def test_ransac_with_invalid_params_fit_correctly():
    ' Check that on a small dataset the RANSAC anomaly search algorithm can\n    adjust the values of hyperparameters\n\n    As stated in the sklearn documentation, min_samples is determined by default\n    based on how many features are in the dataset\n    Therefore, problems can arise when there are more attributes in a dataset\n    than the number of objects\n    '
    input_regression = get_synthetic_regression_data(n_samples=20, n_features=23)
    ransac_pipeline = get_ransac_pipeline()
    ransac_pipeline.fit(input_regression)
    predicted = ransac_pipeline.predict(input_regression)
    assert ransac_pipeline.is_fitted
    assert (predicted is not None)
Check that on a small dataset the RANSAC anomaly search algorithm can
adjust the values of hyperparameters

As stated in the sklearn documentation, min_samples is determined by default
based on how many features are in the dataset
Therefore, problems can arise when there are more attributes in a dataset
than the number of objects
def test_ransac_with_invalid_params_fit_correctly():
    ' Check that on a small dataset the RANSAC anomaly search algorithm can\n    adjust the values of hyperparameters\n\n    As stated in the sklearn documentation, min_samples is determined by default\n    based on how many features are in the dataset\n    Therefore, problems can arise when there are more attributes in a dataset\n    than the number of objects\n    '
    input_regression = get_synthetic_regression_data(n_samples=20, n_features=23)
    ransac_pipeline = get_ransac_pipeline()
    ransac_pipeline.fit(input_regression)
    predicted = ransac_pipeline.predict(input_regression)
    assert ransac_pipeline.is_fitted
    assert (predicted is not None)