estimator_checks.py 文件源码

def check_parameters_default_constructible(name, Estimator):
    classifier = LinearDiscriminantAnalysis()
    # test default-constructibility
    # get rid of deprecation warnings
    with warnings.catch_warnings(record=True):
        if name in META_ESTIMATORS:
            estimator = Estimator(classifier)
        else:
            estimator = Estimator()
        # test cloning
        clone(estimator)
        # test __repr__
        repr(estimator)
        # test that set_params returns self
        assert_true(estimator.set_params() is estimator)

        # test if init does nothing but set parameters
        # this is important for grid_search etc.
        # We get the default parameters from init and then
        # compare these against the actual values of the attributes.

        # this comes from getattr. Gets rid of deprecation decorator.
        init = getattr(estimator.__init__, 'deprecated_original',
                       estimator.__init__)

        try:
            def param_filter(p):
                """Identify hyper parameters of an estimator"""
                return (p.name != 'self'
                        and p.kind != p.VAR_KEYWORD
                        and p.kind != p.VAR_POSITIONAL)

            init_params = [p for p in signature(init).parameters.values()
                           if param_filter(p)]
        except (TypeError, ValueError):
            # init is not a python function.
            # true for mixins
            return
        params = estimator.get_params()
        if name in META_ESTIMATORS:
            # they can need a non-default argument
            init_params = init_params[1:]

        for init_param in init_params:
            assert_not_equal(init_param.default, init_param.empty,
                             "parameter %s for %s has no default value"
                             % (init_param.name, type(estimator).__name__))
            assert_in(type(init_param.default),
                      [str, int, float, bool, tuple, type(None),
                       np.float64, types.FunctionType, Memory])
            if init_param.name not in params.keys():
                # deprecated parameter, not in get_params
                assert_true(init_param.default is None)
                continue

            param_value = params[init_param.name]
            if isinstance(param_value, np.ndarray):
                assert_array_equal(param_value, init_param.default)
            else:
                assert_equal(param_value, init_param.default)