Source code for torch_geometric.graphgym.utils.agg_runs

import logging
import os

import numpy as np

from torch_geometric.graphgym.config import cfg
from torch_geometric.graphgym.utils.io import (
    dict_list_to_json,
    dict_list_to_tb,
    dict_to_json,
    json_to_dict_list,
    makedirs_rm_exist,
    string_to_python,
)

try:
    from tensorboardX import SummaryWriter
except ImportError:
    SummaryWriter = None


def is_seed(s):
    try:
        int(s)
        return True
    except Exception:
        return False


def is_split(s):
    if s in ['train', 'val', 'test']:
        return True
    else:
        return False


def join_list(l1, l2):
    assert len(l1) == len(l2), \
        'Results with different seeds must have the save format'
    for i in range(len(l1)):
        l1[i] += l2[i]
    return l1


def agg_dict_list(dict_list):
    """
    Aggregate a list of dictionaries: mean + std
    Args:
        dict_list: list of dictionaries

    """
    dict_agg = {'epoch': dict_list[0]['epoch']}
    for key in dict_list[0]:
        if key != 'epoch':
            value = np.array([dict[key] for dict in dict_list])
            dict_agg[key] = np.mean(value).round(cfg.round)
            dict_agg['{}_std'.format(key)] = np.std(value).round(cfg.round)
    return dict_agg


def name_to_dict(run):
    cols = run.split('-')[1:]
    keys, vals = [], []
    for col in cols:
        try:
            key, val = col.split('=')
        except Exception:
            print(col)
        keys.append(key)
        vals.append(string_to_python(val))
    return dict(zip(keys, vals))


def rm_keys(dict, keys):
    for key in keys:
        dict.pop(key, None)


[docs]def agg_runs(dir, metric_best='auto'): r''' Aggregate over different random seeds of a single experiment Args: dir (str): Directory of the results, containing 1 experiment metric_best (str, optional): The metric for selecting the best validation performance. Options: auto, accuracy, auc. ''' results = {'train': None, 'val': None, 'test': None} results_best = {'train': None, 'val': None, 'test': None} for seed in os.listdir(dir): if is_seed(seed): dir_seed = os.path.join(dir, seed) split = 'val' if split in os.listdir(dir_seed): dir_split = os.path.join(dir_seed, split) fname_stats = os.path.join(dir_split, 'stats.json') stats_list = json_to_dict_list(fname_stats) if metric_best == 'auto': metric = 'auc' if 'auc' in stats_list[0] else 'accuracy' else: metric = metric_best performance_np = np.array( # noqa [stats[metric] for stats in stats_list]) best_epoch = \ stats_list[ eval("performance_np.{}()".format(cfg.metric_agg))][ 'epoch'] print(best_epoch) for split in os.listdir(dir_seed): if is_split(split): dir_split = os.path.join(dir_seed, split) fname_stats = os.path.join(dir_split, 'stats.json') stats_list = json_to_dict_list(fname_stats) stats_best = [ stats for stats in stats_list if stats['epoch'] == best_epoch ][0] print(stats_best) stats_list = [[stats] for stats in stats_list] if results[split] is None: results[split] = stats_list else: results[split] = join_list(results[split], stats_list) if results_best[split] is None: results_best[split] = [stats_best] else: results_best[split] += [stats_best] results = {k: v for k, v in results.items() if v is not None} # rm None results_best = {k: v for k, v in results_best.items() if v is not None} # rm None for key in results: for i in range(len(results[key])): results[key][i] = agg_dict_list(results[key][i]) for key in results_best: results_best[key] = agg_dict_list(results_best[key]) # save aggregated results for key, value in results.items(): dir_out = os.path.join(dir, 'agg', key) makedirs_rm_exist(dir_out) fname = os.path.join(dir_out, 'stats.json') dict_list_to_json(value, fname) if cfg.tensorboard_agg: if SummaryWriter is None: raise ImportError( 'Tensorboard support requires `tensorboardX`.') writer = SummaryWriter(dir_out) dict_list_to_tb(value, writer) writer.close() for key, value in results_best.items(): dir_out = os.path.join(dir, 'agg', key) fname = os.path.join(dir_out, 'best.json') dict_to_json(value, fname) logging.info('Results aggregated across runs saved in {}'.format( os.path.join(dir, 'agg')))
[docs]def agg_batch(dir, metric_best='auto'): r''' Aggregate across results from multiple experiments via grid search Args: dir (str): Directory of the results, containing multiple experiments metric_best (str, optional): The metric for selecting the best validation performance. Options: auto, accuracy, auc. ''' import pandas as pd results = {'train': [], 'val': [], 'test': []} for run in os.listdir(dir): if run != 'agg': dict_name = name_to_dict(run) dir_run = os.path.join(dir, run, 'agg') if os.path.isdir(dir_run): for split in os.listdir(dir_run): dir_split = os.path.join(dir_run, split) fname_stats = os.path.join(dir_split, 'best.json') dict_stats = json_to_dict_list(fname_stats)[ -1] # get best val epoch rm_keys(dict_stats, ['lr', 'lr_std', 'eta', 'eta_std', 'params_std']) results[split].append({**dict_name, **dict_stats}) dir_out = os.path.join(dir, 'agg') makedirs_rm_exist(dir_out) for key in results: if len(results[key]) > 0: results[key] = pd.DataFrame(results[key]) results[key] = results[key].sort_values( list(dict_name.keys()), ascending=[True] * len(dict_name)) fname = os.path.join(dir_out, '{}_best.csv'.format(key)) results[key].to_csv(fname, index=False) results = {'train': [], 'val': [], 'test': []} for run in os.listdir(dir): if run != 'agg': dict_name = name_to_dict(run) dir_run = os.path.join(dir, run, 'agg') if os.path.isdir(dir_run): for split in os.listdir(dir_run): dir_split = os.path.join(dir_run, split) fname_stats = os.path.join(dir_split, 'stats.json') dict_stats = json_to_dict_list(fname_stats)[ -1] # get last epoch rm_keys(dict_stats, ['lr', 'lr_std', 'eta', 'eta_std', 'params_std']) results[split].append({**dict_name, **dict_stats}) dir_out = os.path.join(dir, 'agg') for key in results: if len(results[key]) > 0: results[key] = pd.DataFrame(results[key]) results[key] = results[key].sort_values( list(dict_name.keys()), ascending=[True] * len(dict_name)) fname = os.path.join(dir_out, '{}.csv'.format(key)) results[key].to_csv(fname, index=False) results = {'train': [], 'val': [], 'test': []} for run in os.listdir(dir): if run != 'agg': dict_name = name_to_dict(run) dir_run = os.path.join(dir, run, 'agg') if os.path.isdir(dir_run): for split in os.listdir(dir_run): dir_split = os.path.join(dir_run, split) fname_stats = os.path.join(dir_split, 'stats.json') dict_stats = json_to_dict_list( fname_stats) # get best epoch if metric_best == 'auto': metric = 'auc' if 'auc' in dict_stats[0] \ else 'accuracy' else: metric = metric_best performance_np = np.array( # noqa [stats[metric] for stats in dict_stats]) dict_stats = dict_stats[eval("performance_np.{}()".format( cfg.metric_agg))] rm_keys(dict_stats, ['lr', 'lr_std', 'eta', 'eta_std', 'params_std']) results[split].append({**dict_name, **dict_stats}) dir_out = os.path.join(dir, 'agg') for key in results: if len(results[key]) > 0: results[key] = pd.DataFrame(results[key]) results[key] = results[key].sort_values( list(dict_name.keys()), ascending=[True] * len(dict_name)) fname = os.path.join(dir_out, '{}_bestepoch.csv'.format(key)) results[key].to_csv(fname, index=False) print('Results aggregated across models saved in {}'.format(dir_out))