def test_pass_decision_function_multiclass_3class(self):
clf = FastLinearClassifier(number_of_threads=1)
clf.fit(X_train_3class, y_train_3class)
s = clf.decision_function(X_test_3class).sum()
assert_almost_equal(s,
38.0,
decimal=4,
err_msg=invalid_decision_function_output)
assert_equal(set(clf.classes_), {'Blue', 'Green', 'Red'})
def test_pass_predict_proba_multiclass_3class_retains_classes_type(self):
clf = FastLinearClassifier(number_of_threads=1)
clf.fit(X_train_3class_int, y_train_3class_int)
s = clf.predict_proba(X_test_3class_int).sum()
assert_almost_equal(s,
38.0,
decimal=4,
err_msg=invalid_predict_proba_output)
assert_equal(set(clf.classes_), {0, 1, 2})
def test_pass_predict_proba_multiclass_3class(self):
clf = FastLinearClassifier(number_of_threads=1)
clf.fit(X_train_3class, y_train_3class, verbose=0)
s = clf.predict_proba(X_test_3class).sum()
assert_almost_equal(s,
38.0,
decimal=4,
err_msg=invalid_predict_proba_output)
assert_equal(set(clf.classes_), {'Blue', 'Green', 'Red'})
def test_text_label(self):
X, y = get_iris()
ap = FastLinearClassifier(
feature=[
'Sepal_Width',
'Sepal_Length',
'Petal_Width',
'Petal_Length'])
ap.fit(X, y)
scores = ap.predict(X)
assert str(scores.dtype) == "object"
def test_decision_function_multiclass_3class_no_y_input_implies_no_classes_attribute(
self):
X_train = X_train_3class_int.join(y_train_3class_int)
X_test = X_test_3class_int.join(y_test_3class_int)
clf = FastLinearClassifier(number_of_threads=1, label='Label')
clf.fit(X_train)
if hasattr(clf, 'classes_'):
# The classes_ attribute is currently not supported
# when fitting when there is no y input specified.
self.fail("classes_ attribute not expected.")
s = clf.decision_function(X_test).sum()
assert_almost_equal(s,
38.0,
decimal=4,
err_msg=invalid_decision_function_output)
if hasattr(clf, 'classes_'):
# The classes_ attribute is currently not supported
# when predicting when there was no y input specified
# during fitting.
self.fail("classes_ attribute not expected.")
