516 lines
19 KiB
Python
516 lines
19 KiB
Python
|
import numpy as np
|
||
|
|
from scipy import sparse
|
||
|
|
|
||
|
|
from numpy.testing import assert_array_almost_equal
|
||
|
|
from numpy.testing import assert_array_equal
|
||
|
|
|
||
|
|
from sklearn.utils import check_random_state
|
||
|
|
from sklearn.utils._testing import assert_warns
|
||
|
|
from sklearn.utils._testing import assert_raises_regexp
|
||
|
|
from sklearn.utils._testing import assert_raises
|
||
|
|
from sklearn.utils._testing import assert_allclose
|
||
|
|
from sklearn.datasets import make_regression
|
||
|
|
from sklearn.linear_model import LinearRegression, RANSACRegressor
|
||
|
|
from sklearn.linear_model import OrthogonalMatchingPursuit
|
||
|
|
from sklearn.linear_model._ransac import _dynamic_max_trials
|
||
|
|
from sklearn.exceptions import ConvergenceWarning
|
||
|
|
|
||
|
|
|
||
|
|
# Generate coordinates of line
|
||
|
|
X = np.arange(-200, 200)
|
||
|
|
y = 0.2 * X + 20
|
||
|
|
data = np.column_stack([X, y])
|
||
|
|
|
||
|
|
# Add some faulty data
|
||
|
|
rng = np.random.RandomState(1000)
|
||
|
|
outliers = np.unique(rng.randint(len(X), size=200))
|
||
|
|
data[outliers, :] += 50 + rng.rand(len(outliers), 2) * 10
|
||
|
|
|
||
|
|
X = data[:, 0][:, np.newaxis]
|
||
|
|
y = data[:, 1]
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_inliers_outliers():
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
residual_threshold=5, 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_is_data_valid():
|
||
|
|
def is_data_valid(X, y):
|
||
|
|
assert X.shape[0] == 2
|
||
|
|
assert y.shape[0] == 2
|
||
|
|
return False
|
||
|
|
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.rand(10, 2)
|
||
|
|
y = rng.rand(10, 1)
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
residual_threshold=5,
|
||
|
|
is_data_valid=is_data_valid,
|
||
|
|
random_state=0)
|
||
|
|
|
||
|
|
assert_raises(ValueError, ransac_estimator.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_is_model_valid():
|
||
|
|
def is_model_valid(estimator, X, y):
|
||
|
|
assert X.shape[0] == 2
|
||
|
|
assert y.shape[0] == 2
|
||
|
|
return False
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
residual_threshold=5,
|
||
|
|
is_model_valid=is_model_valid,
|
||
|
|
random_state=0)
|
||
|
|
|
||
|
|
assert_raises(ValueError, ransac_estimator.fit, X, y)
|
||
|
|
|
||
|
|
|
||
|
|
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_stop_n_inliers():
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
residual_threshold=5, stop_n_inliers=2,
|
||
|
|
random_state=0)
|
||
|
|
ransac_estimator.fit(X, y)
|
||
|
|
|
||
|
|
assert ransac_estimator.n_trials_ == 1
|
||
|
|
|
||
|
|
|
||
|
|
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_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_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_resid_thresh_no_inliers():
|
||
|
|
# When residual_threshold=0.0 there are no inliers and a
|
||
|
|
# ValueError with a message should be raised
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
residual_threshold=0.0, random_state=0,
|
||
|
|
max_trials=5)
|
||
|
|
|
||
|
|
msg = ("RANSAC could not find a valid consensus set")
|
||
|
|
assert_raises_regexp(ValueError, msg, ransac_estimator.fit, X, y)
|
||
|
|
assert ransac_estimator.n_skips_no_inliers_ == 5
|
||
|
|
assert ransac_estimator.n_skips_invalid_data_ == 0
|
||
|
|
assert ransac_estimator.n_skips_invalid_model_ == 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_no_valid_data():
|
||
|
|
def is_data_valid(X, y):
|
||
|
|
return False
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator,
|
||
|
|
is_data_valid=is_data_valid,
|
||
|
|
max_trials=5)
|
||
|
|
|
||
|
|
msg = ("RANSAC could not find a valid consensus set")
|
||
|
|
assert_raises_regexp(ValueError, msg, ransac_estimator.fit, X, y)
|
||
|
|
assert ransac_estimator.n_skips_no_inliers_ == 0
|
||
|
|
assert ransac_estimator.n_skips_invalid_data_ == 5
|
||
|
|
assert ransac_estimator.n_skips_invalid_model_ == 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_no_valid_model():
|
||
|
|
def is_model_valid(estimator, X, y):
|
||
|
|
return False
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator,
|
||
|
|
is_model_valid=is_model_valid,
|
||
|
|
max_trials=5)
|
||
|
|
|
||
|
|
msg = ("RANSAC could not find a valid consensus set")
|
||
|
|
assert_raises_regexp(ValueError, msg, ransac_estimator.fit, X, y)
|
||
|
|
assert ransac_estimator.n_skips_no_inliers_ == 0
|
||
|
|
assert ransac_estimator.n_skips_invalid_data_ == 0
|
||
|
|
assert ransac_estimator.n_skips_invalid_model_ == 5
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_exceed_max_skips():
|
||
|
|
def is_data_valid(X, y):
|
||
|
|
return False
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator,
|
||
|
|
is_data_valid=is_data_valid,
|
||
|
|
max_trials=5,
|
||
|
|
max_skips=3)
|
||
|
|
|
||
|
|
msg = ("RANSAC skipped more iterations than `max_skips`")
|
||
|
|
assert_raises_regexp(ValueError, msg, ransac_estimator.fit, X, y)
|
||
|
|
assert ransac_estimator.n_skips_no_inliers_ == 0
|
||
|
|
assert ransac_estimator.n_skips_invalid_data_ == 4
|
||
|
|
assert ransac_estimator.n_skips_invalid_model_ == 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_warn_exceed_max_skips():
|
||
|
|
global cause_skip
|
||
|
|
cause_skip = False
|
||
|
|
|
||
|
|
def is_data_valid(X, y):
|
||
|
|
global cause_skip
|
||
|
|
if not cause_skip:
|
||
|
|
cause_skip = True
|
||
|
|
return True
|
||
|
|
else:
|
||
|
|
return False
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator,
|
||
|
|
is_data_valid=is_data_valid,
|
||
|
|
max_skips=3,
|
||
|
|
max_trials=5)
|
||
|
|
|
||
|
|
assert_warns(ConvergenceWarning, ransac_estimator.fit, X, y)
|
||
|
|
assert ransac_estimator.n_skips_no_inliers_ == 0
|
||
|
|
assert ransac_estimator.n_skips_invalid_data_ == 4
|
||
|
|
assert ransac_estimator.n_skips_invalid_model_ == 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_sparse_coo():
|
||
|
|
X_sparse = sparse.coo_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_sparse_csr():
|
||
|
|
X_sparse = sparse.csr_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_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_none_estimator():
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
residual_threshold=5, random_state=0)
|
||
|
|
ransac_none_estimator = RANSACRegressor(None, min_samples=2,
|
||
|
|
residual_threshold=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_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_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_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_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_dynamic_max_trials():
|
||
|
|
# Numbers hand-calculated and confirmed on page 119 (Table 4.3) in
|
||
|
|
# Hartley, R.~I. and Zisserman, A., 2004,
|
||
|
|
# Multiple View Geometry in Computer Vision, Second Edition,
|
||
|
|
# Cambridge University Press, ISBN: 0521540518
|
||
|
|
|
||
|
|
# e = 0%, min_samples = X
|
||
|
|
assert _dynamic_max_trials(100, 100, 2, 0.99) == 1
|
||
|
|
|
||
|
|
# e = 5%, min_samples = 2
|
||
|
|
assert _dynamic_max_trials(95, 100, 2, 0.99) == 2
|
||
|
|
# e = 10%, min_samples = 2
|
||
|
|
assert _dynamic_max_trials(90, 100, 2, 0.99) == 3
|
||
|
|
# e = 30%, min_samples = 2
|
||
|
|
assert _dynamic_max_trials(70, 100, 2, 0.99) == 7
|
||
|
|
# e = 50%, min_samples = 2
|
||
|
|
assert _dynamic_max_trials(50, 100, 2, 0.99) == 17
|
||
|
|
|
||
|
|
# e = 5%, min_samples = 8
|
||
|
|
assert _dynamic_max_trials(95, 100, 8, 0.99) == 5
|
||
|
|
# e = 10%, min_samples = 8
|
||
|
|
assert _dynamic_max_trials(90, 100, 8, 0.99) == 9
|
||
|
|
# e = 30%, min_samples = 8
|
||
|
|
assert _dynamic_max_trials(70, 100, 8, 0.99) == 78
|
||
|
|
# e = 50%, min_samples = 8
|
||
|
|
assert _dynamic_max_trials(50, 100, 8, 0.99) == 1177
|
||
|
|
|
||
|
|
# e = 0%, min_samples = 10
|
||
|
|
assert _dynamic_max_trials(1, 100, 10, 0) == 0
|
||
|
|
assert _dynamic_max_trials(1, 100, 10, 1) == float('inf')
|
||
|
|
|
||
|
|
base_estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
stop_probability=-0.1)
|
||
|
|
assert_raises(ValueError, ransac_estimator.fit, X, y)
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
|
||
|
|
stop_probability=1.1)
|
||
|
|
assert_raises(ValueError, ransac_estimator.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_allclose(ransac_estimator.estimator_.coef_, ref_coef_)
|
||
|
|
|
||
|
|
# check that if base_estimator.fit doesn't support
|
||
|
|
# sample_weight, raises error
|
||
|
|
base_estimator = OrthogonalMatchingPursuit()
|
||
|
|
ransac_estimator = RANSACRegressor(base_estimator)
|
||
|
|
assert_raises(ValueError, ransac_estimator.fit, X, y, weights)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_final_model_fit_sample_weight():
|
||
|
|
X, y = make_regression(n_samples=1000, random_state=10)
|
||
|
|
rng = check_random_state(42)
|
||
|
|
sample_weight = rng.randint(1, 4, size=y.shape[0])
|
||
|
|
sample_weight = sample_weight / sample_weight.sum()
|
||
|
|
ransac = RANSACRegressor(base_estimator=LinearRegression(), random_state=0)
|
||
|
|
ransac.fit(X, y, sample_weight=sample_weight)
|
||
|
|
|
||
|
|
final_model = LinearRegression()
|
||
|
|
mask_samples = ransac.inlier_mask_
|
||
|
|
final_model.fit(
|
||
|
|
X[mask_samples], y[mask_samples],
|
||
|
|
sample_weight=sample_weight[mask_samples]
|
||
|
|
)
|
||
|
|
|
||
|
|
assert_allclose(ransac.estimator_.coef_, final_model.coef_)
|