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stacking_classification.py
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# Stacking classifier
# Sebastian Raschka 2014-2022
# mlxtend Machine Learning Library Extensions
#
# An ensemble-learning meta-classifier for stacking
# Author: Sebastian Raschka <sebastianraschka.com>
#
# License: BSD 3 clause
import warnings
import numpy as np
from scipy import sparse
from sklearn.base import TransformerMixin, clone
from ..externals.estimator_checks import check_is_fitted
from ..externals.name_estimators import _name_estimators
from ..utils.base_compostion import _BaseXComposition
from ._base_classification import _BaseStackingClassifier
class StackingClassifier(_BaseXComposition, _BaseStackingClassifier, TransformerMixin):
"""A Stacking classifier for scikit-learn estimators for classification.
Parameters
----------
classifiers : array-like, shape = [n_classifiers]
A list of classifiers.
Invoking the `fit` method on the `StackingClassifer` will fit clones
of these original classifiers that will
be stored in the class attribute
`self.clfs_` if `use_clones=True` (default) and
`fit_base_estimators=True` (default).
meta_classifier : object
The meta-classifier to be fitted on the ensemble of
classifiers
use_probas : bool (default: False)
If True, trains meta-classifier based on predicted probabilities
instead of class labels.
drop_proba_col : string (default: None)
Drops extra "probability" column in the feature set, because it is
redundant:
p(y_c) = 1 - p(y_1) + p(y_2) + ... + p(y_{c-1}).
This can be useful for meta-classifiers that are sensitive to perfectly
collinear features.
If 'last', drops last probability column.
If 'first', drops first probability column.
Only relevant if `use_probas=True`.
average_probas : bool (default: False)
Averages the probabilities as meta features if `True`.
Only relevant if `use_probas=True`.
verbose : int, optional (default=0)
Controls the verbosity of the building process.
- `verbose=0` (default): Prints nothing
- `verbose=1`: Prints the number & name of the regressor being fitted
- `verbose=2`: Prints info about the parameters of the
regressor being fitted
- `verbose>2`: Changes `verbose` param of the underlying regressor to
self.verbose - 2
use_features_in_secondary : bool (default: False)
If True, the meta-classifier will be trained both on the predictions
of the original classifiers and the original dataset.
If False, the meta-classifier will be trained only on the predictions
of the original classifiers.
store_train_meta_features : bool (default: False)
If True, the meta-features computed from the training data used
for fitting the meta-classifier stored in the
`self.train_meta_features_` array, which can be
accessed after calling `fit`.
use_clones : bool (default: True)
Clones the classifiers for stacking classification if True (default)
or else uses the original ones, which will be refitted on the dataset
upon calling the `fit` method. Hence, if use_clones=True, the original
input classifiers will remain unmodified upon using the
StackingClassifier's `fit` method.
Setting `use_clones=False` is
recommended if you are working with estimators that are supporting
the scikit-learn fit/predict API interface but are not compatible
to scikit-learn's `clone` function.
fit_base_estimators: bool (default: True)
Refits classifiers in `classifiers` if True; uses references to the
`classifiers`, otherwise (assumes that the classifiers were
already fit).
Note: fit_base_estimators=False will enforce use_clones to be False,
and is incompatible to most scikit-learn wrappers!
For instance, if any form of cross-validation is performed
this would require the re-fitting classifiers to training folds, which
would raise a NotFitterError if fit_base_estimators=False.
(New in mlxtend v0.6.)
Attributes
----------
clfs_ : list, shape=[n_classifiers]
Fitted classifiers (clones of the original classifiers)
meta_clf_ : estimator
Fitted meta-classifier (clone of the original meta-estimator)
train_meta_features : numpy array, shape = [n_samples, n_classifiers]
meta-features for training data, where n_samples is the
number of samples
in training data and n_classifiers is the number of classfiers.
Examples
-----------
For usage examples, please see
http://rasbt.github.io/mlxtend/user_guide/classifier/StackingClassifier/
"""
def __init__(
self,
classifiers,
meta_classifier,
use_probas=False,
drop_proba_col=None,
average_probas=False,
verbose=0,
use_features_in_secondary=False,
store_train_meta_features=False,
use_clones=True,
fit_base_estimators=True,
):
self.classifiers = classifiers
self.meta_classifier = meta_classifier
self.use_probas = use_probas
allowed = {None, "first", "last"}
if drop_proba_col not in allowed:
raise ValueError(
"`drop_proba_col` must be in %s. Got %s" % (allowed, drop_proba_col)
)
self.drop_proba_col = drop_proba_col
self.average_probas = average_probas
self.verbose = verbose
self.use_features_in_secondary = use_features_in_secondary
self.store_train_meta_features = store_train_meta_features
self.use_clones = use_clones
self.fit_base_estimators = fit_base_estimators
@property
def named_classifiers(self):
return _name_estimators(self.classifiers)
def fit(self, X, y, sample_weight=None):
"""Fit ensemble classifers and the meta-classifier.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
Target values.
sample_weight : array-like, shape = [n_samples], optional
Sample weights passed as sample_weights to each regressor
in the regressors list as well as the meta_regressor.
Raises error if some regressor does not support
sample_weight in the fit() method.
Returns
-------
self : object
"""
if not self.fit_base_estimators:
warnings.warn(
"fit_base_estimators=False " "enforces use_clones to be `False`"
)
self.use_clones = False
if self.use_clones:
self.clfs_ = clone(self.classifiers)
self.meta_clf_ = clone(self.meta_classifier)
else:
self.clfs_ = self.classifiers
self.meta_clf_ = self.meta_classifier
if self.fit_base_estimators:
if self.verbose > 0:
print("Fitting %d classifiers..." % (len(self.classifiers)))
for clf in self.clfs_:
if self.verbose > 0:
i = self.clfs_.index(clf) + 1
print(
"Fitting classifier%d: %s (%d/%d)"
% (i, _name_estimators((clf,))[0][0], i, len(self.clfs_))
)
if self.verbose > 2:
if hasattr(clf, "verbose"):
clf.set_params(verbose=self.verbose - 2)
if self.verbose > 1:
print(_name_estimators((clf,))[0][1])
if sample_weight is None:
clf.fit(X, y)
else:
clf.fit(X, y, sample_weight=sample_weight)
meta_features = self.predict_meta_features(X)
if self.store_train_meta_features:
self.train_meta_features_ = meta_features
if not self.use_features_in_secondary:
pass
elif sparse.issparse(X):
meta_features = sparse.hstack((X, meta_features))
else:
meta_features = np.hstack((X, meta_features))
if sample_weight is None:
self.meta_clf_.fit(meta_features, y)
else:
self.meta_clf_.fit(meta_features, y, sample_weight=sample_weight)
return self
def get_params(self, deep=True):
"""Return estimator parameter names for GridSearch support."""
return self._get_params("named_classifiers", deep=deep)
def set_params(self, **params):
"""Set the parameters of this estimator.
Valid parameter keys can be listed with ``get_params()``.
Returns
-------
self
"""
self._set_params("classifiers", "named_classifiers", **params)
return self
def predict_meta_features(self, X):
"""Get meta-features of test-data.
Parameters
----------
X : numpy array, shape = [n_samples, n_features]
Test vectors, where n_samples is the number of samples and
n_features is the number of features.
Returns
-------
meta-features : numpy array, shape = [n_samples, n_classifiers]
Returns the meta-features for test data.
"""
check_is_fitted(self, "clfs_")
if self.use_probas:
if self.drop_proba_col == "last":
probas = np.asarray(
[clf.predict_proba(X)[:, :-1] for clf in self.clfs_]
)
elif self.drop_proba_col == "first":
probas = np.asarray([clf.predict_proba(X)[:, 1:] for clf in self.clfs_])
else:
probas = np.asarray([clf.predict_proba(X) for clf in self.clfs_])
if self.average_probas:
vals = np.average(probas, axis=0)
else:
vals = np.concatenate(probas, axis=1)
else:
vals = np.column_stack([clf.predict(X) for clf in self.clfs_])
return vals