def test_class_weight_param():
"""Backport of sklearn.utils.estimator_checks.check_class_weight_classifiers
for sklearn <= 0.23.0.
"""
clf = KerasClassifier(
model=dynamic_classifier,
model__hidden_layer_sizes=(100, ),
epochs=50,
random_state=0,
)
problems = (2, 3)
for n_centers in problems:
# create a very noisy dataset
X, y = make_blobs(centers=n_centers, random_state=0, cluster_std=20)
X_train, X_test, y_train, _ = train_test_split(X,
y,
test_size=0.5,
random_state=0)
n_centers = len(np.unique(y_train))
if n_centers == 2:
class_weight = {0: 1000, 1: 0.0001}
else:
class_weight = {0: 1000, 1: 0.0001, 2: 0.0001}
clf.set_params(class_weight=class_weight)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
assert np.mean(y_pred == 0) > 0.87
def test_class_weight_param():
"""Backport of sklearn.utils.estimator_checks.check_class_weight_classifiers
for sklearn <= 0.23.0.
Tests that fit and partial_fit correctly handle the class_weight parameter.
"""
clf = KerasClassifier(
model=dynamic_classifier,
model__hidden_layer_sizes=(100, ),
random_state=0,
)
problems = (2, 3)
for n_centers in problems:
# create a very noisy dataset
X, y = make_blobs(centers=n_centers, random_state=0, cluster_std=20)
X_train, X_test, y_train, _ = train_test_split(X,
y,
test_size=0.5,
random_state=0)
n_centers = len(np.unique(y_train))
if n_centers == 2:
class_weight = {0: 1000, 1: 0.0001}
fit_epochs = 4
partial_fit_epochs = 3
else:
class_weight = {0: 1000, 1: 0.0001, 2: 0.0001}
fit_epochs = 8
partial_fit_epochs = 6
clf.set_params(class_weight=class_weight)
# run fit epochs followed by several partial_fit iterations
# these numbers are purely empirical, just like they are in the
# original sklearn test
clf.set_params(fit__epochs=fit_epochs)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
assert np.mean(y_pred == 0) > 0.8
for _ in range(partial_fit_epochs):
clf.partial_fit(X_train, y_train)
y_pred = clf.predict(X_test)
assert np.mean(y_pred == 0) > 0.95
