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[MRG +1] DOC Modify documentation/examples for the new model_selection module #5566

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13 changes: 6 additions & 7 deletions doc/developers/contributing.rst
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Expand Up @@ -794,7 +794,7 @@ adheres to the scikit-learn interface and standards by running

The main motivation to make a class compatible to the scikit-learn estimator
interface might be that you want to use it together with model assessment and
selection tools such as :class:`grid_search.GridSearchCV`.
selection tools such as :class:`model_selection.GridSearchCV`.

For this to work, you need to implement the following interface.
If a dependency on scikit-learn is okay for your code,
Expand Down Expand Up @@ -856,7 +856,7 @@ implement the interface is::

Parameters and init
-------------------
As :class:`grid_search.GridSearchCV` uses ``set_params``
As :class:`model_selection.GridSearchCV` uses ``set_params``
to apply parameter setting to estimators,
it is essential that calling ``set_params`` has the same effect
as setting parameters using the ``__init__`` method.
Expand All @@ -874,9 +874,8 @@ trailing ``_`` is used to check if the estimator has been fitted.

Cloning
-------
For using :class:`grid_search.GridSearch` or any functionality of the
:mod:`cross_validation` module, an estimator must support the ``base.clone``
function to replicate an estimator.
For use with the :mod:`model_selection` module,
an estimator must support the ``base.clone`` function to replicate an estimator.
This can be done by providing a ``get_params`` method.
If ``get_params`` is present, then ``clone(estimator)`` will be an instance of
``type(estimator)`` on which ``set_params`` has been called with clones of
Expand All @@ -901,8 +900,8 @@ accepts an optional ``y``.
Estimator types
---------------
Some common functionality depends on the kind of estimator passed.
For example, cross-validation in :class:`grid_search.GridSearchCV` and
:func:`cross_validation.cross_val_score` defaults to being stratified when used
For example, cross-validation in :class:`model_selection.GridSearchCV` and
:func:`model_selection.cross_val_score` defaults to being stratified when used
on a classifier, but not otherwise. Similarly, scorers for average precision
that take a continuous prediction need to call ``decision_function`` for classifiers,
but ``predict`` for regressors. This distinction between classifiers and regressors
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1 change: 0 additions & 1 deletion doc/model_selection.rst
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Expand Up @@ -11,4 +11,3 @@ Model selection and evaluation
modules/grid_search
modules/model_evaluation
modules/model_persistence
modules/learning_curve
116 changes: 53 additions & 63 deletions doc/modules/classes.rst
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Expand Up @@ -144,45 +144,76 @@ Classes
covariance.graph_lasso


.. _cross_validation_ref:

:mod:`sklearn.cross_validation`: Cross Validation
=================================================
:mod:`sklearn.model_selection`: Model Selection
===============================================

.. automodule:: sklearn.cross_validation
.. automodule:: sklearn.model_selection
:no-members:
:no-inherited-members:

**User guide:** See the :ref:`cross_validation` section for further details.
**User guide:** See the :ref:`cross_validation`, :ref:`grid_search` and
:ref:`learning_curve` sections for further details.

Splitter Classes
----------------

.. currentmodule:: sklearn

.. autosummary::
:toctree: generated/
:template: class.rst

model_selection.KFold
model_selection.LabelKFold
model_selection.StratifiedKFold
model_selection.LeaveOneLabelOut
model_selection.LeavePLabelOut
model_selection.LeaveOneOut
model_selection.LeavePOut
model_selection.ShuffleSplit
model_selection.LabelShuffleSplit
model_selection.StratifiedShuffleSplit
model_selection.PredefinedSplit

Splitter Functions
------------------

.. currentmodule:: sklearn

.. autosummary::
:toctree: generated/
:template: function.rst

model_selection.train_test_split
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I would put this in the section above.

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This one has a function template and that one is a class template... So I renamed the headings to "splitter classes" and "splitter functions" I hope that is fine?


Hyper-parameter optimizers
--------------------------

.. currentmodule:: sklearn

.. autosummary::
:toctree: generated/
:template: class.rst

cross_validation.KFold
cross_validation.LabelKFold
cross_validation.LabelShuffleSplit
cross_validation.LeaveOneLabelOut
cross_validation.LeaveOneOut
cross_validation.LeavePLabelOut
cross_validation.LeavePOut
cross_validation.PredefinedSplit
cross_validation.ShuffleSplit
cross_validation.StratifiedKFold
cross_validation.StratifiedShuffleSplit
model_selection.GridSearchCV
model_selection.RandomizedSearchCV
model_selection.ParameterGrid
model_selection.ParameterSampler

Model validation
----------------

.. currentmodule:: sklearn

.. autosummary::
:toctree: generated/
:template: function.rst

cross_validation.train_test_split
cross_validation.cross_val_score
cross_validation.cross_val_predict
cross_validation.permutation_test_score
cross_validation.check_cv
model_selection.cross_val_score
model_selection.cross_val_predict
model_selection.permutation_test_score
model_selection.learning_curve
model_selection.validation_curve

.. _datasets_ref:

Expand Down Expand Up @@ -547,29 +578,6 @@ Kernels:
gaussian_process.kernels.CompoundKernel
gaussian_process.kernels.Hyperparameter

.. _grid_search_ref:

:mod:`sklearn.grid_search`: Grid Search
=======================================

.. automodule:: sklearn.grid_search
:no-members:
:no-inherited-members:

**User guide:** See the :ref:`grid_search` section for further details.

.. currentmodule:: sklearn

.. autosummary::
:toctree: generated/
:template: class.rst

grid_search.GridSearchCV
grid_search.ParameterGrid
grid_search.ParameterSampler
grid_search.RandomizedSearchCV


.. _isotonic_ref:

:mod:`sklearn.isotonic`: Isotonic regression
Expand Down Expand Up @@ -658,24 +666,6 @@ Kernels:
discriminant_analysis.QuadraticDiscriminantAnalysis


.. _learning_curve_ref:

:mod:`sklearn.learning_curve` Learning curve evaluation
=======================================================

.. automodule:: sklearn.learning_curve
:no-members:
:no-inherited-members:

.. currentmodule:: sklearn

.. autosummary::
:toctree: generated/
:template: function.rst

learning_curve.learning_curve
learning_curve.validation_curve

.. _linear_model_ref:

:mod:`sklearn.linear_model`: Generalized Linear Models
Expand Down
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