Add power transformation logic to forecaster transforms

crates/augurs-forecaster/Cargo.toml

@@ -13,12 +13,13 @@ description = "A high-level API for the augurs forecasting library."
 bench = false
 
 [dependencies]
+argmin = "0.10.0"
 augurs-core.workspace = true
 itertools.workspace = true
 thiserror.workspace = true
 
 [dev-dependencies]
-augurs.workspace = true
+augurs = { workspace = true, features = ["mstl", "ets", "forecaster"]}
 augurs-testing.workspace = true
 
 [lints]

crates/augurs-forecaster/src/forecaster.rs

@@ -124,4 +124,42 @@ mod test {
         let forecasts = forecaster.predict(4, None).unwrap();
         assert_all_approx_eq(&forecasts.point, &[5.0, 5.0, 5.0, 5.0]);
     }
+
+    #[test]
+    fn test_forecaster_power_positive() {
+        let data = &[1.0_f64, 2.0, 3.0, 4.0, 5.0];
+        let transforms = vec![Transform::power_transform(data)];
+        let model = MSTLModel::new(vec![2], NaiveTrend::new());
+        let mut forecaster = Forecaster::new(model).with_transforms(transforms);
+        forecaster.fit(data).unwrap();
+        let forecasts = forecaster.predict(4, None).unwrap();
+        assert_all_approx_eq(
+            &forecasts.point,
+            &[
+                5.084499064884572,
+                5.000000030329821,
+                5.084499064884572,
+                5.000000030329821,
+            ],
+        );
+    }
+
+    #[test]
+    fn test_forecaster_power_non_positive() {
+        let data = &[0.0, 2.0, 3.0, 4.0, 5.0];
+        let transforms = vec![Transform::power_transform(data)];
+        let model = MSTLModel::new(vec![2], NaiveTrend::new());
+        let mut forecaster = Forecaster::new(model).with_transforms(transforms);
+        forecaster.fit(data).unwrap();
+        let forecasts = forecaster.predict(4, None).unwrap();
+        assert_all_approx_eq(
+            &forecasts.point,
+            &[
+                5.205557727170964,
+                5.000000132803496,
+                5.205557727170964,
+                5.000000132803496,
+            ],
+        );
+    }
 }

crates/augurs-forecaster/src/lib.rs

@@ -3,6 +3,7 @@
 mod data;
 mod error;
 mod forecaster;
+mod power_transforms;
 pub mod transforms;
 
 pub use data::Data;

crates/augurs-forecaster/src/power_transforms.rs

@@ -0,0 +1,221 @@
+use crate::transforms::box_cox;
+use crate::transforms::yeo_johnson;
+use argmin::core::*;
+use argmin::solver::brent::BrentOpt;
+
+fn box_cox_log_likelihood(data: &[f64], lambda: f64) -> Result<f64, Error> {
+    let n = data.len() as f64;
+    if n == 0.0 {
+        return Err(Error::msg("Data must not be empty"));
+    }
+    if data.iter().any(|&x| x <= 0.0) {
+        return Err(Error::msg("All data must be greater than 0"));
+    }
+    let transformed_data: Result<Vec<f64>, _> = data.iter().map(|&x| box_cox(x, lambda)).collect();
+
+    let transformed_data = match transformed_data {
+        Ok(values) => values,
+        Err(e) => return Err(Error::msg(e)),
+    };
+    let mean_transformed: f64 = transformed_data.iter().copied().sum::<f64>() / n;
+    let variance: f64 = transformed_data
+        .iter()
+        .map(|&x| (x - mean_transformed).powi(2))
+        .sum::<f64>()
+        / n;
+
+    // Avoid log(0) by ensuring variance is positive
+    if variance <= 0.0 {
+        return Err(Error::msg("Variance must be positive"));
+    }
+    let log_likelihood =
+        -0.5 * n * variance.ln() + (lambda - 1.0) * data.iter().map(|&x| x.ln()).sum::<f64>();
+    Ok(log_likelihood)
+}
+
+fn yeo_johnson_log_likelihood(data: &[f64], lambda: f64) -> Result<f64, Error> {
+    let n = data.len() as f64;
+
+    if n == 0.0 {
+        return Err(Error::msg("Data array is empty"));
+    }
+
+    let transformed_data: Result<Vec<f64>, _> =
+        data.iter().map(|&x| yeo_johnson(x, lambda)).collect();
+
+    let transformed_data = match transformed_data {
+        Ok(values) => values,
+        Err(e) => return Err(Error::msg(e)),
+    };
+
+    let mean = transformed_data.iter().sum::<f64>() / n;
+
+    let variance = transformed_data
+        .iter()
+        .map(|&x| (x - mean).powi(2))
+        .sum::<f64>()
+        / n;
+
+    if variance <= 0.0 {
+        return Err(Error::msg("Variance is non-positive"));
+    }
+
+    let log_sigma_squared = variance.ln();
+    let log_likelihood = -n / 2.0 * log_sigma_squared;
+
+    let additional_term: f64 = data
+        .iter()
+        .map(|&x| (x.signum() * (x.abs() + 1.0).ln()))
+        .sum::<f64>()
+        * (lambda - 1.0);
+
+    Ok(log_likelihood + additional_term)
+}
+
+#[derive(Clone)]
+struct BoxCoxProblem<'a> {
+    data: &'a [f64],
+}
+
+impl CostFunction for BoxCoxProblem<'_> {
+    type Param = f64;
+    type Output = f64;
+
+    // The goal is to minimize the negative log-likelihood
+    fn cost(&self, lambda: &Self::Param) -> Result<Self::Output, Error> {
+        box_cox_log_likelihood(self.data, *lambda).map(|ll| -ll)
+    }
+}
+
+#[derive(Clone)]
+struct YeoJohnsonProblem<'a> {
+    data: &'a [f64],
+}
+
+impl CostFunction for YeoJohnsonProblem<'_> {
+    type Param = f64;
+    type Output = f64;
+
+    // The goal is to minimize the negative log-likelihood
+    fn cost(&self, lambda: &Self::Param) -> Result<Self::Output, Error> {
+        yeo_johnson_log_likelihood(self.data, *lambda).map(|ll| -ll)
+    }
+}
+
+struct OptimizationParams {
+    initial_param: f64,
+    lower_bound: f64,
+    upper_bound: f64,
+    max_iterations: u64,
+}
+
+fn optimize_lambda<T: CostFunction<Param = f64, Output = f64>>(
+    cost: T,
+    params: OptimizationParams,
+) -> Result<f64, Error> {
+    let solver = BrentOpt::new(params.lower_bound, params.upper_bound);
+    let result = Executor::new(cost, solver)
+        .configure(|state| {
+            state
+                .param(params.initial_param)
+                .max_iters(params.max_iterations)
+        })
+        .run();
+
+    result.and_then(|res| {
+        res.state()
+            .best_param
+            .ok_or_else(|| Error::msg("No best parameter found"))
+    })
+}
+
+/// Optimize the lambda parameter for the Box-Cox or Yeo-Johnson transformation
+pub(crate) fn optimize_box_cox_lambda(data: &[f64]) -> Result<f64, Error> {
+    // Use Box-Cox transformation
+    let cost = BoxCoxProblem { data };
+    let optimization_params = OptimizationParams {
+        initial_param: 0.0,
+        lower_bound: -2.0,
+        upper_bound: 2.0,
+        max_iterations: 1000,
+    };
+    optimize_lambda(cost, optimization_params)
+}
+
+pub(crate) fn optimize_yeo_johnson_lambda(data: &[f64]) -> Result<f64, Error> {
+    // Use Yeo-Johnson transformation
+    let cost = YeoJohnsonProblem { data };
+    let optimization_params = OptimizationParams {
+        initial_param: 0.0,
+        lower_bound: -2.0,
+        upper_bound: 2.0,
+        max_iterations: 1000,
+    };
+    optimize_lambda(cost, optimization_params)
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+    use augurs_testing::assert_approx_eq;
+
+    #[test]
+    fn correct_optimal_box_cox_lambda() {
+        let data = &[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7];
+        let got = optimize_box_cox_lambda(data);
+        assert!(got.is_ok());
+        let lambda = got.unwrap();
+        assert_approx_eq!(lambda, 0.7123778635679304);
+    }
+
+    #[test]
+    fn optimal_box_cox_lambda_lambda_empty_data() {
+        let data = &[];
+        let got = optimize_box_cox_lambda(data);
+        assert!(got.is_err());
+    }
+
+    #[test]
+    fn optimal_box_cox_lambda_non_positive_data() {
+        let data = &[0.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7];
+        let got = optimize_box_cox_lambda(data);
+        assert!(got.is_err());
+    }
+
+    #[test]
+    fn correct_optimal_yeo_johnson_lambda() {
+        let data = &[0.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7];
+        let got = optimize_yeo_johnson_lambda(data);
+        assert!(got.is_ok());
+        let lambda = got.unwrap();
+        assert_approx_eq!(lambda, 1.7458442076987954);
+    }
+
+    #[test]
+    fn test_box_cox_llf() {
+        let data = &[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7];
+        let lambda = 1.0;
+        let got = box_cox_log_likelihood(data, lambda);
+        assert!(got.is_ok());
+        let llf = got.unwrap();
+        assert_approx_eq!(llf, 11.266065387038703);
+    }
+
+    #[test]
+    fn test_box_cox_llf_non_positive() {
+        let data = &[0.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7];
+        let lambda = 0.0;
+        let got = box_cox_log_likelihood(data, lambda);
+        assert!(got.is_err());
+    }
+
+    #[test]
+    fn test_yeo_johnson_llf() {
+        let data = &[0.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7];
+        let lambda = 1.0;
+        let got = yeo_johnson_log_likelihood(data, lambda);
+        assert!(got.is_ok());
+        let llf = got.unwrap();
+        assert_approx_eq!(llf, 10.499377905819307);
+    }
+}