src/leaderboard/read_evals.py

import glob
import json
import math
import os
from dataclasses import dataclass

import dateutil
import numpy as np

from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, Tasks
from src.submission.check_validity import is_model_on_hub
from src.envs import EXTERNAL_PROVIDERS

@dataclass
class EvalResult:
    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
    """
    eval_name: str # org_model_precision (uid)
    full_model: str # org/model (path on hub)
    org: str 
    model: str
    results: dict
    license: str = "?"
    num_params: int = 0
    date: str = "" # submission date of request file
    still_on_hub: bool = False

    @classmethod
    def init_from_json_file(self, json_filepath):
        """Inits the result from the specific model result file"""
        with open(json_filepath) as fp:
            data = json.load(fp)

        config = data.get("config")

        # Get model and org
        org_and_model = config.get("model_name", config.get("model_args", None))
        org_and_model = org_and_model.split("/", 1)

        if len(org_and_model) == 1:
            org = None
            model = org_and_model[0]
            result_key = model
        else:
            org = org_and_model[0]
            model = org_and_model[1]
            result_key = f"{org}_{model}"
        full_model = "/".join(org_and_model)

        still_on_hub = False
        if not any([org.lower() in provider for provider in EXTERNAL_PROVIDERS]):
            still_on_hub, _, model_config = is_model_on_hub(
                full_model, trust_remote_code=True, test_tokenizer=False
            )

        num_params = data.get("params")

        # Extract results available in this file (some results are split in several files)
        results = {}
        for task in Tasks:
            task = task.value

            # We average all scores of a given metric (not all metrics are present in all files)
            accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k])
            if accs.size == 0 or any([acc is None for acc in accs]):
                continue

            mean_acc = np.mean(accs) * 100.0
            results[task.benchmark] = mean_acc

        return self(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            results=results,
            num_params=num_params, 
            still_on_hub=still_on_hub,
        )

    def update_with_request_file(self, requests_path):
        """Finds the relevant request file for the current model and updates info with it"""
        request_file = get_request_file_for_model(requests_path, self.full_model)

        #try:
        with open(request_file, "r") as f:
            request = json.load(f)
        self.license = request.get("license", "?")
        self.num_params = request.get("params", "?")
        self.date = request.get("submitted_time", "")
        #except Exception:
        #    print(f"Could not find request file for {self.org}/{self.model}")


    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)

        auto_eval_instance = AutoEvalColumn()

        data_dict = {
            "eval_name": self.eval_name,  # not a column, just a save name,
            auto_eval_instance.model.name: make_clickable_model(self.full_model) if self.still_on_hub else self.full_model,
            auto_eval_instance.average.name: average,
            auto_eval_instance.license.name: self.license,
            auto_eval_instance.org.name: self.org,
            auto_eval_instance.params.name: self.num_params,
            auto_eval_instance.still_on_hub.name: self.still_on_hub,
        }

        for task in Tasks:
            data_dict[task.value.col_name] = self.results[task.value.benchmark]

        return data_dict


def get_request_file_for_model(requests_path, model_name):
    """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
    request_files = os.path.join(
        requests_path,
        f"{model_name}*.json",
    )
    request_files = glob.glob(request_files)
    request_file = request_files[0]
    return request_file


def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
    """From the path of the results folder root, extract all needed info for results"""
    model_result_filepaths = []

    for root, _, files in os.walk(results_path):
        # We should only have json files in model results
        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
            continue

        # Sort the files by date
        try:
            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
        except dateutil.parser._parser.ParserError:
            files = [files[-1]]

        for file in files:
            model_result_filepaths.append(os.path.join(root, file))

    eval_results = {}
    for model_result_filepath in model_result_filepaths:
        # Creation of result
        eval_result = EvalResult.init_from_json_file(model_result_filepath)
        eval_result.update_with_request_file(requests_path)

        # Store results of same eval together
        eval_name = eval_result.eval_name
        if eval_name in eval_results.keys():
            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
        else:
            eval_results[eval_name] = eval_result

    results = []
    for v in eval_results.values():
        try:
            v.to_dict() # we test if the dict version is complete
            results.append(v)
        except KeyError:  # not all eval values present
            continue

    return results