def generatePlot(exp_paths):
ax = plt.gca()
# ax.semilogx()
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = use_ideal_h
color = colors[exp.agent]
label = exp.agent.replace('adagrad', '')
if use_ideal_h:
label += '-h*'
plot(results,
ax,
label=label,
color=color,
dashed=dashed,
bestBy='end')
# plt.show()
save(exp, f'rmsve_learning-curve', type='svg')
plt.clf()
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 generatePlotSSA(ax, exp_path, bounds):
exp = loadExperiment(exp_path)
results = loadResults(exp, errorfile)
color = colors[exp.agent]
label = exp.agent
b = plot(results, ax, window=window, smoothing=smoothing, label=label, color=color, alpha=0.2, dashed=False, bestBy=bestBy)
bounds.append(b)
def generatePlot(exp_paths):
ax = plt.gca()
# ax.semilogx()
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp)
is_h_star = exp.getPermutation(0)['metaParameters']['use_ideal_h']
agent = exp.agent.replace('adagrad', '')
label = agent
if is_h_star:
label += '-h*'
plot(results, ax, label=label)
plt.show()
# save(exp, f'learning-curve')
plt.clf()
def generatePlot(exp_paths):
b_fig = plt.figure("Mean Update Variance")
w_fig = plt.figure("W Update Variance")
h_fig = plt.figure("H Update Variance")
b_ax = b_fig.add_subplot(1, 1, 1)
w_ax = w_fig.add_subplot(1, 1, 1)
h_ax = h_fig.add_subplot(1, 1, 1)
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp, 'variance_summary.npy')
both, w, h = tee(results, 3)
both = map(lambda r: r.reducer(columnReducer(0)), both)
w = map(lambda r: r.reducer(columnReducer(1)), w)
h = map(lambda r: r.reducer(columnReducer(2)), h)
plot(both, b_ax)
plot(w, w_ax)
plot(h, h_ax)
plt.show()
exit()
exp_name = exp.getExperimentName()
save_path = f'experiments/{exp_name}'
os.makedirs(save_path, exist_ok=True)
b_fig.savefig(f'{save_path}/both_variance-curve.pdf')
w_fig.savefig(f'{save_path}/w_variance-curve.pdf')
h_fig.savefig(f'{save_path}/h_variance-curve.pdf')
def generatePlotTTA(ax, exp_paths, bestBy, bounds):
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp, errorfile)
color = colors[exp.agent]
label = exp.agent
dashed = False
if 'TDC' in label:
dashed = True
results = whereParameterGreaterEq(results, 'ratio', 1)
b = plot(results, ax, label=label, color=color, dashed=dashed, bestBy=bestBy)
bounds.append(b)
def generatePlot(exp_paths):
f, axes = plt.subplots(2, 2)
# ax.semilogx()
# RMSPBE plots
ax = axes[0, 0]
rmspbe_bounds = []
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 = False
color = colors[exp.agent]
label = exp.agent.replace('adagrad', '')
# if use_ideal_h:
# label += '-h*'
bounds = plot(results, ax, label=label, color=color, dashed=dashed)
rmspbe_bounds.append(bounds)
ax.set_ylabel("RMSPBE")
ax.set_title("RMSPBE")
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')
best = getBestEnd(rmsve)
best_rmspbe = find(results, best)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = False
color = colors[exp.agent]
label = exp.agent.replace('adagrad', '')
# if use_ideal_h:
# label += '-h*'
bounds = plotBest(best_rmspbe,
ax,
label=label,
color=color,
dashed=dashed)
rmspbe_bounds.append(bounds)
ax.set_title("RMSVE")
# RMSVE plots
ax = axes[1, 0]
rmsve_bounds = []
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
rmsve = loadResults(exp, 'errors_summary.npy')
rmspbe = loadResults(exp, 'rmspbe_summary.npy')
# if exp.agent == 'TDadagrad':
# continue
# best PBE using AUC
best = getBestEnd(rmspbe)
best_rmsve = find(rmsve, best)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = False
color = colors[exp.agent]
label = exp.agent.replace('adagrad', '')
# if use_ideal_h:
# label += '-h*'
bounds = plotBest(best_rmsve,
ax,
label=label,
color=color,
dashed=dashed)
rmsve_bounds.append(bounds)
ax.set_ylabel("RMSVE")
ax = axes[1, 1]
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = False
color = colors[exp.agent]
label = exp.agent.replace('adagrad', '')
# if use_ideal_h:
# label += '-h*'
bounds = plot(results, ax, label=label, color=color, dashed=dashed)
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
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
axes[1, 0].set_ylim([rmsve_lower, rmsve_upper])
axes[1, 1].set_ylim([rmsve_lower, rmsve_upper])
plt.show()
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])