ml libraries upgraded (#223)

Author: gyrdym

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 16.11.0
+- `ml_preprocessing` version upgraded to 7.0.2
+- `ml_dataframe` version upgraded to 1.0.0
+
 ## 16.10.5
 - KnnClassifier:
     - Proofreading the documentation

README.md

@@ -94,8 +94,8 @@ in your dependencies:
 
 ````
 dependencies:
-  ml_dataframe: ^0.5.1
-  ml_preprocessing: ^6.0.0
+  ml_dataframe: ^1.0.0
+  ml_preprocessing: ^7.0.2
 ````
 
 We need these repos to parse raw data in order to use it further. For more details, please
@@ -128,14 +128,14 @@ final samples = await fromCsv('datasets/pima_indians_diabetes_database.csv');
 
 #### For a flutter application:
 
-Be sure that you have ml_dataframe package version at least 0.5.1 and ml_algo package version at least 16.0.0 
+Be sure that you have ml_dataframe package version at least 1.0.0 and ml_algo package version at least 16.0.0 
 in your pubspec.yaml:
 
 ````
 dependencies:
   ...
   ml_algo: ^16.0.0
-  ml_dataframe: ^0.5.1
+  ml_dataframe: ^1.0.0
   ...
 ````
 

e2e/decision_tree_classifier/decision_tree_classifier_save_as_svg_test.dart

@@ -9,10 +9,10 @@ void main() async {
         () async {
       final samples = (await fromCsv('e2e/_datasets/iris.csv'))
           .shuffle()
-          .dropSeries(seriesNames: ['Id']);
+          .dropSeries(names: ['Id']);
       final pipeline = Pipeline(samples, [
-        encodeAsIntegerLabels(
-          featureNames: ['Species'],
+        toIntegerLabels(
+          columnNames: ['Species'],
         ),
       ]);
       final processed = pipeline.process(samples);

e2e/decision_tree_classifier/decision_tree_classifier_test.dart

@@ -8,10 +8,10 @@ import 'package:test/test.dart';
 Future<Vector> evaluateClassifier(MetricType metric, DType dtype) async {
   final samples = (await fromCsv('e2e/_datasets/iris.csv'))
       .shuffle()
-      .dropSeries(seriesNames: ['Id']);
+      .dropSeries(names: ['Id']);
   final pipeline = Pipeline(samples, [
-    encodeAsIntegerLabels(
-      featureNames: ['Species'],
+    toIntegerLabels(
+      columnNames: ['Species'],
     ),
   ]);
   final numberOfFolds = 5;

e2e/knn_classifier/knn_classifier_test.dart

@@ -8,11 +8,11 @@ import 'package:test/test.dart';
 Future<Vector> evaluateKnnClassifier(MetricType metric, DType dtype) async {
   final samples = (await fromCsv('e2e/_datasets/iris.csv'))
       .shuffle()
-      .dropSeries(seriesNames: ['Id']);
+      .dropSeries(names: ['Id']);
   final targetName = 'Species';
   final pipeline = Pipeline(samples, [
-    encodeAsIntegerLabels(
-      featureNames: [targetName],
+    toIntegerLabels(
+      columnNames: [targetName],
     ),
   ]);
   final processed = pipeline.process(samples);

e2e/lasso_regressor/lasso_regressor_test.dart

@@ -8,7 +8,7 @@ Future<Vector> evaluateLassoRegressor(
     MetricType metricType, DType dtype) async {
   final samples = (await fromCsv('e2e/_datasets/advertising.csv'))
       .shuffle()
-      .dropSeries(seriesNames: ['Num']);
+      .dropSeries(names: ['Num']);
   final targetName = 'Sales';
   final validator = CrossValidator.kFold(
     samples,

e2e/softmax_regressor/softmax_regressor_test.dart

@@ -9,10 +9,10 @@ Future<Vector> evaluateSoftmaxRegressor(
     MetricType metricType, DType dtype) async {
   final samples = (await fromCsv('e2e/_datasets/iris.csv'))
       .shuffle()
-      .dropSeries(seriesNames: ['Id']);
+      .dropSeries(names: ['Id']);
   final pipeline = Pipeline(samples, [
-    encodeAsOneHotLabels(
-      featureNames: ['Species'],
+    toOneHotLabels(
+      columnNames: ['Species'],
     ),
   ]);
   final classNames = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'];

lib/src/di/common/init_common_module.dart

@@ -33,7 +33,7 @@ void initCommonModule() {
   injector
     ..registerSingletonIf<EncoderFactory>(
         () => (DataFrame data, Iterable<String> targetNames) =>
-            Encoder.oneHot(data, featureNames: targetNames),
+            Encoder.oneHot(data, columnNames: targetNames),
         dependencyName: oneHotEncoderFactoryKey)
     ..registerSingletonIf<RandomizerFactory>(
         () => const RandomizerFactoryImpl())

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms, Machine learning models performance evaluation functionality
-version: 16.10.5
+version: 16.11.0
 homepage: https://github.com/gyrdym/ml_algo
 
 environment:
@@ -10,9 +10,9 @@ dependencies:
   collection: ^1.16.0
   injector: ^2.0.0
   json_annotation: ^4.0.0
-  ml_dataframe: ^0.5.1
+  ml_dataframe: ^1.0.0
   ml_linalg: ^13.7.0
-  ml_preprocessing: ^6.0.1
+  ml_preprocessing: ^7.0.2
   quiver: ^3.0.0
   xrange: ^2.0.0
 

test/mocks.mocks.dart

@@ -173,7 +173,10 @@ class _FakeKernel_17 extends _i1.Fake implements _i19.Kernel {}
 
 class _FakeKnnClassifier_18 extends _i1.Fake implements _i20.KnnClassifier {}
 
-class _FakeDataFrame_19 extends _i1.Fake implements _i21.DataFrame {}
+class _FakeDataFrame_19 extends _i1.Fake implements _i21.DataFrame {
+  @override
+  String toString({int? maxRows = 10, int? maxCols = 7}) => super.toString();
+}
 
 class _FakeLinearOptimizer_20 extends _i1.Fake implements _i22.LinearOptimizer {
 }