Merge branch 'develop' into all-contributors/add-Dl-Vv

.all-contributorsrc

@@ -152,6 +152,33 @@
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "chachaleo",
+      "name": "Charlotte",
+      "avatar_url": "https://avatars.githubusercontent.com/u/49371958?v=4",
+      "profile": "https://github.com/chachaleo",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "0xfulanito",
+      "name": "0xfulanito",
+      "avatar_url": "https://avatars.githubusercontent.com/u/145947367?v=4",
+      "profile": "https://github.com/0xfulanito",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "0x73e",
+      "name": "0x73e",
+      "avatar_url": "https://avatars.githubusercontent.com/u/132935850?v=4",
+      "profile": "https://github.com/0x73e",
+      "contributions": [
+        "code"
+      ]
     }
   ],
   "contributorsPerLine": 7,

README.md

@@ -23,7 +23,7 @@
 
 # Orion: An Open-source Framework for Validity and ZK ML ✨
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-16-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-19-orange.svg?style=flat-square)](#contributors-)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 Orion is an open-source, community-driven framework dedicated to Provable Machine Learning. It provides essential components and a new ONNX runtime for building verifiable Machine Learning models using [STARKs](https://starkware.co/stark/).
@@ -89,6 +89,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/vikkydataseo"><img src="https://avatars.githubusercontent.com/u/65757691?v=4?s=100" width="100px;" alt="vikkydataseo"/><br /><sub><b>vikkydataseo</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=vikkydataseo" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://www.brilliantblocks.io/"><img src="https://avatars.githubusercontent.com/u/83556514?v=4?s=100" width="100px;" alt="Daniel"/><br /><sub><b>Daniel</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=Dl-Vv" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/chachaleo"><img src="https://avatars.githubusercontent.com/u/49371958?v=4?s=100" width="100px;" alt="Charlotte"/><br /><sub><b>Charlotte</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=chachaleo" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/0xfulanito"><img src="https://avatars.githubusercontent.com/u/145947367?v=4?s=100" width="100px;" alt="0xfulanito"/><br /><sub><b>0xfulanito</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=0xfulanito" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/0x73e"><img src="https://avatars.githubusercontent.com/u/132935850?v=4?s=100" width="100px;" alt="0x73e"/><br /><sub><b>0x73e</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=0x73e" title="Code">💻</a></td>
     </tr>
   </tbody>
 </table>

docgen/src/main.rs

@@ -42,6 +42,22 @@ fn main() {
     let trait_name: &str = "TreeRegressorTrait";
     doc_trait(trait_path, doc_path, label);
     doc_functions(trait_path, doc_path, trait_name, label);
+
+    // TREE ClASSIFIER DOC
+    let trait_path = "src/operators/ml/tree_classifier/core.cairo";
+    let doc_path = "docs/framework/operators/machine-learning/tree-classifier";
+    let label = "tree";
+    let trait_name: &str = "TreeClassifierTrait";
+    doc_trait(trait_path, doc_path, label);
+    doc_functions(trait_path, doc_path, trait_name, label);
+
+    // XGBOOST REGRESSOR DOC
+    let trait_path = "src/operators/ml/xgboost_regressor/core.cairo";
+    let doc_path = "docs/framework/operators/machine-learning/xgboost-regressor";
+    let label = "xgboost";
+    let trait_name: &str = "XGBoostRegressorTrait";
+    doc_trait(trait_path, doc_path, label);
+    doc_functions(trait_path, doc_path, trait_name, label);
 }
 
 fn doc_trait(trait_path: &str, doc_path: &str, label: &str) {

docs/SUMMARY.md

@@ -59,6 +59,7 @@
     * [tensor.less](framework/operators/tensor/tensor.less.md)
     * [tensor.less\_equal](framework/operators/tensor/tensor.less\_equal.md)
     * [tensor.abs](framework/operators/tensor/tensor.abs.md)
+    * [tensor.neg](framework/operators/tensor/tensor.neg.md)
     * [tensor.ceil](framework/operators/tensor/tensor.ceil.md)
     * [tensor.cumsum](framework/operators/tensor/tensor.cumsum.md)
     * [tensor.sin](framework/operators/tensor/tensor.sin.md)
@@ -86,6 +87,7 @@
     * [tensor.unsqueeze](framework/operators/tensor/tensor.unsqueeze.md)
     * [tensor.sign](framework/operators/tensor/tensor.sign.md)
     * [tensor.clip](framework/operators/tensor/tensor.clip.md)
+    * [tensor.identity](framework/operators/tensor/tensor.identity.md)
   * [Neural Network](framework/operators/neural-network/README.md)
     * [nn.relu](framework/operators/neural-network/nn.relu.md)
     * [nn.leaky\_relu](framework/operators/neural-network/nn.leaky\_relu.md)
@@ -95,10 +97,16 @@
     * [nn.softsign](framework/operators/neural-network/nn.softsign.md)
     * [nn.softplus](framework/operators/neural-network/nn.softplus.md)
     * [nn.linear](framework/operators/neural-network/nn.linear.md)
+    * [nn.hard\_sigmoid](framework/operators/neural-network/nn.hard\_sigmoid.md)
+    * [nn.thresholded\_relu](framework/operators/neural-network/nn.thresholded_relu.md)
   * [Machine Learning](framework/operators/machine-learning/README.md)
     * [Tree Regressor](framework/operators/machine-learning/tree-regressor/README.md)
-      * [tree.fit](framework/operators/machine-learning/tree-regressor/tree.fit.md)
       * [tree.predict](framework/operators/machine-learning/tree-regressor/tree.predict.md)
+    * [Tree Classifier](framework/operators/machine-learning/tree-classifier/README.md)
+      * [tree.predict](framework/operators/machine-learning/tree-classifier/tree.predict.md)
+      * [tree.predict_proba](framework/operators/machine-learning/tree-classifier/tree.predict_proba.md)
+    * [XGBoost Regressor](framework/operators/machine-learning/xgboost-regressor/README.md)
+      * [xgboost.predict](framework/operators/machine-learning/xgboost-regressor/xgboost.predict.md)
 
 ## 🏛 Hub
 

docs/framework/compatibility.md

@@ -18,6 +18,7 @@ You can see below the list of current supported ONNX Operators:
 |             [Less](operators/tensor/tensor.less.md)             | :white\_check\_mark: |
 |      [LessOrEqual](operators/tensor/tensor.less\_equal.md)      | :white\_check\_mark: |
 |              [Abs](operators/tensor/tensor.abs.md)              | :white\_check\_mark: |
+|              [Neg](operators/tensor/tensor.neg.md)              | :white\_check\_mark: |
 |             [Ceil](operators/tensor/tensor.ceil.md)             | :white\_check\_mark: |
 |              [Exp](operators/tensor/tensor.exp.md)              | :white\_check\_mark: |
 |              [Ln](operators/tensor/tensor.log.md)               | :white\_check\_mark: |
@@ -33,12 +34,14 @@ You can see below the list of current supported ONNX Operators:
 |          [Flatten](operators/tensor/tensor.flatten.md)          | :white\_check\_mark: |
 |           [Relu](operators/neural-network/nn.relu.md)           | :white\_check\_mark: |
 |     [LeakyRelu](operators/neural-network/nn.leaky\_relu.md)     | :white\_check\_mark: |
+|[ThresholdedRelu](operators/neural-network/nn.thresholded\_relu.md)| :white\_check\_mark: |
 |        [Sigmoid](operators/neural-network/nn.sigmoid.md)        | :white\_check\_mark: |
 |        [Softmax](operators/neural-network/nn.softmax.md)        | :white\_check\_mark: |
 |     [LogSoftmax](operators/neural-network/nn.logsoftmax.md)     | :white\_check\_mark: |
 |       [Softsign](operators/neural-network/nn.softsign.md)       | :white\_check\_mark: |
 |       [Softplus](operators/neural-network/nn.softplus.md)       | :white\_check\_mark: |
 |         [Linear](operators/neural-network/nn.linear.md)         | :white\_check\_mark: |
+|   [HardSigmoid](operators/neural-network/nn.hard\_sigmoid.md)   | :white\_check\_mark: |
 |             [Sinh](operators/tensor/tensor.sinh.md)             | :white\_check\_mark: |
 |            [Asinh](operators/tensor/tensor.asinh.md)            | :white\_check\_mark: |
 |             [Cosh](operators/tensor/tensor.cosh.md)             | :white\_check\_mark: |
@@ -57,5 +60,6 @@ You can see below the list of current supported ONNX Operators:
 |        [Unsqueeze](operators/tensor/tensor.unsqueeze.md)        | :white\_check\_mark: |
 |             [Sign](operators/tensor/tensor.sign.md)             | :white\_check\_mark: |
 |             [Clip](operators/tensor/tensor.clip.md)             | :white\_check\_mark: |
+|             [Identity](operators/tensor/tensor.identity.md)     | :white\_check\_mark: |
 
-Current Operators support: **50/156 (32%)**
+Current Operators support: **51/156 (33%)**

docs/framework/operators/machine-learning/tree-classifier/README.md

@@ -0,0 +1,23 @@
+# Tree Classifier
+
+`TreeClassifierTrait` provides a trait definition for decision tree classifier. This trait offers functionalities to build a decision tree and predict target values based on input features.
+
+```rust
+use orion::operators::ml::TreeClassifierTrait;
+```
+
+### Data types
+
+Orion supports currently only fixed point data types for `TreeClassifierTrait`.
+
+| Data type            | dtype                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Fixed point (signed) | `TreeClassifierTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
+
+***
+
+| function | description |
+| --- | --- |
+| [`tree.predict`](tree.predict.md) | Given a set of features, predicts the target value using the constructed decision tree. |
+| [`tree.predict_proba`](tree.predict\_proba.md) | Predicts class probabilities based on feature data. |
+

docs/framework/operators/machine-learning/tree-classifier/tree.predict.md

@@ -0,0 +1,35 @@
+# TreeClassifierTrait::predict
+
+```rust 
+   fn predict(ref self: TreeClassifier<T>, features: Span<T>) -> T;
+```
+
+Predicts the target value for a set of features using the provided decision tree.
+
+## Args
+
+* `self`: A reference to the decision tree used for making the prediction.
+* `features`: A span representing the features for which the prediction is to be made.
+
+## Returns
+
+The predicted target value.
+
+## Type Constraints
+
+Constrain input and output types to fixed point.
+
+## Examples
+
+```rust
+use orion::operators::ml::{FP16x16TreeClassifier, TreeClassifierTrait, TreeClassifier};
+use orion::numbers::{FP16x16, FixedTrait};
+
+fn tree_classifier_example(tree: TreeClassifier<FP16x16>) {
+
+ tree.predict(
+       array![FixedTrait::new_unscaled(1, false), FixedTrait::new_unscaled(2, false),].span()
+   );
+
+}
+```

docs/framework/operators/machine-learning/tree-classifier/tree.predict_proba.md

@@ -0,0 +1,38 @@
+# TreeClassifierTrait::predict_proba
+
+```rust 
+   fn predict_proba(ref self: TreeClassifier<T>, features: Span<T>) -> Span<T>;
+```
+
+Given a set of features, this method traverses the decision tree
+represented by `self` and returns the class distribution (probabilities)
+found in the leaf node that matches the provided features. The traversal
+stops once a leaf node is reached in the decision tree.
+
+## Args
+
+* `self`: A reference to the decision tree used for making the prediction.
+* `features`: A span representing the features for which the prediction is to be made.
+
+## Returns
+
+Returns a `Span<T>` representing the class distribution at the leaf node.
+
+## Type Constraints
+
+Constrain input and output types to fixed points.
+
+## Examples
+
+```rust
+use orion::operators::ml::{FP16x16TreeClassifier, TreeClassifierTrait, TreeClassifier};
+use orion::numbers::{FP16x16, FixedTrait};
+
+fn tree_classifier_example(tree: TreeClassifier<FP16x16>) {
+
+ tree.predict_proba(
+       array![FixedTrait::new_unscaled(1, false), FixedTrait::new_unscaled(2, false),].span()
+   );
+
+}
+```

docs/framework/operators/machine-learning/tree-regressor/README.md

@@ -18,6 +18,5 @@ Orion supports currently only fixed point data types for `TreeRegressorTrait`.
 
 | function | description |
 | --- | --- |
-| [`tree.fit`](tree.fit.md) | Constructs a decision tree regressor based on the provided data and target values. |
 | [`tree.predict`](tree.predict.md) | Given a set of features, predicts the target value using the constructed decision tree. |
 

docs/framework/operators/machine-learning/tree-regressor/tree.predict.md

@@ -1,7 +1,7 @@
 # TreeRegressorTrait::predict
 
 ```rust 
-   fn predict(ref self: TreeNode<T>, features: Span<T>) -> T;
+   fn predict(ref self: TreeRegressor<T>, features: Span<T>) -> T;
 ```
 
 Predicts the target value for a set of features using the provided decision tree.
@@ -17,37 +17,19 @@ The predicted target value.
 
 ## Type Constraints
 
-Constrain input and output types to fixed point tensors.
+Constrain input and output types to fixed point.
 
 ## Examples
 
 ```rust
-use orion::operators::ml::{FP16x16TreeRegressor, TreeRegressorTrait};
+use orion::operators::ml::{FP16x16TreeRegressor, TreeRegressorTrait, TreeRegressor};
 use orion::numbers::{FP16x16, FixedTrait};
 
-fn tree_regressor_example() {
+fn tree_regressor_example(tree: TreeRegressor<FP16x16>) {
 
- let data = array![
-     array![FixedTrait::new_unscaled(1, false), FixedTrait::new_unscaled(2, false)].span(),
-     array![FixedTrait::new_unscaled(3, false), FixedTrait::new_unscaled(4, false)].span(),
-     array![FixedTrait::new_unscaled(5, false), FixedTrait::new_unscaled(6, false)].span(),
-     array![FixedTrait::new_unscaled(7, false), FixedTrait::new_unscaled(8, false)].span(),
- ]
-     .span();
-
- let target = array![
-     FixedTrait::new_unscaled(2, false),
-     FixedTrait::new_unscaled(4, false),
-     FixedTrait::new_unscaled(6, false),
-     FixedTrait::new_unscaled(8, false)
- ]
-     .span();
-
- let mut tree = TreeRegressorTrait::fit(data, target, 3);
-
- let prediction_1 = tree
-   .predict(
+ tree.predict(
        array![FixedTrait::new_unscaled(1, false), FixedTrait::new_unscaled(2, false),].span()
    );
+
 }
 ```

docs/framework/operators/machine-learning/xgboost-regressor/README.md

@@ -0,0 +1,22 @@
+# Tree Regressor
+
+`XGBoostRegressorTrait` provides a trait definition for xgboost regression. This trait offers functionalities to predict target values based on input features.
+
+```rust
+use orion::operators::ml::XGBoostRegressorTrait;
+```
+
+### Data types
+
+Orion supports currently only fixed point data types for `XGBoostRegressorTrait`.
+
+| Data type            | dtype                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Fixed point (signed) | `TreeRegressorTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
+
+***
+
+| function | description |
+| --- | --- |
+| [`xgboost.predict`](xgboost.predict.md) | Predicts the target value for a set of features using the provided ensemble of decision trees. |
+