我正在尝试使用Anaconda 2.7中的scikit learn 0.17来解决一个多标签分类问题。以下是我的代码

import pandas as pdimport pickleimport refrom sklearn.cross_validation import train_test_splitfrom sklearn.metrics.metrics import classification_report, accuracy_score, confusion_matrixfrom nltk.stem import WordNetLemmatizerfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNB as MNBfrom sklearn.pipeline import Pipelinefrom sklearn.grid_search import GridSearchCVtraindf = pickle.load(open("train.pkl","rb"))X, y = traindf['colC'], traindf['colB'].as_matrix()Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7)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(Xtest)print ('Accuracy:', accuracy_score(ytest, predictions))print ('Confusion Matrix:', confusion_matrix(ytest, predictions))print ('Classification Report:', classification_report(ytest, predictions))testdf = pickle.load(open("test.pkl","rb"))predictions=gridSearchTS.predict(testdf['colC'])testdf['colB'] = predictionsprint(testdf.info())testdf.to_csv("res.csv")

我的数据看起来像这样

训练数据

colC                colBsome text           [list of tags]some text           [list of tags]

测试数据

colC                    some text           some text

但我遇到了以下错误

raise ValueError('You appear to be using a legacy multi-label data'ValueError: You appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead.

这是什么意思？

这是完整的堆栈跟踪

Traceback (most recent call last):  File "X:\asd.py", line 34, in getTags    gridSearchTS.fit(Xtrain, ytrain)  File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 804, in fit    return self._fit(X, y, ParameterGrid(self.param_grid))  File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 532, in _fit    cv = check_cv(cv, X, y, classifier=is_classifier(estimator))  File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1676, in check_cv    if type_of_target(y) in ['binary', 'multiclass']:  File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\utils\multiclass.py", line 251, in type_of_target    raise ValueError('You appear to be using a legacy multi-label data'ValueError: You appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead.

我该如何解决这个问题？我需要更改数据格式吗？为什么gridSearchTS.fit(Xtrain, ytrain)会失败？如何使X和y适合fit函数？

编辑

我尝试了

        from sklearn.preprocessing import MultiLabelBinarizer          y=MultiLabelBinarizer().fit_transform(y)              random_state = np.random.RandomState(0)        # Split into training and test        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,                                                            random_state=random_state)        # Run classifier        from sklearn import svm, datasets        classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,                                         random_state=random_state))        y_score = classifier.fit(X_train, y_train).decision_function(X_test)

但现在我得到

ValueError: could not convert string to float: <value of ColC here>

在

y_score = classifier.fit(X_train, y_train).decision_function(X_test) 

我是否也需要对X进行二值化处理？为什么我需要将X维度转换为浮点数？

回答：

文档中给出了以下示例：

>>> from sklearn.preprocessing import MultiLabelBinarizer>>> y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]>>> MultiLabelBinarizer().fit_transform(y)array([[0, 0, 1, 1, 1],       [0, 0, 1, 0, 0],       [1, 1, 0, 1, 0],       [1, 1, 1, 1, 1],       [1, 1, 1, 0, 0]])

MultiLabelBinarizer.fit_transform 接受您的标记集并可以输出二进制数组。输出应该可以传递给您的fit函数。