update populate

src/populate.py

@@ -3,10 +3,8 @@
 import os
 import pandas as pd
 import json
-import random
 
-from src.display.utils import COLUMNS, EVAL_COLS, Tasks
-from src.envs import EVAL_RESULTS_PATH, FIXED_QUESTIONS_FILE  # Ensure FIXED_QUESTIONS_FILE is defined in envs.py
+from src.display.utils import COLUMNS, EVAL_COLS
 
 def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_cols):
     # Initialize an empty DataFrame
@@ -14,16 +12,12 @@ def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_co
 
     # Load evaluation results from JSON files
     if os.path.exists(eval_results_path):
-        result_files = [
-            os.path.join(eval_results_path, f)
-            for f in os.listdir(eval_results_path)
-            if f.endswith('.json')
-        ]
+        result_files = [os.path.join(eval_results_path, f) for f in os.listdir(eval_results_path) if f.endswith('.json')]
         data_list = []
         for file in result_files:
             with open(file, 'r') as f:
                 data = json.load(f)
-                # Flatten the JSON structure
+                # Flatten the JSON structure if needed
                 flattened_data = {}
                 flattened_data.update(data.get('config', {}))
                 flattened_data.update(data.get('results', {}))
@@ -31,10 +25,6 @@ def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_co
         if data_list:
             df = pd.DataFrame(data_list)
 
-    # Rename 'model_name' to 'model' if 'model' is missing
-    if 'model' not in df.columns and 'model_name' in df.columns:
-        df.rename(columns={'model_name': 'model'}, inplace=True)
-
     # Ensure DataFrame has all columns
     for col in cols:
         if col not in df.columns:
@@ -54,11 +44,7 @@ def get_evaluation_queue_df(eval_requests_path, eval_cols):
 
     # Load evaluation requests from JSON files
     if os.path.exists(eval_requests_path):
-        request_files = [
-            os.path.join(eval_requests_path, f)
-            for f in os.listdir(eval_requests_path)
-            if f.endswith('.json')
-        ]
+        request_files = [os.path.join(eval_requests_path, f) for f in os.listdir(eval_requests_path) if f.endswith('.json')]
         data_list = []
         for file in request_files:
             with open(file, 'r') as f:
@@ -72,42 +58,3 @@ def get_evaluation_queue_df(eval_requests_path, eval_cols):
             pending_df = df[df['status'] == 'pending']
 
     return finished_df, running_df, pending_df
-
-def preselect_fixed_questions(dataset_path, num_questions_per_subject=30, fixed_questions_file='fixed_questions.json'):
-    """
-    Preselects a fixed number of questions per subject and saves them to a JSON file.
-    """
-    # Load the dataset
-    # Assuming the dataset is in CSV format with a 'Subject' column
-    if not os.path.exists(dataset_path):
-        raise FileNotFoundError(f"Dataset file not found at {dataset_path}")
-
-    dataset = pd.read_csv(dataset_path)
-
-    fixed_questions = {}
-
-    for task in Tasks:
-        subject = task.value.benchmark
-        subject_questions = dataset[dataset['Subject'] == subject]
-
-        if len(subject_questions) < num_questions_per_subject:
-            raise ValueError(f"Not enough questions for subject '{subject}'. Required: {num_questions_per_subject}, Available: {len(subject_questions)}")
-
-        # Randomly select fixed number of questions
-        selected_questions = subject_questions.sample(n=num_questions_per_subject, random_state=42)  # random_state for reproducibility
-        fixed_questions[subject] = selected_questions.to_dict(orient='records')
-
-    # Save fixed questions to a JSON file
-    with open(os.path.join(EVAL_RESULTS_PATH, fixed_questions_file), 'w') as f:
-        json.dump(fixed_questions, f, indent=4)
-
-    print(f"Fixed questions preselected and saved to {fixed_questions_file}")
-
-if __name__ == "__main__":
-    # Example usage:
-    # Define the path to your dataset
-    DATASET_PATH = os.path.join(EVAL_RESULTS_PATH, "your_dataset.csv")  # Update with your actual dataset file
-    FIXED_QUESTIONS_FILE = "fixed_questions.json"  # Define the name for fixed questions file
-
-    # Preselect fixed questions
-    preselect_fixed_questions(DATASET_PATH, num_questions_per_subject=30, fixed_questions_file=FIXED_QUESTIONS_FILE)