单例数组array(<函数在0x7f3a311320d0&gt ;, dtype = object)被认为是有效的集合 [英] Singleton array array(&lt;function train at 0x7f3a311320d0&gt;, dtype=object) cannot be considered a valid collection

问题描述

不确定如何解决.任何帮助，不胜感激.我看到了向量化:不是有效的集合，但不确定我是否理解

Not sure how to fix . Any help much appreciate. I saw thi Vectorization: Not a valid collection but not sure if i understood this

train = df1.iloc[:,[4,6]]
target =df1.iloc[:,[0]]

def train(classifier, X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)
    classifier.fit(X_train, y_train)
    print ("Accuracy: %s" % classifier.score(X_test, y_test))
    return classifier

trial1 = Pipeline([
         ('vectorizer', TfidfVectorizer()),
         ('classifier', MultinomialNB()),])

train(trial1, train, target)

以下错误:

    ----> 6 train(trial1, train, target)

    <ipython-input-140-ac0e8d32795e> in train(classifier, X, y)
          1 def train(classifier, X, y):
    ----> 2     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)
          3 
          4     classifier.fit(X_train, y_train)
          5     print ("Accuracy: %s" % classifier.score(X_test, y_test))

    /home/manisha/anaconda3/lib/python3.5/site-packages/sklearn/model_selection/_split.py in train_test_split(*arrays, **options)
       1687         test_size = 0.25
       1688 
    -> 1689     arrays = indexable(*arrays)
       1690 
       1691     if stratify is not None:

    /home/manisha/anaconda3/lib/python3.5/site-packages/sklearn/utils/validation.py in indexable(*iterables)
        204         else:
        205             result.append(np.array(X))
    --> 206     check_consistent_length(*result)
        207     return result
        208 

    /home/manisha/anaconda3/lib/python3.5/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
        175     """
        176 
    --> 177     lengths = [_num_samples(X) for X in arrays if X is not None]
        178     uniques = np.unique(lengths)
        179     if len(uniques) > 1:

    /home/manisha/anaconda3/lib/python3.5/site-packages/sklearn/utils/validation.py in <listcomp>(.0)
        175     """
        176 
    --> 177     lengths = [_num_samples(X) for X in arrays if X is not None]
        178     uniques = np.unique(lengths)
        179     if len(uniques) > 1:

    /home/manisha/anaconda3/lib/python3.5/site-packages/sklearn/utils/validation.py in _num_samples(x)
        124         if len(x.shape) == 0:
        125             raise TypeError("Singleton array %r cannot be considered"
    --> 126                             " a valid collection." % x)
        127         return x.shape[0]
        128     else:

    TypeError: Singleton array array(<function train at 0x7f3a311320d0>, dtype=object) cannot be considered a valid collection.

 ____

不确定如何解决.任何帮助，不胜感激.我看到了向量化:不是有效的集合，但不确定我是否理解

Not sure how to fix . Any help much appreciate. I saw thi Vectorization: Not a valid collection but not sure if i understood this

推荐答案

出现此错误是因为函数train掩盖了变量train，因此将其传递给自身.

This error arises because your function train masks your variable train, and hence it is passed to itself.

说明:

您可以这样定义变量火车:

You define a variable train like this:

train = df1.iloc[:,[4,6]]

然后在几行之后，您将定义一个方法序列，如下所示:

Then after some lines, you define a method train like this:

def train(classifier, X, y):

因此实际发生的是，您以前的train版本已更新为新版本.这意味着train现在不再指向您想要的Dataframe对象，而是指向您定义的函数.该错误已清除.

So what actually happens is, your previous version of train is updated with new version. That means that the train now does not point to the Dataframe object as you wanted, but points to the function you defined. In the error it is cleared.

array(<function train at 0x7f3a311320d0>, dtype=object)

请参阅错误说明中的功能介绍.

解决方案:

重命名其中之一(变量或方法). 建议:将函数重命名为其他名称，例如training或training_func或类似名称.

Rename one of them (the variable or the method). Suggestion: Rename the function to some other name like training or training_func or something like that.

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