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update plotting_constants.py
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@lenarttreven
lenarttreven committed Feb 16, 2024
1 parent 16564e5 commit f8fcabd
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Showing 3 changed files with 43 additions and 84 deletions.
2 changes: 1 addition & 1 deletion experiments/offline_rl_from_recorded_data/launcher.py
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Expand Up @@ -4,7 +4,7 @@
PROJECT_NAME = 'OfflineRLSim_Jan12'

_applicable_configs = {
'horizon_len': [100],
'horizon_len': [200],
'model_seed': list(range(5)),
'data_seed': list(range(5)),
'project_name': [PROJECT_NAME],
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118 changes: 35 additions & 83 deletions lenart_internal/analysis_simulator_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,10 @@
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from plotting_hyperdata import plotting_constants

LINEWIDTH = 3
SIMULATION = True
BEST_MODEL_FREE = 164.53915

if SIMULATION:
reward_column = 'reward_mean_on_simulator'
Expand All @@ -15,41 +16,37 @@
folder = 'real_data'



def to_pretty_group_name(group_name: str):
if 'use_grey_box=1' in group_name:
return 'Grey box'
elif 'use_sim_prior=0' in group_name:
return 'No sim prior'
return 'No sim prior model'
elif 'use_sim_prior=1' in group_name and 'high_fidelity=0' in group_name:
return 'Low fidelity'
return 'Low fidelity model'
elif 'use_sim_prior=1' in group_name and 'high_fidelity=1' in group_name:
return 'High fidelity'
return 'High fidelity model'


def join_group_names_and_prepare_statistics_mean(data: pd.DataFrame):
def join_group_names_and_prepare_statistics_mean_SIM(data: pd.DataFrame, ):
# Summary of rewards
summary_rewards = data.groupby('Group')[reward_column].agg(['mean', 'std'])

summary = pd.concat([summary_rewards], axis=1)
summary.reset_index(inplace=True)
summary.columns = ['group_name', 'mean', 'std', ]
return summary
summary_rewards = data.groupby('Group')[reward_column].agg([
lambda x: smooth_curve(x, type='mean'),
lambda x: smooth_curve(x, type='std_err'),
'max',


def join_group_names_and_prepare_statistics(data: pd.DataFrame):
summary_rewards = data.groupby('Group')[reward_column].agg(['median',
lambda x: x.quantile(0.2), # 0.2 quantile
lambda x: x.quantile(0.8) # 0.8 quantile
])
])

summary = pd.concat([summary_rewards], axis=1)
summary.reset_index(inplace=True)
summary.columns = ['group_name',
'median_rewards', '0.2_quantile_rewards', '0.8_quantile_rewards']
summary.columns = ['group_name', 'mean', 'std', 'max']
return summary


def plot_rewards_mean(data: pd.DataFrame, max_offline_data: int | None = None, sup_title: str = None):
def plot_rewards_mean_SIM(data: pd.DataFrame,
max_offline_data: int | None = None,
sup_title: str = None,
width: float = 2.0):
offline_transitions = data['num_offline_collected_transitions'].unique()
offline_transitions.sort()

Expand All @@ -60,7 +57,7 @@ def plot_rewards_mean(data: pd.DataFrame, max_offline_data: int | None = None, s

for index, offline_transition in enumerate(offline_transitions):
cur_data = data.loc[data['num_offline_collected_transitions'] == offline_transition]
summary = join_group_names_and_prepare_statistics_mean(cur_data)
summary = join_group_names_and_prepare_statistics_mean_SIM(cur_data)

means.append(summary['mean'].to_numpy())
stds.append(summary['std'].to_numpy())
Expand All @@ -76,15 +73,21 @@ def plot_rewards_mean(data: pd.DataFrame, max_offline_data: int | None = None, s
stds = stds[idx]

for index, group in enumerate(group_names):
ax.plot(offline_transitions, means[:, index], label=group)
ax.fill_between(offline_transitions,
means[:, index] - (stds[:, index]) / (1),
means[:, index] + stds[:, index] / (1), alpha=0.2)
if max_offline_data is None:
max_offline_data = offline_transitions[-1]
ax.hlines(y=BEST_MODEL_FREE, xmin=0, xmax=max_offline_data, linewidth=2, linestyle='--', label='Best model free')
if group in plotting_constants.offline_rl_names_transfer.keys():
method_name = plotting_constants.offline_rl_names_transfer[group]
ax.plot(offline_transitions, means[:, index],
label=method_name,
color=plotting_constants.COLORS[method_name],
linestyle=plotting_constants.LINE_STYLES[method_name],
linewidth=LINEWIDTH)
ax.fill_between(offline_transitions,
means[:, index] - width * stds[:, index],
means[:, index] + width * stds[:, index],
alpha=0.2,
color=plotting_constants.COLORS[method_name], )
ax.set_title(r'Mean $\pm$ std error')
ax.set_xlabel('Number of offline transitions')
ax.set_ylim(0)
ax.set_ylabel('Reward')
fig.suptitle(sup_title)
dir = folder
Expand All @@ -96,65 +99,14 @@ def plot_rewards_mean(data: pd.DataFrame, max_offline_data: int | None = None, s
plt.show()


def plot_rewards(data: pd.DataFrame, max_offline_data: int | None = None, sup_title: str = None):
offline_transitions = data['num_offline_collected_transitions'].unique()
offline_transitions.sort()

medians = []
quantile_20 = []
quantile_80 = []
group_names = []

fig, ax = plt.subplots(1, 1, figsize=(10, 4))

for index, offline_transition in enumerate(offline_transitions):
cur_data = data.loc[data['num_offline_collected_transitions'] == offline_transition]
summary = join_group_names_and_prepare_statistics(cur_data)

medians.append(summary['median_rewards'].to_numpy())
quantile_20.append(summary['0.2_quantile_rewards'].to_numpy())
quantile_80.append(summary['0.8_quantile_rewards'].to_numpy())
if index == 0:
group_names = list(map(lambda x: to_pretty_group_name(x), summary['group_name']))

medians = np.stack(medians, axis=0)
quantile_20 = np.stack(quantile_20, axis=0)
quantile_80 = np.stack(quantile_80, axis=0)
if max_offline_data:
idx = offline_transitions <= max_offline_data
offline_transitions = offline_transitions[idx]
medians = medians[idx]
quantile_20 = quantile_20[idx]
quantile_80 = quantile_80[idx]

for index, group in enumerate(group_names):
ax.plot(offline_transitions, medians[:, index], label=group)
ax.fill_between(offline_transitions, quantile_20[:, index], quantile_80[:, index], alpha=0.2)

if max_offline_data is None:
max_offline_data = offline_transitions[-1]

ax.hlines(y=BEST_MODEL_FREE, xmin=0, xmax=max_offline_data, linewidth=2, linestyle='--', label='Best model free')
ax.set_title(f'Median and 0.2-0.8 confidence interval')
ax.set_xlabel('Number of offline transitions')
ax.set_ylabel('Reward')
fig.suptitle(sup_title)
dir = folder
if not os.path.exists(dir):
os.makedirs(dir)
title = sup_title + 'offline_rl_simulated_data.pdf'
plt.legend()
plt.savefig(os.path.join(dir, title))
plt.show()


if __name__ == '__main__':
max_offline_data = 3000
data = pd.read_csv('wandb_runs.csv')
max_offline_data = 5_000
data = pd.read_csv('wandb_runs_sim_final.csv')
bandwidth_svgd = data.bandwidth_svgd.unique()
length_scale_aditive_sim_gp = data.length_scale_aditive_sim_gp.unique()
for i in bandwidth_svgd:
for j in length_scale_aditive_sim_gp:
filtered_data = data[(data.bandwidth_svgd == i) & (data.length_scale_aditive_sim_gp == j)]
plot_rewards_mean(filtered_data, max_offline_data=max_offline_data,
sup_title=f'bandwidth_svgd={i}, length_scale_aditive_sim_gp={j}')
plot_rewards_mean_SIM(filtered_data, max_offline_data=max_offline_data,
sup_title=f'bandwidth_svgd={i}, length_scale_aditive_sim_gp={j}')
7 changes: 7 additions & 0 deletions plotting_hyperdata/plotting_constants.py
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Expand Up @@ -126,6 +126,13 @@
'nll': 'NLL',
}

offline_rl_names_transfer = {
'No sim prior model': METHODS[1],
'Low fidelity model': METHODS[11],
'High fidelity model': METHODS[12],
'Parameteric model': METHODS[16]
}

online_rl_name_transfer = {
'No sim prior': METHODS[1],
'Low fidelity prior': METHODS[11]
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