tree_ensemble_classifier.predict

   fn predict(classifier: TreeEnsembleClassifier<T>, X: Tensor<T>) -> (Span<usize>, MutMatrix::<T>);

Tree Ensemble classifier. Returns the top class for each of N inputs.

Args

• self: TreeEnsembleClassifier - A TreeEnsembleClassifier object.

• X: Input 2D tensor.

Returns

• N Top class for each point

• The class score Matrix for each class, for each point.

Type Constraints

TreeEnsembleClassifier and X must be fixed points

Examples

use orion::numbers::FP16x16;
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
use orion::operators::ml::{NODE_MODES, TreeEnsembleAttributes, TreeEnsemble};
use orion::operators::ml::{
    TreeEnsembleClassifier, POST_TRANSFORM, TreeEnsembleClassifierTrait
};
use orion::operators::matrix::{MutMatrix, MutMatrixImpl};

fn tree_ensemble_classifier_helper(
   post_transform: POST_TRANSFORM
) -> (TreeEnsembleClassifier<FP16x16>, Tensor<FP16x16>) {
   let class_ids: Span<usize> = array![0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]
       .span();

   let class_nodeids: Span<usize> = array![2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 3, 3, 3, 4, 4, 4]
       .span();

   let class_treeids: Span<usize> = array![0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
       .span();

   let class_weights: Span<FP16x16> = array![
       FP16x16 { mag: 30583, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 2185, sign: false },
       FP16x16 { mag: 13107, sign: false },
       FP16x16 { mag: 15729, sign: false },
       FP16x16 { mag: 3932, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 32768, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 32768, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 29491, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 3277, sign: false },
       FP16x16 { mag: 6746, sign: false },
       FP16x16 { mag: 12529, sign: false },
       FP16x16 { mag: 13493, sign: false },
   ]
       .span();

   let classlabels: Span<usize> = array![0, 1, 2].span();

   let nodes_falsenodeids: Span<usize> = array![4, 3, 0, 0, 0, 2, 0, 4, 0, 0].span();

   let nodes_featureids: Span<usize> = array![1, 0, 0, 0, 0, 1, 0, 0, 0, 0].span();

   let nodes_missing_value_tracks_true: Span<usize> = array![0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span();

   let nodes_modes: Span<NODE_MODES> = array![
       NODE_MODES::BRANCH_LEQ,
       NODE_MODES::BRANCH_LEQ,
       NODE_MODES::LEAF,
       NODE_MODES::LEAF,
       NODE_MODES::LEAF,
       NODE_MODES::BRANCH_LEQ,
       NODE_MODES::LEAF,
       NODE_MODES::BRANCH_LEQ,
       NODE_MODES::LEAF,
       NODE_MODES::LEAF,
   ]
       .span();

   let nodes_nodeids: Span<usize> = array![0, 1, 2, 3, 4, 0, 1, 2, 3, 4].span();

   let nodes_treeids: Span<usize> = array![0, 0, 0, 0, 0, 1, 1, 1, 1, 1].span();

   let nodes_truenodeids: Span<usize> = array![1, 2, 0, 0, 0, 1, 0, 3, 0, 0].span();

   let nodes_values: Span<FP16x16> = array![
       FP16x16 { mag: 81892, sign: false },
       FP16x16 { mag: 19992, sign: true },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 110300, sign: true },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 44245, sign: true },
       FP16x16 { mag: 0, sign: false },
       FP16x16 { mag: 0, sign: false },
   ]
       .span();

   let tree_ids: Span<usize> = array![0, 1].span();

   let mut root_index: Felt252Dict<usize> = Default::default();
   root_index.insert(0, 0);
   root_index.insert(1, 5);

   let mut node_index: Felt252Dict<usize> = Default::default();
   node_index
       .insert(2089986280348253421170679821480865132823066470938446095505822317253594081284, 0);
   node_index
       .insert(2001140082530619239661729809084578298299223810202097622761632384561112390979, 1);
   node_index
       .insert(2592670241084192212354027440049085852792506518781954896144296316131790403900, 2);
   node_index
       .insert(2960591271376829378356567803618548672034867345123727178628869426548453833420, 3);
   node_index
       .insert(458933264452572171106695256465341160654132084710250671055261382009315664425, 4);
   node_index
       .insert(1089549915800264549621536909767699778745926517555586332772759280702396009108, 5);
   node_index
       .insert(1321142004022994845681377299801403567378503530250467610343381590909832171180, 6);
   node_index
       .insert(2592987851775965742543459319508348457290966253241455514226127639100457844774, 7);
   node_index
       .insert(2492755623019086109032247218615964389726368532160653497039005814484393419348, 8);
   node_index
       .insert(1323616023845704258113538348000047149470450086307731200728039607710316625916, 9);

   let atts = TreeEnsembleAttributes {
       nodes_falsenodeids,
       nodes_featureids,
       nodes_missing_value_tracks_true,
       nodes_modes,
       nodes_nodeids,
       nodes_treeids,
       nodes_truenodeids,
       nodes_values
   };

   let mut ensemble: TreeEnsemble<FP16x16> = TreeEnsemble {
       atts, tree_ids, root_index, node_index
   };

   let base_values: Option<Span<FP16x16>> = Option::None;

   let mut classifier: TreeEnsembleClassifier<FP16x16> = TreeEnsembleClassifier {
       ensemble,
       class_ids,
       class_nodeids,
       class_treeids,
       class_weights,
       classlabels,
       base_values,
       post_transform
   };

   let mut X: Tensor<FP16x16> = TensorTrait::new(
       array![3, 3].span(),
       array![
           FP16x16 { mag: 65536, sign: true },
           FP16x16 { mag: 52429, sign: true },
           FP16x16 { mag: 39322, sign: true },
           FP16x16 { mag: 26214, sign: true },
           FP16x16 { mag: 13107, sign: true },
           FP16x16 { mag: 0, sign: false },
           FP16x16 { mag: 13107, sign: false },
           FP16x16 { mag: 26214, sign: false },
           FP16x16 { mag: 39322, sign: false },
       ]
           .span()
   );

   (classifier, X)
}

fn test_tree_ensemble_classifier_multi_pt_softmax() -> (Span<usize>, MutMatrix::<FP16x16>) {
    let (mut classifier, X) = tree_ensemble_classifier_helper(POST_TRANSFORM::SOFTMAX);

    let (labels, scores) = TreeEnsembleClassifierTrait::predict(classifier, X);
    (labels, scores)
}   

>>> 
([0, 0, 1],
 [
   [0.545123, 0.217967, 0.23691],
   [0.416047, 0.284965, 0.298988],
   [0.322535, 0.366664, 0.310801],
  ])