.. _develop:

==================================
Developing scikit-learn estimators
==================================

Whether you are proposing an estimator for inclusion in scikit-learn,
developing a separate package compatible with scikit-learn, or
implementing custom components for your own projects, this chapter
details how to develop objects that safely interact with scikit-learn
Pipelines and model selection tools.

.. currentmodule:: sklearn

.. _api_overview:

APIs of scikit-learn objects
============================

To have a uniform API, we try to have a common basic API for all the
objects. In addition, to avoid the proliferation of framework code, we
try to adopt simple conventions and limit to a minimum the number of
methods an object must implement.

Elements of the scikit-learn API are described more definitively in the
:ref:`glossary`.

Different objects
-----------------

The main objects in scikit-learn are (one class can implement
multiple interfaces):

:Estimator:

    The base object, implements a ``fit`` method to learn from data, either::

      estimator = estimator.fit(data, targets)

    or::

      estimator = estimator.fit(data)

:Predictor:

    For supervised learning, or some unsupervised problems, implements::

      prediction = predictor.predict(data)

    Classification algorithms usually also offer a way to quantify certainty
    of a prediction, either using ``decision_function`` or ``predict_proba``::

      probability = predictor.predict_proba(data)

:Transformer:

    For filtering or modifying the data, in a supervised or unsupervised
    way, implements::

      new_data = transformer.transform(data)

    When fitting and transforming can be performed much more efficiently
    together than separately, implements::

      new_data = transformer.fit_transform(data)

:Model:

    A model that can give a `goodness of fit <https://en.wikipedia.org/wiki/Goodness_of_fit>`_
    measure or a likelihood of unseen data, implements (higher is better)::

      score = model.score(data)

Estimators
----------

The API has one predominant object: the estimator. An estimator is an
object that fits a model based on some training data and is capable of
inferring some properties on new data. It can be, for instance, a
classifier or a regressor. All estimators implement the fit method::

    estimator.fit(X, y)

All built-in estimators also have a ``set_params`` method, which sets
data-independent parameters (overriding previous parameter values passed
to ``__init__``).

All estimators in the main scikit-learn codebase should inherit from
``sklearn.base.BaseEstimator``.

Instantiation
^^^^^^^^^^^^^

This concerns the creation of an object. The object's ``__init__`` method
might accept constants as arguments that determine the estimator's behavior
(like the C constant in SVMs). It should not, however, take the actual training
data as an argument, as this is left to the ``fit()`` method::

    clf2 = SVC(C=2.3)
    clf3 = SVC([[1, 2], [2, 3]], [-1, 1]) # WRONG!


The arguments accepted by ``__init__`` should all be keyword arguments
with a default value. In other words, a user should be able to instantiate
an estimator without passing any arguments to it. The arguments should all
correspond to hyperparameters describing the model or the optimisation
problem the estimator tries to solve. These initial arguments (or parameters)
are always remembered by the estimator.
Also note that they should not be documented under the "Attributes" section,
but rather under the "Parameters" section for that estimator.

In addition, **every keyword argument accepted by** ``__init__`` **should
correspond to an attribute on the instance**. Scikit-learn relies on this to
find the relevant attributes to set on an estimator when doing model selection.

To summarize, an ``__init__`` should look like::

    def __init__(self, param1=1, param2=2):
        self.param1 = param1
        self.param2 = param2

There should be no logic, not even input validation,
and the parameters should not be changed.
The corresponding logic should be put where the parameters are used,
typically in ``fit``.
The following is wrong::

    def __init__(self, param1=1, param2=2, param3=3):
        # WRONG: parameters should not be modified
        if param1 > 1:
            param2 += 1
        self.param1 = param1
        # WRONG: the object's attributes should have exactly the name of
        # the argument in the constructor
        self.param3 = param2

The reason for postponing the validation is that the same validation
would have to be performed in ``set_params``,
which is used in algorithms like ``GridSearchCV``.

Fitting
^^^^^^^

The next thing you will probably want to do is to estimate some
parameters in the model. This is implemented in the ``fit()`` method.

The ``fit()`` method takes the training data as arguments, which can be one
array in the case of unsupervised learning, or two arrays in the case
of supervised learning.

Note that the model is fitted using ``X`` and ``y``, but the object holds no
reference to ``X`` and ``y``. There are, however, some exceptions to this, as in
the case of precomputed kernels where this data must be stored for use by
the predict method.

============= ======================================================
Parameters
============= ======================================================
X             array-like of shape (n_samples, n_features)

y             array-like of shape (n_samples,)

kwargs        optional data-dependent parameters
============= ======================================================

``X.shape[0]`` should be the same as ``y.shape[0]``. If this requisite
is not met, an exception of type ``ValueError`` should be raised.

``y`` might be ignored in the case of unsupervised learning. However, to
make it possible to use the estimator as part of a pipeline that can
mix both supervised and unsupervised transformers, even unsupervised
estimators need to accept a ``y=None`` keyword argument in
the second position that is just ignored by the estimator.
For the same reason, ``fit_predict``, ``fit_transform``, ``score``
and ``partial_fit`` methods need to accept a ``y`` argument in
the second place if they are implemented.

The method should return the object (``self``). This pattern is useful
to be able to implement quick one liners in an IPython session such as::

  y_predicted = SVC(C=100).fit(X_train, y_train).predict(X_test)

Depending on the nature of the algorithm, ``fit`` can sometimes also
accept additional keywords arguments. However, any parameter that can
have a value assigned prior to having access to the data should be an
``__init__`` keyword argument. **fit parameters should be restricted
to directly data dependent variables**. For instance a Gram matrix or
an affinity matrix which are precomputed from the data matrix ``X`` are
data dependent. A tolerance stopping criterion ``tol`` is not directly
data dependent (although the optimal value according to some scoring
function probably is).

When ``fit`` is called, any previous call to ``fit`` should be ignored. In
general, calling ``estimator.fit(X1)`` and then ``estimator.fit(X2)`` should
be the same as only calling ``estimator.fit(X2)``. However, this may not be
true in practice when ``fit`` depends on some random process, see
:term:`random_state`. Another exception to this rule is when the
hyper-parameter ``warm_start`` is set to ``True`` for estimators that
support it. ``warm_start=True`` means that the previous state of the
trainable parameters of the estimator are reused instead of using the
default initialization strategy.

Estimated Attributes
^^^^^^^^^^^^^^^^^^^^

Attributes that have been estimated from the data must always have a name
ending with trailing underscore, for example the coefficients of
some regression estimator would be stored in a ``coef_`` attribute after
``fit`` has been called.

The estimated attributes are expected to be overridden when you call ``fit``
a second time.

Optional Arguments
^^^^^^^^^^^^^^^^^^

In iterative algorithms, the number of iterations should be specified by
an integer called ``n_iter``.

Universal attributes
^^^^^^^^^^^^^^^^^^^^

Estimators that expect tabular input should set a `n_features_in_`
attribute at `fit` time to indicate the number of features that the estimator
expects for subsequent calls to `predict` or `transform`.
See
`SLEP010
<https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep010/proposal.html>`_
for details.

.. _rolling_your_own_estimator:

Rolling your own estimator
==========================
If you want to implement a new estimator that is scikit-learn-compatible,
whether it is just for you or for contributing it to scikit-learn, there are
several internals of scikit-learn that you should be aware of in addition to
the scikit-learn API outlined above. You can check whether your estimator
adheres to the scikit-learn interface and standards by running
:func:`~sklearn.utils.estimator_checks.check_estimator` on an instance. The
:func:`~sklearn.utils.estimator_checks.parametrize_with_checks` pytest
decorator can also be used (see its docstring for details and possible
interactions with `pytest`)::

  >>> from sklearn.utils.estimator_checks import check_estimator
  >>> from sklearn.svm import LinearSVC
  >>> check_estimator(LinearSVC())  # passes

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 evaluation and
selection tools such as :class:`model_selection.GridSearchCV` and
:class:`pipeline.Pipeline`.

Before detailing the required interface below, we describe two ways to achieve
the correct interface more easily.

.. topic:: Project template:

    We provide a `project template <https://github.com/scikit-learn-contrib/project-template/>`_
    which helps in the creation of Python packages containing scikit-learn compatible estimators.
    It provides:

    * an initial git repository with Python package directory structure
    * a template of a scikit-learn estimator
    * an initial test suite including use of ``check_estimator``
    * directory structures and scripts to compile documentation and example
      galleries
    * scripts to manage continuous integration (testing on Linux and Windows)
    * instructions from getting started to publishing on `PyPi <https://pypi.org/>`_

.. topic:: ``BaseEstimator`` and mixins:

    We tend to use "duck typing", so building an estimator which follows
    the API suffices for compatibility, without needing to inherit from or
    even import any scikit-learn classes.

    However, if a dependency on scikit-learn is acceptable in your code,
    you can prevent a lot of boilerplate code
    by deriving a class from ``BaseEstimator``
    and optionally the mixin classes in ``sklearn.base``.
    For example, below is a custom classifier, with more examples included
    in the scikit-learn-contrib
    `project template <https://github.com/scikit-learn-contrib/project-template/blob/master/skltemplate/_template.py>`__.

      >>> import numpy as np
      >>> from sklearn.base import BaseEstimator, ClassifierMixin
      >>> from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
      >>> from sklearn.utils.multiclass import unique_labels
      >>> from sklearn.metrics import euclidean_distances
      >>> class TemplateClassifier(BaseEstimator, ClassifierMixin):
      ...
      ...     def __init__(self, demo_param='demo'):
      ...         self.demo_param = demo_param
      ...
      ...     def fit(self, X, y):
      ...
      ...         # Check that X and y have correct shape
      ...         X, y = check_X_y(X, y)
      ...         # Store the classes seen during fit
      ...         self.classes_ = unique_labels(y)
      ...
      ...         self.X_ = X
      ...         self.y_ = y
      ...         # Return the classifier
      ...         return self
      ...
      ...     def predict(self, X):
      ...
      ...         # Check if fit has been called
      ...         check_is_fitted(self)
      ...
      ...         # Input validation
      ...         X = check_array(X)
      ...
      ...         closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
      ...         return self.y_[closest]


get_params and set_params
-------------------------
All scikit-learn estimators have ``get_params`` and ``set_params`` functions.
The ``get_params`` function takes no arguments and returns a dict of the
``__init__`` parameters of the estimator, together with their values.

It must take one keyword argument, ``deep``, which receives a boolean value
that determines whether the method should return the parameters of
sub-estimators (for most estimators, this can be ignored). The default value
for ``deep`` should be `True`. For instance considering the following
estimator::

    >>> from sklearn.base import BaseEstimator
    >>> from sklearn.linear_model import LogisticRegression
    >>> class MyEstimator(BaseEstimator):
    ...     def __init__(self, subestimator=None, my_extra_param="random"):
    ...         self.subestimator = subestimator
    ...         self.my_extra_param = my_extra_param

The parameter `deep` will control whether or not the parameters of the
`subestimator` should be reported. Thus when `deep=True`, the output will be::

    >>> my_estimator = MyEstimator(subestimator=LogisticRegression())
    >>> for param, value in my_estimator.get_params(deep=True).items():
    ...     print(f"{param} -> {value}")
    my_extra_param -> random
    subestimator__C -> 1.0
    subestimator__class_weight -> None
    subestimator__dual -> False
    subestimator__fit_intercept -> True
    subestimator__intercept_scaling -> 1
    subestimator__l1_ratio -> None
    subestimator__max_iter -> 100
    subestimator__multi_class -> auto
    subestimator__n_jobs -> None
    subestimator__penalty -> l2
    subestimator__random_state -> None
    subestimator__solver -> lbfgs
    subestimator__tol -> 0.0001
    subestimator__verbose -> 0
    subestimator__warm_start -> False
    subestimator -> LogisticRegression()

Often, the `subestimator` has a name (as e.g. named steps in a
:class:`~sklearn.pipeline.Pipeline` object), in which case the key should
become `<name>__C`, `<name>__class_weight`, etc.

While when `deep=False`, the output will be::

    >>> for param, value in my_estimator.get_params(deep=False).items():
    ...     print(f"{param} -> {value}")
    my_extra_param -> random
    subestimator -> LogisticRegression()

On the other hand, ``set_params`` takes the parameters of ``__init__``
as keyword arguments, unpacks them into a dict of the form
``'parameter': value`` and sets the parameters of the estimator using this dict.
Return value must be the estimator itself.

While the ``get_params`` mechanism is not essential (see :ref:`cloning` below),
the ``set_params`` function is necessary as it is used to set parameters during
grid searches.

The easiest way to implement these functions, and to get a sensible
``__repr__`` method, is to inherit from ``sklearn.base.BaseEstimator``. If you
do not want to make your code dependent on scikit-learn, the easiest way to
implement the interface is::

    def get_params(self, deep=True):
        # suppose this estimator has parameters "alpha" and "recursive"
        return {"alpha": self.alpha, "recursive": self.recursive}

    def set_params(self, **parameters):
        for parameter, value in parameters.items():
            setattr(self, parameter, value)
        return self


Parameters and init
-------------------
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.
The easiest and recommended way to accomplish this is to
**not do any parameter validation in** ``__init__``.
All logic behind estimator parameters,
like translating string arguments into functions, should be done in ``fit``.

Also it is expected that parameters with trailing ``_`` are **not to be set
inside the** ``__init__`` **method**. All and only the public attributes set by
fit have a trailing ``_``. As a result the existence of parameters with
trailing ``_`` is used to check if the estimator has been fitted.

.. _cloning:

Cloning
-------
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
the result of ``estimator.get_params()``.

Objects that do not provide this method will be deep-copied
(using the Python standard function ``copy.deepcopy``)
if ``safe=False`` is passed to ``clone``.

Estimators can customize the behavior of :func:`base.clone` by defining a
`__sklearn_clone__` method. `__sklearn_clone__` must return an instance of the
estimator. `__sklearn_clone__` is useful when an estimator needs to hold on to
some state when :func:`base.clone` is called on the estimator. For example, a
frozen meta-estimator for transformers can be defined as follows::

    class FrozenTransformer(BaseEstimator):
        def __init__(self, fitted_transformer):
            self.fitted_transformer = fitted_transformer

        def __getattr__(self, name):
            # `fitted_transformer`'s attributes are now accessible
            return getattr(self.fitted_transformer, name)

        def __sklearn_clone__(self):
            return self

        def fit(self, X, y):
            # Fitting does not change the state of the estimator
            return self

        def fit_transform(self, X, y=None):
            # fit_transform only transforms the data
            return self.fitted_transformer.transform(X, y)

Pipeline compatibility
----------------------
For an estimator to be usable together with ``pipeline.Pipeline`` in any but the
last step, it needs to provide a ``fit`` or ``fit_transform`` function.
To be able to evaluate the pipeline on any data but the training set,
it also needs to provide a ``transform`` function.
There are no special requirements for the last step in a pipeline, except that
it has a ``fit`` function. All ``fit`` and ``fit_transform`` functions must
take arguments ``X, y``, even if y is not used. Similarly, for ``score`` to be
usable, the last step of the pipeline needs to have a ``score`` function that
accepts an optional ``y``.

Estimator types
---------------
Some common functionality depends on the kind of estimator passed.
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
is implemented using the ``_estimator_type`` attribute, which takes a string value.
It should be ``"classifier"`` for classifiers and ``"regressor"`` for
regressors and ``"clusterer"`` for clustering methods, to work as expected.
Inheriting from ``ClassifierMixin``, ``RegressorMixin`` or ``ClusterMixin``
will set the attribute automatically.  When a meta-estimator needs to distinguish
among estimator types, instead of checking ``_estimator_type`` directly, helpers
like :func:`base.is_classifier` should be used.

Specific models
---------------

Classifiers should accept ``y`` (target) arguments to ``fit`` that are
sequences (lists, arrays) of either strings or integers.  They should not
assume that the class labels are a contiguous range of integers; instead, they
should store a list of classes in a ``classes_`` attribute or property.  The
order of class labels in this attribute should match the order in which
``predict_proba``, ``predict_log_proba`` and ``decision_function`` return their
values.  The easiest way to achieve this is to put::

    self.classes_, y = np.unique(y, return_inverse=True)

in ``fit``.  This returns a new ``y`` that contains class indexes, rather than
labels, in the range [0, ``n_classes``).

A classifier's ``predict`` method should return
arrays containing class labels from ``classes_``.
In a classifier that implements ``decision_function``,
this can be achieved with::

    def predict(self, X):
        D = self.decision_function(X)
        return self.classes_[np.argmax(D, axis=1)]

In linear models, coefficients are stored in an array called ``coef_``, and the
independent term is stored in ``intercept_``.  ``sklearn.linear_model._base``
contains a few base classes and mixins that implement common linear model
patterns.

The :mod:`sklearn.utils.multiclass` module contains useful functions
for working with multiclass and multilabel problems.

.. _estimator_tags:

Estimator Tags
--------------
.. warning::

    The estimator tags are experimental and the API is subject to change.

Scikit-learn introduced estimator tags in version 0.21. These are annotations
of estimators that allow programmatic inspection of their capabilities, such as
sparse matrix support, supported output types and supported methods. The
estimator tags are a dictionary returned by the method ``_get_tags()``. These
tags are used in the common checks run by the
:func:`~sklearn.utils.estimator_checks.check_estimator` function and the
:func:`~sklearn.utils.estimator_checks.parametrize_with_checks` decorator.
Tags determine which checks to run and what input data is appropriate. Tags
can depend on estimator parameters or even system architecture and can in
general only be determined at runtime.

The current set of estimator tags are:

allow_nan (default=False)
    whether the estimator supports data with missing values encoded as np.nan

array_api_support (default=False)
    whether the estimator supports Array API compatible inputs.

binary_only (default=False)
    whether estimator supports binary classification but lacks multi-class
    classification support.

multilabel (default=False)
    whether the estimator supports multilabel output

multioutput (default=False)
    whether a regressor supports multi-target outputs or a classifier supports
    multi-class multi-output.

multioutput_only (default=False)
    whether estimator supports only multi-output classification or regression.

no_validation (default=False)
    whether the estimator skips input-validation. This is only meant for
    stateless and dummy transformers!

non_deterministic (default=False)
    whether the estimator is not deterministic given a fixed ``random_state``

pairwise (default=False)
    This boolean attribute indicates whether the data (`X`) :term:`fit` and
    similar methods consists of pairwise measures over samples rather than a
    feature representation for each sample.  It is usually `True` where an
    estimator has a `metric` or `affinity` or `kernel` parameter with value
    'precomputed'. Its primary purpose is to support a :term:`meta-estimator`
    or a cross validation procedure that extracts a sub-sample of data intended
    for a pairwise estimator, where the data needs to be indexed on both axes.
    Specifically, this tag is used by
    :func:`~sklearn.utils.metaestimators._safe_split` to slice rows and
    columns.

preserves_dtype (default=``[np.float64]``)
    applies only on transformers. It corresponds to the data types which will
    be preserved such that `X_trans.dtype` is the same as `X.dtype` after
    calling `transformer.transform(X)`. If this list is empty, then the
    transformer is not expected to preserve the data type. The first value in
    the list is considered as the default data type, corresponding to the data
    type of the output when the input data type is not going to be preserved.

poor_score (default=False)
    whether the estimator fails to provide a "reasonable" test-set score, which
    currently for regression is an R2 of 0.5 on ``make_regression(n_samples=200,
    n_features=10, n_informative=1, bias=5.0, noise=20, random_state=42)``, and
    for classification an accuracy of 0.83 on
    ``make_blobs(n_samples=300, random_state=0)``. These datasets and values
    are based on current estimators in sklearn and might be replaced by
    something more systematic.

requires_fit (default=True)
    whether the estimator requires to be fitted before calling one of
    `transform`, `predict`, `predict_proba`, or `decision_function`.

requires_positive_X (default=False)
    whether the estimator requires positive X.

requires_y (default=False)
    whether the estimator requires y to be passed to `fit`, `fit_predict` or
    `fit_transform` methods. The tag is True for estimators inheriting from
    `~sklearn.base.RegressorMixin` and `~sklearn.base.ClassifierMixin`.

requires_positive_y (default=False)
    whether the estimator requires a positive y (only applicable for regression).

_skip_test (default=False)
    whether to skip common tests entirely. Don't use this unless you have a
    *very good* reason.

_xfail_checks (default=False)
    dictionary ``{check_name: reason}`` of common checks that will be marked
    as `XFAIL` for pytest, when using
    :func:`~sklearn.utils.estimator_checks.parametrize_with_checks`. These
    checks will be simply ignored and not run by
    :func:`~sklearn.utils.estimator_checks.check_estimator`, but a
    `SkipTestWarning` will be raised.
    Don't use this unless there is a *very good* reason for your estimator
    not to pass the check.
    Also note that the usage of this tag is highly subject to change because
    we are trying to make it more flexible: be prepared for breaking changes
    in the future.

stateless (default=False)
    whether the estimator needs access to data for fitting. Even though an
    estimator is stateless, it might still need a call to ``fit`` for
    initialization.

X_types (default=['2darray'])
    Supported input types for X as list of strings. Tests are currently only
    run if '2darray' is contained in the list, signifying that the estimator
    takes continuous 2d numpy arrays as input. The default value is
    ['2darray']. Other possible types are ``'string'``, ``'sparse'``,
    ``'categorical'``, ``dict``, ``'1dlabels'`` and ``'2dlabels'``. The goal is
    that in the future the supported input type will determine the data used
    during testing, in particular for ``'string'``, ``'sparse'`` and
    ``'categorical'`` data. For now, the test for sparse data do not make use
    of the ``'sparse'`` tag.

It is unlikely that the default values for each tag will suit the needs of your
specific estimator. Additional tags can be created or default tags can be
overridden by defining a `_more_tags()` method which returns a dict with the
desired overridden tags or new tags. For example::

    class MyMultiOutputEstimator(BaseEstimator):

        def _more_tags(self):
            return {'multioutput_only': True,
                    'non_deterministic': True}

Any tag that is not in `_more_tags()` will just fall-back to the default values
documented above.

Even if it is not recommended, it is possible to override the method
`_get_tags()`. Note however that **all tags must be present in the dict**. If
any of the keys documented above is not present in the output of `_get_tags()`,
an error will occur.

In addition to the tags, estimators also need to declare any non-optional
parameters to ``__init__`` in the ``_required_parameters`` class attribute,
which is a list or tuple.  If ``_required_parameters`` is only
``["estimator"]`` or ``["base_estimator"]``, then the estimator will be
instantiated with an instance of ``LogisticRegression`` (or
``RidgeRegression`` if the estimator is a regressor) in the tests. The choice
of these two models is somewhat idiosyncratic but both should provide robust
closed-form solutions.

.. _developer_api_set_output:

Developer API for `set_output`
==============================

With
`SLEP018 <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html>`__,
scikit-learn introduces the `set_output` API for configuring transformers to
output pandas DataFrames. The `set_output` API is automatically defined if the
transformer defines :term:`get_feature_names_out` and subclasses
:class:`base.TransformerMixin`. :term:`get_feature_names_out` is used to get the
column names of pandas output.

:class:`base.OneToOneFeatureMixin` and
:class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for defining
:term:`get_feature_names_out`. :class:`base.OneToOneFeatureMixin` is useful when
the transformer has a one-to-one correspondence between input features and output
features, such as :class:`~preprocessing.StandardScaler`.
:class:`base.ClassNamePrefixFeaturesOutMixin` is useful when the transformer
needs to generate its own feature names out, such as :class:`~decomposition.PCA`.

You can opt-out of the `set_output` API by setting `auto_wrap_output_keys=None`
when defining a custom subclass::

    class MyTransformer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):

        def fit(self, X, y=None):
            return self
        def transform(self, X, y=None):
            return X
        def get_feature_names_out(self, input_features=None):
            ...

The default value for `auto_wrap_output_keys` is `("transform",)`, which automatically
wraps `fit_transform` and `transform`. The `TransformerMixin` uses the
`__init_subclass__` mechanism to consume `auto_wrap_output_keys` and pass all other
keyword arguments to it's super class. Super classes' `__init_subclass__` should
**not** depend on `auto_wrap_output_keys`.

For transformers that return multiple arrays in `transform`, auto wrapping will
only wrap the first array and not alter the other arrays.

See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
for an example on how to use the API.

.. _coding-guidelines:

Coding guidelines
=================

The following are some guidelines on how new code should be written for
inclusion in scikit-learn, and which may be appropriate to adopt in external
projects. Of course, there are special cases and there will be exceptions to
these rules. However, following these rules when submitting new code makes
the review easier so new code can be integrated in less time.

Uniformly formatted code makes it easier to share code ownership. The
scikit-learn project tries to closely follow the official Python guidelines
detailed in `PEP8 <https://www.python.org/dev/peps/pep-0008>`_ that
detail how code should be formatted and indented. Please read it and
follow it.

In addition, we add the following guidelines:

* Use underscores to separate words in non class names: ``n_samples``
  rather than ``nsamples``.

* Avoid multiple statements on one line. Prefer a line return after
  a control flow statement (``if``/``for``).

* Use relative imports for references inside scikit-learn.

* Unit tests are an exception to the previous rule;
  they should use absolute imports, exactly as client code would.
  A corollary is that, if ``sklearn.foo`` exports a class or function
  that is implemented in ``sklearn.foo.bar.baz``,
  the test should import it from ``sklearn.foo``.

* **Please don't use** ``import *`` **in any case**. It is considered harmful
  by the `official Python recommendations
  <https://docs.python.org/3.1/howto/doanddont.html#at-module-level>`_.
  It makes the code harder to read as the origin of symbols is no
  longer explicitly referenced, but most important, it prevents
  using a static analysis tool like `pyflakes
  <https://divmod.readthedocs.io/en/latest/products/pyflakes.html>`_ to automatically
  find bugs in scikit-learn.

* Use the `numpy docstring standard
  <https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard>`_
  in all your docstrings.


A good example of code that we like can be found `here
<https://gist.github.com/nateGeorge/5455d2c57fb33c1ae04706f2dc4fee01>`_.

Input validation
----------------

.. currentmodule:: sklearn.utils

The module :mod:`sklearn.utils` contains various functions for doing input
validation and conversion. Sometimes, ``np.asarray`` suffices for validation;
do *not* use ``np.asanyarray`` or ``np.atleast_2d``, since those let NumPy's
``np.matrix`` through, which has a different API
(e.g., ``*`` means dot product on ``np.matrix``,
but Hadamard product on ``np.ndarray``).

In other cases, be sure to call :func:`check_array` on any array-like argument
passed to a scikit-learn API function. The exact parameters to use depends
mainly on whether and which ``scipy.sparse`` matrices must be accepted.

For more information, refer to the :ref:`developers-utils` page.

Random Numbers
--------------

If your code depends on a random number generator, do not use
``numpy.random.random()`` or similar routines.  To ensure
repeatability in error checking, the routine should accept a keyword
``random_state`` and use this to construct a
``numpy.random.RandomState`` object.
See :func:`sklearn.utils.check_random_state` in :ref:`developers-utils`.

Here's a simple example of code using some of the above guidelines::

    from sklearn.utils import check_array, check_random_state

    def choose_random_sample(X, random_state=0):
        """Choose a random point from X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            An array representing the data.
        random_state : int or RandomState instance, default=0
            The seed of the pseudo random number generator that selects a
            random sample. Pass an int for reproducible output across multiple
            function calls.
            See :term:`Glossary <random_state>`.

        Returns
        -------
        x : ndarray of shape (n_features,)
            A random point selected from X.
        """
        X = check_array(X)
        random_state = check_random_state(random_state)
        i = random_state.randint(X.shape[0])
        return X[i]

If you use randomness in an estimator instead of a freestanding function,
some additional guidelines apply.

First off, the estimator should take a ``random_state`` argument to its
``__init__`` with a default value of ``None``.
It should store that argument's value, **unmodified**,
in an attribute ``random_state``.
``fit`` can call ``check_random_state`` on that attribute
to get an actual random number generator.
If, for some reason, randomness is needed after ``fit``,
the RNG should be stored in an attribute ``random_state_``.
The following example should make this clear::

    class GaussianNoise(BaseEstimator, TransformerMixin):
        """This estimator ignores its input and returns random Gaussian noise.

        It also does not adhere to all scikit-learn conventions,
        but showcases how to handle randomness.
        """

        def __init__(self, n_components=100, random_state=None):
            self.random_state = random_state
            self.n_components = n_components

        # the arguments are ignored anyway, so we make them optional
        def fit(self, X=None, y=None):
            self.random_state_ = check_random_state(self.random_state)

        def transform(self, X):
            n_samples = X.shape[0]
            return self.random_state_.randn(n_samples, self.n_components)

The reason for this setup is reproducibility:
when an estimator is ``fit`` twice to the same data,
it should produce an identical model both times,
hence the validation in ``fit``, not ``__init__``.

Numerical assertions in tests
-----------------------------

When asserting the quasi-equality of arrays of continuous values,
do use :func:`sklearn.utils._testing.assert_allclose`.

The relative tolerance is automatically inferred from the provided arrays
dtypes (for float32 and float64 dtypes in particular) but you can override
via ``rtol``.

When comparing arrays of zero-elements, please do provide a non-zero value for
the absolute tolerance via ``atol``.

For more information, please refer to the docstring of
:func:`sklearn.utils._testing.assert_allclose`.
