文本[多等级]分类与许多输出

Question

问题陈述：

要分类文本文档它所属的类别并且还向上分类到的类别的两个层次。

样品训练集：

Description Category Level1 Level2 
The gun shooting that happened in Vegas killed two Crime | High Crime High 
Donald Trump elected as President of America Politics | High Politics High 
Rian won in football qualifier Sports | Low Sports Low 
Brazil won in football final Sports | High Sports High 

初步尝试：

我试图创建一个分类模型，其将尝试使用随机森林法的类别进行分类，它给了我90％总体。

代码1：

import pandas as pd 
#import numpy as np 

from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn.naive_bayes import BernoulliNB 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.model_selection import train_test_split 
#from stemming.porter2 import stem 

from nltk.corpus import stopwords 

from sklearn.model_selection import cross_val_score 

stop = stopwords.words('english') 
data_file = "Training_dataset_70k" 

#Reading the input/ dataset 
data = pd.read_csv(data_file, header = 0, delimiter= "\t", quoting = 3, encoding = "utf8") 
data = data.dropna() 

#Removing stopwords, punctuation and stemming 
data['Description'] = data['Description'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)])) 
data['Description'] = data['Description'].str.replace('[^\w\s]',' ').replace('\s+',' ') 
#data['Description'] = data['Description'].apply(lambda x: ' '.join([stem(word) for word in x.split()])) 

train_data, test_data, train_label, test_label = train_test_split(data.Description, data.Category, test_size=0.3, random_state=100) 

RF = RandomForestClassifier(n_estimators=10) 
vectorizer = TfidfVectorizer(max_features = 40000, ngram_range = (1,3), sublinear_tf = True) 
data_features = vectorizer.fit_transform(train_data) 
RF.fit(data_features, train_label) 
test_data_feature = vectorizer.transform(test_data) 
Output_predict = RF.predict(test_data_feature) 
print "Overall_Accuracy: " + str(np.mean(Output_predict == test_label)) 
with codecs.open("out_Category.txt", "w", "utf8") as out: 
    for inp, pred, act in zip(test_data, Output_predict, test_label): 
     try: 
      out.write("{}\t{}\t{}\n".format(inp, pred, act)) 
     except: 
      continue 

问题：

我想两个级别添加到模型中，他们是Level1和Level2添加它们是当我跑分类为1级的原因独自我有96％的准确性。我被困在分裂训练和测试数据集并且训练有三个分类的模型。

是否可以创建三种分类的模型或创建三种模型？如何拆分火车和测试数据？

EDIT1： 进口串 进口编解码器 进口大熊猫作为PD 进口numpy的为NP

from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn.naive_bayes import BernoulliNB 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.model_selection import train_test_split 
from stemming.porter2 import stem 

from nltk.stem import PorterStemmer 
from nltk.corpus import stopwords 

from sklearn.model_selection import cross_val_score 


stop = stopwords.words('english') 

data_file = "Training_dataset_70k" 
#Reading the input/ dataset 
data = pd.read_csv(data_file, header = 0, delimiter= "\t", quoting = 3, encoding = "utf8") 
data = data.dropna() 
#Removing stopwords, punctuation and stemming 
data['Description'] = data['Description'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)])) 
data['Description'] = data['Description'].str.replace('[^\w\s]',' ').replace('\s+',' ') 

train_data, test_data, train_label, test_label = train_test_split(data.Description, data[["Category", "Level1", "Level2"]], test_size=0.3, random_state=100) 
RF = RandomForestClassifier(n_estimators=2) 
vectorizer = TfidfVectorizer(max_features = 40000, ngram_range = (1,3), sublinear_tf = True) 
data_features = vectorizer.fit_transform(train_data) 
print len(train_data), len(train_label) 
print train_label 
RF.fit(data_features, train_label) 
test_data_feature = vectorizer.transform(test_data) 
#print test_data_feature 
Output_predict = RF.predict(test_data_feature) 
print "BreadCrumb_Accuracy: " + str(np.mean(Output_predict == test_label)) 
with codecs.open("out_bread_crumb.txt", "w", "utf8") as out: 
    for inp, pred, act in zip(test_data, Output_predict, test_label): 
     try: 
      out.write("{}\t{}\t{}\n".format(inp, pred, act)) 
     except: 
      continue 

Answer 1

的scikit学习随机森林分类本身就支持多路输出（见this example）。因此，您不需要创建三个单独的模型。

从RandomForestClassifier.fit文档，输入到fit功能是：

X : array-like or sparse matrix of shape = [n_samples, n_features]

y : array-like, shape = [n_samples] or [n_samples, n_outputs]

因此，需要作为输入大小为N×3的阵列y（您的标签），以您的RandomForestClassifier。为了分割你的训练和测试集，你可以这样做：

train_data, test_data, train_label, test_label = train_test_split(data.Description, data[['Category','Level 1','Level 2']], test_size=0.3, random_state=100) 

你train_label和test_label应该是大小为N×3，你可以用它来适应你的模型比较你的预测（NB阵列：我没有在这里测试它，你可能需要做一些转换）。