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| # -*- coding: utf-8 -*- | |
| """ | |
| Author: Philipp Seidl | |
| ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning | |
| Johannes Kepler University Linz | |
| Contact: seidl@ml.jku.at | |
| Loading log-files from training | |
| """ | |
| from pathlib import Path | |
| import os | |
| import datetime | |
| import pandas as pd | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| def load_experiments(EXP_DIR = Path('data/experiments/')): | |
| dfs = [] | |
| for fn in os.listdir(EXP_DIR): | |
| print(fn, end='\r') | |
| if fn.split('.')[-1]=='tsv': | |
| df = pd.read_csv(EXP_DIR/fn, sep='\t', index_col=0) | |
| try: | |
| with open(df['fn_hist'][0]) as f: | |
| hist = eval(f.readlines()[0] ) | |
| df['hist'] = [hist] | |
| df['fn'] = fn | |
| except: | |
| print('err') | |
| #print(df['fn_hist']) | |
| dfs.append( df ) | |
| df = pd.concat(dfs,ignore_index=True) | |
| return df | |
| def get_x(k, kw, operation='max', index=None): | |
| operation = getattr(np,operation) | |
| try: | |
| if index is not None: | |
| return k[kw][index] | |
| return operation(k[kw]) | |
| except: | |
| return 0 | |
| def get_min_val_loss_idx(k): | |
| return get_x(k, 'loss_valid', 'argmin') #changed from argmax to argmin!! | |
| def get_tauc(hist): | |
| idx = get_min_val_loss_idx(hist) | |
| # takes max TODO take idx | |
| return np.mean([get_x(hist, f't100_acc_nte_{nt}') for nt in [*range(11),'>10']]) | |
| def get_stats_from_hist(df): | |
| df['0shot_acc'] = df['hist'].apply(lambda k: get_x(k, 't100_acc_nte_0')) | |
| df['1shot_acc'] = df['hist'].apply(lambda k: get_x(k, 't100_acc_nte_1')) | |
| df['>49shot_acc'] = df['hist'].apply(lambda k: get_x(k, 't100_acc_nte_>49')) | |
| df['min_loss_valid'] = df['hist'].apply(lambda k: get_x(k, 'loss_valid', 'min')) | |
| return df |