text classification using naive bayes classifier in python

TextClassification.py

import os                        
import pandas as pd              
import re                        
import numpy as np                
from sklearn.metrics import confusion_matrix
import random
import nltk
from sklearn.metrics import recall_score, precision_score, accuracy_score
from sklearn.naive_bayes import MultinomialNB

# change the file name
data_domain = pd.read_csv("Data_Domains_Textclassification.csv") # importing the data into python using pandas data frame
print data_domain.head() # this will print the head of data_domain 
data_domain.columns #to print column names

data_domain['target'] = data_domain['Domain'].astype(str) # taking the target column in seperate variable. we will add this to final data frame when pre processing is done
data_desc = data_domain["Description"] # taking out the description column from main dataset to perform pre-processing steps

for i in range(1,len(data_desc)):
    data_desc[i] = re.sub(" \d+", " ",data_desc[i]) # this will retain only the words

# create the count vectorizer
from sklearn.feature_extraction.text import CountVectorizer
cnt_vct = CountVectorizer(analyzer='word',binary =True,token_pattern=r'\b\w\w+\b',
                             lowercase = True,ngram_range=(1,1),stop_words='english',encoding = 'ISO-8859-1',min_df=3)

# creating the document term matrix
data_mat = cnt_vct.fit_transform(data_desc)
dense = data_mat.todense()

# extracting the features and storing it in a different dataset
featureset = pd.DataFrame(dense,columns=cnt_vct.get_feature_names())
featureset["target"] = data_domain['target']
classlabel = "target" 

# shuffing the data to avoid baising 
featureset = featureset.sample(frac = 1)
featureset.shape
Targetindex = featureset.columns.get_loc("target")

np.random.seed(0) # setting the seed so that we will get same results every time

# split data into training = 70%, and test = 30% sets
from sklearn import cross_validation
dataset_train, dataset_test = cross_validation.train_test_split(np.array(featureset), train_size=0.7, test_size=0.3)
type(dataset_train)

dataset_train = pd.DataFrame(dataset_train)
dataset_test = pd.DataFrame(dataset_test)
dataset_train.columns = featureset.columns
dataset_test.columns = featureset.columns
Targetindex = dataset_train.columns.get_loc("target")
Targetindex

X = dataset_train.drop(dataset_train.columns[[Targetindex]], axis=1) # all the features except the target variable
y = dataset_train["target"] # only the target variable

X1 = dataset_test.drop(dataset_test.columns[[Targetindex]], axis=1) # all the features except the target variable
y1 = dataset_test["target"] # only the target variable

# building the naive bayes model with the data
clf = MultinomialNB().fit(X, y)
trainpred = clf.predict(X)
testpred = clf.predict(X1)

dataset_train["target"].value_counts()
dataset_test["target"].value_counts()

# creating the confusion matrix for both train and test
train_confMat = confusion_matrix(y,trainpred)                  
test_confMat = confusion_matrix(y1,testpred)  

# print the confusion matrix for train
print(train_confMat)
print(test_confMat)

# evaluate the error metrics on the predictions made
accuracy_score(dataset_train['target'],trainpred)
accuracy_score(dataset_test['target'],testpred)
acc = accuracy_score(dataset_test['target'],testpred)
rec = recall_score(dataset_test['target'],testpred,pos_label='3')
prec = precision_score(dataset_test['target'],testpred,pos_label='1')
acc