| | import pandas as pd |
| | import os |
| |
|
| | |
| | |
| | script_dir = os.path.dirname(os.path.abspath(__file__)) |
| |
|
| | |
| | datasets_dir = os.path.join(script_dir, "datasets") |
| |
|
| | |
| | os.makedirs(datasets_dir, exist_ok=True) |
| |
|
| | input_file = os.path.join(datasets_dir, "routerbench_0shot.pkl") |
| | full_csv_output = os.path.join(datasets_dir, "routerbench_0shot.csv") |
| | train_output = os.path.join(datasets_dir, "routerbench_0shot_train.csv") |
| | test_output = os.path.join(datasets_dir, "routerbench_0shot_test.csv") |
| | test_sample_output = os.path.join(datasets_dir, "routerbench_0shot_test_500.csv") |
| |
|
| | try: |
| | print(f"Loading dataset from: {input_file}") |
| | |
| | df = pd.read_pickle(input_file) |
| | |
| | |
| | df.to_csv(full_csv_output, index=False) |
| | print(f"Converted pickle to CSV: {full_csv_output}") |
| |
|
| | |
| | train_df = df.sample(frac=0.01, random_state=42) |
| | train_df.to_csv(train_output, index=False) |
| | print(f"Created 'train' split with {len(train_df)} rows.") |
| |
|
| | |
| | test_df = df.drop(train_df.index) |
| | test_df.to_csv(test_output, index=False) |
| | print(f"Created 'test' split with {len(test_df)} rows.") |
| |
|
| | |
| | sample_size = 500 |
| | if len(test_df) < sample_size: |
| | print(f"Warning: Test data only has {len(test_df)} rows. Sampling all of them.") |
| | sample_size = len(test_df) |
| | |
| | test_sample_df = test_df.sample(n=sample_size, random_state=42) |
| | test_sample_df.to_csv(test_sample_output, index=False) |
| | print(f"Created 'test_500' split with {len(test_sample_df)} rows.") |
| |
|
| | except FileNotFoundError: |
| | print(f"Error: The file '{input_file}' was not found. Please run the download script first.") |
| | except Exception as e: |
| | print(f"An unexpected error occurred: {e}") |