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Add serialization for LogisticRegression #1

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merged 1 commit into from
Oct 6, 2022
Merged

Add serialization for LogisticRegression #1

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1 change: 1 addition & 0 deletions algorithms/linfa-logistic/Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -26,3 +26,4 @@ linfa = { version = "0.6.0", path = "../..", features=["serde"] }
[dev-dependencies]
approx = "0.4"
linfa-datasets = { version = "0.6.0", path = "../../datasets", features = ["winequality"] }
rmp-serde = "1"
22 changes: 16 additions & 6 deletions algorithms/linfa-logistic/src/lib.rs
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Expand Up @@ -30,6 +30,7 @@ use ndarray::{
Dimension, IntoDimension, Ix1, Ix2, RemoveAxis, Slice, Zip,
};
use ndarray_stats::QuantileExt;
use serde::{Deserialize, Serialize};
use std::default::Default;

mod argmin_param;
Expand Down Expand Up @@ -524,8 +525,8 @@ fn multi_logistic_grad<F: Float, A: Data<Elem = F>>(
}

/// A fitted logistic regression which can make predictions
#[derive(PartialEq, Debug, Clone)]
pub struct FittedLogisticRegression<F: Float, C: PartialOrd + Clone> {
#[derive(PartialEq, Debug, Clone, Serialize, Deserialize)]
pub struct FittedLogisticRegression<F, C: PartialOrd + Clone> {
threshold: F,
intercept: F,
params: Array1<F>,
Expand Down Expand Up @@ -610,8 +611,8 @@ impl<C: PartialOrd + Clone + Default, F: Float, D: Data<Elem = F>>
}

/// A fitted multinomial logistic regression which can make predictions
#[derive(PartialEq, Debug, Clone)]
pub struct MultiFittedLogisticRegression<F: Float, C: PartialOrd + Clone> {
#[derive(PartialEq, Debug, Clone, Serialize, Deserialize)]
pub struct MultiFittedLogisticRegression<F, C: PartialOrd + Clone> {
intercept: Array1<F>,
params: Array2<F>,
classes: Vec<C>,
Expand Down Expand Up @@ -685,8 +686,8 @@ impl<C: PartialOrd + Clone + Default, F: Float, D: Data<Elem = F>>
}
}

#[derive(PartialEq, Debug, Clone)]
struct ClassLabel<F: Float, C: PartialOrd> {
#[derive(PartialEq, Debug, Clone, Serialize, Deserialize)]
struct ClassLabel<F, C: PartialOrd> {
class: C,
label: F,
}
Expand Down Expand Up @@ -1066,6 +1067,15 @@ mod test {
&res.predict(dataset.records()),
dataset.targets().as_single_targets()
);

// Test serialization
let ser = rmp_serde::to_vec(&res).unwrap();
let unser: FittedLogisticRegression<f32, f32> = rmp_serde::from_slice(&ser).unwrap();

let x = array![[1.0]];
let y_hat = unser.predict(&x);

assert!(y_hat[0] == 0.0);
}

#[test]
Expand Down