Hey! Thanks for opening this issue!

          Hey! Thanks for opening this issue! 

I am also trying to use the codes for AutoBNN shared here to model this data, but it is giving me very big values of the performance metric, how may i adjust?

-- coding: utf-8 --

"""
Created on Sun Oct 27 15:41:32 2024

@author: iO
"""

import jax
import jax.numpy as jnp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from autobnn import estimators
from autobnn import training_util

Seed setup

seed = jax.random.PRNGKey(0)

Load data

data = pd.read_csv("path_to_data/Bukomansimbi.txt", delim_whitespace=True)
data.columns = data.columns.str.lower()

Date and rainfall extraction

data['date'] = pd.to_datetime(data[['year', 'month', 'day']])
rainfall_by_day = data['value'].values

Train/test split configuration

num_forecast_steps = 365
train_data = rainfall_by_day[:-num_forecast_steps]
test_data = rainfall_by_day[-num_forecast_steps:]
dates_train = data['date'].values[:-num_forecast_steps]
dates_test = data['date'].values[-num_forecast_steps:]

Normalize and scale data

scaler = StandardScaler()
train_scaled = scaler.fit_transform(train_data.reshape(-1, 1)).flatten()
test_scaled = scaler.transform(test_data.reshape(-1, 1)).flatten()

Add time feature for seasonality

month_sin = np.sin(2 * np.pi * data['month'].values / 12)
month_cos = np.cos(2 * np.pi * data['month'].values / 12)
x_train = np.stack([month_sin[:-num_forecast_steps], month_cos[:-num_forecast_steps]], axis=1)
x_test = np.stack([month_sin[-num_forecast_steps:], month_cos[-num_forecast_steps:]], axis=1)

Initialize AutoBNN

est = estimators.AutoBnnMapEstimator(
'sum_of_products',
likelihood_model='normal_likelihood_logistic_noise',
seed=seed,
periods=(1.0,), # one year period as float since date normalization handles scaling
num_particles=32
)

Fit the model

est.fit(x_train, train_scaled[:, None])

Prediction

preds = est.predict(x_test)[:, 0] # Squeeze predictions
quantiles = est.predict_quantiles(x_test, q=[2.5, 50., 90., 97.5])
lo, mid, p90, hi = quantiles[:, 0], quantiles[:, 1], quantiles[:, 2], quantiles[:, 3]

Plotting the results

plt.figure(figsize=(16, 10))
plt.plot(dates_train, train_scaled, label='Training Data')
plt.plot(dates_test, test_scaled, label='Actual')
plt.plot(dates_test, mid, label='Predictions', color='r')
plt.fill_between(dates_test, lo, hi, color='r', alpha=0.3, label='Confidence Interval')
plt.title('Rainfall Prediction in Bukomansimbi')
plt.xlabel('Date')
plt.ylabel('Standardized Rainfall')
plt.legend()
plt.show()

Performance Metrics

mae = np.mean(np.abs(preds - test_scaled))
rmse = np.sqrt(np.mean((preds - test_scaled) ** 2))
r_squared = 1 - np.sum((preds - test_scaled) ** 2) / np.sum((test_scaled - np.mean(test_scaled)) ** 2)

print(f"Mean Absolute Error (MAE): {mae:.2f}")
print(f"Root Mean Square Error (RMSE): {rmse:.2f}")
print(f"R-squared: {r_squared:.2f}")

Start with the pasted Python script and the AutoBnnMapEstimator configuration, then reproduce the reported metric values using the included scaling, likelihood, periods, and prediction calls. Compare the scaled predictions and targets with the metric calculation to determine whether the issue is in model output or evaluation. Done means a clearly identified cause and a reproducible correction or explanation.