com.arosbio.ml.metrics.vap.MeanVAPInterval Maven / Gradle / Ivy

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Conformal AI package, including all data IO, transformations, machine learning models and predictor classes. Without inclusion of chemistry-dependent code.

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/*
 * Copyright (C) Aros Bio AB.
 *
 * CPSign is an Open Source Software that is dual licensed to allow you to choose a license that best suits your requirements:
 *
 * 1) GPLv3 (GNU General Public License Version 3) with Additional Terms, including an attribution clause as well as a limitation to use the software for commercial purposes.
 *
 * 2) CPSign Proprietary License that allows you to use CPSign for commercial activities, such as in a revenue-generating operation or environment, or integrate CPSign in your proprietary software without worrying about disclosing the source code of your proprietary software, which is required if you choose to use the software under GPLv3 license. See arosbio.com/cpsign/commercial-license for details.
 */
package com.arosbio.ml.metrics.vap;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import com.arosbio.commons.MathUtils;
import com.arosbio.commons.mixins.Described;
import com.arosbio.ml.metrics.SingleValuedMetric;

public class MeanVAPInterval implements SingleValuedMetric, VAPMetric, Described {

	public static final String METRIC_NAME = "Mean P0-P1 width";
	public static final String METRIC_DESCRIPTION = "The Venn-ABERS algorithm produces two probabilities, and the P0-P1 width adds information about how much the prediction can be trusted. The smaller the difference between P0 and P1, the more stable the result is and the more the prediction can be trusted.";

	private List meanWidths = new ArrayList<>();

	@Override
	public boolean supportsMulticlass() {
		return false;
	}

	@Override
	public Map asMap() {
		Map res = new HashMap<>();
		res.put(METRIC_NAME, getScore());
		return res;
	}

	public List getMeanWidths(){
		return meanWidths;
	}

	public double getScore() {
		if (meanWidths.isEmpty())
			return Double.NaN;
		return MathUtils.mean(meanWidths);
	}


	public String toString() {
		return SingleValuedMetric.toString(this);
	}

	@Override
	public String getName() {
		return METRIC_NAME;
	}
	
	@Override
	public String getDescription() {
		return METRIC_DESCRIPTION;
	}

	@Override
	public void addPrediction(int trueLabel, Map probabilities, double meanIntervalWidth,
			double medianIntervalWidth) {
		meanWidths.add(meanIntervalWidth);
	}

	@Override
	public int getNumExamples() {
		return meanWidths.size();
	}

	public MeanVAPInterval clone() {
		return new MeanVAPInterval();
	}

	@Override
	public void clear() {
		meanWidths.clear();
	}

	@Override
	public boolean goalIsMinimization() {
		return true;
	}




}