CHANGELOG record added, version updated

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Changelog
 
+## 11.0.1
+- Cross validator refactored
+- Data splitters refactored
+- Unit tests for cross validator added
+
 ## 11.0.0
 - Added immutable state to all the predictor subclasses
 

benchmark/cross_validator.dart

@@ -1,4 +1,4 @@
-// 10 sec
+// 8.5 sec
 import 'dart:async';
 
 import 'package:benchmark_harness/benchmark_harness.dart';

lib/src/model_selection/cross_validator/cross_validator_impl.dart

@@ -26,19 +26,20 @@ class CrossValidatorImpl implements CrossValidator {
     var folds = 0;
 
     for (final testIndices in allIndicesGroups) {
+      final testIndicesAsSet = Set<int>.from(testIndices);
       final trainFeatures =
-          List<Vector>(observations.rowsNum - testIndices.length);
+          List<Vector>(observations.rowsNum - testIndicesAsSet.length);
       final trainLabels =
-          List<Vector>(observations.rowsNum - testIndices.length);
+          List<Vector>(observations.rowsNum - testIndicesAsSet.length);
 
-      final testFeatures = List<Vector>(testIndices.length);
-      final testLabels = List<Vector>(testIndices.length);
+      final testFeatures = List<Vector>(testIndicesAsSet.length);
+      final testLabels = List<Vector>(testIndicesAsSet.length);
 
       int trainPointsCounter = 0;
       int testPointsCounter = 0;
 
       for (int index = 0; index < observations.rowsNum; index++) {
-        if (testIndices.contains(index)) {
+        if (testIndicesAsSet.contains(index)) {
           testFeatures[testPointsCounter] = observations.getRow(index);
           testLabels[testPointsCounter] = labels.getRow(index);
           testPointsCounter++;

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms written in native dart
-version: 11.0.0
+version: 11.0.1
 author: Ilia Gyrdymov <ilgyrd@gmail.com>
 homepage: https://github.com/gyrdym/ml_algo
 

test/cross_validator/cross_validator_impl_test.dart

@@ -58,5 +58,23 @@ void main() {
         [530, 130, 930],
       ])), argThat(equals([[700], [900]])), metric)).called(1);
     });
+
+    test('should throw an exception if observations number and outcomes number '
+        'mismatch', () {
+      final allObservations = Matrix.from([
+        [330, 930, 130],
+        [630, 830, 230],
+      ]);
+      final allOutcomes = Matrix.from([
+        [100],
+      ]);
+      final metric = MetricType.mape;
+      final splitter = SplitterMock();
+      final predictor = PredictorMock();
+      final validator = CrossValidatorImpl(Float32x4, splitter);
+
+      expect(() => validator.evaluate((observations, outcomes) => predictor,
+          allObservations, allOutcomes, metric), throwsException);
+    });
   });
 }