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嗨，我将推文分为 7 类。我有大约 250.000 条训练推文和另外 250.000 条不同的测试推文。我的代码可以在下面找到。training.pkl 是训练推文， testing.pkl 是测试推文。如您所见，我也有相应的标签。

当我执行我的代码时，我发现将测试集（原始）转换为特征空间需要 14.9649999142 秒。我还测量了对测试集中的所有推文进行分类所需的时间，即 0.131999969482 秒。

尽管在我看来，这个框架不太可能在 0.131999969482 秒内对大约 250.000 条推文进行分类。我现在的问题是，这是正确的吗？

file = open("training.pkl", 'rb')
training = cPickle.load(file)
file.close()


file = open("testing.pkl", 'rb')
testing = cPickle.load(file)
file.close()

file = open("ground_truth_testing.pkl", 'rb')
ground_truth_testing = cPickle.load(file)
file.close()

file = open("ground_truth_training.pkl", 'rb')
ground_truth_training = cPickle.load(file)
file.close()


print 'data loaded'
tweetsTestArray = np.array(testing)
tweetsTrainingArray = np.array(training)
y_train = np.array(ground_truth_training)


# Transform dataset to a design matrix with TFIDF and 1,2 gram
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,  ngram_range=(1, 2))

X_train = vectorizer.fit_transform(tweetsTrainingArray)
print "n_samples: %d, n_features: %d" % X_train.shape


print 'COUNT'
_t0 = time.time()
X_test = vectorizer.transform(tweetsTestArray)
print "n_samples: %d, n_features: %d" % X_test.shape
_t1 =  time.time()

print  _t1 - _t0
print 'STOP'

# TRAINING & TESTING

print 'SUPERVISED'
print '----------------------------------------------------------'
print 

print 'SGD'

#Initialize Stochastic Gradient Decent
sgd = linear_model.SGDClassifier(loss='modified_huber',alpha = 0.00003, n_iter = 25)

#Train
sgd.fit(X_train, ground_truth_training)

#Predict

print "START COUNT"
_t2 = time.time()
target_sgd = sgd.predict(X_test)
_t3 = time.time()

print _t3 -_t2
print "END COUNT"

# Print report
report_sgd = classification_report(ground_truth_testing, target_sgd)
print report_sgd
print

X_train 打印

 <248892x213162 sparse matrix of type '<type 'numpy.float64'>'
    with 4346880 stored elements in Compressed Sparse Row format>

X_train 打印

 <249993x213162 sparse matrix of type '<type 'numpy.float64'>'
    with 4205309 stored elements in Compressed Sparse Row format>
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1 回答 1

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X_train提取和X_test稀疏矩阵中非零特征的形状和数量是多少？它们是否与您的语料库中的单词数量大致相关？

分类预计比线性模型的特征提取快得多。它只是计算一个点积，因此与非零的数量直接线性（即近似于测试集中的单词数）。

编辑：获取稀疏矩阵内容的统计信息，X_train然后X_test执行以下操作：

>>> print repr(X_train)
>>> print repr(X_test)

编辑2：你的数字看起来不错。对数值特征的线性模型预测确实比特征提取快得多：

>>> from sklearn.datasets import fetch_20newsgroups
>>> from sklearn.feature_extraction.text import TfidfVectorizer
>>> twenty = fetch_20newsgroups()
>>> %time X = TfidfVectorizer().fit_transform(twenty.data)
CPU times: user 10.74 s, sys: 0.32 s, total: 11.06 s
Wall time: 11.04 s

>>> X
<11314x56436 sparse matrix of type '<type 'numpy.float64'>'
    with 1713894 stored elements in Compressed Sparse Row format>
>>> from sklearn.linear_model import SGDClassifier

>>> %time clf = SGDClassifier().fit(X, twenty.target)
CPU times: user 0.50 s, sys: 0.01 s, total: 0.51 s
Wall time: 0.51 s

>>> %time clf.predict(X)
CPU times: user 0.10 s, sys: 0.00 s, total: 0.11 s
Wall time: 0.11 s
array([7, 4, 4, ..., 3, 1, 8])
于 2013-01-09T11:51:53.700 回答