Adding Multi-Task ElasticNet support (#238)

* added block coordinate descent function

* added duality_gap_mtl computation

* ENH cd pass to be consistent with bcd

* added prox operator for MTL Enet

* added helper functions for tests

* working ent mtl penalties

* bcd lower objective test pass

* added MultiTaskEnet struct

* added MTENET documentation

* added API MTENET

* added variance, z-score, conf interval for multitask ENET

* added multi-task estimators

* added tests for MTL

* added tests for Enet and MTL

* WIP: made variance params generic over the number of tasks

* added z_score and confidence_95th for MTL

* WIP make compute_variance generic over the dimension

* Replace for loops in block_coordinate_descent with general_mat_mul calls

* Bring back generic compute_intercept

* Replace manual norm calculations with norm trait calls

* Add docs and derives to multi task types

* Add example for multitask_elasticnet

* Rename shape() calls to nrows and ncols

Co-authored-by: Pierre-Antoine Bannier <pierreantoine.bannier@gmail.com>

algorithms/linfa-elasticnet/Cargo.toml

@@ -40,6 +40,6 @@ thiserror = "1.0"
 linfa = { version = "0.6.0", path = "../.." }
 
 [dev-dependencies]
-linfa-datasets = { version = "0.6.0", path = "../../datasets", features = ["diabetes"] }
+linfa-datasets = { version = "0.6.0", path = "../../datasets", features = ["diabetes", "linnerud"] }
 ndarray-rand = "0.14"
 rand_xoshiro = "0.6"

algorithms/linfa-elasticnet/examples/elasticnet.rs

@@ -5,7 +5,7 @@ fn main() -> Result<()> {
     // load Diabetes dataset
     let (train, valid) = linfa_datasets::diabetes().split_with_ratio(0.90);
 
-    // train pure LASSO model with 0.1 penalty
+    // train pure LASSO model with 0.3 penalty
     let model = ElasticNet::params()
         .penalty(0.3)
         .l1_ratio(1.0)

algorithms/linfa-elasticnet/examples/multitask_elasticnet.rs

@@ -0,0 +1,24 @@
+use linfa::prelude::*;
+use linfa_elasticnet::{MultiTaskElasticNet, Result};
+
+fn main() -> Result<()> {
+    // load Diabetes dataset
+    let (train, valid) = linfa_datasets::linnerud().split_with_ratio(0.80);
+
+    // train pure LASSO model with 0.1 penalty
+    let model = MultiTaskElasticNet::params()
+        .penalty(0.1)
+        .l1_ratio(1.0)
+        .fit(&train)?;
+
+    println!("intercept:  {}", model.intercept());
+    println!("params: {}", model.hyperplane());
+
+    println!("z score: {:?}", model.z_score());
+
+    // validate
+    let y_est = model.predict(&valid);
+    println!("predicted variance: {}", y_est.r2(&valid)?);
+
+    Ok(())
+}