Hyperparameters added to models interfaces (#168)

Author: gyrdym

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 15.5.0
+- `KnnClassifier`, `DecisionTreeClassifier`, `LogisticRegressor`, `SoftmaxRegressor`, `KnnRegressor`, `LinearRegressor`
+    - hyperparameters added to the interfaces
+
 ## 15.4.1
 - `DTypeJsonConverter` added
 - `MatrixJsonConverter` added

e2e/datasets/advertising.csv renamed to e2e/_datasets/advertising.csv

@@ -0,0 +1,23 @@
+import 'dart:io';
+
+import 'package:ml_algo/src/classifier/decision_tree_classifier/decision_tree_classifier.dart';
+import 'package:ml_linalg/dtype.dart';
+import 'package:test/test.dart';
+
+void main() {
+  group('DecisionTreeClassifier', () {
+    test('should deserialize v0 schema version', () async {
+      final file = File('e2e/decision_tree_classifier/decision_tree_classifier_v0.json');
+      final encodedData = await file.readAsString();
+      final classifier = DecisionTreeClassifier.fromJson(encodedData);
+
+      expect(classifier.dtype, DType.float32);
+      expect(classifier.targetNames, ['Species']);
+      expect(classifier.positiveLabel, isNull);
+      expect(classifier.negativeLabel, isNull);
+      expect(classifier.minSamplesCount, isNull);
+      expect(classifier.maxDepth, isNull);
+      expect(classifier.minError, isNull);
+    });
+  });
+}

e2e/datasets/housing.csv renamed to e2e/_datasets/housing.csv

@@ -6,7 +6,7 @@ import 'package:ml_preprocessing/ml_preprocessing.dart';
 import 'package:test/test.dart';
 
 Future<Vector> evaluateClassifier(MetricType metric, DType dtype) async {
-  final samples = (await fromCsv('e2e/datasets/iris.csv'))
+  final samples = (await fromCsv('e2e/_datasets/iris.csv'))
       .shuffle()
       .dropSeries(seriesNames: ['Id']);
   final pipeline = Pipeline(samples, [

e2e/datasets/iris.csv renamed to e2e/_datasets/iris.csv

@@ -0,0 +1 @@
+{"DT":"F32","T":"Species","R":{"CN":[{"LB":{"V":2.0,"P":1.0000000000000004},"PT":"LT","SV":2.449999988079071,"SI":2,"LV":1},{"CN":[{"LB":{"V":0.0,"P":0.9230769230769238},"PT":"LT","SV":1.7000000476837158,"SI":3,"LV":2},{"LB":{"V":1.0,"P":0.9583333333333339},"PT":"GET","SV":1.7000000476837158,"SI":3,"LV":2}],"PT":"GET","SV":2.449999988079071,"SI":2,"LV":1}],"LV":0}}

e2e/datasets/pima_indians_diabetes_database.csv renamed to e2e/_datasets/pima_indians_diabetes_database.csv

@@ -0,0 +1,25 @@
+import 'dart:io';
+
+import 'package:ml_algo/ml_algo.dart';
+import 'package:ml_algo/src/knn_kernel/kernel_type.dart';
+import 'package:ml_linalg/distance.dart';
+import 'package:ml_linalg/dtype.dart';
+import 'package:test/test.dart';
+
+void main() {
+  group('KnnClassifier', () {
+    test('should deserialize v0 schema version', () async {
+      final file = File('e2e/knn_classifier/knn_classifier_v0.json');
+      final encodedData = await file.readAsString();
+      final classifier = KnnClassifier.fromJson(encodedData);
+
+      expect(classifier.distanceType, Distance.euclidean);
+      expect(classifier.kernelType, KernelType.gaussian);
+      expect(classifier.k, 5);
+      expect(classifier.negativeLabel, isNull);
+      expect(classifier.positiveLabel, isNull);
+      expect(classifier.dtype, DType.float32);
+      expect(classifier.targetNames, ['Species']);
+    });
+  });
+}

e2e/decision_tree_classifier/decision_tree_classifier_serialization_test.dart

@@ -6,7 +6,7 @@ import 'package:ml_preprocessing/ml_preprocessing.dart';
 import 'package:test/test.dart';
 
 Future<Vector> evaluateKnnClassifier(MetricType metric, DType dtype) async {
-  final samples = (await fromCsv('e2e/datasets/iris.csv'))
+  final samples = (await fromCsv('e2e/_datasets/iris.csv'))
       .shuffle()
       .dropSeries(seriesNames: ['Id']);
   final targetName = 'Species';