replace build_tree with fit

gizatechxyz · Sep 28, 2023 · c66ad63 · c66ad63
1 parent 3d751ed
commit c66ad63
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Showing 10 changed files with 24 additions and 24 deletions.
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
@@ -89,7 +89,7 @@
     * [nn.linear](framework/operators/neural-network/nn.linear.md)
   * [Machine Learning](framework/operators/machine-learning/README.md)
     * [Tree Regressor](framework/operators/machine-learning/tree-regressor/README.md)
-      * [tree.build](framework/operators/machine-learning/tree-regressor/tree.build_tree.md)
+      * [tree.build](framework/operators/machine-learning/tree-regressor/tree.fit.md)
       * [tree.predict](framework/operators/machine-learning/tree-regressor/tree.predict.md)
 
 ## 🏛 Hub

diff --git a/docs/framework/operators/machine-learning/tree-regressor/README.md b/docs/framework/operators/machine-learning/tree-regressor/README.md
@@ -19,6 +19,6 @@ Orion supports currently only fixed point data types for `TreeRegressorTrait`.
 
 | function | description |
 | --- | --- |
-| [`tree.build_tree`](tree.build\_tree.md) | Constructs a decision tree regressor based on the provided data and target values. |
+| [`tree.fit`](tree.fit\_tree.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. |
 
diff --git a/docs/framework/operators/machine-learning/tree-regressor/tree.build_tree.md b/docs/framework/operators/machine-learning/tree-regressor/tree.build_tree.md
@@ -1,7 +1,7 @@
-# TreeRegressorTrait::build_tree
+# TreeRegressorTrait::fit
 
 ```rust 
-   fn build_tree(data: Span<Span<T>>, target: Span<T>, max_depth: usize) -> TreeNode<T>;
+   fn fit(data: Span<Span<T>>, target: Span<T>, max_depth: usize) -> TreeNode<T>;
 ```
 
 Builds a decision tree based on the provided data and target values up to a specified maximum depth.
@@ -44,6 +44,6 @@ fn tree_regressor_example() {
  ]
      .span();
 
- TreeRegressorTrait::build_tree(data, target, 3);
+ TreeRegressorTrait::fit(data, target, 3);
 }
 ```
diff --git a/docs/framework/operators/machine-learning/tree-regressor/tree.predict.md b/docs/framework/operators/machine-learning/tree-regressor/tree.predict.md
@@ -43,7 +43,7 @@ fn tree_regressor_example() {
  ]
      .span();
 
- let mut tree = TreeRegressorTrait::build_tree(data, target, 3);
+ let mut tree = TreeRegressorTrait::fit(data, target, 3);
 
  let prediction_1 = tree
    .predict(

diff --git a/src/operators/ml/tree_regressor/core.cairo b/src/operators/ml/tree_regressor/core.cairo
@@ -13,13 +13,13 @@ struct TreeNode<T> {
 
 /// Trait
 ///
-/// build_tree - Constructs a decision tree regressor based on the provided data and target values.
+/// fit - Constructs a decision tree regressor based on the provided data and target values.
 /// predict - Given a set of features, predicts the target value using the constructed decision tree.
 trait TreeRegressorTrait<T> {
-    /// # TreeRegressorTrait::build_tree
+    /// # TreeRegressorTrait::fit
     ///
     /// ```rust 
-    ///    fn build_tree(data: Span<Span<T>>, target: Span<T>, max_depth: usize) -> TreeNode<T>;
+    ///    fn fit(data: Span<Span<T>>, target: Span<T>, max_depth: usize) -> TreeNode<T>;
     /// ```
     ///
     /// Builds a decision tree based on the provided data and target values up to a specified maximum depth.
@@ -62,11 +62,11 @@ trait TreeRegressorTrait<T> {
     ///  ]
     ///      .span();
     ///
-    ///  TreeRegressorTrait::build_tree(data, target, 3);
+    ///  TreeRegressorTrait::fit(data, target, 3);
     /// }
     /// ```
     ///
-    fn build_tree(data: Span<Span<T>>, target: Span<T>, max_depth: usize) -> TreeNode<T>;
+    fn fit(data: Span<Span<T>>, target: Span<T>, max_depth: usize) -> TreeNode<T>;
     /// # TreeRegressorTrait::predict
     ///
     /// ```rust 
@@ -112,7 +112,7 @@ trait TreeRegressorTrait<T> {
     ///  ]
     ///      .span();
     ///
-    ///  let mut tree = TreeRegressorTrait::build_tree(data, target, 3);
+    ///  let mut tree = TreeRegressorTrait::fit(data, target, 3);
     ///
     ///  let prediction_1 = tree
     ///    .predict(
@@ -343,7 +343,7 @@ fn best_split<
     (best_split_feature, best_split_value, best_prediction)
 }
 
-fn build_tree<
+fn fit<
     T,
     MAG,
     impl FFixedTrait: FixedTrait<T, MAG>,
@@ -412,10 +412,10 @@ fn build_tree<
 
     TreeNode {
         left: Option::Some(
-            BoxTrait::new(build_tree(left_data.span(), left_target.span(), depth + 1, max_depth))
+            BoxTrait::new(fit(left_data.span(), left_target.span(), depth + 1, max_depth))
         ),
         right: Option::Some(
-            BoxTrait::new(build_tree(right_data.span(), right_target.span(), depth + 1, max_depth))
+            BoxTrait::new(fit(right_data.span(), right_target.span(), depth + 1, max_depth))
         ),
         split_feature,
         split_value,

diff --git a/src/operators/ml/tree_regressor/implementations/tree_regressor_fp16x16.cairo b/src/operators/ml/tree_regressor/implementations/tree_regressor_fp16x16.cairo
@@ -3,10 +3,10 @@ use orion::operators::ml::tree_regressor::core;
 use orion::numbers::FP16x16;
 
 impl FP16x16TreeRegressor of TreeRegressorTrait<FP16x16> {
-    fn build_tree(
+    fn fit(
         data: Span<Span<FP16x16>>, target: Span<FP16x16>, max_depth: usize
     ) -> TreeNode<FP16x16> {
-        core::build_tree(data, target, 0, max_depth)
+        core::fit(data, target, 0, max_depth)
     }
 
     fn predict(ref self: TreeNode<FP16x16>, features: Span<FP16x16>) -> FP16x16 {

diff --git a/src/operators/ml/tree_regressor/implementations/tree_regressor_fp32x32.cairo b/src/operators/ml/tree_regressor/implementations/tree_regressor_fp32x32.cairo
@@ -3,10 +3,10 @@ use orion::operators::ml::tree_regressor::core;
 use orion::numbers::{FP32x32, FP32x32Impl};
 
 impl FP32x32TreeRegressor of TreeRegressorTrait<FP32x32> {
-    fn build_tree(
+    fn fit(
         data: Span<Span<FP32x32>>, target: Span<FP32x32>, max_depth: usize
     ) -> TreeNode<FP32x32> {
-        core::build_tree(data, target, 0, max_depth)
+        core::fit(data, target, 0, max_depth)
     }
 
     fn predict(ref self: TreeNode<FP32x32>, features: Span<FP32x32>) -> FP32x32 {

diff --git a/src/operators/ml/tree_regressor/implementations/tree_regressor_fp64x64.cairo b/src/operators/ml/tree_regressor/implementations/tree_regressor_fp64x64.cairo
@@ -3,10 +3,10 @@ use orion::operators::ml::tree_regressor::core;
 use orion::numbers::{FP64x64, FP64x64Impl};
 
 impl FP64x64TreeRegressor of TreeRegressorTrait<FP64x64> {
-    fn build_tree(
+    fn fit(
         data: Span<Span<FP64x64>>, target: Span<FP64x64>, max_depth: usize
     ) -> TreeNode<FP64x64> {
-        core::build_tree(data, target, 0, max_depth)
+        core::fit(data, target, 0, max_depth)
     }
 
     fn predict(ref self: TreeNode<FP64x64>, features: Span<FP64x64>) -> FP64x64 {

diff --git a/src/operators/ml/tree_regressor/implementations/tree_regressor_fp8x23.cairo b/src/operators/ml/tree_regressor/implementations/tree_regressor_fp8x23.cairo
@@ -3,10 +3,10 @@ use orion::operators::ml::tree_regressor::core;
 use orion::numbers::FP8x23;
 
 impl FP8x23TreeRegressor of TreeRegressorTrait<FP8x23> {
-    fn build_tree(
+    fn fit(
         data: Span<Span<FP8x23>>, target: Span<FP8x23>, max_depth: usize
     ) -> TreeNode<FP8x23> {
-        core::build_tree(data, target, 0, max_depth)
+        core::fit(data, target, 0, max_depth)
     }
 
     fn predict(ref self: TreeNode<FP8x23>, features: Span<FP8x23>) -> FP8x23 {

diff --git a/src/tests/ml/tree_regressor.cairo b/src/tests/ml/tree_regressor.cairo
@@ -42,7 +42,7 @@ fn test_tree() {
     ]
         .span();
 
-    let mut tree = TreeRegressorTrait::build_tree(data, target, 3);
+    let mut tree = TreeRegressorTrait::fit(data, target, 3);
 
     let prediction_1 = tree
         .predict(