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docs/framework/operators/machine-learning/tree-ensemble-classifier/README.md
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| # Tree Ensemble Classifier | ||
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| `TreeEnsembleClassifierTrait` provides a trait definition for tree ensemble classification problem. | ||
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| ```rust | ||
| use orion::operators::ml::TreeEnsembleClassifierTrait; | ||
| ``` | ||
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| ### Data types | ||
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| Orion supports currently only fixed point data types for `TreeEnsembleClassifierTrait`. | ||
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| | Data type | dtype | | ||
| | -------------------- | ------------------------------------------------------------- | | ||
| | Fixed point (signed) | `TreeRegressorTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` | | ||
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| ### How to construct `TreeEnsembleClassifier` | ||
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| You can utilize [this notebook](https://colab.research.google.com/drive/1qem56rUKJcNongXsLZ16_869q8395prz#scrollTo=V3qGW_kfXudk) to translate parameters from your ONNX TreeEnsembleClassifier model into Cairo code. Efforts are underway to integrate this functionality into Giza-CLI, aiming to enhance the user experience. | ||
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| *** | ||
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| | function | description | | ||
| | --- | --- | | ||
| | [`tree_ensemble_classifier.predict`](tree_ensemble_classifier.predict.md) | Returns the top class for each of N inputs. | | ||
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...s/machine-learning/tree-ensemble-classifier/tree_ensemble_classifier.predict.md
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| # TreeEnsembleClassifier::predict | ||
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| ```rust | ||
| fn predict(ref self: TreeEnsembleClassifier<T>, X: Tensor<T>) -> (Span<usize>, MutMatrix::<T>); | ||
| ``` | ||
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| Tree Ensemble classifier. Returns the top class for each of N inputs. | ||
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| ## Args | ||
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| * `self`: TreeEnsembleClassifier<T> - A TreeEnsembleClassifier object. | ||
| * `X`: Input 2D tensor. | ||
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| ## Returns | ||
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| * N Top class for each point | ||
| * The class score Matrix for each class, for each point. | ||
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| ## Type Constraints | ||
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| `TreeEnsembleClassifier` and `X` must be fixed points | ||
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| ## Examples | ||
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| ```rust | ||
| 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}; | ||
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| 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(); | ||
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| 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(); | ||
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| 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(); | ||
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| 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(); | ||
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| let classlabels: Span<usize> = array![0, 1, 2].span(); | ||
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| let nodes_falsenodeids: Span<usize> = array![4, 3, 0, 0, 0, 2, 0, 4, 0, 0].span(); | ||
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| let nodes_featureids: Span<usize> = array![1, 0, 0, 0, 0, 1, 0, 0, 0, 0].span(); | ||
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| let nodes_missing_value_tracks_true: Span<usize> = array![0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(); | ||
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| 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(); | ||
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| let nodes_nodeids: Span<usize> = array![0, 1, 2, 3, 4, 0, 1, 2, 3, 4].span(); | ||
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| let nodes_treeids: Span<usize> = array![0, 0, 0, 0, 0, 1, 1, 1, 1, 1].span(); | ||
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| let nodes_truenodeids: Span<usize> = array![1, 2, 0, 0, 0, 1, 0, 3, 0, 0].span(); | ||
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| 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(); | ||
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| let tree_ids: Span<usize> = array![0, 1].span(); | ||
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| let mut root_index: Felt252Dict<usize> = Default::default(); | ||
| root_index.insert(0, 0); | ||
| root_index.insert(1, 5); | ||
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| 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); | ||
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| let atts = TreeEnsembleAttributes { | ||
| nodes_falsenodeids, | ||
| nodes_featureids, | ||
| nodes_missing_value_tracks_true, | ||
| nodes_modes, | ||
| nodes_nodeids, | ||
| nodes_treeids, | ||
| nodes_truenodeids, | ||
| nodes_values | ||
| }; | ||
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| let mut ensemble: TreeEnsemble<FP16x16> = TreeEnsemble { | ||
| atts, tree_ids, root_index, node_index | ||
| }; | ||
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| let base_values: Option<Span<FP16x16>> = Option::None; | ||
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| let mut classifier: TreeEnsembleClassifier<FP16x16> = TreeEnsembleClassifier { | ||
| ensemble, | ||
| class_ids, | ||
| class_nodeids, | ||
| class_treeids, | ||
| class_weights, | ||
| classlabels, | ||
| base_values, | ||
| post_transform | ||
| }; | ||
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| 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() | ||
| ); | ||
|
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| (classifier, X) | ||
| } | ||
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| fn test_tree_ensemble_classifier_multi_pt_softmax() -> (Span<usize>, MutMatrix::<FP16x16>) { | ||
| let (mut classifier, X) = tree_ensemble_classifier_helper(POST_TRANSFORM::SOFTMAX); | ||
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| let (labels, scores) = TreeEnsembleClassifierTrait::predict(ref classifier, X); | ||
| (labels, scores) | ||
| } | ||
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| >>> | ||
| ([0, 0, 1], | ||
| [ | ||
| [0.545123, 0.217967, 0.23691], | ||
| [0.416047, 0.284965, 0.298988], | ||
| [0.322535, 0.366664, 0.310801], | ||
| ]) | ||
| ``` |
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