def test_ransac_stop_score():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
stop_score=0,
random_state=0)
ransac_estimator.fit(X, y)
assert ransac_estimator.n_trials_ == 1
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_default_residual_threshold():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
random_state=0)
# Estimate parameters of corrupted data
ransac_estimator.fit(X, y)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(
np.bool_)
ref_inlier_mask[outliers] = False
assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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_sparse_csc():
X_sparse = sparse.csc_matrix(X)
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_estimator.fit(X_sparse, y)
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(
np.bool_)
ref_inlier_mask[outliers] = False
assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_score():
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 ransac_estimator.score(X[2:], y[2:]) == 1
assert ransac_estimator.score(X[:2], y[:2]) < 1
def test_ransac_max_trials():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
max_trials=0,
random_state=0)
assert_raises(ValueError, ransac_estimator.fit, X, y)
# there is a 1e-9 chance it will take these many trials. No good reason
# 1e-2 isn't enough, can still happen
# 2 is the what ransac defines as min_samples = X.shape[1] + 1
max_trials = _dynamic_max_trials(
len(X) - len(outliers), X.shape[0], 2, 1 - 1e-9)
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2)
for i in range(50):
ransac_estimator.set_params(min_samples=2, random_state=i)
ransac_estimator.fit(X, y)
assert ransac_estimator.n_trials_ < max_trials + 1
def test_ransac_multi_dimensional_targets():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
# 3-D target values
yyy = np.column_stack([y, y, y])
# Estimate parameters of corrupted data
ransac_estimator.fit(X, yyy)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(
np.bool_)
ref_inlier_mask[outliers] = False
assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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)
def test_ransac_fit_sample_weight():
ransac_estimator = RANSACRegressor(random_state=0)
n_samples = y.shape[0]
weights = np.ones(n_samples)
ransac_estimator.fit(X, y, weights)
# sanity check
assert ransac_estimator.inlier_mask_.shape[0] == n_samples
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(
np.bool_)
ref_inlier_mask[outliers] = False
# check that mask is correct
assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
# check that fit(X) = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
# X = X1 repeated n1 times, X2 repeated n2 times and so forth
random_state = check_random_state(0)
X_ = random_state.randint(0, 200, [10, 1])
y_ = np.ndarray.flatten(0.2 * X_ + 2)
sample_weight = random_state.randint(0, 10, 10)
outlier_X = random_state.randint(0, 1000, [1, 1])
outlier_weight = random_state.randint(0, 10, 1)
outlier_y = random_state.randint(-1000, 0, 1)
X_flat = np.append(np.repeat(X_, sample_weight, axis=0),
np.repeat(outlier_X, outlier_weight, axis=0),
axis=0)
y_flat = np.ndarray.flatten(
np.append(np.repeat(y_, sample_weight, axis=0),
np.repeat(outlier_y, outlier_weight, axis=0),
axis=0))
ransac_estimator.fit(X_flat, y_flat)
ref_coef_ = ransac_estimator.estimator_.coef_
sample_weight = np.append(sample_weight, outlier_weight)
X_ = np.append(X_, outlier_X, axis=0)
y_ = np.append(y_, outlier_y)
ransac_estimator.fit(X_, y_, sample_weight)
assert_almost_equal(ransac_estimator.estimator_.coef_, ref_coef_)
# check that if base_estimator.fit doesn't support
# sample_weight, raises error
base_estimator = Lasso()
ransac_estimator = RANSACRegressor(base_estimator)
assert_raises(ValueError, ransac_estimator.fit, X, y, weights)
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))
