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| # Read the CSV data into a DataFrame | |
| data = pd.read_csv('models.csv') | |
| # Define the score columns | |
| score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench'] | |
| # Function to calculate the highest combined score for a given column | |
| def calculate_highest_combined_score(column): | |
| start_time = time.time() | |
| scores = data[column].tolist() | |
| models = data['Model'].tolist() | |
| top_combinations = {2: [], 3: [], 4: [], 5: [], 6: []} | |
| calculations = {2: 0, 3: 0, 4: 0, 5: 0, 6: 0} | |
| # Generate all unique combinations of two, three, four, and five models | |
| for r in range(2, 7): # r is the combination size (2, 3, 4, 5 or 6) | |
| for combination in combinations(zip(scores, models), r): | |
| combined_score = sum(score for score, _ in combination) | |
| # Add the combination to the list along with its score | |
| top_combinations[r].append((combined_score, tuple(model for _, model in combination))) | |
| calculations[r] += 1 | |
| # Sort the list in descending order by score and keep only the top three | |
| top_combinations[r] = sorted(top_combinations[r], key=lambda x: x[0], reverse=True)[:3] | |
| elapsed_time = time.time() - start_time | |
| return column, top_combinations, calculations, elapsed_time | |
| # Function to be executed in parallel | |
| def worker(column): | |
| return calculate_highest_combined_score(column) | |
| if __name__ == '__main__': | |
| with Pool() as pool: | |
| results = pool.map(worker, score_columns) | |
| # Sort results by max_score in descending order | |
| sorted_results = sorted(results, key=lambda x: max(x[1][5])[0] if 5 in x[1] else 0, reverse=True) | |
| # Print the sorted results | |
| for column, top_combinations, calculations, elapsed_time in sorted_results: | |
| for r in range(2, 7): | |
| print(f"Column: {column}, Number of Models: {r}") | |
| for score, combination in top_combinations[r]: | |
| print(f"Combination: {combination}, Score: {score}") | |
| print(f"Calculations required: {calculations[r]}") | |
| print(f"Time taken: {elapsed_time:.4f} seconds") | |
| print() # Add an empty line for better readability | |
| # Count how many times each model is mentioned | |
| model_mentions = Counter() | |
| for _, top_combinations, _, _ in sorted_results: | |
| for r in range(2, 7): | |
| for _, combination in top_combinations[r]: | |
| model_mentions.update(combination) | |
| # Print the top 5 most mentioned models | |
| print("Top 5 most mentioned models:") | |
| for model, count in model_mentions.most_common(5): | |
| print(f"{model}: {count} times") |