src/leaderboard/read_evals.py

import os
import glob
import json
import dateutil
import numpy as np
from dataclasses import dataclass
from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType, generate_column_name
from src.submission.check_validity import is_model_on_hub


@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_feature-set_nb-shots (uid)
    full_model: str # org/model (path on hub)
    org: str 
    model: str
    revision: str # commit hash, "" if main
    results: dict
    raw_data: dict
    nb_shots: int
    feature_set: str
    precision: Precision = Precision.Unknown
    model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
    weight_type: WeightType = WeightType.Original # Original or Adapter
    architecture: str = "Unknown" 
    license: str = "?"
    likes: int = 0
    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)
        
        # Get config
        config = data.get("config")
        full_model = config.get("model")
        org = full_model.split("/")[0]
        model = full_model.split("/")[1]
        precision = Precision.from_str(config.get("precision"))
        revision = config.get("revision", "")
        nb_shots = config.get("nb_shots", None)
        feature_set = config.get("feature_set", None)
        model_type = ModelType.from_str(config.get("model_type", ""))
        weight_type = WeightType[config.get("weight_type", "Original")]
        license = config.get("license", "?")
        likes = config.get("likes", 0)
        num_params = config.get("params", 0)
        date = config.get("submitted_time", "")
        
        # Check if model is still on hub
        still_on_hub, _, model_config = is_model_on_hub(
            full_model, revision, trust_remote_code=True, test_tokenizer=False, token=os.environ.get("TOKEN")
        )
        architecture = "?"
        if model_config is not None:
            architectures = getattr(model_config, "architectures", None)
            if architectures:
                architecture = ";".join(architectures)

        results = {}
        for task in Tasks:
            task = task.value
            mean = data["results"].get(task.phenotype, {}).get("metrics", {}).get("_".join(["mean", task.metric]), None)
            lower = data["results"].get(task.phenotype, {}).get("metrics", {}).get("_".join(["lower", task.metric]), None)
            upper = data["results"].get(task.phenotype, {}).get("metrics", {}).get("_".join(["upper", task.metric]), None)
            formated_score = f"{mean:.2f} ({lower:.2f}-{upper:.2f})" if mean is not None else None
            results["_".join([task.phenotype, task.metric])] = formated_score

        return self(
            eval_name=f"{org}_{model}_{precision.value.name}_{feature_set}_{nb_shots}",
            full_model=full_model,
            org=full_model.split("/")[0],
            model=full_model.split("/")[1],
            results=results,
            raw_data=data,
            nb_shots=nb_shots,
            feature_set=feature_set,
            precision=precision,
            revision=revision,
            still_on_hub=still_on_hub,
            architecture=architecture,
            model_type=model_type,
            weight_type=weight_type,
            license=license,
            likes=likes,
            num_params=num_params,
            date=date
        )

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        average_auroc = np.mean(np.array([d["metrics"]["mean_auroc"] for d in self.raw_data["results"].values() if "mean_auroc" in d["metrics"].keys()]))
        average_auprc = np.mean(np.array([d["metrics"]["mean_auprc"] for d in self.raw_data["results"].values() if "mean_auprc" in d["metrics"].keys()]))
        data_dict = {
            "eval_name": self.eval_name,  # not a column, just a save name,
            AutoEvalColumn.feature_set.name: self.feature_set,
            AutoEvalColumn.nb_shots.name: self.nb_shots,
            AutoEvalColumn.precision.name: self.precision.value.name,
            AutoEvalColumn.model_type.name: self.model_type.value.name,
            AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
            AutoEvalColumn.weight_type.name: self.weight_type.value.name,
            AutoEvalColumn.architecture.name: self.architecture,
            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
            AutoEvalColumn.revision.name: self.revision,
            AutoEvalColumn.average_auroc.name: average_auroc,
            AutoEvalColumn.average_auprc.name: average_auprc,
            AutoEvalColumn.license.name: self.license,
            AutoEvalColumn.likes.name: self.likes,
            AutoEvalColumn.params.name: self.num_params,
            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
        }

        for task in Tasks:
            data_dict[generate_column_name(task.value.phenotype, task.value.metric.upper())] = self.results["_".join([task.value.phenotype, task.value.metric])]
        return data_dict


def get_request_file_for_model(requests_path, model_name, precision):
    """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}_eval_request_*.json",
    )
    request_files = glob.glob(request_files)

    # Select correct request file (precision)
    request_file = ""
    request_files = sorted(request_files, reverse=True)
    for tmp_request_file in request_files:
        with open(tmp_request_file, "r") as f:
            req_content = json.load(f)
            if (
                req_content["status"] in ["FINISHED"]
                and req_content["precision"] == precision.split(".")[-1]
            ):
                request_file = tmp_request_file
    return request_file


def get_raw_eval_results(results_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)

        # 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