toOneHotLabels function

PipeableOperatorFn toOneHotLabels({ Iterable<int>? columnIndices, Iterable<String>? columnNames, String headerPrefix = '', String headerPostfix = '', UnknownValueHandlingType unknownValueHandlingType = defaultUnknownValueHandlingType, })

A factory function to use one hot categorical data encoder in the pipeline

A usage example:

import 'package:ml_dataframe/ml_dataframe.dart';
import 'package:ml_preprocessing/ml_preprocessing.dart';

void main() {
  final dataframe = DataFrame([
    ['col_1', 'col_2', 'col_3'],
    ['val_1',       1,   false],
    ['val_2',     0.4,    true],
    ['val_1',       5,   false],
  ]);

  // let's fit a pipeline
  final pipeline = Pipeline(dataframe, [
    // 'col_1' column contains categorical data, let's encode it
    toOneHotLabels(columnNames: ['col_1']),
  ]);
  final processed = pipeline.process(dataframe);

  // since there are only two values in the series 'col_1', they will be
  // converted as follows:
  //
  // 'val_1' => 10
  // 'val_2' => 01
  print(processed);
  // DataFrame (3 x 4)
  // val_1   val_2   col_2   col_3
  //     1       0       1   false
  //     0       1     0.4    true
  //     1       0       5   false
}

Implementation

PipeableOperatorFn toOneHotLabels({
  Iterable<int>? columnIndices,
  Iterable<String>? columnNames,
  String headerPrefix = '',
  String headerPostfix = '',
  UnknownValueHandlingType unknownValueHandlingType =
      defaultUnknownValueHandlingType,
}) =>
    (data, {dtype}) => EncoderImpl(
          data,
          EncoderType.oneHot,
          const SeriesEncoderFactoryImpl(),
          columnIndices: columnIndices,
          columnNames: columnNames,
          encodedHeaderPostfix: headerPostfix,
          encodedHeaderPrefix: headerPrefix,
          unknownValueHandlingType: unknownValueHandlingType,
        );