449 lines
17 KiB
Python
449 lines
17 KiB
Python
from scipy import sparse
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import numpy as np
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from scipy import sparse
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from numpy.testing import assert_equal, assert_raises
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from numpy.testing import assert_array_almost_equal
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from numpy.testing import assert_array_equal
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from sklearn.utils import check_random_state
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from sklearn.utils.testing import assert_raises_regexp
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from sklearn.utils.testing import assert_less
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from sklearn.utils.testing import assert_warns
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.linear_model import LinearRegression, RANSACRegressor, Lasso
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from sklearn.linear_model.ransac import _dynamic_max_trials
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# Generate coordinates of line
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X = np.arange(-200, 200)
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y = 0.2 * X + 20
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data = np.column_stack([X, y])
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# Add some faulty data
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outliers = np.array((10, 30, 200))
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data[outliers[0], :] = (1000, 1000)
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data[outliers[1], :] = (-1000, -1000)
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data[outliers[2], :] = (-100, -50)
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X = data[:, 0][:, np.newaxis]
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y = data[:, 1]
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def test_ransac_inliers_outliers():
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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# Estimate parameters of corrupted data
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ransac_estimator.fit(X, y)
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# Ground truth / reference inlier mask
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
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).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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def test_ransac_is_data_valid():
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def is_data_valid(X, y):
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assert_equal(X.shape[0], 2)
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assert_equal(y.shape[0], 2)
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return False
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X = np.random.rand(10, 2)
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y = np.random.rand(10, 1)
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5,
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is_data_valid=is_data_valid,
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random_state=0)
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assert_raises(ValueError, ransac_estimator.fit, X, y)
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def test_ransac_is_model_valid():
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def is_model_valid(estimator, X, y):
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assert_equal(X.shape[0], 2)
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assert_equal(y.shape[0], 2)
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return False
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5,
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is_model_valid=is_model_valid,
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random_state=0)
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assert_raises(ValueError, ransac_estimator.fit, X, y)
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def test_ransac_max_trials():
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, max_trials=0,
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random_state=0)
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assert_raises(ValueError, ransac_estimator.fit, X, y)
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, max_trials=11,
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random_state=0)
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assert getattr(ransac_estimator, 'n_trials_', None) is None
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ransac_estimator.fit(X, y)
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assert_equal(ransac_estimator.n_trials_, 2)
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def test_ransac_stop_n_inliers():
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, stop_n_inliers=2,
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random_state=0)
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ransac_estimator.fit(X, y)
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assert_equal(ransac_estimator.n_trials_, 1)
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def test_ransac_stop_score():
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, stop_score=0,
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random_state=0)
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ransac_estimator.fit(X, y)
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assert_equal(ransac_estimator.n_trials_, 1)
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def test_ransac_score():
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X = np.arange(100)[:, None]
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y = np.zeros((100, ))
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y[0] = 1
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y[1] = 100
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=0.5, random_state=0)
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ransac_estimator.fit(X, y)
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assert_equal(ransac_estimator.score(X[2:], y[2:]), 1)
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assert_less(ransac_estimator.score(X[:2], y[:2]), 1)
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def test_ransac_predict():
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X = np.arange(100)[:, None]
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y = np.zeros((100, ))
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y[0] = 1
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y[1] = 100
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=0.5, random_state=0)
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ransac_estimator.fit(X, y)
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assert_equal(ransac_estimator.predict(X), np.zeros(100))
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def test_ransac_resid_thresh_no_inliers():
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# When residual_threshold=0.0 there are no inliers and a
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# ValueError with a message should be raised
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=0.0, random_state=0)
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assert_raises_regexp(ValueError,
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"No inliers.*residual_threshold.*0\.0",
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ransac_estimator.fit, X, y)
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def test_ransac_sparse_coo():
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X_sparse = sparse.coo_matrix(X)
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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ransac_estimator.fit(X_sparse, y)
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
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).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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def test_ransac_sparse_csr():
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X_sparse = sparse.csr_matrix(X)
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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ransac_estimator.fit(X_sparse, y)
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
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).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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def test_ransac_sparse_csc():
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X_sparse = sparse.csc_matrix(X)
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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ransac_estimator.fit(X_sparse, y)
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
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).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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def test_ransac_none_estimator():
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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ransac_none_estimator = RANSACRegressor(None, 2, 5, random_state=0)
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ransac_estimator.fit(X, y)
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ransac_none_estimator.fit(X, y)
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assert_array_almost_equal(ransac_estimator.predict(X),
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ransac_none_estimator.predict(X))
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def test_ransac_min_n_samples():
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base_estimator = LinearRegression()
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ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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ransac_estimator2 = RANSACRegressor(base_estimator,
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min_samples=2. / X.shape[0],
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residual_threshold=5, random_state=0)
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ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=-1,
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residual_threshold=5, random_state=0)
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ransac_estimator4 = RANSACRegressor(base_estimator, min_samples=5.2,
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residual_threshold=5, random_state=0)
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ransac_estimator5 = RANSACRegressor(base_estimator, min_samples=2.0,
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residual_threshold=5, random_state=0)
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ransac_estimator6 = RANSACRegressor(base_estimator,
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residual_threshold=5, random_state=0)
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ransac_estimator7 = RANSACRegressor(base_estimator,
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min_samples=X.shape[0] + 1,
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residual_threshold=5, random_state=0)
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ransac_estimator1.fit(X, y)
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ransac_estimator2.fit(X, y)
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ransac_estimator5.fit(X, y)
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ransac_estimator6.fit(X, y)
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assert_array_almost_equal(ransac_estimator1.predict(X),
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ransac_estimator2.predict(X))
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assert_array_almost_equal(ransac_estimator1.predict(X),
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ransac_estimator5.predict(X))
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assert_array_almost_equal(ransac_estimator1.predict(X),
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ransac_estimator6.predict(X))
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assert_raises(ValueError, ransac_estimator3.fit, X, y)
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assert_raises(ValueError, ransac_estimator4.fit, X, y)
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assert_raises(ValueError, ransac_estimator7.fit, X, y)
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def test_ransac_multi_dimensional_targets():
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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# 3-D target values
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yyy = np.column_stack([y, y, y])
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# Estimate parameters of corrupted data
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ransac_estimator.fit(X, yyy)
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# Ground truth / reference inlier mask
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
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).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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# XXX: Remove in 0.20
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def test_ransac_residual_metric():
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residual_metric1 = lambda dy: np.sum(np.abs(dy), axis=1)
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residual_metric2 = lambda dy: np.sum(dy ** 2, axis=1)
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yyy = np.column_stack([y, y, y])
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base_estimator = LinearRegression()
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ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0,
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residual_metric=residual_metric1)
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ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0,
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residual_metric=residual_metric2)
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# multi-dimensional
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ransac_estimator0.fit(X, yyy)
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assert_warns(DeprecationWarning, ransac_estimator1.fit, X, yyy)
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assert_warns(DeprecationWarning, ransac_estimator2.fit, X, yyy)
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assert_array_almost_equal(ransac_estimator0.predict(X),
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ransac_estimator1.predict(X))
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assert_array_almost_equal(ransac_estimator0.predict(X),
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ransac_estimator2.predict(X))
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# one-dimensional
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ransac_estimator0.fit(X, y)
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assert_warns(DeprecationWarning, ransac_estimator2.fit, X, y)
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assert_array_almost_equal(ransac_estimator0.predict(X),
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ransac_estimator2.predict(X))
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def test_ransac_residual_loss():
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loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred), axis=1)
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loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred) ** 2, axis=1)
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loss_mono = lambda y_true, y_pred : np.abs(y_true - y_pred)
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yyy = np.column_stack([y, y, y])
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base_estimator = LinearRegression()
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ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0)
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ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0,
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loss=loss_multi1)
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ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0,
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loss=loss_multi2)
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# multi-dimensional
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ransac_estimator0.fit(X, yyy)
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ransac_estimator1.fit(X, yyy)
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ransac_estimator2.fit(X, yyy)
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assert_array_almost_equal(ransac_estimator0.predict(X),
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ransac_estimator1.predict(X))
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assert_array_almost_equal(ransac_estimator0.predict(X),
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ransac_estimator2.predict(X))
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# one-dimensional
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ransac_estimator0.fit(X, y)
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ransac_estimator2.loss = loss_mono
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ransac_estimator2.fit(X, y)
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assert_array_almost_equal(ransac_estimator0.predict(X),
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ransac_estimator2.predict(X))
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ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=2,
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residual_threshold=5, random_state=0,
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loss="squared_loss")
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ransac_estimator3.fit(X, y)
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assert_array_almost_equal(ransac_estimator0.predict(X),
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ransac_estimator2.predict(X))
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def test_ransac_default_residual_threshold():
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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random_state=0)
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# Estimate parameters of corrupted data
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ransac_estimator.fit(X, y)
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# Ground truth / reference inlier mask
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
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).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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def test_ransac_dynamic_max_trials():
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# Numbers hand-calculated and confirmed on page 119 (Table 4.3) in
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# Hartley, R.~I. and Zisserman, A., 2004,
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# Multiple View Geometry in Computer Vision, Second Edition,
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# Cambridge University Press, ISBN: 0521540518
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# e = 0%, min_samples = X
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assert_equal(_dynamic_max_trials(100, 100, 2, 0.99), 1)
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# e = 5%, min_samples = 2
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assert_equal(_dynamic_max_trials(95, 100, 2, 0.99), 2)
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# e = 10%, min_samples = 2
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assert_equal(_dynamic_max_trials(90, 100, 2, 0.99), 3)
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# e = 30%, min_samples = 2
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assert_equal(_dynamic_max_trials(70, 100, 2, 0.99), 7)
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# e = 50%, min_samples = 2
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assert_equal(_dynamic_max_trials(50, 100, 2, 0.99), 17)
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# e = 5%, min_samples = 8
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assert_equal(_dynamic_max_trials(95, 100, 8, 0.99), 5)
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# e = 10%, min_samples = 8
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assert_equal(_dynamic_max_trials(90, 100, 8, 0.99), 9)
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# e = 30%, min_samples = 8
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assert_equal(_dynamic_max_trials(70, 100, 8, 0.99), 78)
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# e = 50%, min_samples = 8
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assert_equal(_dynamic_max_trials(50, 100, 8, 0.99), 1177)
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# e = 0%, min_samples = 10
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assert_equal(_dynamic_max_trials(1, 100, 10, 0), 0)
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assert_equal(_dynamic_max_trials(1, 100, 10, 1), float('inf'))
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base_estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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stop_probability=-0.1)
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assert_raises(ValueError, ransac_estimator.fit, X, y)
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ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
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stop_probability=1.1)
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assert_raises(ValueError, ransac_estimator.fit, X, y)
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def test_ransac_fit_sample_weight():
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ransac_estimator = RANSACRegressor(random_state=0)
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n_samples = y.shape[0]
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weights = np.ones(n_samples)
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ransac_estimator.fit(X, y, weights)
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# sanity check
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assert_equal(ransac_estimator.inlier_mask_.shape[0], n_samples)
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
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).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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# check that mask is correct
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assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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# check that fit(X) = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
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# X = X1 repeated n1 times, X2 repeated n2 times and so forth
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random_state = check_random_state(0)
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X_ = random_state.randint(0, 200, [10, 1])
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y_ = np.ndarray.flatten(0.2 * X_ + 2)
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sample_weight = random_state.randint(0, 10, 10)
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outlier_X = random_state.randint(0, 1000, [1, 1])
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outlier_weight = random_state.randint(0, 10, 1)
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outlier_y = random_state.randint(-1000, 0, 1)
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X_flat = np.append(np.repeat(X_, sample_weight, axis=0),
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np.repeat(outlier_X, outlier_weight, axis=0), axis=0)
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y_flat = np.ndarray.flatten(np.append(np.repeat(y_, sample_weight, axis=0),
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np.repeat(outlier_y, outlier_weight, axis=0),
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axis=0))
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ransac_estimator.fit(X_flat, y_flat)
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ref_coef_ = ransac_estimator.estimator_.coef_
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sample_weight = np.append(sample_weight, outlier_weight)
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X_ = np.append(X_, outlier_X, axis=0)
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y_ = np.append(y_, outlier_y)
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ransac_estimator.fit(X_, y_, sample_weight)
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assert_almost_equal(ransac_estimator.estimator_.coef_, ref_coef_)
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# check that if base_estimator.fit doesn't support
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# sample_weight, raises error
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base_estimator = Lasso()
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ransac_estimator = RANSACRegressor(base_estimator)
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assert_raises(ValueError, ransac_estimator.fit, X, y, weights)
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