python - 将特征名称更新为 scikit TFIdfVectorizer

Question

我正在试用这段代码

from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np

train_data = ["football is the sport","gravity is the movie", "education is imporatant"]
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                                 stop_words='english')

print "Applying first train data"
X_train = vectorizer.fit_transform(train_data)
print vectorizer.get_feature_names()

print "\n\nApplying second train data"
train_data = ["cricket", "Transformers is a film","AIMS is a college"]
X_train = vectorizer.transform(train_data)
print vectorizer.get_feature_names()

print "\n\nApplying fit transform onto second train data"
X_train = vectorizer.fit_transform(train_data)
print vectorizer.get_feature_names()

这个的输出是

Applying first train data
[u'education', u'football', u'gravity', u'imporatant', u'movie', u'sport']


Applying second train data
[u'education', u'football', u'gravity', u'imporatant', u'movie', u'sport']


 Applying fit transform onto second train data
[u'aims', u'college', u'cricket', u'film', u'transformers']

我使用 fit_transform 将第一组数据提供给矢量化器，所以它给我的特征名称如 [u'education', u'football', u'gravity', u'imporatant', u'movie', u'sport'] 之后，我将另一个训练集应用到同一个矢量化器，但它给了我相同的特征名称，因为我没有使用 fit 或 fit_transform。但我想知道如何在不覆盖以前的 oncs 的情况下更新矢量化器的功能。如果我再次使用 fit_transform，之前的功能将被覆盖。所以我想更新矢量化器的功能列表。所以我想要类似 [u'education', u'football', u'gravity', u'imporatant', u'movie', u'sport',u'aims', u'college', u 'cricket', u'film', u'transformers'] 我怎样才能得到它。

Answer 1

在 sklearn 术语中，这称为部分拟合，您不能使用 TfidfVectorizer 来完成。有两种解决方法:

• 连接两个训练集并重新向量化
• 使用支持部分拟合的HashingVectorizer。但是，由于是散列特征，因此没有 get_feature_names 方法，因此不会保留原始特征。另一个优点是内存效率更高。

第一种方法的例子:

from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np

train_data1 = ["football is the sport", "gravity is the movie", "education is important"]
vectorizer = TfidfVectorizer(stop_words='english')

print("Applying first train data")
X_train = vectorizer.fit_transform(train_data1)
print(vectorizer.get_feature_names())

print("\n\nApplying second train data")
train_data2 = ["cricket", "Transformers is a film", "AIMS is a college"]
X_train = vectorizer.transform(train_data2)
print(vectorizer.get_feature_names())

print("\n\nApplying fit transform onto second train data")
X_train = vectorizer.fit_transform(train_data1 + train_data2)
print(vectorizer.get_feature_names())

输出:

Applying first train data
['education', 'football', 'gravity', 'important', 'movie', 'sport']

Applying second train data
['education', 'football', 'gravity', 'important', 'movie', 'sport']

Applying fit transform onto second train data
['aims', 'college', 'cricket', 'education', 'film', 'football', 'gravity', 'important', 'movie', 'sport', 'transformers']