1087 lines
38 KiB
Python
1087 lines
38 KiB
Python
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"""
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The :mod:`sklearn.pipeline` module implements utilities to build a composite
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estimator, as a chain of transforms and estimators.
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"""
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# Author: Edouard Duchesnay
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# Gael Varoquaux
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# Virgile Fritsch
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# Alexandre Gramfort
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# Lars Buitinck
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# License: BSD
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from collections import defaultdict
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from itertools import islice
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import warnings
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import numpy as np
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from scipy import sparse
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from joblib import Parallel, delayed
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from .base import clone, TransformerMixin
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from .utils._estimator_html_repr import _VisualBlock
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from .utils.metaestimators import if_delegate_has_method
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from .utils import Bunch, _print_elapsed_time
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from .utils.validation import check_memory
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from .utils.validation import _deprecate_positional_args
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from .utils.metaestimators import _BaseComposition
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__all__ = ['Pipeline', 'FeatureUnion', 'make_pipeline', 'make_union']
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class Pipeline(_BaseComposition):
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"""
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Pipeline of transforms with a final estimator.
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Sequentially apply a list of transforms and a final estimator.
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Intermediate steps of the pipeline must be 'transforms', that is, they
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must implement fit and transform methods.
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The final estimator only needs to implement fit.
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The transformers in the pipeline can be cached using ``memory`` argument.
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The purpose of the pipeline is to assemble several steps that can be
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cross-validated together while setting different parameters.
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For this, it enables setting parameters of the various steps using their
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names and the parameter name separated by a '__', as in the example below.
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A step's estimator may be replaced entirely by setting the parameter
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with its name to another estimator, or a transformer removed by setting
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it to 'passthrough' or ``None``.
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Read more in the :ref:`User Guide <pipeline>`.
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.. versionadded:: 0.5
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Parameters
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----------
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steps : list
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List of (name, transform) tuples (implementing fit/transform) that are
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chained, in the order in which they are chained, with the last object
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an estimator.
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memory : str or object with the joblib.Memory interface, default=None
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Used to cache the fitted transformers of the pipeline. By default,
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no caching is performed. If a string is given, it is the path to
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the caching directory. Enabling caching triggers a clone of
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the transformers before fitting. Therefore, the transformer
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instance given to the pipeline cannot be inspected
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directly. Use the attribute ``named_steps`` or ``steps`` to
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inspect estimators within the pipeline. Caching the
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transformers is advantageous when fitting is time consuming.
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verbose : bool, default=False
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If True, the time elapsed while fitting each step will be printed as it
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is completed.
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Attributes
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----------
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named_steps : :class:`~sklearn.utils.Bunch`
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Dictionary-like object, with the following attributes.
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Read-only attribute to access any step parameter by user given name.
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Keys are step names and values are steps parameters.
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See Also
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--------
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sklearn.pipeline.make_pipeline : Convenience function for simplified
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pipeline construction.
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Examples
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--------
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>>> from sklearn.svm import SVC
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>>> from sklearn.preprocessing import StandardScaler
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>>> from sklearn.datasets import make_classification
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>>> from sklearn.model_selection import train_test_split
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>>> from sklearn.pipeline import Pipeline
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>>> X, y = make_classification(random_state=0)
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>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
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... random_state=0)
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>>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
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>>> # The pipeline can be used as any other estimator
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>>> # and avoids leaking the test set into the train set
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>>> pipe.fit(X_train, y_train)
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Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])
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>>> pipe.score(X_test, y_test)
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0.88
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"""
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# BaseEstimator interface
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_required_parameters = ['steps']
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@_deprecate_positional_args
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def __init__(self, steps, *, memory=None, verbose=False):
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self.steps = steps
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self.memory = memory
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self.verbose = verbose
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self._validate_steps()
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def get_params(self, deep=True):
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"""Get parameters for this estimator.
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Parameters
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----------
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deep : bool, default=True
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If True, will return the parameters for this estimator and
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contained subobjects that are estimators.
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Returns
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-------
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params : mapping of string to any
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Parameter names mapped to their values.
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"""
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return self._get_params('steps', deep=deep)
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def set_params(self, **kwargs):
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"""Set the parameters of this estimator.
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Valid parameter keys can be listed with ``get_params()``.
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Returns
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-------
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self
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"""
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self._set_params('steps', **kwargs)
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return self
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def _validate_steps(self):
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names, estimators = zip(*self.steps)
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# validate names
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self._validate_names(names)
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# validate estimators
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transformers = estimators[:-1]
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estimator = estimators[-1]
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for t in transformers:
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if t is None or t == 'passthrough':
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continue
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if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not
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hasattr(t, "transform")):
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raise TypeError("All intermediate steps should be "
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"transformers and implement fit and transform "
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"or be the string 'passthrough' "
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"'%s' (type %s) doesn't" % (t, type(t)))
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# We allow last estimator to be None as an identity transformation
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if (estimator is not None and estimator != 'passthrough'
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and not hasattr(estimator, "fit")):
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raise TypeError(
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"Last step of Pipeline should implement fit "
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"or be the string 'passthrough'. "
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"'%s' (type %s) doesn't" % (estimator, type(estimator)))
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def _iter(self, with_final=True, filter_passthrough=True):
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"""
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Generate (idx, (name, trans)) tuples from self.steps
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When filter_passthrough is True, 'passthrough' and None transformers
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are filtered out.
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"""
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stop = len(self.steps)
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if not with_final:
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stop -= 1
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for idx, (name, trans) in enumerate(islice(self.steps, 0, stop)):
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if not filter_passthrough:
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yield idx, name, trans
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elif trans is not None and trans != 'passthrough':
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yield idx, name, trans
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def __len__(self):
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"""
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Returns the length of the Pipeline
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"""
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return len(self.steps)
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def __getitem__(self, ind):
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"""Returns a sub-pipeline or a single esimtator in the pipeline
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Indexing with an integer will return an estimator; using a slice
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returns another Pipeline instance which copies a slice of this
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Pipeline. This copy is shallow: modifying (or fitting) estimators in
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the sub-pipeline will affect the larger pipeline and vice-versa.
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However, replacing a value in `step` will not affect a copy.
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"""
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if isinstance(ind, slice):
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if ind.step not in (1, None):
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raise ValueError('Pipeline slicing only supports a step of 1')
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return self.__class__(self.steps[ind])
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try:
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name, est = self.steps[ind]
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except TypeError:
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# Not an int, try get step by name
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return self.named_steps[ind]
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return est
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@property
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def _estimator_type(self):
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return self.steps[-1][1]._estimator_type
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@property
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def named_steps(self):
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# Use Bunch object to improve autocomplete
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return Bunch(**dict(self.steps))
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@property
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def _final_estimator(self):
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estimator = self.steps[-1][1]
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return 'passthrough' if estimator is None else estimator
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def _log_message(self, step_idx):
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if not self.verbose:
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return None
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name, step = self.steps[step_idx]
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return '(step %d of %d) Processing %s' % (step_idx + 1,
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len(self.steps),
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name)
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def _check_fit_params(self, **fit_params):
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fit_params_steps = {name: {} for name, step in self.steps
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if step is not None}
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for pname, pval in fit_params.items():
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if '__' not in pname:
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raise ValueError(
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"Pipeline.fit does not accept the {} parameter. "
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"You can pass parameters to specific steps of your "
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"pipeline using the stepname__parameter format, e.g. "
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"`Pipeline.fit(X, y, logisticregression__sample_weight"
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"=sample_weight)`.".format(pname))
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step, param = pname.split('__', 1)
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fit_params_steps[step][param] = pval
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return fit_params_steps
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# Estimator interface
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def _fit(self, X, y=None, **fit_params_steps):
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# shallow copy of steps - this should really be steps_
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self.steps = list(self.steps)
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self._validate_steps()
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# Setup the memory
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memory = check_memory(self.memory)
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fit_transform_one_cached = memory.cache(_fit_transform_one)
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for (step_idx,
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name,
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transformer) in self._iter(with_final=False,
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filter_passthrough=False):
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if (transformer is None or transformer == 'passthrough'):
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with _print_elapsed_time('Pipeline',
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self._log_message(step_idx)):
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continue
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if hasattr(memory, 'location'):
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# joblib >= 0.12
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if memory.location is None:
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# we do not clone when caching is disabled to
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# preserve backward compatibility
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cloned_transformer = transformer
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else:
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cloned_transformer = clone(transformer)
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elif hasattr(memory, 'cachedir'):
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# joblib < 0.11
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if memory.cachedir is None:
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# we do not clone when caching is disabled to
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# preserve backward compatibility
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cloned_transformer = transformer
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else:
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cloned_transformer = clone(transformer)
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else:
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cloned_transformer = clone(transformer)
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# Fit or load from cache the current transformer
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X, fitted_transformer = fit_transform_one_cached(
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cloned_transformer, X, y, None,
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message_clsname='Pipeline',
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message=self._log_message(step_idx),
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**fit_params_steps[name])
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# Replace the transformer of the step with the fitted
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# transformer. This is necessary when loading the transformer
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# from the cache.
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self.steps[step_idx] = (name, fitted_transformer)
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return X
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def fit(self, X, y=None, **fit_params):
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"""Fit the model
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Fit all the transforms one after the other and transform the
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data, then fit the transformed data using the final estimator.
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Parameters
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----------
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X : iterable
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Training data. Must fulfill input requirements of first step of the
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pipeline.
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y : iterable, default=None
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Training targets. Must fulfill label requirements for all steps of
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the pipeline.
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**fit_params : dict of string -> object
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Parameters passed to the ``fit`` method of each step, where
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each parameter name is prefixed such that parameter ``p`` for step
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``s`` has key ``s__p``.
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Returns
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-------
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self : Pipeline
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This estimator
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"""
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fit_params_steps = self._check_fit_params(**fit_params)
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Xt = self._fit(X, y, **fit_params_steps)
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with _print_elapsed_time('Pipeline',
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self._log_message(len(self.steps) - 1)):
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if self._final_estimator != 'passthrough':
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fit_params_last_step = fit_params_steps[self.steps[-1][0]]
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self._final_estimator.fit(Xt, y, **fit_params_last_step)
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return self
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def fit_transform(self, X, y=None, **fit_params):
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"""Fit the model and transform with the final estimator
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Fits all the transforms one after the other and transforms the
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data, then uses fit_transform on transformed data with the final
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estimator.
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Parameters
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----------
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X : iterable
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Training data. Must fulfill input requirements of first step of the
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pipeline.
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y : iterable, default=None
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Training targets. Must fulfill label requirements for all steps of
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the pipeline.
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**fit_params : dict of string -> object
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Parameters passed to the ``fit`` method of each step, where
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each parameter name is prefixed such that parameter ``p`` for step
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``s`` has key ``s__p``.
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Returns
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-------
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Xt : array-like of shape (n_samples, n_transformed_features)
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Transformed samples
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"""
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fit_params_steps = self._check_fit_params(**fit_params)
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Xt = self._fit(X, y, **fit_params_steps)
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last_step = self._final_estimator
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with _print_elapsed_time('Pipeline',
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self._log_message(len(self.steps) - 1)):
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if last_step == 'passthrough':
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return Xt
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fit_params_last_step = fit_params_steps[self.steps[-1][0]]
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if hasattr(last_step, 'fit_transform'):
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return last_step.fit_transform(Xt, y, **fit_params_last_step)
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else:
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return last_step.fit(Xt, y,
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**fit_params_last_step).transform(Xt)
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|
|
@if_delegate_has_method(delegate='_final_estimator')
|
||
|
|
def predict(self, X, **predict_params):
|
||
|
|
"""Apply transforms to the data, and predict with the final estimator
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : iterable
|
||
|
|
Data to predict on. Must fulfill input requirements of first step
|
||
|
|
of the pipeline.
|
||
|
|
|
||
|
|
**predict_params : dict of string -> object
|
||
|
|
Parameters to the ``predict`` called at the end of all
|
||
|
|
transformations in the pipeline. 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.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_pred : array-like
|
||
|
|
"""
|
||
|
|
Xt = X
|
||
|
|
for _, name, transform in self._iter(with_final=False):
|
||
|
|
Xt = transform.transform(Xt)
|
||
|
|
return self.steps[-1][-1].predict(Xt, **predict_params)
|
||
|
|
|
||
|
|
@if_delegate_has_method(delegate='_final_estimator')
|
||
|
|
def fit_predict(self, X, y=None, **fit_params):
|
||
|
|
"""Applies fit_predict of last step in pipeline after transforms.
|
||
|
|
|
||
|
|
Applies fit_transforms of a pipeline to the data, followed by the
|
||
|
|
fit_predict method of the final estimator in the pipeline. Valid
|
||
|
|
only 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.
|
||
|
|
|
||
|
|
**fit_params : dict of string -> object
|
||
|
|
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``.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_pred : array-like
|
||
|
|
"""
|
||
|
|
fit_params_steps = self._check_fit_params(**fit_params)
|
||
|
|
Xt = self._fit(X, y, **fit_params_steps)
|
||
|
|
|
||
|
|
fit_params_last_step = fit_params_steps[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,
|
||
|
|
**fit_params_last_step)
|
||
|
|
return y_pred
|
||
|
|
|
||
|
|
@if_delegate_has_method(delegate='_final_estimator')
|
||
|
|
def predict_proba(self, X):
|
||
|
|
"""Apply transforms, and predict_proba of the final estimator
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : iterable
|
||
|
|
Data to predict on. Must fulfill input requirements of first step
|
||
|
|
of the pipeline.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_proba : array-like of shape (n_samples, n_classes)
|
||
|
|
"""
|
||
|
|
Xt = X
|
||
|
|
for _, name, transform in self._iter(with_final=False):
|
||
|
|
Xt = transform.transform(Xt)
|
||
|
|
return self.steps[-1][-1].predict_proba(Xt)
|
||
|
|
|
||
|
|
@if_delegate_has_method(delegate='_final_estimator')
|
||
|
|
def decision_function(self, X):
|
||
|
|
"""Apply transforms, and decision_function of the final estimator
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : iterable
|
||
|
|
Data to predict on. Must fulfill input requirements of first step
|
||
|
|
of the pipeline.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_score : array-like of shape (n_samples, n_classes)
|
||
|
|
"""
|
||
|
|
Xt = X
|
||
|
|
for _, name, transform in self._iter(with_final=False):
|
||
|
|
Xt = transform.transform(Xt)
|
||
|
|
return self.steps[-1][-1].decision_function(Xt)
|
||
|
|
|
||
|
|
@if_delegate_has_method(delegate='_final_estimator')
|
||
|
|
def score_samples(self, X):
|
||
|
|
"""Apply transforms, and score_samples of the final estimator.
|
||
|
|
|
||
|
|
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,)
|
||
|
|
"""
|
||
|
|
Xt = X
|
||
|
|
for _, _, transformer in self._iter(with_final=False):
|
||
|
|
Xt = transformer.transform(Xt)
|
||
|
|
return self.steps[-1][-1].score_samples(Xt)
|
||
|
|
|
||
|
|
@if_delegate_has_method(delegate='_final_estimator')
|
||
|
|
def predict_log_proba(self, X):
|
||
|
|
"""Apply transforms, and predict_log_proba of the final estimator
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : iterable
|
||
|
|
Data to predict on. Must fulfill input requirements of first step
|
||
|
|
of the pipeline.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_score : array-like of shape (n_samples, n_classes)
|
||
|
|
"""
|
||
|
|
Xt = X
|
||
|
|
for _, name, transform in self._iter(with_final=False):
|
||
|
|
Xt = transform.transform(Xt)
|
||
|
|
return self.steps[-1][-1].predict_log_proba(Xt)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def transform(self):
|
||
|
|
"""Apply transforms, and transform with the final estimator
|
||
|
|
|
||
|
|
This also works where final estimator is ``None``: all prior
|
||
|
|
transformations are applied.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : iterable
|
||
|
|
Data to transform. Must fulfill input requirements of first step
|
||
|
|
of the pipeline.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Xt : array-like of shape (n_samples, n_transformed_features)
|
||
|
|
"""
|
||
|
|
# _final_estimator is None or has transform, otherwise attribute error
|
||
|
|
# XXX: Handling the None case means we can't use if_delegate_has_method
|
||
|
|
if self._final_estimator != 'passthrough':
|
||
|
|
self._final_estimator.transform
|
||
|
|
return self._transform
|
||
|
|
|
||
|
|
def _transform(self, X):
|
||
|
|
Xt = X
|
||
|
|
for _, _, transform in self._iter():
|
||
|
|
Xt = transform.transform(Xt)
|
||
|
|
return Xt
|
||
|
|
|
||
|
|
@property
|
||
|
|
def inverse_transform(self):
|
||
|
|
"""Apply inverse transformations in 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.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Xt : array-like of shape (n_samples, n_features)
|
||
|
|
"""
|
||
|
|
# raise AttributeError if necessary for hasattr behaviour
|
||
|
|
# XXX: Handling the None case means we can't use if_delegate_has_method
|
||
|
|
for _, _, transform in self._iter():
|
||
|
|
transform.inverse_transform
|
||
|
|
return self._inverse_transform
|
||
|
|
|
||
|
|
def _inverse_transform(self, X):
|
||
|
|
Xt = X
|
||
|
|
reverse_iter = reversed(list(self._iter()))
|
||
|
|
for _, _, transform in reverse_iter:
|
||
|
|
Xt = transform.inverse_transform(Xt)
|
||
|
|
return Xt
|
||
|
|
|
||
|
|
@if_delegate_has_method(delegate='_final_estimator')
|
||
|
|
def score(self, X, y=None, sample_weight=None):
|
||
|
|
"""Apply transforms, and score with the final estimator
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
score : float
|
||
|
|
"""
|
||
|
|
Xt = X
|
||
|
|
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)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def classes_(self):
|
||
|
|
return self.steps[-1][-1].classes_
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _pairwise(self):
|
||
|
|
# check if first estimator expects pairwise input
|
||
|
|
return getattr(self.steps[0][1], '_pairwise', False)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def n_features_in_(self):
|
||
|
|
# delegate to first step (which will call _check_is_fitted)
|
||
|
|
return self.steps[0][1].n_features_in_
|
||
|
|
|
||
|
|
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 _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, **kwargs):
|
||
|
|
"""Construct a Pipeline from the given estimators.
|
||
|
|
|
||
|
|
This is a shorthand for the 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 estimators.
|
||
|
|
|
||
|
|
memory : str or object with the joblib.Memory interface, default=None
|
||
|
|
Used to cache the fitted transformers of the pipeline. 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.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
sklearn.pipeline.Pipeline : Class for creating a pipeline of
|
||
|
|
transforms with a final estimator.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from sklearn.naive_bayes import GaussianNB
|
||
|
|
>>> from sklearn.preprocessing import StandardScaler
|
||
|
|
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
|
||
|
|
Pipeline(steps=[('standardscaler', StandardScaler()),
|
||
|
|
('gaussiannb', GaussianNB())])
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
p : Pipeline
|
||
|
|
"""
|
||
|
|
memory = kwargs.pop('memory', None)
|
||
|
|
verbose = kwargs.pop('verbose', False)
|
||
|
|
if kwargs:
|
||
|
|
raise TypeError('Unknown keyword arguments: "{}"'
|
||
|
|
.format(list(kwargs.keys())[0]))
|
||
|
|
return Pipeline(_name_estimators(steps), memory=memory, verbose=verbose)
|
||
|
|
|
||
|
|
|
||
|
|
def _transform_one(transformer, X, y, weight, **fit_params):
|
||
|
|
res = transformer.transform(X)
|
||
|
|
# 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,
|
||
|
|
**fit_params):
|
||
|
|
"""
|
||
|
|
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``.
|
||
|
|
"""
|
||
|
|
with _print_elapsed_time(message_clsname, message):
|
||
|
|
if hasattr(transformer, 'fit_transform'):
|
||
|
|
res = transformer.fit_transform(X, y, **fit_params)
|
||
|
|
else:
|
||
|
|
res = transformer.fit(X, y, **fit_params).transform(X)
|
||
|
|
|
||
|
|
if weight is None:
|
||
|
|
return res, transformer
|
||
|
|
return res * weight, transformer
|
||
|
|
|
||
|
|
|
||
|
|
def _fit_one(transformer,
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
weight,
|
||
|
|
message_clsname='',
|
||
|
|
message=None,
|
||
|
|
**fit_params):
|
||
|
|
"""
|
||
|
|
Fits ``transformer`` to ``X`` and ``y``.
|
||
|
|
"""
|
||
|
|
with _print_elapsed_time(message_clsname, message):
|
||
|
|
return transformer.fit(X, y, **fit_params)
|
||
|
|
|
||
|
|
|
||
|
|
class FeatureUnion(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,
|
||
|
|
or removed by setting to 'drop'.
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <feature_union>`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.13
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
transformer_list : list of (string, transformer) tuples
|
||
|
|
List of transformer objects to be applied to the data. The first
|
||
|
|
half of each tuple is the name of the transformer.
|
||
|
|
|
||
|
|
.. 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.
|
||
|
|
|
||
|
|
verbose : bool, default=False
|
||
|
|
If True, the time elapsed while fitting each transformer will be
|
||
|
|
printed as it is completed.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
sklearn.pipeline.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...]])
|
||
|
|
"""
|
||
|
|
_required_parameters = ["transformer_list"]
|
||
|
|
|
||
|
|
@_deprecate_positional_args
|
||
|
|
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
|
||
|
|
self._validate_transformers()
|
||
|
|
|
||
|
|
def get_params(self, deep=True):
|
||
|
|
"""Get parameters for this estimator.
|
||
|
|
|
||
|
|
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()``.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self
|
||
|
|
"""
|
||
|
|
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:
|
||
|
|
# TODO: Remove in 0.24 when None is removed
|
||
|
|
if t is None:
|
||
|
|
warnings.warn("Using None as a transformer is deprecated "
|
||
|
|
"in version 0.22 and will be removed in "
|
||
|
|
"version 0.24. Please use 'drop' instead.",
|
||
|
|
FutureWarning)
|
||
|
|
continue
|
||
|
|
if t == 'drop':
|
||
|
|
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 _iter(self):
|
||
|
|
"""
|
||
|
|
Generate (name, trans, weight) tuples excluding None and
|
||
|
|
'drop' transformers.
|
||
|
|
"""
|
||
|
|
get_weight = (self.transformer_weights or {}).get
|
||
|
|
return ((name, trans, get_weight(name))
|
||
|
|
for name, trans in self.transformer_list
|
||
|
|
if trans is not None and trans != 'drop')
|
||
|
|
|
||
|
|
def get_feature_names(self):
|
||
|
|
"""Get feature names from all transformers.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
feature_names : list of strings
|
||
|
|
Names of the features produced by transform.
|
||
|
|
"""
|
||
|
|
feature_names = []
|
||
|
|
for name, trans, weight in self._iter():
|
||
|
|
if not hasattr(trans, 'get_feature_names'):
|
||
|
|
raise AttributeError("Transformer %s (type %s) does not "
|
||
|
|
"provide get_feature_names."
|
||
|
|
% (str(name), type(trans).__name__))
|
||
|
|
feature_names.extend([name + "__" + f for f in
|
||
|
|
trans.get_feature_names()])
|
||
|
|
return feature_names
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : FeatureUnion
|
||
|
|
This estimator
|
||
|
|
"""
|
||
|
|
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.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
X_t : array-like or sparse matrix of \
|
||
|
|
shape (n_samples, sum_n_components)
|
||
|
|
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)
|
||
|
|
|
||
|
|
if any(sparse.issparse(f) for f in Xs):
|
||
|
|
Xs = sparse.hstack(Xs).tocsr()
|
||
|
|
else:
|
||
|
|
Xs = np.hstack(Xs)
|
||
|
|
return 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()
|
||
|
|
transformers = list(self._iter())
|
||
|
|
|
||
|
|
return Parallel(n_jobs=self.n_jobs)(delayed(func)(
|
||
|
|
transformer, X, y, weight,
|
||
|
|
message_clsname='FeatureUnion',
|
||
|
|
message=self._log_message(name, idx, len(transformers)),
|
||
|
|
**fit_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)
|
||
|
|
hstack of results of transformers. sum_n_components is the
|
||
|
|
sum of n_components (output dimension) over transformers.
|
||
|
|
"""
|
||
|
|
for _, t in self.transformer_list:
|
||
|
|
# TODO: Remove in 0.24 when None is removed
|
||
|
|
if t is None:
|
||
|
|
warnings.warn("Using None as a transformer is deprecated "
|
||
|
|
"in version 0.22 and will be removed in "
|
||
|
|
"version 0.24. Please use 'drop' instead.",
|
||
|
|
FutureWarning)
|
||
|
|
continue
|
||
|
|
Xs = Parallel(n_jobs=self.n_jobs)(
|
||
|
|
delayed(_transform_one)(trans, X, None, weight)
|
||
|
|
for name, trans, weight in self._iter())
|
||
|
|
if not Xs:
|
||
|
|
# All transformers are None
|
||
|
|
return np.zeros((X.shape[0], 0))
|
||
|
|
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 is None or old == 'drop'
|
||
|
|
else next(transformers))
|
||
|
|
for name, old in self.transformer_list]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def n_features_in_(self):
|
||
|
|
# X is passed to all transformers so we just delegate to the first one
|
||
|
|
return self.transformer_list[0][1].n_features_in_
|
||
|
|
|
||
|
|
def _sk_visual_block_(self):
|
||
|
|
names, transformers = zip(*self.transformer_list)
|
||
|
|
return _VisualBlock('parallel', transformers, names=names)
|
||
|
|
|
||
|
|
|
||
|
|
def make_union(*transformers, **kwargs):
|
||
|
|
"""
|
||
|
|
Construct a FeatureUnion from the given transformers.
|
||
|
|
|
||
|
|
This is a shorthand for the 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
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
sklearn.pipeline.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())])
|
||
|
|
"""
|
||
|
|
n_jobs = kwargs.pop('n_jobs', None)
|
||
|
|
verbose = kwargs.pop('verbose', False)
|
||
|
|
if kwargs:
|
||
|
|
# We do not currently support `transformer_weights` as we may want to
|
||
|
|
# change its type spec in make_union
|
||
|
|
raise TypeError('Unknown keyword arguments: "{}"'
|
||
|
|
.format(list(kwargs.keys())[0]))
|
||
|
|
return FeatureUnion(
|
||
|
|
_name_estimators(transformers), n_jobs=n_jobs, verbose=verbose)
|