def test_ransac_none_estimator():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_none_estimator = RANSACRegressor(None, 2, 5, random_state=0)
ransac_estimator.fit(X, y)
ransac_none_estimator.fit(X, y)
assert_array_almost_equal(ransac_estimator.predict(X),
ransac_none_estimator.predict(X))
def test_ransac_predict():
X = np.arange(100)[:, None]
y = np.zeros((100, ))
y[0] = 1
y[1] = 100
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=0.5,
random_state=0)
ransac_estimator.fit(X, y)
assert_array_equal(ransac_estimator.predict(X), np.zeros(100))
def test_ransac_residual_loss():
loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred),
axis=1)
loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred)**2, axis=1)
loss_mono = lambda y_true, y_pred: np.abs(y_true - y_pred)
yyy = np.column_stack([y, y, y])
base_estimator = LinearRegression()
ransac_estimator0 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_estimator1 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
loss=loss_multi1)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
loss=loss_multi2)
# multi-dimensional
ransac_estimator0.fit(X, yyy)
ransac_estimator1.fit(X, yyy)
ransac_estimator2.fit(X, yyy)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator1.predict(X))
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
# one-dimensional
ransac_estimator0.fit(X, y)
ransac_estimator2.loss = loss_mono
ransac_estimator2.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
ransac_estimator3 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
loss="squared_loss")
ransac_estimator3.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
def test_ransac_min_n_samples():
base_estimator = LinearRegression()
ransac_estimator1 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2. / X.shape[0],
residual_threshold=5,
random_state=0)
ransac_estimator3 = RANSACRegressor(base_estimator,
min_samples=-1,
residual_threshold=5,
random_state=0)
ransac_estimator4 = RANSACRegressor(base_estimator,
min_samples=5.2,
residual_threshold=5,
random_state=0)
ransac_estimator5 = RANSACRegressor(base_estimator,
min_samples=2.0,
residual_threshold=5,
random_state=0)
ransac_estimator6 = RANSACRegressor(base_estimator,
residual_threshold=5,
random_state=0)
ransac_estimator7 = RANSACRegressor(base_estimator,
min_samples=X.shape[0] + 1,
residual_threshold=5,
random_state=0)
ransac_estimator1.fit(X, y)
ransac_estimator2.fit(X, y)
ransac_estimator5.fit(X, y)
ransac_estimator6.fit(X, y)
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator2.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator5.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator6.predict(X))
assert_raises(ValueError, ransac_estimator3.fit, X, y)
assert_raises(ValueError, ransac_estimator4.fit, X, y)
assert_raises(ValueError, ransac_estimator7.fit, X, y)