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import argparse
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from download_ixi import DEFAULT_DESTINATION
def evaluate_ixi_predictions(
labels: str,
predictions: str
) -> None:
labels = pd.read_excel(labels)
predictions = pd.read_csv(predictions)
predictions['IXI_ID'] = predictions['source'].apply(
lambda path: int(path.split('/')[-1][3:6])
)
predictions['age_prediction'] = predictions['age']
predictions = pd.merge(
predictions[['IXI_ID', 'age_prediction']],
labels[['IXI_ID', 'AGE']],
on='IXI_ID',
how='left'
)
mae = np.mean(np.abs(predictions['AGE'] - predictions['age_prediction']))
print(f'MAE: {mae:.2f}')
plt.scatter(predictions['AGE'], predictions['age_prediction'])
plt.xlabel('True age')
plt.ylabel('Predicted age')
plt.title('Age prediction')
plt.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser(
'Evaluates predictions for the IXI dataset'
)
parser.add_argument(
'-l', '--labels',
required=False,
default=os.path.join(DEFAULT_DESTINATION, 'IXI.xls'),
help='Path to XLSX containing labels'
)
parser.add_argument(
'-p', '--predictions',
required=False,
default=os.path.join(
DEFAULT_DESTINATION,
'outputs',
'predictions.csv'
),
help='Path to CSV containing predictions'
)
args = parser.parse_args()
evaluate_ixi_predictions(
labels=args.labels,
predictions=args.predictions
)
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