File size: 2,786 Bytes
8c49cb6 ebe77ac 8c49cb6 bc7fa0c 465544f b2e7d0b b1a1395 8c49cb6 3dfaf22 657d04f b2e7d0b 8c49cb6 ebe77ac 8c49cb6 a392379 bc7fa0c b1a1395 8c49cb6 adb0416 8c49cb6 ebe77ac 8c49cb6 eed1ccd 8c49cb6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import json
import os
import numpy as np
import pandas as pd
from src.display.formatting import has_no_nan_values, make_clickable_model, model_hyperlink
from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn, HEADER_MAP
from src.leaderboard.read_evals import get_raw_eval_results
def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
raw_data = get_raw_eval_results(results_path, requests_path)
#all_data_json = [v.to_dict() for v in raw_data]
df = pd.DataFrame.from_records(raw_data)
#df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
df = df.rename(columns=HEADER_MAP)
df = df[cols].round(decimals=2)
df.replace(r'\s+', np.nan, regex=True)
# filter out if any of the benchmarks have not been produced
df = df[has_no_nan_values(df, benchmark_cols)]
df['Model'] = df.apply(lambda row: model_hyperlink(row['Model link (temporary)'], row['Model']), axis=1)
return raw_data, df
def get_evaluation_queue_df(save_path: str, cols: list) -> list[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")
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")
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]
|