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discrete_optimization9.py
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discrete_optimization9.py
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import copy
import logging
import sys
import multiprocessing as mp
import time
from mmlrose.opt_probs import (
TSPOpt,
DiscreteOpt
)
from mmlrose.algorithms import (
random_hill_climb,
simulated_annealing,
genetic_alg,
mimic
)
from mlrose import (
TravellingSales,
FourPeaks,
SixPeaks,
ContinuousPeaks,
Knapsack,
MaxKColor,
Queens,
#random_hill_climb,
#simulated_annealing,
#genetic_alg,
#mimic,
ExpDecay,
GeomDecay,
ArithDecay,
#TSPOpt,
#DiscreteOpt
)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from utils import (
file_join,
log_filename
)
idx = pd.IndexSlice
STEM = __file__[:-3]
print(STEM)
def get_problems():
return {
'TSP': {
'class': TravellingSales,
'opt_prob': TSPOpt,
'experiments': [
{'length': 15, #10, # fixed 2/17 after seeing error
'kwargs': {
'coords': #[(0, 0), (3, 0), (3, 2), (2, 4), (1, 3),
# (7, 9), (2, 5), (4, 1), (2, 3), (0, 7)
#]
# https://people.sc.fsu.edu/~jburkardt/datasets/tsp/p01_sxy.txt
[
(-65.0865638413727368, 36.0624693073746769),
(-0.1358203773809326, 28.7292896751977480),
(-14.6577381710829471, 43.3895496964974043),
(-29.0584693142401171, 43.2167287683090606),
(-43.0700258454450875, -14.5548396888330487),
(-36.0366489745023770, 21.6135482886620949),
(-0.0000000400893815, 0.0000000358808126),
(-50.4808382862985496, -7.3744722432402208),
(-50.5859026832315024, 21.5881966132975371),
(-21.4983260706612533, -7.3194159498090388),
(-64.7472605264735108, -21.8981713360336698),
(-72.0785319657452987, -0.1815834632498404),
(-28.8732862244731230, -0.0000008724121069),
(-79.2915791686897506, 21.4033307581457670),
(-57.5687244704708050, 43.2505562436354225),
]
}
},
]
},
'Four_Peaks': {
'class': FourPeaks,
'opt_prob': DiscreteOpt,
'experiments': [
{'length': 30,
'kwargs': {
't_pct': 0.1
}
},
]
},
'Six_Peaks': {
'class': SixPeaks,
'opt_prob': DiscreteOpt,
'experiments': [
{'length': 30,
'kwargs': {
't_pct': 0.1
}
},
]
},
'Continuous_Peaks': {
'class': ContinuousPeaks,
'opt_prob': DiscreteOpt,
'experiments': [
{'length': 30,
'kwargs': {
't_pct': 0.1
}
},
]
},
'Knapsack': {
'class': Knapsack,
'opt_prob': DiscreteOpt,
'experiments': [
{'length': 40,
'kwargs':
{
'weights':[
1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
],
'values': [
1, 10, 2, 9, 3, 8, 4, 7, 5, 6,
1, 10, 2, 9, 3, 8, 4, 7, 5, 6,
1, 10, 2, 9, 3, 8, 4, 7, 5, 6,
1, 10, 2, 9, 3, 8, 4, 7, 5, 6,
],
'max_weight_pct': 0.25
}
#{
# 'weights':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
# 'values': [1, 10, 2, 9, 3, 8, 4, 7, 5, 6],
# 'max_weight_pct': 0.25
#}
},
#{
# 'length': 10,
# 'kwargs':
# {
# 'weights':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
# 'values': [1, 10, 2, 9, 3, 8, 4, 7, 5, 6],
# 'max_weight_pct': 0.5
# },
#},
#{
# 'length': 5,
# 'kwargs':
# {
# 'weights':[1, 2, 3, 4, 5],
# 'values': [1, 10, 2, 9, 3],
# 'max_weight_pct': 0.25
# },
#},
#{
# 'length': 5,
# 'kwargs':
# {
# 'weights':[1, 2, 3, 4, 5],
# 'values': [1, 10, 2, 9, 3],
# 'max_weight_pct': 0.5
# }
#}
]
},
'k_Color': {
'class': MaxKColor,
'opt_prob': DiscreteOpt,
'experiments': [
{'length': 40,
'kwargs': {
'edges': [(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4),
(5, 6), (5, 7), (5, 9), (6, 8), (7, 5), (7, 8), (8, 9),
(0, 9),
(10, 11), (10, 12), (10, 14), (11, 13), (12, 10), (12, 13), (13, 14),
(15, 16), (15, 17), (15, 19), (16, 18), (17, 15), (17, 18), (18, 19),
(10, 19),
(20, 21), (20, 22), (20, 24), (21, 23), (22, 20), (22, 23), (23, 24),
(25, 26), (25, 27), (25, 29), (26, 28), (27, 25), (27, 28), (28, 29),
(20, 29),
(30, 31), (30, 32), (30, 34), (31, 33), (32, 30), (32, 33), (33, 34),
(35, 36), (35, 37), (35, 39), (36, 38), (37, 35), (37, 38), (38, 39),
(30, 39),
]
}
},
]
},
'Eight_Queens': {
'class': Queens,
'opt_prob': DiscreteOpt,
'experiments': [
{'length': 8,
'kwargs': {}
},
]
},
}
def get_optimizers():
return {
'RHC': {
'function': random_hill_climb,
'kwargs': [
{}
],
},
'SA': {
'function': simulated_annealing,
'kwargs': [
{'schedule': ExpDecay()},
{'schedule': ExpDecay(exp_const=0.01)},
{'schedule': ExpDecay(exp_const=0.002)},
{'schedule': GeomDecay()},
{'schedule': GeomDecay(decay=.98)},
{'schedule': GeomDecay(decay=.96)},
{'schedule': ArithDecay()},
{'schedule': ArithDecay(decay=0.001)},
{'schedule': ArithDecay(decay=1/5000)},
{'schedule': ExpDecay(exp_const=0.002)},
]
},
'GA': {
'function': genetic_alg,
'kwargs': [
{'pop_size': 200,
'mutation_prob': 0.1
},
{'pop_size': 200,
'mutation_prob': 0.2
},
{'pop_size': 200,
'mutation_prob': 0.05
},
{'pop_size': 800,
'mutation_prob': 0.1
},
]
},
'MIMIC': {
'function': mimic,
'kwargs': [
{'pop_size': 200,
'keep_pct': 0.2,
#'fast_mimic': True
},
{'pop_size': 200,
'keep_pct': 0.4,
#'fast_mimic': True
},
{'pop_size': 400,
'keep_pct': 0.2,
},
]
}
}
#### add # of runs for each problem X algorithm
def get_runs_per_problem():
return {
'TSP': 10,
'Four_Peaks': 10,
'Six_Peaks': 10,
'Continuous_Peaks': 10,
'Knapsack': 10,
'k_Color': 10,
'Eight_Queens': 10,
}
def get_func_kwargs_per_problem():
return {
'Four_Peaks': {
'GA': 2,
'MIMIC': 0,
'SA': 5,
'RHC': 0
},
'Knapsack': {
'GA': 0,
'MIMIC': 0,
'SA': 6,
'RHC': 0
},
'k_Color': {
'GA': 3,
'MIMIC': 2,
'SA': 9,
'RHC': 0
},
}
def kwarg_str(kwargs):
s = ''
for val in kwargs.values():
s += str(val)
return s
def run_opt(params):
problem_fit = params['problem_fit']
max_attempts = params['max_attempts']
#max_iters = params['max_iters']
run = params['run']
func_kwargs = params['func_kwargs']
func = params['func']
start_time = time.time()
best_state, best_fitness, curve, eval_curve = (
func(
problem_fit,
max_attempts=max_attempts,
#max_iters=max_iters,
curve=True,
random_state=run,
**func_kwargs
)
)
end_time = time.time()
duration = end_time - start_time
outcome = {
'best_state': best_state,
'best_fitness': best_fitness,
'curve': curve,
'eval_curve': eval_curve,
'duration': duration
}
return outcome
def run_experiments(problems):
lfname = log_filename(problems, STEM)
logging.basicConfig(filename='./logs/' + lfname + '.txt', level=logging.DEBUG)
problems_dict = get_problems()
optimizers = get_optimizers()
runs_per_problem = get_runs_per_problem()
func_kwargs_per_problem = get_func_kwargs_per_problem()
best_state_collection = {}
best_fitness_collection = {}
curves = {}
eval_curves = {}
times = {}
iters = {}
for problem in problems:
print('problem', problem)
best_state_collection[problem] = {}
best_fitness_collection[problem] = {}
curves[problem] = {}
eval_curves[problem] = {}
times[problem] = {}
iters[problem] = {}
prob_dict = problems_dict[problem]
#print('prob_dict', prob_dict)
for exp_id, experiment in enumerate(prob_dict['experiments']):
print('exp_id', exp_id)
kwargs = experiment['kwargs']
problem_kwargs = kwarg_str(kwargs)
logging.info('problem ' + problem + ' experiment id ' + str(exp_id) + ' for ' + problem_kwargs)
best_state_collection[problem][exp_id] = {}
best_fitness_collection[problem][exp_id] = {}
curves[problem][exp_id] = {}
eval_curves[problem][exp_id] = {}
times[problem][exp_id] = {}
iters[problem][exp_id] = {}
fitness = prob_dict['class']
opt_prob = prob_dict['opt_prob']
for opt_name, optimizer in optimizers.items():
best_state_collection[problem][exp_id][opt_name] = {}
best_fitness_collection[problem][exp_id][opt_name] = {}
curves[problem][exp_id][opt_name] = {}
eval_curves[problem][exp_id][opt_name] = {}
times[problem][exp_id][opt_name] = {}
iters[problem][exp_id][opt_name] = {}
func = optimizer['function']
#for f_kwargs_id, func_kwargs in enumerate(optimizer['kwargs']):
f_kwargs_id = func_kwargs_per_problem[problem][opt_name]
func_kwargs = optimizer['kwargs'][f_kwargs_id]
print('fitness', fitness.__name__)
print('kwargs', kwargs)
print('func', func)
print('func_kwargs', func_kwargs)
func_kwargs_str = kwarg_str(func_kwargs)
logging.info('problem ' + problem + 'experiment id ' + str(exp_id) + ' fkwargs_id ' + str(f_kwargs_id) + ' is ' + func_kwargs_str)
fit_func = fitness(**kwargs)
problem_fit = opt_prob(experiment['length'], fit_func)
print('problem_fit', problem_fit)
best_state_collection[problem][exp_id][opt_name][f_kwargs_id] = {}
best_fitness_collection[problem][exp_id][opt_name][f_kwargs_id] = {}
curves[problem][exp_id][opt_name][f_kwargs_id] = {}
eval_curves[problem][exp_id][opt_name][f_kwargs_id] = {}
times[problem][exp_id][opt_name][f_kwargs_id] = {}
iters[problem][exp_id][opt_name][f_kwargs_id] = {}
param_list = []
for run in range(runs_per_problem[problem]):
params = {
'problem_fit': problem_fit,
'max_attempts': 200, #1000,
#'max_iters': 5000,
'run': run,
'func_kwargs': func_kwargs,
'func': func
}
param_list.append(params)
logging.info('time ' + str(time.time()))
with mp.Pool() as pool:
outcome = pool.map(run_opt, param_list)
pool.close()
pool.join()
#outcome = run_opt(params)
#start_time = time.time()
#best_state, best_fitness, curve = (
# func(
# problem_fit,
# max_attempts=1000,
# max_iters=10000,
# curve=True,
# random_state=run,
# **func_kwargs
# )
#)
#end_time = time.time()
#time = end_time - start_time
#best_state_collection[problem][problem_kwargs][opt_name][func_kwargs_str][run] = best_state
#best_fitness_collection[problem][problem_kwargs][opt_name][func_kwargs_str][run] = best_fitness
#curves[problem][problem_kwargs][opt_name][func_kwargs_str][run] = curve
#times[problem][problem_kwargs][opt_name][func_kwargs_str][run] = time
#best_state_collection[problem][problem_kwargs][opt_name][func_kwargs_str][run] = outcome['best_state']
#best_fitness_collection[problem][problem_kwargs][opt_name][func_kwargs_str][run] = outcome['best_fitness']
#curves[problem][problem_kwargs][opt_name][func_kwargs_str][run] = outcome['curve']
#times[problem][problem_kwargs][opt_name][func_kwargs_str][run] = outcome['duration']
for run in range(runs_per_problem[problem]):
best_state_collection[problem][exp_id][opt_name][f_kwargs_id][run] = outcome[run]['best_state']
best_fitness_collection[problem][exp_id][opt_name][f_kwargs_id][run] = outcome[run]['best_fitness']
curves[problem][exp_id][opt_name][f_kwargs_id][run] = outcome[run]['curve']
eval_curves[problem][exp_id][opt_name][f_kwargs_id][run] = outcome[run]['eval_curve']
times[problem][exp_id][opt_name][f_kwargs_id][run] = outcome[run]['duration']
iters[problem][exp_id][opt_name][f_kwargs_id][run] = len(outcome[run]['curve'])
#best_state_df = pd.DataFrame.from_dict(
# {(i, j, k, l, m): best_state_collection[i][j][k][l][m]
# for i in best_state_collection.keys()
# for j in best_state_collection[i].keys()
# for k in best_state_collection[i][j].keys()
# for l in best_state_collection[i][j][k].keys()
# for m in best_state_collection[i][j][k][l].keys()
# }, orient='index'
# )
best_fitness_df = pd.DataFrame.from_dict(
{(i, j, k, l, m): best_fitness_collection[i][j][k][l][m]
for i in best_fitness_collection.keys()
for j in best_fitness_collection[i].keys()
for k in best_fitness_collection[i][j].keys()
for l in best_fitness_collection[i][j][k].keys()
for m in best_fitness_collection[i][j][k][l].keys()
}, orient='index'
)
best_fitness_df.columns = ['fitness']
best_fitness_df.index = pd.MultiIndex.from_tuples(best_fitness_df.index,
names=('problem', 'experiment',
'algo', 'params', 'run'))
#best_fitness_df.to_pickle('./output/best_fitness_df.pkl')
for problem in problems:
for exp_id, experiment in enumerate(prob_dict['experiments']):
fig, ax = plt.subplots(figsize=(6, 4))
plt.subplots_adjust(bottom=.2)
#f, ax = plt.subplots(1, 1)
#plt.subplots_adjust(bottom=.3)
summary = best_fitness_df.loc[idx[(problem, exp_id)], :].unstack().droplevel(1)
#summary = best_fitness_df.loc[idx[(problem)], :].unstack()
summary.plot(kind='bar', rot=45, ax=ax, legend=False, color="C0", edgecolor='black')
plt.xlabel('Each Run Grouped By Algorithm')
plt.ylabel('fitness')
plt.suptitle('Fitness Result on ' + problem + '\nfor ' + str(runs_per_problem[problem]) + ' runs of each Algorithm ')
#plt.legend(loc='lower right')
plt.savefig('./plots/' + STEM + '_end_fitness_by_run_' + file_join([problem, exp_id]) + '.png')
plt.clf()
curves_df = pd.DataFrame.from_dict(
{(i, j, k, l, m): curves[i][j][k][l][m]
for i in curves.keys()
for j in curves[i].keys()
for k in curves[i][j].keys()
for l in curves[i][j][k].keys()
for m in curves[i][j][k][l].keys()
}, orient='index'
)
curves_df = curves_df.ffill(axis=1)
curves_df.index = pd.MultiIndex.from_tuples(curves_df.index,
names=('problem', 'experiment',
'algo', 'params', 'run'))
avg_curves = curves_df.mean(level=list(range(4)))
for problem in problems:
for exp_id, experiment in enumerate(prob_dict['experiments']):
fig, ax = plt.subplots(figsize=(6, 4))
plt.subplots_adjust(bottom=.15)
#plot_df = avg_curves.loc[idx[problem, exp_id, : , ], :]
plot_df = avg_curves.loc[idx[problem, exp_id, : , ], :].droplevel(level=[0, 1, 3])
plot_df.T.plot(ax=ax)
plt.xlabel('iteration')
plt.ylabel('fitness')
plt.suptitle('Fitness on ' + problem + ' by iteration')
plt.legend(loc='lower right')
plt.savefig('./plots/' + STEM + '_fitness_curves_' + file_join([problem, exp_id]) + '.png')
plt.clf()
#curves_df.to_pickle('./output/curves_df.pkl')
#print(curves_df.unstack(-1))
eval_curves_df = pd.DataFrame.from_dict(
{(i, j, k, l, m): eval_curves[i][j][k][l][m]
for i in eval_curves.keys()
for j in eval_curves[i].keys()
for k in eval_curves[i][j].keys()
for l in eval_curves[i][j][k].keys()
for m in eval_curves[i][j][k][l].keys()
}, orient='index'
)
eval_curves_df.index = pd.MultiIndex.from_tuples(eval_curves_df.index,
names=('problem', 'experiment',
'algo', 'params', 'run'))
avg_eval_curves = eval_curves_df.mean(level=list(range(4)))
#print(avg_eval_curves)
for problem in problems:
for exp_id, experiment in enumerate(prob_dict['experiments']):
fig, ax = plt.subplots(figsize=(6, 4))
plt.subplots_adjust(bottom=.15)
plot_df = avg_eval_curves.loc[idx[problem, exp_id, : , ], :].droplevel(level=[0, 1, 3])
plot_df.T.plot(ax=ax)
plt.xlabel('iteration')
plt.ylabel('evaluations')
plt.yscale('log')
plt.suptitle('Evaluations on ' + problem + ' by Iteration')
plt.legend(loc='lower right')
plt.savefig('./plots/' + STEM + '_eval_curves_' + file_join([problem, exp_id]) + '.png')
plt.clf()
colors = ['C0', 'C1', 'C2', 'C3']
#print('curves')
#print(curves_df.head())
#print('evals')
#print(eval_curves_df.head())
for problem in problems:
for exp_id, experiment in enumerate(prob_dict['experiments']):
fig, ax = plt.subplots(figsize=(6, 4))
plt.subplots_adjust(bottom=.15)
#for i, algo in enumerate(algos): # change
for i, (opt_name, optimizer) in enumerate(optimizers.items()):
color = colors[i]
f_kwargs_id = func_kwargs_per_problem[problem][opt_name]
for run in range(runs_per_problem[problem]):
fitness = curves_df.loc[idx[problem, exp_id, opt_name, f_kwargs_id, run], :]
#print('fitness')
#print(fitness)
evals = eval_curves_df.loc[idx[problem, exp_id, opt_name, f_kwargs_id, run], :]
#print('evals')
#print(evals)
#plt.plot(evals, fitness)
#plt.show()
if run == 0:
ax.plot(evals, fitness, color=color, linestyle=':', label=opt_name)
else:
ax.plot(evals, fitness, color=color, linestyle=':', label='_nolegend_')
ax.set_xlabel('evaluations')
ax.set_ylabel('fitness')
ax.set_xscale('log')
ax.set_title('Fitness by Evaluation for ' + problem)
ax.legend()
plt.savefig('./plots/' + STEM + '_fitness_by_eval_' + file_join([problem, exp_id]) + '.png')
plt.clf()
times_df = pd.DataFrame.from_dict(
{(i, j, k, l, m): times[i][j][k][l][m]
for i in times.keys()
for j in times[i].keys()
for k in times[i][j].keys()
for l in times[i][j][k].keys()
for m in times[i][j][k][l].keys()
}, orient='index'
)
times_df.columns = ['time']
times_df.index = pd.MultiIndex.from_tuples(times_df.index,
names=('problem', 'experiment',
'algo', 'params', 'run'))
iters_df = pd.DataFrame.from_dict(
{(i, j, k, l, m): iters[i][j][k][l][m]
for i in iters.keys()
for j in iters[i].keys()
for k in iters[i][j].keys()
for l in iters[i][j][k].keys()
for m in iters[i][j][k][l].keys()
}, orient='index'
)
iters_df.columns = ['iterations']
iters_df.index = pd.MultiIndex.from_tuples(iters_df.index,
names=('problem', 'experiment',
'algo', 'params', 'run'))
combined = pd.merge(left=times_df,
right=iters_df,
how='left',
left_index=True,
right_index=True
)
#combined.to_pickle('./output/combined.pkl')
avgtimes = {}
for problem in problems:
avgtimes[problem] = {}
for exp_id, experiment in enumerate(prob_dict['experiments']):
avgtimes[problem][exp_id] = {}
fig, ax = plt.subplots(figsize=(6, 4))
plt.subplots_adjust(bottom=.15)
#fig, ax = plt.subplots()
plot_df = combined.loc[idx[(problem, exp_id)], :]
#print(plot_df)
for opt_name, optimizer in optimizers.items():
df = plot_df.loc[idx[opt_name], :]
avgtime = df['time'].sum()/df['iterations'].sum()
avgtimes[problem][exp_id][opt_name] = avgtime
ax.scatter(df['iterations'], df['time'], label=f'{opt_name}: {avgtime:.2e} s/iter')
ax.set_yscale('log')
plt.xlabel('iterations')
plt.ylabel('completion time (seconds)')
plt.suptitle('Time vs Iterations for each algorithm on ' + problem)
plt.legend()
plt.savefig('./plots/' + STEM + '_time_iterations_' + file_join([problem, exp_id]) + '.png')
combined = pd.merge(left=times_df,
right=best_fitness_df,
how='left',
left_index=True,
right_index=True
)
#combined.to_pickle('./output/combined.pkl')
for problem in problems:
for exp_id, experiment in enumerate(prob_dict['experiments']):
fig, ax = plt.subplots(figsize=(6, 4))
plt.subplots_adjust(bottom=.15)
#fig, ax = plt.subplots()
plot_df = combined.loc[idx[(problem, exp_id)], :]
#print(plot_df)
for opt_name, optimizer in optimizers.items():
avgtime = avgtimes[problem][exp_id][opt_name]
df = plot_df.loc[idx[opt_name], :]
ax.scatter(df['time'], df['fitness'], label=f'{opt_name}: {avgtime:.2e} s/iter')
ax.set_xscale('log')
plt.xlabel('time (seconds)')
plt.ylabel('Fitness')
plt.suptitle('Fitness vs Time for each algorithm on ' + problem)
plt.legend()
plt.savefig('./plots/' + STEM + '_fitness_time_' + file_join([problem, exp_id]) + '.png')
#best_state_df.to_csv('./results/' + lfname + '_best_states.csv')
best_fitness_df.to_csv('./results/' + lfname + '_best_fitness.csv')
curves_df.to_csv('./results/' + lfname + '_curves.csv')
eval_curves_df.to_csv('./results/' + lfname + '_eval_curves.csv')
times_df.to_csv('./results/' + lfname + '_times.csv')
iters_df.to_csv('./results/' + lfname + '_iterations.csv')
#logging.info(str(best_state_collection))
logging.info(str(best_fitness_collection))
logging.info(str(curves))
logging.info(str(times))
if __name__=="__main__":
problems = []
if len(sys.argv) > 1:
problems = sys.argv[1:]
run_experiments(problems)