def plot_session(session_spec, info_space, session_data):
'''Plot the session graph, 2 panes: reward, loss & explore_var. Each aeb_df gets its own color'''
graph_x = session_spec['meta'].get('graph_x', 'epi')
aeb_count = len(session_data)
palette = viz.get_palette(aeb_count)
fig = viz.tools.make_subplots(rows=3, cols=1, shared_xaxes=True)
for idx, (a, e, b) in enumerate(session_data):
aeb_str = f'{a}{e}{b}'
aeb_df = session_data[(a, e, b)]
aeb_df.fillna(0, inplace=True) # for saving plot, cant have nan
fig_1 = viz.plot_line(aeb_df, 'reward', graph_x, legend_name=aeb_str, draw=False, trace_kwargs={'legendgroup': aeb_str, 'line': {'color': palette[idx]}})
fig.append_trace(fig_1.data[0], 1, 1)
fig_2 = viz.plot_line(aeb_df, ['loss'], graph_x, y2_col=['explore_var'], trace_kwargs={'legendgroup': aeb_str, 'showlegend': False, 'line': {'color': palette[idx]}}, draw=False)
fig.append_trace(fig_2.data[0], 2, 1)
fig.append_trace(fig_2.data[1], 3, 1)
fig.layout['xaxis1'].update(title=graph_x, zerolinewidth=1)
fig.layout['yaxis1'].update(fig_1.layout['yaxis'])
fig.layout['yaxis1'].update(domain=[0.55, 1])
fig.layout['yaxis2'].update(fig_2.layout['yaxis'])
fig.layout['yaxis2'].update(showgrid=False, domain=[0, 0.45])
fig.layout['yaxis3'].update(fig_2.layout['yaxis2'])
fig.layout['yaxis3'].update(overlaying='y2', anchor='x2')
fig.layout.update(ps.pick(fig_1.layout, ['legend']))
fig.layout.update(title=f'session graph: {session_spec["name"]} t{info_space.get("trial")} s{info_space.get("session")}', width=500, height=600)
viz.plot(fig)
return fig
def plot_trial(trial_spec, info_space):
'''Plot the trial graph, 1 pane: mean and error envelope of reward graphs from all sessions. Each aeb_df gets its own color'''
prepath = util.get_prepath(trial_spec, info_space)
predir = util.prepath_to_predir(prepath)
session_datas = session_datas_from_file(predir, trial_spec,
info_space.get('trial'))
aeb_count = len(session_datas[0])
palette = viz.get_palette(aeb_count)
fig = None
for idx, (a, e, b) in enumerate(session_datas[0]):
aeb = (a, e, b)
aeb_str = f'{a}{e}{b}'
color = palette[idx]
aeb_rewards_df = gather_aeb_rewards_df(aeb, session_datas)
aeb_fig = build_aeb_reward_fig(aeb_rewards_df, aeb_str, color)
if fig is None:
fig = aeb_fig
else:
fig.data.extend(aeb_fig.data)
fig.layout.update(
title=f'trial graph: {trial_spec["name"]} t{info_space.get("trial")}',
width=500,
height=600)
viz.plot(fig)
return fig
def plot_trial(trial_spec, info_space):
'''Plot the trial graph, 1 pane: mean and error envelope of reward graphs from all sessions. Each aeb_df gets its own color'''
prepath = util.get_prepath(trial_spec, info_space)
predir, _, _, _, _, _ = util.prepath_split(prepath)
session_datas = session_datas_from_file(predir, trial_spec,
info_space.get('trial'))
rand_session_data = session_datas[list(session_datas.keys())[0]]
max_tick_unit = ps.get(trial_spec, 'env.0.max_tick_unit')
aeb_count = len(rand_session_data)
palette = viz.get_palette(aeb_count)
fig = None
for idx, (a, e, b) in enumerate(rand_session_data):
aeb = (a, e, b)
aeb_str = f'{a}{e}{b}'
color = palette[idx]
aeb_rewards_df = gather_aeb_rewards_df(aeb, session_datas,
max_tick_unit)
aeb_fig = build_aeb_reward_fig(aeb_rewards_df, aeb_str, color,
max_tick_unit)
if fig is None:
fig = aeb_fig
else:
fig.add_traces(aeb_fig.data)
fig.layout.update(
title=
f'trial graph: {trial_spec["name"]} t{info_space.get("trial")}, {len(session_datas)} sessions',
width=500,
height=600)
viz.plot(fig)
return fig
'bert': 'Qbert',
'eaquest': 'Seaquest',
'humanoid': 'RoboschoolHumanoid',
'humanoidflagrun': 'RoboschoolHumanoidFlagrun',
'humanoidflagrunharder': 'RoboschoolHumanoidFlagrunHarder',
}
master_legend_list = [
'DQN',
'DDQN+PER',
'A2C (GAE)',
'A2C (n-step)',
'PPO',
'SAC',
]
master_palette_dict = dict(
zip(master_legend_list, viz.get_palette(len(master_legend_list))))
master_palette_dict['Async SAC'] = master_palette_dict['SAC']
def guard_env_name(env):
env = env.strip('_').strip('-')
if env in env_name_map:
return env_name_map[env]
else:
return env
def get_trial_metrics_scalar(algo, env, data_folder):
try:
filepaths = glob(
f'{data_folder}/{algo}*{env}*/{trial_metrics_scalar_path}')