_train, y_train):
"""
Plot the effect of varying threshold for self-training
Parameters
___________
base_classifier: Supervised classifier implementing both fit and predict_proba
X_train: Scaled feature matrix of the training set
y_train: Class label of the training set
Returns
_____________
Matplotlib figure
"""
total_samples = y_train.shape[0]
x_values = np.arange(0.4, 1.05, 0.05)
x_values = np.append(x_values, 0.99999)
no_labeled = np.zeros(x_values.shape[0])
no_iterations = np.zeros(x_values.shape[0])
for (i, threshold) in enumerate(x_values):
# Fit model with chosen base classifier
self_training_clf = SelfTrainingClassifier(base_classifier,threshold=threshold)
self_training_clf.fit(X_train, y_train)
# The number of labeled samples that the classifier has available by the end of fit
no_labeled[i] = total_samples - \
np.unique(self_training_clf.labeled_iter_, return_counts=True)[1][0]
# The last iteration the classifier labeled a sample in
no_iterations[i] = np.max(self_training_clf.labeled_iter_)
# Plot figures
plt.rcParams.update({'font.size': 15})
fig, (ax1, ax2) = plt.subplots(1,2, figsize = (15,4))
ax1.plot(x_values, no_labeled, color='b')
ax1.set_xlabel('Threshold')
ax1.set_ylabel('Number of labeled samples')
ax2.plot(x_values, no_iterations, color='b')
ax2.set_ylabel('Number of iterations')
ax2.set_xlabel('Threshold')
plt.show()
def test_k_best():
st = SelfTrainingClassifier(KNeighborsClassifier(n_neighbors=1),
criterion='k_best',
k_best=10,
max_iter=None)
y_train_only_one_label = np.copy(y_train)
y_train_only_one_label[1:] = -1
n_samples = y_train.shape[0]
n_expected_iter = ceil((n_samples - 1) / 10)
st.fit(X_train, y_train_only_one_label)
assert st.n_iter_ == n_expected_iter
# Check labeled_iter_
assert np.sum(st.labeled_iter_ == 0) == 1
for i in range(1, n_expected_iter):
assert np.sum(st.labeled_iter_ == i) == 10
assert np.sum(st.labeled_iter_ == n_expected_iter) == (n_samples - 1) % 10
assert st.termination_condition_ == 'all_labeled'
def test_classification(base_estimator, selection_crit):
# Check classification for various parameter settings.
# Also assert that predictions for strings and numerical labels are equal.
# Also test for multioutput classification
threshold = 0.75
max_iter = 10
st = SelfTrainingClassifier(base_estimator,
max_iter=max_iter,
threshold=threshold,
criterion=selection_crit)
st.fit(X_train, y_train_missing_labels)
pred = st.predict(X_test)
proba = st.predict_proba(X_test)
st_string = SelfTrainingClassifier(base_estimator,
max_iter=max_iter,
criterion=selection_crit,
threshold=threshold)
st_string.fit(X_train, y_train_missing_strings)
pred_string = st_string.predict(X_test)
proba_string = st_string.predict_proba(X_test)
assert_array_equal(np.vectorize(mapping.get)(pred), pred_string)
assert_array_equal(proba, proba_string)
assert st.termination_condition_ == st_string.termination_condition_
# Check consistency between labeled_iter, n_iter and max_iter
labeled = y_train_missing_labels != -1
# assert that labeled samples have labeled_iter = 0
assert_array_equal(st.labeled_iter_ == 0, labeled)
# assert that labeled samples do not change label during training
assert_array_equal(y_train_missing_labels[labeled],
st.transduction_[labeled])
# assert that the max of the iterations is less than the total amount of
# iterations
assert np.max(st.labeled_iter_) <= st.n_iter_ <= max_iter
assert np.max(st_string.labeled_iter_) <= st_string.n_iter_ <= max_iter
# check shapes
assert st.labeled_iter_.shape == st.transduction_.shape
assert st_string.labeled_iter_.shape == st_string.transduction_.shape
def test_verbose_k_best(capsys):
st = SelfTrainingClassifier(KNeighborsClassifier(n_neighbors=1),
criterion='k_best',
k_best=10,
verbose=True,
max_iter=None)
y_train_only_one_label = np.copy(y_train)
y_train_only_one_label[1:] = -1
n_samples = y_train.shape[0]
n_expected_iter = ceil((n_samples - 1) / 10)
st.fit(X_train, y_train_only_one_label)
captured = capsys.readouterr()
msg = 'End of iteration {}, added {} new labels.'
for i in range(1, n_expected_iter):
assert msg.format(i, 10) in captured.out
assert msg.format(n_expected_iter, (n_samples - 1) % 10) in captured.out
def test_base_estimator_meta_estimator():
# Check that a meta-estimator relying on an estimator implementing
# `predict_proba` will work even if it does expose this method before being
# fitted.
# Non-regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/19119
base_estimator = StackingClassifier(estimators=[
("svc_1", SVC(probability=True)),
("svc_2", SVC(probability=True)),
],
final_estimator=SVC(probability=True),
cv=2)
# make sure that the `base_estimator` does not expose `predict_proba`
# without being fitted
assert not hasattr(base_estimator, "predict_proba")
clf = SelfTrainingClassifier(base_estimator=base_estimator)
clf.fit(X_train, y_train_missing_labels)
clf.predict_proba(X_test)
def test_invalid_params_selection_crit():
st = SelfTrainingClassifier(KNeighborsClassifier(), criterion="foo")
with pytest.raises(ValueError, match="criterion must be either"):
st.fit(X_train, y_train)
def test_warns_k_best():
st = SelfTrainingClassifier(KNeighborsClassifier(), criterion="k_best", k_best=1000)
with pytest.warns(UserWarning, match="k_best is larger than"):
st.fit(X_train, y_train_missing_labels)
assert st.termination_condition_ == "all_labeled"
def test_none_classifier():
st = SelfTrainingClassifier(None)
with pytest.raises(ValueError, match="base_estimator cannot be None"):
st.fit(X_train, y_train_missing_labels)
# will behave as a # semi-supervised classifier, allowing it to learn from unlabeled data. # Read more in the :ref:`User guide <self_training>`. import numpy as np from sklearn import datasets from sklearn.semi_supervised import SelfTrainingClassifier from sklearn.svm import SVC rng = np.random.RandomState(42) iris = datasets.load_iris() random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3 iris.target[random_unlabeled_points] = -1 svc = SVC(probability=True, gamma="auto") self_training_model = SelfTrainingClassifier(svc) self_training_model.fit(iris.data, iris.target) ############################################################################## # New SequentialFeatureSelector transformer # ----------------------------------------- # A new iterative transformer to select features is available: # :class:`~sklearn.feature_selection.SequentialFeatureSelector`. # Sequential Feature Selection can add features one at a time (forward # selection) or remove features from the list of the available features # (backward selection), based on a cross-validated score maximization. # See the :ref:`User Guide <sequential_feature_selection>`. from sklearn.feature_selection import SequentialFeatureSelector from sklearn.neighbors import KNeighborsClassifier from sklearn.datasets import load_iris
for (i, threshold) in enumerate(x_values):
self_training_clf = SelfTrainingClassifier(base_classifier,
threshold=threshold)
# We need manual cross validation so that we don't treat -1 as a separate
# class when computing accuracy
skfolds = StratifiedKFold(n_splits=n_splits)
for fold, (train_index, test_index) in enumerate(skfolds.split(X, y)):
X_train = X[train_index]
y_train = y[train_index]
X_test = X[test_index]
y_test = y[test_index]
y_test_true = y_true[test_index]
self_training_clf.fit(X_train, y_train)
# The amount of labeled samples that at the end of fitting
amount_labeled[i, fold] = (total_samples - np.unique(
self_training_clf.labeled_iter_, return_counts=True)[1][0])
# The last iteration the classifier labeled a sample in
amount_iterations[i, fold] = np.max(self_training_clf.labeled_iter_)
y_pred = self_training_clf.predict(X_test)
scores[i, fold] = accuracy_score(y_test_true, y_pred)
ax1 = plt.subplot(211)
ax1.errorbar(x_values,
scores.mean(axis=1),
yerr=scores.std(axis=1),
capsize=2,
