酸洗训练的分类器产生与直接从新的但相同训练的分类器获得的结果不同的结果

Question

我正在尝试从Scikit-learn库中挑选一个经过训练的SVM分类器，这样我就不必一遍又一遍地训练它。但是当我将测试数据传递给从泡菜加载的分类器时，我得到了异常高的精确度、f度量等值。如果将测试数据直接传递给未酸洗的分类器，则得到的值要低得多。我不明白为什么酸洗和取消酸洗分类器对象会改变它的行为方式。有人能帮我解决这个问题吗？

我正在做这样的事情：

from sklearn.externals import joblib
joblib.dump(grid, 'grid_trained.pkl')

这里，grid是经过训练的分类器对象。当我解开它时，它的行为与直接使用时有很大的不同。

Answer 1

正如@AndreasMueller所说，应该没有任何区别，这里是一个使用pickle的http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#loading-the-20-newgroups-dataset修改后的示例

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB

# Set labels and data
categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']
twenty_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42)

# Vectorize data
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(twenty_train.data)

# TF-IDF transformation
tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)

# Train classifier
clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)

# Tag new data
docs_new = ['God is love', 'OpenGL on the GPU is fast']
X_new_counts = count_vect.transform(docs_new)
X_new_tfidf = tfidf_transformer.transform(X_new_counts)
predicted = clf.predict(X_new_tfidf)

answers = [(doc, twenty_train.target_names[category]) for doc, category in zip(docs_new, predicted)]


# Pickle the classifier
import pickle
with open('clf.pk', 'wb') as fout:
    pickle.dump(clf, fout)

# Let's clear the classifier
clf = None

with open('clf.pk', 'rb') as fin:
    clf = pickle.load(fin)

# Retag new data
docs_new = ['God is love', 'OpenGL on the GPU is fast']
X_new_counts = count_vect.transform(docs_new)
X_new_tfidf = tfidf_transformer.transform(X_new_counts)
predicted = clf.predict(X_new_tfidf)

answers_from_loaded_clf = [(doc, twenty_train.target_names[category]) for doc, category in zip(docs_new, predicted)]

assert answers_from_loaded_clf == answers
print "Answers from freshly trained classifier and loaded pre-trained classifer are the same !!!"

使用sklearn.externals.joblib时也是如此：

# Pickle the classifier
from sklearn.externals import joblib
joblib.dump(clf, 'clf.pk')

# Let's clear the classifier
clf = None

# Loads the pretrained classifier
clf = joblib.load('clf.pk')

# Retag new data
docs_new = ['God is love', 'OpenGL on the GPU is fast']
X_new_counts = count_vect.transform(docs_new)
X_new_tfidf = tfidf_transformer.transform(X_new_counts)
predicted = clf.predict(X_new_tfidf)

answers_from_loaded_clf = [(doc, twenty_train.target_names[category]) for doc, category in zip(docs_new, predicted)]

assert answers_from_loaded_clf == answers
print "Answers from freshly trained classifier and loaded pre-trained classifer are the same !!!"