NBClassifier_.py

# -*- coding: utf-8 -*-
"""
Created on Mon Feb 19 14:50:22 2018

@author: velmurugan.m
"""
'''
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# Load dataset
data = load_breast_cancer()

# Organize our data
label_names = data['target_names']
labels = data['target']
feature_names = data['feature_names']
features = data['data']

# Split our data
train, test, train_labels, test_labels = train_test_split(features, labels, test_size=0.1, random_state=42)

# Initialize our classifier
gnb = GaussianNB()

# Train our classifier
model = gnb.fit(train, train_labels)

# Make predictions
preds = gnb.predict(test)
#print(preds)

# Evaluate accuracy
#print(accuracy_score(test_labels, preds))
'''


import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

iris = datasets.load_iris()

X = iris.data
y = iris.target
target_names = iris.target_names

pca = PCA(n_components=2)
X_r = pca.fit(X).transform(X)

print y.shape

lda = LinearDiscriminantAnalysis(n_components=2)
X_r2 = lda.fit(X, y).transform(X)

# Percentage of variance explained for each components
print('explained variance ratio (first two components): %s' % str(pca.explained_variance_ratio_))

plt.figure()
colors = ['navy', 'turquoise', 'darkorange']
lw = 2

for color, i, target_name in zip(colors, [0, 1, 2], target_names):
    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=.8, lw=lw, label=target_name)
plt.legend(loc='best', shadow=False, scatterpoints=1)
plt.title('PCA of IRIS dataset')

plt.figure()

print zip(colors, [0, 1, 2], target_names)

for color, i, target_name in zip(colors, [0, 1, 2], target_names):    
    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], alpha=.8, color=color, label=target_name)
plt.legend(loc='best', shadow=False, scatterpoints=1)
plt.title('LDA of IRIS dataset')

plt.show()