397 lines
16 KiB
Python
397 lines
16 KiB
Python
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from importlib import import_module
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from inspect import signature
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from numbers import Integral, Real
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import pytest
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from sklearn.utils._param_validation import (
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Interval,
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InvalidParameterError,
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generate_invalid_param_val,
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generate_valid_param,
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make_constraint,
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)
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def _get_func_info(func_module):
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module_name, func_name = func_module.rsplit(".", 1)
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module = import_module(module_name)
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func = getattr(module, func_name)
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func_sig = signature(func)
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func_params = [
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p.name
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for p in func_sig.parameters.values()
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if p.kind not in (p.VAR_POSITIONAL, p.VAR_KEYWORD)
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]
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# The parameters `*args` and `**kwargs` are ignored since we cannot generate
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# constraints.
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required_params = [
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p.name
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for p in func_sig.parameters.values()
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if p.default is p.empty and p.kind not in (p.VAR_POSITIONAL, p.VAR_KEYWORD)
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]
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return func, func_name, func_params, required_params
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def _check_function_param_validation(
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func, func_name, func_params, required_params, parameter_constraints
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):
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"""Check that an informative error is raised when the value of a parameter does not
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have an appropriate type or value.
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"""
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# generate valid values for the required parameters
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valid_required_params = {}
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for param_name in required_params:
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if parameter_constraints[param_name] == "no_validation":
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valid_required_params[param_name] = 1
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else:
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valid_required_params[param_name] = generate_valid_param(
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make_constraint(parameter_constraints[param_name][0])
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)
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# check that there is a constraint for each parameter
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if func_params:
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validation_params = parameter_constraints.keys()
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unexpected_params = set(validation_params) - set(func_params)
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missing_params = set(func_params) - set(validation_params)
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err_msg = (
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"Mismatch between _parameter_constraints and the parameters of"
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f" {func_name}.\nConsider the unexpected parameters {unexpected_params} and"
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f" expected but missing parameters {missing_params}\n"
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)
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assert set(validation_params) == set(func_params), err_msg
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# this object does not have a valid type for sure for all params
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param_with_bad_type = type("BadType", (), {})()
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for param_name in func_params:
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constraints = parameter_constraints[param_name]
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if constraints == "no_validation":
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# This parameter is not validated
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continue
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# Mixing an interval of reals and an interval of integers must be avoided.
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if any(
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isinstance(constraint, Interval) and constraint.type == Integral
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for constraint in constraints
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) and any(
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isinstance(constraint, Interval) and constraint.type == Real
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for constraint in constraints
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):
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raise ValueError(
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f"The constraint for parameter {param_name} of {func_name} can't have a"
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" mix of intervals of Integral and Real types. Use the type"
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" RealNotInt instead of Real."
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)
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match = (
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rf"The '{param_name}' parameter of {func_name} must be .* Got .* instead."
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)
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err_msg = (
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f"{func_name} does not raise an informative error message when the "
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f"parameter {param_name} does not have a valid type. If any Python type "
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"is valid, the constraint should be 'no_validation'."
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)
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# First, check that the error is raised if param doesn't match any valid type.
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with pytest.raises(InvalidParameterError, match=match):
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func(**{**valid_required_params, param_name: param_with_bad_type})
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pytest.fail(err_msg)
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# Then, for constraints that are more than a type constraint, check that the
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# error is raised if param does match a valid type but does not match any valid
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# value for this type.
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constraints = [make_constraint(constraint) for constraint in constraints]
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for constraint in constraints:
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try:
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bad_value = generate_invalid_param_val(constraint)
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except NotImplementedError:
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continue
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err_msg = (
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f"{func_name} does not raise an informative error message when the "
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f"parameter {param_name} does not have a valid value.\n"
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"Constraints should be disjoint. For instance "
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"[StrOptions({'a_string'}), str] is not a acceptable set of "
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"constraint because generating an invalid string for the first "
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"constraint will always produce a valid string for the second "
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"constraint."
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)
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with pytest.raises(InvalidParameterError, match=match):
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func(**{**valid_required_params, param_name: bad_value})
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pytest.fail(err_msg)
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PARAM_VALIDATION_FUNCTION_LIST = [
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"sklearn.calibration.calibration_curve",
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"sklearn.cluster.cluster_optics_dbscan",
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"sklearn.cluster.compute_optics_graph",
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"sklearn.cluster.estimate_bandwidth",
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"sklearn.cluster.kmeans_plusplus",
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"sklearn.cluster.cluster_optics_xi",
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"sklearn.cluster.ward_tree",
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"sklearn.covariance.empirical_covariance",
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"sklearn.covariance.ledoit_wolf_shrinkage",
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"sklearn.covariance.log_likelihood",
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"sklearn.covariance.shrunk_covariance",
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"sklearn.datasets.clear_data_home",
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"sklearn.datasets.dump_svmlight_file",
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"sklearn.datasets.fetch_20newsgroups",
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"sklearn.datasets.fetch_20newsgroups_vectorized",
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"sklearn.datasets.fetch_california_housing",
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"sklearn.datasets.fetch_covtype",
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"sklearn.datasets.fetch_kddcup99",
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"sklearn.datasets.fetch_lfw_pairs",
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"sklearn.datasets.fetch_lfw_people",
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"sklearn.datasets.fetch_olivetti_faces",
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"sklearn.datasets.fetch_rcv1",
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"sklearn.datasets.fetch_openml",
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"sklearn.datasets.fetch_species_distributions",
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"sklearn.datasets.get_data_home",
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"sklearn.datasets.load_breast_cancer",
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"sklearn.datasets.load_diabetes",
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"sklearn.datasets.load_digits",
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"sklearn.datasets.load_files",
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"sklearn.datasets.load_iris",
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"sklearn.datasets.load_linnerud",
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"sklearn.datasets.load_sample_image",
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"sklearn.datasets.load_svmlight_file",
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"sklearn.datasets.load_svmlight_files",
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"sklearn.datasets.load_wine",
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"sklearn.datasets.make_biclusters",
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"sklearn.datasets.make_blobs",
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"sklearn.datasets.make_checkerboard",
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"sklearn.datasets.make_circles",
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"sklearn.datasets.make_classification",
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"sklearn.datasets.make_friedman1",
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"sklearn.datasets.make_friedman2",
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"sklearn.datasets.make_friedman3",
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"sklearn.datasets.make_gaussian_quantiles",
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"sklearn.datasets.make_hastie_10_2",
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"sklearn.datasets.make_low_rank_matrix",
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"sklearn.datasets.make_moons",
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"sklearn.datasets.make_multilabel_classification",
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"sklearn.datasets.make_regression",
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"sklearn.datasets.make_s_curve",
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"sklearn.datasets.make_sparse_coded_signal",
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"sklearn.datasets.make_sparse_spd_matrix",
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"sklearn.datasets.make_sparse_uncorrelated",
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"sklearn.datasets.make_spd_matrix",
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"sklearn.datasets.make_swiss_roll",
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"sklearn.decomposition.sparse_encode",
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"sklearn.feature_extraction.grid_to_graph",
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"sklearn.feature_extraction.img_to_graph",
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"sklearn.feature_extraction.image.extract_patches_2d",
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"sklearn.feature_extraction.image.reconstruct_from_patches_2d",
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"sklearn.feature_selection.chi2",
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"sklearn.feature_selection.f_classif",
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"sklearn.feature_selection.f_regression",
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"sklearn.feature_selection.mutual_info_classif",
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"sklearn.feature_selection.mutual_info_regression",
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"sklearn.feature_selection.r_regression",
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"sklearn.inspection.partial_dependence",
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"sklearn.inspection.permutation_importance",
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"sklearn.isotonic.check_increasing",
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"sklearn.isotonic.isotonic_regression",
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"sklearn.linear_model.enet_path",
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"sklearn.linear_model.lars_path",
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"sklearn.linear_model.lars_path_gram",
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"sklearn.linear_model.lasso_path",
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"sklearn.linear_model.orthogonal_mp",
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"sklearn.linear_model.orthogonal_mp_gram",
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"sklearn.linear_model.ridge_regression",
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"sklearn.manifold.trustworthiness",
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"sklearn.metrics.accuracy_score",
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"sklearn.manifold.smacof",
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"sklearn.metrics.auc",
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"sklearn.metrics.average_precision_score",
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"sklearn.metrics.balanced_accuracy_score",
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"sklearn.metrics.brier_score_loss",
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"sklearn.metrics.calinski_harabasz_score",
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"sklearn.metrics.check_scoring",
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"sklearn.metrics.completeness_score",
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"sklearn.metrics.class_likelihood_ratios",
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"sklearn.metrics.classification_report",
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"sklearn.metrics.cluster.adjusted_mutual_info_score",
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"sklearn.metrics.cluster.contingency_matrix",
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"sklearn.metrics.cluster.entropy",
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"sklearn.metrics.cluster.fowlkes_mallows_score",
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"sklearn.metrics.cluster.homogeneity_completeness_v_measure",
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"sklearn.metrics.cluster.normalized_mutual_info_score",
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"sklearn.metrics.cluster.silhouette_samples",
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"sklearn.metrics.cluster.silhouette_score",
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"sklearn.metrics.cohen_kappa_score",
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"sklearn.metrics.confusion_matrix",
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"sklearn.metrics.consensus_score",
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"sklearn.metrics.coverage_error",
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"sklearn.metrics.d2_absolute_error_score",
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"sklearn.metrics.d2_pinball_score",
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"sklearn.metrics.d2_tweedie_score",
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"sklearn.metrics.davies_bouldin_score",
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"sklearn.metrics.dcg_score",
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"sklearn.metrics.det_curve",
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"sklearn.metrics.explained_variance_score",
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"sklearn.metrics.f1_score",
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"sklearn.metrics.fbeta_score",
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"sklearn.metrics.get_scorer",
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"sklearn.metrics.hamming_loss",
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"sklearn.metrics.hinge_loss",
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"sklearn.metrics.homogeneity_score",
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"sklearn.metrics.jaccard_score",
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"sklearn.metrics.label_ranking_average_precision_score",
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"sklearn.metrics.label_ranking_loss",
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"sklearn.metrics.log_loss",
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"sklearn.metrics.make_scorer",
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"sklearn.metrics.matthews_corrcoef",
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"sklearn.metrics.max_error",
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"sklearn.metrics.mean_absolute_error",
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"sklearn.metrics.mean_absolute_percentage_error",
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"sklearn.metrics.mean_gamma_deviance",
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"sklearn.metrics.mean_pinball_loss",
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"sklearn.metrics.mean_poisson_deviance",
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"sklearn.metrics.mean_squared_error",
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"sklearn.metrics.mean_squared_log_error",
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"sklearn.metrics.mean_tweedie_deviance",
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"sklearn.metrics.median_absolute_error",
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"sklearn.metrics.multilabel_confusion_matrix",
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"sklearn.metrics.mutual_info_score",
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"sklearn.metrics.ndcg_score",
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"sklearn.metrics.pair_confusion_matrix",
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"sklearn.metrics.adjusted_rand_score",
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"sklearn.metrics.pairwise.additive_chi2_kernel",
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"sklearn.metrics.pairwise.chi2_kernel",
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"sklearn.metrics.pairwise.cosine_distances",
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"sklearn.metrics.pairwise.cosine_similarity",
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"sklearn.metrics.pairwise.euclidean_distances",
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"sklearn.metrics.pairwise.haversine_distances",
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"sklearn.metrics.pairwise.laplacian_kernel",
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"sklearn.metrics.pairwise.linear_kernel",
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"sklearn.metrics.pairwise.manhattan_distances",
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"sklearn.metrics.pairwise.nan_euclidean_distances",
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"sklearn.metrics.pairwise.paired_cosine_distances",
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"sklearn.metrics.pairwise.paired_distances",
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"sklearn.metrics.pairwise.paired_euclidean_distances",
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"sklearn.metrics.pairwise.paired_manhattan_distances",
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"sklearn.metrics.pairwise.pairwise_distances_argmin_min",
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"sklearn.metrics.pairwise.pairwise_kernels",
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"sklearn.metrics.pairwise.polynomial_kernel",
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"sklearn.metrics.pairwise.rbf_kernel",
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"sklearn.metrics.pairwise.sigmoid_kernel",
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"sklearn.metrics.pairwise_distances",
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"sklearn.metrics.pairwise_distances_argmin",
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"sklearn.metrics.pairwise_distances_chunked",
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"sklearn.metrics.precision_recall_curve",
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"sklearn.metrics.precision_recall_fscore_support",
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"sklearn.metrics.precision_score",
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"sklearn.metrics.r2_score",
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"sklearn.metrics.rand_score",
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"sklearn.metrics.recall_score",
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"sklearn.metrics.roc_auc_score",
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"sklearn.metrics.roc_curve",
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"sklearn.metrics.root_mean_squared_error",
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"sklearn.metrics.root_mean_squared_log_error",
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"sklearn.metrics.top_k_accuracy_score",
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"sklearn.metrics.v_measure_score",
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"sklearn.metrics.zero_one_loss",
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"sklearn.model_selection.cross_val_predict",
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"sklearn.model_selection.cross_val_score",
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"sklearn.model_selection.cross_validate",
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"sklearn.model_selection.learning_curve",
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"sklearn.model_selection.permutation_test_score",
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"sklearn.model_selection.train_test_split",
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"sklearn.model_selection.validation_curve",
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"sklearn.neighbors.kneighbors_graph",
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"sklearn.neighbors.radius_neighbors_graph",
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"sklearn.neighbors.sort_graph_by_row_values",
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"sklearn.preprocessing.add_dummy_feature",
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"sklearn.preprocessing.binarize",
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"sklearn.preprocessing.label_binarize",
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"sklearn.preprocessing.normalize",
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"sklearn.preprocessing.scale",
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"sklearn.random_projection.johnson_lindenstrauss_min_dim",
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"sklearn.svm.l1_min_c",
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"sklearn.tree.export_graphviz",
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"sklearn.tree.export_text",
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"sklearn.tree.plot_tree",
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"sklearn.utils.gen_batches",
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"sklearn.utils.gen_even_slices",
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"sklearn.utils.resample",
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|
"sklearn.utils.safe_mask",
|
||
|
|
"sklearn.utils.extmath.randomized_svd",
|
||
|
|
"sklearn.utils.class_weight.compute_class_weight",
|
||
|
|
"sklearn.utils.class_weight.compute_sample_weight",
|
||
|
|
"sklearn.utils.graph.single_source_shortest_path_length",
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("func_module", PARAM_VALIDATION_FUNCTION_LIST)
|
||
|
|
def test_function_param_validation(func_module):
|
||
|
|
"""Check param validation for public functions that are not wrappers around
|
||
|
|
estimators.
|
||
|
|
"""
|
||
|
|
func, func_name, func_params, required_params = _get_func_info(func_module)
|
||
|
|
|
||
|
|
parameter_constraints = getattr(func, "_skl_parameter_constraints")
|
||
|
|
|
||
|
|
_check_function_param_validation(
|
||
|
|
func, func_name, func_params, required_params, parameter_constraints
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
PARAM_VALIDATION_CLASS_WRAPPER_LIST = [
|
||
|
|
("sklearn.cluster.affinity_propagation", "sklearn.cluster.AffinityPropagation"),
|
||
|
|
("sklearn.cluster.dbscan", "sklearn.cluster.DBSCAN"),
|
||
|
|
("sklearn.cluster.k_means", "sklearn.cluster.KMeans"),
|
||
|
|
("sklearn.cluster.mean_shift", "sklearn.cluster.MeanShift"),
|
||
|
|
("sklearn.cluster.spectral_clustering", "sklearn.cluster.SpectralClustering"),
|
||
|
|
("sklearn.covariance.graphical_lasso", "sklearn.covariance.GraphicalLasso"),
|
||
|
|
("sklearn.covariance.ledoit_wolf", "sklearn.covariance.LedoitWolf"),
|
||
|
|
("sklearn.covariance.oas", "sklearn.covariance.OAS"),
|
||
|
|
("sklearn.decomposition.dict_learning", "sklearn.decomposition.DictionaryLearning"),
|
||
|
|
("sklearn.decomposition.fastica", "sklearn.decomposition.FastICA"),
|
||
|
|
("sklearn.decomposition.non_negative_factorization", "sklearn.decomposition.NMF"),
|
||
|
|
("sklearn.preprocessing.maxabs_scale", "sklearn.preprocessing.MaxAbsScaler"),
|
||
|
|
("sklearn.preprocessing.minmax_scale", "sklearn.preprocessing.MinMaxScaler"),
|
||
|
|
("sklearn.preprocessing.power_transform", "sklearn.preprocessing.PowerTransformer"),
|
||
|
|
(
|
||
|
|
"sklearn.preprocessing.quantile_transform",
|
||
|
|
"sklearn.preprocessing.QuantileTransformer",
|
||
|
|
),
|
||
|
|
("sklearn.preprocessing.robust_scale", "sklearn.preprocessing.RobustScaler"),
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"func_module, class_module", PARAM_VALIDATION_CLASS_WRAPPER_LIST
|
||
|
|
)
|
||
|
|
def test_class_wrapper_param_validation(func_module, class_module):
|
||
|
|
"""Check param validation for public functions that are wrappers around
|
||
|
|
estimators.
|
||
|
|
"""
|
||
|
|
func, func_name, func_params, required_params = _get_func_info(func_module)
|
||
|
|
|
||
|
|
module_name, class_name = class_module.rsplit(".", 1)
|
||
|
|
module = import_module(module_name)
|
||
|
|
klass = getattr(module, class_name)
|
||
|
|
|
||
|
|
parameter_constraints_func = getattr(func, "_skl_parameter_constraints")
|
||
|
|
parameter_constraints_class = getattr(klass, "_parameter_constraints")
|
||
|
|
parameter_constraints = {
|
||
|
|
**parameter_constraints_class,
|
||
|
|
**parameter_constraints_func,
|
||
|
|
}
|
||
|
|
parameter_constraints = {
|
||
|
|
k: v for k, v in parameter_constraints.items() if k in func_params
|
||
|
|
}
|
||
|
|
|
||
|
|
_check_function_param_validation(
|
||
|
|
func, func_name, func_params, required_params, parameter_constraints
|
||
|
|
)
|