|
import re |
|
import sys |
|
import numpy as np |
|
|
|
from collections import defaultdict |
|
from pathlib import Path |
|
from tabulate import tabulate |
|
|
|
|
|
pattern = sys.argv[1] |
|
|
|
log_dirs = Path("./").rglob(f"{pattern}") |
|
|
|
dev_results = defaultdict(list) |
|
test_results = defaultdict(list) |
|
|
|
for log_dir in log_dirs: |
|
training_log = log_dir / "training.log" |
|
|
|
if not training_log.exists(): |
|
print(f"No training.log found in {log_dir}") |
|
|
|
matches = re.match(".*(bs.*?)-(ws.*?)-(e.*?)-(lr.*?)-layers-1-crfFalse-(\d+)", str(log_dir)) |
|
|
|
batch_size = matches.group(1) |
|
ws = matches.group(2) |
|
epochs = matches.group(3) |
|
lr = matches.group(4) |
|
seed = matches.group(5) |
|
|
|
result_identifier = f"{ws}-{batch_size}-{epochs}-{lr}" |
|
|
|
with open(training_log, "rt") as f_p: |
|
all_dev_results = [] |
|
for line in f_p: |
|
line = line.rstrip() |
|
|
|
if "f1-score (micro avg)" in line: |
|
dev_result = line.split(" ")[-1] |
|
all_dev_results.append(dev_result) |
|
|
|
|
|
if "F-score (micro" in line: |
|
test_result = line.split(" ")[-1] |
|
test_results[result_identifier].append(test_result) |
|
|
|
best_dev_result = max([float(value) for value in all_dev_results]) |
|
dev_results[result_identifier].append(best_dev_result) |
|
|
|
mean_dev_results = {} |
|
|
|
print("Debug:", dev_results) |
|
|
|
for dev_result in dev_results.items(): |
|
result_identifier, results = dev_result |
|
|
|
mean_result = np.mean([float(value) for value in results]) |
|
|
|
mean_dev_results[result_identifier] = mean_result |
|
|
|
print("Averaged Development Results:") |
|
|
|
sorted_mean_dev_results = dict(sorted(mean_dev_results.items(), key=lambda item: item[1], reverse=True)) |
|
|
|
for mean_dev_config, score in sorted_mean_dev_results.items(): |
|
print(f"{mean_dev_config} : {round(score * 100, 2)}") |
|
|
|
best_dev_configuration = max(mean_dev_results, key=mean_dev_results.get) |
|
|
|
print("Markdown table:") |
|
|
|
print("") |
|
|
|
print("Best configuration:", best_dev_configuration) |
|
|
|
print("\n") |
|
|
|
print("Best Development Score:", |
|
round(mean_dev_results[best_dev_configuration] * 100, 2)) |
|
|
|
print("\n") |
|
|
|
header = ["Configuration"] + [f"Run {i + 1}" for i in range(len(dev_results[best_dev_configuration]))] + ["Avg."] |
|
|
|
table = [] |
|
|
|
for mean_dev_config, score in sorted_mean_dev_results.items(): |
|
current_std = np.std(dev_results[mean_dev_config]) |
|
current_row = [f"`{mean_dev_config}`", *[round(res * 100, 2) for res in dev_results[mean_dev_config]], |
|
f"{round(score * 100, 2)} ± {round(current_std * 100, 2)}"] |
|
table.append(current_row) |
|
|
|
print(tabulate(table, headers=header, tablefmt="github") + "\n") |
|
|
