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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") |