python - sklearn StackingClassifier 和样本权重
问题描述
我有一个类似于的堆叠工作流程
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
import xgboost as xgb
X = np.random.random(size=(1000, 5))
y = np.random.choice([0,1], 1000)
w = np.random.random(size=(1000,))
scaler = StandardScaler()
log_reg = LogisticRegression()
params = {
'n_estimators': 10,
'max_depth': 3,
'learning_rate': 0.1
}
log_reg_pipe = make_pipeline(
scaler,
log_reg
)
stack_pipe = make_pipeline(
StackingClassifier(
estimators=[('lr', lr_stack_pipe)],
final_estimator=xgb.XGBClassifier(**params),
passthrough=True,
cv=2
)
)
我希望能够将样本权重传递给 xgboost。我的问题是如何在最终估计器中设置样本权重？
我努力了
stack_pipe.fit(X, y, sample_weights=w)抛出
ValueError: Pipeline.fit does not accept the sample_weights 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)`
解决方案
我最近还意识到堆叠估计器无法处理样本加权管道。我通过子类化 scikit-learn 中的StackingRegressor和StackingClassifier类并覆盖它的fit()方法来更好地管理管道来解决这个问题。看看以下内容：
"""Implement StackingClassifier that can handle sample-weighted Pipelines."""
from sklearn.ensemble import StackingRegressor, StackingClassifier
from copy import deepcopy
import numpy as np
from joblib import Parallel
from sklearn.base import clone
from sklearn.base import is_classifier, is_regressor
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import check_cv
from sklearn.utils import Bunch
from sklearn.utils.fixes import delayed
from sklearn.pipeline import Pipeline
ESTIMATOR_NAME_IN_PIPELINE = 'estimator'
def new_fit_single_estimator(estimator, X, y, sample_weight=None,
message_clsname=None, message=None):
"""Private function used to fit an estimator within a job."""
if sample_weight is not None:
try:
if isinstance(estimator, Pipeline):
# determine name of final estimator
estimator_name = estimator.steps[-1][0]
kwargs = {estimator_name + '__sample_weight': sample_weight}
estimator.fit(X, y, **kwargs)
else:
estimator.fit(X, y, sample_weight=sample_weight)
except TypeError as exc:
if "unexpected keyword argument 'sample_weight'" in str(exc):
raise TypeError(
"Underlying estimator {} does not support sample weights."
.format(estimator.__class__.__name__)
) from exc
raise
else:
estimator.fit(X, y)
return estimator
class FlexibleStackingClassifier(StackingClassifier):
def __init__(self, estimators, final_estimator=None, *, cv=None,
n_jobs=None, passthrough=False, verbose=0):
super().__init__(
estimators=estimators,
final_estimator=final_estimator,
cv=cv,
n_jobs=n_jobs,
passthrough=passthrough,
verbose=verbose
)
def fit(self, X, y, sample_weight=None):
"""Fit the estimators.
Parameters
----------
X : {array-like, sparse matrix} of 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 of shape (n_samples,)
Target values.
sample_weight : array-like of shape (n_samples,) or default=None
Sample weights. If None, then samples are equally weighted.
Note that this is supported only if all underlying estimators
support sample weights.
.. versionchanged:: 0.23
when not None, `sample_weight` is passed to all underlying
estimators
Returns
-------
self : object
"""
# all_estimators contains all estimators, the one to be fitted and the
# 'drop' string.
names, all_estimators = self._validate_estimators()
self._validate_final_estimator()
stack_method = [self.stack_method] * len(all_estimators)
# Fit the base estimators on the whole training data. Those
# base estimators will be used in transform, predict, and
# predict_proba. They are exposed publicly.
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(new_fit_single_estimator)(clone(est), X, y, sample_weight)
for est in all_estimators if est != 'drop'
)
self.named_estimators_ = Bunch()
est_fitted_idx = 0
for name_est, org_est in zip(names, all_estimators):
if org_est != 'drop':
self.named_estimators_[name_est] = self.estimators_[
est_fitted_idx]
est_fitted_idx += 1
else:
self.named_estimators_[name_est] = 'drop'
# To train the meta-classifier using the most data as possible, we use
# a cross-validation to obtain the output of the stacked estimators.
# To ensure that the data provided to each estimator are the same, we
# need to set the random state of the cv if there is one and we need to
# take a copy.
cv = check_cv(self.cv, y=y, classifier=is_classifier(self))
if hasattr(cv, 'random_state') and cv.random_state is None:
cv.random_state = np.random.RandomState()
self.stack_method_ = [
self._method_name(name, est, meth)
for name, est, meth in zip(names, all_estimators, stack_method)
]
fit_params = ({f"{ESTIMATOR_NAME_IN_PIPELINE}__sample_weight": sample_weight}
if sample_weight is not None
else None)
predictions = Parallel(n_jobs=self.n_jobs)(
delayed(cross_val_predict)(clone(est), X, y, cv=deepcopy(cv),
method=meth, n_jobs=self.n_jobs,
fit_params=fit_params,
verbose=self.verbose)
for est, meth in zip(all_estimators, self.stack_method_)
if est != 'drop'
)
# Only not None or not 'drop' estimators will be used in transform.
# Remove the None from the method as well.
self.stack_method_ = [
meth for (meth, est) in zip(self.stack_method_, all_estimators)
if est != 'drop'
]
X_meta = self._concatenate_predictions(X, predictions)
new_fit_single_estimator(self.final_estimator_, X_meta, y,
sample_weight=sample_weight)
return self
class FlexibleStackingRegressor(StackingRegressor):
def __init__(self, estimators, final_estimator=None, *, cv=None,
n_jobs=None, passthrough=False, verbose=0):
super().__init__(
estimators=estimators,
final_estimator=final_estimator,
cv=cv,
n_jobs=n_jobs,
passthrough=passthrough,
verbose=verbose
)
def fit(self, X, y, sample_weight=None):
"""Fit the estimators.
Parameters
----------
X : {array-like, sparse matrix} of 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 of shape (n_samples,)
Target values.
sample_weight : array-like of shape (n_samples,) or default=None
Sample weights. If None, then samples are equally weighted.
Note that this is supported only if all underlying estimators
support sample weights.
.. versionchanged:: 0.23
when not None, `sample_weight` is passed to all underlying
estimators
Returns
-------
self : object
"""
# all_estimators contains all estimators, the one to be fitted and the
# 'drop' string.
names, all_estimators = self._validate_estimators()
self._validate_final_estimator()
stack_method = [self.stack_method] * len(all_estimators)
# Fit the base estimators on the whole training data. Those
# base estimators will be used in transform, predict, and
# predict_proba. They are exposed publicly.
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(new_fit_single_estimator)(clone(est), X, y, sample_weight)
for est in all_estimators if est != 'drop'
)
self.named_estimators_ = Bunch()
est_fitted_idx = 0
for name_est, org_est in zip(names, all_estimators):
if org_est != 'drop':
self.named_estimators_[name_est] = self.estimators_[
est_fitted_idx]
est_fitted_idx += 1
else:
self.named_estimators_[name_est] = 'drop'
# To train the meta-classifier using the most data as possible, we use
# a cross-validation to obtain the output of the stacked estimators.
# To ensure that the data provided to each estimator are the same, we
# need to set the random state of the cv if there is one and we need to
# take a copy.
cv = check_cv(self.cv, y=y, classifier=is_classifier(self))
if hasattr(cv, 'random_state') and cv.random_state is None:
cv.random_state = np.random.RandomState()
self.stack_method_ = [
self._method_name(name, est, meth)
for name, est, meth in zip(names, all_estimators, stack_method)
]
fit_params = ({f"{ESTIMATOR_NAME_IN_PIPELINE}__sample_weight": sample_weight}
if sample_weight is not None
else None)
predictions = Parallel(n_jobs=self.n_jobs)(
delayed(cross_val_predict)(clone(est), X, y, cv=deepcopy(cv),
method=meth, n_jobs=self.n_jobs,
fit_params=fit_params,
verbose=self.verbose)
for est, meth in zip(all_estimators, self.stack_method_)
if est != 'drop'
)
# Only not None or not 'drop' estimators will be used in transform.
# Remove the None from the method as well.
self.stack_method_ = [
meth for (meth, est) in zip(self.stack_method_, all_estimators)
if est != 'drop'
]
X_meta = self._concatenate_predictions(X, predictions)
new_fit_single_estimator(self.final_estimator_, X_meta, y,
sample_weight=sample_weight)
return self
我包含了 Regressor 和 Classifier 版本，尽管您似乎只需要能够使用 Classifier 子类。
但请注意：您必须在管道中为估算器指定相同的名称，并且该名称必须与ESTIMATOR_NAME_IN_PIPELINE下面定义的字段一致。否则代码将无法工作。例如，这将是一个适当定义Pipeline的实例，它使用与上面显示的类定义脚本中定义的名称相同的名称：
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import TweedieRegressor
from sklearn.feature_selection import VarianceThreshold
validly_named_pipeline = Pipeline([
('variance_threshold', VarianceThreshold()),
('scaler', StandardScaler()),
('estimator', TweedieRegressor())
])
这并不理想，但这是我现在所拥有的，无论如何都应该工作。
编辑：为了清楚起见，当我覆盖该fit()方法时，我只是从 scikit 存储库中复制并粘贴了代码并进行了必要的更改，这仅构成几行。这么多粘贴的代码不是我的原创作品，而是 scikit 开发者的作品。
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