[python][tests] minor Python tests cleanup

[StrikerRUS]

• Use pytest.approx for scalars instead of numpy.testing.assert_allclose.
• Use default tolerance in asserts where possible.
• Bring back one assertion lost during [python-package] migrate test_sklearn.py to pytest #3844.
• Remove misleading comment.

[jameslamb]

thanks!

[wyfSunflower]

Failed to run test_sklearn.py with scikit-learn 0.23.1 in python 3.8 on Ubuntu 20

test_sklearn.py::TestSklearn::test_check_is_fitted FAILED [100%]
test_sklearn.py:1095 (TestSklearn.test_check_is_fitted)
self = <test_sklearn.TestSklearn testMethod=test_check_is_fitted>

@unittest.skipIf(sk_version < '0.22.0', 'scikit-learn version is less than 0.22')
def test_check_is_fitted(self):
    X, y = load_digits(n_class=2, return_X_y=True)
    est = lgb.LGBMModel(n_estimators=5, objective="binary")
    clf = lgb.LGBMClassifier(n_estimators=5)
    reg = lgb.LGBMRegressor(n_estimators=5)
    rnk = lgb.LGBMRanker(n_estimators=5)
    models = (est, clf, reg, rnk)
    for model in models:
        self.assertRaises(lgb.compat.LGBMNotFittedError,
                          check_is_fitted,
                          model)
    est.fit(X, y)
    clf.fit(X, y)
    reg.fit(X, y)
    rnk.fit(X, y, group=np.ones(X.shape[0]))
    for model in models:

      check_is_fitted(model)

test_sklearn.py:1113:

---

../../../../anaconda3/envs/py38/lib/python3.8/site-packages/sklearn/utils/validation.py:73: in inner_f
return f(**kwargs)

---

estimator = LGBMModel(n_estimators=5, objective='binary'), attributes = None

@_deprecate_positional_args
def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all):
    """Perform is_fitted validation for estimator.

    Checks if the estimator is fitted by verifying the presence of
    fitted attributes (ending with a trailing underscore) and otherwise
    raises a NotFittedError with the given message.

    This utility is meant to be used internally by estimators themselves,
    typically in their own predict / transform methods.

    Parameters
    ----------
    estimator : estimator instance.
        estimator instance for which the check is performed.

    attributes : str, list or tuple of str, default=None
        Attribute name(s) given as string or a list/tuple of strings
        Eg.: ``["coef_", "estimator_", ...], "coef_"``

        If `None`, `estimator` is considered fitted if there exist an
        attribute that ends with a underscore and does not start with double
        underscore.

    msg : string
        The default error message is, "This %(name)s instance is not fitted
        yet. Call 'fit' with appropriate arguments before using this
        estimator."

        For custom messages if "%(name)s" is present in the message string,
        it is substituted for the estimator name.

        Eg. : "Estimator, %(name)s, must be fitted before sparsifying".

    all_or_any : callable, {all, any}, default all
        Specify whether all or any of the given attributes must exist.

    Returns
    -------
    None

    Raises
    ------
    NotFittedError
        If the attributes are not found.
    """
    if isclass(estimator):
        raise TypeError("{} is a class, not an instance.".format(estimator))
    if msg is None:
        msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
               "appropriate arguments before using this estimator.")

    if not hasattr(estimator, 'fit'):
        raise TypeError("%s is not an estimator instance." % (estimator))

    if attributes is not None:
        if not isinstance(attributes, (list, tuple)):
            attributes = [attributes]
        attrs = all_or_any([hasattr(estimator, attr) for attr in attributes])
    else:
        attrs = [v for v in vars(estimator)
                 if v.endswith("_") and not v.startswith("__")]

    if not attrs:

      raise NotFittedError(msg % {'name': type(estimator).__name__})

E sklearn.exceptions.NotFittedError: This LGBMModel instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

../../../../anaconda3/envs/py38/lib/python3.8/site-packages/sklearn/utils/validation.py:1020: NotFittedError

Assertion failed

[github-actions]

This pull request has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/microsoft/LightGBM/issues including a reference to this.