Python 中的 NLP:向量化后从 SelectKBest 中获取词名 [英] NLP in Python: Obtain word names from SelectKBest after vectorizing

问题描述

我似乎无法找到我的确切问题的答案.有人可以帮忙吗?

I can't seem to find an answer to my exact problem. Can anyone help?

我的数据框(df")的简化描述:它有两列:一列是一堆文本(注释")，另一列是一个二进制变量，指示解析时间是否高于平均水平(y").

A simplified description of my dataframe ("df"): It has 2 columns: one is a bunch of text ("Notes"), and the other is a binary variable indicating if the resolution time was above average or not ("y").

我对文本做了词袋处理:

I did bag-of-words on the text:

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(lowercase=True, stop_words="english")
matrix = vectorizer.fit_transform(df["Notes"])

我的矩阵是 6290 x 4650.获取单词名称(即特征名称)没问题:

My matrix is 6290 x 4650. No problem getting the word names (i.e. feature names) :

feature_names = vectorizer.get_feature_names()
feature_names

接下来，我想知道这 4650 个中哪些与高于平均分辨率的时间最相关；并减少我可能想在预测模型中使用的矩阵.我进行了卡方检验以找出前 20 个最重要的词.

Next, I want to know which of these 4650 are most associated with above average resolution times; and reduce the matrix I may want to use in a predictive model. I do a chi-square test to find the top 20 most important words.

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
selector = SelectKBest(chi2, k=20)
selector.fit(matrix, y)
top_words = selector.get_support().nonzero()

# Pick only the most informative columns in the data.
chi_matrix = matrix[:,top_words[0]]

现在我卡住了.我如何从这个简化的矩阵(chi_matrix")中得到单词?我的功能名称是什么?我正在尝试这个:

Now I'm stuck. How do I get the words from this reduced matrix ("chi_matrix")? What are my feature names? I was trying this:

chi_matrix.feature_names[selector.get_support(indices=True)].tolist()

或

chi_matrix.feature_names[features.get_support()]

这些给了我一个错误:未找到功能名称.我错过了什么?

These gives me an error: feature_names not found. What am I missing?

A

推荐答案

在弄清楚我真正想要做什么(感谢 Daniel)并做了更多研究之后，我找到了一些其他方法来实现我的目标.

After figuring out really what I wanted to do (thanks Daniel) and doing more research, I found a couple other ways to meet my objective.

方式 1 - https://glowingpython.blogspot.com/2014/02/terms-selection-with-chi-square.html

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(lowercase=True,stop_words='english')
X = vectorizer.fit_transform(df["Notes"])

from sklearn.feature_selection import chi2
chi2score = chi2(X,df['AboveAverage'])[0]

wscores = zip(vectorizer.get_feature_names(),chi2score)
wchi2 = sorted(wscores,key=lambda x:x[1]) 
topchi2 = zip(*wchi2[-20:])
show=list(topchi2)
show

方式 2 - 这是我使用的方式，因为它对我来说最容易理解，并生成了一个很好的输出，列出了单词、chi2 分数和 p 值.此处的另一个主题:Sklearn Chi2 用于特征选择

Way 2 - This is the way I used because it was the easiest for me to understand and produced a nice output listing the word, chi2 score, and p-value. Another thread on here: Sklearn Chi2 For Feature Selection

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_selection import SelectKBest, chi2

vectorizer = CountVectorizer(lowercase=True,stop_words='english')
X = vectorizer.fit_transform(df["Notes"])

y = df['AboveAverage']

# Select 10 features with highest chi-squared statistics
chi2_selector = SelectKBest(chi2, k=10)
chi2_selector.fit(X, y)

# Look at scores returned from the selector for each feature
chi2_scores = pd.DataFrame(list(zip(vectorizer.get_feature_names(), chi2_selector.scores_, chi2_selector.pvalues_)), 
                                       columns=['ftr', 'score', 'pval'])
chi2_scores

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