def calculate_distance(y, y_pred):
distance = distance75(y, y_pred)
return np.mean(distance)
best_params = param_search.best_params_
# %% Training the model with full data and optimized hyperparameters
knn_model = KNeighborsRegressor(**best_params)
knn_model.fit(X, y)
pred = knn_model.predict(X_valid)
pred_lon = pred[:, 0]
pred_lat = pred[:, 1]
pred_floor = np.round(pred[:, 2], decimals=0)
pred_building = np.round(pred[:, 3], decimals=0)
distance = distance75(y_valid, pred)
score = np.mean(distance)
lon_score = np.mean(np.absolute(pred_lon - y_valid.lon))
lat_score = np.mean(np.absolute(pred_lat - y_valid.lat))
right_floor = np.round(np.mean(pred_floor == y_valid.floor) * 100, 2)
right_building = np.round(np.mean(pred_building == y_valid.building) * 100, 2)
predictions = pd.DataFrame({
"LATITUDE": pred_lat,
"LONGITUDE": pred_lon,
"FLOOR": pred_floor,
"distance": distance,
})
true_values = pd.DataFrame({
"LATITUDE": y_valid.lat,