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import json
import os
from tqdm import tqdm
import copy
import pandas as pd
import numpy as np
from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn
from src.leaderboard.filter_models import filter_models
from src.leaderboard.read_evals import get_raw_eval_results, EvalResult, update_model_type_with_open_llm_request_file
from src.backend.envs import Tasks as BackendTasks
from src.display.utils import Tasks
from src.display.utils import system_metrics_to_name_map, gpu_metrics_to_name_map
def get_leaderboard_df(
results_path: str,
requests_path: str,
requests_path_open_llm: str,
cols: list,
benchmark_cols: list,
is_backend: bool = False,
) -> tuple[list[EvalResult], pd.DataFrame]:
# Returns a list of EvalResult
raw_data: list[EvalResult] = get_raw_eval_results(results_path, requests_path, requests_path_open_llm)
if requests_path_open_llm != "":
for result_idx in tqdm(range(len(raw_data)), desc="updating model type with open llm leaderboard"):
raw_data[result_idx] = update_model_type_with_open_llm_request_file(
raw_data[result_idx], requests_path_open_llm
)
# all_data_json_ = [v.to_dict() for v in raw_data if v.is_complete()]
all_data_json_ = [v.to_dict() for v in raw_data] # include incomplete evals
name_to_bm_map = {}
task_iterator = Tasks
if is_backend is True:
task_iterator = BackendTasks
for task in task_iterator:
task = task.value
name = task.col_name
bm = (task.benchmark, task.metric)
name_to_bm_map[name] = bm
all_data_json = []
for entry in all_data_json_:
new_entry = copy.deepcopy(entry)
for k, v in entry.items():
if k in name_to_bm_map:
benchmark, metric = name_to_bm_map[k]
new_entry[k] = entry[k][metric]
for sys_metric, metric_namne in system_metrics_to_name_map.items():
if sys_metric in entry[k]:
new_entry[f"{k} {metric_namne}"] = entry[k][sys_metric]
for gpu_metric, metric_namne in gpu_metrics_to_name_map.items():
if gpu_metric in entry[k]:
new_entry[f"{k} {metric_namne}"] = entry[k][gpu_metric]
all_data_json += [new_entry]
# all_data_json.append(baseline_row)
filter_models(all_data_json)
df = pd.DataFrame.from_records(all_data_json)
# if AutoEvalColumn.average.name in df:
# df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
for col in cols:
if col not in df.columns:
df[col] = np.nan
if not df.empty:
df = df.round(decimals=2)
# filter out if any of the benchmarks have not been produced
# df = df[has_no_nan_values(df, benchmark_cols)]
return raw_data, df
def get_evaluation_queue_df(save_path: str, cols: list) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
all_evals = []
for entry in entries:
if ".json" in entry:
file_path = os.path.join(save_path, entry)
with open(file_path) as fp:
data = json.load(fp)
data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
data[EvalQueueColumn.revision.name] = data.get("revision", "main")
data[EvalQueueColumn.model_framework.name] = data.get("inference_framework", "-")
all_evals.append(data)
elif ".md" not in entry:
# this is a folder
sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")]
for sub_entry in sub_entries:
file_path = os.path.join(save_path, entry, sub_entry)
with open(file_path) as fp:
data = json.load(fp)
data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
data[EvalQueueColumn.revision.name] = data.get("revision", "main")
data[EvalQueueColumn.model_framework.name] = data.get("inference_framework", "-")
all_evals.append(data)
pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
running_list = [e for e in all_evals if e["status"] == "RUNNING"]
finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
df_running = pd.DataFrame.from_records(running_list, columns=cols)
df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
return df_finished[cols], df_running[cols], df_pending[cols]
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