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, ModelType, Tasks, Precision, WeightType, Domains
from src.submission.check_validity import is_model_on_hub


@dataclass
class RankResult:
    """Represents one the overall ranking table
    """
    eval_name: str
    full_model: str
    org: str 
    model: str
    results: dict
    license: str = "?"
    knowledge_cutoff: str = ""
    
    @classmethod
    def init_from_json_dict(self, data):
        
        config = data.get("config")
        # Get model and org
        model = config.get("model_name")
        org = config.get("organization")
        license = config.get("license")
        knowledge_cutoff = config.get("knowledge_cutoff")
        
        model_results = data.get("results")
        
        # Extract results available in this file (some results are split in several files)
        results = {}
        for domain in Domains:
            domain = domain.value
            results[domain.dimension] = model_results.get(domain.dimension).get(domain.metric, None)
        
        return self(
            eval_name=f"{org}_{model}",
            full_model=f"{org}/{model}",
            org=org,
            model=model,
            results=results,
            license=license,
            knowledge_cutoff=knowledge_cutoff
        )

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        
        # score = 1 / self.results[Domains.dim0.dimension] if self.results[Domains.dim0.dimension] != 0 else 0
        # average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
        data_dict = {
            # "eval_name": self.eval_name,  # not a column, just a save name,
            # AutoEvalColumn.model.name: make_clickable_model(self.full_model),
            AutoEvalColumn.rank.name: None, # placeholder for the rank
            AutoEvalColumn.model.name: self.model,
            AutoEvalColumn.score.name: self.results[Domains.dim0.value.dimension],
            AutoEvalColumn.score_sd.name: None, # placeholder for the score sd 
            AutoEvalColumn.license.name: self.license,
            AutoEvalColumn.organization.name: self.org,
            AutoEvalColumn.knowledge_cutoff.name: self.knowledge_cutoff,

            # AutoEvalColumn.precision.name: self.precision.value.name,
            # AutoEvalColumn.model_type.name: self.model_type.value.name,
            # AutoEvalColumn.model_type_symbol.name
            # AutoEvalColumn.weight_type.name: self.weight_type.value.name,
            # AutoEvalColumn.architecture.name: self.architecture,
            # AutoEvalColumn.revision.name: self.revision,
            # AutoEvalColumn.average.name: average,
            # AutoEvalColumn.likes.name: self.likes,
            # AutoEvalColumn.params.name: self.num_params,
            # AutoEvalColumn.still_on_hub.name: self.still_on_hub,
        }
        


@dataclass
class ModelResult:
    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
    """
    eval_name: str
    full_model: str
    org: str 
    model: str
    results: dict
    license: str = "?"
    knowledge_cutoff: str = ""
    
    @classmethod
    def init_from_json_dict(self, data):
        
        config = data.get("config")
        # Get model and org
        model = config.get("model_name")
        org = config.get("organization")
        license = config.get("license")
        knowledge_cutoff = config.get("knowledge_cutoff")
        
        model_results = data.get("results")
        new_results = {}
        for k, v in model_results.items():
            new_v = {}
            for kk, vv in v.items():
                if vv == 'N/A':
                    new_v[kk] = None
                else:
                    new_v[kk] = vv
                    
            new_results[k] = new_v

        # Extract results available in this file (some results are split in several files)
        # results = {}
        # for domain in Domains:
        #     domain = domain.value
        #     results[domain.dimension] = model_results.get(domain.dimension).get(domain.metric, None)
        
        return self(
            eval_name=f"{org}_{model}",
            full_model=f"{org}/{model}",
            org=org,
            model=model,
            results=new_results,
            license=license,
            knowledge_cutoff=knowledge_cutoff
        )

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        
        data_dict = {
            # "eval_name": self.eval_name,  # not a column, just a save name,
            # AutoEvalColumn.model.name: make_clickable_model(self.full_model),
            # AutoEvalColumn.rank.name: None, # placeholder for the rank
            AutoEvalColumn.model.name: self.model,
            # AutoEvalColumn.score.name: self.results[Domains.dim0.value.dimension],
            # AutoEvalColumn.score_sd.name: None, # placeholder for the score sd 

            # AutoEvalColumn.score_overall.name: float(self.results.get("OVERALL").get("Average Score", None)),
            # AutoEvalColumn.score_math_algebra.name: float(self.results.get("Algebra").get("Average Score", None)),
            # AutoEvalColumn.score_math_geometry.name: float(self.results.get("Geometry").get("Average Score", None)),
            # AutoEvalColumn.score_math_probability.name: float(self.results.get("Probability").get("Average Score", None)),
            # AutoEvalColumn.score_reason_logical.name: float(self.results.get("Logical").get("Average Score", None)),
            # AutoEvalColumn.score_reason_social.name: float(self.results.get("Social").get("Average Score", None)),
            
            # AutoEvalColumn.sd_overall.name: float(self.results.get("OVERALL").get("Standard Deviation", None)),
            # AutoEvalColumn.sd_math_algebra.name: float(self.results.get("Algebra").get("Standard Deviation", None)),
            # AutoEvalColumn.sd_math_geometry.name: float(self.results.get("Geometry").get("Standard Deviation", None)),
            # AutoEvalColumn.sd_math_probability.name: float(self.results.get("Probability").get("Standard Deviation", None)),
            # AutoEvalColumn.sd_reason_logical.name: float(self.results.get("Logical").get("Standard Deviation", None)),
            # AutoEvalColumn.sd_reason_social.name: float(self.results.get("Social").get("Standard Deviation", None)),

            # AutoEvalColumn.rank_overall.name: int(self.results.get("OVERALL").get("Rank", None)),
            # AutoEvalColumn.rank_math_algebra.name: int(self.results.get("Algebra").get("Rank", None)),
            # AutoEvalColumn.rank_math_geometry.name: int(self.results.get("Geometry").get("Rank", None)),
            # AutoEvalColumn.rank_math_probability.name: int(self.results.get("Probability").get("Rank", None)),
            # AutoEvalColumn.rank_reason_logical.name: int(self.results.get("Logical").get("Rank", None)),
            # AutoEvalColumn.rank_reason_social.name: int(self.results.get("Social").get("Rank", None)),
            
            AutoEvalColumn.score_overall.name: self.results.get("OVERALL").get("Average Score", None),
            AutoEvalColumn.score_math_algebra.name: self.results.get("Algebra").get("Average Score", None),
            AutoEvalColumn.score_math_geometry.name: self.results.get("Geometry").get("Average Score", None),
            AutoEvalColumn.score_math_probability.name: self.results.get("Probability").get("Average Score", None),
            AutoEvalColumn.score_reason_logical.name: self.results.get("Logical").get("Average Score", None),
            AutoEvalColumn.score_reason_social.name: self.results.get("Social").get("Average Score", None),
            
            AutoEvalColumn.sd_overall.name: self.results.get("OVERALL").get("Standard Deviation", None),
            AutoEvalColumn.sd_math_algebra.name: self.results.get("Algebra").get("Standard Deviation", None),
            AutoEvalColumn.sd_math_geometry.name: self.results.get("Geometry").get("Standard Deviation", None),
            AutoEvalColumn.sd_math_probability.name: self.results.get("Probability").get("Standard Deviation", None),
            AutoEvalColumn.sd_reason_logical.name: self.results.get("Logical").get("Standard Deviation", None),
            AutoEvalColumn.sd_reason_social.name: self.results.get("Social").get("Standard Deviation", None),

            AutoEvalColumn.rank_overall.name: self.results.get("OVERALL").get("Rank", None),
            AutoEvalColumn.rank_math_algebra.name: self.results.get("Algebra").get("Rank", None),
            AutoEvalColumn.rank_math_geometry.name: self.results.get("Geometry").get("Rank", None),
            AutoEvalColumn.rank_math_probability.name: self.results.get("Probability").get("Rank", None),
            AutoEvalColumn.rank_reason_logical.name: self.results.get("Logical").get("Rank", None),
            AutoEvalColumn.rank_reason_social.name: self.results.get("Social").get("Rank", None),
            
            AutoEvalColumn.license.name: self.license,
            AutoEvalColumn.organization.name: self.org,
            AutoEvalColumn.knowledge_cutoff.name: self.knowledge_cutoff,
        }

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

        # for domain in Domains:
        #     data_dict[domain.value.col_name] = self.results[domain.value.dimension]

        return data_dict

@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
    revision: str # commit hash, "" if main
    results: dict
    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)

        config = data.get("config")

        # Precision
        precision = Precision.from_str(config.get("model_dtype"))

        # 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 = f"{model}_{precision.value.name}"
        else:
            org = org_and_model[0]
            model = org_and_model[1]
            result_key = f"{org}_{model}_{precision.value.name}"
        full_model = "/".join(org_and_model)

        still_on_hub, _, model_config = is_model_on_hub(
            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
        )
        architecture = "?"
        if model_config is not None:
            architectures = getattr(model_config, "architectures", None)
            if architectures:
                architecture = ";".join(architectures)

        # 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,
            precision=precision,  
            revision= config.get("model_sha", ""),
            still_on_hub=still_on_hub,
            architecture=architecture
        )

    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, self.precision.value.name)

        try:
            with open(request_file, "r") as f:
                request = json.load(f)
            self.model_type = ModelType.from_str(request.get("model_type", ""))
            self.weight_type = WeightType[request.get("weight_type", "Original")]
            self.license = request.get("license", "?")
            self.likes = request.get("likes", 0)
            self.num_params = request.get("params", 0)
            self.date = request.get("submitted_time", "")
        except Exception:
            print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}")

    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)
        # print(AutoEvalColumn.precision.name, self.precision.value.name)
        data_dict = {
            "eval_name": self.eval_name,  # not a column, just a save name,
            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.name: average,
            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[task.value.col_name] = self.results[task.value.benchmark]

        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, 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


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

    try:
        with open(results_path) as fp:
            data = json.load(fp)
    except:
        data = eval(open(results_path).read()) # a list of dicts   

    # print("data", len(data))
    # print(data[0]) 
    # {'config': {'model_name': 'ChatGPT-4o-latest (2024-09-03)', 
    # 'organization': 'OpenAI', 'license': 'Proprietary', 
    # 'knowledge_cutoff': '2023/10'}, 
    # 'results': {'math-algebra': 
    # {'Score': 99.19484702, 'Avg Rank': 1.666666667, 'Min Rank': 1, 'Max Rank': 3}, 
    # 'math-probability': {'Score': 100, 'Avg Rank': 1, 'Min Rank': 1, 'Max Rank': 1}, 
    # 'reasoning-logical': {'Avg Rank': 1, 'Min Rank': 1, 'Max Rank': 1}, 
    # 'overall': {'Avg Rank': 2, 'Min Rank': 2, 'Max Rank': 2}}}
    eval_results = {}

    for result in data:
        # Creation of result
        eval_result = ModelResult.init_from_json_dict(result)
        # print(eval_result)
        # ModelResult(eval_name='OpenAI_ChatGPT-4o-latest (2024-09-03)', 
        # full_model='OpenAI/ChatGPT-4o-latest (2024-09-03)', 
        # org='OpenAI', model='ChatGPT-4o-latest (2024-09-03)', 
        # results={'overall': None}, license='Proprietary', knowledge_cutoff='2023/10')

        # all_num_results = eval_result.results

        # def get_terminal_values(data):
        #     terminal_values = []
        #     for key, value in data.items():
        #         if isinstance(value, dict):
        #             terminal_values.extend(get_terminal_values(value))
        #         else:
        #             terminal_values.append(value)
        #     return terminal_values
    
        # all_values = get_terminal_values(all_num_results)
        
        # if 'N/A' in all_values:
        #     continue
            
        eval_name = eval_result.eval_name
        eval_results[eval_name] = eval_result

        # # Store results of same eval together
        # 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():
        # print(v.to_dict())
        # exit()
        # {'eval_name': 'OpenAI_ChatGPT-4o-latest (2024-09-03)', 
        # 'Model': '<a target="_blank" href="https://huggingface.co/OpenAI/ChatGPT-4o-latest (2024-09-03)" 
        # style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">OpenAI/ChatGPT-4o-latest (2024-09-03)</a>', 
        # 'Hub License': 'Proprietary', 'Organization': 'OpenAI', 'Knowledge cutoff': '2023/10', 'Overall': None}
        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