保存模型以供以后预测 (OneVsRest)

Question

我想知道如何保存 OnevsRest classifier model 用于以后的预测。

我在保存它时遇到问题，因为这意味着还要保存矢量化器。我在这学到了.

这是我创建的模型：

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(strip_accents='unicode', analyzer='word', ngram_range=(1,3), norm='l2')
vectorizer.fit(train_text)
vectorizer.fit(test_text)

x_train = vectorizer.transform(train_text)
y_train = train.drop(labels = ['id','comment_text'], axis=1)

x_test = vectorizer.transform(test_text)
y_test = test.drop(labels = ['id','comment_text'], axis=1)


from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.multiclass import OneVsRestClassifier

%%time

# Using pipeline for applying logistic regression and one vs rest classifier
LogReg_pipeline = Pipeline([
                ('clf', OneVsRestClassifier(LogisticRegression(solver='sag'), n_jobs=-1)),
            ])

for category in categories:
    printmd('**Processing {} comments...**'.format(category))

    # Training logistic regression model on train data
    LogReg_pipeline.fit(x_train, train[category])

    # calculating test accuracy
    prediction = LogReg_pipeline.predict(x_test)
    print('Test accuracy is {}'.format(accuracy_score(test[category], prediction)))
    print("\n")

非常感谢任何帮助。

此致，

Answer 1

使用 joblib 您可以保存任何 Scikit-learn Pipeline 完整的所有元素，因此还包含拟合的 TfidfVectorizer.

我在这里使用 Newsgroups20 数据集的前 200 个示例重写了您的示例：

from sklearn.datasets import fetch_20newsgroups
data = fetch_20newsgroups()

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.multiclass import OneVsRestClassifier

vectorizer = TfidfVectorizer(strip_accents='unicode', analyzer='word', ngram_range=(1,3), norm='l2')

x_train = data.data[:100]
y_train = data.target[:100]

x_test =  data.data[100:200]
y_test = data.target[100:200]

# Using pipeline for applying logistic regression and one vs rest classifier
LogReg_pipeline = Pipeline([
    ('vectorizer', vectorizer),
    ('clf', OneVsRestClassifier(LogisticRegression(solver='sag', 
                                                   class_weight='balanced'), 
                                n_jobs=-1))
                           ])

# Training logistic regression model on train data
LogReg_pipeline.fit(x_train, y_train)

在上面的代码中，您只需开始定义您的训练和测试数据，然后实例化您的 TfidfVectorizer。然后，您定义包含矢量化器和 OVR 分类器的管道，并将其与训练数据相匹配。它将学习一次预测所有类。

现在您只需使用 joblib:

将整个拟合管道保存为单个预测变量

from joblib import dump, load
dump(LogReg_pipeline, 'LogReg_pipeline.joblib')

您的整个模型未以名称 'LogReg_pipeline.joblib' 保存到磁盘。您可以通过以下代码片段调用它并直接在原始数据上使用它：

clf = load('LogReg_pipeline.joblib') 
clf.predict(x_test)

您将获得对原始文本的预测，因为管道会自动对其进行矢量化处理。

保存模型以供以后预测 (OneVsRest)

Save model for later prediction (OneVsRest)

python

save

scikit-learn

multilabel-classification

tfidfvectorizer