SequentialFeatureSelector ValueError：不支持连续格式

Question

我是机器学习的新手，并试图了解 SequentialFeatureSelector sklearn 的概念。我正在为 poc 使用 Anaconda 和 Jupyter 笔记本。我已经导入了

from mlxtend.feature_selection import SequentialFeatureSelector as SFS

包。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。

如何解决这个错误？

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

Answer 1

仔细看trace，你会发现这个错误不是mlxtend引发的——是scikit-learn的scorer.py模块引发的，这是因为你使用的roc_auc_score是合适的仅用于分类问题；对于回归问题，比如你这里的问题，它是没有意义的。

来自docs（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者操作特征曲线下的面积 (ROC AUC)。

注意：此实现仅限于标签指示器格式的二进制分类任务或多标签分类任务。

另请参阅 scikit-learn list of metrics 每种问题，您可以在其中确认 roc_auc 不适合回归。

所以，在您的 sfs 定义中将其更改为类似

scoring='neg_mean_squared_error'

就像SequentialFeatureSelector 的文档example 或任何其他适合回归的指标一样，你会没事的。