def n_queens_sa(nq_problem,
initial_state,
max_iters=np.inf,
num_runs=20,
verbose=False):
hp_name = 'schedule'
hp_values = [mlrose.ArithDecay(), mlrose.GeomDecay(), mlrose.ExpDecay()]
hp_values_strings = [
val.get_info__()['schedule_type'] for val in hp_values
]
# run for each hp value and append results to list
fitness_dfs = []
runs = np.arange(num_runs)
for hp_value, hp_value_string in zip(hp_values, hp_values_strings):
schedule = hp_value # set varied HP at beginning of loop
run_times = np.zeros(num_runs)
fitness_data = pd.DataFrame()
for run in runs:
run_t0 = time()
best_state, best_fitness, fitness_curve = mlrose.simulated_annealing(
problem=nq_problem,
schedule=schedule,
max_attempts=10,
max_iters=max_iters,
curve=True,
)
run_time = time() - run_t0
run_times[run] = run_time
fitness_data = pd.concat(
[fitness_data, pd.DataFrame(fitness_curve)],
axis=1,
sort=False)
fitness_data.columns = runs
fitness_data = fitness_data.fillna(method='ffill')
fitness_dfs.append(fitness_data)
# calculate and print avg time per run
avg_run_time = np.average(run_times)
print("N-Queens - SA avg run time,", hp_value_string, hp_name, ":",
avg_run_time)
# generate plots
plot_title = "N-Queens SA: fitness vs. iterations"
plotting.plot_fitness_curves(
fitness_dfs=fitness_dfs,
hp_values=hp_values_strings,
hp_name=hp_name,
title=plot_title,
)
plt.savefig('graphs/n_queens_sa_fitness.png')
plt.clf()
return fitness_dfs
def n_queens_mimic(nq_problem, max_iters=np.inf, num_runs=20, verbose=False):
# HP to vary
hp_name = 'keep_pct'
hp_values = [0.2, 0.4, 0.6]
# other hyperparameters for genetic algorithm
population_size = 200
# run for each hp value and append results to list
fitness_dfs = []
runs = np.arange(num_runs)
for hp_value in hp_values:
keep_pct = hp_value # set varied HP at beginning of loop
run_times = np.zeros(num_runs)
fitness_data = pd.DataFrame()
for run in runs:
run_t0 = time()
best_state, best_fitness, fitness_curve = mlrose.mimic(
problem=nq_problem,
pop_size=population_size,
keep_pct=keep_pct,
max_attempts=10,
max_iters=max_iters,
curve=True,
)
run_time = time() - run_t0
run_times[run] = run_time
fitness_data = pd.concat(
[fitness_data, pd.DataFrame(fitness_curve)],
axis=1,
sort=False)
fitness_data.columns = runs
fitness_data = fitness_data.fillna(method='ffill')
fitness_dfs.append(fitness_data)
# calculate and print avg time per run
avg_run_time = np.average(run_times)
print("N-Queens - MIMIC avg run time,", hp_value, hp_name, ":",
avg_run_time)
# generate plots
plot_title = "N-Queens MIMIC - " \
+ str(population_size) + " pop, " \
+ ": fit vs iter"
plotting.plot_fitness_curves(
fitness_dfs=fitness_dfs,
hp_values=hp_values,
hp_name=hp_name,
title=plot_title,
)
plt.savefig('graphs/n_queens_mimic_fitness.png')
plt.clf()
return fitness_dfs
def n_queens_rhc(nq_problem,
initial_state,
max_iters=np.inf,
num_runs=20,
verbose=False):
hp_name = 'restarts'
hp_values = [10, 20, 30]
# run for each hp value and append results to list
fitness_dfs = []
runs = np.arange(num_runs)
for hp_value in hp_values:
restarts = hp_value # set varied HP at beginning of loop
run_times = np.zeros(num_runs)
fitness_data = pd.DataFrame()
for run in runs:
run_t0 = time()
best_state, best_fitness, fitness_curve = mlrose.random_hill_climb(
problem=nq_problem,
restarts=restarts,
max_attempts=10,
max_iters=max_iters,
init_state=initial_state,
curve=True,
)
run_time = time() - run_t0
run_times[run] = run_time
fitness_data = pd.concat(
[fitness_data, pd.DataFrame(fitness_curve)],
axis=1,
sort=False)
fitness_data.columns = runs
fitness_data = fitness_data.fillna(method='ffill')
fitness_dfs.append(fitness_data)
# calculate and print avg time per run
avg_run_time = np.average(run_times)
print("N-Queens - RHC avg run time,", hp_value, hp_name, ":",
avg_run_time)
# generate plots
plot_title = "N-Queens RHC: fitness vs. iterations"
plotting.plot_fitness_curves(
fitness_dfs=fitness_dfs,
hp_values=hp_values,
hp_name=hp_name,
title=plot_title,
)
plt.savefig('graphs/n_queens_rhc_fitness.png')
plt.clf()
return fitness_dfs
def flip_flop_ga(ff_problem, max_iters=np.inf, num_runs=20, verbose=False):
# HP to vary
hp_name = 'pop_mate_pct'
hp_values = [0.25, 0.50, 0.75]
# other hyperparameters for genetic algorithm
population_size = 200
elite_dreg_ratio = 0.95
mutation_prob = 0.1
# run for each hp value and append results to list
fitness_dfs = []
runs = np.arange(num_runs)
for hp_value in hp_values:
pop_mate_pct = hp_value # set varied HP at beginning of loop
run_times = np.zeros(num_runs)
fitness_data = pd.DataFrame()
for run in runs:
run_t0 = time()
best_state, best_fitness, fitness_curve = mlrose.genetic_alg(
problem=ff_problem,
pop_size=population_size,
pop_breed_percent=pop_mate_pct,
elite_dreg_ratio=elite_dreg_ratio,
mutation_prob=mutation_prob,
max_attempts=10,
max_iters=max_iters,
curve=True,
)
run_time = time() - run_t0
run_times[run] = run_time
fitness_data = pd.concat(
[fitness_data, pd.DataFrame(fitness_curve)],
axis=1,
sort=False)
fitness_data.columns = runs
fitness_data = fitness_data.fillna(method='ffill')
fitness_dfs.append(fitness_data)
# calculate and print avg time per run
avg_run_time = np.average(run_times)
print("Flip Flop - GA avg run time,", hp_value, hp_name, ":",
avg_run_time)
# generate plots
plot_title = "Flip Flop GA - " \
+ str(population_size) + " pop, " \
+ str(mutation_prob) + " mut prob, " \
+ ": fit vs iter"
plotting.plot_fitness_curves(
fitness_dfs=fitness_dfs,
hp_values=hp_values,
hp_name=hp_name,
title=plot_title,
)
plt.savefig('graphs/flip_flop_ga_fitness.png')
plt.clf()
return fitness_dfs