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import gc
import json
import logging
import os
from datetime import datetime
from typing import Dict, List
import torch
import wandb
from datasets import Dataset
from tqdm import tqdm
from src.language_model.language_model_abstraction import LanguageModel
from src.task.task import Task
from src.task.task_factory import tasks_factory
class ModelEvaluator:
"""
The model evaluator acts as a pipeline for evaluating models on tasks available from tasks_factory.
"""
def __init__(self):
self.last_predictions = {}
self.last_metrics = {}
self.last_model_name = None
def compute_metrics(self) -> Dict:
"""
Compute metrics over the last tested model's predictions,
must have called one the evaluate functions before or loaded predictions with load_predictions_from_file.
"""
metrics = []
for task_dict in tqdm(
self.last_predictions["tasks"], desc="Computing metrics: "
):
task_name, preds = list(task_dict.items())[0]
if not preds:
warning_message = f"Task '{task_name}' ignored due to no predictions"
logging.warning(warning_message)
continue
try:
tasks = tasks_factory([task_name])
task = tasks[0]
metric_score, warning = task.compute(preds)
except Exception as e:
error_message = f"Error while calculating metrics'{task_name}' : {e}"
logging.error(error_message)
continue
metric_name = task.metric_name
task_entry = {
task_name: {
metric_name: {**metric_score, f"{metric_name}_warning": warning}
}
}
metrics.append(task_entry)
wandb.log({f"{task_name}.{metric_name}": {**metric_score}})
self.last_metrics = metrics
metrics = self.last_predictions
metrics["tasks"] = self.last_metrics
self.last_metrics = metrics
return metrics
def load_predictions_from_file(self, file_path: str) -> None:
"""
Load predictions from file to compute metrics :param file_path:path to the predictions file.
"""
try:
with open(file_path, "r", encoding="utf-8") as f:
self.last_predictions = json.load(f)
except FileNotFoundError:
error = f"File not found: {file_path}"
logging.error(error)
self.last_predictions = None
except json.JSONDecodeError:
error = f"Invalid JSON in file: {file_path}"
logging.error(error)
self.last_predictions = None
def save_metrics(self, save_path):
"""
Saves computed metrics to a json file.
:param save_path : the path to which the json file will be saved.
"""
if self.last_metrics is None:
logging.info("No metrics saved")
return None
return self.save_object(
save_path,
self.last_metrics,
f"{self.last_model_name.replace('/', '_')}_metrics.json",
)
def evaluate(self, model: LanguageModel, tasks: List[Task]):
"""
Evaluates a given model on the given tasks.
:param model : the model that will infer on the given tasks.
:param tasks : the tasks to be evaluated on.
"""
return self.evaluate_subset(model, tasks)
def evaluate_subset(
self, model: LanguageModel, tasks: List[Task], subset_size=None
) -> Dict:
"""
Evaluates a given model on the given tasks, but only on a given size.
:param model : the model that will infer on the given tasks.
:param tasks : the tasks to be evaluated on.
:param subset_size : the size of the subset to be evaluated.
"""
predictions = []
for task in tasks:
info_log = (
f"-----Doing task '{task.task_name}' with model '{model.name}-----'."
)
logging.info(info_log)
try:
if subset_size is None:
prompts = task.dataset.prompts[:]
else:
prompts = task.dataset.prompts[:subset_size]
evaluate_dataset = Dataset.from_dict({"text": prompts})
task_predictions = model.predict(
evaluation_dataset=evaluate_dataset, task=task
)
task_predictions = {task.task_name: task_predictions}
predictions.append(task_predictions)
except Exception as e:
error_message = f"Task '{task.task_name}' has failed : {e}"
logging.error(error_message)
wandb.log({task.task_name: "Failed"})
continue
finally:
# Memory cleaning
if "evaluate_dataset" in locals():
del evaluate_dataset
if "prompts" in locals():
del prompts
torch.cuda.empty_cache()
gc.collect()
wandb.log({task.task_name: "Success"})
logging.info("Finished evaluating tasks.")
self.last_predictions = {
"model_name": model.name,
"model_url": f"https://huggingface.co/{model.name}",
"tasks": predictions,
}
self.last_model_name = model.name
return self.last_predictions
def save_results(self, save_path):
"""
Saves inferred metrics to a json file.
:param save_path : the path to which the json file will be saved.
"""
if self.last_model_name is None:
logging.error("Please evaluate before saving results")
return None
date_time_stamp = datetime.now().strftime("%Y%m%d-%H%M")
return self.save_object(
save_path,
self.last_predictions,
f"{self.last_model_name.replace('/', '_')}_{date_time_stamp}.json",
)
def save_object(self, save_dir_path, saved_object, filename):
"""
Utility method to save the given object into a json file.
"""
os.makedirs(save_dir_path, exist_ok=True)
full_path = os.path.join(save_dir_path, filename)
if os.path.isfile(full_path):
logging.info("Appending results to previous results file.")
try:
with open(full_path, "r", encoding="utf-8") as f:
data = json.load(f)
data.get("tasks").extend(saved_object.get("tasks"))
with open(full_path, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2)
info_message = f"Results saved to {save_dir_path}"
logging.info(info_message)
except Exception as e:
error_message = f"Failed to save object: {e}"
logging.error(error_message)
else:
try:
with open(full_path, "w", encoding="utf-8") as f:
json.dump(saved_object, f, indent=2)
info_message = f"Results saved to {save_dir_path}"
logging.info(info_message)
except Exception as e:
error_message = f"Failed to save object: {e}"
logging.error(error_message)
return full_path
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