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import re
import sys
import numpy as np
from collections import defaultdict
from pathlib import Path
from tabulate import tabulate
# pattern = "bert-tiny-historic-multilingual-cased-*" # sys.argv[1]
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)
# dev_results[result_identifier].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")
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