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DOC Sphinx Unexpected unindent warnings (scikit-learn#15130)
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‎doc/contents.rst

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.. include:: tune_toc.rst
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.. Places global toc into the sidebar
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:globalsidebartoc: True
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=================

‎doc/developers/index.rst

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.. Places global toc into the sidebar
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:globalsidebartoc: True
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.. _developers_guide:

‎doc/preface.rst

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useful for PDF output as this section is not linked from elsewhere.
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:globalsidebartoc: True
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.. _preface_menu:

‎doc/tutorial/index.rst

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.. Places global toc into the sidebar
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:globalsidebartoc: True
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.. _tutorial_menu:

‎doc/user_guide.rst

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.. Places global toc into the sidebar
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:globalsidebartoc: True
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.. title:: User guide: contents

‎examples/neighbors/approximate_nearest_neighbors.py

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compatibility reasons, one extra neighbor is computed when
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`mode == 'distance'`. Please note that we do the same in the proposed wrappers.
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Sample output:
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```
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Benchmarking on MNIST_2000:
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---------------------------
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AnnoyTransformer: 0.583 sec
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NMSlibTransformer: 0.321 sec
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KNeighborsTransformer: 1.225 sec
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TSNE with AnnoyTransformer: 4.903 sec
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TSNE with NMSlibTransformer: 5.009 sec
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TSNE with KNeighborsTransformer: 6.210 sec
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TSNE with internal NearestNeighbors: 6.365 sec
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Benchmarking on MNIST_10000:
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----------------------------
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AnnoyTransformer: 4.457 sec
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NMSlibTransformer: 2.080 sec
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KNeighborsTransformer: 30.680 sec
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TSNE with AnnoyTransformer: 30.225 sec
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TSNE with NMSlibTransformer: 43.295 sec
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TSNE with KNeighborsTransformer: 64.845 sec
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TSNE with internal NearestNeighbors: 64.984 sec
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```
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Sample output::
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Benchmarking on MNIST_2000:
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---------------------------
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AnnoyTransformer: 0.583 sec
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NMSlibTransformer: 0.321 sec
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KNeighborsTransformer: 1.225 sec
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TSNE with AnnoyTransformer: 4.903 sec
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TSNE with NMSlibTransformer: 5.009 sec
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TSNE with KNeighborsTransformer: 6.210 sec
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TSNE with internal NearestNeighbors: 6.365 sec
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Benchmarking on MNIST_10000:
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----------------------------
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AnnoyTransformer: 4.457 sec
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NMSlibTransformer: 2.080 sec
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KNeighborsTransformer: 30.680 sec
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TSNE with AnnoyTransformer: 30.225 sec
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TSNE with NMSlibTransformer: 43.295 sec
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TSNE with KNeighborsTransformer: 64.845 sec
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TSNE with internal NearestNeighbors: 64.984 sec
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"""
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# Author: Tom Dupre la Tour
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#

‎sklearn/ensemble/_stacking.py

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`'predict_proba'`, `'decision_function'` or `'predict'` in that
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order.
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* otherwise, one of `'predict_proba'`, `'decision_function'` or
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`'predict'`. If the method is not implemented by the estimator, it
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will raise an error.
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`'predict'`. If the method is not implemented by the estimator, it
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will raise an error.
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n_jobs : int, default=None
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The number of jobs to run in parallel all `estimators` `fit`.

‎sklearn/ensemble/forest.py

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max_samples : int or float, default=None
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If bootstrap is True, the number of samples to draw from X
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to train each base estimator.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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.. versionadded:: 0.22
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max_samples : int or float, default=None
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If bootstrap is True, the number of samples to draw from X
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to train each base estimator.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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.. versionadded:: 0.22
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max_samples : int or float, default=None
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If bootstrap is True, the number of samples to draw from X
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to train each base estimator.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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max_samples : int or float, default=None
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If bootstrap is True, the number of samples to draw from X
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to train each base estimator.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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to train each base estimator.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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`max_samples` should be in the interval `(0, 1)`.
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.. versionadded:: 0.22
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‎sklearn/ensemble/partial_dependence.py

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----------
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gbrt : BaseGradientBoosting
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A fitted gradient boosting model.
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X : array-like of shape (n_samples, n_features)
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The data on which ``gbrt`` was trained.
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‎sklearn/inspection/partial_dependence.py

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Feature names corrsponding to the indicies in ``features``.
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target_idx : int
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- In a multiclass setting, specifies the class for which the PDPs
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should be computed. Note that for binary classification, the
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positive class (index 1) is always used.
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- In a multioutput setting, specifies the task for which the PDPs
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should be computed.
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Ignored in binary classification or classical regression settings.
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pdp_lim : dict

‎sklearn/manifold/isomap.py

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self.embedding_ = self.kernel_pca_.fit_transform(G)
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@property
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@deprecated("Attribute training_data_ was deprecated in version 0.22 and "
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"will be removed in 0.24.")
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@deprecated("Attribute `training_data_` was deprecated in version 0.22 and"
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" will be removed in 0.24.")
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def training_data_(self):
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check_is_fitted(self)
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return self.nbrs_._fit_X

‎sklearn/utils/__init__.py

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X : array-like, sparse-matrix, list, pandas.DataFrame, pandas.Series
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Data from which to sample rows, items or columns. `list` are only
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supported when `axis=0`.
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indices : bool, int, str, slice, array-like
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- If `axis=0`, boolean and integer array-like, integer slice,
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and scalar integer are supported.
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- If `axis=1`:
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- to select a single column, `indices` can be of `int` type for
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all `X` types and `str` only for dataframe. The selected subset
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these containers can be one of the following: `int`, 'bool' and
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The selected subset will be 2D.
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axis : int, default=0
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The axis along which `X` will be subsampled. `axis=0` will select
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rows while `axis=1` will select columns.

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