Python 如何将sklearn管道转换为普通代码？

我有一个学习代码：

我想将其转换为普通代码，如下所示：

X_train = predictors.fit_transform(X_train)
X_train = bow_vector.fit_transform(X_train)
classifier.fit(X_train)

但我经常出错。快速阅读文档没有帮助

UPD

我的确切密码是

from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer
df = pd.read_excel('data.xlsx')
from sklearn.model_selection import train_test_split

X = df['X'] 
ylabels = df['y']

X_train, X_test, y_train, y_test = train_test_split(X, ylabels, test_size=0.3, random_state=42)

标点符号列表

punctuations = string.punctuation

自然语言处理引擎

nlp = spacy.load('en')

停止词列表

stop_words = spacy.lang.en.stop_words.STOP_WORDS

加载英语标记器、标记器、解析器、NER和单词向量

parser = English()

标记器

def spacy_tokenizer(sentence):
    # Creating an token object
    mytokens = parser(sentence)

    # Lemmatizing each token and converting each token into lowercase
    mytokens = [ word.lemma_.lower().strip() if word.lemma_ != "-PRON-" else word.lower_ for word in mytokens ]

    # Removing stop words
    mytokens = [ word for word in mytokens if word not in stop_words and word not in punctuations ]

    # return preprocessed list of tokens
    return mytokens

管道的第一要素

class predictors(TransformerMixin):
    def transform(self, X, **transform_params):
        # Cleaning Text
        return [clean_text(text) for text in X]

    def fit(self, X, y=None, **fit_params):
        return self

    def get_params(self, deep=True):
        return {}

清理文本的基本功能

def clean_text(text):
    # Removing spaces and converting text into lowercase
    return text.strip().lower()

我解决了我的问题

tfidf_vector = TfidfVectorizer(tokenizer = spacy_tokenizer)

cleaner = predictors()

X_train_cleaned = cleaner.transform(X_train)

X_train_transformed = tfidf_vector.fit_transform(X_train_cleaned)

classifier = LogisticRegression(solver='lbfgs')

classifier.fit(X_train_transformed, y_train)

cleaner = predictors()

X_test_cleaned = cleaner.transform(X_test)

X_test_transformed = tfidf_vector.transform(X_test_cleaned)

---

那么，错误是什么呢？你的确切密码是什么？我现在需要知道变量的数据类型，您应该能够在

管道上调用所有这些方法，例如
X\u train=pipe.fit\u transform（X\u train）
。如果您只想使用管道，则无需“拆卸”管道。docsRelated：我知道我可以一次完成管道中的所有操作，但我想深入挖掘，一步一步地了解X_列发生了什么，要做到这一点，我想先做第一个操作，然后看X_列，然后做第二个操作，依此类推on@sokolov0我们的目标是发布一个，而不是整个代码
tfidf_vector = TfidfVectorizer(tokenizer = spacy_tokenizer)

cleaner = predictors()

X_train_cleaned = cleaner.transform(X_train)

X_train_transformed = tfidf_vector.fit_transform(X_train_cleaned)

classifier = LogisticRegression(solver='lbfgs')

classifier.fit(X_train_transformed, y_train)

cleaner = predictors()

X_test_cleaned = cleaner.transform(X_test)

X_test_transformed = tfidf_vector.transform(X_test_cleaned)