我不断收到

IndexError:仅整数、切片 (:)、省略号 (...)、numpy.newaxis (Không có)整数或 bool 数组是有效索引

同时尝试将我的数据框适合以下管道。训练和测试是两个具有相同列的数据框。有不同的列，但我只想通过 ItemSelector 关注其中的三个。

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn import preprocessing
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import Pipeline

    class ItemSelector(BaseEstimator, TransformerMixin):

        def __init__(self, column):
            self.column = column

        def fit(self, X, y=None):
            return self

        def transform(self, X):
            return X[self.column]


    def predictCases(train, test):
        target_names = sorted(list(set(train['TARGET'].values)))
        y_train = np.array([target_names.index(x) for x in train['TARGET'].values])
        y_test = np.array([target_names.index(x) for x in test['TARGET'].values])

        # train and predict
        classifier = Pipeline([
                    ('union', FeatureUnion([

                            ('text', Pipeline([
                                ('selector', ItemSelector(column='TEXT')),
                                ('tfidf_vec', TfidfVectorizer())
                            ])),

                            ('feature1', Pipeline([
                                ('selector', ItemSelector(column='CATEG_FEAT1')),
                                ('lbe', LabelEncoder())
                            ])),

                            ('feature2', Pipeline([
                                ('selector', ItemSelector(column='CATEG_FEAT2')),
                                ('lbe', LabelEncoder())
                            ]))
                    ])),
                    ('clf', OneVsRestClassifier(LinearSVC()))])
        classifier.fit(train.values, y_train)
        predicted = classifier.predict(test.values)
        return(metrics.precision_recall_fscore_support(y_test, predicted))

完全错误:

IndexError Traceback (most recent call last)
 in ()
----> 1 tt = predictCases(train_resampled, validate)

 in predictCases(train, test)
     24 ])),
     25 ('clf', OneVsRestClassifier(LinearSVC()))])
---> 26 classifier.fit(train.values, y_train)
     27 predicted = classifier.predict(test.values)
     28 return(metrics.precision_recall_fscore_support(y_test, predicted))

C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
    266 This estimator
    267 """
--> 268 Xt, fit_params = self._fit(X, y, **fit_params)
    269 if self._final_estimator is not None:
    270 self._final_estimator.fit(Xt, y, **fit_params)

C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params)
    232 pass
    233 elif hasattr(transform, "fit_transform"):
--> 234 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
    235 else:
    236 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \

C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit_transform(self, X, y, **fit_params)
    732 delayed(_fit_transform_one)(trans, name, weight, X, y,
    733 **fit_params)
--> 734 for name, trans, weight in self._iter())
    735 
    736 if not result:

C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
    756 # was dispatched. In particular this covers the edge
    757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
    759 self._iterating = True
    760 else:

C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
    606 return False
    607 else:
--> 608 self._dispatch(tasks)
    609 return True
    610 

C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
    569 dispatch_timestamp = time.time()
    570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
    572 self._jobs.append(job)
    573 

C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
    107 def apply_async(self, func, callback=None):
    108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
    110 if callback:
    111 callback(result)

    C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
        324 # Don't delay the application, to avoid keeping the input
        325 # arguments in memory
    --> 326 self.results = batch()
        327 
        328 def get(self):

    C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
        129 
        130 def __call__(self):
    --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
        132 
        133 def __len__(self):

    C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in (.0)
        129 
        130 def __call__(self):
    --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
        132 
        133 def __len__(self):

    C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, name, weight, X, y, **fit_params)
        575 **fit_params):
        576 if hasattr(transformer, 'fit_transform'):
    --> 577 res = transformer.fit_transform(X, y, **fit_params)
        578 else:
        579 res = transformer.fit(X, y, **fit_params).transform(X)

    C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit_transform(self, X, y, **fit_params)
        299 """
        300 last_step = self._final_estimator
    --> 301 Xt, fit_params = self._fit(X, y, **fit_params)
        302 if hasattr(last_step, 'fit_transform'):
        303 return last_step.fit_transform(Xt, y, **fit_params)

    C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params)
        232 pass
        233 elif hasattr(transform, "fit_transform"):
    --> 234 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
        235 else:
        236 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \

    C:\\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
        495 else:
        496 # fit method of arity 2 (supervised transformation)
    --> 497 return self.fit(X, y, **fit_params).transform(X)
        498 
        499 

     in transform(self, X)
         10 
         11 def transform(self, X):
    ---> 12 return X[self.column]

    IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

biên tập:

如果我在 fit 中使用 train 而不是 train.values ，则会出现以下错误:

TypeError: fit_transform() takes 2 positional arguments but 3 were given

1 Câu trả lời

您将 test.values(即具有原始 DataFrame 值的 numpy 数组)传递给 classifier.predict 和 classifier.fit，而您的转换器需要一个 DataFrame 对象。

关于python - 使用 FeatureUnion 拟合管道时出现 IndexError，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/44696741/