Spaces:
Sleeping
Sleeping
File size: 2,880 Bytes
6e8eb41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import pandas as pd
import os
from sklearn.metrics import root_mean_squared_error
import numpy as np
SEED = 42
TTM_MODEL_PATH = "iaravagni/ttm-finetune-model"
CONTEXT_LENGTH = 52 # 4.33 hrs
PREDICTION_LENGTH = 6 # 30 mins
def main():
"""
Main function to execute the metrics for the three approaches.
"""
# Get dataset path
script_dir = os.path.dirname(os.path.abspath(__file__))
naive_pred_path = os.path.join(script_dir, '..', 'data', 'outputs', 'naive_predictions.csv')
ml_pred_path = os.path.join(script_dir, '..', 'data', 'outputs', 'ml_predictions.csv')
dl_pred_path = os.path.join(script_dir, '..', 'data', 'outputs', 'dl_predictions.csv')
# Load and prepare data
naive_pred_df = pd.read_csv(naive_pred_path)
ml_pred_df = pd.read_csv(ml_pred_path)
dl_pred_df = pd.read_csv(dl_pred_path)
y_true = naive_pred_df[['Glucose', 'patient_id']]
naive_pred = naive_pred_df['averaged_prediction']
ml_pred = ml_pred_df['averaged_prediction']
dl_pred = dl_pred_df['averaged_prediction']
rmse_list = []
for patient in y_true['patient_id'].unique():
patient_mask = y_true['patient_id'] == patient
y_true_patient = y_true[patient_mask].reset_index(drop=True)
naive_pred_patient = naive_pred[patient_mask].reset_index(drop=True)
ml_pred_patient = ml_pred[patient_mask].reset_index(drop=True)
dl_pred_patient = dl_pred[patient_mask].reset_index(drop=True)
y_true_aux = y_true_patient['Glucose'][CONTEXT_LENGTH:].reset_index(drop=True)
naive_pred_aux = naive_pred_patient[CONTEXT_LENGTH:].reset_index(drop=True)
ml_pred_aux = ml_pred_patient[CONTEXT_LENGTH:].reset_index(drop=True)
dl_pred_aux = dl_pred_patient[CONTEXT_LENGTH:].reset_index(drop=True)
rmse_naive = np.sqrt(root_mean_squared_error(y_true_aux, naive_pred_aux))
rmse_ml = np.sqrt(root_mean_squared_error(y_true_aux, ml_pred_aux))
rmse_dl = np.sqrt(root_mean_squared_error(y_true_aux, dl_pred_aux))
rmse_list.append([rmse_naive, rmse_ml, rmse_dl])
model_names = ['Naive', 'ML', 'DL']
rmse_array = np.array(rmse_list)
print("Average RMSEs:")
for name, avg in zip(model_names, rmse_array.mean(axis=0)):
print(f" {name}: {avg:.4f}")
print("\nHighest and Lowest RMSE per model:")
for i, model in enumerate(model_names):
max_val = np.max(rmse_array[:, i])
min_val = np.min(rmse_array[:, i])
max_patient = y_true['patient_id'].unique()[np.argmax(rmse_array[:, i])]
min_patient = y_true['patient_id'].unique()[np.argmin(rmse_array[:, i])]
print(f" {model}:")
print(f" Highest RMSE = {max_val:.4f} (Patient {max_patient})")
print(f" Lowest RMSE = {min_val:.4f} (Patient {min_patient})")
return
if __name__ == "__main__":
main()
|