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我是机器学习的新手，并试图从 sklearn 中理解 SequentialFeatureSelector 概念。我正在为 poc 使用 Anaconda 和 Jupyter 笔记本。我已经进口了

from mlxtend.feature_selection import SequentialFeatureSelector as SFS

包裹。默认情况下 mlxtend 包不是 Anaconda 的一部分，然后我通过pip install mlxtend命令安装。

我已经为此 poc 使用了 sklearn 波士顿住房数据集，并执行了以下代码。在安装 sfs 时，我遇到了错误。

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
4

1 回答 1

1

仔细看trace，你会发现这个错误不是由scikit-learnmlxtend的模块引发的scorer.py，而是因为roc_auc_score你使用的只适用于分类问题；对于回归问题，例如您的回归问题，这是无意义的。

从文档（强调添加）：

sklearn.metrics.roc_auc_score(y_true, y_score, average='macro', sample_weight=None, max_fpr=None)

根据预测分数计算接收器操作特征曲线 (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-learn指标列表roc_auc，您可以在其中确认不适合回归。

因此，将其在您的sfs定义中更改为类似

scoring='neg_mean_squared_error'

就像在文档示例中一样SequentialFeatureSelector，或者任何其他适合回归的指标，你会没事的。

于 2019-05-14T10:39:48.953 回答