python - 单例数组 array(, dtype=object) 不能被视为有效集合

Question

不确定如何修复。非常感谢任何帮助。我看到了 Vectorization: Not a valid collection但不确定我是否理解这一点

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)

错误如下:

    ----> 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.

 ____

不确定如何修复。非常感谢任何帮助。我看到了 Vectorization: Not a valid collection但不确定我是否理解这一点

Answer 1

出现这个错误是因为你的函数 train 屏蔽了你的变量 train，因此它被传递给了它自己。

解释:

您可以像这样定义一个变量序列:

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

然后在几行之后，你定义了一个这样的方法训练:

def train(classifier, X, y):

所以实际发生的是，您以前版本的 train 已更新为新版本。这意味着 train 现在不指向您想要的 Dataframe 对象，而是指向您定义的函数。在错误中它被清除。

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

查看错误语句中的函数序列。

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。