Set up github actions

.github/workflows/dart.yml

@@ -0,0 +1,40 @@
+name: Dart
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    container:
+      image:  google/dart:latest
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Print Dart SDK version
+        run: dart --version
+
+      - name: Install dependencies
+        run: dart pub get
+
+      - name: Analyze project source
+        run: dart analyze --fatal-infos
+
+      - name: Run tests
+        run: dart test
+
+      - name: Run e2e tests
+        run: dart pub run test e2e -p vm
+
+      - name: Code coverage
+        run: dart pub run test_coverage
+
+      - name: Coveralls GitHub Action
+        uses: coverallsapp/github-action@v1.1.2
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}

CHANGELOG.md

@@ -1,5 +1,8 @@
 # Changelog
 
+## 15.3.5
+- Github actions set up
+
 ## 15.3.4
 - `DI logic`: 
     - conditional dependency registering added

analysis_options.yaml

@@ -1 +1 @@
-include: package:ml_tech/analysis_options.yaml
+include: package:pedantic/analysis_options.yaml

benchmark/logistic_regressor.dart

@@ -28,7 +28,7 @@ class LogisticRegressorBenchmark extends BenchmarkBase {
 
   @override
   void setup() {
-    final Matrix observations = Matrix.fromRows(List.generate(observationsNum,
+    final observations = Matrix.fromRows(List.generate(observationsNum,
             (i) => Vector.randomFilled(columnsNum)));
 
     _data = DataFrame.fromMatrix(observations);

e2e_tests.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+pub run test e2e -p vm

lib/src/classifier/softmax_regressor/softmax_regressor_impl.dart

@@ -51,6 +51,7 @@ class SoftmaxRegressorImpl
   factory SoftmaxRegressorImpl.fromJson(Map<String, dynamic> json) =>
       _$SoftmaxRegressorImplFromJson(json);
 
+  @override
   Map<String, dynamic> toJson() => _$SoftmaxRegressorImplToJson(this);
 
   @override

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms, Machine learning models performance evaluation functionality
-version: 15.3.4
+version: 15.3.5
 homepage: https://github.com/gyrdym/ml_algo
 
 environment:
@@ -21,7 +21,8 @@ dev_dependencies:
   build_runner: ^1.1.2
   build_test: ^0.10.2
   grinder: ^0.8.3
-  ml_tech: ^0.0.8
   mockito: ^3.0.0
+  pedantic: ^1.9.2
   process_run: ^0.10.12
   test: ^1.2.0
+  test_coverage: ^0.5.0

test/classifier/decision_tree_classifier/decision_tree_classifier_impl_test.dart

@@ -16,10 +16,10 @@ import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/dtype_to_json.dart';
 import 'package:ml_linalg/matrix.dart';
 import 'package:ml_linalg/vector.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:mockito/mockito.dart';
 import 'package:test/test.dart';
 
+import '../../helpers.dart';
 import '../../mocks.dart';
 
 void main() {

test/classifier/knn_classifier/knn_classifier_impl_test.dart

@@ -2,13 +2,12 @@ import 'package:ml_algo/src/classifier/knn_classifier/_injector.dart';
 import 'package:ml_algo/src/classifier/knn_classifier/knn_classifier_impl.dart';
 import 'package:ml_algo/src/di/injector.dart';
 import 'package:ml_algo/src/knn_solver/neigbour.dart';
-import 'package:ml_algo/src/metric/metric_type.dart';
 import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/linalg.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:mockito/mockito.dart';
 import 'package:test/test.dart';
 
+import '../../helpers.dart';
 import '../../mocks.dart';
 
 void main() {

test/classifier/logistic_regressor/integration_test/logistic_regressor_default_constructor_integration_test.dart

@@ -5,9 +5,10 @@ import 'package:ml_algo/src/linear_optimizer/gradient_optimizer/learning_rate_ge
 import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/linalg.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:test/test.dart';
 
+import '../../../helpers.dart';
+
 void main() {
   group('LogisticRegressor', () {
     final createClassifier = ({

test/classifier/logistic_regressor/unit_test/logistic_regressor_impl_test.dart

@@ -3,7 +3,6 @@ import 'package:ml_algo/src/classifier/logistic_regressor/logistic_regressor_imp
 import 'package:ml_algo/src/common/exception/invalid_class_labels_exception.dart';
 import 'package:ml_algo/src/common/exception/invalid_probability_threshold_exception.dart';
 import 'package:ml_algo/src/di/injector.dart';
-import 'package:ml_algo/src/metric/metric_type.dart';
 import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/linalg.dart';
 import 'package:mockito/mockito.dart';

test/classifier/logistic_regressor/unit_test/logistic_regressor_test.dart

@@ -18,10 +18,10 @@ import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/matrix.dart';
 import 'package:ml_linalg/vector.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:mockito/mockito.dart';
 import 'package:test/test.dart';
 
+import '../../../helpers.dart';
 import '../../../mocks.dart';
 
 void main() {

test/classifier/softmax_regressor/integration_test/softmax_regressor_serialization_integration_test.dart

@@ -1,7 +1,6 @@
 import 'dart:io';
 
 import 'package:ml_algo/ml_algo.dart';
-import 'package:ml_algo/src/classifier/softmax_regressor/_init_module.dart';
 import 'package:ml_algo/src/classifier/softmax_regressor/_injector.dart';
 import 'package:ml_algo/src/classifier/softmax_regressor/softmax_regressor_json_keys.dart';
 import 'package:ml_algo/src/di/injector.dart';

test/classifier/softmax_regressor/unit_test/softmax_regressor_impl_test.dart

@@ -1,15 +1,14 @@
 import 'package:ml_algo/src/classifier/softmax_regressor/_injector.dart';
 import 'package:ml_algo/src/classifier/softmax_regressor/softmax_regressor_impl.dart';
 import 'package:ml_algo/src/di/injector.dart';
-import 'package:ml_algo/src/metric/metric_type.dart';
 import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/matrix.dart';
 import 'package:ml_linalg/vector.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:mockito/mockito.dart';
 import 'package:test/test.dart';
 
+import '../../../helpers.dart';
 import '../../../mocks.dart';
 
 void main() {

test/classifier/softmax_regressor/unit_test/softmax_regressor_test.dart

@@ -1,4 +1,3 @@
-import 'package:injector/injector.dart';
 import 'package:ml_algo/ml_algo.dart';
 import 'package:ml_algo/src/classifier/softmax_regressor/_helpers/create_softmax_regressor.dart';
 import 'package:ml_algo/src/classifier/softmax_regressor/_injector.dart';
@@ -19,10 +18,10 @@ import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/linalg.dart';
 import 'package:ml_linalg/matrix.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:mockito/mockito.dart';
 import 'package:test/test.dart';
 
+import '../../../helpers.dart';
 import '../../../mocks.dart';
 
 void main() {

test/cost_function/least_square_cost_function_test.dart

@@ -2,9 +2,10 @@ import 'dart:math' as math;
 
 import 'package:ml_algo/src/cost_function/least_square_cost_function.dart';
 import 'package:ml_linalg/linalg.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:test/test.dart';
 
+import '../helpers.dart';
+
 void main() {
   group('LeastSquareCostFunction', () {
     final x11 = 21.5;

test/helpers.dart

@@ -0,0 +1,34 @@
+import 'dart:convert' as convert;
+import 'dart:io';
+
+import 'package:test/test.dart';
+
+Matcher iterable2dAlmostEqualTo(Iterable<Iterable<double>> expected,
+    [double precision = 1e-5]) =>
+    pairwiseCompare<Iterable<double>, Iterable<double>>(expected,
+            (Iterable<double> expected, Iterable<double> actual) {
+          if (expected.length != actual.length) {
+            return false;
+          }
+          for (var i = 0; i < expected.length; i++) {
+            if ((expected.elementAt(i) - actual.elementAt(i)).abs() >= precision) {
+              return false;
+            }
+          }
+          return true;
+        }, '');
+
+Matcher iterableAlmostEqualTo(Iterable<double> expected,
+    [double precision = 1e-5]) =>
+    pairwiseCompare<double, double>(
+        expected,
+            (expectedVal, actualVal) =>
+        (expectedVal - actualVal).abs() <= precision,
+        '');
+
+Future<Map<String, dynamic>> readJSON(String fileName) async =>
+    await File(fileName)
+        .openRead()
+        .transform(convert.utf8.decoder)
+        .transform(convert.json.decoder)
+        .first as Map<String, dynamic>;

test/linear_optimizer/coordinate_optimizer/coordinate_descent_optimizer_integration_test.dart

@@ -4,9 +4,10 @@ import 'package:ml_algo/src/di/injector.dart';
 import 'package:ml_algo/src/linear_optimizer/coordinate_optimizer/coordinate_descent_optimizer.dart';
 import 'package:ml_algo/src/linear_optimizer/initial_coefficients_generator/initial_coefficients_type.dart';
 import 'package:ml_linalg/linalg.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_almost_equal_to.dart';
 import 'package:test/test.dart';
 
+import '../../helpers.dart';
+
 /// L1 regularization, as known as Lasso, is aimed to penalize unimportant features, setting their weights to the zero,
 /// therefore, we can treat the objective of the Lasso Optimizer like feature selection. Since lasso solver regularizes
 /// coefficients by adding to their magnitude their L1-norm, we cannot use gradient methods any longer. Instead, we can

test/link_function/logit/inverse_logit_link_function_test.dart

@@ -4,9 +4,10 @@ import 'package:ml_algo/src/link_function/logit/float32_inverse_logit_function.d
 import 'package:ml_algo/src/link_function/logit/float64_inverse_logit_function.dart';
 import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/matrix.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:test/test.dart';
 
+import '../../helpers.dart';
+
 void main() {
   void testInverseLogitLinkFunction(LinkFunction inverseLogitLink, DType dtype) {
     group(inverseLogitLink.runtimeType, () {
@@ -36,7 +37,6 @@ void main() {
           [-4.0],
         ], dtype: dtype);
         final probabilities = inverseLogitLink.link(scores);
-        final a = 2.3;
 
         expect(probabilities, iterable2dAlmostEqualTo([
           [0.268],

test/link_function/softmax/softmax_link_function_test.dart

@@ -3,9 +3,10 @@ import 'package:ml_algo/src/link_function/softmax/float32_softmax_link_function.
 import 'package:ml_algo/src/link_function/softmax/float64_softmax_link_function.dart';
 import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/matrix.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:test/test.dart';
 
+import '../../helpers.dart';
+
 void main() {
   void testSoftmaxLinkFunction(LinkFunction linkFunction, DType dtype) {
     group(linkFunction.runtimeType, () {

test/math/randomizer_test.dart

@@ -8,7 +8,7 @@ import 'package:test/test.dart';
 class RandomMock extends Mock implements Random {}
 
 void main() {
-  const int maxEpoch = 400;
+  const maxEpoch = 400;
 
   Random baseGenerator;
   Randomizer randomizer;
@@ -100,7 +100,7 @@ void main() {
       final start = 2;
       final end = 13;
 
-      for (int i = 0; i < maxEpoch; i++) {
+      for (var i = 0; i < maxEpoch; i++) {
         final value = randomizer.getIntegerFromInterval(start, end);
         expect(start <= value && value < end, isTrue);
       }
@@ -112,7 +112,7 @@ void main() {
       final start = 6;
       final end = 17;
 
-      for (int i = 0; i < maxEpoch; i++) {
+      for (var i = 0; i < maxEpoch; i++) {
         final interval = randomizer.getIntegerInterval(start, end,
             intervalLength: intervalLength);
 
@@ -135,7 +135,7 @@ void main() {
       final start = 0;
       final end = 1;
 
-      for (int i = 0; i < maxEpoch; i++) {
+      for (var i = 0; i < maxEpoch; i++) {
         final value = randomizer.getIntegerFromInterval(start, end);
         expect(value, isZero);
       }
@@ -155,7 +155,7 @@ void main() {
       final start = 5.3;
       final end = 123.4;
 
-      for (int i = 0; i < maxEpoch; i++) {
+      for (var i = 0; i < maxEpoch; i++) {
         final value = randomizer.getDoubleFromInterval(start, end);
         expect(start <= value && value < end, isTrue);
       }

test/metric/classification/recall_test.dart

@@ -1,4 +1,3 @@
-import 'package:ml_algo/src/metric/classification/precision.dart';
 import 'package:ml_algo/src/metric/classification/recall.dart';
 import 'package:ml_linalg/matrix.dart';
 import 'package:test/test.dart';