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_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)