def main():
parser = argparse.ArgumentParser()
parser.add_argument("folder_path", type=str)
# parser.add_argument("--num_iters", type=int)
args = parser.parse_args()
base = Path(os.getcwd())
base = base / args.folder_path
path_and_iter = get_path_and_iters(base)
resolution = 20
x_bounds = (-1, 1)
y_bounds = (-1, 1)
report = HTMLReport(str(base / 'report.html'), images_per_row=1)
# for path, itr in takespread(path_and_iter, args):
for path, itr in [path_and_iter[-1]]:
report.add_text("Path: %s" % path)
print("Loading: %s" % path)
data = joblib.load(str(path))
qf = data['qf']
env = data['env']
qf.train(False)
start_state = env.reset()
report.add_text("Start State = {}".format(start_state))
report.add_text("Start XY = {}".format(
position_from_angles(np.expand_dims(start_state, 0))))
goal_states = [start_state]
goal_states += [env.sample_goal_for_rollout() for _ in range(5)]
for goal_state in goal_states:
qf_eval = create_qf_eval_fnct(qf, start_state, goal_state)
qf_heatmap = vu.make_heat_map(
qf_eval,
x_bounds=x_bounds,
y_bounds=y_bounds,
resolution=resolution,
)
fig = create_figure(
['Estimated'],
[qf_heatmap],
)
img = vu.save_image(fig)
report.add_image(
img, "Goal State = {}\nGoal XY = {}".format(
goal_state,
position_from_angles(np.expand_dims(goal_state, 0))))
abs_path = osp.abspath(report.path)
print("Report saved to: {}".format(abs_path))
report.save()
open_report = query_yes_no("Open report?", default="yes")
if open_report:
cmd = "xdg-open {}".format(abs_path)
print(cmd)
subprocess.call(cmd, shell=True)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('dir', type=str)
parser.add_argument('--report_name', type=str,
default='report_retroactive.html')
args = parser.parse_args()
directory = args.dir
report_name = args.report_name
with open(join(directory, 'variant.json')) as variant_file:
variant = json.load(variant_file)
skew_config = get_key_recursive(variant, 'skew_config')
pkl_paths = glob.glob(directory + '/*.pkl')
numbered_paths = append_itr(pkl_paths)
ordered_numbered_paths = sorted(numbered_paths, key=lambda x: x[1])
report = HTMLReport(join(directory, report_name), images_per_row=5)
vae_heatmap_imgs = []
sample_imgs = []
for path, itr in ordered_numbered_paths:
print("Processing iteration {}".format(itr))
snapshot = pickle.load(open(path, "rb"))
if 'vae' in snapshot:
vae = snapshot['vae']
else:
vae = snapshot['p_theta']
vae.to('cpu')
vae_train_data = snapshot['train_data']
dynamics = snapshot.get('dynamics', project_square_border_np_4x4)
report.add_header("Iteration {}".format(itr))
vae.xy_range = ((-4, 4), (-4, 4))
vae_heatmap_img = visualize_vae_samples(
itr,
vae_train_data,
vae,
report,
xlim=vae.get_plot_ranges()[0],
ylim=vae.get_plot_ranges()[1],
dynamics=dynamics,
)
sample_img = visualize_vae(
vae,
skew_config,
report,
title="Post-skew",
)
vae_heatmap_imgs.append(vae_heatmap_img)
sample_imgs.append(sample_img)
report.add_header("Summary GIFs")
for filename, imgs in [
("vae_heatmaps", vae_heatmap_imgs),
("samples", sample_imgs),
]:
video = np.stack(imgs)
vwrite(
'{}/{}.mp4'.format(directory, filename),
video,
)
gif_file_path = '{}/{}.gif'.format(directory, filename)
gif(gif_file_path, video)
report.add_image(gif_file_path, txt=filename, is_url=True)
report.save()
print("Report saved to")
print(report.path)
def train(
dataset_generator,
n_start_samples,
projection=project_samples_square_np,
n_samples_to_add_per_epoch=1000,
n_epochs=100,
z_dim=1,
hidden_size=32,
save_period=10,
append_all_data=True,
full_variant=None,
dynamics_noise=0,
decoder_output_var='learned',
num_bins=5,
skew_config=None,
use_perfect_samples=False,
use_perfect_density=False,
vae_reset_period=0,
vae_kwargs=None,
use_dataset_generator_first_epoch=True,
**kwargs
):
"""
Sanitize Inputs
"""
assert skew_config is not None
if not (use_perfect_density and use_perfect_samples):
assert vae_kwargs is not None
if vae_kwargs is None:
vae_kwargs = {}
report = HTMLReport(
logger.get_snapshot_dir() + '/report.html',
images_per_row=10,
)
dynamics = Dynamics(projection, dynamics_noise)
if full_variant:
report.add_header("Variant")
report.add_text(
json.dumps(
ppp.dict_to_safe_json(
full_variant,
sort=True),
indent=2,
)
)
vae, decoder, decoder_opt, encoder, encoder_opt = get_vae(
decoder_output_var,
hidden_size,
z_dim,
vae_kwargs,
)
vae.to(ptu.device)
epochs = []
losses = []
kls = []
log_probs = []
hist_heatmap_imgs = []
vae_heatmap_imgs = []
sample_imgs = []
entropies = []
tvs_to_uniform = []
entropy_gains_from_reweighting = []
p_theta = Histogram(num_bins)
p_new = Histogram(num_bins)
orig_train_data = dataset_generator(n_start_samples)
train_data = orig_train_data
start = time.time()
for epoch in progressbar(range(n_epochs)):
p_theta = Histogram(num_bins)
if epoch == 0 and use_dataset_generator_first_epoch:
vae_samples = dataset_generator(n_samples_to_add_per_epoch)
else:
if use_perfect_samples and epoch != 0:
# Ideally the VAE = p_new, but in practice, it won't be...
vae_samples = p_new.sample(n_samples_to_add_per_epoch)
else:
vae_samples = vae.sample(n_samples_to_add_per_epoch)
projected_samples = dynamics(vae_samples)
if append_all_data:
train_data = np.vstack((train_data, projected_samples))
else:
train_data = np.vstack((orig_train_data, projected_samples))
p_theta.fit(train_data)
if use_perfect_density:
prob = p_theta.compute_density(train_data)
else:
prob = vae.compute_density(train_data)
all_weights = prob_to_weight(prob, skew_config)
p_new.fit(train_data, weights=all_weights)
if epoch == 0 or (epoch + 1) % save_period == 0:
epochs.append(epoch)
report.add_text("Epoch {}".format(epoch))
hist_heatmap_img = visualize_histogram(p_theta, skew_config, report)
vae_heatmap_img = visualize_vae(
vae, skew_config, report,
resolution=num_bins,
)
sample_img = visualize_vae_samples(
epoch, train_data, vae, report, dynamics,
)
visualize_samples(
p_theta.sample(n_samples_to_add_per_epoch),
report,
title="P Theta/RB Samples",
)
visualize_samples(
p_new.sample(n_samples_to_add_per_epoch),
report,
title="P Adjusted Samples",
)
hist_heatmap_imgs.append(hist_heatmap_img)
vae_heatmap_imgs.append(vae_heatmap_img)
sample_imgs.append(sample_img)
report.save()
Image.fromarray(hist_heatmap_img).save(
logger.get_snapshot_dir() + '/hist_heatmap{}.png'.format(epoch)
)
Image.fromarray(vae_heatmap_img).save(
logger.get_snapshot_dir() + '/hist_heatmap{}.png'.format(epoch)
)
Image.fromarray(sample_img).save(
logger.get_snapshot_dir() + '/samples{}.png'.format(epoch)
)
"""
train VAE to look like p_new
"""
if sum(all_weights) == 0:
all_weights[:] = 1
if vae_reset_period > 0 and epoch % vae_reset_period == 0:
vae, decoder, decoder_opt, encoder, encoder_opt = get_vae(
decoder_output_var,
hidden_size,
z_dim,
vae_kwargs,
)
vae.to(ptu.device)
vae.fit(train_data, weights=all_weights)
epoch_stats = vae.get_epoch_stats()
losses.append(np.mean(epoch_stats['losses']))
kls.append(np.mean(epoch_stats['kls']))
log_probs.append(np.mean(epoch_stats['log_probs']))
entropies.append(p_theta.entropy())
tvs_to_uniform.append(p_theta.tv_to_uniform())
entropy_gain = p_new.entropy() - p_theta.entropy()
entropy_gains_from_reweighting.append(entropy_gain)
for k in sorted(epoch_stats.keys()):
logger.record_tabular(k, epoch_stats[k])
logger.record_tabular("Epoch", epoch)
logger.record_tabular('Entropy ', p_theta.entropy())
logger.record_tabular('KL from uniform', p_theta.kl_from_uniform())
logger.record_tabular('TV to uniform', p_theta.tv_to_uniform())
logger.record_tabular('Entropy gain from reweight', entropy_gain)
logger.record_tabular('Total Time (s)', time.time() - start)
logger.dump_tabular()
logger.save_itr_params(epoch, {
'vae': vae,
'train_data': train_data,
'vae_samples': vae_samples,
'dynamics': dynamics,
})
report.add_header("Training Curves")
plot_curves(
[
("Training Loss", losses),
("KL", kls),
("Log Probs", log_probs),
("Entropy Gain from Reweighting", entropy_gains_from_reweighting),
],
report,
)
plot_curves(
[
("Entropy", entropies),
("TV to Uniform", tvs_to_uniform),
],
report,
)
report.add_text("Max entropy: {}".format(p_theta.max_entropy()))
report.save()
for filename, imgs in [
("hist_heatmaps", hist_heatmap_imgs),
("vae_heatmaps", vae_heatmap_imgs),
("samples", sample_imgs),
]:
video = np.stack(imgs)
vwrite(
logger.get_snapshot_dir() + '/{}.mp4'.format(filename),
video,
)
local_gif_file_path = '{}.gif'.format(filename)
gif_file_path = '{}/{}'.format(
logger.get_snapshot_dir(),
local_gif_file_path
)
gif(gif_file_path, video)
report.add_image(local_gif_file_path, txt=filename, is_url=True)
report.save()
def train(dataset_generator,
n_start_samples,
projection=project_samples_square_np,
n_samples_to_add_per_epoch=1000,
n_epochs=100,
save_period=10,
append_all_data=True,
full_variant=None,
dynamics_noise=0,
num_bins=5,
weight_type='sqrt_inv_p',
**kwargs):
report = HTMLReport(
logger.get_snapshot_dir() + '/report.html',
images_per_row=3,
)
dynamics = Dynamics(projection, dynamics_noise)
if full_variant:
report.add_header("Variant")
report.add_text(
json.dumps(
ppp.dict_to_safe_json(full_variant, sort=True),
indent=2,
))
orig_train_data = dataset_generator(n_start_samples)
train_data = orig_train_data
heatmap_imgs = []
sample_imgs = []
entropies = []
tvs_to_uniform = []
"""
p_theta = previous iteration's model
p_new = this iteration's distribution
"""
p_theta = Histogram(num_bins, weight_type=weight_type)
for epoch in range(n_epochs):
logger.record_tabular('Epoch', epoch)
logger.record_tabular('Entropy ', p_theta.entropy())
logger.record_tabular('KL from uniform', p_theta.kl_from_uniform())
logger.record_tabular('TV to uniform', p_theta.tv_to_uniform())
entropies.append(p_theta.entropy())
tvs_to_uniform.append(p_theta.tv_to_uniform())
samples = p_theta.sample(n_samples_to_add_per_epoch)
empirical_samples = dynamics(samples)
if append_all_data:
train_data = np.vstack((train_data, empirical_samples))
else:
train_data = np.vstack((orig_train_data, empirical_samples))
if epoch == 0 or (epoch + 1) % save_period == 0:
report.add_text("Epoch {}".format(epoch))
heatmap_img = visualize_histogram(epoch, p_theta, report)
sample_img = visualize_samples(epoch, train_data, p_theta, report,
dynamics)
heatmap_imgs.append(heatmap_img)
sample_imgs.append(sample_img)
report.save()
from PIL import Image
Image.fromarray(heatmap_img).save(logger.get_snapshot_dir() +
'/heatmap{}.png'.format(epoch))
Image.fromarray(sample_img).save(logger.get_snapshot_dir() +
'/samples{}.png'.format(epoch))
weights = p_theta.compute_per_elem_weights(train_data)
p_new = Histogram(num_bins, weight_type=weight_type)
p_new.fit(
train_data,
weights=weights,
)
p_theta = p_new
logger.dump_tabular()
plot_curves([
("Entropy", entropies),
("TVs to Uniform", tvs_to_uniform),
], report)
report.add_text("Max entropy: {}".format(p_theta.max_entropy()))
report.save()
heatmap_video = np.stack(heatmap_imgs)
sample_video = np.stack(sample_imgs)
vwrite(
logger.get_snapshot_dir() + '/heatmaps.mp4',
heatmap_video,
)
vwrite(
logger.get_snapshot_dir() + '/samples.mp4',
sample_video,
)
try:
gif(
logger.get_snapshot_dir() + '/samples.gif',
sample_video,
)
gif(
logger.get_snapshot_dir() + '/heatmaps.gif',
heatmap_video,
)
report.add_image(
logger.get_snapshot_dir() + '/samples.gif',
"Samples GIF",
is_url=True,
)
report.add_image(
logger.get_snapshot_dir() + '/heatmaps.gif',
"Heatmaps GIF",
is_url=True,
)
report.save()
except ImportError as e:
print(e)
def generate_report(fanova_info: FanovaInfo, base_dir, param_name_to_log=None):
if param_name_to_log is None:
param_name_to_log = {}
f, config_space, X, Y, categorical_remapping, variants_list = fanova_info
report = HTMLReport(
osp.join(base_dir, 'report.html'),
images_per_row=3,
)
vis = visualizer.Visualizer(f, config_space)
cs_params = config_space.get_hyperparameters()
importances = [get_param_importance(f, param) for param in cs_params]
are_logs = [
is_data_log_uniformly_distributed(X[:, i])
for i in range(len(cs_params))
]
data = sorted(
zip(cs_params, importances, are_logs),
key=lambda x: -x[1],
)
"""
List out how the categorical hyperparameters were mapped.
"""
for name, remapping in categorical_remapping.items():
report.add_text("Remapping for {}:".format(name))
for key, value in remapping.inverse_dict.items():
report.add_text("\t{} = {}\n".format(key, value))
"""
Plot individual marginals.
"""
print("Creating marginal plots")
for param, importance, is_log in data:
param_name = param.name
if param_name in param_name_to_log:
is_log = param_name_to_log[param_name]
if isinstance(param, CategoricalHyperparameter):
vis.plot_categorical_marginal(param_name, show=False)
else:
vis.plot_marginal(param_name, show=False, log_scale=is_log)
img = vu.save_image()
report.add_image(
img,
"Marginal for {}.\nImportance = {}".format(param_name, importance),
)
"""
Plot pairwise marginals.
"""
print("Creating pairwise-marginal plots")
num_params = len(cs_params)
num_pairs = num_params * (num_params + 1) // 2
pair_and_importance = (f.get_most_important_pairwise_marginals(num_pairs))
for combi, importance in pair_and_importance.items():
param_names = []
for p in combi:
param_names.append(cs_params[p].name)
info_text = "Pairwise Marginal for {}.\nImportance = {}".format(
param_names,
importance,
),
if any(is_categorical(f, name) for name in param_names):
report.add_text(info_text)
continue
plot_pairwise_marginal(vis, combi, show=False)
img = vu.save_image()
report.add_image(
img,
txt=info_text,
)
"""
List the top 10 parameters.
"""
N = min(10, len(Y))
Y[np.isnan(Y)] = np.nanmin(Y) - 1
best_idxs = Y.argsort()[-N:][::-1]
all_param_names = [p.name for p in config_space.get_hyperparameters()]
for rank, i in enumerate(best_idxs):
variant = variants_list[i]
report.add_text("Rank {} params, with score = {}:".format(
rank + 1, Y[i]))
for name, value in zip(all_param_names, X[i, :]):
report.add_text("\t{} = {}\n".format(name, value))
report.add_text("\texp_name = {}\n".format(variant['exp_name']))
report.add_text("\tunique_id = {}\n".format(variant['unique_id']))
print("Guesses for is_log")
print("{")
for param, _, is_log in data:
name = param.name
print(" '{}': {},".format(name, is_log))
print("}")
"""
Ask user if they want to see the report
"""
abs_path = osp.abspath(report.path)
print("Report saved to: {}".format(abs_path))
report.save()
open_report = query_yes_no("Open report?", default="yes")
if open_report:
cmd = "xdg-open {}".format(abs_path)
print(cmd)
subprocess.call(cmd, shell=True)