def get_group_selectors(exps, custom_series_splitter):
"""
:param exps:
:param custom_series_splitter:
:return: A dictionary with (splitted_keys, group_selectors). Group selectors can be used to extract progresses.
"""
splitted_dict = dict()
for exp in exps:
# Group exps by their series_splitter key
# splitted_dict: {key:[exp1, exp2, ...]}
key = custom_series_splitter(exp)
if key not in splitted_dict:
splitted_dict[key] = list()
splitted_dict[key].append(exp)
splitted = list(splitted_dict.items())
# Group selectors: All the exps in one of the keys/legends
# Group legends: All the different legends
group_selectors = [core.Selector(list(x[1])) for x in splitted]
group_legends = [x[0] for x in splitted]
all_tuples = sorted(list(zip(group_selectors, group_legends)),
key=lambda x: x[1],
reverse=True)
group_selectors = [x[0] for x in all_tuples]
group_legends = [x[1] for x in all_tuples]
return group_selectors, group_legends
def get_plot_instruction(plot_key,
split_key=None,
group_key=None,
filters=None,
use_median=False,
only_show_best=False,
only_show_best_final=False,
gen_eps=False,
only_show_best_sofar=False,
clip_plot_value=None,
plot_width=None,
plot_height=None,
filter_nan=False,
smooth_curve=False,
custom_filter=None,
legend_post_processor=None,
normalize_error=False,
custom_series_splitter=None):
print(plot_key, split_key, group_key, filters)
if filter_nan:
nonnan_exps_data = list(filter(check_nan, exps_data))
selector = core.Selector(nonnan_exps_data)
else:
selector = core.Selector(exps_data)
if legend_post_processor is None:
legend_post_processor = lambda x: x
if filters is None:
filters = dict()
for k, v in filters.items():
selector = selector.where(k, str(v))
if custom_filter is not None:
selector = selector.custom_filter(custom_filter)
# print selector._filters
if split_key is not None:
vs = [vs for k, vs in distinct_params if k == split_key][0]
split_selectors = [selector.where(split_key, v) for v in vs]
split_legends = list(map(str, vs))
else:
split_selectors = [selector]
split_legends = ["Plot"]
plots = []
counter = 1
for split_selector, split_legend in zip(split_selectors, split_legends):
if custom_series_splitter is not None:
exps = split_selector.extract()
splitted_dict = dict()
for exp in exps:
key = custom_series_splitter(exp)
if key not in splitted_dict:
splitted_dict[key] = list()
splitted_dict[key].append(exp)
splitted = list(splitted_dict.items())
group_selectors = [core.Selector(list(x[1])) for x in splitted]
group_legends = [x[0] for x in splitted]
else:
if group_key and group_key is not "exp_name":
vs = [vs for k, vs in distinct_params if k == group_key][0]
group_selectors = [
split_selector.where(group_key, v) for v in vs
]
group_legends = [str(x) for x in vs]
else:
group_key = "exp_name"
vs = sorted(
[x.params["exp_name"] for x in split_selector.extract()])
group_selectors = [
split_selector.where(group_key, v) for v in vs
]
group_legends = [
summary_name(x.extract()[0], split_selector)
for x in group_selectors
]
# group_selectors = [split_selector]
# group_legends = [split_legend]
to_plot = []
for group_selector, group_legend in zip(group_selectors,
group_legends):
filtered_data = group_selector.extract()
if len(filtered_data) > 0:
if only_show_best or only_show_best_final or only_show_best_sofar:
# Group by seed and sort.
# -----------------------
filtered_params = core.extract_distinct_params(
filtered_data, l=0)
filtered_params2 = [p[1] for p in filtered_params]
filtered_params_k = [p[0] for p in filtered_params]
product_space = list(itertools.product(*filtered_params2))
data_best_regret = None
best_regret = -np.inf
kv_string_best_regret = None
for idx, params in enumerate(product_space):
selector = core.Selector(exps_data)
for k, v in zip(filtered_params_k, params):
selector = selector.where(k, str(v))
data = selector.extract()
if len(data) > 0:
progresses = [
exp.progress.get(plot_key, np.array([np.nan]))
for exp in data
]
# progresses = [progress[:500] for progress in progresses ]
sizes = list(map(len, progresses))
max_size = max(sizes)
progresses = [
np.concatenate(
[ps,
np.ones(max_size - len(ps)) * np.nan])
for ps in progresses
]
if only_show_best_final:
progresses = np.asarray(progresses)[:, -1]
if only_show_best_sofar:
progresses = np.max(np.asarray(progresses),
axis=1)
if use_median:
medians = np.nanmedian(progresses, axis=0)
regret = np.mean(medians)
else:
means = np.nanmean(progresses, axis=0)
regret = np.mean(means)
distinct_params_k = [p[0] for p in distinct_params]
distinct_params_v = [
v for k, v in zip(filtered_params_k, params)
if k in distinct_params_k
]
distinct_params_kv = [(k, v) for k, v in zip(
distinct_params_k, distinct_params_v)]
distinct_params_kv_string = str(
distinct_params_kv).replace('), ', ')\t')
print('{}\t{}\t{}'.format(
regret, len(progresses),
distinct_params_kv_string))
if regret > best_regret:
best_regret = regret
best_progress = progresses
data_best_regret = data
kv_string_best_regret = distinct_params_kv_string
print(group_selector._filters)
print('best regret: {}'.format(best_regret))
# -----------------------
if best_regret != -np.inf:
progresses = [
exp.progress.get(plot_key, np.array([np.nan]))
for exp in data_best_regret
]
# progresses = [progress[:500] for progress in progresses ]
sizes = list(map(len, progresses))
# more intelligent:
max_size = max(sizes)
progresses = [
np.concatenate(
[ps, np.ones(max_size - len(ps)) * np.nan])
for ps in progresses
]
legend = '{} (mu: {:.3f}, std: {:.5f})'.format(
group_legend, best_regret, np.std(best_progress))
window_size = np.maximum(
int(np.round(max_size / float(1000))), 1)
if use_median:
percentile25 = np.nanpercentile(progresses,
q=25,
axis=0)
percentile50 = np.nanpercentile(progresses,
q=50,
axis=0)
percentile75 = np.nanpercentile(progresses,
q=75,
axis=0)
if smooth_curve:
percentile25 = sliding_mean(percentile25,
window=window_size)
percentile50 = sliding_mean(percentile50,
window=window_size)
percentile75 = sliding_mean(percentile75,
window=window_size)
if clip_plot_value is not None:
percentile25 = np.clip(percentile25,
-clip_plot_value,
clip_plot_value)
percentile50 = np.clip(percentile50,
-clip_plot_value,
clip_plot_value)
percentile75 = np.clip(percentile75,
-clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(
percentile25=percentile25,
percentile50=percentile50,
percentile75=percentile75,
legend=legend_post_processor(legend)))
else:
means = np.nanmean(progresses, axis=0)
stds = np.nanstd(progresses, axis=0)
if normalize_error: # and len(progresses) > 0:
stds /= np.sqrt(
np.sum((1. - np.isnan(progresses)),
axis=0))
if smooth_curve:
means = sliding_mean(means, window=window_size)
stds = sliding_mean(stds, window=window_size)
if clip_plot_value is not None:
means = np.clip(means, -clip_plot_value,
clip_plot_value)
stds = np.clip(stds, -clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(
means=means,
stds=stds,
legend=legend_post_processor(legend)))
if len(to_plot) > 0 and len(data) > 0:
to_plot[-1]["footnote"] = "%s; e.g. %s" % (
kv_string_best_regret, data[0].params.get(
"exp_name", "NA"))
else:
to_plot[-1]["footnote"] = ""
else:
progresses = [
exp.progress.get(plot_key, np.array([np.nan]))
for exp in filtered_data
]
sizes = list(map(len, progresses))
# more intelligent:
max_size = max(sizes)
progresses = [
np.concatenate(
[ps, np.ones(max_size - len(ps)) * np.nan])
for ps in progresses
]
window_size = np.maximum(
int(np.round(max_size / float(1000))), 1)
if use_median:
percentile25 = np.nanpercentile(progresses,
q=25,
axis=0)
percentile50 = np.nanpercentile(progresses,
q=50,
axis=0)
percentile75 = np.nanpercentile(progresses,
q=75,
axis=0)
if smooth_curve:
percentile25 = sliding_mean(percentile25,
window=window_size)
percentile50 = sliding_mean(percentile50,
window=window_size)
percentile75 = sliding_mean(percentile75,
window=window_size)
if clip_plot_value is not None:
percentile25 = np.clip(percentile25,
-clip_plot_value,
clip_plot_value)
percentile50 = np.clip(percentile50,
-clip_plot_value,
clip_plot_value)
percentile75 = np.clip(percentile75,
-clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(
percentile25=percentile25,
percentile50=percentile50,
percentile75=percentile75,
legend=legend_post_processor(group_legend)))
else:
means = np.nanmean(progresses, axis=0)
stds = np.nanstd(progresses, axis=0)
if smooth_curve:
means = sliding_mean(means, window=window_size)
stds = sliding_mean(stds, window=window_size)
if clip_plot_value is not None:
means = np.clip(means, -clip_plot_value,
clip_plot_value)
stds = np.clip(stds, -clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(
means=means,
stds=stds,
legend=legend_post_processor(group_legend)))
if len(to_plot) > 0 and not gen_eps:
fig_title = "%s: %s" % (split_key, split_legend)
# plots.append("<h3>%s</h3>" % fig_title)
plots.append(
make_plot(to_plot,
use_median=use_median,
title=fig_title,
plot_width=plot_width,
plot_height=plot_height))
if gen_eps:
make_plot_eps(to_plot, use_median=use_median, counter=counter)
counter += 1
return "\n".join(plots)
len(data_array))))
avg = 0
for j in indices:
avg += data_array[j]
avg /= float(len(indices))
new_list.append(avg)
return np.array(new_list)
if __name__ == '__main__':
data_path = '/Users/Dora/Projects/baselines_hrl/data/seuss/visual_rss_RopeFloat_0407'
exps_data, plottable_keys, distinct_params = reload_data(data_path)
# Example of extracting a single curve
selector = core.Selector(exps_data)
selector = selector.where('her_replay_strategy', 'balance_filter')
y, y_lower, y_upper = get_shaded_curve(selector, 'test/success_state')
_, ax = plt.subplots()
color = core.color_defaults[0]
ax.fill_between(range(len(y)),
y_lower,
y_upper,
interpolate=True,
facecolor=color,
linewidth=0.0,
alpha=0.2)
ax.plot(range(len(y)), y, color=color, label=plt.legend, linewidth=2.0)
# Example of extracting all the curves
def get_plot_instruction(plot_key,
split_key=None,
group_key=None,
filters=None,
use_median=False,
only_show_best=False,
gen_eps=False,
clip_plot_value=None,
plot_width=None,
plot_height=None,
filter_nan=False):
print(plot_key, split_key, group_key, filters)
if filter_nan:
nonnan_exps_data = filter(check_nan, exps_data)
selector = core.Selector(nonnan_exps_data)
else:
selector = core.Selector(exps_data)
if filters is None:
filters = dict()
for k, v in filters.iteritems():
selector = selector.where(k, str(v))
# print selector._filters
if split_key is not None:
vs = [vs for k, vs in distinct_params if k == split_key][0]
split_selectors = [selector.where(split_key, v) for v in vs]
split_legends = map(str, vs)
else:
split_selectors = [selector]
split_legends = ["Plot"]
plots = []
counter = 1
for split_selector, split_legend in zip(split_selectors, split_legends):
if group_key and group_key is not "exp_name":
vs = [vs for k, vs in distinct_params if k == group_key][0]
group_selectors = [split_selector.where(group_key, v) for v in vs]
group_legends = map(lambda x: str(x), vs)
else:
group_key = "exp_name"
vs = sorted(
[x.params["exp_name"] for x in split_selector.extract()])
group_selectors = [split_selector.where(group_key, v) for v in vs]
group_legends = [
summary_name(x.extract()[0], split_selector)
for x in group_selectors
]
# group_selectors = [split_selector]
# group_legends = [split_legend]
to_plot = []
for group_selector, group_legend in zip(group_selectors,
group_legends):
filtered_data = group_selector.extract()
if len(filtered_data) > 0:
if only_show_best:
# Group by seed and sort.
# -----------------------
filtered_params = core.extract_distinct_params(
filtered_data, l=0)
filtered_params2 = [p[1] for p in filtered_params]
filtered_params_k = [p[0] for p in filtered_params]
product_space = list(itertools.product(*filtered_params2))
data_best_regret = None
best_regret = -np.inf
for idx, params in enumerate(product_space):
selector = core.Selector(exps_data)
for k, v in zip(filtered_params_k, params):
selector = selector.where(k, str(v))
data = selector.extract()
if len(data) > 0:
progresses = [
exp.progress.get(plot_key, np.array([np.nan]))
for exp in data
]
sizes = map(len, progresses)
max_size = max(sizes)
progresses = [
np.concatenate(
[ps,
np.ones(max_size - len(ps)) * np.nan])
for ps in progresses
]
if use_median:
medians = np.nanmedian(progresses, axis=0)
regret = np.median(medians)
else:
means = np.nanmean(progresses, axis=0)
regret = np.mean(means)
if regret > best_regret:
best_regret = regret
data_best_regret = data
distinct_params_k = [p[0] for p in distinct_params]
distinct_params_v = [
v for k, v in zip(filtered_params_k, params)
if k in distinct_params_k
]
distinct_params_kv = [(k, v) for k, v in zip(
distinct_params_k, distinct_params_v)]
distinct_params_kv_string = str(
distinct_params_kv).replace('), ', ')\t')
print('{}\t{}\t{}'.format(
regret, len(progresses),
distinct_params_kv_string))
print(group_selector._filters)
print('best regret: {}'.format(best_regret))
# -----------------------
if best_regret != -np.inf:
progresses = [
exp.progress.get(plot_key, np.array([np.nan]))
for exp in data_best_regret
]
sizes = map(len, progresses)
# more intelligent:
max_size = max(sizes)
progresses = [
np.concatenate(
[ps, np.ones(max_size - len(ps)) * np.nan])
for ps in progresses
]
legend = '{} ({:.1f})'.format(group_legend,
best_regret)
window_size = np.maximum(
int(np.round(max_size / float(1000))), 1)
if use_median:
percentile25 = np.nanpercentile(progresses,
q=25,
axis=0)
percentile50 = np.nanpercentile(progresses,
q=50,
axis=0)
percentile75 = np.nanpercentile(progresses,
q=75,
axis=0)
percentile25 = sliding_mean(percentile25,
window=window_size)
percentile50 = sliding_mean(percentile50,
window=window_size)
percentile75 = sliding_mean(percentile75,
window=window_size)
if clip_plot_value is not None:
percentile25 = np.clip(percentile25,
-clip_plot_value,
clip_plot_value)
percentile50 = np.clip(percentile50,
-clip_plot_value,
clip_plot_value)
percentile75 = np.clip(percentile75,
-clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(percentile25=percentile25,
percentile50=percentile50,
percentile75=percentile75,
legend=legend))
else:
means = np.nanmean(progresses, axis=0)
stds = np.nanstd(progresses, axis=0)
means = sliding_mean(means, window=window_size)
stds = sliding_mean(stds, window=window_size)
if clip_plot_value is not None:
means = np.clip(means, -clip_plot_value,
clip_plot_value)
stds = np.clip(stds, -clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(means=means,
stds=stds,
legend=legend))
else:
progresses = [
exp.progress.get(plot_key, np.array([np.nan]))
for exp in filtered_data
]
sizes = map(len, progresses)
# more intelligent:
max_size = max(sizes)
progresses = [
np.concatenate(
[ps, np.ones(max_size - len(ps)) * np.nan])
for ps in progresses
]
window_size = np.maximum(
int(np.round(max_size / float(1000))), 1)
if use_median:
percentile25 = np.nanpercentile(progresses,
q=25,
axis=0)
percentile50 = np.nanpercentile(progresses,
q=50,
axis=0)
percentile75 = np.nanpercentile(progresses,
q=75,
axis=0)
percentile25 = sliding_mean(percentile25,
window=window_size)
percentile50 = sliding_mean(percentile50,
window=window_size)
percentile75 = sliding_mean(percentile75,
window=window_size)
if clip_plot_value is not None:
percentile25 = np.clip(percentile25,
-clip_plot_value,
clip_plot_value)
percentile50 = np.clip(percentile50,
-clip_plot_value,
clip_plot_value)
percentile75 = np.clip(percentile75,
-clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(percentile25=percentile25,
percentile50=percentile50,
percentile75=percentile75,
legend=group_legend))
else:
means = np.nanmean(progresses, axis=0)
stds = np.nanstd(progresses, axis=0)
means = sliding_mean(means, window=window_size)
stds = sliding_mean(stds, window=window_size)
if clip_plot_value is not None:
means = np.clip(means, -clip_plot_value,
clip_plot_value)
stds = np.clip(stds, -clip_plot_value,
clip_plot_value)
to_plot.append(
ext.AttrDict(means=means,
stds=stds,
legend=group_legend))
if len(to_plot) > 0 and not gen_eps:
plots.append("<div>%s: %s</div>" % (split_key, split_legend))
plots.append(
make_plot(to_plot,
use_median=use_median,
plot_width=plot_width,
plot_height=plot_height))
if gen_eps:
make_plot_eps(to_plot, use_median=use_median, counter=counter)
counter += 1
return "\n".join(plots)
