def generatePlot(exp_paths):
ax = plt.gca()
# ax.semilogx()
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp, 'rmspbe_summary.npy')
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = use_ideal_h
label = exp.agent
if use_ideal_h:
label += '-h*'
if use_ideal_h:
continue
if 'SmoothTDC' in exp.agent:
agents = splitOverParameter(results, 'averageType')
smooth_colors = {
'ema': 'pink',
'buffer': 'grey',
'window': 'black',
}
plot(agents['ema'],
ax,
label=label + '_ema',
bestBy='auc',
color=smooth_colors['ema'],
dashed=dashed)
# plot(agents['buffer'], ax, label=label + '_buffer', bestBy='auc', color=smooth_colors['buffer'], dashed=dashed)
# plot(agents['window'], ax, label=label + '_window', bestBy='auc', color=smooth_colors['window'], dashed=dashed)
else:
color = colors[exp.agent]
plot(results,
ax,
label=label,
bestBy='auc',
color=color,
dashed=dashed)
plt.show()
exit()
# save(exp, f'rmspbe', type='png')
problem = fileName(exp.getExperimentName())
save_path = f'plots/'
os.makedirs(save_path, exist_ok=True)
fig = plt.gcf()
fig.set_size_inches((13, 12), forward=False)
plt.savefig(f'{save_path}/{problem}_rmspbe.png')
def generatePlot(exp_paths):
ax = plt.gca()
# ax.semilogx()
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
# load the errors and hnorm files
errors = loadResults(exp, 'errors_summary.npy')
results = loadResults(exp, 'hnorm_summary.npy')
# choose the best parameters from the _errors_
best = getBestEnd(errors)
best_hnorm = find(results, best)
label = fileName(exp_path).replace('.json', '')
plotBest(best_hnorm, ax, label=label)
plt.show()
# save(exp, f'learning-curve')
plt.clf()
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)
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)