@angela97lin ist meine Erklärung sinnvoll / gab es einen Grund, warum wir dies beim Einrichten des Stapelns nicht getan haben? :)

@dsherry Ich finde deine Erklärung sinnvoll! IIRC als wir Stacking einrichteten und versuchten, es performanter zu machen / das Stacking schneller laufen zu lassen, wollten wir standardmäßig etwas verwenden, das nicht zu viele Folds hat - daher die self._default_cv(n_splits=3, random_state=random_state) Zeile, in der wir die Standardwert von scikit-learn und hartcodiert n_splits auf 3.

Ich habe mich noch etwas genauer damit befasst und versucht, die von AutoML verwendete Datenaufteilungsmethode mit der gestapelten Ensemble-Komponente zu verweben. Ich bin jedoch auf dieses Problem gestoßen (nachdem ich die API-Updates behoben hatte, die erforderlich sind, um unsere TrainingValidationSplit Klasse zum Laufen zu bringen):

estimator = WrappedSKClassifier(pipeline=LogisticRegressionBinaryPipeline(parameters={'Imputer':{'categorical_impute_strategy': 'm...Logistic Regression Classifier':{'penalty': 'l2', 'C': 1.0, 'n_jobs': -1, 'multi_class': 'auto', 'solver': 'lbfgs'},}))
X =            0         1         2         3         4
0   0.965469  0.041236  0.028701  0.659165  0.213375
1   0.043831...978  0.079577
48  0.376344  0.920154  0.314640  0.180086  0.197598
49  0.682661  0.046529  0.400513  0.412513  0.751464
y = array([1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 0])

    <strong i="7">@_deprecate_positional_args</strong>
    def cross_val_predict(estimator, X, y=None, *, groups=None, cv=None,
                          n_jobs=None, verbose=0, fit_params=None,
                          pre_dispatch='2*n_jobs', method='predict'):
        """Generate cross-validated estimates for each input data point

        The data is split according to the cv parameter. Each sample belongs
        to exactly one test set, and its prediction is computed with an
        estimator fitted on the corresponding training set.

        Passing these predictions into an evaluation metric may not be a valid
        way to measure generalization performance. Results can differ from
        :func:`cross_validate` and :func:`cross_val_score` unless all tests sets
        have equal size and the metric decomposes over samples.

        Read more in the :ref:`User Guide <cross_validation>`.

        Parameters
        ----------
        estimator : estimator object implementing 'fit' and 'predict'
            The object to use to fit the data.

        X : array-like of shape (n_samples, n_features)
            The data to fit. Can be, for example a list, or an array at least 2d.

        y : array-like of shape (n_samples,) or (n_samples, n_outputs), \
                default=None
            The target variable to try to predict in the case of
            supervised learning.

        groups : array-like of shape (n_samples,), default=None
            Group labels for the samples used while splitting the dataset into
            train/test set. Only used in conjunction with a "Group" :term:`cv`
            instance (e.g., :class:`GroupKFold`).

        cv : int, cross-validation generator or an iterable, default=None
            Determines the cross-validation splitting strategy.
            Possible inputs for cv are:

            - None, to use the default 5-fold cross validation,
            - int, to specify the number of folds in a `(Stratified)KFold`,
            - :term:`CV splitter`,
            - An iterable yielding (train, test) splits as arrays of indices.

            For int/None inputs, if the estimator is a classifier and ``y`` is
            either binary or multiclass, :class:`StratifiedKFold` is used. In all
            other cases, :class:`KFold` is used.

            Refer :ref:`User Guide <cross_validation>` for the various
            cross-validation strategies that can be used here.

            .. versionchanged:: 0.22
                ``cv`` default value if None changed from 3-fold to 5-fold.

        n_jobs : int, default=None
            Number of jobs to run in parallel. Training the estimator and
            predicting are parallelized over the cross-validation splits.
            ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
            ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
            for more details.

        verbose : int, default=0
            The verbosity level.

        fit_params : dict, defualt=None
            Parameters to pass to the fit method of the estimator.

        pre_dispatch : int or str, default='2*n_jobs'
            Controls the number of jobs that get dispatched during parallel
            execution. Reducing this number can be useful to avoid an
            explosion of memory consumption when more jobs get dispatched
            than CPUs can process. This parameter can be:

                - None, in which case all the jobs are immediately
                  created and spawned. Use this for lightweight and
                  fast-running jobs, to avoid delays due to on-demand
                  spawning of the jobs

                - An int, giving the exact number of total jobs that are
                  spawned

                - A str, giving an expression as a function of n_jobs,
                  as in '2*n_jobs'

        method : {'predict', 'predict_proba', 'predict_log_proba', \
                  'decision_function'}, default='predict'
            The method to be invoked by `estimator`.

        Returns
        -------
        predictions : ndarray
            This is the result of calling `method`. Shape:

                - When `method` is 'predict' and in special case where `method` is
                  'decision_function' and the target is binary: (n_samples,)
                - When `method` is one of {'predict_proba', 'predict_log_proba',
                  'decision_function'} (unless special case above):
                  (n_samples, n_classes)
                - If `estimator` is :term:`multioutput`, an extra dimension
                  'n_outputs' is added to the end of each shape above.

        See Also
        --------
        cross_val_score : Calculate score for each CV split.
        cross_validate : Calculate one or more scores and timings for each CV
            split.

        Notes
        -----
        In the case that one or more classes are absent in a training portion, a
        default score needs to be assigned to all instances for that class if
        ``method`` produces columns per class, as in {'decision_function',
        'predict_proba', 'predict_log_proba'}.  For ``predict_proba`` this value is
        0.  In order to ensure finite output, we approximate negative infinity by
        the minimum finite float value for the dtype in other cases.

        Examples
        --------
        >>> from sklearn import datasets, linear_model
        >>> from sklearn.model_selection import cross_val_predict
        >>> diabetes = datasets.load_diabetes()
        >>> X = diabetes.data[:150]
        >>> y = diabetes.target[:150]
        >>> lasso = linear_model.Lasso()
        >>> y_pred = cross_val_predict(lasso, X, y, cv=3)
        """
        X, y, groups = indexable(X, y, groups)

        cv = check_cv(cv, y, classifier=is_classifier(estimator))
        splits = list(cv.split(X, y, groups))

        test_indices = np.concatenate([test for _, test in splits])
        if not _check_is_permutation(test_indices, _num_samples(X)):
>           raise ValueError('cross_val_predict only works for partitions')
E           ValueError: cross_val_predict only works for partitions

../venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:845: ValueError

Dies ist ein Fehler, der ausgegeben wird, wenn wir versuchen, Folgendes aufzurufen:

clf = StackedEnsembleClassifier(input_pipelines=[logistic_regression_binary_pipeline_class(parameters={})], cv=TrainingValidationSplit())
    clf.fit(X, y)

Der Grund dafür ist, dass scikit-learn validiert, dass der übergebene Lebenslauf tatsächlich eine Kreuzvalidierungsmethode ist; es ist nicht zufrieden mit einzelnen Aufteilungen wie TrainingValidationSplit denen einige der Daten nie in den Testdaten enthalten sind (da es nur eine Aufteilung gibt).

Daher denke ich, dass der beste Plan im Moment darin besteht, scikit-learn 0.24 zu unterstützen und den Standard-Cv shuffle=True festzulegen. Wir können dies überdenken, wenn wir es für sinnvoll erachten. Gedanken, @dsherry?

Verwandte: https://stackoverflow.com/questions/41753795/sklearn-timeseriessplit-cross-val-predict-only-works-for-partitions

1593 sollte von diesem Problem nicht mehr blockiert werden, da das, was für 0.24.0 notwendig war, in #1613 behoben sein sollte.