def test_TimeSeriesForest_predictions(n_estimators, n_intervals):
random_state = 1234
X_train, y_train = load_gunpoint(split="train", return_X_y=True)
X_test, y_test = load_gunpoint(split="test", return_X_y=True)
features = [np.mean, np.std, _slope]
steps = [
(
"transform",
RandomIntervalFeatureExtractor(
random_state=random_state, features=features
),
),
("clf", DecisionTreeClassifier()),
]
estimator = Pipeline(steps)
clf1 = ComposableTimeSeriesForestClassifier(
estimator=estimator, random_state=random_state, n_estimators=n_estimators
)
clf1.fit(X_train, y_train)
a = clf1.predict_proba(X_test)
# default, semi-modular implementation using
# RandomIntervalFeatureExtractor internally
clf2 = ComposableTimeSeriesForestClassifier(
random_state=random_state, n_estimators=n_estimators
)
clf2.fit(X_train, y_train)
b = clf2.predict_proba(X_test)
np.testing.assert_array_equal(a, b)
def test_predict_proba():
clf = ComposableTimeSeriesForestClassifier(n_estimators=2)
clf.fit(X, y)
proba = clf.predict_proba(X)
assert proba.shape == (X.shape[0], n_classes)
np.testing.assert_array_equal(np.ones(X.shape[0]), np.sum(proba, axis=1))
# test single row input
y_proba = clf.predict_proba(X.iloc[[0], :])
assert y_proba.shape == (1, n_classes)
y_pred = clf.predict(X.iloc[[0], :])
assert y_pred.shape == (1,)