Add target names to Dataset

.github/workflows/codequality.yml

@@ -33,7 +33,7 @@ jobs:
     needs: codequality
     name: coverage
     runs-on: ubuntu-latest
-    if: github.event.pull_request.draft == false && (github.event_name == 'pull_request' || github.ref == 'refs/heads/master')
+    if: github.event_name == 'pull_request' || github.ref == 'refs/heads/master'
 
     steps:
       - name: Checkout sources

algorithms/linfa-preprocessing/src/linear_scaling.rs

@@ -307,12 +307,14 @@ impl<F: Float, D: Data<Elem = F>, T: AsTargets>
     /// Substitutes the records of the dataset with their scaled version.
     /// Panics if the shape of the records is not compatible with the shape of the dataset used for fitting.
     fn transform(&self, x: DatasetBase<ArrayBase<D, Ix2>, T>) -> DatasetBase<Array2<F>, T> {
-        let feature_names = x.feature_names();
+        let feature_names = x.feature_names().to_vec();
+        let target_names = x.target_names().to_vec();
         let (records, targets, weights) = (x.records, x.targets, x.weights);
         let records = self.transform(records.to_owned());
         DatasetBase::new(records, targets)
             .with_weights(weights)
             .with_feature_names(feature_names)
+            .with_target_names(target_names)
     }
 }
 
@@ -575,7 +577,7 @@ mod tests {
     #[test]
     fn test_retain_feature_names() {
         let dataset = linfa_datasets::diabetes();
-        let original_feature_names = dataset.feature_names();
+        let original_feature_names = dataset.feature_names().to_vec();
         let transformed = LinearScaler::standard()
             .fit(&dataset)
             .unwrap()

algorithms/linfa-preprocessing/src/norm_scaling.rs

@@ -94,12 +94,14 @@ impl<F: Float, D: Data<Elem = F>, T: AsTargets>
 {
     /// Substitutes the records of the dataset with their scaled versions with unit norm.
     fn transform(&self, x: DatasetBase<ArrayBase<D, Ix2>, T>) -> DatasetBase<Array2<F>, T> {
-        let feature_names = x.feature_names();
+        let feature_names = x.feature_names().to_vec();
+        let target_names = x.target_names().to_vec();
         let (records, targets, weights) = (x.records, x.targets, x.weights);
         let records = self.transform(records.to_owned());
         DatasetBase::new(records, targets)
             .with_weights(weights)
             .with_feature_names(feature_names)
+            .with_target_names(target_names)
     }
 }
 
@@ -160,7 +162,7 @@ mod tests {
     #[test]
     fn test_retain_feature_names() {
         let dataset = linfa_datasets::diabetes();
-        let original_feature_names = dataset.feature_names();
+        let original_feature_names = dataset.feature_names().to_vec();
         let transformed = NormScaler::l2().transform(dataset);
         assert_eq!(original_feature_names, transformed.feature_names())
     }

algorithms/linfa-preprocessing/src/whitening.rs

@@ -209,12 +209,14 @@ impl<F: Float, D: Data<Elem = F>, T: AsTargets>
     for FittedWhitener<F>
 {
     fn transform(&self, x: DatasetBase<ArrayBase<D, Ix2>, T>) -> DatasetBase<Array2<F>, T> {
-        let feature_names = x.feature_names();
+        let feature_names = x.feature_names().to_vec();
+        let target_names = x.target_names().to_vec();
         let (records, targets, weights) = (x.records, x.targets, x.weights);
         let records = self.transform(records.to_owned());
         DatasetBase::new(records, targets)
             .with_weights(weights)
             .with_feature_names(feature_names)
+            .with_target_names(target_names)
     }
 }
 
@@ -334,7 +336,7 @@ mod tests {
     #[test]
     fn test_retain_feature_names() {
         let dataset = linfa_datasets::diabetes();
-        let original_feature_names = dataset.feature_names();
+        let original_feature_names = dataset.feature_names().to_vec();
         let transformed = Whitener::cholesky()
             .fit(&dataset)
             .unwrap()

algorithms/linfa-trees/src/decision_trees/algorithm.rs

@@ -523,7 +523,13 @@
     /// a matrix of features `x` and an array of labels `y`.
     fn fit(&self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>) -> Result<Self::Object> {
         let x = dataset.records();
-        let feature_names = dataset.feature_names();
+        let feature_names = if dataset.feature_names().is_empty() {
+            (0..x.nfeatures())
+                .map(|idx| format!("feature-{idx}"))
+                .collect()
+        } else {
+            dataset.feature_names().to_vec()
+        };
         let all_idxs = RowMask::all(x.nrows());
         let sorted_indices: Vec<_> = (0..(x.ncols()))
             .map(|feature_idx| {

datasets/src/dataset.rs

@@ -131,8 +131,11 @@ pub fn linnerud() -> Dataset<f64, f64> {
     let output_array = array_from_gz_csv(&output_data[..], true, b',').unwrap();
 
     let feature_names = vec!["Chins", "Situps", "Jumps"];
+    let target_names = vec!["Weight", "Waist", "Pulse"];
 
-    Dataset::new(input_array, output_array).with_feature_names(feature_names)
+    Dataset::new(input_array, output_array)
+        .with_feature_names(feature_names)
+        .with_target_names(target_names)
 }
 
 #[cfg(test)]
@@ -261,6 +264,10 @@ mod tests {
         let feature_names = vec!["Chins", "Situps", "Jumps"];
         assert_eq!(ds.feature_names(), feature_names);
 
+        // check for target names
+        let target_names = vec!["Weight", "Waist", "Pulse"];
+        assert_eq!(ds.target_names(), target_names);
+
         // get the mean per target: Weight, Waist, Pulse
         let mean_targets = ds.targets().mean_axis(Axis(0)).unwrap();
         assert_abs_diff_eq!(mean_targets, array![178.6, 35.4, 56.1]);

src/correlation.rs

@@ -169,7 +169,7 @@
         PearsonCorrelation {
             pearson_coeffs,
             p_values,
-            feature_names: dataset.feature_names(),
+            feature_names: dataset.feature_names().to_vec(),
         }
     }
 

src/dataset/impl_dataset.rs

@@ -30,6 +30,7 @@
             targets,
             weights: Array1::zeros(0),
             feature_names: Vec::new(),
+            target_names: Vec::new(),
         }
     }
 
@@ -60,14 +61,8 @@
     /// A feature name gives a human-readable string describing the purpose of a single feature.
     /// This allow the reader to understand its purpose while analysing results, for example
     /// correlation analysis or feature importance.
-    pub fn feature_names(&self) -> Vec<String> {
-        if !self.feature_names.is_empty() {
-            self.feature_names.clone()
-        } else {
-            (0..self.records.nfeatures())
-                .map(|idx| format!("feature-{idx}"))
-                .collect()
-        }
+    pub fn feature_names(&self) -> &[String] {
+        &self.feature_names
     }
 
     /// Return records of a dataset
@@ -81,13 +76,14 @@
     /// Updates the records of a dataset
     ///
     /// This function overwrites the records in a dataset. It also invalidates the weights and
-    /// feature names.
+    /// feature/target names.
     pub fn with_records<T: Records>(self, records: T) -> DatasetBase<T, S> {
         DatasetBase {
             records,
             targets: self.targets,
             weights: Array1::zeros(0),
             feature_names: Vec::new(),
+            target_names: Vec::new(),
         }
     }
 
@@ -100,6 +96,7 @@
             targets,
             weights: self.weights,
             feature_names: self.feature_names,
+            target_names: self.target_names,
         }
     }
 
@@ -111,11 +108,14 @@
     }
 
     /// Updates the feature names of a dataset
+    ///
+    /// **Panics** when given names not empty and length does not equal to the number of features
     pub fn with_feature_names<I: Into<String>>(mut self, names: Vec<I>) -> DatasetBase<R, S> {
-        let feature_names = names.into_iter().map(|x| x.into()).collect();
-
-        self.feature_names = feature_names;
-
+        assert!(
+            names.is_empty() || names.len() == self.nfeatures(),
+            "Wrong number of feature names"
+        );
+        self.feature_names = names.into_iter().map(|x| x.into()).collect();
         self
     }
 }
@@ -131,6 +131,18 @@
 }
 
 impl<L, R: Records, T: AsTargets<Elem = L>> DatasetBase<R, T> {
+    /// Updates the target names of a dataset
+    ///
+    /// **Panics**  when given names not empty and length does not equal to the number of targets
+    pub fn with_target_names<I: Into<String>>(mut self, names: Vec<I>) -> DatasetBase<R, T> {
+        assert!(
+            names.is_empty() || names.len() == self.ntargets(),
+            "Wrong number of target names"
+        );
+        self.target_names = names.into_iter().map(|x| x.into()).collect();
+        self
+    }
+
     /// Map targets with a function `f`
     ///
     /// # Example
@@ -153,6 +165,7 @@
             targets,
             weights,
             feature_names,
+            target_names,
             ..
         } = self;
 
@@ -163,9 +176,17 @@
             targets: targets.map(fnc),
             weights,
             feature_names,
+            target_names,
         }
     }
 
+    /// Returns target names
+    ///
+    /// A target name gives a human-readable string describing the purpose of a single target.
+    pub fn target_names(&self) -> &[String] {
+        &self.target_names
+    }
+
     /// Return the number of targets in the dataset
     ///
     /// # Example
@@ -217,6 +238,7 @@
 where
     D: Data<Elem = F>,
     T: AsTargets<Elem = L> + FromTargetArray<'a>,
+    T::View: AsTargets<Elem = L>,
 {
     /// Creates a view of a dataset
     pub fn view(&'a self) -> DatasetBase<ArrayView2<'a, F>, T::View> {
@@ -226,6 +248,7 @@
         DatasetBase::new(records, targets)
             .with_feature_names(self.feature_names.clone())
             .with_weights(self.weights.clone())
+            .with_target_names(self.target_names.clone())
     }
 
     /// Iterate over features
@@ -268,6 +291,7 @@
 impl<'a, L: 'a, F, T> DatasetBase<ArrayView2<'a, F>, T>
 where
     T: AsTargets<Elem = L> + FromTargetArray<'a>,
+    T::View: AsTargets<Elem = L>,
 {
     /// Split dataset into two disjoint chunks
     ///
@@ -299,11 +323,13 @@
         };
         let dataset1 = DatasetBase::new(records_first, targets_first)
             .with_weights(first_weights)
-            .with_feature_names(self.feature_names.clone());
+            .with_feature_names(self.feature_names.clone())
+            .with_target_names(self.target_names.clone());
 
         let dataset2 = DatasetBase::new(records_second, targets_second)
             .with_weights(second_weights)
-            .with_feature_names(self.feature_names.clone());
+            .with_feature_names(self.feature_names.clone())
+            .with_target_names(self.target_names.clone());
 
         (dataset1, dataset2)
     }
@@ -349,7 +375,8 @@
                     label,
                     DatasetBase::new(self.records().view(), targets)
                         .with_feature_names(self.feature_names.clone())
-                        .with_weights(self.weights.clone()),
+                        .with_weights(self.weights.clone())
+                        .with_target_names(self.target_names.clone()),
                 )
             })
             .collect())
@@ -405,6 +432,7 @@
             targets: empty_targets,
             weights: Array1::zeros(0),
             feature_names: Vec::new(),
+            target_names: Vec::new(),
         }
     }
 }
@@ -421,6 +449,7 @@
             targets: rec_tar.1,
             weights: Array1::zeros(0),
             feature_names: Vec::new(),
+            target_names: Vec::new(),
         }
     }
 }
@@ -957,7 +986,8 @@
         let n1 = (self.nsamples() as f32 * ratio).ceil() as usize;
         let n2 = self.nsamples() - n1;
 
-        let feature_names = self.feature_names();
+        let feature_names = self.feature_names().to_vec();
+        let target_names = self.target_names().to_vec();
 
         // split records into two disjoint arrays
         let mut array_buf = self.records.into_raw_vec();
@@ -990,10 +1020,12 @@
         // create new datasets with attached weights
         let dataset1 = Dataset::new(first, first_targets)
             .with_weights(self.weights)
-            .with_feature_names(feature_names.clone());
+            .with_feature_names(feature_names.clone())
+            .with_target_names(target_names.clone());
         let dataset2 = Dataset::new(second, second_targets)
             .with_weights(second_weights)
-            .with_feature_names(feature_names);
+            .with_feature_names(feature_names.clone())
+            .with_target_names(target_names.clone());
 
         (dataset1, dataset2)
     }

src/dataset/impl_targets.rs

@@ -83,7 +83,7 @@ impl<'a, L: Label + 'a, T> FromTargetArray<'a> for CountedTargets<L, T>
 where
     T: FromTargetArray<'a, Elem = L>,
     T::Owned: Labels<Elem = L>,
-    T::View: Labels<Elem = L>,
+    T::View: Labels<Elem = L> + AsTargets,
 {
     type Owned = CountedTargets<L, T::Owned>;
     type View = CountedTargets<L, T::View>;
@@ -231,6 +231,7 @@ where
             weights: Array1::from(weights),
             targets,
             feature_names: self.feature_names.clone(),
+            target_names: self.target_names.clone(),
         }
     }
 }

src/dataset/iter.rs

@@ -77,18 +77,24 @@
         if self.target_or_feature && self.dataset.nfeatures() <= self.idx {
             return None;
         }
-
         let mut records = self.dataset.records.view();
         let mut targets = self.dataset.targets.as_targets();
         let feature_names;
+        let target_names;
         let weights = self.dataset.weights.clone();
 
         if !self.target_or_feature {
             // This branch should only run for 2D targets
             targets.collapse_axis(Axis(1), self.idx);
             feature_names = self.dataset.feature_names.clone();
+            if self.dataset.target_names.is_empty() {
+                target_names = Vec::new();
+            } else {
+                target_names = vec![self.dataset.target_names[self.idx].clone()];
+            }
         } else {
             records.collapse_axis(Axis(1), self.idx);
+            target_names = self.dataset.target_names.clone();
             if self.dataset.feature_names.len() == records.len_of(Axis(1)) {
                 feature_names = vec![self.dataset.feature_names[self.idx].clone()];
             } else {
@@ -103,6 +109,7 @@
             targets,
             weights,
             feature_names,
+            target_names,
         };
 
         Some(dataset_view)