Merge branch 'master' into master

.github/workflows/checking.yml

@@ -10,7 +10,7 @@ jobs:
       fail-fast: false
       matrix:
         toolchain:
-          - 1.67.0
+          - 1.70.0
           - stable
           - nightly
         os:

.github/workflows/codequality.yml

@@ -10,7 +10,7 @@ jobs:
     strategy:
       matrix:
         toolchain:
-          - 1.67.0
+          - 1.70.0
           - stable
 
     steps:

.github/workflows/testing.yml

@@ -10,7 +10,7 @@ jobs:
       fail-fast: false
       matrix:
         toolchain:
-          - 1.67.0
+          - 1.70.0
           - stable
         os:
           - ubuntu-latest
@@ -35,7 +35,7 @@ jobs:
       fail-fast: false
       matrix:
         toolchain:
-          - 1.67.0
+          - 1.70.0
           - stable
         os:
           - ubuntu-latest

algorithms/linfa-bayes/src/gaussian_nb.rs

@@ -84,7 +84,7 @@ where
             let nclass = xclass.nrows();
 
             // We compute the update of the gaussian mean and variance
-            let mut class_info = model
+            let class_info = model
                 .class_info
                 .entry(class)
                 .or_insert_with(GaussianClassInfo::default);

algorithms/linfa-bayes/src/multinomial_nb.rs

@@ -71,7 +71,7 @@ where
             let nclass = xclass.nrows();
 
             // We compute the feature log probabilities and feature counts on the slice corresponding to the current class
-            let mut class_info = model
+            let class_info = model
                 .class_info
                 .entry(class)
                 .or_insert_with(MultinomialClassInfo::default);

algorithms/linfa-clustering/src/gaussian_mixture/algorithm.rs

@@ -1,4 +1,4 @@
-use crate::gaussian_mixture::errors::{GmmError, Result};
+use crate::gaussian_mixture::errors::GmmError;
 use crate::gaussian_mixture::hyperparams::{
     GmmCovarType, GmmInitMethod, GmmParams, GmmValidParams,
 };
@@ -126,7 +126,7 @@ impl<F: Float> GaussianMixtureModel<F> {
         hyperparameters: &GmmValidParams<F, R>,
         dataset: &DatasetBase<ArrayBase<D, Ix2>, T>,
         mut rng: R,
-    ) -> Result<GaussianMixtureModel<F>> {
+    ) -> Result<GaussianMixtureModel<F>, GmmError> {
         let observations = dataset.records().view();
         let n_samples = observations.nrows();
 
@@ -216,7 +216,7 @@ impl<F: Float> GaussianMixtureModel<F> {
         resp: &Array2<F>,
         _covar_type: &GmmCovarType,
         reg_covar: F,
-    ) -> Result<(Array1<F>, Array2<F>, Array3<F>)> {
+    ) -> Result<(Array1<F>, Array2<F>, Array3<F>), GmmError> {
         let nk = resp.sum_axis(Axis(0));
         if nk.min()? < &(F::cast(10.) * F::epsilon()) {
             return Err(GmmError::EmptyCluster(format!(
@@ -255,7 +255,7 @@ impl<F: Float> GaussianMixtureModel<F> {
 
     fn compute_precisions_cholesky_full<D: Data<Elem = F>>(
         covariances: &ArrayBase<D, Ix3>,
-    ) -> Result<Array3<F>> {
+    ) -> Result<Array3<F>, GmmError> {
         let n_clusters = covariances.shape()[0];
         let n_features = covariances.shape()[1];
         let mut precisions_chol = Array::zeros((n_clusters, n_features, n_features));
@@ -290,7 +290,7 @@ impl<F: Float> GaussianMixtureModel<F> {
     fn e_step<D: Data<Elem = F>>(
         &self,
         observations: &ArrayBase<D, Ix2>,
-    ) -> Result<(F, Array2<F>)> {
+    ) -> Result<(F, Array2<F>), GmmError> {
         let (log_prob_norm, log_resp) = self.estimate_log_prob_resp(observations);
         let log_mean = log_prob_norm.mean().unwrap();
         Ok((log_mean, log_resp))
@@ -301,7 +301,7 @@ impl<F: Float> GaussianMixtureModel<F> {
         reg_covar: F,
         observations: &ArrayBase<D, Ix2>,
         log_resp: &Array2<F>,
-    ) -> Result<()> {
+    ) -> Result<(), GmmError> {
         let n_samples = observations.nrows();
         let (weights, means, covariances) = Self::estimate_gaussian_parameters(
             observations,
@@ -407,7 +407,7 @@ impl<F: Float, R: Rng + Clone, D: Data<Elem = F>, T> Fit<ArrayBase<D, Ix2>, T, G
 {
     type Object = GaussianMixtureModel<F>;
 
-    fn fit(&self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>) -> Result<Self::Object> {
+    fn fit(&self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>) -> Result<Self::Object, GmmError> {
         let observations = dataset.records().view();
         let mut gmm = GaussianMixtureModel::<F>::new(self, dataset, self.rng())?;
 

algorithms/linfa-clustering/src/gaussian_mixture/errors.rs

@@ -4,7 +4,6 @@ use linfa_linalg::LinalgError;
 #[cfg(feature = "blas")]
 use ndarray_linalg::error::LinalgError;
 use thiserror::Error;
-pub type Result<T> = std::result::Result<T, GmmError>;
 
 /// An error when modeling a GMM algorithm
 #[derive(Error, Debug)]

algorithms/linfa-clustering/src/gaussian_mixture/hyperparams.rs

@@ -1,4 +1,4 @@
-use crate::gaussian_mixture::errors::{GmmError, Result};
+use crate::gaussian_mixture::errors::GmmError;
 use ndarray_rand::rand::{Rng, SeedableRng};
 use rand_xoshiro::Xoshiro256Plus;
 #[cfg(feature = "serde")]
@@ -170,7 +170,7 @@ impl<F: Float, R: Rng> ParamGuard for GmmParams<F, R> {
     type Checked = GmmValidParams<F, R>;
     type Error = GmmError;
 
-    fn check_ref(&self) -> Result<&Self::Checked> {
+    fn check_ref(&self) -> Result<&Self::Checked, GmmError> {
         if self.0.n_clusters == 0 {
             Err(GmmError::InvalidValue(
                 "`n_clusters` cannot be 0!".to_string(),
@@ -194,7 +194,7 @@ impl<F: Float, R: Rng> ParamGuard for GmmParams<F, R> {
         }
     }
 
-    fn check(self) -> Result<Self::Checked> {
+    fn check(self) -> Result<Self::Checked, GmmError> {
         self.check_ref()?;
         Ok(self.0)
     }

algorithms/linfa-clustering/src/optics/errors.rs

@@ -1,5 +1,4 @@
 use thiserror::Error;
-pub type Result<T> = std::result::Result<T, OpticsError>;
 
 /// An error when performing OPTICS Analysis
 #[derive(Error, Debug)]

algorithms/linfa-clustering/src/optics/hyperparams.rs

@@ -1,4 +1,4 @@
-use crate::optics::errors::{OpticsError, Result};
+use crate::optics::errors::OpticsError;
 use linfa::{param_guard::TransformGuard, Float, ParamGuard};
 #[cfg(feature = "serde")]
 use serde_crate::{Deserialize, Serialize};
@@ -91,7 +91,7 @@ impl<F: Float, D, N> ParamGuard for OpticsParams<F, D, N> {
     type Checked = OpticsValidParams<F, D, N>;
     type Error = OpticsError;
 
-    fn check_ref(&self) -> Result<&Self::Checked> {
+    fn check_ref(&self) -> Result<&Self::Checked, OpticsError> {
         if self.0.tolerance <= F::zero() {
             Err(OpticsError::InvalidValue(
                 "`tolerance` must be greater than 0!".to_string(),
@@ -106,7 +106,7 @@ impl<F: Float, D, N> ParamGuard for OpticsParams<F, D, N> {
         }
     }
 
-    fn check(self) -> Result<Self::Checked> {
+    fn check(self) -> Result<Self::Checked, OpticsError> {
         self.check_ref()?;
         Ok(self.0)
     }

src/benchmarks/mod.rs

@@ -1,6 +1,8 @@
 #[cfg(feature = "benchmarks")]
 pub mod config {
-    use criterion::{measurement::WallTime, BenchmarkGroup, Criterion};
+    #[cfg(not(target_os = "windows"))]
+    use criterion::Criterion;
+    use criterion::{measurement::WallTime, BenchmarkGroup};
     #[cfg(not(target_os = "windows"))]
     use pprof::criterion::{Output, PProfProfiler};
     use std::time::Duration;