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pickle加载的分类器时，我得到了非常高的准确度，f测量等值。如果测试数据直接传递给未被腌制的分类器，则它会提供更低的值。我不明白为什么pickling和unpickling分类器对象正在改变它的行为方式。有人可以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是训练有素的分类器对象。当我取消它时，它与直接使用时的行为非常不同。

Answer 1

@AndreasMueller说不应该有任何区别，这是http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#loading-the-20-newgroups-dataset使用pickle的修改示例：

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 !!!"

挑选训练有素的分类器会产生与直接从新近但训练相同的分类器获得的结果不同的结果

1 个答案: