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"""
EVALITA LLM EVALUATION PROCESSOR
Transforms raw model evaluation outputs into structured performance reports for leaderboard integration.
DATA PIPELINE OVERVIEW:
1. Inputs:
- Evaluation Results: Raw .out files from lm-eval-harness
- Model Metadata: Pre-collected .json files from HuggingFace
2. Output:
- Comprehensive evaluation reports in JSON format
- Ready for ingestion into the evaluation leaderboard
--------------------------------------------------------------------
INPUT SPECIFICATION
Evaluation Results (.out format):
hf (pretrained=model-org/model-name), num_fewshot: 5, batch_size: 1
| Task | Metric | Value | Stderr |
|---------------|--------|--------|--------|
| main-task | acc | 0.5605 | 0.0052 |
| - sub-task | acc | 0.4640 | 0.0088 |
| - prompt-1 | acc | 0.3720 | 0.0216 |
Model Metadata (.json format):
{
"model": "model-org/model-name",
"base_model": "ModelArchitecture",
"revision": "git_commit_hash",
"parameters": 8.03,
"language": "en_it"
}
--------------------------------------------------------------------
OUTPUT SPECIFICATION
Evaluation Report (.json format):
{
"summary_metrics": {
"average_CPS": 41.74,
"num_tasks": 12
},
"model_config": {
"identifier": "model-org/model-name",
"architecture": "ModelArchitecture",
"parameters": 8.03,
"evaluation_settings": {
"fewshot": 5,
"batch_size": 1
}
},
"task_results": {
"task-name": {
"average_score": 52.60,
"best_prompt": {
"id": "prompt-6",
"score": 66.57
},
"prompt_analysis": [
{
"prompt_id": "prompt-1",
"score": 37.20,
"stderr": 0.0216
}
]
}
}
}
"""
import json
import os
import re
def safe_float(value):
"""Safely converts a value to float, returning None if the conversion fails."""
try:
return float(value)
except ValueError:
return None
def calculate_task_metrics(task_info):
"""Calculates average accuracy, best prompt accuracy, and CPS for a given 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):
"""Extracts task and prompt data from a specified file."""
with open(file_path, 'r') as file:
lines = file.readlines()
tasks_data = {}
current_task = None
for line in lines:
line = line.strip()
# Skips empty lines
if not line:
continue
# Skips header lines
if line.startswith("| Tasks"):
continue
# Extracts model configuration details
if line.startswith("hf (pretrained="):
start = line.find("pretrained=") + len("pretrained=")
end = line.find(",", start)
pretrained_model = line[start:end]
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
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])
# Skips normalized accuracy metrics
if metric == "acc_norm":
continue
# Identifies task and prompt sections in the file
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 task to calculate weighted prompt averages
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}]
# Calculates task metrics for each task
for task_info in tasks_data.values():
calculate_task_metrics(task_info)
# Calculates the average CPS across all tasks
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}
"""
MAIN PROCESSING PIPELINE
This script executes the complete evaluation data processing workflow:
1. Input Sources:
- Raw evaluation results (.out files) from: ../evalita_llm_models_output/
- Model metadata JSON files from: ../evalita_llm_requests/
2. Processing Steps:
- Parses evaluation metrics from .out files
- Combines with model metadata
- Calculates aggregated performance statistics
3. Output:
- Structured JSON results saved to: ../evalita_llm_results/
- Organized by model organization/name
- Contains complete evaluation results with metadata
"""
directory_in_path = '../evalita_llm_models_output/'
directory_in_requests_path = '../evalita_llm_requests/'
directory_out_results_path = '../evalita_llm_results/'
for filename in os.listdir(directory_in_path):
if filename.endswith('.out'):
file_path = os.path.join(directory_in_path, filename)
json_output = extract_data_from_file(file_path)
model_org_name, model_name = json_output['config']['model_name'].split('/')
config_file_path = os.path.join(directory_in_requests_path, model_org_name, f"{model_name}.json")
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)
json_output['config'].update(additional_config)
org_folder_path = os.path.join(directory_out_results_path, model_org_name)
os.makedirs(org_folder_path, exist_ok=True)
file_suffix = f"{json_output['config']['num_fewshot']}"
output_file_path = os.path.join(org_folder_path, f"{model_name}_{file_suffix}.json")
with open(output_file_path, 'w', newline="\n") as outfile:
json.dump(json_output, outfile, indent=4)
print(f"File {filename} processed and saved to {output_file_path}") |