Question

问题陈述：

将文本文档分类到它所属的类别，并将该类别最多分为两个级别。

示例训练集：

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：

我尝试创建一个分类器模型，尝试使用随机森林方法对类别进行分类，它总体上给了我90％。

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：

我想为模型添加两个级别，它们是Level1和Level2，添加它们的原因是当我单独运行Level1分类时，我得到了96％的准确率。我坚持分裂训练和测试数据集，并训练一个有三个分类的模型。

是否可以创建具有三种分类的模型，还是应该创建三种模型？如何拆分列车和测试数据？

EDIT1： 导入字符串导入编解码器将pandas导入为pd 导入numpy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

scikit-learn随机森林分类器本身支持多个输出（参见this example）。因此，您不需要创建三个单独的模型。

从RandomForestClassifier.fit的文档中，fit函数的输入是：

X：形状的数组或稀疏矩阵= [n_samples，n_features]

y：类似数组，shape = [n_samples]或[n_samples，n_outputs]

因此，您需要一个大小为N x 3的数组y（您的标签）作为RandomForestClassifier的输入。为了分割您的训练和测试集，您可以：

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)

您的train_label和test_label应该是大小为N x 3的数组，您可以使用它来匹配您的模型比较您的预测（注意：我没有在这里测试过，您可能需要做一些调换）。

文本[多级]具有多个输出的分类

1 个答案: