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import json
import os
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.read_evals import get_raw_eval_results
# def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
# """Creates a dataframe from all the individual experiment results"""
# 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(all_data_json)
# df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
# 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_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
"""Creates a dataframe from all the individual experiment results"""
# iterate thorugh all files in the results path and read them into json
all_data_json = []
res_path = os.path.join(results_path, "demo-leaderboard", "syntherela-demo")
for entry in os.listdir(res_path):
if entry.endswith(".json"):
file_path = os.path.join(res_path, entry)
with open(file_path) as fp:
data = json.load(fp)
all_data_json.append(data)
multi_table_metrics = [
"AggregationDetection-LogisticRegression",
"AggregationDetection-XGBClassifier",
"CardinalityShapeSimilarity",
]
# create empty dataframe with the columns multi_table_metrics
multitable_df = pd.DataFrame(columns=["Dataset", "Model"] + multi_table_metrics)
# iterate through all json files and add the data to the dataframe
for data in all_data_json:
model = data["model"]
dataset = data["dataset"]
row = {"Dataset": dataset, "Model": model}
for metric in multi_table_metrics:
if metric in data["multi_table_metrics"]:
metric_values = []
for table in data["multi_table_metrics"][metric].keys():
if "accuracy" in data["multi_table_metrics"][metric][table]:
metric_values.append(data["multi_table_metrics"][metric][table]["accuracy"])
if "statistic" in data["multi_table_metrics"][metric][table]:
metric_values.append(data["multi_table_metrics"][metric][table]["statistic"])
row[metric] = np.mean(metric_values)
multitable_df = pd.concat([multitable_df, pd.DataFrame([row])], ignore_index=True)
return multitable_df
def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
"""Creates the different dataframes for the evaluation queues requestes"""
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 os.path.isfile(e) and 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]
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