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我正在使用已经在另一个上下文中运行的多标签分类器。

数据分类器包含来自图表的节点访问的数据，可在此处获得： https ://drive.google.com/file/d/1xD2dq4UL0UqQsuvEWgjSnFLBpjC5xlVL/view?usp=sharing

使用以下命令保存 csv：

    data_classifier2.to_csv('data_classifier2.csv', encoding='utf-8', sep=',')

代码：


    size_test = 0.2
    splits = 10
    train = []
    test = []

    X = data_classifier2

    rs = ShuffleSplit(n_splits=splits, test_size= size_test, random_state=52)
    rs.get_n_splits(X)

    for train_index, test_index in rs.split(X):
         #print("%s %s" % (train_index, test_index))
         train.append(train_index)
         test.append(test_index)

    for i in range(0, len(train)):

        size_features = len(X.columns)
        X_train = data_classifier2.iloc[train[i],65:size_features]
        y_train = data_classifier2.iloc[train[i],6:64]

        X_test = data_classifier2.iloc[test[i],65:size_features]
        y_test = data_classifier2.iloc[test[i],6:64]

        categories = y_test.columns.values.tolist()
        ids = y_test.index

        classifier_setup = build_model(test_type)

        clf = classifier_setup
        clf.fit(X_train,y_train)

        predict = clf.predict(X_test).toarray()
        probability = clf.predict_proba(X_test).toarray()

        predictions = pd.DataFrame(predict, index=ids, columns=categories) # with header
        probabilities = pd.DataFrame(probability, index=ids, columns=categories) # with header

X_train、X_test、y_train 和 y_test 的形状似乎还可以。我想这可能是任何标签概率都缺乏价值，但我不确定！无论如何，如果这是真的，我们怎么能避免呢？

错误信息：


    ---------------------------------------------------------------------------
    IndexError                                Traceback (most recent call last)
    <ipython-input-74-17328f2b4b23> in <module>
    ----> 1 probability = clf.predict_proba(X_test).toarray()

    /opt/anaconda3/lib/python3.7/site-packages/skmultilearn/problem_transform/br.py in predict_proba(self, X)
       211                     classifier.predict_proba(
        212                         self._ensure_input_format(X))
    --> 213                 )[:, 1]  # probability that label is assigned
        214 
        215         return result

    /opt/anaconda3/lib/python3.7/site-packages/scipy/sparse/_index.py in __getitem__(self, key)
         33     """
         34     def __getitem__(self, key):
    ---> 35         row, col = self._validate_indices(key)
         36         # Dispatch to specialized methods.
         37         if isinstance(row, INT_TYPES):

    /opt/anaconda3/lib/python3.7/site-packages/scipy/sparse/_index.py in _validate_indices(self, key)
        142             col = int(col)
        143             if col < -N or col >= N:
    --> 144                 raise IndexError('column index (%d) out of range' % col)
        145             if col < 0:
        146                 col += N

    IndexError: column index (1) out of range
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