import matplotlib.pyplot as plt
import numpy as np
from forkan.common.utils import read_keys, setup_plotting, get_figure_size
logger = logging.getLogger(__name__)
home = os.environ['HOME']
models_dir = f'{home}/.forkan/done/pendulum/ppo2-gt'
ylims, tick_setup = setup_plotting()
fig, ax = plt.subplots(1, 1, figsize=get_figure_size())
for fi, name in [('', 'beta=1')]:
data = read_keys(models_dir, fi, ['mean_reward', 'total_timesteps'])
xs = data['total_timesteps'][0]
ys = data['mean_reward']
ax.plot(xs, np.nanmedian(ys, axis=0))
ax.fill_between(xs,
np.nanpercentile(ys, 25, axis=0),
np.nanpercentile(ys, 75, axis=0),
alpha=0.33)
plt.ylim(**ylims)
ax.set_ylabel('Median Reward')
ax.set_xlabel('Steps')
plt.xticks(tick_setup[0], tick_setup[1])
from forkan.common.utils import read_keys, setup_plotting, get_figure_size
logger = logging.getLogger(__name__)
ylims, tick_setup = setup_plotting()
home = os.environ['HOME']
models_dir = f'{home}/.forkan/done/pendulum/ppo2-beta-exp'
col = ['#8cd17d', '#b6992d', '#f1ce63', '#499894', '#86bcb6']
for rlc in [1, 30]:
fig, ax = plt.subplots(1, 1, figsize=get_figure_size())
i = 0
for beta in [5, 20, 30, 60, 80]:
data = read_keys(models_dir, [f'rlc{rlc}-', f'b{beta}-'],
['mean_reward', 'total_timesteps'])
xs = data['total_timesteps'][0]
ys = data['mean_reward']
ax.plot(xs,
np.nanmedian(ys, axis=0),
label=f'$\\beta={beta}$',
color=col[i])
ax.fill_between(xs,
np.nanpercentile(ys, 25, axis=0),
np.nanpercentile(ys, 75, axis=0),
alpha=0.33,
color=col[i])
i += 1
plt.ylim(**ylims)
logger = logging.getLogger(__name__)
setup_plotting()
home = os.environ['HOME']
models_dir = f'{home}/.forkan/done/classify-ball/'
def smooth(l, N=1000):
l = np.asarray(l, np.float32)
return np.convolve(l, np.ones((N, )) / N, mode='valid')
# return medfilt(np.asarray(l, dtype=np.float32), 51)
fig, ax = plt.subplots(1, 1, figsize=get_figure_size())
data = read_keys(models_dir, 'RETRAIN-N128',
['nbatch', 'mae_train', 'mse_train', 'mae_test', 'mse_test'])
ret_mae_train = np.squeeze(data['mae_train'])
ret_mae_test = np.squeeze(data['mae_test'])
ret_mse_train = np.squeeze(data['mse_train'])
ret_mse_test = np.squeeze(data['mse_test'])
ret_nbatch = np.squeeze(data['nbatch'])
data = read_keys(models_dir, 'VAE-N128',
['nbatch', 'mae_train', 'mse_train', 'mae_test', 'mse_test'])
vae_mae_train = np.squeeze(data['mae_train'])
vae_mae_test = np.squeeze(data['mae_test'])
vae_mse_train = np.squeeze(data['mse_train'])
vae_mse_test = np.squeeze(data['mse_test'])
vae_nbatch = np.squeeze(data['nbatch'])
import matplotlib.pyplot as plt
import numpy as np
from forkan.common.utils import read_keys, setup_plotting, get_figure_size
logger = logging.getLogger(__name__)
ylims, tick_setup = setup_plotting()
fig, ax = plt.subplots(1, 1, figsize=get_figure_size())
home = os.environ['HOME']
models_dir = f'{home}/.forkan/done/pendulum/ppo2-pendcnn'
for num_hid in [16, 32, 64, 512]:
data = read_keys(models_dir, f'lathid{num_hid}-',
['mean_reward', 'total_timesteps'])
xs = data['total_timesteps']
ys = data['mean_reward']
ax.plot(xs[0], np.nanmedian(ys, axis=0), label=f'$N={num_hid}$')
ax.fill_between(xs[0],
np.nanpercentile(ys, 25, axis=0),
np.nanpercentile(ys, 75, axis=0),
alpha=0.33)
plt.ylim(**ylims)
ax.set_ylabel('Median Reward')
ax.set_xlabel('Steps')
plt.xticks(tick_setup[0], tick_setup[1])
