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178 lines (140 loc) · 6.66 KB
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import statistics
import argparse
import json
import os
parser = argparse.ArgumentParser(description='Computing statistics over 15 runs')
parser.add_argument('path', default='', type=str, metavar='PATH',
help='path to statistics file')
parser.add_argument('--mode', default='', type=str)
parser.add_argument('--path_out', default='', type=str)
parser.add_argument('--index', default=-1, type=int)
parser.add_argument('--concepts', default='', type=str)
def compute_statistics(path, path_out, mode, index, concepts):
listObj = []
with open(path, 'r') as fp:
if len(fp.readlines()) != 0:
fp.seek(0)
listObj = json.load(fp)
train_losses, train_accs, train_aucs = [], [], []
val_losses, val_accs, val_aucs = [], [], []
test_losses, test_accs, test_aucs = [], [], []
for entry in listObj:
if 'train_loss' in entry.keys():
train_losses.append(entry['train_loss'])
train_accs.append(entry['train_acc'])
train_aucs.append(entry['train_auc'])
val_losses.append(entry['val_loss'])
val_accs.append(entry['val_acc'])
val_aucs.append(entry['val_auc'])
test_losses.append(entry['test_loss'])
test_accs.append(entry['test_acc'])
test_aucs.append(entry['test_auc'])
if mode == 'top10':
d = {}
for i in range(len(test_accs)):
d[i] = test_accs[i]
sorted_d = {k : v for k, v in sorted(d.items(), key = lambda v: v[1], reverse=True)}
top10 = list(sorted_d.items())[:10]
train_losses_10, train_accs_10, train_aucs_10 = [], [], []
val_losses_10, val_accs_10, val_aucs_10 = [], [], []
test_losses_10, test_accs_10, test_aucs_10 = [], [], []
for elem in top10:
train_losses_10.append(train_losses[elem[0]])
train_accs_10.append(train_accs[elem[0]])
train_aucs_10.append(train_aucs[elem[0]])
val_losses_10.append(val_losses[elem[0]])
val_accs_10.append(val_accs[elem[0]])
val_aucs_10.append(val_aucs[elem[0]])
test_losses_10.append(test_losses[elem[0]])
test_accs_10.append(test_accs[elem[0]])
test_aucs_10.append(test_aucs[elem[0]])
train_losses, train_accs, train_aucs = train_losses_10, train_accs_10, train_aucs_10
val_losses, val_accs, val_aucs = val_losses_10, val_accs_10, val_aucs_10
test_losses, test_accs, test_aucs = test_losses_10, test_accs_10, test_aucs_10
if mode == 'median':
mean_train_loss = statistics.median(train_losses)
mean_train_acc = statistics.median(train_accs)
mean_train_auc = statistics.median(train_aucs)
mean_val_loss = statistics.median(val_losses)
mean_val_acc = statistics.median(val_accs)
mean_val_auc = statistics.median(val_aucs)
mean_test_loss = statistics.median(test_losses)
mean_test_acc = statistics.median(test_accs)
mean_test_auc = statistics.median(test_aucs)
test_acc_stddev = statistics.pstdev(test_accs)
test_auc_stddev = statistics.pstdev(test_aucs)
else:
mean_train_loss = sum(train_losses)/len(train_losses)
mean_train_acc = sum(train_accs)/len(train_accs)
mean_train_auc = sum(train_aucs)/len(train_aucs)
mean_val_loss = sum(val_losses)/len(val_losses)
mean_val_acc = sum(val_accs)/len(val_accs)
mean_val_auc = sum(val_aucs)/len(val_aucs)
mean_test_loss = sum(test_losses)/len(test_losses)
mean_test_acc = sum(test_accs)/len(test_accs)
mean_test_auc = sum(test_aucs)/len(test_aucs)
test_acc_stddev = statistics.pstdev(test_accs)
test_auc_stddev = statistics.pstdev(test_aucs)
listObj = []
print(index)
if index != -1:
if os.path.isfile(path_out) is False:
print('Creating new saving file')
open(path_out, 'a').close()
with open(path_out, 'r') as fp:
if len(fp.readlines()) != 0:
fp.seek(0)
listObj = json.load(fp)
listObj.append({
"Index": index,
"Concepts:": concepts,
"mean_train_loss": mean_train_loss,
"mean_train_acc": mean_train_acc,
"mean_train_auc":mean_train_auc,
"mean_val_loss": mean_val_loss,
"mean_val_acc": mean_val_acc,
"mean_val_auc": mean_val_auc,
"mean_test_loss": mean_test_loss,
"mean_test_acc": mean_test_acc,
"test_acc_stddev": test_acc_stddev,
"test_auc_stddev": test_auc_stddev,
"mean_test_auc": mean_test_auc,
})
elif index == -1:
path_out = path
with open(path_out, 'r') as fp:
if len(fp.readlines()) != 0:
fp.seek(0)
listObj = json.load(fp)
listObj.append({
"Runs": len(train_losses),
"mean_train_loss": mean_train_loss,
"mean_train_acc": mean_train_acc,
"mean_train_auc":mean_train_auc,
"mean_val_loss": mean_val_loss,
"mean_val_acc": mean_val_acc,
"mean_val_auc": mean_val_auc,
"mean_test_loss": mean_test_loss,
"mean_test_acc": mean_test_acc,
"test_acc_stddev": test_acc_stddev,
"test_auc_stddev": test_auc_stddev,
"mean_test_auc": mean_test_auc,
})
with open(path_out, 'w') as json_file:
json.dump(listObj, json_file, indent=4, separators=(',',': '))
print(f'Mean train loss = {mean_train_loss}')
print(f'Mean train accuracy = {mean_train_acc}')
print(f'Mean train auc roc = {mean_train_auc}')
print(f'Mean val loss = {mean_val_loss}')
print(f'Mean val accuracy = {mean_val_acc}')
print(f'Mean val auc roc = {mean_val_auc}')
print(f'Mean test loss = {mean_test_loss}')
print(f'Mean test accuracy = {mean_test_acc}')
print(f'Mean test auc roc = {mean_test_auc}')
print(f'Test acc std dev = {test_acc_stddev}')
print(f'Test auc std dev = {test_auc_stddev}')
def main():
args = parser.parse_args()
compute_statistics(args.path, args.path_out, args.mode, args.index, args.concepts)
if __name__ == '__main__':
main()