| import os.path | |
| import numpy as np | |
| import pandas as pd | |
| import argparse | |
| from sklearn.metrics import mean_squared_error | |
| from sklearn.metrics import mean_squared_log_error | |
| from sklearn.metrics import mean_absolute_error | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--path', type=str, required=True) | |
| parser.add_argument('--name', type=str, required=True) | |
| parser.add_argument('--answer_file', type=str, required=True) | |
| parser.add_argument('--predict_file', type=str, required=True) | |
| parser.add_argument('--value', type=str, default="yield") | |
| args = parser.parse_args() | |
| answers = pd.read_csv( args.answer_file) | |
| predictions = pd.read_csv( args.predict_file) | |
| answers.sort_values(by=['id']) | |
| predictions.sort_values(by=['id']) | |
| if 'Age' in predictions: | |
| performance = mean_absolute_error(answers['Age'], predictions['Age']) | |
| else: | |
| performance = mean_absolute_error(answers['Age'], predictions[args.value]) | |
| with open(os.path.join(args.path, args.name, "result.txt"), "w") as f: | |
| f.write(str(performance)) | |