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import json
import os
import re
def safe_float(value):
"""Convert a value to float safely. Returns None if conversion fails."""
try:
return float(value)
except ValueError:
return None
def calculate_task_metrics(task_info):
"""Calculate average accuracy, best prompt, and CPS for a task."""
accuracies = [prompt['value'] for prompt in task_info['prompts'] if prompt['value'] is not None]
if not accuracies:
return None
task_info['average_accuracy'] = sum(accuracies) / len(accuracies)
best_prompt_data = max(task_info['prompts'], key=lambda x: x['value'])
task_info['best_prompt'] = best_prompt_data['value']
task_info['prompt_id'] = best_prompt_data['prompt']
# Calculate CPS
avg_acc = task_info['average_accuracy']
best_acc = task_info['best_prompt']
task_info['CPS'] = (1 - (best_acc - avg_acc) / 100) * best_acc
def extract_data_from_file(file_path):
"""Extract task and prompt data from the given file."""
with open(file_path, 'r') as file:
lines = file.readlines()
tasks_data = {}
current_task = None
for line in lines:
line = line.strip()
# Skip irrelevant lines
if not line:
continue
if line.startswith("| Tasks"):
continue
if line.startswith("hf (pretrained="):
# Estrai la parte dopo "pretrained="
start = line.find("pretrained=") + len("pretrained=")
end = line.find(",", start) # Trova la virgola successiva
# Estrai la stringa desiderata
pretrained_model = line[start:end]
# Estrarre num_fewshot
num_fewshot_match = re.search(r"num_fewshot:\s*([\w\d]+)", line)
num_fewshot = num_fewshot_match.group(1) if num_fewshot_match else None
# Estrarre batch_size
batch_size_match = re.search(r"batch_size:\s*(\d+)", line)
batch_size = int(batch_size_match.group(1)) if batch_size_match else None
continue
columns = line.split('|')
if len(columns) != 11:
continue
task_name = columns[1]
metric = columns[5].strip()
value = safe_float(columns[7])
stderr = safe_float(columns[9])
if metric == "acc_norm":
continue
# Identify task and prompts
if task_name.startswith(" - "):
task_name = task_name[3:].strip()
current_task = task_name
tasks_data.setdefault(current_task,
{'prompts': [], 'average_accuracy': 0, 'best_prompt': None, 'prompt_id': None,
'CPS': None})
elif task_name.startswith(" - ") and current_task:
prompt_name = task_name[4:].strip()
prompt_data = {'prompt': prompt_name, 'metric': metric, 'value': value * 100,
'stderr': stderr}
tasks_data[current_task]['prompts'].append(prompt_data)
# Special handling for evalita NER
if "evalita NER" in tasks_data:
task_info = tasks_data["evalita NER"]
weight_map = {"ADG prompt-1": 521, "ADG prompt-2": 521, "FIC prompt-1": 1517, "FIC prompt-2": 1517,
"WN prompt-1": 2088, "WN prompt-2": 2088}
weighted_values = {"prompt-1": 0, "prompt-2": 0}
total_weights = sum(weight_map.values())
for prompt in task_info['prompts']:
if prompt['prompt'] in weight_map:
if "prompt-1" in prompt['prompt']:
weighted_values["prompt-1"] += weight_map[prompt['prompt']] * prompt['value']
elif "prompt-2" in prompt['prompt']:
weighted_values["prompt-2"] += weight_map[prompt['prompt']] * prompt['value']
task_info['prompts'] = [
{"prompt": "prompt-1", "metric": "acc", "value": weighted_values["prompt-1"] / total_weights,
'stderr': None},
{"prompt": "prompt-2", "metric": "acc", "value": weighted_values["prompt-2"] / total_weights,
'stderr': None}]
# Calculate metrics for each task
for task_info in tasks_data.values():
calculate_task_metrics(task_info)
# Calculate average CPS
tasks_with_cps = [task['CPS'] for task in tasks_data.values() if task['CPS'] is not None]
average_CPS = sum(tasks_with_cps) / len(tasks_with_cps) if tasks_with_cps else 0
config = {
"model_name": pretrained_model,
"num_fewshot": num_fewshot,
"batch_size": batch_size
}
return {'average_CPS': average_CPS, 'config': config, 'tasks': tasks_data}
# Example usage
#file_path = '../evalita_llm_results/models_output/slurm-7769.out'
#json_output = extract_data_from_file(file_path)
#print(json_output)
# Directory da cui leggere i file .out
directory_in_path = '../evalita_llm_models_output/'
directory_out_results_path = '../evalita_llm_results/'
directory_out_requests_path = '../evalita_llm_requests/'
# Itera sui file nella directory
for filename in os.listdir(directory_in_path):
if filename.endswith('.out'):
# Costruisci il percorso completo del file
file_path = os.path.join(directory_in_path, filename)
# Esegui la funzione extract_data_from_file
json_output = extract_data_from_file(file_path)
# Estrai model_org_name e model_name da model_name
model_org_name, model_name = json_output['config']['model_name'].split('/')
# Percorso del file JSON di configurazione in ../evalita_llm_requests2/
config_file_path = os.path.join(directory_out_requests_path, model_org_name, f"{model_name}.json")
# Se il file esiste, caricalo e aggiorna il dizionario config
if os.path.exists(config_file_path):
with open(config_file_path, 'r', encoding='utf-8') as config_file:
additional_config = json.load(config_file)
# Aggiorna la configurazione con i nuovi dati
json_output['config'].update(additional_config)
# Crea il percorso della cartella per model_org_name
org_folder_path = os.path.join(directory_out_results_path, model_org_name)
os.makedirs(org_folder_path, exist_ok=True) # Crea la cartella se non esiste
# Crea il percorso completo del file JSON
file_suffix = f"{json_output['config']['num_fewshot']}"
output_file_path = os.path.join(org_folder_path, f"{model_name}_{file_suffix}.json")
# Salva il JSON in un file con ritorni a capo compatibili con Linux
with open(output_file_path, 'w', newline="\n") as outfile:
json.dump(json_output, outfile, indent=4)
# Stampa il risultato
print(f"File {filename} elaborato e salvato in {output_file_path}")
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