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geneticAlgo.py
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geneticAlgo.py
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from deap import base, creator
import random
import numpy as np
from deap import tools
import fitness_function as ff
from InformationGain import FeatureSelection
from sklearn import svm
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
import time
import pandas as pd
start = time.time()
class FeatureSelectionGA(FeatureSelection):
# def __init__(self,model, x, y, cv_split=5, verbose=0):
def __init__(self, csv, model = svm.SVC(), cv_split=5, verbose=0, num_feature_select = 10):
"""
Parameters:
model : scikit-learn supported model,
x : {array-like}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : {array-like}, shape = [n_samples]
Target Values
cv_split: int
Number of splits for cross_validation to calculate fitness.
verbose: 0 or 1
"""
super().__init__(csv, num_feature_select)
self.exp_IG()
self.model = model
self.csv_data = pd.read_csv(csv)
self.x = self.csv_data.iloc[:, self.top_n_features]
# self.x = self.csv_data.iloc[:, 0:14]
self.y = self.csv_data.iloc[:, 20: 21].values.reshape(-1,)
# print(self.y.shape, self.x.shape)
self.n_features = self.x.shape[1]
self.toolbox = None
self.creator = self._create()
self.cv_split = cv_split
self.verbose = verbose
if self.verbose==1:
print("Model {} will select best features among {} features using cv_split :{}.".format(model,x.shape[1],cv_split))
print("Shape od train_x: {} and target: {}".format(x.shape,y.shape))
self.final_fitness = []
self.fitness_in_generation = {}
self.best_ind = None
def evaluate(self,individual):
fit_obj = ff.FitenessFunction(self.cv_split)
np_ind = np.asarray(individual)
if np.sum(np_ind) == 0:
fitness = 0.0
else:
feature_idx = np.where(np_ind==1)[0]
fitness = fit_obj.calculate_fitness(self.model,self.x.iloc[:,feature_idx],self.y)
if self.verbose == 1:
print("Individual: {} Fitness_score: {} ".format(individual,fitness))
return fitness,
def _create(self):
creator.create("FeatureSelect", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FeatureSelect)
return creator
def _init_toolbox(self):
toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
# Structure initializers
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, self.n_features)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
return toolbox
def _default_toolbox(self):
toolbox = self._init_toolbox()
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("evaluate", self.evaluate)
return toolbox
def get_final_scores(self,pop,fits):
self.final_fitness = list(zip(pop,fits))
def generate(self,n_pop,cxpb = 0.5,mutxpb = 0.2,ngen=5,set_toolbox = False):
"""
Generate evolved population
Parameters
-----------
n_pop : {int}
population size
cxpb : {float}
crossover probablity
mutxpb: {float}
mutation probablity
n_gen : {int}
number of generations
set_toolbox : {boolean}
If True then you have to create custom toolbox before calling
method. If False use default toolbox.
Returns
--------
Fittest population
"""
if self.verbose==1:
print("Population: {}, crossover_probablity: {}, mutation_probablity: {}, total generations: {}".format(n_pop,cxpb,mutxpb,ngen))
if not set_toolbox:
self.toolbox = self._default_toolbox()
else:
raise Exception("Please create a toolbox.Use create_toolbox to create and register_toolbox to register. Else set set_toolbox = False to use defualt toolbox")
pop = self.toolbox.population(n_pop)
CXPB, MUTPB, NGEN = cxpb,mutxpb,ngen
# print(pop)
# Evaluate the entire population
print("EVOLVING.......")
fitnesses = list(map(self.toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
# print(ind, ind.fitness, ind.fitness.values)
for g in range(NGEN):
print("-- GENERATION {} --".format(g+1))
offspring = self.toolbox.select(pop, len(pop))
self.fitness_in_generation[str(g+1)] = max([ind.fitness.values[0] for ind in pop])
# Clone the selected individuals
offspring = list(map(self.toolbox.clone, offspring))
# Apply crossover and mutation on the offspring
# print(offspring)
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CXPB:
self.toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
if random.random() < MUTPB:
self.toolbox.mutate(mutant)
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
weak_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = list(map(self.toolbox.evaluate, weak_ind))
for ind, fit in zip(weak_ind, fitnesses):
ind.fitness.values = fit
print("Evaluated %i individuals" % len(weak_ind))
# The population is entirely replaced by the offspring
pop[:] = offspring
# Gather all the fitnesses in one list and print the stats
fits = [ind.fitness.values[0] for ind in pop]
length = len(pop)
mean = sum(fits) / length
sum2 = sum(x*x for x in fits)
std = abs(sum2 / length - mean**2)**0.5
# if self.verbose==1:
# print(" Min %s" % min(fits))
# print(" Max %s" % max(fits))
# print(" Avg %s" % mean)
# print(" Std %s" % std)
print("-- Only the fittest survives --")
self.best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s, %s" % (self.best_ind, self.best_ind.fitness.values))
self.get_final_scores(pop,fits)
return pop
def train(self):
fit_obj = ff.FitenessFunction(10)
feature_idx = np.where(np.asarray(self.best_ind)==1)[0]
# print(feature_idx, self.best_ind)
fitness = fit_obj.calculate_fitness(self.model,self.x.iloc[:,feature_idx],self.y)
print("The accuracy using feature set {} is {}%".format(feature_idx,fitness * 100))
# clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
clf = GaussianNB()
# f = FeatureSelectionGA("australian.csv", GaussianNB)
f = FeatureSelectionGA("GermanData.csv", clf)
f.generate(30)
# f.train()
end = time.time()
print("Script run time : ", end - start)