def processResultPath(new_base, resultAndPath):
result, path = resultAndPath
mean, stderr, count = result
# sampled_mean = windowAverage(mean)
# sampled_stderr = windowAverage(stderr)
sampled_mean = everyN(mean, EVERY)
sampled_stderr = everyN(stderr, EVERY)
sampled = [sampled_mean, sampled_stderr, count]
new_path = new_base + '/' + rest(path)
os.makedirs(up(new_path), exist_ok=True)
np.save(new_path, sampled)
bounds.append(b)
if __name__ == "__main__":
f, axes = plt.subplots(len(algorithms), len(problems))
for i, alg in enumerate(algorithms):
for j, problem in enumerate(problems):
bounds = []
exp_paths = glob.glob(f'experiments/stepsizes/{problem}/{alg}/*.json')
if len(exp_paths) == 0:
continue
exp = loadExperiment(exp_paths[0])
path = up(up(first(exp_paths))) + '/lstd.json'
lstd_exp = loadExperiment(path)
LSTD_res = loadResults(lstd_exp, errorfile)
LSTD_best = getBest(LSTD_res)
b = plotBest(LSTD_best, axes[i, j], color=colors['LSTD'], label='LSTD', alphaMain=0.5)
bounds.append(b)
if '_h' in alg or alg == 'td':
generatePlotSSA(axes[i, j], exp_paths, bounds)
else:
generatePlotTTA(axes[i, j], exp_paths, bounds)
lower = min(map(lambda x: x[0], bounds)) * 0.9
upper = max(map(lambda x: x[1], bounds)) * 1.05
import sys
import json
import glob
import os
sys.path.append(os.getcwd())
from src.utils.path import fileName, up
old_agent_path = sys.argv[1]
problems = sys.argv[2:]
problems = filter(
lambda p: '.' not in p and 'plots' not in p and 'LeftZero' not in p and
'Random' not in p and '5050' not in p, problems)
with open(old_agent_path, 'r') as f:
old_agent = f.read()
old_agent_base_name = fileName(up(old_agent_path))
old_agent_name = fileName(old_agent_path).replace('.json', '')
old_problem = fileName(up(up(old_agent_path)))
for problem in problems:
path = f'{problem}/{old_agent_base_name}/{old_agent_name}.json'
new = old_agent.replace(old_problem, fileName(problem))
os.makedirs(up(path), exist_ok=True)
with open(path, 'w') as f:
f.write(new)
import os
import sys
import json
import numpy as np
sys.path.append(os.getcwd())
from src.analysis.results import loadResults, getBest
from src.utils.model import loadExperiment
from src.utils.path import up, fileName
exp_paths = sys.argv[1:]
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp, 'rmspbe_summary.npy')
best = getBest(results)
print('---------------------')
print('agent:', exp.agent)
print(best.params)
f = fileName(exp_path)
new_path = up(exp_path) + '/best_rmspbe_auc/' + f
d = exp._d
d['metaParameters'] = best.params
os.makedirs(up(new_path), exist_ok=True)
with open(new_path, 'w') as f:
json.dump(d, f, indent=4)
import sys
import json
import glob
import os
sys.path.append(os.getcwd())
from src.utils.path import fileName, up
prob_folder = sys.argv[1]
new_prob = sys.argv[2]
jsons = glob.glob(f'{prob_folder}/**/*.json', recursive=True)
old_prob = fileName(prob_folder)
for json in jsons:
with open(json, 'r') as f:
d = f.read()
new = d.replace(old_prob, new_prob)
new_path = json.replace(old_prob, new_prob)
os.makedirs(up(new_path), exist_ok=True)
with open(new_path, 'w') as f:
f.write(new)
def generatePlot(exp_paths):
f, axes = plt.subplots(2, 2)
# get LSTD solution
path = up(up(first(exp_paths))) + '/lstd.json'
exp = loadExperiment(path)
LSTD_rmsve_results = loadResults(exp, 'errors_summary.npy')
LSTD_rmspbe_results = loadResults(exp, 'rmspbe_summary.npy')
LSTD_rmsve = metric(LSTD_rmsve_results)
LSTD_rmspbe = metric(LSTD_rmspbe_results)
rmspbe_bounds = []
rmsve_bounds = []
bounds = plotBest(LSTD_rmspbe,
axes[0, 0],
color=colors['LSTD'],
label='LSTD',
alphaMain=0.5)
rmspbe_bounds.append(bounds)
bounds = plotBest(LSTD_rmspbe,
axes[0, 1],
color=colors['LSTD'],
label='LSTD',
alphaMain=0.5)
rmspbe_bounds.append(bounds)
bounds = plotBest(LSTD_rmsve,
axes[1, 0],
color=colors['LSTD'],
label='LSTD',
alphaMain=0.5)
rmsve_bounds.append(bounds)
bounds = plotBest(LSTD_rmsve,
axes[1, 1],
color=colors['LSTD'],
label='LSTD',
alphaMain=0.5)
rmsve_bounds.append(bounds)
# RMSPBE plots
ax = axes[0, 0]
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp, 'rmspbe_summary.npy')
const, unconst = tee(results)
const = whereParameterGreaterEq(const, 'ratio', 1)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
agent = exp.agent
if 'SmoothTDC' in agent:
average = exp._d['metaParameters']['averageType']
agent += '_' + average
color = colors[agent]
label = agent.replace('adagrad', '')
if not (exp.agent in ['TDadagrad', 'TDschedule', 'TD', 'TDamsgrad']
or use_ideal_h):
if UNCONSTRAINED:
bounds = plot(unconst,
ax,
label=label + '_unc',
color=color,
dashed=True,
bestBy=bestBy)
rmspbe_bounds.append(bounds)
bounds = plot(const,
ax,
label=label,
color=color,
dashed=False,
bestBy=bestBy)
rmspbe_bounds.append(bounds)
else:
bounds = plot(unconst,
ax,
label=label,
color=color,
dashed=False,
bestBy=bestBy)
rmspbe_bounds.append(bounds)
ax.set_ylabel("MSPBE")
ax.set_title("MSPBE")
ax = axes[0, 1]
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
rmsve = loadResults(exp, 'errors_summary.npy')
results = loadResults(exp, 'rmspbe_summary.npy')
const, unconst = tee(rmsve)
const_res, unconst_res = tee(results)
const = whereParameterGreaterEq(const, 'ratio', 1)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
agent = exp.agent
if 'SmoothTDC' in agent:
average = exp._d['metaParameters']['averageType']
agent += '_' + average
color = colors[agent]
label = agent.replace('adagrad', '')
if not (exp.agent in ['TDadagrad', 'TDschedule', 'TD', 'TDamsgrad']
or use_ideal_h):
best = metric(const)
best_unc = metric(unconst)
best_rmspbe = find(const_res, best)
best_rmspbe_unc = find(unconst_res, best_unc)
bounds = plotBest(best_rmspbe,
ax,
label=label,
color=color,
dashed=False)
rmspbe_bounds.append(bounds)
if UNCONSTRAINED:
bounds = plotBest(best_rmspbe_unc,
ax,
label=label + '_unc',
color=color,
dashed=True)
rmspbe_bounds.append(bounds)
else:
best = metric(unconst)
best_rmspbe = find(unconst_res, best)
bounds = plotBest(best_rmspbe,
ax,
label=label,
color=color,
dashed=False)
rmspbe_bounds.append(bounds)
ax.set_title("MSVE")
# RMSVE plots
ax = axes[1, 0]
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
rmsve = loadResults(exp, 'errors_summary.npy')
rmspbe = loadResults(exp, 'rmspbe_summary.npy')
const, unconst = tee(rmspbe)
const_res, unconst_res = tee(rmsve)
const = whereParameterGreaterEq(const, 'ratio', 1)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
agent = exp.agent
if 'SmoothTDC' in agent:
average = exp._d['metaParameters']['averageType']
agent += '_' + average
color = colors[agent]
label = agent.replace('adagrad', '')
if not (exp.agent in ['TDadagrad', 'TDschedule', 'TD', 'TDamsgrad']
or use_ideal_h):
# best PBE using AUC
best = metric(const)
best_unc = metric(unconst)
best_rmsve = find(const_res, best)
best_rmsve_unc = find(unconst_res, best_unc)
print('rmsve_over_rmspbe')
print(label, best_rmsve.params)
print(label, best_rmsve_unc.params)
bounds = plotBest(best_rmsve,
ax,
label=label,
color=color,
dashed=False)
rmsve_bounds.append(bounds)
if UNCONSTRAINED:
bounds = plotBest(best_rmsve_unc,
ax,
label=label + '_unc',
color=color,
dashed=True)
rmsve_bounds.append(bounds)
else:
best = metric(unconst)
best_rmsve = find(unconst_res, best)
bounds = plotBest(best_rmsve,
ax,
label=label,
color=color,
dashed=False)
rmsve_bounds.append(bounds)
ax.set_ylabel("MSVE")
ax = axes[1, 1]
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp)
const, unconst = tee(results)
const = whereParameterGreaterEq(const, 'ratio', 1)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
agent = exp.agent
if 'SmoothTDC' in agent:
average = exp._d['metaParameters']['averageType']
agent += '_' + average
color = colors[agent]
label = agent.replace('adagrad', '')
if not (exp.agent in ['TDadagrad', 'TDschedule', 'TD', 'TDamsgrad']
or use_ideal_h):
bounds = plot(const,
ax,
label=label,
color=color,
dashed=False,
bestBy=bestBy)
rmsve_bounds.append(bounds)
if UNCONSTRAINED:
bounds = plot(unconst,
ax,
label=label + '_unc',
color=color,
dashed=True,
bestBy=bestBy)
rmsve_bounds.append(bounds)
else:
bounds = plot(unconst,
ax,
label=label,
color=color,
dashed=False,
bestBy=bestBy)
rmsve_bounds.append(bounds)
# rmspbe
rmspbe_lower = min(map(lambda x: x[0], rmspbe_bounds)) * 0.9
rmspbe_upper = max(map(lambda x: x[1], rmspbe_bounds)) * 1.05
if rmspbe_lower < 0.01:
rmspbe_lower = -0.01
axes[0, 0].set_ylim([rmspbe_lower, rmspbe_upper])
axes[0, 1].set_ylim([rmspbe_lower, rmspbe_upper])
# rmsve
rmsve_lower = min(map(lambda x: x[0], rmsve_bounds)) * 0.9
rmsve_upper = max(map(lambda x: x[1], rmsve_bounds)) * 1.05
if rmsve_lower < 0.01:
rmsve_lower = -0.01
axes[1, 0].set_ylim([0, 20])
axes[1, 1].set_ylim([0, 20])
if 'lstd' in exp_path or 'htd' in exp_path or (
'tdc_ema' in exp_path and not 'tdc_ema_x' in exp_path):
continue
if stepsize != 'constant' and stepsize not in exp_path:
continue
if stepsize == 'constant' and ('amsgrad' in exp_path or 'adagrad'
in exp_path or 'schedule' in exp_path):
continue
exp = loadExperiment(exp_path)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
if use_ideal_h:
continue
paths.append(exp_path)
generatePlot(paths)
plt.show()
exit()
exp_name = fileName(up(exp.getExperimentName()))
save_path = f'experiments/stepsizes/plots/2x2/{bestBy}'
os.makedirs(save_path, exist_ok=True)
fig = plt.gcf()
fig.set_size_inches((13, 12), forward=False)
plt.savefig(f'{save_path}/{exp_name}_square_{stepsize}.png', dpi=125)