MeanEmbeddingVectorizer对象无transform属性报错问题排查

当前在开展文本分类相关工作，使用的数据集包含2列：一列为文本内容，另一列为对应分类标签。作为初学者跟随Word2Vec教程实操验证方案适配场景时，运行代码持续出现报错。

class MeanEmbeddingVectorizer(object):
    def __init__(self, word2vec):
        self.word2vec = word2vec
        # if a text is empty we should return a vector of zeros
        # with the same dimensionality as all the other vectors
        self.dim = len(next(iter(word2vec.values())))
def fit(self, X, y):
        return self
def transform(self, X):
        return np.array([
            np.mean([self.word2vec[w] for w in words if w in self.word2vec]
                    or [np.zeros(self.dim)], axis=0)
            for words in X
        ])

train_df['clean_text_tok']=[nltk.word_tokenize(i) for i in train_df['clean_text']]
model = Word2Vec(train_df['clean_text_tok'],min_count=1)
w2v = dict(zip(model.wv.index_to_key, model.wv.vectors))
modelw = MeanEmbeddingVectorizer(w2v)
# converting text to numerical data using Word2Vec
X_train_vectors_w2v = modelw.transform(X_train_tok)
X_val_vectors_w2v = modelw.transform(X_test_tok)

Dimension:  100
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-127-289141692350> in <module>
      4 modelw = MeanEmbeddingVectorizer(w2v)
      5 # converting text to numerical data using Word2Vec
----> 6 X_train_vectors_w2v = modelw.transform(X_train_tok)
      7 X_val_vectors_w2v = modelw.transform(X_test_tok)

AttributeError: 'MeanEmbeddingVectorizer' object has no attribute 'transform'

核心问题是Python类定义的缩进错误。
定义MeanEmbeddingVectorizer类时，fit()和transform()两个方法没有和类内的__init__()方法保持同一缩进层级，二者实际被定义为了独立的全局函数，不属于MeanEmbeddingVectorizer类的成员方法。因此实例化类对象后调用transform()时，会提示对象不存在该属性。

调整fit()、transform()方法的缩进，将两个方法纳入MeanEmbeddingVectorizer类的定义范围内，修正后的类定义代码如下：

class MeanEmbeddingVectorizer(object):
    def __init__(self, word2vec):
        self.word2vec = word2vec
        # 空文本返回与其他向量维度一致的零向量
        self.dim = len(next(iter(word2vec.values())))

    def fit(self, X, y):
        return self

    def transform(self, X):
        return np.array([
            np.mean([self.word2vec[w] for w in words if w in self.word2vec]
                    or [np.zeros(self.dim)], axis=0)
            for words in X
        ])

重新运行修正后的类定义代码，再执行后续的文本向量化逻辑即可解决该报错。

内容的提问来源于stack exchange，提问作者Gabry