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import json
import os
import pandas as pd
from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumnQA, AutoEvalColumnLongDoc, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results, EvalResult, FullEvalResult
from typing import Tuple, List
def get_leaderboard_df(raw_data: List[FullEvalResult], cols: list, benchmark_cols: list, task: str, metric: str) -> pd.DataFrame:
"""Creates a dataframe from all the individual experiment results"""
all_data_json = []
for v in raw_data:
all_data_json += v.to_dict(task=task, metric=metric)
df = pd.DataFrame.from_records(all_data_json)
print(f'dataframe created: {df.shape}')
# calculate the average score for selected benchmarks
_benchmark_cols = frozenset(benchmark_cols).intersection(frozenset(df.columns.to_list()))
if task == 'qa':
df[AutoEvalColumnQA.average.name] = df[list(_benchmark_cols)].mean(axis=1).round(decimals=2)
df = df.sort_values(by=[AutoEvalColumnQA.average.name], ascending=False)
elif task == "long_doc":
df[AutoEvalColumnLongDoc.average.name] = df[list(_benchmark_cols)].mean(axis=1).round(decimals=2)
df = df.sort_values(by=[AutoEvalColumnLongDoc.average.name], ascending=False)
df.reset_index(inplace=True)
_cols = frozenset(cols).intersection(frozenset(df.columns.to_list()))
df = df[_cols].round(decimals=2)
# filter out if any of the benchmarks have not been produced
df = df[has_no_nan_values(df, _benchmark_cols)]
return df
def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
"""Creates the different dataframes for the evaluation queues requests"""
# 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)
cols = ["Retrieval Model", "Submitted Time", "Status"]
df_finished = pd.DataFrame(
{
"Retrieval Model": ["bge-m3", "jina-embeddings-v2"],
"Submitted Time": ["2024-05-01 12:34:20", "2024-05-02 12:34:20"],
"Status": ["FINISHED", "FINISHED"]
}
)
df_running = pd.DataFrame(
{
"Retrieval Model": ["bge-m3", "jina-embeddings-v2"],
"Submitted Time": ["2024-05-01 12:34:20", "2024-05-02 12:34:20"],
"Status": ["RUNNING", "RUNNING"]
}
)
df_pending = pd.DataFrame(
{
"Retrieval Model": ["bge-m3", "jina-embeddings-v2"],
"Submitted Time": ["2024-05-01 12:34:20", "2024-05-02 12:34:20"],
"Status": ["PENDING", "PENDING"]
}
)
return df_finished, df_running, df_pending
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