使用 TF-IDF 分数进行文本分类的 KNN

Question

我有一个 CSV 文件 (corpus.csv)，其中包含语料库中以下格式的分级摘要（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.

我正在尝试在 python 中创建一个 KNN 分类程序，它能够获取用户输入摘要，例如 "This is a new unique abstract"，然后对最接近语料库的用户输入摘要进行分类（ CSV) 以及预测摘要的 returns score/grade。我怎样才能做到这一点？

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，如何使用TF-IDF计算的特征进行上述KNN分类？（可能使用sklearn.neighborsKNeighborsClassifier框架）

P.S。此应用案例的类是相应的 scores/grades 摘要。

我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。任何帮助将非常感激。提前谢谢你。

Answer 1

KNN 是一种 class 化算法 - 这意味着您必须具有 class 属性。 KNN 可以使用 TFIDF 的输出作为输入矩阵 - TrainX，但您仍然需要 TrainY - 数据中每一行的 class。但是，您可以使用 KNN 回归器。使用您的分数作为 class 变量：

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 = score_list
feature_names = vectorizer.get_feature_names()

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

"predict" 将预测每个实例的分数。

使用 TF-IDF 分数进行文本分类的 KNN

KNN for Text Classification using TF-IDF scores

python

machine-learning

knn

python-3.x

scikit-learn