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Refactor moe::predict_smooth #221

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merged 2 commits into from
Dec 12, 2024
Merged

Refactor moe::predict_smooth #221

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113a85d
Refactor predict_smooth in moe
relf Dec 12, 2024
783f561
Cleanup
relf Dec 12, 2024
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5 changes: 3 additions & 2 deletions ego/examples/mopta08.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -191,9 +191,10 @@ fn mopta(x: &ArrayView2<f64>, indices: Option<&[usize]>) -> Array2<f64> {
path_exe.push(r"ego/examples");
path_exe.push(mopta_exe);

Command::new(path_exe)
let _ = Command::new(path_exe)
.spawn()
.expect("ls command failed to start");
.expect("ls command failed to start")
.wait();

std::thread::sleep(std::time::Duration::from_secs(1));
let y_i = get_output().unwrap();
Expand Down
3 changes: 0 additions & 3 deletions gp/src/algorithm.rs
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Expand Up @@ -1574,9 +1574,6 @@ mod tests {
let _gp = Kriging::params()
.fit(&Dataset::new(xt.clone(), yt.clone()))
.expect("GP fit error");
// println!("theta = {}", gp.theta());
// let xtest = array![[0.1]];
// println!("pred({}) = {}", &xtest, gp.predict(&xtest).unwrap());
}

fn x2sinx(x: &Array2<f64>) -> Array2<f64> {
Expand Down
59 changes: 26 additions & 33 deletions moe/src/algorithm.rs
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Expand Up @@ -346,7 +346,7 @@ impl GpMixtureValidParams<f64> {
let errors = scale_factors.map(move |&factor| {
let gmx2 = gmx.clone();
let gmx2 = gmx2.heaviside_factor(factor);
let pred = predict_smooth(experts, &gmx2, xtest).unwrap();
let pred = predict_smooth(experts, &gmx2, xtest, ytest.ncols()).unwrap();
pred.sub(ytest).mapv(|x| x * x).sum().sqrt() / xtest.mapv(|x| x * x).sum().sqrt()
});

Expand Down Expand Up @@ -387,22 +387,17 @@ fn predict_smooth(
experts: &[Box<dyn FullGpSurrogate>],
gmx: &GaussianMixture<f64>,
points: &ArrayBase<impl Data<Elem = f64>, Ix2>,
ny: usize,
) -> Result<Array2<f64>> {
let probas = gmx.predict_probas(points);
let mut preds = Array1::<f64>::zeros(points.nrows());

Zip::from(&mut preds)
.and(points.rows())
.and(probas.rows())
.for_each(|y, x, p| {
let x = x.insert_axis(Axis(0));
let preds: Array1<f64> = experts
.iter()
.map(|gp| gp.predict(&x).unwrap()[[0, 0]])
.collect();
*y = (preds * p).sum();
});
Ok(preds.insert_axis(Axis(1)))
let preds: Array2<f64> = experts
.iter()
.enumerate()
.map(|(i, gp)| {
gp.predict(&points.view()).unwrap() * probas.column(i).to_owned().insert_axis(Axis(1))
})
.fold(Array2::zeros((points.nrows(), ny)), |acc, pred| acc + pred);
Ok(preds)
}

/// Mixture of gaussian process experts
Expand Down Expand Up @@ -597,7 +592,7 @@ impl GpMixture {
/// or another (ie responsabilities).
/// The smooth recombination of each cluster expert responsabilty is used to get the result.
pub fn predict_smooth(&self, x: &ArrayBase<impl Data<Elem = f64>, Ix2>) -> Result<Array2<f64>> {
predict_smooth(&self.experts, &self.gmx, x)
predict_smooth(&self.experts, &self.gmx, x, self.output_dim())
}

/// Predict variances at a set of points `x` specified as (n, nx) matrix.
Expand All @@ -609,21 +604,19 @@ impl GpMixture {
x: &ArrayBase<impl Data<Elem = f64>, Ix2>,
) -> Result<Array2<f64>> {
let probas = self.gmx.predict_probas(x);
let mut preds = Array1::<f64>::zeros(x.nrows());

Zip::from(&mut preds)
.and(x.rows())
.and(probas.rows())
.for_each(|y, x, p| {
let x = x.insert_axis(Axis(0));
let preds: Array1<f64> = self
.experts
.iter()
.map(|gp| gp.predict_var(&x).unwrap()[[0, 0]])
.collect();
*y = (preds * p * p).sum();
});
Ok(preds.insert_axis(Axis(1)))
let preds: Array2<f64> = self
.experts
.iter()
.enumerate()
.map(|(i, gp)| {
let p = probas.column(i).to_owned().insert_axis(Axis(1));
gp.predict_var(&x.view()).unwrap() * &p * &p
})
.fold(
Array2::zeros((x.nrows(), self.output_dim())),
|acc, pred| acc + pred,
);
Ok(preds)
}

/// Predict derivatives of the output at a set of points `x` specified as (n, nx) matrix.
Expand Down Expand Up @@ -731,7 +724,7 @@ impl GpMixture {
pub fn predict_hard(&self, x: &ArrayBase<impl Data<Elem = f64>, Ix2>) -> Result<Array2<f64>> {
let clustering = self.gmx.predict(x);
trace!("Clustering {:?}", clustering);
let mut preds = Array2::zeros((x.nrows(), 1));
let mut preds = Array2::zeros((x.nrows(), self.output_dim()));
Zip::from(preds.rows_mut())
.and(x.rows())
.and(&clustering)
Expand All @@ -756,7 +749,7 @@ impl GpMixture {
) -> Result<Array2<f64>> {
let clustering = self.gmx.predict(x);
trace!("Clustering {:?}", clustering);
let mut variances = Array2::zeros((x.nrows(), 1));
let mut variances = Array2::zeros((x.nrows(), self.output_dim()));
Zip::from(variances.rows_mut())
.and(x.rows())
.and(&clustering)
Expand Down
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