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"""
The :mod:`sklearn.pipeline` module implements utilities to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Lars Buitinck
# License: BSD
from collections import defaultdict
from itertools import islice
import numpy as np
from scipy import sparse
from .base import TransformerMixin, _fit_context, clone
from .exceptions import NotFittedError
from .preprocessing import FunctionTransformer
from .utils import Bunch, _print_elapsed_time
from .utils._estimator_html_repr import _VisualBlock
from .utils._metadata_requests import METHODS
from .utils._param_validation import HasMethods, Hidden
from .utils._set_output import (
_get_container_adapter,
_safe_set_output,
)
from .utils._tags import _safe_tags
from .utils.metadata_routing import (
MetadataRouter,
MethodMapping,
_raise_for_params,
_raise_for_unsupported_routing,
_routing_enabled,
_RoutingNotSupportedMixin,
process_routing,
)
from .utils.metaestimators import _BaseComposition, available_if
from .utils.parallel import Parallel, delayed
from .utils.validation import check_is_fitted, check_memory
__all__ = ["Pipeline", "FeatureUnion", "make_pipeline", "make_union"]
def _final_estimator_has(attr):
"""Check that final_estimator has `attr`.
Used together with `available_if` in `Pipeline`."""
def check(self):
# raise original `AttributeError` if `attr` does not exist
getattr(self._final_estimator, attr)
return True
return check
class Pipeline(_BaseComposition):
"""
A sequence of data transformers with an optional final predictor.
`Pipeline` allows you to sequentially apply a list of transformers to
preprocess the data and, if desired, conclude the sequence with a final
:term:`predictor` for predictive modeling.
Intermediate steps of the pipeline must be 'transforms', that is, they
must implement `fit` and `transform` methods.
The final :term:`estimator` only needs to implement `fit`.
The transformers in the pipeline can be cached using ``memory`` argument.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters. For this, it
enables setting parameters of the various steps using their names and the
parameter name separated by a `'__'`, as in the example below. A step's
estimator may be replaced entirely by setting the parameter with its name
to another estimator, or a transformer removed by setting it to
`'passthrough'` or `None`.
For an example use case of `Pipeline` combined with
:class:`~sklearn.model_selection.GridSearchCV`, refer to
:ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The
example :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` shows how
to grid search on a pipeline using `'__'` as a separator in the parameter names.
Read more in the :ref:`User Guide <pipeline>`.
.. versionadded:: 0.5
Parameters
----------
steps : list of tuples
List of (name of step, estimator) tuples that are to be chained in
sequential order. To be compatible with the scikit-learn API, all steps
must define `fit`. All non-last steps must also define `transform`. See
:ref:`Combining Estimators <combining_estimators>` for more details.
memory : str or object with the joblib.Memory interface, default=None
Used to cache the fitted transformers of the pipeline. The last step
will never be cached, even if it is a transformer. By default, no
caching is performed. If a string is given, it is the path to the
caching directory. Enabling caching triggers a clone of the transformers
before fitting. Therefore, the transformer instance given to the
pipeline cannot be inspected directly. Use the attribute ``named_steps``
or ``steps`` to inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.
verbose : bool, default=False
If True, the time elapsed while fitting each step will be printed as it
is completed.
Attributes
----------
named_steps : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
Read-only attribute to access any step parameter by user given name.
Keys are step names and values are steps parameters.
classes_ : ndarray of shape (n_classes,)
The classes labels. Only exist if the last step of the pipeline is a
classifier.
n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying first estimator in `steps` exposes such an attribute
when fit.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
underlying estimator exposes such an attribute when fit.
.. versionadded:: 1.0
See Also
--------
make_pipeline : Convenience function for simplified pipeline construction.
Examples
--------
>>> from sklearn.svm import SVC
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.pipeline import Pipeline
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
... random_state=0)
>>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
>>> # The pipeline can be used as any other estimator
>>> # and avoids leaking the test set into the train set
>>> pipe.fit(X_train, y_train).score(X_test, y_test)
0.88
>>> # An estimator's parameter can be set using '__' syntax
>>> pipe.set_params(svc__C=10).fit(X_train, y_train).score(X_test, y_test)
0.76
"""
# BaseEstimator interface
_required_parameters = ["steps"]
_parameter_constraints: dict = {
"steps": [list, Hidden(tuple)],
"memory": [None, str, HasMethods(["cache"])],
"verbose": ["boolean"],
}
def __init__(self, steps, *, memory=None, verbose=False):
self.steps = steps
self.memory = memory
self.verbose = verbose
def set_output(self, *, transform=None):
"""Set the output container when `"transform"` and `"fit_transform"` are called.
Calling `set_output` will set the output of all estimators in `steps`.
Parameters
----------
transform : {"default", "pandas"}, default=None
Configure output of `transform` and `fit_transform`.
- `"default"`: Default output format of a transformer
- `"pandas"`: DataFrame output
- `"polars"`: Polars output
- `None`: Transform configuration is unchanged
.. versionadded:: 1.4
`"polars"` option was added.
Returns
-------
self : estimator instance
Estimator instance.
"""
for _, _, step in self._iter():
_safe_set_output(step, transform=transform)
return self
def get_params(self, deep=True):
"""Get parameters for this estimator.
Returns the parameters given in the constructor as well as the
estimators contained within the `steps` of the `Pipeline`.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : mapping of string to any
Parameter names mapped to their values.
"""
return self._get_params("steps", deep=deep)
def set_params(self, **kwargs):
"""Set the parameters of this estimator.
Valid parameter keys can be listed with ``get_params()``. Note that
you can directly set the parameters of the estimators contained in
`steps`.
Parameters
----------
**kwargs : dict
Parameters of this estimator or parameters of estimators contained
in `steps`. Parameters of the steps may be set using its name and
the parameter name separated by a '__'.
Returns
-------
self : object
Pipeline class instance.
"""
self._set_params("steps", **kwargs)
return self
def _validate_steps(self):
names, estimators = zip(*self.steps)
# validate names
self._validate_names(names)
# validate estimators
transformers = estimators[:-1]
estimator = estimators[-1]
for t in transformers:
if t is None or t == "passthrough":
continue
if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(
t, "transform"
):
raise TypeError(
"All intermediate steps should be "
"transformers and implement fit and transform "
"or be the string 'passthrough' "
"'%s' (type %s) doesn't" % (t, type(t))
)
# We allow last estimator to be None as an identity transformation
if (
estimator is not None
and estimator != "passthrough"
and not hasattr(estimator, "fit")
):
raise TypeError(
"Last step of Pipeline should implement fit "
"or be the string 'passthrough'. "
"'%s' (type %s) doesn't" % (estimator, type(estimator))
)
def _iter(self, with_final=True, filter_passthrough=True):
"""
Generate (idx, (name, trans)) tuples from self.steps
When filter_passthrough is True, 'passthrough' and None transformers
are filtered out.
"""
stop = len(self.steps)
if not with_final:
stop -= 1
for idx, (name, trans) in enumerate(islice(self.steps, 0, stop)):
if not filter_passthrough:
yield idx, name, trans
elif trans is not None and trans != "passthrough":
yield idx, name, trans
def __len__(self):
"""
Returns the length of the Pipeline
"""
return len(self.steps)
def __getitem__(self, ind):
"""Returns a sub-pipeline or a single estimator in the pipeline
Indexing with an integer will return an estimator; using a slice
returns another Pipeline instance which copies a slice of this
Pipeline. This copy is shallow: modifying (or fitting) estimators in
the sub-pipeline will affect the larger pipeline and vice-versa.
However, replacing a value in `step` will not affect a copy.
"""
if isinstance(ind, slice):
if ind.step not in (1, None):
raise ValueError("Pipeline slicing only supports a step of 1")
return self.__class__(
self.steps[ind], memory=self.memory, verbose=self.verbose
)
try:
name, est = self.steps[ind]
except TypeError:
# Not an int, try get step by name
return self.named_steps[ind]
return est
@property
def _estimator_type(self):
return self.steps[-1][1]._estimator_type
@property
def named_steps(self):
"""Access the steps by name.
Read-only attribute to access any step by given name.
Keys are steps names and values are the steps objects."""
# Use Bunch object to improve autocomplete
return Bunch(**dict(self.steps))
@property
def _final_estimator(self):
try:
estimator = self.steps[-1][1]
return "passthrough" if estimator is None else estimator
except (ValueError, AttributeError, TypeError):
# This condition happens when a call to a method is first calling
# `_available_if` and `fit` did not validate `steps` yet. We
# return `None` and an `InvalidParameterError` will be raised
# right after.
return None
def _log_message(self, step_idx):
if not self.verbose:
return None
name, _ = self.steps[step_idx]
return "(step %d of %d) Processing %s" % (step_idx + 1, len(self.steps), name)
def _check_method_params(self, method, props, **kwargs):
if _routing_enabled():
routed_params = process_routing(self, method, **props, **kwargs)
return routed_params
else:
fit_params_steps = Bunch(
**{
name: Bunch(**{method: {} for method in METHODS})
for name, step in self.steps
if step is not None
}
)
for pname, pval in props.items():
if "__" not in pname:
raise ValueError(
"Pipeline.fit does not accept the {} parameter. "
"You can pass parameters to specific steps of your "
"pipeline using the stepname__parameter format, e.g. "
"`Pipeline.fit(X, y, logisticregression__sample_weight"
"=sample_weight)`.".format(pname)
)
step, param = pname.split("__", 1)
fit_params_steps[step]["fit"][param] = pval
# without metadata routing, fit_transform and fit_predict
# get all the same params and pass it to the last fit.
fit_params_steps[step]["fit_transform"][param] = pval
fit_params_steps[step]["fit_predict"][param] = pval
return fit_params_steps
# Estimator interface
def _fit(self, X, y=None, routed_params=None):
# shallow copy of steps - this should really be steps_
self.steps = list(self.steps)
self._validate_steps()
# Setup the memory
memory = check_memory(self.memory)
fit_transform_one_cached = memory.cache(_fit_transform_one)
for step_idx, name, transformer in self._iter(
with_final=False, filter_passthrough=False
):
if transformer is None or transformer == "passthrough":
with _print_elapsed_time("Pipeline", self._log_message(step_idx)):
continue
if hasattr(memory, "location") and memory.location is None:
# we do not clone when caching is disabled to
# preserve backward compatibility
cloned_transformer = transformer
else:
cloned_transformer = clone(transformer)
# Fit or load from cache the current transformer
X, fitted_transformer = fit_transform_one_cached(
cloned_transformer,
X,
y,
None,
message_clsname="Pipeline",
message=self._log_message(step_idx),
params=routed_params[name],
)
# Replace the transformer of the step with the fitted
# transformer. This is necessary when loading the transformer
# from the cache.
self.steps[step_idx] = (name, fitted_transformer)
return X
@_fit_context(
# estimators in Pipeline.steps are not validated yet
prefer_skip_nested_validation=False
)
def fit(self, X, y=None, **params):
"""Fit the model.
Fit all the transformers one after the other and sequentially transform the
data. Finally, fit the transformed data using the final estimator.
Parameters
----------
X : iterable
Training data. Must fulfill input requirements of first step of the
pipeline.
y : iterable, default=None
Training targets. Must fulfill label requirements for all steps of
the pipeline.
**params : dict of str -> object
- If `enable_metadata_routing=False` (default):
Parameters passed to the ``fit`` method of each step, where
each parameter name is prefixed such that parameter ``p`` for step
``s`` has key ``s__p``.
- If `enable_metadata_routing=True`:
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionchanged:: 1.4
Parameters are now passed to the ``transform`` method of the
intermediate steps as well, if requested, and if
`enable_metadata_routing=True` is set via
:func:`~sklearn.set_config`.
See :ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
self : object
Pipeline with fitted steps.
"""
routed_params = self._check_method_params(method="fit", props=params)
Xt = self._fit(X, y, routed_params)
with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
if self._final_estimator != "passthrough":
last_step_params = routed_params[self.steps[-1][0]]
self._final_estimator.fit(Xt, y, **last_step_params["fit"])
return self
def _can_fit_transform(self):
return (
self._final_estimator == "passthrough"
or hasattr(self._final_estimator, "transform")
or hasattr(self._final_estimator, "fit_transform")
)
@available_if(_can_fit_transform)
@_fit_context(
# estimators in Pipeline.steps are not validated yet
prefer_skip_nested_validation=False
)
def fit_transform(self, X, y=None, **params):
"""Fit the model and transform with the final estimator.
Fit all the transformers one after the other and sequentially transform
the data. Only valid if the final estimator either implements
`fit_transform` or `fit` and `transform`.
Parameters
----------
X : iterable
Training data. Must fulfill input requirements of first step of the
pipeline.
y : iterable, default=None
Training targets. Must fulfill label requirements for all steps of
the pipeline.
**params : dict of str -> object
- If `enable_metadata_routing=False` (default):
Parameters passed to the ``fit`` method of each step, where
each parameter name is prefixed such that parameter ``p`` for step
``s`` has key ``s__p``.
- If `enable_metadata_routing=True`:
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionchanged:: 1.4
Parameters are now passed to the ``transform`` method of the
intermediate steps as well, if requested, and if
`enable_metadata_routing=True`.
See :ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
Xt : ndarray of shape (n_samples, n_transformed_features)
Transformed samples.
"""
routed_params = self._check_method_params(method="fit_transform", props=params)
Xt = self._fit(X, y, routed_params)
last_step = self._final_estimator
with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
if last_step == "passthrough":
return Xt
last_step_params = routed_params[self.steps[-1][0]]
if hasattr(last_step, "fit_transform"):
return last_step.fit_transform(
Xt, y, **last_step_params["fit_transform"]
)
else:
return last_step.fit(Xt, y, **last_step_params["fit"]).transform(
Xt, **last_step_params["transform"]
)
@available_if(_final_estimator_has("predict"))
def predict(self, X, **params):
"""Transform the data, and apply `predict` with the final estimator.
Call `transform` of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls `predict`
method. Only valid if the final estimator implements `predict`.
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
**params : dict of str -> object
- If `enable_metadata_routing=False` (default):
Parameters to the ``predict`` called at the end of all
transformations in the pipeline.
- If `enable_metadata_routing=True`:
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 0.20
.. versionchanged:: 1.4
Parameters are now passed to the ``transform`` method of the
intermediate steps as well, if requested, and if
`enable_metadata_routing=True` is set via
:func:`~sklearn.set_config`.
See :ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Note that while this may be used to return uncertainties from some
models with ``return_std`` or ``return_cov``, uncertainties that are
generated by the transformations in the pipeline are not propagated
to the final estimator.
Returns
-------
y_pred : ndarray
Result of calling `predict` on the final estimator.
"""
Xt = X
if not _routing_enabled():
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
return self.steps[-1][1].predict(Xt, **params)
# metadata routing enabled
routed_params = process_routing(self, "predict", **params)
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt, **routed_params[name].transform)
return self.steps[-1][1].predict(Xt, **routed_params[self.steps[-1][0]].predict)
@available_if(_final_estimator_has("fit_predict"))
@_fit_context(
# estimators in Pipeline.steps are not validated yet
prefer_skip_nested_validation=False
)
def fit_predict(self, X, y=None, **params):
"""Transform the data, and apply `fit_predict` with the final estimator.
Call `fit_transform` of each transformer in the pipeline. The
transformed data are finally passed to the final estimator that calls
`fit_predict` method. Only valid if the final estimator implements
`fit_predict`.
Parameters
----------
X : iterable
Training data. Must fulfill input requirements of first step of
the pipeline.
y : iterable, default=None
Training targets. Must fulfill label requirements for all steps
of the pipeline.
**params : dict of str -> object
- If `enable_metadata_routing=False` (default):
Parameters to the ``predict`` called at the end of all
transformations in the pipeline.
- If `enable_metadata_routing=True`:
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 0.20
.. versionchanged:: 1.4
Parameters are now passed to the ``transform`` method of the
intermediate steps as well, if requested, and if
`enable_metadata_routing=True`.
See :ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Note that while this may be used to return uncertainties from some
models with ``return_std`` or ``return_cov``, uncertainties that are
generated by the transformations in the pipeline are not propagated
to the final estimator.
Returns
-------
y_pred : ndarray
Result of calling `fit_predict` on the final estimator.
"""
routed_params = self._check_method_params(method="fit_predict", props=params)
Xt = self._fit(X, y, routed_params)
params_last_step = routed_params[self.steps[-1][0]]
with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
y_pred = self.steps[-1][1].fit_predict(
Xt, y, **params_last_step.get("fit_predict", {})
)
return y_pred
@available_if(_final_estimator_has("predict_proba"))
def predict_proba(self, X, **params):
"""Transform the data, and apply `predict_proba` with the final estimator.
Call `transform` of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
`predict_proba` method. Only valid if the final estimator implements
`predict_proba`.
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
**params : dict of str -> object
- If `enable_metadata_routing=False` (default):
Parameters to the `predict_proba` called at the end of all
transformations in the pipeline.
- If `enable_metadata_routing=True`:
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 0.20
.. versionchanged:: 1.4
Parameters are now passed to the ``transform`` method of the
intermediate steps as well, if requested, and if
`enable_metadata_routing=True`.
See :ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
y_proba : ndarray of shape (n_samples, n_classes)
Result of calling `predict_proba` on the final estimator.
"""
Xt = X
if not _routing_enabled():
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
return self.steps[-1][1].predict_proba(Xt, **params)
# metadata routing enabled
routed_params = process_routing(self, "predict_proba", **params)
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt, **routed_params[name].transform)
return self.steps[-1][1].predict_proba(
Xt, **routed_params[self.steps[-1][0]].predict_proba
)
@available_if(_final_estimator_has("decision_function"))
def decision_function(self, X, **params):
"""Transform the data, and apply `decision_function` with the final estimator.
Call `transform` of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
`decision_function` method. Only valid if the final estimator
implements `decision_function`.
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
**params : dict of string -> object
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 1.4
Only available if `enable_metadata_routing=True`. See
:ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
y_score : ndarray of shape (n_samples, n_classes)
Result of calling `decision_function` on the final estimator.
"""
_raise_for_params(params, self, "decision_function")
# not branching here since params is only available if
# enable_metadata_routing=True
routed_params = process_routing(self, "decision_function", **params)
Xt = X
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(
Xt, **routed_params.get(name, {}).get("transform", {})
)
return self.steps[-1][1].decision_function(
Xt, **routed_params.get(self.steps[-1][0], {}).get("decision_function", {})
)
@available_if(_final_estimator_has("score_samples"))
def score_samples(self, X):
"""Transform the data, and apply `score_samples` with the final estimator.
Call `transform` of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
`score_samples` method. Only valid if the final estimator implements
`score_samples`.
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
y_score : ndarray of shape (n_samples,)
Result of calling `score_samples` on the final estimator.
"""
Xt = X
for _, _, transformer in self._iter(with_final=False):
Xt = transformer.transform(Xt)
return self.steps[-1][1].score_samples(Xt)
@available_if(_final_estimator_has("predict_log_proba"))
def predict_log_proba(self, X, **params):
"""Transform the data, and apply `predict_log_proba` with the final estimator.
Call `transform` of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
`predict_log_proba` method. Only valid if the final estimator
implements `predict_log_proba`.
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
**params : dict of str -> object
- If `enable_metadata_routing=False` (default):
Parameters to the `predict_log_proba` called at the end of all
transformations in the pipeline.
- If `enable_metadata_routing=True`:
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 0.20
.. versionchanged:: 1.4
Parameters are now passed to the ``transform`` method of the
intermediate steps as well, if requested, and if
`enable_metadata_routing=True`.
See :ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
y_log_proba : ndarray of shape (n_samples, n_classes)
Result of calling `predict_log_proba` on the final estimator.
"""
Xt = X
if not _routing_enabled():
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
return self.steps[-1][1].predict_log_proba(Xt, **params)
# metadata routing enabled
routed_params = process_routing(self, "predict_log_proba", **params)
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt, **routed_params[name].transform)
return self.steps[-1][1].predict_log_proba(
Xt, **routed_params[self.steps[-1][0]].predict_log_proba
)
def _can_transform(self):
return self._final_estimator == "passthrough" or hasattr(
self._final_estimator, "transform"
)
@available_if(_can_transform)
def transform(self, X, **params):
"""Transform the data, and apply `transform` with the final estimator.
Call `transform` of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
`transform` method. Only valid if the final estimator
implements `transform`.
This also works where final estimator is `None` in which case all prior
transformations are applied.
Parameters
----------
X : iterable
Data to transform. Must fulfill input requirements of first step
of the pipeline.
**params : dict of str -> object
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 1.4
Only available if `enable_metadata_routing=True`. See
:ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
Xt : ndarray of shape (n_samples, n_transformed_features)
Transformed data.
"""
_raise_for_params(params, self, "transform")
# not branching here since params is only available if
# enable_metadata_routing=True
routed_params = process_routing(self, "transform", **params)
Xt = X
for _, name, transform in self._iter():
Xt = transform.transform(Xt, **routed_params[name].transform)
return Xt
def _can_inverse_transform(self):
return all(hasattr(t, "inverse_transform") for _, _, t in self._iter())
@available_if(_can_inverse_transform)
def inverse_transform(self, Xt, **params):
"""Apply `inverse_transform` for each step in a reverse order.
All estimators in the pipeline must support `inverse_transform`.
Parameters
----------
Xt : array-like of shape (n_samples, n_transformed_features)
Data samples, where ``n_samples`` is the number of samples and
``n_features`` is the number of features. Must fulfill
input requirements of last step of pipeline's
``inverse_transform`` method.
**params : dict of str -> object
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 1.4
Only available if `enable_metadata_routing=True`. See
:ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
Xt : ndarray of shape (n_samples, n_features)
Inverse transformed data, that is, data in the original feature
space.
"""
_raise_for_params(params, self, "inverse_transform")
# we don't have to branch here, since params is only non-empty if
# enable_metadata_routing=True.
routed_params = process_routing(self, "inverse_transform", **params)
reverse_iter = reversed(list(self._iter()))
for _, name, transform in reverse_iter:
Xt = transform.inverse_transform(
Xt, **routed_params[name].inverse_transform
)
return Xt
@available_if(_final_estimator_has("score"))
def score(self, X, y=None, sample_weight=None, **params):
"""Transform the data, and apply `score` with the final estimator.
Call `transform` of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
`score` method. Only valid if the final estimator implements `score`.
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
y : iterable, default=None
Targets used for scoring. Must fulfill label requirements for all
steps of the pipeline.
sample_weight : array-like, default=None
If not None, this argument is passed as ``sample_weight`` keyword
argument to the ``score`` method of the final estimator.
**params : dict of str -> object
Parameters requested and accepted by steps. Each step must have
requested certain metadata for these parameters to be forwarded to
them.
.. versionadded:: 1.4
Only available if `enable_metadata_routing=True`. See
:ref:`Metadata Routing User Guide <metadata_routing>` for more
details.
Returns
-------
score : float
Result of calling `score` on the final estimator.
"""
Xt = X
if not _routing_enabled():
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
score_params = {}
if sample_weight is not None:
score_params["sample_weight"] = sample_weight
return self.steps[-1][1].score(Xt, y, **score_params)
# metadata routing is enabled.
routed_params = process_routing(
self, "score", sample_weight=sample_weight, **params
)
Xt = X
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt, **routed_params[name].transform)
return self.steps[-1][1].score(Xt, y, **routed_params[self.steps[-1][0]].score)
@property
def classes_(self):
"""The classes labels. Only exist if the last step is a classifier."""
return self.steps[-1][1].classes_
def _more_tags(self):
tags = {
"_xfail_checks": {
"check_dont_overwrite_parameters": (
"Pipeline changes the `steps` parameter, which it shouldn't."
"Therefore this test is x-fail until we fix this."
),
"check_estimators_overwrite_params": (
"Pipeline changes the `steps` parameter, which it shouldn't."
"Therefore this test is x-fail until we fix this."
),
}
}
try:
tags["pairwise"] = _safe_tags(self.steps[0][1], "pairwise")
except (ValueError, AttributeError, TypeError):
# This happens when the `steps` is not a list of (name, estimator)
# tuples and `fit` is not called yet to validate the steps.
pass
try:
tags["multioutput"] = _safe_tags(self.steps[-1][1], "multioutput")
except (ValueError, AttributeError, TypeError):
# This happens when the `steps` is not a list of (name, estimator)
# tuples and `fit` is not called yet to validate the steps.
pass
return tags
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Transform input features using the pipeline.
Parameters
----------
input_features : array-like of str or None, default=None
Input features.
Returns
-------
feature_names_out : ndarray of str objects
Transformed feature names.
"""
feature_names_out = input_features
for _, name, transform in self._iter():
if not hasattr(transform, "get_feature_names_out"):
raise AttributeError(
"Estimator {} does not provide get_feature_names_out. "
"Did you mean to call pipeline[:-1].get_feature_names_out"
"()?".format(name)
)
feature_names_out = transform.get_feature_names_out(feature_names_out)
return feature_names_out
@property
def n_features_in_(self):
"""Number of features seen during first step `fit` method."""
# delegate to first step (which will call _check_is_fitted)
return self.steps[0][1].n_features_in_
@property
def feature_names_in_(self):
"""Names of features seen during first step `fit` method."""
# delegate to first step (which will call _check_is_fitted)
return self.steps[0][1].feature_names_in_
def __sklearn_is_fitted__(self):
"""Indicate whether pipeline has been fit."""
try:
# check if the last step of the pipeline is fitted
# we only check the last step since if the last step is fit, it
# means the previous steps should also be fit. This is faster than
# checking if every step of the pipeline is fit.
check_is_fitted(self.steps[-1][1])
return True
except NotFittedError:
return False
def _sk_visual_block_(self):
_, estimators = zip(*self.steps)
def _get_name(name, est):
if est is None or est == "passthrough":
return f"{name}: passthrough"
# Is an estimator
return f"{name}: {est.__class__.__name__}"
names = [_get_name(name, est) for name, est in self.steps]
name_details = [str(est) for est in estimators]
return _VisualBlock(
"serial",
estimators,
names=names,
name_details=name_details,
dash_wrapped=False,
)
def get_metadata_routing(self):
"""Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
routing information.
"""
router = MetadataRouter(owner=self.__class__.__name__)
# first we add all steps except the last one
for _, name, trans in self._iter(with_final=False, filter_passthrough=True):
method_mapping = MethodMapping()
# fit, fit_predict, and fit_transform call fit_transform if it
# exists, or else fit and transform
if hasattr(trans, "fit_transform"):
(
method_mapping.add(caller="fit", callee="fit_transform")
.add(caller="fit_transform", callee="fit_transform")
.add(caller="fit_predict", callee="fit_transform")
)
else:
(
method_mapping.add(caller="fit", callee="fit")
.add(caller="fit", callee="transform")
.add(caller="fit_transform", callee="fit")
.add(caller="fit_transform", callee="transform")
.add(caller="fit_predict", callee="fit")
.add(caller="fit_predict", callee="transform")
)
(
method_mapping.add(caller="predict", callee="transform")
.add(caller="predict", callee="transform")
.add(caller="predict_proba", callee="transform")
.add(caller="decision_function", callee="transform")
.add(caller="predict_log_proba", callee="transform")
.add(caller="transform", callee="transform")
.add(caller="inverse_transform", callee="inverse_transform")
.add(caller="score", callee="transform")
)
router.add(method_mapping=method_mapping, **{name: trans})
final_name, final_est = self.steps[-1]
if final_est is None or final_est == "passthrough":
return router
# then we add the last step
method_mapping = MethodMapping()
if hasattr(final_est, "fit_transform"):
method_mapping.add(caller="fit_transform", callee="fit_transform")
else:
method_mapping.add(caller="fit", callee="fit").add(
caller="fit", callee="transform"
)
(
method_mapping.add(caller="fit", callee="fit")
.add(caller="predict", callee="predict")
.add(caller="fit_predict", callee="fit_predict")
.add(caller="predict_proba", callee="predict_proba")
.add(caller="decision_function", callee="decision_function")
.add(caller="predict_log_proba", callee="predict_log_proba")
.add(caller="transform", callee="transform")
.add(caller="inverse_transform", callee="inverse_transform")
.add(caller="score", callee="score")
)
router.add(method_mapping=method_mapping, **{final_name: final_est})
return router
def _name_estimators(estimators):
"""Generate names for estimators."""
names = [
estimator if isinstance(estimator, str) else type(estimator).__name__.lower()
for estimator in estimators
]
namecount = defaultdict(int)
for est, name in zip(estimators, names):
namecount[name] += 1
for k, v in list(namecount.items()):
if v == 1:
del namecount[k]
for i in reversed(range(len(estimators))):
name = names[i]
if name in namecount:
names[i] += "-%d" % namecount[name]
namecount[name] -= 1
return list(zip(names, estimators))
def make_pipeline(*steps, memory=None, verbose=False):
"""Construct a :class:`Pipeline` from the given estimators.
This is a shorthand for the :class:`Pipeline` constructor; it does not
require, and does not permit, naming the estimators. Instead, their names
will be set to the lowercase of their types automatically.
Parameters
----------
*steps : list of Estimator objects
List of the scikit-learn estimators that are chained together.
memory : str or object with the joblib.Memory interface, default=None
Used to cache the fitted transformers of the pipeline. The last step
will never be cached, even if it is a transformer. By default, no
caching is performed. If a string is given, it is the path to the
caching directory. Enabling caching triggers a clone of the transformers
before fitting. Therefore, the transformer instance given to the
pipeline cannot be inspected directly. Use the attribute ``named_steps``
or ``steps`` to inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.
verbose : bool, default=False
If True, the time elapsed while fitting each step will be printed as it
is completed.
Returns
-------
p : Pipeline
Returns a scikit-learn :class:`Pipeline` object.
See Also
--------
Pipeline : Class for creating a pipeline of transforms with a final
estimator.
Examples
--------
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.pipeline import make_pipeline
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
Pipeline(steps=[('standardscaler', StandardScaler()),
('gaussiannb', GaussianNB())])
"""
return Pipeline(_name_estimators(steps), memory=memory, verbose=verbose)
def _transform_one(transformer, X, y, weight, params):
"""Call transform and apply weight to output.
Parameters
----------
transformer : estimator
Estimator to be used for transformation.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data to be transformed.
y : ndarray of shape (n_samples,)
Ignored.
weight : float
Weight to be applied to the output of the transformation.
params : dict
Parameters to be passed to the transformer's ``transform`` method.
This should be of the form ``process_routing()["step_name"]``.
"""
res = transformer.transform(X, **params.transform)
# if we have a weight for this transformer, multiply output
if weight is None:
return res
return res * weight
def _fit_transform_one(
transformer, X, y, weight, message_clsname="", message=None, params=None
):
"""
Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned
with the fitted transformer. If ``weight`` is not ``None``, the result will
be multiplied by ``weight``.
``params`` needs to be of the form ``process_routing()["step_name"]``.
"""
params = params or {}
with _print_elapsed_time(message_clsname, message):
if hasattr(transformer, "fit_transform"):
res = transformer.fit_transform(X, y, **params.get("fit_transform", {}))
else:
res = transformer.fit(X, y, **params.get("fit", {})).transform(
X, **params.get("transform", {})
)
if weight is None:
return res, transformer
return res * weight, transformer
def _fit_one(transformer, X, y, weight, message_clsname="", message=None, params=None):
"""
Fits ``transformer`` to ``X`` and ``y``.
"""
with _print_elapsed_time(message_clsname, message):
return transformer.fit(X, y, **params["fit"])
class FeatureUnion(_RoutingNotSupportedMixin, TransformerMixin, _BaseComposition):
"""Concatenates results of multiple transformer objects.
This estimator applies a list of transformer objects in parallel to the
input data, then concatenates the results. This is useful to combine
several feature extraction mechanisms into a single transformer.
Parameters of the transformers may be set using its name and the parameter
name separated by a '__'. A transformer may be replaced entirely by
setting the parameter with its name to another transformer, removed by
setting to 'drop' or disabled by setting to 'passthrough' (features are
passed without transformation).
Read more in the :ref:`User Guide <feature_union>`.
.. versionadded:: 0.13
Parameters
----------
transformer_list : list of (str, transformer) tuples
List of transformer objects to be applied to the data. The first
half of each tuple is the name of the transformer. The transformer can
be 'drop' for it to be ignored or can be 'passthrough' for features to
be passed unchanged.
.. versionadded:: 1.1
Added the option `"passthrough"`.
.. versionchanged:: 0.22
Deprecated `None` as a transformer in favor of 'drop'.
n_jobs : int, default=None
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
.. versionchanged:: v0.20
`n_jobs` default changed from 1 to None
transformer_weights : dict, default=None
Multiplicative weights for features per transformer.
Keys are transformer names, values the weights.
Raises ValueError if key not present in ``transformer_list``.
verbose : bool, default=False
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
Attributes
----------
named_transformers : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
Read-only attribute to access any transformer parameter by user
given name. Keys are transformer names and values are
transformer parameters.
.. versionadded:: 1.2
n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying first transformer in `transformer_list` exposes such an
attribute when fit.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when
`X` has feature names that are all strings.
.. versionadded:: 1.3
See Also
--------
make_union : Convenience function for simplified feature union
construction.
Examples
--------
>>> from sklearn.pipeline import FeatureUnion
>>> from sklearn.decomposition import PCA, TruncatedSVD
>>> union = FeatureUnion([("pca", PCA(n_components=1)),
... ("svd", TruncatedSVD(n_components=2))])
>>> X = [[0., 1., 3], [2., 2., 5]]
>>> union.fit_transform(X)
array([[ 1.5 , 3.0..., 0.8...],
[-1.5 , 5.7..., -0.4...]])
>>> # An estimator's parameter can be set using '__' syntax
>>> union.set_params(svd__n_components=1).fit_transform(X)
array([[ 1.5 , 3.0...],
[-1.5 , 5.7...]])
For a more detailed example of usage, see
:ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`.
"""
_required_parameters = ["transformer_list"]
def __init__(
self, transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False
):
self.transformer_list = transformer_list
self.n_jobs = n_jobs
self.transformer_weights = transformer_weights
self.verbose = verbose
def set_output(self, *, transform=None):
"""Set the output container when `"transform"` and `"fit_transform"` are called.
`set_output` will set the output of all estimators in `transformer_list`.
Parameters
----------
transform : {"default", "pandas"}, default=None
Configure output of `transform` and `fit_transform`.
- `"default"`: Default output format of a transformer
- `"pandas"`: DataFrame output
- `None`: Transform configuration is unchanged
Returns
-------
self : estimator instance
Estimator instance.
"""
super().set_output(transform=transform)
for _, step, _ in self._iter():
_safe_set_output(step, transform=transform)
return self
@property
def named_transformers(self):
# Use Bunch object to improve autocomplete
return Bunch(**dict(self.transformer_list))
def get_params(self, deep=True):
"""Get parameters for this estimator.
Returns the parameters given in the constructor as well as the
estimators contained within the `transformer_list` of the
`FeatureUnion`.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : mapping of string to any
Parameter names mapped to their values.
"""
return self._get_params("transformer_list", deep=deep)
def set_params(self, **kwargs):
"""Set the parameters of this estimator.
Valid parameter keys can be listed with ``get_params()``. Note that
you can directly set the parameters of the estimators contained in
`transformer_list`.
Parameters
----------
**kwargs : dict
Parameters of this estimator or parameters of estimators contained
in `transform_list`. Parameters of the transformers may be set
using its name and the parameter name separated by a '__'.
Returns
-------
self : object
FeatureUnion class instance.
"""
self._set_params("transformer_list", **kwargs)
return self
def _validate_transformers(self):
names, transformers = zip(*self.transformer_list)
# validate names
self._validate_names(names)
# validate estimators
for t in transformers:
if t in ("drop", "passthrough"):
continue
if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(
t, "transform"
):
raise TypeError(
"All estimators should implement fit and "
"transform. '%s' (type %s) doesn't" % (t, type(t))
)
def _validate_transformer_weights(self):
if not self.transformer_weights:
return
transformer_names = set(name for name, _ in self.transformer_list)
for name in self.transformer_weights:
if name not in transformer_names:
raise ValueError(
f'Attempting to weight transformer "{name}", '
"but it is not present in transformer_list."
)
def _iter(self):
"""
Generate (name, trans, weight) tuples excluding None and
'drop' transformers.
"""
get_weight = (self.transformer_weights or {}).get
for name, trans in self.transformer_list:
if trans == "drop":
continue
if trans == "passthrough":
trans = FunctionTransformer(feature_names_out="one-to-one")
yield (name, trans, get_weight(name))
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Input features.
Returns
-------
feature_names_out : ndarray of str objects
Transformed feature names.
"""
feature_names = []
for name, trans, _ in self._iter():
if not hasattr(trans, "get_feature_names_out"):
raise AttributeError(
"Transformer %s (type %s) does not provide get_feature_names_out."
% (str(name), type(trans).__name__)
)
feature_names.extend(
[f"{name}__{f}" for f in trans.get_feature_names_out(input_features)]
)
return np.asarray(feature_names, dtype=object)
def fit(self, X, y=None, **fit_params):
"""Fit all transformers using X.
Parameters
----------
X : iterable or array-like, depending on transformers
Input data, used to fit transformers.
y : array-like of shape (n_samples, n_outputs), default=None
Targets for supervised learning.
**fit_params : dict, default=None
Parameters to pass to the fit method of the estimator.
Returns
-------
self : object
FeatureUnion class instance.
"""
_raise_for_unsupported_routing(self, "fit", **fit_params)
transformers = self._parallel_func(X, y, fit_params, _fit_one)
if not transformers:
# All transformers are None
return self
self._update_transformer_list(transformers)
return self
def fit_transform(self, X, y=None, **fit_params):
"""Fit all transformers, transform the data and concatenate results.
Parameters
----------
X : iterable or array-like, depending on transformers
Input data to be transformed.
y : array-like of shape (n_samples, n_outputs), default=None
Targets for supervised learning.
**fit_params : dict, default=None
Parameters to pass to the fit method of the estimator.
Returns
-------
X_t : array-like or sparse matrix of \
shape (n_samples, sum_n_components)
The `hstack` of results of transformers. `sum_n_components` is the
sum of `n_components` (output dimension) over transformers.
"""
results = self._parallel_func(X, y, fit_params, _fit_transform_one)
if not results:
# All transformers are None
return np.zeros((X.shape[0], 0))
Xs, transformers = zip(*results)
self._update_transformer_list(transformers)
return self._hstack(Xs)
def _log_message(self, name, idx, total):
if not self.verbose:
return None
return "(step %d of %d) Processing %s" % (idx, total, name)
def _parallel_func(self, X, y, fit_params, func):
"""Runs func in parallel on X and y"""
self.transformer_list = list(self.transformer_list)
self._validate_transformers()
self._validate_transformer_weights()
transformers = list(self._iter())
params = Bunch(fit=fit_params, fit_transform=fit_params)
return Parallel(n_jobs=self.n_jobs)(
delayed(func)(
transformer,
X,
y,
weight,
message_clsname="FeatureUnion",
message=self._log_message(name, idx, len(transformers)),
params=params,
)
for idx, (name, transformer, weight) in enumerate(transformers, 1)
)
def transform(self, X):
"""Transform X separately by each transformer, concatenate results.
Parameters
----------
X : iterable or array-like, depending on transformers
Input data to be transformed.
Returns
-------
X_t : array-like or sparse matrix of \
shape (n_samples, sum_n_components)
The `hstack` of results of transformers. `sum_n_components` is the
sum of `n_components` (output dimension) over transformers.
"""
# TODO(SLEP6): accept **params here in `transform` and route it to the
# underlying estimators.
params = Bunch(transform={})
Xs = Parallel(n_jobs=self.n_jobs)(
delayed(_transform_one)(trans, X, None, weight, params)
for name, trans, weight in self._iter()
)
if not Xs:
# All transformers are None
return np.zeros((X.shape[0], 0))
return self._hstack(Xs)
def _hstack(self, Xs):
adapter = _get_container_adapter("transform", self)
if adapter and all(adapter.is_supported_container(X) for X in Xs):
return adapter.hstack(Xs)
if any(sparse.issparse(f) for f in Xs):
Xs = sparse.hstack(Xs).tocsr()
else:
Xs = np.hstack(Xs)
return Xs
def _update_transformer_list(self, transformers):
transformers = iter(transformers)
self.transformer_list[:] = [
(name, old if old == "drop" else next(transformers))
for name, old in self.transformer_list
]
@property
def n_features_in_(self):
"""Number of features seen during :term:`fit`."""
# X is passed to all transformers so we just delegate to the first one
return self.transformer_list[0][1].n_features_in_
@property
def feature_names_in_(self):
"""Names of features seen during :term:`fit`."""
# X is passed to all transformers -- delegate to the first one
return self.transformer_list[0][1].feature_names_in_
def __sklearn_is_fitted__(self):
# Delegate whether feature union was fitted
for _, transformer, _ in self._iter():
check_is_fitted(transformer)
return True
def _sk_visual_block_(self):
names, transformers = zip(*self.transformer_list)
return _VisualBlock("parallel", transformers, names=names)
def __getitem__(self, name):
"""Return transformer with name."""
if not isinstance(name, str):
raise KeyError("Only string keys are supported")
return self.named_transformers[name]
def make_union(*transformers, n_jobs=None, verbose=False):
"""Construct a :class:`FeatureUnion` from the given transformers.
This is a shorthand for the :class:`FeatureUnion` constructor; it does not
require, and does not permit, naming the transformers. Instead, they will
be given names automatically based on their types. It also does not allow
weighting.
Parameters
----------
*transformers : list of estimators
One or more estimators.
n_jobs : int, default=None
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
.. versionchanged:: v0.20
`n_jobs` default changed from 1 to None.
verbose : bool, default=False
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
Returns
-------
f : FeatureUnion
A :class:`FeatureUnion` object for concatenating the results of multiple
transformer objects.
See Also
--------
FeatureUnion : Class for concatenating the results of multiple transformer
objects.
Examples
--------
>>> from sklearn.decomposition import PCA, TruncatedSVD
>>> from sklearn.pipeline import make_union
>>> make_union(PCA(), TruncatedSVD())
FeatureUnion(transformer_list=[('pca', PCA()),
('truncatedsvd', TruncatedSVD())])
"""
return FeatureUnion(_name_estimators(transformers), n_jobs=n_jobs, verbose=verbose)
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