feat: puts ndarray and nalgebra bindings behind feature flags

.circleci/config.yml

@@ -15,10 +15,10 @@ jobs:
           command: cargo fmt -- --check
       - run:
           name: Stable Build
-          command: cargo build
+          command: cargo build --features "nalgebra-bindings ndarray-bindings"
       - run:
           name: Test
-          command: cargo test
+          command: cargo test --features "nalgebra-bindings ndarray-bindings"
       - save_cache:
           key: project-cache
           paths:

Cargo.toml

@@ -4,9 +4,14 @@ version = "0.1.0"
 authors = ["SmartCore Developers"]
 edition = "2018"
 
+[features]
+default = []
+ndarray-bindings = ["ndarray"]
+nalgebra-bindings = ["nalgebra"]
+
 [dependencies]
-ndarray = "0.13"
+ndarray = { version = "0.13", optional = true }
-nalgebra = "0.22.0"
+nalgebra = { version = "0.22.0", optional = true }
 num-traits = "0.2.12"
 num = "0.3.0"
 rand = "0.7.3"

src/ensemble/random_forest_regressor.rs

@@ -124,7 +124,6 @@ impl<T: FloatExt> RandomForestRegressor<T> {
 mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::DenseMatrix;
-    use ndarray::{arr1, arr2};
 
     #[test]
     fn fit_longley() {
@@ -173,53 +172,6 @@ mod tests {
         }
     }
 
-    #[test]
-    fn my_fit_longley_ndarray() {
-        let x = arr2(&[
-            [234.289, 235.6, 159., 107.608, 1947., 60.323],
-            [259.426, 232.5, 145.6, 108.632, 1948., 61.122],
-            [258.054, 368.2, 161.6, 109.773, 1949., 60.171],
-            [284.599, 335.1, 165., 110.929, 1950., 61.187],
-            [328.975, 209.9, 309.9, 112.075, 1951., 63.221],
-            [346.999, 193.2, 359.4, 113.27, 1952., 63.639],
-            [365.385, 187., 354.7, 115.094, 1953., 64.989],
-            [363.112, 357.8, 335., 116.219, 1954., 63.761],
-            [397.469, 290.4, 304.8, 117.388, 1955., 66.019],
-            [419.18, 282.2, 285.7, 118.734, 1956., 67.857],
-            [442.769, 293.6, 279.8, 120.445, 1957., 68.169],
-            [444.546, 468.1, 263.7, 121.95, 1958., 66.513],
-            [482.704, 381.3, 255.2, 123.366, 1959., 68.655],
-            [502.601, 393.1, 251.4, 125.368, 1960., 69.564],
-            [518.173, 480.6, 257.2, 127.852, 1961., 69.331],
-            [554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ]);
-        let y = arr1(&[
-            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
-            114.2, 115.7, 116.9,
-        ]);
-
-        let expected_y: Vec<f64> = vec![
-            85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.,
-        ];
-
-        let y_hat = RandomForestRegressor::fit(
-            &x,
-            &y,
-            RandomForestRegressorParameters {
-                max_depth: None,
-                min_samples_leaf: 1,
-                min_samples_split: 2,
-                n_trees: 1000,
-                mtry: Option::None,
-            },
-        )
-        .predict(&x);
-
-        for i in 0..y_hat.len() {
-            assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
-        }
-    }
-
     #[test]
     fn serde() {
         let x = DenseMatrix::from_array(&[

src/linalg/mod.rs

@@ -1,7 +1,9 @@
 pub mod evd;
 pub mod lu;
 pub mod naive;
+#[cfg(feature = "nalgebra-bindings")]
 pub mod nalgebra_bindings;
+#[cfg(feature = "ndarray-bindings")]
 pub mod ndarray_bindings;
 pub mod qr;
 pub mod svd;

src/linalg/nalgebra_bindings.rs

@@ -368,6 +368,7 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
 #[cfg(test)]
 mod tests {
     use super::*;
+    use crate::linear::linear_regression::*;
     use nalgebra::{DMatrix, Matrix2x3, RowDVector};
 
     #[test]
@@ -674,4 +675,49 @@ mod tests {
         assert_eq!(res.len(), 7);
         assert_eq!(res, vec![-7., -6., -2., 1., 2., 3., 4.]);
     }
+
+    #[test]
+    fn ols_fit_predict() {
+        let x = DMatrix::from_row_slice(
+            16,
+            6,
+            &[
+                234.289, 235.6, 159.0, 107.608, 1947., 60.323, 259.426, 232.5, 145.6, 108.632,
+                1948., 61.122, 258.054, 368.2, 161.6, 109.773, 1949., 60.171, 284.599, 335.1,
+                165.0, 110.929, 1950., 61.187, 328.975, 209.9, 309.9, 112.075, 1951., 63.221,
+                346.999, 193.2, 359.4, 113.270, 1952., 63.639, 365.385, 187.0, 354.7, 115.094,
+                1953., 64.989, 363.112, 357.8, 335.0, 116.219, 1954., 63.761, 397.469, 290.4,
+                304.8, 117.388, 1955., 66.019, 419.180, 282.2, 285.7, 118.734, 1956., 67.857,
+                442.769, 293.6, 279.8, 120.445, 1957., 68.169, 444.546, 468.1, 263.7, 121.950,
+                1958., 66.513, 482.704, 381.3, 255.2, 123.366, 1959., 68.655, 502.601, 393.1,
+                251.4, 125.368, 1960., 69.564, 518.173, 480.6, 257.2, 127.852, 1961., 69.331,
+                554.894, 400.7, 282.7, 130.081, 1962., 70.551,
+            ],
+        );
+
+        let y: RowDVector<f64> = RowDVector::from_vec(vec![
+            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
+            114.2, 115.7, 116.9,
+        ]);
+
+        let y_hat_qr = LinearRegression::fit(
+            &x,
+            &y,
+            LinearRegressionParameters {
+                solver: LinearRegressionSolverName::QR,
+            },
+        )
+        .predict(&x);
+
+        let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
+
+        assert!(y
+            .iter()
+            .zip(y_hat_qr.iter())
+            .all(|(&a, &b)| (a - b).abs() <= 5.0));
+        assert!(y
+            .iter()
+            .zip(y_hat_svd.iter())
+            .all(|(&a, &b)| (a - b).abs() <= 5.0));
+    }
 }

src/linalg/ndarray_bindings.rs

@@ -336,7 +336,9 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
 #[cfg(test)]
 mod tests {
     use super::*;
-    use ndarray::{arr1, arr2, Array2};
+    use crate::ensemble::random_forest_regressor::*;
+    use crate::linear::logistic_regression::*;
+    use ndarray::{arr1, arr2, Array1, Array2};
 
     #[test]
     fn vec_get_set() {
@@ -654,4 +656,92 @@ mod tests {
         a.abs_mut();
         assert_eq!(a, expected);
     }
+
+    #[test]
+    fn lr_fit_predict_iris() {
+        let x = arr2(&[
+            [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],
+            [5.4, 3.9, 1.7, 0.4],
+            [4.6, 3.4, 1.4, 0.3],
+            [5.0, 3.4, 1.5, 0.2],
+            [4.4, 2.9, 1.4, 0.2],
+            [4.9, 3.1, 1.5, 0.1],
+            [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],
+            [5.7, 2.8, 4.5, 1.3],
+            [6.3, 3.3, 4.7, 1.6],
+            [4.9, 2.4, 3.3, 1.0],
+            [6.6, 2.9, 4.6, 1.3],
+            [5.2, 2.7, 3.9, 1.4],
+        ]);
+        let y: Array1<f64> = arr1(&[
+            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+        ]);
+
+        let lr = LogisticRegression::fit(&x, &y);
+
+        let y_hat = lr.predict(&x);
+
+        let error: f64 = y
+            .into_iter()
+            .zip(y_hat.into_iter())
+            .map(|(&a, &b)| (a - b).abs())
+            .sum();
+
+        assert!(error <= 1.0);
+    }
+
+    #[test]
+    fn my_fit_longley_ndarray() {
+        let x = arr2(&[
+            [234.289, 235.6, 159., 107.608, 1947., 60.323],
+            [259.426, 232.5, 145.6, 108.632, 1948., 61.122],
+            [258.054, 368.2, 161.6, 109.773, 1949., 60.171],
+            [284.599, 335.1, 165., 110.929, 1950., 61.187],
+            [328.975, 209.9, 309.9, 112.075, 1951., 63.221],
+            [346.999, 193.2, 359.4, 113.27, 1952., 63.639],
+            [365.385, 187., 354.7, 115.094, 1953., 64.989],
+            [363.112, 357.8, 335., 116.219, 1954., 63.761],
+            [397.469, 290.4, 304.8, 117.388, 1955., 66.019],
+            [419.18, 282.2, 285.7, 118.734, 1956., 67.857],
+            [442.769, 293.6, 279.8, 120.445, 1957., 68.169],
+            [444.546, 468.1, 263.7, 121.95, 1958., 66.513],
+            [482.704, 381.3, 255.2, 123.366, 1959., 68.655],
+            [502.601, 393.1, 251.4, 125.368, 1960., 69.564],
+            [518.173, 480.6, 257.2, 127.852, 1961., 69.331],
+            [554.894, 400.7, 282.7, 130.081, 1962., 70.551],
+        ]);
+        let y = arr1(&[
+            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
+            114.2, 115.7, 116.9,
+        ]);
+
+        let expected_y: Vec<f64> = vec![
+            85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.,
+        ];
+
+        let y_hat = RandomForestRegressor::fit(
+            &x,
+            &y,
+            RandomForestRegressorParameters {
+                max_depth: None,
+                min_samples_leaf: 1,
+                min_samples_split: 2,
+                n_trees: 1000,
+                mtry: Option::None,
+            },
+        )
+        .predict(&x);
+
+        for i in 0..y_hat.len() {
+            assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
+        }
+    }
 }

src/linear/linear_regression.rs

@@ -13,7 +13,7 @@ pub enum LinearRegressionSolverName {
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct LinearRegressionParameters {
-    solver: LinearRegressionSolverName,
+    pub solver: LinearRegressionSolverName,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
@@ -81,55 +81,9 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
 mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::*;
-    use nalgebra::{DMatrix, RowDVector};
 
     #[test]
     fn ols_fit_predict() {
-        let x = DMatrix::from_row_slice(
-            16,
-            6,
-            &[
-                234.289, 235.6, 159.0, 107.608, 1947., 60.323, 259.426, 232.5, 145.6, 108.632,
-                1948., 61.122, 258.054, 368.2, 161.6, 109.773, 1949., 60.171, 284.599, 335.1,
-                165.0, 110.929, 1950., 61.187, 328.975, 209.9, 309.9, 112.075, 1951., 63.221,
-                346.999, 193.2, 359.4, 113.270, 1952., 63.639, 365.385, 187.0, 354.7, 115.094,
-                1953., 64.989, 363.112, 357.8, 335.0, 116.219, 1954., 63.761, 397.469, 290.4,
-                304.8, 117.388, 1955., 66.019, 419.180, 282.2, 285.7, 118.734, 1956., 67.857,
-                442.769, 293.6, 279.8, 120.445, 1957., 68.169, 444.546, 468.1, 263.7, 121.950,
-                1958., 66.513, 482.704, 381.3, 255.2, 123.366, 1959., 68.655, 502.601, 393.1,
-                251.4, 125.368, 1960., 69.564, 518.173, 480.6, 257.2, 127.852, 1961., 69.331,
-                554.894, 400.7, 282.7, 130.081, 1962., 70.551,
-            ],
-        );
-
-        let y: RowDVector<f64> = RowDVector::from_vec(vec![
-            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
-            114.2, 115.7, 116.9,
-        ]);
-
-        let y_hat_qr = LinearRegression::fit(
-            &x,
-            &y,
-            LinearRegressionParameters {
-                solver: LinearRegressionSolverName::QR,
-            },
-        )
-        .predict(&x);
-
-        let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
-
-        assert!(y
-            .iter()
-            .zip(y_hat_qr.iter())
-            .all(|(&a, &b)| (a - b).abs() <= 5.0));
-        assert!(y
-            .iter()
-            .zip(y_hat_svd.iter())
-            .all(|(&a, &b)| (a - b).abs() <= 5.0));
-    }
-
-    #[test]
-    fn ols_fit_predict_nalgebra() {
         let x = DenseMatrix::from_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
             &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],

src/linear/logistic_regression.rs

@@ -265,7 +265,6 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
 mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::*;
-    use ndarray::{arr1, arr2, Array1};
 
     #[test]
     fn multiclass_objective_f() {
@@ -426,31 +425,31 @@ mod tests {
 
     #[test]
     fn lr_fit_predict_iris() {
-        let x = arr2(&[
+        let x = DenseMatrix::from_array(&[
-            [5.1, 3.5, 1.4, 0.2],
+            &[5.1, 3.5, 1.4, 0.2],
-            [4.9, 3.0, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
-            [4.7, 3.2, 1.3, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
-            [4.6, 3.1, 1.5, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
-            [5.0, 3.6, 1.4, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
-            [5.4, 3.9, 1.7, 0.4],
+            &[5.4, 3.9, 1.7, 0.4],
-            [4.6, 3.4, 1.4, 0.3],
+            &[4.6, 3.4, 1.4, 0.3],
-            [5.0, 3.4, 1.5, 0.2],
+            &[5.0, 3.4, 1.5, 0.2],
-            [4.4, 2.9, 1.4, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
-            [4.9, 3.1, 1.5, 0.1],
+            &[4.9, 3.1, 1.5, 0.1],
-            [7.0, 3.2, 4.7, 1.4],
+            &[7.0, 3.2, 4.7, 1.4],
-            [6.4, 3.2, 4.5, 1.5],
+            &[6.4, 3.2, 4.5, 1.5],
-            [6.9, 3.1, 4.9, 1.5],
+            &[6.9, 3.1, 4.9, 1.5],
-            [5.5, 2.3, 4.0, 1.3],
+            &[5.5, 2.3, 4.0, 1.3],
-            [6.5, 2.8, 4.6, 1.5],
+            &[6.5, 2.8, 4.6, 1.5],
-            [5.7, 2.8, 4.5, 1.3],
+            &[5.7, 2.8, 4.5, 1.3],
-            [6.3, 3.3, 4.7, 1.6],
+            &[6.3, 3.3, 4.7, 1.6],
-            [4.9, 2.4, 3.3, 1.0],
+            &[4.9, 2.4, 3.3, 1.0],
-            [6.6, 2.9, 4.6, 1.3],
+            &[6.6, 2.9, 4.6, 1.3],
-            [5.2, 2.7, 3.9, 1.4],
+            &[5.2, 2.7, 3.9, 1.4],
         ]);
-        let y: Array1<f64> = arr1(&[
+        let y: Vec<f64> = vec![
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
-        ]);
+        ];
 
         let lr = LogisticRegression::fit(&x, &y);
 
@@ -459,7 +458,7 @@ mod tests {
         let error: f64 = y
             .into_iter()
             .zip(y_hat.into_iter())
-            .map(|(&a, &b)| (a - b).abs())
+            .map(|(a, b)| (a - b).abs())
             .sum();
 
         assert!(error <= 1.0);