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ga_utility.py
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ga_utility.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jun 21 16:12:21 2020
@author: Sweta Shaw
"""
import scipy.io
#from PIL import Image
import numpy as np
#import matplotlib.pyplot as plt
#import numpy as np
#import seaborn as sns
#import random
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from statistics import mean
def read_HSI():
X = scipy.io.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']
y = scipy.io.loadmat('Indian_pines_gt.mat')['indian_pines_gt']
#print(f"X shape: {X.shape}\ny shape: {y.shape}")
return X, y
# flatten the 3-d data
def flatten_data(x, y):
X_flat = []
for i in range(0,200):
x_flatten = x[:,:,i].flatten()
X_flat.append(x_flatten)
y_flat = y.flatten()
return X_flat, y_flat
# initiate population with 20 individuals out of 200
# @params: x : hyperspectral 2-d data
# n : required no. of bands out of 200
# ind : no. of individuals in the given population
def initiate_population(x, n, ind):
population = np.zeros((ind, n))
for i in range(ind):
for j in range(n):
population[i][j] = np.random.randint(200)
return population
def crossover(m1, m2, population):
mate1 = []
mate2 = []
#print("m1", m1)
#print("m2", m2)
k = np.random.randint(1,4)
print("Crossover point : ", k)
#print("Population inside crossover", population)
for val in m1:
mate1.append(val)
for vals in m2:
mate2.append(vals)
#print("mate1", mate1)
#print("mate2", mate2)
for i in range(k, len(mate1)):
mate1[i], mate2[i] = m2[i], m1[i]
#print("Crossover function returns",mate1)
#print("Population inside crossover before return statement ", population)
return mate1, mate2
def mutate(ofsp):
m_ofsp = []
m_ofsp.append(ofsp)
#print("offspring before mutation", m_ofsp)
k = np.random.randint(len(ofsp))
#print("size of m_ofsp", m_ofsp[0])
m_ofsp[0][k] = np.random.randint(200)
#print("Offspring after mutation", m_ofsp)
return m_ofsp[0]
# fitness function calculates the fitness of each individual and returns the average score
# @params : x : individual (chromosome)
# X : Band dataset
# y : ground truth
def fitness(x, X, y):
score_list = []
for i in range(len(x)):
index = int(x[i])
x_data = np.reshape(X[index], (21025,1))
y_data = np.reshape(y, (21025, 1))
X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.20, random_state=42)
clf = svm.SVC(decision_function_shape='ovo', probability=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
fitness_score = f1_score(y_test, y_pred, average='weighted')
score_list.append(fitness_score)
score = mean(score_list)
print("Score list : ", score_list)
print("Average Score : ", score)
return score
def fitness_all(npool, X, Y):
fitnessScore_list = []
for i in range(len(npool)):
x = npool[i]
score = fitness(x, X, Y)
fitnessScore_list.append(score)
return fitnessScore_list