ml_dataframe 0.3.0 supported, github actions set up (#22)

Author: gyrdym

.github/workflows/ci_pipeline.yml

@@ -0,0 +1,37 @@
+name: CI pipeline
+
+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: Code coverage
+        run: dart pub run test_coverage
+
+      - name: Coveralls GitHub Action
+        uses: coverallsapp/[email protected]
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}

.travis.yml

@@ -1,5 +1,9 @@
 # Changelog
 
+## 5.2.1
+- `ml_dataframe`: version 0.3.0 supported
+- `CI`: github actions set up
+
 ## 5.2.0
 - `UnknownValueHandlingType` enum added to the lib's public API
 

CHANGELOG.md

@@ -1,4 +1,4 @@
-[![Build Status](https://travis-ci.com/gyrdym/ml_algo.svg?branch=master)](https://travis-ci.com/gyrdym/ml_preprocessing)
+[![Build Status](https://github.com/gyrdym/ml_preprocessing/workflows/CI%20pipeline/badge.svg)](https://github.com/gyrdym/ml_preprocessing/actions?query=branch%3Amaster+)
 [![Coverage Status](https://coveralls.io/repos/github/gyrdym/ml_preprocessing/badge.svg)](https://coveralls.io/github/gyrdym/ml_preprocessing)
 [![pub package](https://img.shields.io/pub/v/ml_preprocessing.svg)](https://pub.dartlang.org/packages/ml_preprocessing)
 [![Gitter Chat](https://badges.gitter.im/gyrdym/gyrdym.svg)](https://gitter.im/gyrdym/)
@@ -194,4 +194,4 @@ final processed = pipeline.process(dataFrame);
 
 `encodeAsOneHotLabels`, `encodeAsIntegerLabels`, `normalize` and `standardize` are pipeable operator functions. 
 Pipeable operator function is a factory, that takes fitting data and creates a fitted pipeable entity (e.g., 
-`Normalizer` instance)  
+`Normalizer` instance)  

README.md

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

analysis_options.yaml

@@ -6,7 +6,7 @@ Map<String, SeriesEncoder> createEncoderToSeriesMapping(
     DataFrame dataFrame,
     Iterable<String> predefinedSeriesNames,
     Iterable<int> seriesIndices,
-    SeriesEncoder seriesEncoderFactory(Series series),
+    SeriesEncoder Function(Series series) seriesEncoderFactory,
 ) {
   final seriesNames = predefinedSeriesNames ??
       getSeriesNamesByIndices(dataFrame.header, seriesIndices);
@@ -16,4 +16,4 @@ Map<String, SeriesEncoder> createEncoderToSeriesMapping(
     return MapEntry(name, encoder);
   });
   return Map.fromEntries(entries);
-}
+}

lib/src/encoder/helpers/create_encoder_to_series_mapping.dart

@@ -1,22 +1,22 @@
 name: ml_preprocessing
 description: Popular algorithms of data preprocessing for machine learning
-version: 5.2.0
+version: 5.2.1
 author: Ilia Gyrdymov <[email protected]>
 homepage: https://github.com/gyrdym/ml_preprocessing
 
 environment:
   sdk: '>=2.7.0 <3.0.0'
 
 dependencies:
-  ml_dataframe: ^0.2.0
+  ml_dataframe: ^0.3.0
   ml_linalg: ^12.17.0
   quiver: ^2.0.2
 
 dev_dependencies:
   benchmark_harness: '>=1.0.0 <2.0.0'
   build_runner: ^1.1.2
   build_test: ^0.10.2
-  grinder: ^0.8.3
-  ml_tech: ^0.0.5
   mockito: ^3.0.0
+  pedantic: ^1.9.2
   test: ^1.2.0
+  test_coverage: ^0.5.0

pubspec.yaml

@@ -0,0 +1,24 @@
+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,
+        '');

test/helpers.dart

@@ -1,9 +1,10 @@
 import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/linalg.dart';
 import 'package:ml_preprocessing/src/normalizer/normalizer.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('Normalizer', () {
     test('should divide each row-vector by its euclidean norm and preserve '

test/normalizer/normalizer_test.dart

@@ -2,9 +2,10 @@ import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/dtype.dart';
 import 'package:ml_linalg/linalg.dart';
 import 'package:ml_preprocessing/ml_preprocessing.dart';
-import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
 import 'package:test/test.dart';
 
+import '../helpers.dart';
+
 void main() {
   const dtype = DType.float32;