Merge branch 'develop' into pr/431

.all-contributorsrc

@@ -197,6 +197,42 @@
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "Ephraim-nonso",
+      "name": "Ephraim Chukwu",
+      "avatar_url": "https://avatars.githubusercontent.com/u/68496788?v=4",
+      "profile": "https://audits.quillhash.com/smart-contract-audit",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "Bal7hazar",
+      "name": "Bal7hazar",
+      "avatar_url": "https://avatars.githubusercontent.com/u/97087040?v=4",
+      "profile": "https://github.com/Bal7hazar",
+      "contributions": [
+        "bug"
+      ]
+    },
+    {
+      "login": "okhaimie-dev",
+      "name": "Tony Stark",
+      "avatar_url": "https://avatars.githubusercontent.com/u/57156589?v=4",
+      "profile": "https://okhaimie.com",
+      "contributions": [
+        "doc"
+      ]
+    },
+    {
+      "login": "bilgin-kocak",
+      "name": "Bilgin Koçak",
+      "avatar_url": "https://avatars.githubusercontent.com/u/30844607?v=4",
+      "profile": "https://www.bilginkocak.com/",
+      "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-21-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-25-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/).
@@ -61,7 +61,7 @@ See [LICENSE](https://github.com/franalgaba/onnx-cairo/blob/main/LICENSE/README.
 
 ## Contributors ✨
 
-Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
+Thanks goes to these wonderful people:
 
 <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
 <!-- prettier-ignore-start -->
@@ -95,6 +95,12 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://brilliantblocks.io"><img src="https://avatars.githubusercontent.com/u/25390947?v=4?s=100" width="100px;" alt="Thomas S. Bauer"/><br /><sub><b>Thomas S. Bauer</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=TsBauer" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="http://andresmayorca.github.io"><img src="https://avatars.githubusercontent.com/u/70079260?v=4?s=100" width="100px;" alt="Andres"/><br /><sub><b>Andres</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=andresmayorca" title="Code">💻</a></td>
     </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://audits.quillhash.com/smart-contract-audit"><img src="https://avatars.githubusercontent.com/u/68496788?v=4?s=100" width="100px;" alt="Ephraim Chukwu"/><br /><sub><b>Ephraim Chukwu</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=Ephraim-nonso" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Bal7hazar"><img src="https://avatars.githubusercontent.com/u/97087040?v=4?s=100" width="100px;" alt="Bal7hazar"/><br /><sub><b>Bal7hazar</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/issues?q=author%3ABal7hazar" title="Bug reports">🐛</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://okhaimie.com"><img src="https://avatars.githubusercontent.com/u/57156589?v=4?s=100" width="100px;" alt="Tony Stark"/><br /><sub><b>Tony Stark</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=okhaimie-dev" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://www.bilginkocak.com/"><img src="https://avatars.githubusercontent.com/u/30844607?v=4?s=100" width="100px;" alt="Bilgin Koçak"/><br /><sub><b>Bilgin Koçak</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=bilgin-kocak" title="Code">💻</a></td>
+    </tr>
   </tbody>
 </table>
 

Scarb.toml

@@ -1,14 +1,15 @@
 [package]
 name = "orion"
-version = "0.1.5"
+version = "0.1.7"
 description = "ONNX Runtime in Cairo for verifiable ML inference using STARK"
 homepage = "https://github.com/gizatechxyz/orion"
 
 [dependencies]
 alexandria_data_structures = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "f37d73d" }
-cubit = { git = "https://github.com/raphaelDkhn/cubit.git" }
+alexandria_sorting = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "f37d73d" }
+cubit = { git = "https://github.com/influenceth/cubit.git", rev = "b459053" }
 
 [scripts]
 sierra = "cairo-compile . -r"
 docgen = "cd docgen && cargo run"
-nodegen = "python3 nodegen/node/__init__.py"
+nodegen = "python3 nodegen/node/__init__.py"

docgen/src/main.rs

@@ -58,6 +58,14 @@ fn main() {
     let trait_name: &str = "XGBoostRegressorTrait";
     doc_trait(trait_path, doc_path, label);
     doc_functions(trait_path, doc_path, trait_name, label);
+
+    // TREE ENSEMBLE CLASSIFIER DOC
+    let trait_path = "src/operators/ml/tree_ensemble/tree_ensemble_classifier.cairo";
+    let doc_path = "docs/framework/operators/machine-learning/tree-ensemble-classifier";
+    let label = "tree_ensemble_classifier";
+    let trait_name: &str = "TreeEnsembleClassifierTrait";
+    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/CHANGELOG.md

@@ -4,12 +4,28 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased] - 2023-11-06
+
+## Added
+- Reduce Sum Square Operator.
+
+## [Unreleased] - 2023-11-05
+
+### Added
+- ReduceL2 operator.
+
+## [Unreleased] - 2023-11-06
+
+## Added
+- Reduce L1 Operator.
+
 
 ## [Unreleased] - 2023-11-03
 
 ## Added
 - Scatter Elements Operator.
 
+
 ## [Unreleased] - 2023-09-27
 
 ## Added

docs/SUMMARY.md

@@ -95,8 +95,19 @@
     * [tensor.identity](framework/operators/tensor/tensor.identity.md)
     * [tensor.and](framework/operators/tensor/tensor.and.md)
     * [tensor.where](framework/operators/tensor/tensor.where.md)
+    * [tensor.bitwise_and](framework/operators/tensor/tensor.bitwise_and.md)
     * [tensor.round](framework/operators/tensor/tensor.round.md)
     * [tensor.scatter](framework/operators/tensor/tensor.scatter.md)
+    * [tensor.array_feature_extractor](framework/operators/tensor/tensor.array\_feature\_extractor.md)
+    * [tensor.binarizer](framework/operators/tensor/tensor.binarizer.md)
+    * [tensor.reduce\_sum\_square](framework/operators/tensor/tensor.reduce\_sum\_square.md)
+    * [tensor.reduce\_l2](framework/operators/tensor/tensor.reduce\_l2.md)
+    * [tensor.reduce\_l1](framework/operators/tensor/tensor.reduce\_l1.md)
+    * [tensor.reduce\_min](framework/operators/tensor/tensor.reduce\_min.md)
+    * [tensor.sequence\_construct](framework/operators/tensor/tensor.sequence\_construct.md)
+    * [tensor.shrink](framework/operators/tensor/tensor.shrink.md)
+    * [tensor.sequence\_empty](framework/operators/tensor/tensor.sequence\_empty.md)
+    * [tensor.reduce_mean](framework/operators/tensor/tensor.reduce\_mean.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)
@@ -110,6 +121,8 @@
     * [nn.thresholded\_relu](framework/operators/neural-network/nn.thresholded_relu.md)
     * [nn.gemm](framework/operators/neural-network/nn.gemm.md)
   * [Machine Learning](framework/operators/machine-learning/README.md)
+    * [Tree Ensemble Classifier](framework/operators/machine-learning/tree-ensemble-classifier/README.md)
+      * [tree_ensemble_classifier.predict](framework/operators/machine-learning/tree-ensemble-classifier/tree_ensemble_classifier.predict.md)
     * [Tree Regressor](framework/operators/machine-learning/tree-regressor/README.md)
       * [tree.predict](framework/operators/machine-learning/tree-regressor/tree.predict.md)
     * [Tree Classifier](framework/operators/machine-learning/tree-classifier/README.md)

docs/academy/tutorials/implement-new-operators-in-orion.md

@@ -268,7 +268,7 @@ Finally, we create a Softmax class, containing tests for each dtypes.
 ```python
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_node, make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
 
 def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
     x_max = np.max(x, axis=axis, keepdims=True)
@@ -293,8 +293,6 @@ class Softmax(RunAll):
 
         # Define the name of the generated folder. 
         name = "softmax_fp8x23"
-        # Invoke `make_node` method to generate Cairo representation of `x` and `y`:
-        make_node([x], [y], name)
         # Invoke `make_test` method to generate corresponding Cairo tests:
         make_test(
             [x], # List of input tensors.
@@ -316,7 +314,6 @@ class Softmax(RunAll):
             y.flatten(), FixedImpl.FP16x16))
 
         name = "softmax_fp16x16"
-        make_node([x], [y], name)
         make_test([x], y, "NNTrait::softmax(@input_0, 1)",
                     name, Trait.NN)
 ```

docs/framework/compatibility.md

@@ -69,12 +69,24 @@ You can see below the list of current supported ONNX Operators:
 |                [Xor](operators/tensor/tensor.xor.md)                | :white\_check\_mark: |
 |                 [Or](operators/tensor/tensor.or.md)                 | :white\_check\_mark: |
 |             [Gemm](operators/neural-network/nn.gemm.md)             | :white\_check\_mark: |
-|    [MinInTensor](operators/tensor/tensor.min\_in\_tensor.md)        | :white\_check\_mark: |
+|      [MinInTensor](operators/tensor/tensor.min\_in\_tensor.md)      | :white\_check\_mark: |
 |                [Min](operators/tensor/tensor.min.md)                | :white\_check\_mark: |
-|             [Where](operators/tensor/tensor.where.md)               | :white\_check\_mark: |
-|             [Round](operators/tensor/tensor.round.md)               | :white\_check\_mark: |
+|              [Where](operators/tensor/tensor.where.md)              | :white\_check\_mark: |
+|        [BitwiseAnd](operators/tensor/tensor.bitwise_and.md)         | :white\_check\_mark: |
+|              [Round](operators/tensor/tensor.round.md)              | :white\_check\_mark: |
 |      [MaxInTensor](operators/tensor/tensor.max\_in\_tensor.md)      | :white\_check\_mark: |
-|                 [Max](operators/tensor/tensor.max.md)               | :white\_check\_mark: |
+|                [Max](operators/tensor/tensor.max.md)                | :white\_check\_mark: |
+|  [ReduceSumSquare](operators/tensor/tensor.reduce\_sum\_square.md)  | :white\_check\_mark: |
+|              [Trilu](operators/tensor/tensor.trilu.md)              | :white\_check\_mark: |
 |             [Scatter](operators/tensor/scatter.max.md)              | :white\_check\_mark: |
+|  [ArrayFeatureExtractor](operators/tensor/tensor.array\_feature\_extractor.md)     | :white\_check\_mark: |
+|          [Binarizer](operators/tensor/tensor.binarizer.md)          | :white\_check\_mark: |
+|   [ConstantOfShape](operators/tensor/tensor.constant_of_shape.md)   | :white\_check\_mark: |
+|          [ReduceL1](operators/tensor/tensor.reduce\_l1.md)          | :white\_check\_mark: |
+|         [ReduceL2](operators/tensor/tensor.reduce\_l2.md)           | :white\_check\_mark: |
+| [SequenceConstruct](operators/tensor/tensor.sequence\_construct.md) | :white\_check\_mark: |
+|               [Shrink](operators/tensor/tensor.shrink.md)           | :white\_check\_mark: |
+|         [SequenceEmpty](operators/tensor/tensor.sequence\_empty.md) | :white\_check\_mark: |
+|          [ReduceL2](operators/tensor/tensor.reduce\_l2.md)          | :white\_check\_mark: |
 
-Current Operators support: **68/156 (43%)**
+Current Operators support: **81/156 (52%)**

docs/framework/get-started.md

@@ -14,7 +14,7 @@ Orion supports <mark style="color:orange;">**Cairo and Scarb v2.3.0**</mark>
 
 **Step 1: Install Cairo**
 
-There are different ways to install Cairo. Use the one that suits you best: [Cairo installer](https://cairo-book.github.io/ch01-01-installation.html).
+There are different ways to install Cairo. Use the one that suits you best: [Cairo installer.](https://book.cairo-lang.org/ch01-01-installation.html)
 
 **Step 2: Setup Language Server**
 

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

@@ -0,0 +1,27 @@
+# Tree Ensemble Classifier
+
+`TreeEnsembleClassifierTrait` provides a trait definition for tree ensemble classification problem.
+
+```rust
+use orion::operators::ml::TreeEnsembleClassifierTrait;
+```
+
+### Data types
+
+Orion supports currently only fixed point data types for `TreeEnsembleClassifierTrait`.
+
+| Data type            | dtype                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Fixed point (signed) | `TreeRegressorTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
+
+### How to construct `TreeEnsembleClassifier` 
+
+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.
+
+
+***
+
+| function | description |
+| --- | --- |
+| [`tree_ensemble_classifier.predict`](tree_ensemble_classifier.predict.md) | Returns the top class for each of N inputs. |
+