如何存储 TfidfVectorizer 以供将来在 scikit-learn 中使用？

Question

我有一个TfidfVectorizer，它对文章集合进行矢量化，然后进行特征选择。

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。我不想在训练数据集上重新运行 TfidfVectorizer() 和特征选择器。我怎么做？我知道如何使用joblib 使模型持久化，但我想知道这是否与使模型持久化相同。

Answer 1

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好了：

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

test = selector.trasnform(vectorizer.transform(['this is test']))

Answer 2

你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您需要时再次将它们加载到内存中

pickle lib docs

Answer 3

“使对象持久化”基本上意味着您要将存储在内存中的二进制代码转储到硬盘驱动器上的文件中，以便稍后在您的程序或任何其他程序中对象可以从硬盘驱动器中的文件重新加载到内存中。

包含joblib 的scikit-learn 或pickle 和cPickle 的stdlib 都可以完成这项工作。 我更喜欢cPickle，因为它明显更快。使用ipython's %timeit command：

>>> 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，如果您想在一个文件中存储多个对象，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。这适用于tuple、list 或dict。 从您的问题示例中：

# 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)