def test_random_forest():
# toy sample
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
y = [-1, -1, -1, 1, 1, 1]
T = [[-1, -1], [2, 2], [3, 2]]
true_result = [-1, 1, 1]
clf = RandomForestRegressor(n_estimators=10, random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
clf = RandomForestRegressor(n_estimators=10,
min_impurity_decrease=0.1,
random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
clf = RandomForestRegressor(n_estimators=10,
criterion="mse",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_decrease=0.,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=1,
verbose=0,
warm_start=False)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
clf = RandomForestRegressor(n_estimators=10,
max_features=1,
random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
# also test apply
leaf_indices = clf.apply(X)
assert leaf_indices.shape == (len(X), clf.n_estimators)
def test_min_variance():
rng = np.random.RandomState(0)
X = rng.normal(size=(1000, 1))
y = np.ones(1000)
rf = RandomForestRegressor(min_variance=0.1)
rf.fit(X, y)
mean, std = rf.predict(X, return_std=True)
assert_array_almost_equal(mean, y)
assert_array_almost_equal(std, np.sqrt(0.1 * np.ones(1000)))
def test_min_variance():
rng = np.random.RandomState(0)
X = rng.normal(size=(1000, 1))
y = np.ones(1000)
rf = RandomForestRegressor(min_variance=0.1)
rf.fit(X, y)
mean, std = rf.predict(X, return_std=True)
assert_array_almost_equal(mean, y)
assert_array_almost_equal(std, np.sqrt(0.1*np.ones(1000)))