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,
)
assert hasattr(base_estimator, "predict_proba")
clf = SelfTrainingClassifier(base_estimator=base_estimator)
clf.fit(X_train, y_train_missing_labels)
clf.predict_proba(X_test)
base_estimator = StackingClassifier(
estimators=[
("svc_1", SVC(probability=False)),
("svc_2", SVC(probability=False)),
],
final_estimator=SVC(probability=False),
cv=2,
)
assert not hasattr(base_estimator, "predict_proba")
clf = SelfTrainingClassifier(base_estimator=base_estimator)
with pytest.raises(AttributeError):
clf.fit(X_train, y_train_missing_labels)
def self_training_clf(self, base_classifier, X_train, y_train,
threshold= None, max_iter = None,verbose = None):
"""
Train self-training classifier from scikit-learn >= 0.24.1
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
threshold (float): The decision threshold for use with criterion='threshold'. Should be in [0, 1)
max_iter (int): Maximum number of iterations allowed. Should be greater than or equal to 0
verbose (bool): Enable verbose output
Returns
_____________
Predicted labels and probability
"""
# Self training model
model = SelfTrainingClassifier(base_classifier,threshold= threshold,
max_iter = max_iter, verbose = verbose)
# Fit the training set
model.fit(X_train, y_train)
# Predict the labels of the unlabeled data points
predicted_labels = model.predict(X_train)
# Predict probability
predicted_proba = model.predict_proba(X_train)
return predicted_labels, predicted_proba
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
