Update src/populate.py
Browse files- src/populate.py +17 -5
src/populate.py
CHANGED
@@ -4,7 +4,8 @@ import os
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import pandas as pd
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import json
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from src.display.utils import COLUMNS, EVAL_COLS
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def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_cols):
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# Initialize an empty DataFrame
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@@ -12,12 +13,15 @@ def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_co
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# Load evaluation results from JSON files
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if os.path.exists(eval_results_path):
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result_files = [
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data_list = []
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for file in result_files:
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with open(file, 'r') as f:
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data = json.load(f)
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# Flatten the JSON structure if needed
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flattened_data = {}
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flattened_data.update(data.get('config', {}))
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flattened_data.update(data.get('results', {}))
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@@ -30,6 +34,10 @@ def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_co
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if col not in df.columns:
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df[col] = None
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# Sort by 'average' column if it exists
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if 'average' in df.columns:
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df = df.sort_values(by=['average'], ascending=False)
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@@ -44,7 +52,11 @@ def get_evaluation_queue_df(eval_requests_path, eval_cols):
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# Load evaluation requests from JSON files
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if os.path.exists(eval_requests_path):
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request_files = [
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data_list = []
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for file in request_files:
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with open(file, 'r') as f:
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@@ -57,4 +69,4 @@ def get_evaluation_queue_df(eval_requests_path, eval_cols):
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running_df = df[df['status'] == 'running']
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pending_df = df[df['status'] == 'pending']
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return finished_df, running_df, pending_df
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import pandas as pd
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import json
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from src.display.utils import COLUMNS, EVAL_COLS, Tasks
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from src.envs import EVAL_RESULTS_PATH
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def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_cols):
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# Initialize an empty DataFrame
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# Load evaluation results from JSON files
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if os.path.exists(eval_results_path):
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result_files = [
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os.path.join(eval_results_path, f)
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for f in os.listdir(eval_results_path)
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if f.endswith('.json')
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]
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data_list = []
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for file in result_files:
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with open(file, 'r') as f:
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data = json.load(f)
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flattened_data = {}
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flattened_data.update(data.get('config', {}))
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flattened_data.update(data.get('results', {}))
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if col not in df.columns:
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df[col] = None
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# Convert 'average' column to float and handle errors
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if 'average' in df.columns:
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df['average'] = pd.to_numeric(df['average'], errors='coerce')
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# Sort by 'average' column if it exists
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if 'average' in df.columns:
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df = df.sort_values(by=['average'], ascending=False)
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# Load evaluation requests from JSON files
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if os.path.exists(eval_requests_path):
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request_files = [
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os.path.join(eval_requests_path, f)
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for f in os.listdir(eval_requests_path)
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if f.endswith('.json')
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]
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data_list = []
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for file in request_files:
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with open(file, 'r') as f:
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running_df = df[df['status'] == 'running']
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pending_df = df[df['status'] == 'pending']
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return finished_df, running_df, pending_df
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