mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-10-31 12:37:27 +01:00
204 lines
8.4 KiB
Python
204 lines
8.4 KiB
Python
import pickle
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import re
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from os import PathLike
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from pathlib import Path
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from typing import Union, List
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import pandas as pd
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from environment.utils.helpers import IGNORED_DF_COLUMNS
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from plotting.plotting import prepare_plot
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MODEL_MAP = None
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def plot_single_run(run_path: Union[str, PathLike], use_tex: bool = False, column_keys=None):
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run_path = Path(run_path)
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df_list = list()
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if run_path.is_dir():
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monitor_file = next(run_path.glob('*monitor*.pick'))
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elif run_path.exists() and run_path.is_file():
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monitor_file = run_path
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else:
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raise ValueError
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with monitor_file.open('rb') as f:
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monitor_df = pickle.load(f)
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monitor_df = monitor_df.fillna(0)
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df_list.append(monitor_df)
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df = pd.concat(df_list, ignore_index=True)
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df = df.fillna(0).rename(columns={'episode': 'Episode'}).sort_values(['Episode'])
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if column_keys is not None:
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columns = [col for col in column_keys if col in df.columns]
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else:
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columns = [col for col in df.columns if col not in IGNORED_DF_COLUMNS]
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roll_n = 50
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non_overlapp_window = df.groupby(['Episode']).rolling(roll_n, min_periods=1).mean()
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df_melted = df[columns + ['Episode']].reset_index().melt(
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id_vars=['Episode'], value_vars=columns, var_name="Measurement", value_name="Score"
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)
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if df_melted['Episode'].max() > 800:
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skip_n = round(df_melted['Episode'].max() * 0.02)
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df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
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prepare_plot(run_path.parent / f'{run_path.parent.name}_monitor_lineplot.png', df_melted, use_tex=use_tex)
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print('Plotting done.')
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def compare_seed_runs(run_path: Union[str, PathLike], use_tex: bool = False):
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run_path = Path(run_path)
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df_list = list()
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for run, monitor_file in enumerate(run_path.rglob('monitor*.pick')):
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with monitor_file.open('rb') as f:
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monitor_df = pickle.load(f)
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monitor_df['run'] = run
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monitor_df = monitor_df.fillna(0)
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df_list.append(monitor_df)
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df = pd.concat(df_list, ignore_index=True)
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df = df.fillna(0).rename(columns={'episode': 'Episode', 'run': 'Run'}).sort_values(['Run', 'Episode'])
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columns = [col for col in df.columns if col not in IGNORED_DF_COLUMNS]
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roll_n = 50
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non_overlapp_window = df.groupby(['Run', 'Episode']).rolling(roll_n, min_periods=1).mean()
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df_melted = non_overlapp_window[columns].reset_index().melt(id_vars=['Episode', 'Run'],
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value_vars=columns, var_name="Measurement",
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value_name="Score")
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if df_melted['Episode'].max() > 800:
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skip_n = round(df_melted['Episode'].max() * 0.02)
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df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
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run_path.mkdir(parents=True, exist_ok=True)
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if run_path.exists() and run_path.is_file():
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prepare_plot(run_path.parent / f'{run_path.name}_monitor_lineplot.png', df_melted, use_tex=use_tex)
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else:
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prepare_plot(run_path / f'{run_path.name}_monitor_lineplot.png', df_melted, use_tex=use_tex)
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print('Plotting done.')
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def compare_model_runs(run_path: Path, run_identifier: Union[str, int], parameter: Union[str, List[str]],
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use_tex: bool = False):
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run_path = Path(run_path)
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df_list = list()
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parameter = [parameter] if isinstance(parameter, str) else parameter
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for path in run_path.iterdir():
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if path.is_dir() and str(run_identifier) in path.name:
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for run, monitor_file in enumerate(path.rglob('monitor*.pick')):
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with monitor_file.open('rb') as f:
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monitor_df = pickle.load(f)
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monitor_df['run'] = run
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monitor_df['model'] = next((x for x in path.name.split('_') if x in MODEL_MAP.keys()))
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monitor_df = monitor_df.fillna(0)
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df_list.append(monitor_df)
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df = pd.concat(df_list, ignore_index=True)
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df = df.fillna(0).rename(columns={'episode': 'Episode', 'run': 'Run', 'model': 'Model'})
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columns = [col for col in df.columns if col in parameter]
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last_episode_to_report = min(df.groupby(['Model'])['Episode'].max())
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df = df[df['Episode'] < last_episode_to_report]
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roll_n = 40
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non_overlapp_window = df.groupby(['Model', 'Run', 'Episode']).rolling(roll_n, min_periods=1).mean()
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df_melted = non_overlapp_window[columns].reset_index().melt(id_vars=['Episode', 'Run', 'Model'],
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value_vars=columns, var_name="Measurement",
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value_name="Score")
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if df_melted['Episode'].max() > 80:
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skip_n = round(df_melted['Episode'].max() * 0.02)
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df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
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style = 'Measurement' if len(columns) > 1 else None
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prepare_plot(run_path / f'{run_identifier}_compare_{parameter}.png', df_melted, hue='Model', style=style,
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use_tex=use_tex)
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print('Plotting done.')
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def compare_all_parameter_runs(run_root_path: Path, parameter: Union[str, List[str]],
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param_names: Union[List[str], None] = None, str_to_ignore='', use_tex: bool = False):
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run_root_path = Path(run_root_path)
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df_list = list()
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parameter = [parameter] if isinstance(parameter, str) else parameter
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for monitor_idx, monitor_file in enumerate(run_root_path.rglob('monitor*.pick')):
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with monitor_file.open('rb') as f:
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monitor_df = pickle.load(f)
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params = [x.name for x in monitor_file.parents if x.parent not in run_root_path.parents]
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if str_to_ignore:
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params = [re.sub(f'_*({str_to_ignore})', '', param) for param in params]
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if monitor_idx == 0:
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if param_names is not None:
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if len(param_names) < len(params):
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# FIXME: Missing Seed Detection, see below @111
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param_names = [next(param_names) if param not in MODEL_MAP.keys() else 'Model' for param in params]
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elif len(param_names) == len(params):
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pass
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else:
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raise ValueError
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else:
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param_names = []
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for param_idx, param in enumerate(params):
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dtype = None
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if param in MODEL_MAP.keys():
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param_name = 'Model'
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elif '_' in param:
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param_split = param.split('_')
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if len(param_split) == 2 and any(split in MODEL_MAP.keys() for split in param_split):
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# Extract the seed
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param = int(next(x for x in param_split if x not in MODEL_MAP))
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param_name = 'Seed'
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dtype = int
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else:
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param_name = f'param_{param_idx}'
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else:
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param_name = f'param_{param_idx}'
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dtype = dtype if dtype is not None else str
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monitor_df[param_name] = str(param)
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monitor_df[param_name] = monitor_df[param_name].astype(dtype)
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if monitor_idx == 0:
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param_names.append(param_name)
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monitor_df = monitor_df.fillna(0)
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df_list.append(monitor_df)
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df = pd.concat(df_list, ignore_index=True)
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df = df.fillna(0).rename(columns={'episode': 'Episode'}).sort_values(['Episode'])
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for param_name in param_names:
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df[param_name] = df[param_name].astype(str)
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columns = [col for col in df.columns if col in parameter]
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last_episode_to_report = min(df.groupby(['Model'])['Episode'].max())
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df = df[df['Episode'] < last_episode_to_report]
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if df['Episode'].max() > 80:
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skip_n = round(df['Episode'].max() * 0.02)
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df = df[df['Episode'] % skip_n == 0]
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combinations = [x for x in param_names if x not in ['Model', 'Seed']]
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df['Parameter Combination'] = df[combinations].apply(lambda row: '_'.join(row.values.astype(str)), axis=1)
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df.drop(columns=combinations, inplace=True)
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# non_overlapp_window = df.groupby(param_names).sum()
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df_melted = df.reset_index().melt(id_vars=['Parameter Combination', 'Episode'],
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value_vars=columns, var_name="Measurement",
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value_name="Score")
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style = 'Measurement' if len(columns) > 1 else None
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prepare_plot(run_root_path / f'compare_{parameter}.png', df_melted, hue='Parameter Combination',
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style=style, use_tex=use_tex)
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print('Plotting done.')
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