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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 |