我正在尝试为多标签分类问题创建一个LogisticRegression分类器

    traindf = pickle.load(open(self.dataLocation+"train"+self.fname+".pkl","rb"))    X, y = traindf[self.predX], traindf[self.predY]    from sklearn.preprocessing import MultiLabelBinarizer      y=MultiLabelBinarizer().fit_transform(y)     Xtrain, Xvalidate , ytrain, yvalidate = train_test_split(X, y, train_size=self.splitFactor)    pip = Pipeline([('vect', TfidfVectorizer(                        analyzer='word',                        binary=False,                        decode_error='ignore',                        dtype=<type 'numpy.int64'>,                        encoding=u'utf-8',                        input=u'content',                        lowercase=True,                        max_df=0.25,                        max_features=None,                        min_df=1,                        ngram_range=(1, 1),                        norm=u'l2',                        preprocessor=None,                        smooth_idf=True,                        stop_words='english',                        strip_accents=None,                        sublinear_tf=True,                        token_pattern=u'(?u)\\b\\w\\w+\\b',                        tokenizer=nltk.data.load('tokenizers/punkt/english.pickle'),                        use_idf=True, vocabulary=None)),('clf', LogisticRegression(                        C=10,                        class_weight=None,                        dual=False,                        fit_intercept=True,                        intercept_scaling=1,                        max_iter=100,                        multi_class='multinomial',                        n_jobs=1,                        penalty='l2',                         random_state=None,                         solver='lbfgs',                        tol=0.0001,                        verbose=0,                         warm_start=False))                ])parameters = {}   gridSearchTS = GridSearchCV(pip,parameters,n_jobs=3, verbose=1, scoring='accuracy')    gridSearchTS.fit(Xtrain, ytrain)    predictions = gridSearchTS.predict(Xvalidate )    print ('Accuracy:', accuracy_score(yvalidate, predictions))    print ('Confusion Matrix:', confusion_matrix(yvalidate, predictions))    print ('Classification Report:', classification_report(yvalidate, predictions))    testdf = pickle.load(open(self.dataLocation+"test"+self.fname+".pkl","rb"))    predictions=gridSearchTS.predict(testdf[self.predX])    testdf[self.predY] = predictions    print(testdf.info())    testdf.to_csv(self.resLocation+self.prefix+self.fname+".csv")

但我得到了以下错误

ValueError: bad input shape (326L, 559L)

完整的堆栈跟踪如下

gridSearchTS.fit(Xtrain, ytrain)  File "X:Anaconda2\lib\site-packages\sklearn\grid_search.py", line 804, in fit    return self._fit(X, y, ParameterGrid(self.param_grid))  File "X:Anaconda2\lib\site-packages\sklearn\grid_search.py", line 553, in _fit    for parameters in parameter_iterable  File "X:Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 812, in __call__    self.retrieve()  File "X:Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 762, in retrieve    raise exceptionsklearn.externals.joblib.my_exceptions.JoblibValueError: JoblibValueError___________________________________________________________________________Multiprocessing exception:...........................................................................X:\myScript.py in getUniTags(self=<predict.RbcSolver.Predictor object>, multiNomial=True)     75         Xtrain, Xvalidate , ytrain, yvalidate = train_test_split(X, y, train_size=self.splitFactor)     76                  77         parameters = {}     78          79         gridSearchTS = GridSearchCV(self.pipClassifier,parameters,n_jobs=3, verbose=1, scoring='accuracy')---> 80         gridSearchTS.fit(Xtrain, ytrain)        gridSearchTS.fit = <bound method GridSearchCV.fit of GridSearchCV(c...obs', refit=True, scoring='accuracy', verbose=1)>        Xtrain = 123     <some text here>Name: Content, dtype: object        ytrain = array([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0,..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]])     81              82         predictions = gridSearchTS.predict(Xvalidate )     83          84         print ('Accuracy:', accuracy_score(yvalidate, predictions))...........................................................................X:Anaconda2\lib\site-packages\sklearn\grid_search.py in fit(self=GridSearchCV(cv=None, error_score='raise',     ...jobs', refit=True, scoring='accuracy', verbose=1), X = 123    <some text here>Name: Content, dtype: object, y=array([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0,..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]]))    799         y : array-like, shape = [n_samples] or [n_samples, n_output], optional    800             Target relative to X for classification or regression;    801             None for unsupervised learning.    802     803         """--> 804         return self._fit(X, y, ParameterGrid(self.param_grid))        self._fit = <bound method GridSearchCV._fit of GridSearchCV(...obs', refit=True, scoring='accuracy', verbose=1)>        X = 161    <some text here>Name: Content, dtype: object        y = array([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0,..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]])        self.param_grid = {}    805     806     807 class RandomizedSearchCV(BaseSearchCV):    808     """Randomized search on hyper parameters............................................................................X:Anaconda2\lib\site-packages\sklearn\grid_search.py in _fit(self=GridSearchCV(cv=None, error_score='raise',     ...jobs', refit=True, scoring='accuracy', verbose=1), X = 123    <some text here>Name: Content, dtype: object, y=array([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0,..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]]), parameter_iterable=<sklearn.grid_search.ParameterGrid object>)    548         )(    549             delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_,    550                                     train, test, self.verbose, parameters,    551                                     self.fit_params, return_parameters=True,    552                                     error_score=self.error_score)--> 553                 for parameters in parameter_iterable        parameters = undefined        parameter_iterable = <sklearn.grid_search.ParameterGrid object>    554                 for train, test in cv)    555     556         # Out is a list of triplet: score, estimator, n_test_samples    557         n_fits = len(out)...........................................................................X:Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=Parallel(n_jobs=3), iterable=<generator object <genexpr>>)    807             if pre_dispatch == "all" or n_jobs == 1:    808                 # The iterable was consumed all at once by the above for loop.    809                 # No need to wait for async callbacks to trigger to    810                 # consumption.    811                 self._iterating = False--> 812             self.retrieve()        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=3)>    813             # Make sure that we get a last message telling us we are done    814             elapsed_time = time.time() - self._start_time    815             self._print('Done %3i out of %3i | elapsed: %s finished',    816                         (len(self._output), len(self._output),---------------------------------------------------------------------------Sub-process traceback:---------------------------------------------------------------------------ValueError                                         PID: 5360Python 2.7.11: X:Anaconda2\python.exe...........................................................................X:Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)     67     def __init__(self, iterator_slice):     68         self.items = list(iterator_slice)     69         self._size = len(self.items)     70      71     def __call__(self):---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]     73      74     def __len__(self):     75         return self._size     76 ...........................................................................X:Anaconda2\lib\site-packages\sklearn\cross_validation.pyc in _fit_and_score(estimator=Pipeline(steps=[('vect', TfidfVectorizer(analyze...      tol=0.0001, verbose=0, warm_start=False))]), X = 123    <some text here>Name: Content, dtype: object, y=memmap([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]]), scorer=make_scorer(accuracy_score), train=array([163, 164, 165, 166, 167, 168, 169, 170, 1...79, 480, 481, 482, 483, 484, 485, 486, 487, 488]), test=array([  0,   1,   2,   3,   4,   5,   6,   7,  ..., 155,       156, 157, 158, 159, 160, 161, 162]), verbose=1, parameters={}, fit_params={}, return_train_score=False, return_parameters=True, error_score='raise')   1526    1527     try:   1528         if y_train is None:   1529             estimator.fit(X_train, **fit_params)   1530         else:-> 1531             estimator.fit(X_train, y_train, **fit_params)   1532    1533     except Exception as e:   1534         if error_score == 'raise':   1535             raise...........................................................................X:Anaconda2\lib\site-packages\sklearn\pipeline.pyc in fit(self=Pipeline(steps=[('vect', TfidfVectorizer(analyze...      tol=0.0001, verbose=0, warm_start=False))]), X=29     research weeks feb rel sep hvlo diff clos...rd loihi diff aoo...Name: Content, dtype: object, y=memmap([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]]), **fit_params={})    160         y : iterable, default=None    161             Training targets. Must fulfill label requirements for all steps of    162             the pipeline.    163         """    164         Xt, fit_params = self._pre_transform(X, y, **fit_params)--> 165         self.steps[-1][-1].fit(Xt, y, **fit_params)    166         return self    167     168     def fit_transform(self, X, y=None, **fit_params):    169         """Fit all the transforms one after the other and transform the...........................................................................X:Anaconda2\lib\site-packages\sklearn\linear_model\logistic.pyc in fit(self=LogisticRegression(C=10, class_weight=None, dual...         tol=0.0001, verbose=0, warm_start=False), X=<326x17576 sparse matrix of type '<type 'numpy.f... stored elements in Compressed Sparse Row format>, y=memmap([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]]), sample_weight=None)   1137         if not isinstance(self.tol, numbers.Number) or self.tol < 0:   1138             raise ValueError("Tolerance for stopping criteria must be "   1139                              "positive; got (tol=%r)" % self.tol)   1140    1141         X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64, -> 1142                          order="C")   1143         check_classification_targets(y)   1144         self.classes_ = np.unique(y)   1145         n_samples, n_features = X.shape   1146 ...........................................................................X:Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in check_X_y(X=<326x17576 sparse matrix of type '<type 'numpy.f... stored elements in Compressed Sparse Row format>, y=memmap([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]]), accept_sparse='csr', dtype=<type 'numpy.float64'>, order='C', copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, multi_output=False, ensure_min_samples=1, ensure_min_features=1, y_numeric=False, warn_on_dtype=False, estimator=None)    510                     ensure_min_features, warn_on_dtype, estimator)    511     if multi_output:    512         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,    513                         dtype=None)    514     else:--> 515         y = column_or_1d(y, warn=True)    516         _assert_all_finite(y)    517     if y_numeric and y.dtype.kind == 'O':    518         y = y.astype(np.float64)    519 ...........................................................................X:Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in column_or_1d(y=memmap([[0, 0, 0, ..., 0, 0, 0],       [0, 0, 0..., ..., 0, 0, 0],       [0, 0, 0, ..., 0, 0, 0]]), warn=True)    546                           " expected. Please change the shape of y to "    547                           "(n_samples, ), for example using ravel().",    548                           DataConversionWarning, stacklevel=2)    549         return np.ravel(y)    550 --> 551     raise ValueError("bad input shape {0}".format(shape))    552     553     554 def check_random_state(seed):    555     """Turn seed into a np.random.RandomState instanceValueError: bad input shape (326L, 559L)___________________________________________________________________________

我应该如何转换/格式化我的X维度？

回答：

根据LogisticRegression.fit的文档：

y : array-like, shape (n_samples,)

所以y必须是一个一维数组，但你的MulitlabelBinarizer的输出将是一个具有两个维度的0-1矩阵。看起来是(326, 559)，即326行和559个不同的类。y的格式在多标签文档中有解释。你需要将LogisitcRegression模型放入一个多标签分类器中，比如one-vs-rest，这在该页面的下方有解释。还有一个多标签示例。