20

我有一个TfidfVectorizer矢量化文章集合，然后是特征选择。

vectroizer = TfidfVectorizer()
X_train = vectroizer.fit_transform(corpus)
selector = SelectKBest(chi2, k = 5000 )
X_train_sel = selector.fit_transform(X_train, y_train)

现在，我想存储它并在其他程序中使用它。我不想TfidfVectorizer()在训练数据集上重新运行和特征选择器。我怎么做？我知道如何使用使模型持久化，joblib但我想知道这是否与使模型持久化相同。

4

3 回答 3

19

您可以简单地使用内置的 pickle 库：

import pickle
pickle.dump(vectorizer, open("vectorizer.pickle", "wb"))
pickle.dump(selector, open("selector.pickle", "wb"))

并加载它：

vectorizer = pickle.load(open("vectorizer.pickle", "rb"))
selector = pickle.load(open("selector.pickle", "rb"))

Pickle 会将对象序列化到磁盘并在需要时再次将它们加载到内存中

泡菜库文档

于 2015-09-29T14:15:53.297 回答
10

这是我使用joblib的答案：

import joblib
joblib.dump(vectorizer, 'vectorizer.pkl')
joblib.dump(selector, 'selector.pkl')

稍后，我可以加载它并准备就绪：

vectorizer = joblib.load('vectorizer.pkl')
selector = joblib.load('selector.pkl')

test = selector.trasnform(vectorizer.transform(['this is test']))
于 2015-09-24T17:21:40.243 回答
8

“使对象持久化”基本上意味着您将转储存储在内存中的二进制代码，该二进制代码表示硬盘驱动器上的文件中的对象，以便稍后在您的程序或任何其他程序中该对象可以是从硬盘驱动器中的文件重新加载到内存中。

包括 scikit-learnjoblib或 stdlibpickle都cPickle可以完成这项工作。我倾向于更喜欢cPickle，因为它明显更快。使用ipython 的 %timeit 命令：

>>> from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF
>>> t = TFIDF()
>>> t.fit_transform(['hello world'], ['this is a test'])

# generic serializer - deserializer test
>>> def dump_load_test(tfidf, serializer):
...:    with open('vectorizer.bin', 'w') as f:
...:        serializer.dump(tfidf, f)
...:    with open('vectorizer.bin', 'r') as f:
...:        return serializer.load(f)

# joblib has a slightly different interface
>>> def joblib_test(tfidf):
...:    joblib.dump(tfidf, 'tfidf.bin')
...:    return joblib.load('tfidf.bin')

# Now, time it!
>>> %timeit joblib_test(t)
100 loops, best of 3: 3.09 ms per loop

>>> %timeit dump_load_test(t, pickle)
100 loops, best of 3: 2.16 ms per loop

>>> %timeit dump_load_test(t, cPickle)
1000 loops, best of 3: 879 µs per loop

现在，如果您想将多个对象存储在一个文件中，您可以轻松地创建一个数据结构来存储它们，然后转储数据结构本身。这将与或tuple一起使用。从您的问题示例中：listdict

# train
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(corpus)
selector = SelectKBest(chi2, k = 5000 )
X_train_sel = selector.fit_transform(X_train, y_train)

# dump as a dict
data_struct = {'vectorizer': vectorizer, 'selector': selector}
# use the 'with' keyword to automatically close the file after the dump
with open('storage.bin', 'wb') as f: 
    cPickle.dump(data_struct, f)

稍后或在另一个程序中，以下语句将带回程序内存中的数据结构：

# reload
with open('storage.bin', 'rb') as f:
    data_struct = cPickle.load(f)
    vectorizer, selector = data_struct['vectorizer'], data_struct['selector']

# do stuff...
vectors = vectorizer.transform(...)
vec_sel = selector.transform(vectors)
于 2016-03-18T15:03:38.323 回答