Constants for allowed metrics added

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 15.2.1
+- `classificationMetrics` constant list added
+- `regressionMetrics` constant list added
+
 ## 15.2.0
 - Recall metric added
 

e2e/decision_tree_classifier_test.dart

@@ -69,5 +69,21 @@ void main() async {
 
       expect(scores.mean(), greaterThan(0.5));
     });
+
+    test('should return adequate score on iris dataset using recall '
+        'metric, dtype=DType.float32', () async {
+      final scores = await evaluateClassifier(
+          MetricType.recall, DType.float32);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
+
+    test('should return adequate score on iris dataset using recall '
+        'metric, dtype=DType.float64', () async {
+      final scores = await evaluateClassifier(
+          MetricType.recall, DType.float64);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
   });
 }

e2e/knn_classifier_test.dart

@@ -69,5 +69,21 @@ void main() async {
 
       expect(scores.mean(), greaterThan(0.5));
     });
+
+    test('should return adequate score on iris dataset using recall '
+        'metric, dtype=DType.float32', () async {
+      final scores = await evaluateKnnClassifier(MetricType.recall,
+          DType.float32);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
+
+    test('should return adequate score on iris dataset using recall '
+        'metric, dtype=DType.float64', () async {
+      final scores = await evaluateKnnClassifier(MetricType.recall,
+          DType.float64);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
   });
 }

e2e/logistic_regressor_test.dart

@@ -65,5 +65,21 @@ Future main() async {
 
       expect(scores.mean(), greaterThan(0.5));
     });
+
+    test('should return adequate score on pima indians diabetes dataset using '
+        'recall metric, dtype=DType.float32', () async {
+      final scores = await evaluateLogisticRegressor(MetricType.recall,
+          DType.float32);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
+
+    test('should return adequate score on pima indians diabetes dataset using '
+        'recall metric, dtype=DType.float64', () async {
+      final scores = await evaluateLogisticRegressor(MetricType.recall,
+          DType.float32);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
   });
 }

e2e/softmax_regressor_test.dart

@@ -72,5 +72,21 @@ Future main() async {
 
       expect(scores.mean(), greaterThan(0.5));
     });
+
+    test('should return adequate score on iris dataset using recall '
+        'metric, dtype=DType.float32', () async {
+      final scores = await evaluateSoftmaxRegressor(MetricType.recall,
+          DType.float32);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
+
+    test('should return adequate score on iris dataset using recall '
+        'metric, dtype=DType.float64', () async {
+      final scores = await evaluateSoftmaxRegressor(MetricType.recall,
+          DType.float64);
+
+      expect(scores.mean(), greaterThan(0.5));
+    });
   });
 }

lib/src/metric/metric.constants.dart

@@ -0,0 +1,12 @@
+import 'package:ml_algo/src/metric/metric_type.dart';
+
+const classificationMetrics = [
+  MetricType.recall,
+  MetricType.precision,
+  MetricType.accuracy,
+];
+
+const regressionMetrics = [
+  MetricType.mape,
+  MetricType.rmse,
+];

lib/src/model_selection/model_assessor/classifier_assessor.dart

@@ -3,6 +3,7 @@ import 'package:ml_algo/src/common/exception/invalid_metric_type_exception.dart'
 import 'package:ml_algo/src/di/common/init_common_module.dart';
 import 'package:ml_algo/src/helpers/features_target_split_interface.dart';
 import 'package:ml_algo/src/helpers/normalize_class_labels_interface.dart';
+import 'package:ml_algo/src/metric/metric.constants.dart';
 import 'package:ml_algo/src/metric/metric_factory.dart';
 import 'package:ml_algo/src/metric/metric_type.dart';
 import 'package:ml_algo/src/model_selection/model_assessor/model_assessor.dart';
@@ -16,11 +17,6 @@ class ClassifierAssessor implements ModelAssessor<Classifier> {
       this._normalizeClassLabels,
   );
 
-  static const List<MetricType> _allowedMetricTypes = [
-    MetricType.precision,
-    MetricType.accuracy,
-  ];
-
   final MetricFactory _metricFactory;
   final EncoderFactory _encoderFactory;
   final FeaturesTargetSplit _featuresTargetSplit;
@@ -32,9 +28,9 @@ class ClassifierAssessor implements ModelAssessor<Classifier> {
       MetricType metricType,
       DataFrame samples,
   ) {
-    if (!_allowedMetricTypes.contains(metricType)) {
+    if (!classificationMetrics.contains(metricType)) {
       throw InvalidMetricTypeException(
-          metricType, _allowedMetricTypes);
+          metricType, classificationMetrics);
     }
 
     final splits = _featuresTargetSplit(

lib/src/model_selection/model_assessor/regressor_assessor.dart

@@ -1,6 +1,7 @@
 import 'package:ml_algo/ml_algo.dart';
 import 'package:ml_algo/src/common/exception/invalid_metric_type_exception.dart';
 import 'package:ml_algo/src/helpers/features_target_split_interface.dart';
+import 'package:ml_algo/src/metric/metric.constants.dart';
 import 'package:ml_algo/src/metric/metric_factory.dart';
 import 'package:ml_algo/src/metric/metric_type.dart';
 import 'package:ml_algo/src/model_selection/model_assessor/model_assessor.dart';
@@ -13,11 +14,6 @@ class RegressorAssessor implements ModelAssessor<Predictor> {
       this._featuresTargetSplit,
   );
 
-  static const List<MetricType> _allowedMetricTypes = [
-    MetricType.rmse,
-    MetricType.mape,
-  ];
-
   final MetricFactory _metricFactory;
   final FeaturesTargetSplit _featuresTargetSplit;
 
@@ -27,9 +23,9 @@ class RegressorAssessor implements ModelAssessor<Predictor> {
       MetricType metricType,
       DataFrame samples,
   ) {
-    if (!_allowedMetricTypes.contains(metricType)) {
+    if (!regressionMetrics.contains(metricType)) {
       throw InvalidMetricTypeException(
-          metricType, _allowedMetricTypes);
+          metricType, regressionMetrics);
     }
 
     final splits = _featuresTargetSplit(

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms, Machine learning models performance evaluation functionality
-version: 15.2.0
+version: 15.2.1
 homepage: https://github.com/gyrdym/ml_algo
 
 environment: