def test_with_X_nan(self, iris_X, iris_y):
iris_X_nan = iris_X.copy()
index = np.random.choice(iris_X_nan.size, 100, replace=False)
iris_X_nan.ravel()[index] = np.nan
assert np.sum(np.isnan(iris_X_nan)) == 100
forest = GRFForestClassifier()
forest.fit(iris_X_nan, iris_y)
pred = forest.predict(iris_X_nan)
assert len(pred) == iris_X_nan.shape[0]
def test_accuracy(self, iris_X, iris_y):
X_train, X_test, y_train, y_test = train_test_split(iris_X,
iris_y,
test_size=0.33,
random_state=42)
# train and test a random forest classifier
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred_rf = rf.predict(X_test)
rf_acc = accuracy_score(y_test, y_pred_rf)
# train and test a ranger classifier
ra = GRFForestClassifier()
ra.fit(X_train, y_train)
y_pred_ra = ra.predict(X_test)
ranger_acc = accuracy_score(y_test, y_pred_ra)
# the accuracy should be good
assert rf_acc > 0.9
assert ranger_acc > 0.9
def test_predict(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y)
pred = forest.predict(iris_X)
assert len(pred) == iris_X.shape[0]