我是机器学习的新手，正在尝试了解SequentialFeatureSelector sklearn的概念。我正在使用Anaconda和Jupyter笔记本进行POC。我已经导入
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
软件包。默认情况下，mlxtend软件包不是Anaconda的一部分，那么我已经通过 pip install mlxtend 命令进行了安装。
我为此数据使用了sklearn Boston住房数据集，并在以下代码中进行了操作。在安装sfs时，出现错误。
如何解决此错误？
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from mlxtend.feature_selection import SequentialFeatureSelector as sfs
from sklearn.metrics import roc_curve, roc_auc_score
%matplotlib inline
data = load_boston()
print(data.keys())
X = pd.DataFrame(data.data)
X.columns = data.feature_names
y = data.target
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)
sfs1=sfs(RandomForestRegressor(n_jobs=1),
k_features=7,
forward=True,
floating=False,
verbose=3,
scoring='roc_auc',
cv=3
)
sfs1=sfs1.fit(X_train,y_train)
错误
ValueError Traceback (most recent call last)
<ipython-input-77-96b29660189d> in <module>
1 #sfs1.fit(X_train,y_train)
2 X_train.shape
----> 3 sfs2=sfs1.fit(X_train,y_train)
C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\feature_selection\sequential_feature_selector.py in fit(self, X, y, custom_feature_names, **fit_params)
371 X=X_,
372 y=y,
--> 373 **fit_params
374 )
375 else:
C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\feature_selection\sequential_feature_selector.py in _inclusion(self, orig_set, subset, X, y, ignore_feature, **fit_params)
528 tuple(subset | {feature}),
529 **fit_params)
--> 530 for feature in remaining
531 if feature != ignore_feature)
532
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
915 # remaining jobs.
916 self._iterating = False
--> 917 if self.dispatch_one_batch(iterator):
918 self._iterating = self._original_iterator is not None
919
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
757 return False
758 else:
--> 759 self._dispatch(tasks)
760 return True
761
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
714 with self._lock:
715 job_idx = len(self._jobs)
--> 716 job = self._backend.apply_async(batch, callback=cb)
717 # A job can complete so quickly than its callback is
718 # called before we get here, causing self._jobs to
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
180 def apply_async(self, func, callback=None):
181 """Schedule a func to be run"""
--> 182 result = ImmediateResult(func)
183 if callback:
184 callback(result)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
547 # Don't delay the application, to avoid keeping the input
548 # arguments in memory
--> 549 self.results = batch()
550
551 def get(self):
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\feature_selection\sequential_feature_selector.py in _calc_score(selector, X, y, indices, **fit_params)
32 n_jobs=1,
33 pre_dispatch=selector.pre_dispatch,
---> 34 fit_params=fit_params)
35 else:
36 selector.est_.fit(X[:, indices], y, **fit_params)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
400 fit_params=fit_params,
401 pre_dispatch=pre_dispatch,
--> 402 error_score=error_score)
403 return cv_results['test_score']
404
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
238 return_times=True, return_estimator=return_estimator,
239 error_score=error_score)
--> 240 for train, test in cv.split(X, y, groups))
241
242 zipped_scores = list(zip(*scores))
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
915 # remaining jobs.
916 self._iterating = False
--> 917 if self.dispatch_one_batch(iterator):
918 self._iterating = self._original_iterator is not None
919
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
757 return False
758 else:
--> 759 self._dispatch(tasks)
760 return True
761
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
714 with self._lock:
715 job_idx = len(self._jobs)
--> 716 job = self._backend.apply_async(batch, callback=cb)
717 # A job can complete so quickly than its callback is
718 # called before we get here, causing self._jobs to
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
180 def apply_async(self, func, callback=None):
181 """Schedule a func to be run"""
--> 182 result = ImmediateResult(func)
183 if callback:
184 callback(result)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
547 # Don't delay the application, to avoid keeping the input
548 # arguments in memory
--> 549 self.results = batch()
550
551 def get(self):
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
566 fit_time = time.time() - start_time
567 # _score will return dict if is_multimetric is True
--> 568 test_scores = _score(estimator, X_test, y_test, scorer, is_multimetric)
569 score_time = time.time() - start_time - fit_time
570 if return_train_score:
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _score(estimator, X_test, y_test, scorer, is_multimetric)
603 """
604 if is_multimetric:
--> 605 return _multimetric_score(estimator, X_test, y_test, scorer)
606 else:
607 if y_test is None:
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _multimetric_score(estimator, X_test, y_test, scorers)
633 score = scorer(estimator, X_test)
634 else:
--> 635 score = scorer(estimator, X_test, y_test)
636
637 if hasattr(score, 'item'):
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\scorer.py in __call__(self, clf, X, y, sample_weight)
174 y_type = type_of_target(y)
175 if y_type not in ("binary", "multilabel-indicator"):
--> 176 raise ValueError("{0} format is not supported".format(y_type))
177
178 if is_regressor(clf):
ValueError: continuous format is not supported
答案 0 :(得分：1)
仔细观察一下踪迹，您会发现错误不是由mlxtend引发的，而是由scikit-learn的scorer.py模块引发的，这是因为{{1} }您使用的仅适用于分类问题；对于回归问题（例如您在此处的问题），它是毫无意义的。
在docs中（添加了重点）：
roc_auc_score（y_true，y_score，平均值=“宏”，sample_weight = None，max_fpr = None）根据预测得分计算接收器工作特性曲线（ROC AUC）下的面积。
注意：此实现仅限于标签指示符格式的二进制分类任务或多标签分类任务。
另请参阅每种问题的scikit学习list of metrics，在这里您可以确认sklearn.metrics.roc_auc_score不适合回归。
因此，将您的roc_auc定义中的内容更改为类似的内容
sfs就像scoring='neg_mean_squared_error'
的文档example中一样，或者适合任何其他适合回归的指标，就可以了。