sklearn.preprocessing.OneHotEncoder.java Source code

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Here is the source code for sklearn.preprocessing.OneHotEncoder.java

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/*
 * Copyright (c) 2015 Villu Ruusmann
 *
 * This file is part of JPMML-SkLearn
 *
 * JPMML-SkLearn is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * JPMML-SkLearn is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with JPMML-SkLearn.  If not, see <http://www.gnu.org/licenses/>.
 */
package sklearn.preprocessing;

import java.util.ArrayList;
import java.util.List;

import com.google.common.collect.ContiguousSet;
import com.google.common.collect.DiscreteDomain;
import com.google.common.collect.Range;
import org.dmg.pmml.DataType;
import org.dmg.pmml.OpType;
import org.jpmml.converter.BinaryFeature;
import org.jpmml.converter.Feature;
import org.jpmml.converter.ListFeature;
import org.jpmml.converter.ValueUtil;
import org.jpmml.converter.WildcardFeature;
import org.jpmml.sklearn.ClassDictUtil;
import org.jpmml.sklearn.FeatureMapper;
import sklearn.Transformer;
import sklearn.TypeUtil;

public class OneHotEncoder extends Transformer {

    public OneHotEncoder(String module, String name) {
        super(module, name);
    }

    @Override
    public OpType getOpType() {
        return OpType.CATEGORICAL;
    }

    @Override
    public DataType getDataType() {
        List<? extends Number> values = getValues();

        return TypeUtil.getDataType(values, DataType.INTEGER);
    }

    @Override
    public List<Feature> encodeFeatures(List<String> ids, List<Feature> inputFeatures,
            FeatureMapper featureMapper) {
        List<? extends Number> values = getValues();

        if (ids.size() != 1 || inputFeatures.size() != 1) {
            throw new IllegalArgumentException();
        }

        Feature inputFeature = inputFeatures.get(0);

        List<String> categories = new ArrayList<>();

        List<Feature> features = new ArrayList<>();

        for (int i = 0; i < values.size(); i++) {
            int value = ValueUtil.asInt(values.get(i));

            String category;

            if (inputFeature instanceof WildcardFeature) {
                category = ValueUtil.formatValue((Integer) value);
            } else

            if (inputFeature instanceof ListFeature) {
                ListFeature listFeature = (ListFeature) inputFeature;

                category = listFeature.getValue(value);
            } else

            {
                throw new IllegalArgumentException();
            }

            categories.add(category);

            features.add(new BinaryFeature(inputFeature.getName(), DataType.STRING, category));
        }

        featureMapper.updateValueSpace(inputFeature.getName(), categories);

        return features;
    }

    public List<? extends Number> getValues() {
        List<Integer> featureSizes = getFeatureSizes();

        if (featureSizes.size() != 1) {
            throw new IllegalArgumentException();
        }

        Object numberOfValues = get("n_values");

        if (("auto").equals(numberOfValues)) {
            return getActiveFeatures();
        }

        Integer featureSize = featureSizes.get(0);

        List<Integer> result = new ArrayList<>();
        result.addAll(ContiguousSet.create(Range.closedOpen(0, featureSize), DiscreteDomain.integers()));

        return result;
    }

    public List<? extends Number> getActiveFeatures() {
        return (List) ClassDictUtil.getArray(this, "active_features_");
    }

    public List<Integer> getFeatureSizes() {
        return ValueUtil.asIntegers((List) ClassDictUtil.getArray(this, "n_values_"));
    }
}