我正在做一個(gè)關(guān)于情感分析的基本 NLP 項(xiàng)目，我想使用 GridsearchCV 來優(yōu)化我的模型。下面的代碼顯示了我正在使用的示例數(shù)據(jù)框?！癈ontent”是要傳遞給 CountVectorizer 的列，“l(fā)abel”是要預(yù)測(cè)的 y 列，feature_1、feature_2 也是我希望包含在模型中的列。'content': 'Got flat way today Pot hole Another thing tick crap thing happen week list','feature_1': '1', 'feature_2': '34', 'label':1}, {'content': 'UP today Why doe head hurt badly','feature_1': '5', 'feature_2': '142', 'label':1},{'content': 'spray tan fail leg foot Ive scrubbing foot look better ', 'feature_1': '7', 'feature_2': '123', 'label':0},])我正在關(guān)注 stackoverflow 的答案：使用管道和網(wǎng)格搜索執(zhí)行功能選擇from sklearn.pipeline import FeatureUnion, Pipelinefrom sklearn.base import TransformerMixin, BaseEstimatorclass CustomFeatureExtractor(BaseEstimator, TransformerMixin):    def __init__(self, feature_1=True, feature_2=True):        self.feature_1=feature_1        self.feature_2=feature_2            def extractor(self, tweet):        features = []        if self.feature_2:                        features.append(df['feature_2'])        if self.feature_1:            features.append(df['feature_1'])                          return np.array(features)    def fit(self, raw_docs, y):        return self    def transform(self, raw_docs):                return np.vstack(tuple([self.extractor(tweet) for tweet in raw_docs]))下面是我嘗試將數(shù)據(jù)框放入的網(wǎng)格搜索：lr = LogisticRegression()# Pipelinepipe = Pipeline([('features', FeatureUnion([("vectorizer", CountVectorizer(df['content'])),                                            ("extractor", CustomFeatureExtractor())]))                 ,('classifier', lr())                ])But yields results: TypeError: 'LogisticRegression' object is not callable想知道是否還有其他更簡(jiǎn)單的方法可以做到這一點(diǎn)？