In [2]:

    

%matplotlib inline
%config InlineBackend.figure_formats = {'png', 'retina'}

import scipy.stats
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.naive_bayes import BernoulliNB


    

In [55]:

    

# spam division
# making sample data values are 0 or 1
# 4 level sample data(X) ( word1, word2, word3, word4 ), category(Y) 0: no spam, 1: spam
X = np.random.randint(2, size=(10, 4))
y = np.array([0,0,0,0,1,1,1,1,1,1])

# make pandas dataframe
df = pd.DataFrame(X, columns=["word1","word2","word3","word4"])
df["result"] = y
df


    

    
Out[55]:






  

    

      

      
word1
      
word2
      
word3
      
word4
      
result
    
  
  

    

      
0
      
0
      
1
      
0
      
1
      
0
    
    

      
1
      
1
      
1
      
1
      
1
      
0
    
    

      
2
      
0
      
1
      
0
      
1
      
0
    
    

      
3
      
1
      
1
      
1
      
0
      
0
    
    

      
4
      
1
      
0
      
0
      
1
      
1
    
    

      
5
      
1
      
0
      
1
      
0
      
1
    
    

      
6
      
1
      
0
      
0
      
0
      
1
    
    

      
7
      
0
      
0
      
1
      
1
      
1
    
    

      
8
      
0
      
0
      
0
      
1
      
1
    
    

      
9
      
1
      
0
      
1
      
0
      
1
    
  

In [56]:

    

clf_bern = BernoulliNB().fit(df.ix[:,:-1], df.ix[:,-1])


    

In [57]:

    

# the number of categories(y)
clf_bern.classes_


    

    
Out[57]:





array([0, 1])

In [58]:

    

# the number of (result 0, result 1) samples
clf_bern.class_count_


    

    
Out[58]:





array([ 4.,  6.])

In [59]:

    

# the rate of (result 0, result 1) samples 
np.exp(clf_bern.class_log_prior_)


    

    
Out[59]:





array([ 0.4,  0.6])

In [60]:

    

# count of columns of each category sample
fc = clf_bern.feature_count_
fc


    

    
Out[60]:





array([[ 2.,  4.,  2.,  3.],
       [ 4.,  0.,  3.,  3.]])

In [61]:

    

fc / np.repeat(clf_bern.class_count_[:, np.newaxis], 4, axis=1)


    

    
Out[61]:





array([[ 0.5       ,  1.        ,  0.5       ,  0.75      ],
       [ 0.66666667,  0.        ,  0.5       ,  0.5       ]])

In [62]:

    

theta = np.exp(clf_bern.feature_log_prob_)
theta


    

    
Out[62]:





array([[ 0.5       ,  0.83333333,  0.5       ,  0.66666667],
       [ 0.625     ,  0.125     ,  0.5       ,  0.5       ]])

In [63]:

    

x_new = np.array([1, 1, 0, 0])


    

In [66]:

    

clf_bern.predict_proba([x_new])
# the rate of result 0(no spam) is 0.703...


    

    
Out[66]:





array([[ 0.7032967,  0.2967033]])

In [67]:

    

p = ((theta**x_new)*(1-theta)**(1-x_new)).prod(axis=1)*np.exp(clf_bern.class_log_prior_)
p / p.sum()


    

    
Out[67]:





array([ 0.7032967,  0.2967033])

In [ ]: