scikit-learn:分类时机正确

Question

嗨，我把tweet分为7类。我有大约25万个训练推文和另一个不同的25万个测试推文。我的代码可以在下面找到。培训。PKL是训练推文，测试。PKL测试推文。我也有相应的标签，如你所见。

当我执行我的代码时，我看到将测试集(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tweet进行分类所需的时间，即0.131999969482秒。

虽然这对我来说似乎很不可能，但这个框架能够在0.131999969482秒内对大约25万条推文进行分类。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印刷

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

X_train printen

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

Answer 1

在提取的X_train和X_test稀疏矩阵中，非零特征的形状和个数是多少?它们是否与语料库中的单词数量近似相关?

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量)。

编辑:获取稀疏矩阵X_train和X_test的内容统计:

>>> 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])