From 68fd27f8f4b9933986ce90eec789e1f487e9c7ca Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 14:59:50 +0000
Subject: [PATCH 01/12] Implement predict_proba for DecisionTreeClassifier
---
src/tree/decision_tree_classifier.rs | 110 +++++++++++++++++++++++++++
1 file changed, 110 insertions(+)
diff --git a/src/tree/decision_tree_classifier.rs b/src/tree/decision_tree_classifier.rs
index c6596517..712cd87d 100644
--- a/src/tree/decision_tree_classifier.rs
+++ b/src/tree/decision_tree_classifier.rs
@@ -78,6 +78,8 @@ use serde::{Deserialize, Serialize};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::linalg::basic::arrays::{Array1, Array2, MutArrayView1};
+use crate::linalg::basic::matrix::DenseMatrix;
+use crate::linalg::basic::arrays::MutArray;
use crate::numbers::basenum::Number;
use crate::rand_custom::get_rng_impl;
@@ -887,12 +889,79 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
}
importances
}
+
+
+ /// Predict class probabilities for the input samples.
+ ///
+ /// # Arguments
+ ///
+ /// * `x` - The input samples as a matrix where each row is a sample and each column is a feature.
+ ///
+ /// # Returns
+ ///
+ /// A `Result` containing a `DenseMatrix<f64>` where each row corresponds to a sample and each column
+ /// corresponds to a class. The values represent the probability of the sample belonging to each class.
+ ///
+ /// # Errors
+ ///
+ /// Returns an error if the prediction process fails.
+ pub fn predict_proba(&self, x: &X) -> Result<DenseMatrix<f64>, Failed> {
+ let (n_samples, _) = x.shape();
+ let n_classes = self.classes().len();
+ let mut result = DenseMatrix::<f64>::zeros(n_samples, n_classes);
+
+ for i in 0..n_samples {
+ let probs = self.predict_proba_for_row(x, i);
+ for (j, &prob) in probs.iter().enumerate() {
+ result.set((i, j), prob);
+ }
+ }
+
+ Ok(result)
+ }
+
+ /// Predict class probabilities for a single input sample.
+ ///
+ /// # Arguments
+ ///
+ /// * `x` - The input matrix containing all samples.
+ /// * `row` - The index of the row in `x` for which to predict probabilities.
+ ///
+ /// # Returns
+ ///
+ /// A vector of probabilities, one for each class, representing the probability
+ /// of the input sample belonging to each class.
+ fn predict_proba_for_row(&self, x: &X, row: usize) -> Vec<f64> {
+ let mut node = 0;
+
+ while let Some(current_node) = self.nodes().get(node) {
+ if current_node.true_child.is_none() && current_node.false_child.is_none() {
+ // Leaf node reached
+ let mut probs = vec![0.0; self.classes().len()];
+ probs[current_node.output] = 1.0;
+ return probs;
+ }
+
+ let split_feature = current_node.split_feature;
+ let split_value = current_node.split_value.unwrap_or(f64::NAN);
+
+ if x.get((row, split_feature)).to_f64().unwrap() <= split_value {
+ node = current_node.true_child.unwrap();
+ } else {
+ node = current_node.false_child.unwrap();
+ }
+ }
+
+ // This should never happen if the tree is properly constructed
+ vec![0.0; self.classes().len()]
+ }
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::basic::matrix::DenseMatrix;
+ use crate::linalg::basic::arrays::Array;
#[test]
fn search_parameters() {
@@ -934,6 +1003,47 @@ mod tests {
);
}
+ #[cfg_attr(
+ all(target_arch = "wasm32", not(target_os = "wasi")),
+ wasm_bindgen_test::wasm_bindgen_test
+ )]
+ #[test]
+ fn test_predict_proba() {
+ let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
+ &[5.1, 3.5, 1.4, 0.2],
+ &[4.9, 3.0, 1.4, 0.2],
+ &[4.7, 3.2, 1.3, 0.2],
+ &[4.6, 3.1, 1.5, 0.2],
+ &[5.0, 3.6, 1.4, 0.2],
+ &[7.0, 3.2, 4.7, 1.4],
+ &[6.4, 3.2, 4.5, 1.5],
+ &[6.9, 3.1, 4.9, 1.5],
+ &[5.5, 2.3, 4.0, 1.3],
+ &[6.5, 2.8, 4.6, 1.5],
+ ]).unwrap();
+ let y: Vec<usize> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
+
+ let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
+ let probabilities = tree.predict_proba(&x).unwrap();
+
+ assert_eq!(probabilities.shape(), (10, 2));
+
+ for row in 0..10 {
+ let row_sum: f64 = probabilities.get_row(row).sum();
+ assert!((row_sum - 1.0).abs() < 1e-6, "Row probabilities should sum to 1");
+ }
+
+ // Check if the first 5 samples have higher probability for class 0
+ for i in 0..5 {
+ assert!(probabilities.get((i, 0)) > probabilities.get((i, 1)));
+ }
+
+ // Check if the last 5 samples have higher probability for class 1
+ for i in 5..10 {
+ assert!(probabilities.get((i, 1)) > probabilities.get((i, 0)));
+ }
+ }
+
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
From 58ee0cb8d18b933e4e132a0cd8c953ebffde8abf Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 15:04:21 +0000
Subject: [PATCH 02/12] Some automated fixes suggested by cargo clippy --fix
---
src/linalg/basic/matrix.rs | 22 +++++++++++-----------
src/linalg/basic/vector.rs | 12 ++++++------
src/linalg/ndarray/matrix.rs | 16 ++++++++--------
src/linalg/ndarray/vector.rs | 12 ++++++------
src/linear/logistic_regression.rs | 8 ++++----
src/naive_bayes/mod.rs | 2 +-
src/preprocessing/numerical.rs | 10 ++--------
src/readers/csv.rs | 2 +-
src/svm/svc.rs | 4 ++--
src/svm/svr.rs | 4 ++--
10 files changed, 43 insertions(+), 49 deletions(-)
diff --git a/src/linalg/basic/matrix.rs b/src/linalg/basic/matrix.rs
index 47c5e9d2..4be6a2da 100644
--- a/src/linalg/basic/matrix.rs
+++ b/src/linalg/basic/matrix.rs
@@ -91,7 +91,7 @@ impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixView<'a, T> {
}
}
-impl<'a, T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixView<'_, T> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
writeln!(
f,
@@ -169,7 +169,7 @@ impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixMutView<'a, T> {
}
}
-impl<'a, T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixMutView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixMutView<'_, T> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
writeln!(
f,
@@ -493,7 +493,7 @@ impl<T: Number + RealNumber> EVDDecomposable<T> for DenseMatrix<T> {}
impl<T: Number + RealNumber> LUDecomposable<T> for DenseMatrix<T> {}
impl<T: Number + RealNumber> SVDDecomposable<T> for DenseMatrix<T> {}
-impl<'a, T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixView<'_, T> {
fn get(&self, pos: (usize, usize)) -> &T {
if self.column_major {
&self.values[pos.0 + pos.1 * self.stride]
@@ -515,7 +515,7 @@ impl<'a, T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMa
}
}
-impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for DenseMatrixView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> Array<T, usize> for DenseMatrixView<'_, T> {
fn get(&self, i: usize) -> &T {
if self.nrows == 1 {
if self.column_major {
@@ -553,11 +553,11 @@ impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for DenseMatrixView<
}
}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixView<'a, T> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixView<'_, T> {}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for DenseMatrixView<'a, T> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for DenseMatrixView<'_, T> {}
-impl<'a, T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixMutView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixMutView<'_, T> {
fn get(&self, pos: (usize, usize)) -> &T {
if self.column_major {
&self.values[pos.0 + pos.1 * self.stride]
@@ -579,8 +579,8 @@ impl<'a, T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMa
}
}
-impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
- for DenseMatrixMutView<'a, T>
+impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
+ for DenseMatrixMutView<'_, T>
{
fn set(&mut self, pos: (usize, usize), x: T) {
if self.column_major {
@@ -595,9 +595,9 @@ impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
}
}
-impl<'a, T: Debug + Display + Copy + Sized> MutArrayView2<T> for DenseMatrixMutView<'a, T> {}
+impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for DenseMatrixMutView<'_, T> {}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixMutView<'a, T> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixMutView<'_, T> {}
impl<T: RealNumber> MatrixStats<T> for DenseMatrix<T> {}
diff --git a/src/linalg/basic/vector.rs b/src/linalg/basic/vector.rs
index 05c03756..d2e0bae6 100644
--- a/src/linalg/basic/vector.rs
+++ b/src/linalg/basic/vector.rs
@@ -119,7 +119,7 @@ impl<T: Debug + Display + Copy + Sized> Array1<T> for Vec<T> {
}
}
-impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for VecMutView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> Array<T, usize> for VecMutView<'_, T> {
fn get(&self, i: usize) -> &T {
&self.ptr[i]
}
@@ -138,7 +138,7 @@ impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for VecMutView<'a, T
}
}
-impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, usize> for VecMutView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for VecMutView<'_, T> {
fn set(&mut self, i: usize, x: T) {
self.ptr[i] = x;
}
@@ -149,10 +149,10 @@ impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, usize> for VecMutView<'a
}
}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for VecMutView<'a, T> {}
-impl<'a, T: Debug + Display + Copy + Sized> MutArrayView1<T> for VecMutView<'a, T> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for VecMutView<'_, T> {}
+impl<T: Debug + Display + Copy + Sized> MutArrayView1<T> for VecMutView<'_, T> {}
-impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for VecView<'a, T> {
+impl<T: Debug + Display + Copy + Sized> Array<T, usize> for VecView<'_, T> {
fn get(&self, i: usize) -> &T {
&self.ptr[i]
}
@@ -171,7 +171,7 @@ impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for VecView<'a, T> {
}
}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for VecView<'a, T> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for VecView<'_, T> {}
#[cfg(test)]
mod tests {
diff --git a/src/linalg/ndarray/matrix.rs b/src/linalg/ndarray/matrix.rs
index adc8d7e8..e406a198 100644
--- a/src/linalg/ndarray/matrix.rs
+++ b/src/linalg/ndarray/matrix.rs
@@ -68,7 +68,7 @@ impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayBase<OwnedRepr<T>
impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
-impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayView<'a, T, Ix2> {
+impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayView<'_, T, Ix2> {
fn get(&self, pos: (usize, usize)) -> &T {
&self[[pos.0, pos.1]]
}
@@ -144,10 +144,10 @@ impl<T: Number + RealNumber> EVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2>
impl<T: Number + RealNumber> LUDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
impl<T: Number + RealNumber> SVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayView<'a, T, Ix2> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayView<'_, T, Ix2> {}
-impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
- for ArrayViewMut<'a, T, Ix2>
+impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
+ for ArrayViewMut<'_, T, Ix2>
{
fn get(&self, pos: (usize, usize)) -> &T {
&self[[pos.0, pos.1]]
@@ -175,8 +175,8 @@ impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
}
}
-impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
- for ArrayViewMut<'a, T, Ix2>
+impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
+ for ArrayViewMut<'_, T, Ix2>
{
fn set(&mut self, pos: (usize, usize), x: T) {
self[[pos.0, pos.1]] = x
@@ -195,9 +195,9 @@ impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
}
}
-impl<'a, T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayViewMut<'a, T, Ix2> {}
+impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayViewMut<'_, T, Ix2> {}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayViewMut<'a, T, Ix2> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayViewMut<'_, T, Ix2> {}
#[cfg(test)]
mod tests {
diff --git a/src/linalg/ndarray/vector.rs b/src/linalg/ndarray/vector.rs
index 7105da89..de3f7d93 100644
--- a/src/linalg/ndarray/vector.rs
+++ b/src/linalg/ndarray/vector.rs
@@ -41,7 +41,7 @@ impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayBase<OwnedRepr<T>
impl<T: Debug + Display + Copy + Sized> MutArrayView1<T> for ArrayBase<OwnedRepr<T>, Ix1> {}
-impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayView<'a, T, Ix1> {
+impl<T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayView<'_, T, Ix1> {
fn get(&self, i: usize) -> &T {
&self[i]
}
@@ -60,9 +60,9 @@ impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayView<'a
}
}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayView<'a, T, Ix1> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayView<'_, T, Ix1> {}
-impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayViewMut<'a, T, Ix1> {
+impl<T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayViewMut<'_, T, Ix1> {
fn get(&self, i: usize) -> &T {
&self[i]
}
@@ -81,7 +81,7 @@ impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayViewMut
}
}
-impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, usize> for ArrayViewMut<'a, T, Ix1> {
+impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for ArrayViewMut<'_, T, Ix1> {
fn set(&mut self, i: usize, x: T) {
self[i] = x;
}
@@ -92,8 +92,8 @@ impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, usize> for ArrayViewMut<
}
}
-impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayViewMut<'a, T, Ix1> {}
-impl<'a, T: Debug + Display + Copy + Sized> MutArrayView1<T> for ArrayViewMut<'a, T, Ix1> {}
+impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayViewMut<'_, T, Ix1> {}
+impl<T: Debug + Display + Copy + Sized> MutArrayView1<T> for ArrayViewMut<'_, T, Ix1> {}
impl<T: Debug + Display + Copy + Sized> Array1<T> for ArrayBase<OwnedRepr<T>, Ix1> {
fn slice<'a>(&'a self, range: Range<usize>) -> Box<dyn ArrayView1<T> + 'a> {
diff --git a/src/linear/logistic_regression.rs b/src/linear/logistic_regression.rs
index 7e934288..c28dc347 100644
--- a/src/linear/logistic_regression.rs
+++ b/src/linear/logistic_regression.rs
@@ -258,8 +258,8 @@ impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y:
}
}
-impl<'a, T: Number + FloatNumber, X: Array2<T>> ObjectiveFunction<T, X>
- for BinaryObjectiveFunction<'a, T, X>
+impl<T: Number + FloatNumber, X: Array2<T>> ObjectiveFunction<T, X>
+ for BinaryObjectiveFunction<'_, T, X>
{
fn f(&self, w_bias: &[T]) -> T {
let mut f = T::zero();
@@ -313,8 +313,8 @@ struct MultiClassObjectiveFunction<'a, T: Number + FloatNumber, X: Array2<T>> {
_phantom_t: PhantomData<T>,
}
-impl<'a, T: Number + FloatNumber + RealNumber, X: Array2<T>> ObjectiveFunction<T, X>
- for MultiClassObjectiveFunction<'a, T, X>
+impl<T: Number + FloatNumber + RealNumber, X: Array2<T>> ObjectiveFunction<T, X>
+ for MultiClassObjectiveFunction<'_, T, X>
{
fn f(&self, w_bias: &[T]) -> T {
let mut f = T::zero();
diff --git a/src/naive_bayes/mod.rs b/src/naive_bayes/mod.rs
index 31cdd46d..26d91545 100644
--- a/src/naive_bayes/mod.rs
+++ b/src/naive_bayes/mod.rs
@@ -147,7 +147,7 @@ mod tests {
#[derive(Debug, PartialEq, Clone)]
struct TestDistribution<'d>(&'d Vec<i32>);
- impl<'d> NBDistribution<i32, i32> for TestDistribution<'d> {
+ impl NBDistribution<i32, i32> for TestDistribution<'_> {
fn prior(&self, _class_index: usize) -> f64 {
1.
}
diff --git a/src/preprocessing/numerical.rs b/src/preprocessing/numerical.rs
index ddb74a45..8593d9f8 100644
--- a/src/preprocessing/numerical.rs
+++ b/src/preprocessing/numerical.rs
@@ -172,18 +172,12 @@ where
T: Number + RealNumber,
M: Array2<T>,
{
- if let Some(output_matrix) = columns.first().cloned() {
- return Some(
- columns
+ columns.first().cloned().map(|output_matrix| columns
.iter()
.skip(1)
.fold(output_matrix, |current_matrix, new_colum| {
current_matrix.h_stack(new_colum)
- }),
- );
- } else {
- None
- }
+ }))
}
#[cfg(test)]
diff --git a/src/readers/csv.rs b/src/readers/csv.rs
index f8a03ebd..e9a88436 100644
--- a/src/readers/csv.rs
+++ b/src/readers/csv.rs
@@ -30,7 +30,7 @@ pub struct CSVDefinition<'a> {
/// What seperates the fields in your csv-file?
field_seperator: &'a str,
}
-impl<'a> Default for CSVDefinition<'a> {
+impl Default for CSVDefinition<'_> {
fn default() -> Self {
Self {
n_rows_header: 1,
diff --git a/src/svm/svc.rs b/src/svm/svc.rs
index 6477778b..67ffdc33 100644
--- a/src/svm/svc.rs
+++ b/src/svm/svc.rs
@@ -360,8 +360,8 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX> + 'a, Y: Array
}
}
-impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> PartialEq
- for SVC<'a, TX, TY, X, Y>
+impl<TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> PartialEq
+ for SVC<'_, TX, TY, X, Y>
{
fn eq(&self, other: &Self) -> bool {
if (self.b.unwrap().sub(other.b.unwrap())).abs() > TX::epsilon() * TX::two()
diff --git a/src/svm/svr.rs b/src/svm/svr.rs
index e68ebf85..85b48e4b 100644
--- a/src/svm/svr.rs
+++ b/src/svm/svr.rs
@@ -281,8 +281,8 @@ impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> SVR<'
}
}
-impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> PartialEq
- for SVR<'a, T, X, Y>
+impl<T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> PartialEq
+ for SVR<'_, T, X, Y>
{
fn eq(&self, other: &Self) -> bool {
if (self.b - other.b).abs() > T::epsilon() * T::two()
From 609f8024bc0ba20d885cb391bd043b28538cf1a8 Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 15:23:36 +0000
Subject: [PATCH 03/12] more clippy fixes
---
src/algorithm/neighbour/fastpair.rs | 2 +-
src/linalg/basic/matrix.rs | 3 ++-
src/linalg/traits/stats.rs | 1 -
src/svm/svc.rs | 2 +-
src/svm/svr.rs | 2 +-
5 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/src/algorithm/neighbour/fastpair.rs b/src/algorithm/neighbour/fastpair.rs
index 9f663f67..671517df 100644
--- a/src/algorithm/neighbour/fastpair.rs
+++ b/src/algorithm/neighbour/fastpair.rs
@@ -212,7 +212,7 @@ mod tests_fastpair {
use crate::linalg::basic::{arrays::Array, matrix::DenseMatrix};
/// Brute force algorithm, used only for comparison and testing
- pub fn closest_pair_brute(fastpair: &FastPair<f64, DenseMatrix<f64>>) -> PairwiseDistance<f64> {
+ pub fn closest_pair_brute(fastpair: &FastPair<'_, f64, DenseMatrix<f64>>) -> PairwiseDistance<f64> {
use itertools::Itertools;
let m = fastpair.samples.shape().0;
diff --git a/src/linalg/basic/matrix.rs b/src/linalg/basic/matrix.rs
index 4be6a2da..979e5a55 100644
--- a/src/linalg/basic/matrix.rs
+++ b/src/linalg/basic/matrix.rs
@@ -142,7 +142,7 @@ impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixMutView<'a, T> {
}
}
- fn iter_mut<'b>(&'b mut self, axis: u8) -> Box<dyn Iterator<Item = &mut T> + 'b> {
+ fn iter_mut<'b>(&'b mut self, axis: u8) -> Box<dyn Iterator<Item = &'b mut T> + 'b> {
let column_major = self.column_major;
let stride = self.stride;
let ptr = self.values.as_mut_ptr();
@@ -604,6 +604,7 @@ impl<T: RealNumber> MatrixStats<T> for DenseMatrix<T> {}
impl<T: RealNumber> MatrixPreprocessing<T> for DenseMatrix<T> {}
#[cfg(test)]
+#[warn(clippy::reversed_empty_ranges)]
mod tests {
use super::*;
use approx::relative_eq;
diff --git a/src/linalg/traits/stats.rs b/src/linalg/traits/stats.rs
index 8702a81a..6c3db820 100644
--- a/src/linalg/traits/stats.rs
+++ b/src/linalg/traits/stats.rs
@@ -142,7 +142,6 @@ pub trait MatrixPreprocessing<T: RealNumber>: MutArrayView2<T> + Clone {
///
/// assert_eq!(a, expected);
/// ```
-
fn binarize_mut(&mut self, threshold: T) {
let (nrows, ncols) = self.shape();
for row in 0..nrows {
diff --git a/src/svm/svc.rs b/src/svm/svc.rs
index 67ffdc33..cc5a0beb 100644
--- a/src/svm/svc.rs
+++ b/src/svm/svc.rs
@@ -1110,7 +1110,7 @@ mod tests {
let svc = SVC::fit(&x, &y, ¶ms).unwrap();
// serialization
- let deserialized_svc: SVC<f64, i32, _, _> =
+ let deserialized_svc: SVC<'_, f64, i32, _, _> =
serde_json::from_str(&serde_json::to_string(&svc).unwrap()).unwrap();
assert_eq!(svc, deserialized_svc);
diff --git a/src/svm/svr.rs b/src/svm/svr.rs
index 85b48e4b..4ce0aa28 100644
--- a/src/svm/svr.rs
+++ b/src/svm/svr.rs
@@ -702,7 +702,7 @@ mod tests {
let svr = SVR::fit(&x, &y, ¶ms).unwrap();
- let deserialized_svr: SVR<f64, DenseMatrix<f64>, _> =
+ let deserialized_svr: SVR<'_, f64, DenseMatrix<f64>, _> =
serde_json::from_str(&serde_json::to_string(&svr).unwrap()).unwrap();
assert_eq!(svr, deserialized_svr);
From fc7f2e61d9eb7785017d855a1c803cf6d0a2e814 Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 15:27:39 +0000
Subject: [PATCH 04/12] format
---
src/algorithm/neighbour/fastpair.rs | 4 +++-
src/linalg/basic/matrix.rs | 4 +---
src/linalg/ndarray/matrix.rs | 8 ++-----
src/preprocessing/numerical.rs | 14 +++++++------
src/tree/decision_tree_classifier.rs | 31 +++++++++++++++-------------
5 files changed, 31 insertions(+), 30 deletions(-)
diff --git a/src/algorithm/neighbour/fastpair.rs b/src/algorithm/neighbour/fastpair.rs
index 671517df..4e99261b 100644
--- a/src/algorithm/neighbour/fastpair.rs
+++ b/src/algorithm/neighbour/fastpair.rs
@@ -212,7 +212,9 @@ mod tests_fastpair {
use crate::linalg::basic::{arrays::Array, matrix::DenseMatrix};
/// Brute force algorithm, used only for comparison and testing
- pub fn closest_pair_brute(fastpair: &FastPair<'_, f64, DenseMatrix<f64>>) -> PairwiseDistance<f64> {
+ pub fn closest_pair_brute(
+ fastpair: &FastPair<'_, f64, DenseMatrix<f64>>,
+ ) -> PairwiseDistance<f64> {
use itertools::Itertools;
let m = fastpair.samples.shape().0;
diff --git a/src/linalg/basic/matrix.rs b/src/linalg/basic/matrix.rs
index 979e5a55..88a0849c 100644
--- a/src/linalg/basic/matrix.rs
+++ b/src/linalg/basic/matrix.rs
@@ -579,9 +579,7 @@ impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrix
}
}
-impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
- for DenseMatrixMutView<'_, T>
-{
+impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for DenseMatrixMutView<'_, T> {
fn set(&mut self, pos: (usize, usize), x: T) {
if self.column_major {
self.values[pos.0 + pos.1 * self.stride] = x;
diff --git a/src/linalg/ndarray/matrix.rs b/src/linalg/ndarray/matrix.rs
index e406a198..5040497a 100644
--- a/src/linalg/ndarray/matrix.rs
+++ b/src/linalg/ndarray/matrix.rs
@@ -146,9 +146,7 @@ impl<T: Number + RealNumber> SVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2>
impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayView<'_, T, Ix2> {}
-impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
- for ArrayViewMut<'_, T, Ix2>
-{
+impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayViewMut<'_, T, Ix2> {
fn get(&self, pos: (usize, usize)) -> &T {
&self[[pos.0, pos.1]]
}
@@ -175,9 +173,7 @@ impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
}
}
-impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
- for ArrayViewMut<'_, T, Ix2>
-{
+impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for ArrayViewMut<'_, T, Ix2> {
fn set(&mut self, pos: (usize, usize), x: T) {
self[[pos.0, pos.1]] = x
}
diff --git a/src/preprocessing/numerical.rs b/src/preprocessing/numerical.rs
index 8593d9f8..674f6814 100644
--- a/src/preprocessing/numerical.rs
+++ b/src/preprocessing/numerical.rs
@@ -172,12 +172,14 @@ where
T: Number + RealNumber,
M: Array2<T>,
{
- columns.first().cloned().map(|output_matrix| columns
- .iter()
- .skip(1)
- .fold(output_matrix, |current_matrix, new_colum| {
- current_matrix.h_stack(new_colum)
- }))
+ columns.first().cloned().map(|output_matrix| {
+ columns
+ .iter()
+ .skip(1)
+ .fold(output_matrix, |current_matrix, new_colum| {
+ current_matrix.h_stack(new_colum)
+ })
+ })
}
#[cfg(test)]
diff --git a/src/tree/decision_tree_classifier.rs b/src/tree/decision_tree_classifier.rs
index 712cd87d..f63cc2d9 100644
--- a/src/tree/decision_tree_classifier.rs
+++ b/src/tree/decision_tree_classifier.rs
@@ -77,9 +77,9 @@ use serde::{Deserialize, Serialize};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
+use crate::linalg::basic::arrays::MutArray;
use crate::linalg::basic::arrays::{Array1, Array2, MutArrayView1};
use crate::linalg::basic::matrix::DenseMatrix;
-use crate::linalg::basic::arrays::MutArray;
use crate::numbers::basenum::Number;
use crate::rand_custom::get_rng_impl;
@@ -890,7 +890,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
importances
}
-
/// Predict class probabilities for the input samples.
///
/// # Arguments
@@ -933,7 +932,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
/// of the input sample belonging to each class.
fn predict_proba_for_row(&self, x: &X, row: usize) -> Vec<f64> {
let mut node = 0;
-
+
while let Some(current_node) = self.nodes().get(node) {
if current_node.true_child.is_none() && current_node.false_child.is_none() {
// Leaf node reached
@@ -941,17 +940,17 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
probs[current_node.output] = 1.0;
return probs;
}
-
+
let split_feature = current_node.split_feature;
let split_value = current_node.split_value.unwrap_or(f64::NAN);
-
+
if x.get((row, split_feature)).to_f64().unwrap() <= split_value {
node = current_node.true_child.unwrap();
} else {
node = current_node.false_child.unwrap();
}
}
-
+
// This should never happen if the tree is properly constructed
vec![0.0; self.classes().len()]
}
@@ -960,8 +959,8 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
#[cfg(test)]
mod tests {
use super::*;
- use crate::linalg::basic::matrix::DenseMatrix;
use crate::linalg::basic::arrays::Array;
+ use crate::linalg::basic::matrix::DenseMatrix;
#[test]
fn search_parameters() {
@@ -1020,24 +1019,28 @@ mod tests {
&[6.9, 3.1, 4.9, 1.5],
&[5.5, 2.3, 4.0, 1.3],
&[6.5, 2.8, 4.6, 1.5],
- ]).unwrap();
+ ])
+ .unwrap();
let y: Vec<usize> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
-
+
let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
let probabilities = tree.predict_proba(&x).unwrap();
-
+
assert_eq!(probabilities.shape(), (10, 2));
-
+
for row in 0..10 {
let row_sum: f64 = probabilities.get_row(row).sum();
- assert!((row_sum - 1.0).abs() < 1e-6, "Row probabilities should sum to 1");
+ assert!(
+ (row_sum - 1.0).abs() < 1e-6,
+ "Row probabilities should sum to 1"
+ );
}
-
+
// Check if the first 5 samples have higher probability for class 0
for i in 0..5 {
assert!(probabilities.get((i, 0)) > probabilities.get((i, 1)));
}
-
+
// Check if the last 5 samples have higher probability for class 1
for i in 5..10 {
assert!(probabilities.get((i, 1)) > probabilities.get((i, 0)));
From 5711788fd82b7d38d0c6832b42acdab67bb178be Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 16:08:29 +0000
Subject: [PATCH 05/12] add proper error handling
---
src/tree/decision_tree_classifier.rs | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/src/tree/decision_tree_classifier.rs b/src/tree/decision_tree_classifier.rs
index f63cc2d9..5679516a 100644
--- a/src/tree/decision_tree_classifier.rs
+++ b/src/tree/decision_tree_classifier.rs
@@ -903,14 +903,14 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
///
/// # Errors
///
- /// Returns an error if the prediction process fails.
+ /// Returns an error if at least one row prediction process fails.
pub fn predict_proba(&self, x: &X) -> Result<DenseMatrix<f64>, Failed> {
let (n_samples, _) = x.shape();
let n_classes = self.classes().len();
let mut result = DenseMatrix::<f64>::zeros(n_samples, n_classes);
for i in 0..n_samples {
- let probs = self.predict_proba_for_row(x, i);
+ let probs = self.predict_proba_for_row(x, i)?;
for (j, &prob) in probs.iter().enumerate() {
result.set((i, j), prob);
}
@@ -930,7 +930,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
///
/// A vector of probabilities, one for each class, representing the probability
/// of the input sample belonging to each class.
- fn predict_proba_for_row(&self, x: &X, row: usize) -> Vec<f64> {
+ fn predict_proba_for_row(&self, x: &X, row: usize) -> Result<Vec<f64>, Failed> {
let mut node = 0;
while let Some(current_node) = self.nodes().get(node) {
@@ -938,7 +938,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
// Leaf node reached
let mut probs = vec![0.0; self.classes().len()];
probs[current_node.output] = 1.0;
- return probs;
+ return Ok(probs);
}
let split_feature = current_node.split_feature;
@@ -952,7 +952,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
}
// This should never happen if the tree is properly constructed
- vec![0.0; self.classes().len()]
+ Err(Failed::predict("Nodes iteration did not reach leaf"))
}
}
From 40ee35b04fe67776193934d9853fdcba3fe46e7b Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 17:15:52 +0000
Subject: [PATCH 06/12] Implement predict_proba for RandomForestClassifier
---
src/ensemble/random_forest_classifier.rs | 132 +++++++++++++++++++++++
1 file changed, 132 insertions(+)
diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index dabb2480..7f15be00 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -58,6 +58,8 @@ use crate::error::{Failed, FailedError};
use crate::linalg::basic::arrays::{Array1, Array2};
use crate::numbers::basenum::Number;
use crate::numbers::floatnum::FloatNumber;
+use crate::linalg::basic::matrix::DenseMatrix;
+use crate::linalg::basic::arrays::MutArray;
use crate::rand_custom::get_rng_impl;
use crate::tree::decision_tree_classifier::{
@@ -602,6 +604,72 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
}
samples
}
+
+ /// Predict class probabilities for X.
+ ///
+ /// The predicted class probabilities of an input sample are computed as
+ /// the mean predicted class probabilities of the trees in the forest.
+ /// The class probability of a single tree is the fraction of samples of
+ /// the same class in a leaf.
+ ///
+ /// # Arguments
+ ///
+ /// * `x` - The input samples. A matrix of shape (n_samples, n_features).
+ ///
+ /// # Returns
+ ///
+ /// * `Result<DenseMatrix<f64>, Failed>` - The class probabilities of the input samples.
+ /// The order of the classes corresponds to that in the attribute `classes_`.
+ /// The matrix has shape (n_samples, n_classes).
+ ///
+ /// # Errors
+ ///
+ /// Returns a `Failed` error if:
+ /// * The model has not been fitted yet.
+ /// * The input `x` is not compatible with the model's expected input.
+ /// * Any of the tree predictions fail.
+ ///
+ /// # Examples
+ ///
+ /// ```
+ /// use smartcore::ensemble::random_forest_classifier::RandomForestClassifier;
+ /// use smartcore::linalg::basic::matrix::DenseMatrix;
+ /// use smartcore::linalg::basic::arrays::Array;
+ ///
+ /// let x = DenseMatrix::from_2d_array(&[
+ /// &[5.1, 3.5, 1.4, 0.2],
+ /// &[4.9, 3.0, 1.4, 0.2],
+ /// &[7.0, 3.2, 4.7, 1.4],
+ /// ]).unwrap();
+ /// let y = vec![0, 0, 1];
+ ///
+ /// let forest = RandomForestClassifier::fit(&x, &y, Default::default()).unwrap();
+ /// let probas = forest.predict_proba(&x).unwrap();
+ ///
+ /// assert_eq!(probas.shape(), (3, 2));
+ /// ```
+ pub fn predict_proba(&self, x: &X) -> Result<DenseMatrix<f64>, Failed> {
+ let (n_samples, _) = x.shape();
+ let n_classes = self.classes.as_ref().unwrap().len();
+ let mut probas = DenseMatrix::<f64>::zeros(n_samples, n_classes);
+
+ for tree in self.trees.as_ref().unwrap().iter() {
+ let tree_predictions: Y = tree.predict(x).unwrap();
+
+ let mut i = 0;
+ for &class_idx in tree_predictions.iterator(0) {
+ let class_ = class_idx.to_usize().unwrap();
+ probas.add_element_mut((i, class_), 1.0);
+ i += 1;
+ }
+ }
+
+ let n_trees = self.trees.as_ref().unwrap().len() as f64;
+ probas.mul_scalar_mut(1.0 / n_trees);
+
+ Ok(probas)
+ }
+
}
#[cfg(test)]
@@ -609,6 +677,8 @@ mod tests {
use super::*;
use crate::linalg::basic::matrix::DenseMatrix;
use crate::metrics::*;
+ use crate::ensemble::random_forest_classifier::RandomForestClassifier;
+ use crate::linalg::basic::arrays::Array;
#[test]
fn search_parameters() {
@@ -760,6 +830,68 @@ mod tests {
);
}
+ #[cfg_attr(
+ all(target_arch = "wasm32", not(target_os = "wasi")),
+ wasm_bindgen_test::wasm_bindgen_test
+ )]
+ #[test]
+ fn test_random_forest_predict_proba() {
+ // Iris-like dataset (subset)
+ let x = DenseMatrix::from_2d_array(&[
+ &[5.1, 3.5, 1.4, 0.2],
+ &[4.9, 3.0, 1.4, 0.2],
+ &[4.7, 3.2, 1.3, 0.2],
+ &[4.6, 3.1, 1.5, 0.2],
+ &[5.0, 3.6, 1.4, 0.2],
+ &[7.0, 3.2, 4.7, 1.4],
+ &[6.4, 3.2, 4.5, 1.5],
+ &[6.9, 3.1, 4.9, 1.5],
+ &[5.5, 2.3, 4.0, 1.3],
+ &[6.5, 2.8, 4.6, 1.5],
+ ]).unwrap();
+ let y: Vec<u32> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
+
+ let forest = RandomForestClassifier::fit(&x, &y, Default::default()).unwrap();
+ let probas = forest.predict_proba(&x).unwrap();
+
+ // Test shape
+ assert_eq!(probas.shape(), (10, 2));
+
+ // Test probability sum
+ for i in 0..10 {
+ let row_sum: f64 = probas.get_row(i).sum();
+ assert!((row_sum - 1.0).abs() < 1e-6, "Row probabilities should sum to 1");
+ }
+
+ // Test class prediction
+ let predictions: Vec<u32> = (0..10)
+ .map(|i| if probas.get((i, 0)) > probas.get((i, 1)) { 0 } else { 1 })
+ .collect();
+ let acc = accuracy(&y, &predictions);
+ assert!(acc > 0.8, "Accuracy should be high for the training set");
+
+ // Test probability values
+ // These values are approximate and based on typical random forest behavior
+ for i in 0..5 {
+ assert!(*probas.get((i, 0)) > 0.6, "Class 0 samples should have high probability for class 0");
+ assert!(*probas.get((i, 1)) < 0.4, "Class 0 samples should have low probability for class 1");
+ }
+ for i in 5..10 {
+ assert!(*probas.get((i, 1)) > 0.6, "Class 1 samples should have high probability for class 1");
+ assert!(*probas.get((i, 0)) < 0.4, "Class 1 samples should have low probability for class 0");
+ }
+
+ // Test with new data
+ let x_new = DenseMatrix::from_2d_array(&[
+ &[5.0, 3.4, 1.5, 0.2], // Should be close to class 0
+ &[6.3, 3.3, 4.7, 1.6], // Should be close to class 1
+ ]).unwrap();
+ let probas_new = forest.predict_proba(&x_new).unwrap();
+ assert_eq!(probas_new.shape(), (2, 2));
+ assert!(probas_new.get((0, 0)) > probas_new.get((0, 1)), "First sample should be predicted as class 0");
+ assert!(probas_new.get((1, 1)) > probas_new.get((1, 0)), "Second sample should be predicted as class 1");
+ }
+
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
From 63fa00334b7ddb8d24e111ed746e5b96491c3273 Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 17:17:41 +0000
Subject: [PATCH 07/12] Fix clippy error
---
src/ensemble/random_forest_classifier.rs | 6 ++----
1 file changed, 2 insertions(+), 4 deletions(-)
diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index 7f15be00..f03c9cc7 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -655,12 +655,10 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
for tree in self.trees.as_ref().unwrap().iter() {
let tree_predictions: Y = tree.predict(x).unwrap();
-
- let mut i = 0;
- for &class_idx in tree_predictions.iterator(0) {
+
+ for (i, &class_idx) in tree_predictions.iterator(0).enumerate() {
let class_ = class_idx.to_usize().unwrap();
probas.add_element_mut((i, class_), 1.0);
- i += 1;
}
}
From 52b797d520ce592eb11b63711622a481d29408bd Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 17:18:09 +0000
Subject: [PATCH 08/12] format
---
src/ensemble/random_forest_classifier.rs | 62 +++++++++++++++++-------
1 file changed, 45 insertions(+), 17 deletions(-)
diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index f03c9cc7..19d75f38 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -55,11 +55,11 @@ use serde::{Deserialize, Serialize};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::{Failed, FailedError};
+use crate::linalg::basic::arrays::MutArray;
use crate::linalg::basic::arrays::{Array1, Array2};
+use crate::linalg::basic::matrix::DenseMatrix;
use crate::numbers::basenum::Number;
use crate::numbers::floatnum::FloatNumber;
-use crate::linalg::basic::matrix::DenseMatrix;
-use crate::linalg::basic::arrays::MutArray;
use crate::rand_custom::get_rng_impl;
use crate::tree::decision_tree_classifier::{
@@ -667,16 +667,15 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
Ok(probas)
}
-
}
#[cfg(test)]
mod tests {
use super::*;
- use crate::linalg::basic::matrix::DenseMatrix;
- use crate::metrics::*;
use crate::ensemble::random_forest_classifier::RandomForestClassifier;
use crate::linalg::basic::arrays::Array;
+ use crate::linalg::basic::matrix::DenseMatrix;
+ use crate::metrics::*;
#[test]
fn search_parameters() {
@@ -846,7 +845,8 @@ mod tests {
&[6.9, 3.1, 4.9, 1.5],
&[5.5, 2.3, 4.0, 1.3],
&[6.5, 2.8, 4.6, 1.5],
- ]).unwrap();
+ ])
+ .unwrap();
let y: Vec<u32> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
let forest = RandomForestClassifier::fit(&x, &y, Default::default()).unwrap();
@@ -858,12 +858,21 @@ mod tests {
// Test probability sum
for i in 0..10 {
let row_sum: f64 = probas.get_row(i).sum();
- assert!((row_sum - 1.0).abs() < 1e-6, "Row probabilities should sum to 1");
+ assert!(
+ (row_sum - 1.0).abs() < 1e-6,
+ "Row probabilities should sum to 1"
+ );
}
// Test class prediction
let predictions: Vec<u32> = (0..10)
- .map(|i| if probas.get((i, 0)) > probas.get((i, 1)) { 0 } else { 1 })
+ .map(|i| {
+ if probas.get((i, 0)) > probas.get((i, 1)) {
+ 0
+ } else {
+ 1
+ }
+ })
.collect();
let acc = accuracy(&y, &predictions);
assert!(acc > 0.8, "Accuracy should be high for the training set");
@@ -871,23 +880,42 @@ mod tests {
// Test probability values
// These values are approximate and based on typical random forest behavior
for i in 0..5 {
- assert!(*probas.get((i, 0)) > 0.6, "Class 0 samples should have high probability for class 0");
- assert!(*probas.get((i, 1)) < 0.4, "Class 0 samples should have low probability for class 1");
+ assert!(
+ *probas.get((i, 0)) > 0.6,
+ "Class 0 samples should have high probability for class 0"
+ );
+ assert!(
+ *probas.get((i, 1)) < 0.4,
+ "Class 0 samples should have low probability for class 1"
+ );
}
for i in 5..10 {
- assert!(*probas.get((i, 1)) > 0.6, "Class 1 samples should have high probability for class 1");
- assert!(*probas.get((i, 0)) < 0.4, "Class 1 samples should have low probability for class 0");
+ assert!(
+ *probas.get((i, 1)) > 0.6,
+ "Class 1 samples should have high probability for class 1"
+ );
+ assert!(
+ *probas.get((i, 0)) < 0.4,
+ "Class 1 samples should have low probability for class 0"
+ );
}
// Test with new data
let x_new = DenseMatrix::from_2d_array(&[
- &[5.0, 3.4, 1.5, 0.2], // Should be close to class 0
- &[6.3, 3.3, 4.7, 1.6], // Should be close to class 1
- ]).unwrap();
+ &[5.0, 3.4, 1.5, 0.2], // Should be close to class 0
+ &[6.3, 3.3, 4.7, 1.6], // Should be close to class 1
+ ])
+ .unwrap();
let probas_new = forest.predict_proba(&x_new).unwrap();
assert_eq!(probas_new.shape(), (2, 2));
- assert!(probas_new.get((0, 0)) > probas_new.get((0, 1)), "First sample should be predicted as class 0");
- assert!(probas_new.get((1, 1)) > probas_new.get((1, 0)), "Second sample should be predicted as class 1");
+ assert!(
+ probas_new.get((0, 0)) > probas_new.get((0, 1)),
+ "First sample should be predicted as class 0"
+ );
+ assert!(
+ probas_new.get((1, 1)) > probas_new.get((1, 0)),
+ "Second sample should be predicted as class 1"
+ );
}
#[cfg_attr(
From bb356e6a289209ad586eaf7af20217c12125a2ae Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 17:29:29 +0000
Subject: [PATCH 09/12] fix test
---
src/ensemble/random_forest_classifier.rs | 12 +++++++-----
1 file changed, 7 insertions(+), 5 deletions(-)
diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index 19d75f38..f398d135 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -833,8 +833,9 @@ mod tests {
)]
#[test]
fn test_random_forest_predict_proba() {
+ use num_traits::FromPrimitive;
// Iris-like dataset (subset)
- let x = DenseMatrix::from_2d_array(&[
+ let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
&[5.1, 3.5, 1.4, 0.2],
&[4.9, 3.0, 1.4, 0.2],
&[4.7, 3.2, 1.3, 0.2],
@@ -881,21 +882,22 @@ mod tests {
// These values are approximate and based on typical random forest behavior
for i in 0..5 {
assert!(
- *probas.get((i, 0)) > 0.6,
+ *probas.get((i, 0)) > f64::from_f32(0.6).unwrap(),
"Class 0 samples should have high probability for class 0"
);
assert!(
- *probas.get((i, 1)) < 0.4,
+ *probas.get((i, 1)) < f64::from_f32(0.4).unwrap(),
"Class 0 samples should have low probability for class 1"
);
}
+
for i in 5..10 {
assert!(
- *probas.get((i, 1)) > 0.6,
+ *probas.get((i, 1)) > f64::from_f32(0.6).unwrap(),
"Class 1 samples should have high probability for class 1"
);
assert!(
- *probas.get((i, 0)) < 0.4,
+ *probas.get((i, 0)) < f64::from_f32(0.4).unwrap(),
"Class 1 samples should have low probability for class 0"
);
}
From d427c91cef5bfdec9f0d629760e7da8b7703b810 Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Mon, 20 Jan 2025 20:12:41 +0000
Subject: [PATCH 10/12] try to fix test error
---
.github/CONTRIBUTING.md | 12 ++++++++++++
src/ensemble/random_forest_classifier.rs | 10 ++++++----
2 files changed, 18 insertions(+), 4 deletions(-)
diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
index 895db0f5..06d3e86c 100644
--- a/.github/CONTRIBUTING.md
+++ b/.github/CONTRIBUTING.md
@@ -70,3 +70,15 @@ $ rust-code-analysis-cli -p src/algorithm/neighbour/fastpair.rs --ls 22 --le 213
* **PRs on develop**: any change should be PRed first in `development`
* **testing**: everything should work and be tested as defined in the workflow. If any is failing for non-related reasons, annotate the test failure in the PR comment.
+
+
+## Suggestions for debugging
+1. Install `lldb` for your platform
+2. Run `rust-lldb target/debug/libsmartcore.rlib` in your command-line
+3. In lldb, set up some breakpoints using `b func_name` or `b src/path/to/file.rs:linenumber`
+4. In lldb, run a single test with `r the_name_of_your_test`
+
+Display variables in scope: `frame variable <name>`
+
+Execute expression: `p <expr>`
+
diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index f398d135..6c0258eb 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -856,8 +856,10 @@ mod tests {
// Test shape
assert_eq!(probas.shape(), (10, 2));
+ let (pro_n_rows, _) = probas.shape();
+
// Test probability sum
- for i in 0..10 {
+ for i in 0..pro_n_rows {
let row_sum: f64 = probas.get_row(i).sum();
assert!(
(row_sum - 1.0).abs() < 1e-6,
@@ -866,7 +868,7 @@ mod tests {
}
// Test class prediction
- let predictions: Vec<u32> = (0..10)
+ let predictions: Vec<u32> = (0..pro_n_rows)
.map(|i| {
if probas.get((i, 0)) > probas.get((i, 1)) {
0
@@ -880,7 +882,7 @@ mod tests {
// Test probability values
// These values are approximate and based on typical random forest behavior
- for i in 0..5 {
+ for i in 0..(pro_n_rows / 2) {
assert!(
*probas.get((i, 0)) > f64::from_f32(0.6).unwrap(),
"Class 0 samples should have high probability for class 0"
@@ -891,7 +893,7 @@ mod tests {
);
}
- for i in 5..10 {
+ for i in (pro_n_rows / 2)..pro_n_rows {
assert!(
*probas.get((i, 1)) > f64::from_f32(0.6).unwrap(),
"Class 1 samples should have high probability for class 1"
From 4aee603ae4634c44150150de84c2d9b37bd803db Mon Sep 17 00:00:00 2001
From: Lorenzo Mec-iS <tunedconsulting@gmail.com>
Date: Wed, 22 Jan 2025 12:08:11 +0000
Subject: [PATCH 11/12] fix test conditions
---
src/ensemble/random_forest_classifier.rs | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index 6c0258eb..b302ef4d 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -662,7 +662,7 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
}
}
- let n_trees = self.trees.as_ref().unwrap().len() as f64;
+ let n_trees: f64 = self.trees.as_ref().unwrap().len() as f64;
probas.mul_scalar_mut(1.0 / n_trees);
Ok(probas)
@@ -884,22 +884,22 @@ mod tests {
// These values are approximate and based on typical random forest behavior
for i in 0..(pro_n_rows / 2) {
assert!(
- *probas.get((i, 0)) > f64::from_f32(0.6).unwrap(),
+ f64::from_f32(0.6).unwrap().lt(probas.get((i, 0))),
"Class 0 samples should have high probability for class 0"
);
assert!(
- *probas.get((i, 1)) < f64::from_f32(0.4).unwrap(),
+ f64::from_f32(0.4).unwrap().gt(probas.get((i, 1))),
"Class 0 samples should have low probability for class 1"
);
}
for i in (pro_n_rows / 2)..pro_n_rows {
assert!(
- *probas.get((i, 1)) > f64::from_f32(0.6).unwrap(),
+ f64::from_f32(0.6).unwrap().lt(probas.get((i, 1))),
"Class 1 samples should have high probability for class 1"
);
assert!(
- *probas.get((i, 0)) < f64::from_f32(0.4).unwrap(),
+ f64::from_f32(0.4).unwrap().gt(probas.get((i, 0))),
"Class 1 samples should have low probability for class 0"
);
}
From 78780787db8cebcb99500322c62a12a7198ea781 Mon Sep 17 00:00:00 2001
From: Lorenzo <tunedconsulting@gmail.com>
Date: Wed, 22 Jan 2025 12:12:07 +0000
Subject: [PATCH 12/12] Update ci.yml
---
.github/workflows/ci.yml | 1 +
1 file changed, 1 insertion(+)
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d7942c8f..71f200b4 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -23,6 +23,7 @@ jobs:
]
env:
TZ: "/usr/share/zoneinfo/your/location"
+ RUST_BACKTRACE: "1"
steps:
- uses: actions/checkout@v3
- name: Cache .cargo and target