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python - 在新的（看不见的）文本上部署文本分类模型

问题描述

我正在研究文本分类问题。我附上了一个我训练过的文本分类模型的简单虚拟片段。

如何在 new_text 上部署模型？当模型在 上使用时check_predictions，它对文本进行了正确分类，但是，当使用新数据时，分类不正确。

这是因为new_text需要矢量化吗？我错过了一些基本的东西吗？

from collections import Counter
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score, precision_score, recall_score

df = pd.read_csv("/Users/veg.csv")
print (df)

df 的前 15 行

X_train, X_test, y_train, y_test = train_test_split(df['Text'], df['Label'],random_state=1, test_size=0.2)
cv = CountVectorizer()

X_train_vectorized = cv.fit_transform(X_train)
X_test_vectorized = cv.transform(X_test)

naive_bayes = MultinomialNB()
naive_bayes.fit(X_train_vectorized, y_train)
predictions = naive_bayes.predict(X_test_vectorized)

print("Accuracy score: ", accuracy_score(y_test, predictions))
print('accuracy %s' % accuracy_score(predictions, y_test))
print(classification_report(y_test, predictions))

输出

check_predictions = []
for i in range(len(X_test)):   
    if predictions[i] == 0:
        check_predictions.append('vegetable')
    if predictions[i] == 1:
        check_predictions.append('fruit')
    if predictions[i] == 2:
        check_predictions.append('tree')
        
dummy_df = pd.DataFrame({'actual_label': list(y_test), 'prediction': check_predictions, 'Text':list(X_test)})
dummy_df.replace(to_replace=0, value='vegetable', inplace=True)
dummy_df.replace(to_replace=1, value='fruit', inplace=True)
dummy_df.replace(to_replace=2, value='tree', inplace=True)
print("DUMMY DF")
print(dummy_df.head(10))

测试df

new_data=['carrot', 'grapes',
          'banana', 'potato',
          'birch','carrot', 'grapes',
          'banana', 'potato', 'birch','carrot','grapes',
          'banana', 'potato',
          'birch','carrot', 'grapes',
          'banana', 'potato', 'birch','grapes',
          'banana', 'potato', 'birch']

new_predictions = []
for i in range(len(new_data)):    
    if predictions[i] == 0:
        new_predictions.append('vegetable')
    if predictions[i] == 1:
        new_predictions.append('fruit')
    if predictions[i] == 2:
        new_predictions.append('tree')
        
new_df = pd.DataFrame({'actual_label': list(y_test), 'prediction': new_predictions, 'Text':list(new_data)})        
new_df.replace(to_replace=0, value='vegetable', inplace=True)
new_df.replace(to_replace=1, value='fruit', inplace=True)
new_df.replace(to_replace=2, value='tree', inplace=True)
print("NEW DF")
print(new_df.head(10))

新数据df

标签： pythonmachine-learningscikit-learnnlptext-classification

解决方案

无论您输入模型的任何（新）文本都必须经过与您的训练数据完全相同的预处理步骤 - 这里 CountVectorizer已经适合您的X_train：

new_data_vectorized = cv.transform(new_data) # NOT fit_transform
new_predictions = naive_bayes.predict(new_data_vectorized)

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