#!/usr/bin/env python

# coding: utf-8

# In[15]:

import numpy as np

from sklearn.naive_bayes import MultinomialNB

from sklearn.preprocessing import StandardScaler

import pandas as pd

# Architecture

num_features = 4

num_classes = 1

df = pd.read_csv('bucketed.csv')

data = df[['PM2.5 Weighted Mean 24-hr', 'CO 2nd Max 8-hr', 'SO2 2nd Max 24-hr', 'PM10 Mean 24-hr', 'Income_Bracket']] #select features

scaler = StandardScaler()

scaler.fit(data)

#prepare training and test data

msk = np.random.rand(len(df)) < 0.7

train = data[msk]

test = data[~msk]

xtrain = np.array(train.iloc[:,0:num_features], dtype=np.float32)

ytrain = np.array(train.iloc[:, num_features:(num_features + num_classes + 1)], dtype=np.float32)

xtest = np.array(test.iloc[:,0:num_features], dtype=np.float32)

ytest = np.array(test.iloc[:, num_features:(num_features + num_classes + 1)], dtype=np.float32)

#Model

clf = MultinomialNB()

clf.fit(xtrain, ytrain)

MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)

# In[16]:

#Validation

prediction = clf.predict(xtest)

correct = 0

for i in range(len(prediction)):

if prediction[i] == ytest[i]:

correct+=1

print(float(correct)/len(prediction))