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Optimize Logger Class for Improved Readability and Efficiency #46

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55 changes: 27 additions & 28 deletions logger.py
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Original file line number Diff line number Diff line change
@@ -1,44 +1,43 @@
import torch


class Logger(object):
class Logger:
def __init__(self, runs, info=None):
self.info = info
self.results = [[] for _ in range(runs)]

def add_result(self, run, result):
assert len(result) == 3
assert run >= 0 and run < len(self.results)
if len(result) != 3 or not (0 <= run < len(self.results)):
raise ValueError("Invalid result format or run index.")
self.results[run].append(result)

def print_statistics(self, run=None):
def calculate_statistics(data):
train_max = data[:, 0].max().item()
valid_max = data[:, 1].max().item()
best_index = data[:, 1].argmax()
final_train = data[best_index, 0].item()
final_test = data[best_index, 2].item()
return train_max, valid_max, final_train, final_test

if run is not None:
result = 100 * torch.tensor(self.results[run])
argmax = result[:, 1].argmax().item()
data = 100 * torch.tensor(self.results[run])
train_max, valid_max, final_train, final_test = calculate_statistics(data)
print(f'Run {run + 1:02d}:')
print(f'Highest Train: {result[:, 0].max():.2f}')
print(f'Highest Valid: {result[:, 1].max():.2f}')
print(f' Final Train: {result[argmax, 0]:.2f}')
print(f' Final Test: {result[argmax, 2]:.2f}')
print(f'Highest Train: {train_max:.2f}')
print(f'Highest Valid: {valid_max:.2f}')
print(f' Final Train: {final_train:.2f}')
print(f' Final Test: {final_test:.2f}')
else:
result = 100 * torch.tensor(self.results)

best_results = []
for r in result:
train1 = r[:, 0].max().item()
valid = r[:, 1].max().item()
train2 = r[r[:, 1].argmax(), 0].item()
test = r[r[:, 1].argmax(), 2].item()
best_results.append((train1, valid, train2, test))
all_results = 100 * torch.tensor(self.results)
stats = [calculate_statistics(run_data) for run_data in all_results]
stats_tensor = torch.tensor(stats)

best_result = torch.tensor(best_results)
def print_mean_std(idx, label):
values = stats_tensor[:, idx]
print(f'{label}: {values.mean():.2f} ± {values.std():.2f}')

print(f'All runs:')
r = best_result[:, 0]
print(f'Highest Train: {r.mean():.2f} ± {r.std():.2f}')
r = best_result[:, 1]
print(f'Highest Valid: {r.mean():.2f} ± {r.std():.2f}')
r = best_result[:, 2]
print(f' Final Train: {r.mean():.2f} ± {r.std():.2f}')
r = best_result[:, 3]
print(f' Final Test: {r.mean():.2f} ± {r.std():.2f}')
print_mean_std(0, 'Highest Train')
print_mean_std(1, 'Highest Valid')
print_mean_std(2, 'Final Train')
print_mean_std(3, 'Final Test')