/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* 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.
*
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*/
package org.encog.ml.importance;
import org.encog.ml.MLInputOutput;
import org.encog.ml.MLMethod;
import org.encog.ml.MLRegression;
import org.encog.ml.data.MLDataSet;
import java.util.Collection;
import java.util.List;
import java.util.Set;
/**
* Defines an interface for classes that are used to rank the importance of the input features to a model.
*/
public interface FeatureImportance {
/**
* Initialize a model
* @param theModel The model that will be used for ranking.
* @param names The names of the fields.
*/
void init(MLRegression theModel, String[] names);
/**
* Perform the ranking, without using a specific training set. Not all ranking algorithms support this.
*/
void performRanking();
/**
* Perform the ranking, using a specific training set. Not all ranking algorithms can make use of a dataset.
* @param theDataset The dataset.
*/
void performRanking(MLDataSet theDataset);
/**
* @return The individual rankings of each feature.
*/
List<FeatureRank> getFeatures();
/**
* @return The sorted individual rankings of each feature.
*/
Collection<FeatureRank> getFeaturesSorted();
/**
* @return The model that was evaluated.
*/
MLRegression getModel();
}