proj3_5_naive_Bayes_classification_simp.py

# -*- coding: utf-8 -*-
"""
Created on Fri Apr 16 00:47:48 2021

@author: changai
"""
from proj1_1_load_data import *
from sklearn.naive_bayes import MultinomialNB
from sklearn import model_selection
from sklearn.preprocessing import OneHotEncoder
from matplotlib.pyplot import figure, plot, xlabel, ylabel, show

# y = X[:,9].astype('float')
y = y.squeeze()
# y = np.reshape(y,(244,1))
print(y)

X = X.squeeze()
# X = X[:,range(0,8)].astype(float)
X = X.astype(float)
N, M = X.shape
print(X.shape)
X = X - np.ones((N,1)) * X.mean(axis=0)

#normalizing matrix
X = X*(1/np.std(X,axis=0))
print(X)

# attributeNames = attributeNames1[range(0,8)].tolist()
attributeNames = attributeNames1.tolist()
classNames = classNames
C = len(classNames)

np.random.seed(2450)

# Naive Bayes classifier parameters
alpha = 1 # pseudo-count, additive parameter (Laplace correction if 1.0 or Lidtstone smoothing otherwise)
fit_prior = True   # uniform prior (change to True to estimate prior from data)

# K-fold crossvalidation
K = 10
CV = model_selection.KFold(n_splits=K,shuffle=True)

# X = X[:,0:4] # using all 4 letters,
# for using e.g. only third letter or first and last try X[:,[2]] and X[:, [0,3]]

# We need to specify that the data is categorical.
# MultinomialNB does not have this functionality, but we can achieve similar
# results by doing a one-hot-encoding - the intermediate steps in in training
# the classifier are off, but the final result is corrent.
# If we didn't do the converstion MultinomialNB assumes that the numbers are
# e.g. discrete counts of tokens. Without the encoding, the value 26 wouldn't
# mean "the token 'z'", but it would mean 26 counts of some token,
# resulting in 1 and 2 meaning a difference in one count of a given token as
# opposed to the desired 'a' versus 'b'.
X = OneHotEncoder().fit_transform(X=X)
print(X.shape)

errors = np.zeros(K)
k=0
for train_index, test_index in CV.split(X):
    #print('Crossvalidation fold: {0}/{1}'.format(k+1,K))

    # extract training and test set for current CV fold
    X_train = X[train_index,:]
    y_train = y[train_index]
    X_test = X[test_index,:]
    y_test = y[test_index]

    nb_classifier = MultinomialNB(alpha=alpha,
                                  fit_prior=fit_prior)
    nb_classifier.fit(X_train, y_train)
    y_est_prob = nb_classifier.predict_proba(X_test)
    y_est = np.argmax(y_est_prob,1)
    print(y_est.shape, y_test.shape)

    errors[k] = np.sum(y_est!=y_test,dtype=float)/y_test.shape[0]
    k+=1

# Plot the classification error rate
print(errors)

figure()
plot(range(K), 100*errors)
xlabel('Numbers of K')
ylabel('Classification error rate (%)')
show()

print('Error rate: {0}%'.format(100*np.mean(errors)))