sklearn管道不工作

我是sklearn管道的新手，並從sklearn文檔研究它。我用它在movie review數據的情緒分析。數據包含兩列，第一個爲class，第二個爲text。sklearn管道不工作

input_file_df = pd.read_csv("movie-pang.csv") 
x_train = input_file_df["text"] #used complete data as train data 
y_train = input_file_df["class"]

我只用一個特點，sentiment score for each sentence.我寫了這個自定義變壓器：

class GetWorldLevelSentiment(BaseEstimator, TransformerMixin): 

def __init__(self): 
    pass 

def get_word_level_sentiment(self, word_list): 
    sentiment_score = 1 
    for word in word_list: 
     word_sentiment = swn.senti_synsets(word) 

     if len(word_sentiment) > 0: 
      word_sentiment = word_sentiment[0] 
     else: 
      continue 

     if word_sentiment.pos_score() > word_sentiment.neg_score(): 
      word_sentiment_score = word_sentiment.pos_score() 
     elif word_sentiment.pos_score() < word_sentiment.neg_score(): 
      word_sentiment_score = word_sentiment.neg_score()*(-1) 
     else: 
      word_sentiment_score = word_sentiment.pos_score() 

     print word, " " , word_sentiment_score 
     if word_sentiment_score != 0: 
      sentiment_score = sentiment_score * word_sentiment_score 

    return sentiment_score 

def transform(self, review_list, y=None): 
    sentiment_score_list = list() 
    for review in review_list: 
     sentiment_score_list.append(self.get_word_level_sentiment(review.split())) 

    return np.asarray(sentiment_score_list) 

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

管道，我用的是：

pipeline = Pipeline([ 
("word_level_sentiment",GetWorldLevelSentiment()), 
("clf", MultinomialNB())])

，然後調用合適的管道：

pipeline.fit(x_train, y_train)

但這是給下面的錯誤對我說：

This MultinomialNB instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

是否有人可以指導我什麼，我做錯了什麼？這將是一個很大的幫助。

來源

2017-07-26 sdream

請張貼錯誤和完整代碼的完整的堆棧跟蹤複製的行爲。 –

嘗試刪除這樣的括號：（「clf」，MultinomialNB） – CrazyElf

@CrazyElf。刪除括號不起作用。管道需要一個實例，而不是類。 –

這爲我工作：

class GetWorldLevelSentiment(BaseEstimator, TransformerMixin): 

def __init__(self): 
    pass 

def get_word_level_sentiment(self, word_list): 
    sentiment_score = 1 
    for word in word_list: 
     word_sentiment = swn.senti_synsets(word) 

     if len(word_sentiment) > 0: 
      word_sentiment = word_sentiment[0] 
     else: 
      continue 

     if word_sentiment.pos_score() > word_sentiment.neg_score(): 
      word_sentiment_score = word_sentiment.pos_score() 
     elif word_sentiment.pos_score() < word_sentiment.neg_score(): 
      word_sentiment_score = word_sentiment.neg_score()*(-1) 
     else: 
      word_sentiment_score = word_sentiment.pos_score() 

     print word, " " , word_sentiment_score 
     if word_sentiment_score != 0: 
      sentiment_score = sentiment_score * word_sentiment_score 

    return sentiment_score 

def transform(self, review_list, y=None): 
    sentiment_score_list = list() 
    for review in review_list: 
     sentiment_score_list.append(self.get_word_level_sentiment(review.split())) 

    return pandas.DataFrame(sentiment_score-list) 

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

來源

2017-07-27 15:46:26 sdream

sklearn管道不工作

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