Merge 5f5f666 into 8e479db

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 5.0.2
+- `xrange` dependency removed
+- `ml_dataframe` 0.0.11 supported
+
 ## 5.0.1
 - `xrange` package version locked
 

example/black_friday/black_friday.dart

@@ -1,6 +1,5 @@
 import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_preprocessing/ml_preprocessing.dart';
-import 'package:xrange/zrange.dart';
 
 Future processDataSetWithCategoricalData() async {
   final dataFrame = await fromCsv('example/black_friday/black_friday.csv',
@@ -15,14 +14,19 @@ Future processDataSetWithCategoricalData() async {
   ).process(dataFrame);
 
   final observations = encoded.toMatrix();
-  final genderEncoded = observations.submatrix(columns: ZRange.closed(0, 1));
-  final ageEncoded = observations.submatrix(columns: ZRange.closed(2, 8));
+
+  final genderEncoded = observations.sample(columnIndices: [0, 1]);
+
+  final ageEncoded = observations.sample(columnIndices: [2, 3, 4, 5, 6, 7, 8]);
+
   final cityCategoryEncoded = observations
-      .submatrix(columns: ZRange.closed(9, 11));
+      .sample(columnIndices: [9, 10, 11]);
+
   final stayInCityEncoded = observations
-      .submatrix(columns: ZRange.closed(12, 16));
+      .sample(columnIndices: [12, 13, 14, 15, 16]);
+
   final maritalStatusEncoded = observations
-      .submatrix(columns: ZRange.closed(17, 18));
+      .sample(columnIndices: [17, 18]);
 
   print('Features:');
 

example/main.dart

@@ -1,5 +1,3 @@
-import 'dart:async';
-
 import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_preprocessing/ml_preprocessing.dart';
 import 'package:ml_preprocessing/src/encoder/encode_as_integer_labels.dart';

pubspec.yaml

@@ -1,17 +1,16 @@
 name: ml_preprocessing
 description: Popular algorithms of data preprocessing for machine learning
-version: 5.0.1
+version: 5.0.2
 author: Ilia Gyrdymov <ilgyrd@gmail.com>
 homepage: https://github.com/gyrdym/ml_preprocessing
 
 environment:
   sdk: '>=2.4.0 <3.0.0'
 
 dependencies:
-  ml_dataframe: ^0.0.4
-  ml_linalg: ^11.0.0
+  ml_dataframe: 0.0.11
+  ml_linalg: ^12.1.0
   quiver: ^2.0.2
-  xrange: 0.0.6
 
 dev_dependencies:
   benchmark_harness: '>=1.0.0 <2.0.0'