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Steffen Illium
106
studies/e_1.py
106
studies/e_1.py
@ -66,8 +66,8 @@ There are further distinctions to be made:
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"""
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n_agents = 4
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ood_monitor_file = f'e_1_monitor_{n_agents}_agents.pick'
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baseline_monitor_file = 'e_1_baseline_monitor.pick'
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ood_monitor_file = f'e_1_{n_agents}_agents'
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baseline_monitor_file = 'e_1_baseline'
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def policy_model_kwargs():
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@ -103,7 +103,7 @@ def load_model_run_baseline(seed_path, env_to_run):
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env_kwargs = simplejson.load(f)
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env_kwargs.update(done_at_collision=True)
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# Monitor Init
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with MonitorCallback(filepath=seed_path / baseline_monitor_file) as monitor:
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with MonitorCallback(filepath=seed_path / f'{baseline_monitor_file}.pick') as monitor:
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# Init Env
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with env_to_run(**env_kwargs) as env_factory:
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# Evaluation Loop for i in range(n Episodes)
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@ -139,7 +139,7 @@ def load_model_run_study(seed_path, env_to_run, additional_kwargs_dict):
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done_at_collision=True,
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**additional_kwargs_dict.get('post_training_kwargs', {}))
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# Monitor Init
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with MonitorCallback(filepath=seed_path / ood_monitor_file) as monitor:
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with MonitorCallback(filepath=seed_path / f'{ood_monitor_file}.pick') as monitor:
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# Init Env
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with env_to_run(**env_kwargs) as env_factory:
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# Evaluation Loop for i in range(n Episodes)
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@ -172,7 +172,7 @@ def load_model_run_study(seed_path, env_to_run, additional_kwargs_dict):
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def start_mp_study_run(envs_map, policies_path):
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paths = list(y for y in policies_path.iterdir() if y.is_dir() and not (y / ood_monitor_file).exists())
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paths = list(y for y in policies_path.iterdir() if y.is_dir() and not (y / f'{ood_monitor_file}.pick').exists())
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if paths:
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import multiprocessing as mp
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pool = mp.Pool(mp.cpu_count())
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@ -185,7 +185,8 @@ def start_mp_study_run(envs_map, policies_path):
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def start_mp_baseline_run(envs_map, policies_path):
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paths = list(y for y in policies_path.iterdir() if y.is_dir() and not (y / baseline_monitor_file).exists())
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paths = list(y for y in policies_path.iterdir() if y.is_dir() and
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not (y / f'{baseline_monitor_file}.pick').exists())
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if paths:
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import multiprocessing as mp
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pool = mp.Pool(mp.cpu_count())
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@ -197,11 +198,17 @@ def start_mp_baseline_run(envs_map, policies_path):
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if __name__ == '__main__':
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# What to do:
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train = True
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baseline_run = True
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ood_run = True
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plotting = True
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train_steps = 5e6
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n_seeds = 3
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# Define a global studi save path
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start_time = 'Now_with_doors' # int(time.time())
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start_time = 'exploring_obs_stack' # int(time.time())
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study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
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# Define Global Env Parameters
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@ -209,7 +216,7 @@ if __name__ == '__main__':
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obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT,
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omit_agent_self=True,
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additional_agent_placeholder=None,
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frames_to_stack=3,
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frames_to_stack=6,
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pomdp_r=2
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)
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move_props = MovementProperties(allow_diagonal_movement=True,
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@ -327,7 +334,7 @@ if __name__ == '__main__':
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# Train starts here ############################################################
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# Build Major Loop parameters, parameter versions, Env Classes and models
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if False:
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if train:
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for obs_mode in observation_modes.keys():
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for env_name in env_names:
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for model_cls in [h.MODEL_MAP['A2C']]:
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@ -417,7 +424,7 @@ if __name__ == '__main__':
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# Evaluation starts here #####################################################
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# First Iterate over every model and monitor "as trained"
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if True:
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if baseline_run:
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print('Start Baseline Tracking')
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for obs_mode in observation_modes:
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obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == obs_mode)
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@ -432,7 +439,7 @@ if __name__ == '__main__':
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print('Baseline Tracking done')
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# Then iterate over every model and monitor "ood behavior" - "is it ood?"
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if True:
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if ood_run:
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print('Start OOD Tracking')
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for obs_mode in observation_modes:
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obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == obs_mode)
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@ -449,17 +456,18 @@ if __name__ == '__main__':
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print('OOD Tracking Done')
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# Plotting
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if True:
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if plotting:
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# TODO: Plotting
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print('Start Plotting')
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df_list = list()
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for observation_folder in (x for x in study_root_path.iterdir() if x.is_dir()):
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df_list = list()
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for env_folder in (x for x in observation_folder.iterdir() if x.is_dir()):
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for model_folder in (x for x in env_folder.iterdir() if x.is_dir()):
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# Gather per seed results in this list
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for seed_folder in (x for x in model_folder.iterdir() if x.is_dir()):
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for monitor_file in [baseline_monitor_file, ood_monitor_file]:
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for monitor_file in [f'{baseline_monitor_file}.pick', f'{ood_monitor_file}.pick']:
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with (seed_folder / monitor_file).open('rb') as f:
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monitor_df = pickle.load(f)
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@ -476,47 +484,47 @@ if __name__ == '__main__':
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df_list.append(monitor_df)
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id_cols = ['monitor', 'env', 'obs_mode', 'model']
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df = pd.concat(df_list, ignore_index=True)
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df = df.fillna(0)
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id_cols = ['monitor', 'env', 'obs_mode', 'model']
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df = pd.concat(df_list, ignore_index=True)
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df = df.fillna(0)
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for env_name in env_names:
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for id_col in id_cols:
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df[id_col] = df[id_col].astype(str)
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if True:
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# df['fail_sum'] = df.loc[:, df.columns.str.contains("failed")].sum(1)
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df['pick_up'] = df.loc[:, df.columns.str.contains("]_item_pickup")].sum(1)
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df['drop_off'] = df.loc[:, df.columns.str.contains("]_item_dropoff")].sum(1)
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df['failed_item_action'] = df.loc[:, df.columns.str.contains("]_failed_item_action")].sum(1)
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df['failed_cleanup'] = df.loc[:, df.columns.str.contains("]_failed_dirt_cleanup")].sum(1)
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df['coll_lvl'] = df.loc[:, df.columns.str.contains("]_vs_LEVEL")].sum(1)
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df['coll_agent'] = df.loc[:, df.columns.str.contains("]_vs_Agent")].sum(1) / 2
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# df['collisions'] = df['coll_lvl'] + df['coll_agent']
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if True:
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# df['fail_sum'] = df.loc[:, df.columns.str.contains("failed")].sum(1)
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df['pick_up'] = df.loc[:, df.columns.str.contains("]_item_pickup")].sum(1)
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df['drop_off'] = df.loc[:, df.columns.str.contains("]_item_dropoff")].sum(1)
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df['failed_item_action'] = df.loc[:, df.columns.str.contains("]_failed_item_action")].sum(1)
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df['failed_cleanup'] = df.loc[:, df.columns.str.contains("]_failed_dirt_cleanup")].sum(1)
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df['coll_lvl'] = df.loc[:, df.columns.str.contains("]_vs_LEVEL")].sum(1)
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df['coll_agent'] = df.loc[:, df.columns.str.contains("]_vs_Agent")].sum(1) / 2
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# df['collisions'] = df['coll_lvl'] + df['coll_agent']
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value_vars = ['pick_up', 'drop_off', 'failed_item_action', 'failed_cleanup',
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'coll_lvl', 'coll_agent', 'dirt_cleaned']
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value_vars = ['pick_up', 'drop_off', 'failed_item_action', 'failed_cleanup',
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'coll_lvl', 'coll_agent', 'dirt_cleaned']
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df_grouped = df.groupby(id_cols + ['seed']
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).agg({key: 'sum' if "Agent" in key else 'mean' for key in df.columns
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if key not in (id_cols + ['seed'])})
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df_melted = df_grouped.reset_index().melt(id_vars=id_cols,
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value_vars=value_vars, # 'step_reward',
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var_name="Measurement",
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value_name="Score")
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# df_melted["Measurements"] = df_melted["Measurement"] + " " + df_melted["monitor"]
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df_grouped = df.groupby(id_cols + ['seed']
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).agg({key: 'sum' if "Agent" in key else 'mean' for key in df.columns
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if key not in (id_cols + ['seed'])})
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df_melted = df_grouped.reset_index().melt(id_vars=id_cols,
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value_vars=value_vars, # 'step_reward',
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var_name="Measurement",
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value_name="Score")
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# df_melted["Measurements"] = df_melted["Measurement"] + " " + df_melted["monitor"]
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# Plotting
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# fig, ax = plt.subplots(figsize=(11.7, 8.27))
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# Plotting
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# fig, ax = plt.subplots(figsize=(11.7, 8.27))
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c = sns.catplot(data=df_melted[df_melted['obs_mode'] == observation_folder.name],
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x='Measurement', hue='monitor', row='model', col='env', y='Score',
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sharey=False, kind="box", height=4, aspect=.7, legend_out=False, legend=False,
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showfliers=False)
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c.set_xticklabels(rotation=65, horizontalalignment='right')
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# c.fig.subplots_adjust(top=0.9) # adjust the Figure in rp
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c.fig.suptitle(f"Cat plot for {observation_folder.name}")
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# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
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plt.tight_layout()
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plt.savefig(study_root_path / f'results_{n_agents}_agents_{observation_folder.name}.png')
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c = sns.catplot(data=df_melted[df_melted['env'] == env_name],
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x='Measurement', hue='monitor', row='model', col='obs_mode', y='Score',
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sharey=True, kind="box", height=4, aspect=.7, legend_out=False, legend=False,
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showfliers=False)
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c.set_xticklabels(rotation=65, horizontalalignment='right')
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# c.fig.subplots_adjust(top=0.9) # adjust the Figure in rp
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c.fig.suptitle(f"Cat plot for {env_name}")
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# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
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plt.tight_layout()
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plt.savefig(study_root_path / f'results_{n_agents}_agents_{env_name}.png')
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pass
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