<jupyter_start><jupyter_text>init<jupyter_code>from RLA.easy_plot.plot_func import plot_res_func
perf_map = {
    "img--Hopper-v4v2": 0.2,
    "img--InvertedPendulum-v2v2": 0.67,
    "img--InvertedDouble-v5v2": 0.2,
    "img--HalfCheetah-v2v2": 0.8,
    "img--Swimmer-v4v2": 0.08,
    "img--Walker2d-v4v2": 0.48
}
# color_map = ['royalblue', 'orange', 'seagreen', 'deeppink', 'darkred', 'deepskyblue']<jupyter_output><empty_output><jupyter_text>walker<jupyter_code>env = 'Walker2d'
task = f'img--{env}-v4v2'
prefix_dir = f'./log/{task}'
regs = [
    "2021/05/09/*codas*",
    
]
_ = plot_res_func(prefix_dir=prefix_dir, regs=regs , param_keys=['alg_type', 'emb_dynamic'], 
                  value_keys=['performance/reward_ratio'], 
#                   replace_legend_keys=["CODAS"],
                  bound_line=[[perf_map[task], 'grey', '--', 'BC']],
                  resample=1024,
                  smooth_step=10, legend_outside=False, 
                  title='', use_buf=False, 
                  xlabel='epochs', ylabel='reward ratio', 
#                   colors=color_map,
                  shaded_range=False,
                  pretty=True, 
                  save_name=env + '-benchmark.pdf')<jupyter_output>check regs ./log/img--Walker2d-v4v2/2021/05/09/*codas*. log found: 
./log/img--Walker2d-v4v2/2021/05/09/02-39-56-298867 10.83.152.17 &info=traj-dis-final&seed=888&trajectory_batch=10&alg_type=codas&emb_dynamic=True&traj_dis=True
./log/img--Walker2d-v4v2/2021/05/09/02-39-42-261444 10.83.152.17 &info=traj-dis-final&seed=88&trajectory_batch=10&alg_type=codas&emb_dynamic=True&traj_dis=True
./log/img--Walker2d-v4v2/2021/05/09/02-39-27-179454 10.83.152.75 &info=traj-dis-final&seed=8&trajectory_batch=10&alg_type=codas&emb_dynamic=True&traj_dis=True
---- load dataset 3---- 
alg_type=codas.emb_dynamic=True eval:performance/reward_ratio (3)-err : (0.7544 \pm 0.0260)
--- complete process ---
saved location: ./results/easy_plot/img--Walker2d-v4v2/Walker2d-benchmark.pdf
{'fraction_non_alphanumeric': 0.17303822937625754, 'fraction_numerical': 0.09054325955734406, 'mean_word_length': 3.1785714285714284, 'pattern_counts': {'":': 6, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 3, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '455422', 'license': 'MIT', 'n_tokens_mistral': 921, 'n_tokens_neox': 805, 'n_words': 80, 'path': 'plot_demo.ipynb', 'repo_name': 'xionghuichen/CODAS', 'size': '1988'}