python - 加载泡菜 NotFittedError：CountVectorizer - 未安装词汇

Question

我正在尝试使用 scikit 机器学习对垃圾邮件进行分类。一旦我将矢量化器和分类器转储到各自的 .pkl 文件中并在 temp.py 中导入 tem 以进行预测，我就会收到此错误：

raise NotFittedError(msg % {'name': type(estimator).__name__})
NotFittedError: CountVectorizer - Vocabulary wasn't fitted

一旦我建立一个模型保存模型名称（my_model.pkl），（vectorizer.pkl）并重新启动我的内核，但是当我在示例文本的预测过程中加载保存的模型（sample.pkl）时，它给出了相同的Volcubary not发现错误。

应用程序.py：

import pandas as pd
df = pd.read_csv('spam.csv', encoding="latin-1")
#Drop the columns not needed
df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)

#Create a new column label which has the same values as v1 then set the ham and spam values to 0 and 1 which is the standard format for our prediction
df['label'] = df['v1'].map({'ham': 0, 'spam': 1})

#Create a new column having the same values as v2 column
df['message'] = df['v2']

#Now drop the v1 and v2
df.drop(['v1', 'v2'], axis=1, inplace=True)

#print(df.head(10))

from sklearn.feature_extraction.text import CountVectorizer

bow_transformer = CountVectorizer().fit_transform(df['message'])

from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report

#Split the data
X_train, X_test, y_train, y_test = train_test_split(bow_transformer, df['label'], test_size=0.33, random_state=42)

#Naive Bayes Classifier
clf = MultinomialNB()
clf.fit(X_train,y_train)
clf.score(X_test,y_test)

y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))

pickle.dump(bow_transformer, open("vector.pkl", "wb"))
pickle.dump(clf, open("my_model.pkl", "wb"))

temp.py:::我在这个文件中做预测

from sklearn.feature_extraction.text import CountVectorizer


cv=CountVectorizer()


vectorizer = pickle.load(open("my_model.pkl", "rb"))
selector = pickle.load(open("vector.pkl", "rb"))

test_set=["heloo how are u"]

new_test=cv.transform(test_set)

标签： pythonmachine-learningscikit-learnnlpclassification

Answer 1

在您app.py中，您正在腌制文档术语矩阵而不是矢量化器，

pickle.dump(bow_transformer, open("vector.pkl", "wb"))

bow_transformer 在哪里

bow_transformer = CountVectorizer().fit_transform(df['message'])

当你temp.py解开它时，你只有文档术语矩阵。腌制它的正确方法是：

bow_transformer = CountVectorizer().fit(df['message'])
bow_transformer_dtm = bow_transformer.transform(df['message'])

现在你可以腌制你的bow_transformer使用

pickle.dump(bow_transformer, open("vector.pkl", "wb"))

这将是一个转换器，而不是文档术语矩阵。

在你的temp.py你可以解开它并使用它，如下图所示：

selector = pickle.load(open("vector.pkl", "rb"))
test_set=["heloo how are u"]
new_test=selector.transform(test_set)

希望这可以帮助！