Question

我之前只使用一列（字符串类型数据）作为我的列车集，我想将另一个相应的列（浮点类型的Amount列）作为列车集与Details列一起考虑。在金额列中，负值表示借方，正值表示贷方。我如何处理这个问题，我尝试将两列连在一起但是我必须将浮点类型数量转换为不生成的字符串类型我的数据集中的任何意义。我想要包括Amount列以检查机器是否可以了解变化，这在这种情况下非常重要。提前谢谢。

Details                    |Amount               |Category
-------------------------------------------------------------                                
Tanishq Jwellery Bangalore |-990                 |jwellery
ODESK***BAL-28APR13        |240                  |Others
AEGON RELIGARE LIFE IN     |456                  |Others
INTERNET PAYMENT #999999   |-250                 |Transfer in for Card Payment
WWW.VISTAPRINT.IN          |245                  |Print
Khazana Jwellery           |-9000                |jwellery
INTERNET PAYMENT #999999   |785                  |Transfer in for Card Payment
Indian Oil                 |344                  |Fuel
Touch foot wear            |-782                 |Clothing

我的部分内容：

import pandas as pd
import numpy as np
import scipy as sp
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
import time
import matplotlib.pyplot as plt  
from sklearn.model_selection import train_test_split 

# TRAIN DATA
data= pd.read_csv('ds1.csv', delimiter=',',usecols=['Details','Amount','Category'],encoding='utf-8')
data=data[data.Category !="Others"]

target_one=data['Category']
target_list=data['Category'].unique()

# TEST DATASET
test_data=pd.read_csv('ds2.csv', delimiter='\t',usecols=['Details','Amount','Category'],encoding='utf-8')

x_train, y_train = (data.Details, data.Category )
x_test, y_test = (test_data.Details, test_data.Category)

vect = CountVectorizer(ngram_range=(1,2))
X_train = vect.fit_transform(x_train)

X_test = vect.transform(x_test)
start = time.clock()

mnb = MultinomialNB(alpha =0.13)
mnb.fit(X_train,y_train)

result= mnb.predict(X_test)
print (time.clock()-start)

accuracy_score(result,y_test)

Answer 1

如果您只想将“金额”列堆叠到使用CountVectorizer获得的文本文件矩阵，请在插入MultinomialNB之前执行此操作：

import numpy as np

X_amount = data["Amount"].as_matrix().reshape(-1, 1)
X_train = X_train.toarray()
X_train = np.hstack((X_train, X_amount))
X_test_amount = test_data["Amount"].as_matrix().reshape(-1, 1)
X_test = X_test.toarray()
X_test = np.hstack((X_test, X_test_amount))

或者如果你想继续处理X_train的稀疏矩阵：

import scipy as sp

X_amount = data["Amount"].as_matrix().reshape(-1, 1)
X_train = sp.sparse.hstack((X_train, X_amount))
X_test_amount = test_data["Amount"].as_matrix().reshape(-1, 1)
X_test = sp.sparse.hstack((X_test, X_test_amount))

但是，我认为您最终会使用ValueError: Input X must be non-negative，因为MultinomialNB适用于非负特征值......

将不同类型的列作为训练数据集

1 个答案: