[英]Keep TFIDF result for predicting new content using Scikit for Python
[英]Keep model made with TFIDF for predicting new content using Scikit for Python
这是一个用 tf-idf 制作的用于特征提取的情感分析模型我想知道如何保存这个模型并重用它。 我尝试以这种方式保存它,但是当我加载它时,对测试文本和 fit_transform 进行相同的预处理,它给出了一个错误,模型期望 X 个特征但得到 Y
这就是我保存它的方式
filename = "model.joblib"
joblib.dump(model, filename)
这是我的 tf-idf 模型的代码
import pandas as pd
import re
import nltk
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import BernoulliNB
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
nltk.download('stopwords')
from nltk.corpus import stopwords
processed_text = ['List of pre-processed text']
y = ['List of labels']
tfidfconverter = TfidfVectorizer(max_features=10000, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))
X = tfidfconverter.fit_transform(processed_text).toarray()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
text_classifier = BernoulliNB()
text_classifier.fit(X_train, y_train)
predictions = text_classifier.predict(X_test)
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))
print(accuracy_score(y_test, predictions))
编辑:只是为了准确地将每一行放在哪里之后:
tfidfconverter = TfidfVectorizer(max_features=10000, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))
然后
tfidf_obj = tfidfconverter.fit(processed_text)//this is what will be used again
joblib.dump(tfidf_obj, 'tf-idf.joblib')
然后你做剩下的步骤,你将在训练后保存分类器,所以在之后:
text_classifier.fit(X_train, y_train)
当你想预测任何文本时,现在放 joblib.dump(model, "classifier.joblib")
tf_idf_converter = joblib.load("tf-idf.joblib")
classifier = joblib.load("classifier.joblib")
现在你有要预测的句子列表
sent = []
classifier.predict(tf_idf_converter.transform(sent))
现在打印每个句子的情绪列表
您可以首先使用以下方法将tfidf
拟合到您的训练集:
tfidfconverter = TfidfVectorizer(max_features=10000, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))
tfidf_obj = tfidfconverter.fit(processed_text)
然后找到一种方法来存储tfidf_obj
例如使用pickle
或joblib
例如:
joblib.dump(tfidf_obj, filename)
然后加载保存的tfidf_obj
并仅在您的测试集上应用transform
loaded_tfidf = joblib.load(filename)
test_new = loaded_tfidf.transform(X_test)
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