显示 k 个最近邻用于文本分类

Posted 2023-03-12

【中文标题】显示 k 个最近邻用于文本分类

【英文标题】：Show k nearest neighbors for text classification

【发布时间】：2020-05-14 08:54:04

【问题描述】：

我在语料库中有一个 CSV 文件 (corpus.csv)，其中包含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，该程序能够获取用户输入摘要，例如“这是一个新的独特摘要”，然后将此用户输入摘要分类为最接近语料库 (CSV) 和还返回预测摘要的分数/等级。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
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)
predicted = clf.predict(response)

目前，如果我使用上述代码，则“predicted”会给出输出，例如 [3.2]。但是，我也希望输出为 [3.2, UoM, "Hello, this is abstract 2 and yet counted."]

我想显示k个最近的邻居（不仅是分数，还有对应的机构名称和摘要）。我怎样才能做到这一点？

【参考方案1】：

拟合模型后，您需要run the model against a point:

>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples)
NearestNeighbors(n_neighbors=1)
>>> print(neigh.kneighbors([[1., 1., 1.]]))
(array([[0.5]]), array([[2]]))

这将返回两个数组，其中第一个是距离列表，第二个是最近邻居的索引列表。为了以您想要的格式打印，您需要根据第二个列表的索引来查找摘要。

【讨论】：

感谢您的回答。您能否告诉我如何将 .kneighbors() 用于我的用例？