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Create a new OneHotEncoder preprocessing class.
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119
...rc/main/java/com/datumbox/framework/core/machinelearning/preprocessing/OneHotEncoder.java
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/** | ||
* Copyright (C) 2013-2016 Vasilis Vryniotis <bbriniotis@datumbox.com> | ||
* | ||
* Licensed under the Apache License, Version 2.0 (the "License"); | ||
* you may not use this file except in compliance with the License. | ||
* You may obtain a copy of the License at | ||
* | ||
* http://www.apache.org/licenses/LICENSE-2.0 | ||
* | ||
* Unless required by applicable law or agreed to in writing, software | ||
* distributed under the License is distributed on an "AS IS" BASIS, | ||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
* See the License for the specific language governing permissions and | ||
* limitations under the License. | ||
*/ | ||
package com.datumbox.framework.core.machinelearning.preprocessing; | ||
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import com.datumbox.framework.common.Configuration; | ||
import com.datumbox.framework.common.concurrency.StreamMethods; | ||
import com.datumbox.framework.common.dataobjects.*; | ||
import com.datumbox.framework.common.storageengines.interfaces.StorageEngine; | ||
import com.datumbox.framework.core.machinelearning.common.abstracts.AbstractTrainer; | ||
import com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractCategoricalEncoder; | ||
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import java.util.Arrays; | ||
import java.util.List; | ||
import java.util.Map; | ||
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/** | ||
* Encodes the categorical columns of the dataset into booleans using the One Hot Encoding method. | ||
* | ||
* @author Vasilis Vryniotis <bbriniotis@datumbox.com> | ||
*/ | ||
public class OneHotEncoder extends AbstractCategoricalEncoder<OneHotEncoder.ModelParameters, OneHotEncoder.TrainingParameters> { | ||
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/** {@inheritDoc} */ | ||
public static class ModelParameters extends AbstractCategoricalEncoder.AbstractModelParameters { | ||
private static final long serialVersionUID = 1L; | ||
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/** | ||
* @param storageEngine | ||
* @see AbstractTrainer.AbstractModelParameters#AbstractModelParameters(StorageEngine) | ||
*/ | ||
protected ModelParameters(StorageEngine storageEngine) { | ||
super(storageEngine); | ||
} | ||
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} | ||
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/** {@inheritDoc} */ | ||
public static class TrainingParameters extends AbstractCategoricalEncoder.AbstractTrainingParameters { | ||
private static final long serialVersionUID = 1L; | ||
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} | ||
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/** | ||
* @param trainingParameters | ||
* @param configuration | ||
* @see AbstractTrainer#AbstractTrainer(AbstractTrainer.AbstractTrainingParameters, Configuration) | ||
*/ | ||
protected OneHotEncoder(TrainingParameters trainingParameters, Configuration configuration) { | ||
super(trainingParameters, configuration); | ||
} | ||
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/** | ||
* @param storageName | ||
* @param configuration | ||
* @see AbstractTrainer#AbstractTrainer(String, Configuration) | ||
*/ | ||
protected OneHotEncoder(String storageName, Configuration configuration) { | ||
super(storageName, configuration); | ||
} | ||
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/** {@inheritDoc} */ | ||
@Override | ||
protected void _fit(Dataframe trainingData) { | ||
//does not learn anything | ||
} | ||
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/** {@inheritDoc} */ | ||
@Override | ||
protected void _transform(Dataframe newData) { | ||
Map<Object, TypeInference.DataType> columnTypes = newData.getXDataTypes(); | ||
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//Replace variables with dummy versions | ||
streamExecutor.forEach(StreamMethods.stream(newData.entries(), isParallelized()), e -> { | ||
Integer rId = e.getKey(); | ||
Record r = e.getValue(); | ||
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AssociativeArray xData = r.getX().copy(); | ||
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boolean modified = false; | ||
for(Object column : r.getX().keySet()) { | ||
if(covert2dummy(columnTypes.get(column))==false) { | ||
continue; | ||
} | ||
Object value = xData.remove(column); //remove the original column | ||
modified = true; | ||
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//create a new column | ||
List<Object> newColumn = Arrays.asList(column,value); | ||
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//add a new dummy variable for this column-value combination | ||
xData.put(newColumn, true); | ||
} | ||
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if(modified) { | ||
Record newR = new Record(xData, r.getY(), r.getYPredicted(), r.getYPredictedProbabilities()); | ||
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//we call below the recalculateMeta() | ||
newData._unsafe_set(rId, newR); | ||
} | ||
}); | ||
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//Reset Meta info | ||
newData.recalculateMeta(); | ||
} | ||
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} |
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