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ga.py
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ga.py
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import numpy as np
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, mean_absolute_error
PROBABILITY_OF_CROSSOVER = 0.9
PROBABILITY_OF_MUTATION = 0.01
POPULATION_SIZE = 200
TOURNAMENT_SIZE = 20
MAX_GENERATIONS = 1000
NUM_USERS = 943
NUM_MOVIES = 1682
rng = np.random.default_rng()
ratings_base = np.genfromtxt('ml-100k/ua.base', dtype='int32')
ratings_test = np.genfromtxt('ml-100k/ua.test', dtype='int32')
matrix_base = np.zeros((NUM_USERS, NUM_MOVIES), dtype='int32')
matrix_test = np.zeros((NUM_USERS, NUM_MOVIES), dtype='int32')
for r in ratings_base:
matrix_base[r[0]-1, r[1]-1] = r[2]
for r in ratings_test:
matrix_test[r[0] - 1, r[1] - 1] = r[2]
matrix_filled = matrix_base.copy()
unrated = np.where(matrix_filled == 0)
matrix_filled[unrated] = np.random.randint(low=1, high=6, size=len(unrated[1]))
def fitness(x):
return sum([pearsonr(x[neighborhood_rated[i]], neigh[neighborhood_rated[i]])[0] for i, neigh in enumerate(neighborhood)])
def selection(pop_fitness):
num_pairs = int(POPULATION_SIZE * PROBABILITY_OF_CROSSOVER / 2) # Amount of parent pairs that should be selected
parents = np.empty((num_pairs, 2), dtype='int32')
for idx in range(num_pairs):
sample = rng.choice(POPULATION_SIZE, TOURNAMENT_SIZE, replace=False)
max_pair = pop_fitness[sample].argsort()[-2:][::-1]
parents[idx] = sample[max_pair]
return parents
def crossover(parent_pairs):
offspring = []
for parent in parent_pairs:
positions = np.random.randint(low=0, high=2, size=NUM_MOVIES)
offspring.append(np.where(positions == 0, parent[0], parent[1]))
offspring.append(np.where(positions == 0, parent[1], parent[0]))
return offspring
def mutation(chromosomes):
num_mutations = int(PROBABILITY_OF_MUTATION * NUM_MOVIES)
for chrom in chromosomes:
positions = rng.choice(NUM_MOVIES, num_mutations, replace=False)
values = np.random.randint(1, 6, num_mutations)
chrom[positions] = values
chrom[user_rated] = user[user_rated]
# random_users = rng.choice(NUM_USERS, 20, replace=False)
random_users = [382]
for ind, current_user in enumerate(random_users):
user = matrix_base[current_user] # User we are evaluating
user_rated = np.where(user != 0)
user_test_pos = np.where(matrix_test[current_user] != 0)
user_test = matrix_test[current_user][user_test_pos]
user_unrated = np.where(user == 0)
others = np.delete(matrix_base, current_user, axis=0)
others_filled = np.delete(matrix_filled, current_user, axis=0) # Other users with randomly filled values
# Pair wise similarity with every user
similarity = np.array([pearsonr(user[user_rated], us[user_rated])[0] for us in others_filled])
max_users = similarity.argsort()[-10:][::-1] # Top 10 most similar users
neighborhood = others[max_users] # Neighborhood of user
# Actually rated movies of neighborhood
neighborhood_rated = [np.where(neigh != 0) for neigh in neighborhood]
best = np.empty((MAX_GENERATIONS, 10))
rmse = np.empty((MAX_GENERATIONS, 10))
mae = np.empty((MAX_GENERATIONS, 10))
optimals = []
generations = []
for i in range(10):
# Create initial population
initial_population = np.random.randint(low=1, high=6, size=(POPULATION_SIZE, NUM_MOVIES))
for chromosome in initial_population:
chromosome[user_rated] = user[user_rated] # Inserts users actual ratings
next_pop = initial_population
next_pop_fitness = np.array([fitness(x) for x in next_pop])
next_sorted_keys = next_pop_fitness.argsort()
generation = 0
last_leader_change = 0
earlier_performance = 0
finished = False
while not finished:
current_pop = next_pop
current_pop_fitness = next_pop_fitness
current_sorted_keys = next_sorted_keys
parents = selection(current_pop_fitness)
children = crossover(current_pop[parents])
mutation(children)
next_pop = current_pop.copy()
next_pop[current_sorted_keys[:len(children)]] = np.array(children)
next_pop_fitness = np.array([fitness(x) for x in next_pop])
next_sorted_keys = next_pop_fitness.argsort()
improvement = np.sum(next_pop_fitness) / np.sum(current_pop_fitness)
best_sol = next_pop[next_sorted_keys[-1]]
best_fitness = next_pop_fitness[next_sorted_keys[-1]]
last_leader_change = generation if current_pop_fitness[current_sorted_keys[-1]] < best_fitness else last_leader_change
best[generation][i] = best_fitness
rmse[generation][i] = np.sqrt(mean_squared_error(best_sol[user_test_pos], user_test))
mae[generation][i] = mean_absolute_error(best_sol[user_test_pos], user_test)
finished = generation > MAX_GENERATIONS or generation - last_leader_change > 50
if not finished and generation >= 100:
finished = best[generation, i] / best[generation - 100, i] < 1.01
generation += 1
best[generation:, i] = max(next_pop_fitness)
rmse[generation:, i] = rmse[generation-1, i]
mae[generation:, i] = mae[generation-1, i]
optimals.append(max(next_pop_fitness))
generations.append(generation)
best_avg = np.average(best[:max(generations)], axis=1)
rmse_avg = np.average(rmse[:max(generations)], axis=1)
mae_avg = np.average(mae[:max(generations)], axis=1)
print('RUN', ind, 'USER', current_user)
print('Average optimal', np.average(np.array(optimals)))
print('Average generations', np.average(np.array(generations)))
print('Average RMSE', rmse_avg[-1])
print('Average MAE', mae_avg[-1])
print('----------------------------')
plt.plot(np.arange(max(generations)), best_avg)
plt.title('Population size: ' + str(POPULATION_SIZE) + ' Crossover: ' + str(PROBABILITY_OF_CROSSOVER) + '% Mutation: '
+ str(PROBABILITY_OF_MUTATION) + '%')
plt.xlabel('Number of generations')
plt.ylabel('Average fitness of best value')
plt.grid(b=True, axis='y')
plt.show()
plt.plot(np.arange(max(generations)), rmse_avg)
plt.title('Population size: ' + str(POPULATION_SIZE) + ' Crossover: ' + str(PROBABILITY_OF_CROSSOVER) + '% Mutation: '
+ str(PROBABILITY_OF_MUTATION) + '%')
plt.xlabel('Number of generations')
plt.ylabel('Average RMSE of best value')
plt.grid(b=True, axis='y')
plt.show()
plt.plot(np.arange(max(generations)), mae_avg)
plt.title('Population size: ' + str(POPULATION_SIZE) + ' Crossover: ' + str(PROBABILITY_OF_CROSSOVER) + '% Mutation: '
+ str(PROBABILITY_OF_MUTATION) + '%')
plt.xlabel('Number of generations')
plt.ylabel('Average MAE of best value')
plt.grid(b=True, axis='y')
plt.show()