[使用TF-IDF分数进行文本分类的KNN

Question

我有一个CSV文件（corpus.csv），其中带有以下格式的语料库分级摘要（文本）：

Institute,    Score,    Abstract


----------------------------------------------------------------------


UoM,    3.0,    Hello, this is abstract one

UoM,    3.2,    Hello, this is abstract two and yet counting.

UoE,    3.1,    Hello, yet another abstract but this is a unique one.

UoE,    2.2,    Hello, please no more abstract.

我正在尝试使用python创建一个KNN分类程序，该程序能够获取用户输入摘要，例如“这是一个新的唯一摘要”，然后对该用户输入摘要进行分类，使其最接近语料库（CSV），并且返回预测摘要的分数/等级。我该如何实现？

我有以下代码：

from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
import numpy as np
import pandas as pd
from csv import reader,writer
import operator as op
import string

#Read data from corpus
r = reader(open('corpus.csv','r'))
abstract_list = []
score_list = []
institute_list = []
row_count = 0
for row in list(r)[1:]:
    institute,score,abstract = row
    if len(abstract.split()) > 0:
      institute_list.append(institute)
      score = float(score)
      score_list.append(score)
      abstract = abstract.translate(string.punctuation).lower()
      abstract_list.append(abstract)
      row_count = row_count + 1

print("Total processed data: ", row_count)

#Vectorize (TF-IDF, ngrams 1-4, no stop words) using sklearn -->
vectorizer = TfidfVectorizer(analyzer='word', ngram_range=(1,4),
                     min_df = 0, stop_words = 'english', sublinear_tf=True)
response = vectorizer.fit_transform(abstract_list)
feature_names = vectorizer.get_feature_names()

在上述代码中，如何将TF-IDF计算中的功能用于如上所述的KNN分类？ （可能使用sklearn.neighborsKNeighborsClassifier框架）

我具有视觉深度学习的背景，但是，我在文本分类方面缺乏很多知识，尤其是使用KNN时。任何帮助将非常感激。预先谢谢你。

Answer 1

KNN是分类算法-意味着您必须具有class属性。 KNN可以将TFIDF的输出用作输入矩阵-TrainX，但是您仍然需要TrainY-数据中每一行的类。我在前两个样本中添加了一个随机类别1，并在后两个样本中添加了一个随机类别2：

from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
import numpy as np
import pandas as pd
from csv import reader,writer
import operator as op
import string
from sklearn import neighbors

#Read data from corpus
r = reader(open('corpus.csv','r'))
abstract_list = []
score_list = []
institute_list = []
row_count = 0
for row in list(r)[1:]:
    institute,score,abstract = row[0], row[1], row[2]
    if len(abstract.split()) > 0:
      institute_list.append(institute)
      score = float(score)
      score_list.append(score)
      abstract = abstract.translate(string.punctuation).lower()
      abstract_list.append(abstract)
      row_count = row_count + 1

print("Total processed data: ", row_count)

#Vectorize (TF-IDF, ngrams 1-4, no stop words) using sklearn -->
vectorizer = TfidfVectorizer(analyzer='word', ngram_range=(1,4),
                     min_df = 0, stop_words = 'english', sublinear_tf=True)
response = vectorizer.fit_transform(abstract_list)
classes = [1,1,2,2]
feature_names = vectorizer.get_feature_names()

clf = neighbors.KNeighborsClassifier(n_neighbors=1)
clf.fit(response, classes)
clf.predict(response)

“预测”将预测每行属于哪个类。