class RotationForest():
def __init__(self):
super(RotationForest, self).__init__()
self.rf = RotationForestClassifier()
def train_step(self, X, y_train, plot=False):
y_train = y_train["label"].astype('category').cat.codes
self.rf.fit(X, y_train)
def test_forced(self, X, y_test):
y_test = y_test["label"].astype('category').cat.codes
probabilities = self.rf.predict_proba(X)
return probabilities, y_test
def test_rotation_forest(self):
""" Smoke test for rotation forest """
X, y = classification_data()
xt, xv, yt, yv = train_test_split(X, y, test_size=0.3, random_state=77)
clf = RotationForestClassifier(random_state=1234)
clf.fit(xt, yt)
proba = clf.predict_proba(xv)
assert proba.shape[0] == xv.shape[0]
assert np.all(proba <= 1)
assert np.all(proba >= 0)
yhat = clf.predict(xv)
assert yhat.shape[0] == xv.shape[0]
assert np.unique(yhat).tolist() == [0, 1]
