546 lines
16 KiB
Python
546 lines
16 KiB
Python
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import numpy as np
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import pytest
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from numpy.testing import assert_array_almost_equal, assert_array_equal
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from sklearn.datasets import make_regression
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.linear_model import (
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LinearRegression,
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OrthogonalMatchingPursuit,
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RANSACRegressor,
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Ridge,
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)
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from sklearn.linear_model._ransac import _dynamic_max_trials
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS
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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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rng = np.random.RandomState(1000)
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outliers = np.unique(rng.randint(len(X), size=200))
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data[outliers, :] += 50 + rng.rand(len(outliers), 2) * 10
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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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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=5, random_state=0
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)
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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_).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_array_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 X.shape[0] == 2
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assert y.shape[0] == 2
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return False
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rng = np.random.RandomState(0)
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X = rng.rand(10, 2)
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y = rng.rand(10, 1)
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator,
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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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)
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with pytest.raises(ValueError):
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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 X.shape[0] == 2
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assert y.shape[0] == 2
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return False
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator,
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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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)
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with pytest.raises(ValueError):
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ransac_estimator.fit(X, y)
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def test_ransac_max_trials():
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator,
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min_samples=2,
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residual_threshold=5,
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max_trials=0,
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random_state=0,
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)
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with pytest.raises(ValueError):
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ransac_estimator.fit(X, y)
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# there is a 1e-9 chance it will take these many trials. No good reason
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# 1e-2 isn't enough, can still happen
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# 2 is the what ransac defines as min_samples = X.shape[1] + 1
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max_trials = _dynamic_max_trials(len(X) - len(outliers), X.shape[0], 2, 1 - 1e-9)
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ransac_estimator = RANSACRegressor(estimator, min_samples=2)
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for i in range(50):
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ransac_estimator.set_params(min_samples=2, random_state=i)
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ransac_estimator.fit(X, y)
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assert ransac_estimator.n_trials_ < max_trials + 1
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def test_ransac_stop_n_inliers():
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator,
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min_samples=2,
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residual_threshold=5,
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stop_n_inliers=2,
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random_state=0,
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)
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ransac_estimator.fit(X, y)
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assert ransac_estimator.n_trials_ == 1
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def test_ransac_stop_score():
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator,
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min_samples=2,
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residual_threshold=5,
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stop_score=0,
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random_state=0,
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)
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ransac_estimator.fit(X, y)
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assert 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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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=0.5, random_state=0
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)
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ransac_estimator.fit(X, y)
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assert ransac_estimator.score(X[2:], y[2:]) == 1
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assert 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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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=0.5, random_state=0
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)
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ransac_estimator.fit(X, y)
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assert_array_equal(ransac_estimator.predict(X), np.zeros(100))
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def test_ransac_no_valid_data():
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def is_data_valid(X, y):
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return False
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, is_data_valid=is_data_valid, max_trials=5
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)
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msg = "RANSAC could not find a valid consensus set"
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with pytest.raises(ValueError, match=msg):
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ransac_estimator.fit(X, y)
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assert ransac_estimator.n_skips_no_inliers_ == 0
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assert ransac_estimator.n_skips_invalid_data_ == 5
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assert ransac_estimator.n_skips_invalid_model_ == 0
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def test_ransac_no_valid_model():
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def is_model_valid(estimator, X, y):
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return False
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, is_model_valid=is_model_valid, max_trials=5
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)
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msg = "RANSAC could not find a valid consensus set"
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with pytest.raises(ValueError, match=msg):
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ransac_estimator.fit(X, y)
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assert ransac_estimator.n_skips_no_inliers_ == 0
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assert ransac_estimator.n_skips_invalid_data_ == 0
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assert ransac_estimator.n_skips_invalid_model_ == 5
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def test_ransac_exceed_max_skips():
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def is_data_valid(X, y):
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return False
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, is_data_valid=is_data_valid, max_trials=5, max_skips=3
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)
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msg = "RANSAC skipped more iterations than `max_skips`"
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with pytest.raises(ValueError, match=msg):
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ransac_estimator.fit(X, y)
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assert ransac_estimator.n_skips_no_inliers_ == 0
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assert ransac_estimator.n_skips_invalid_data_ == 4
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assert ransac_estimator.n_skips_invalid_model_ == 0
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def test_ransac_warn_exceed_max_skips():
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global cause_skip
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cause_skip = False
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def is_data_valid(X, y):
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global cause_skip
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if not cause_skip:
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cause_skip = True
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return True
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else:
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return False
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, is_data_valid=is_data_valid, max_skips=3, max_trials=5
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)
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warning_message = (
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"RANSAC found a valid consensus set but exited "
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"early due to skipping more iterations than "
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"`max_skips`. See estimator attributes for "
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"diagnostics."
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)
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with pytest.warns(ConvergenceWarning, match=warning_message):
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ransac_estimator.fit(X, y)
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assert ransac_estimator.n_skips_no_inliers_ == 0
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assert ransac_estimator.n_skips_invalid_data_ == 4
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assert ransac_estimator.n_skips_invalid_model_ == 0
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@pytest.mark.parametrize(
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"sparse_container", COO_CONTAINERS + CSR_CONTAINERS + CSC_CONTAINERS
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)
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def test_ransac_sparse(sparse_container):
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X_sparse = sparse_container(X)
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=5, random_state=0
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)
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ransac_estimator.fit(X_sparse, y)
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ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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def test_ransac_none_estimator():
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=5, random_state=0
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)
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ransac_none_estimator = RANSACRegressor(
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None, min_samples=2, residual_threshold=5, random_state=0
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)
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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(
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ransac_estimator.predict(X), ransac_none_estimator.predict(X)
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)
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def test_ransac_min_n_samples():
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estimator = LinearRegression()
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ransac_estimator1 = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=5, random_state=0
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)
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ransac_estimator2 = RANSACRegressor(
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estimator,
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min_samples=2.0 / X.shape[0],
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residual_threshold=5,
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random_state=0,
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)
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ransac_estimator5 = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=5, random_state=0
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)
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ransac_estimator6 = RANSACRegressor(estimator, residual_threshold=5, random_state=0)
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ransac_estimator7 = RANSACRegressor(
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estimator, min_samples=X.shape[0] + 1, residual_threshold=5, random_state=0
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)
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# GH #19390
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ransac_estimator8 = RANSACRegressor(
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Ridge(), min_samples=None, residual_threshold=5, random_state=0
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)
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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(
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ransac_estimator1.predict(X), ransac_estimator2.predict(X)
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)
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assert_array_almost_equal(
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ransac_estimator1.predict(X), ransac_estimator5.predict(X)
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)
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assert_array_almost_equal(
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ransac_estimator1.predict(X), ransac_estimator6.predict(X)
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)
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with pytest.raises(ValueError):
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ransac_estimator7.fit(X, y)
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err_msg = "`min_samples` needs to be explicitly set"
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with pytest.raises(ValueError, match=err_msg):
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ransac_estimator8.fit(X, y)
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def test_ransac_multi_dimensional_targets():
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estimator = LinearRegression()
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ransac_estimator = RANSACRegressor(
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estimator, min_samples=2, residual_threshold=5, random_state=0
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)
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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_).astype(np.bool_)
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ref_inlier_mask[outliers] = False
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assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
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def test_ransac_residual_loss():
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def loss_multi1(y_true, y_pred):
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return np.sum(np.abs(y_true - y_pred), axis=1)
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def loss_multi2(y_true, y_pred):
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return np.sum((y_true - y_pred) ** 2, axis=1)
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|
|
def loss_mono(y_true, y_pred):
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||
|
|
return np.abs(y_true - y_pred)
|
||
|
|
|
||
|
|
yyy = np.column_stack([y, y, y])
|
||
|
|
|
||
|
|
estimator = LinearRegression()
|
||
|
|
ransac_estimator0 = RANSACRegressor(
|
||
|
|
estimator, min_samples=2, residual_threshold=5, random_state=0
|
||
|
|
)
|
||
|
|
ransac_estimator1 = RANSACRegressor(
|
||
|
|
estimator,
|
||
|
|
min_samples=2,
|
||
|
|
residual_threshold=5,
|
||
|
|
random_state=0,
|
||
|
|
loss=loss_multi1,
|
||
|
|
)
|
||
|
|
ransac_estimator2 = RANSACRegressor(
|
||
|
|
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(
|
||
|
|
estimator,
|
||
|
|
min_samples=2,
|
||
|
|
residual_threshold=5,
|
||
|
|
random_state=0,
|
||
|
|
loss="squared_error",
|
||
|
|
)
|
||
|
|
ransac_estimator3.fit(X, y)
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ransac_estimator0.predict(X), ransac_estimator2.predict(X)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ransac_default_residual_threshold():
|
||
|
|
estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(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")
|
||
|
|
|
||
|
|
|
||
|
|
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 estimator.fit doesn't support
|
||
|
|
# sample_weight, raises error
|
||
|
|
estimator = OrthogonalMatchingPursuit()
|
||
|
|
ransac_estimator = RANSACRegressor(estimator, min_samples=10)
|
||
|
|
|
||
|
|
err_msg = f"{estimator.__class__.__name__} does not support sample_weight."
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
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(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_, atol=1e-12)
|
||
|
|
|
||
|
|
|
||
|
|
def test_perfect_horizontal_line():
|
||
|
|
"""Check that we can fit a line where all samples are inliers.
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/19497
|
||
|
|
"""
|
||
|
|
X = np.arange(100)[:, None]
|
||
|
|
y = np.zeros((100,))
|
||
|
|
|
||
|
|
estimator = LinearRegression()
|
||
|
|
ransac_estimator = RANSACRegressor(estimator, random_state=0)
|
||
|
|
ransac_estimator.fit(X, y)
|
||
|
|
|
||
|
|
assert_allclose(ransac_estimator.estimator_.coef_, 0.0)
|
||
|
|
assert_allclose(ransac_estimator.estimator_.intercept_, 0.0)
|