com.arosbio.ml.metrics.cp.classification.EmptyLabelPredictionsPlotBuilder Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of confai Show documentation

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

There is a newer version: 2.0.0

Show newest version

/*
 * 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.cp.classification;

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

import com.arosbio.commons.MathUtils;
import com.arosbio.ml.cp.PValueTools;
import com.arosbio.ml.metrics.cp.EfficiencyPlot;
import com.arosbio.ml.metrics.plots.Plot2D.X_Axis;
import com.arosbio.ml.metrics.plots.PlotMetric;

public class EmptyLabelPredictionsPlotBuilder implements PlotMetric, CPClassifierMetric {
	
	public static final X_Axis X_AXIS = X_Axis.CONFIDENCE;
	public static final String Y_AXIS = "Proportion empty-label prediction sets";
	
	public static final String METRIC_NAME = Y_AXIS;


	private Map numExamples = new HashMap<>();
	private Map numEmptyPredictions = new HashMap<>();

	
	public EmptyLabelPredictionsPlotBuilder() {
		setEvaluationPoints(DEFAULT_EVALUATION_POINTS);
	}
	
	public EmptyLabelPredictionsPlotBuilder(List evaluationPoints) {
		setEvaluationPoints(evaluationPoints);
	}
	
	public String toString() {
		return PlotMetric.toString(this);
	}
	
	@Override
	public boolean supportsMulticlass() {
		return true;
	}
	@Override
	public EfficiencyPlot buildPlot() {

		List fraqEmptyLabel = new ArrayList<>();
		List confs = new ArrayList<>(numExamples.keySet());
		Collections.sort(confs);

		for (Double conf : confs) {
			fraqEmptyLabel.add(((double)numEmptyPredictions.getOrDefault(conf,0))/numExamples.get(conf));
		}
		
		Map> plotValues = new HashMap<>();
		plotValues.put(X_AXIS.label(), new ArrayList<>(confs));
		plotValues.put(Y_AXIS, fraqEmptyLabel);
		
		EfficiencyPlot plot = new EfficiencyPlot(plotValues, X_AXIS, Y_AXIS);
		if (!numExamples.isEmpty()){
			plot.setNumExamplesUsed(numExamples.values().iterator().next());
		}
		return plot;
	}
	
	@Override
	public void addPrediction(int trueLabel, Map pValues) {
		for (double confidence : numExamples.keySet()) {
			numExamples.put(confidence, numExamples.getOrDefault(confidence,0)+1);
			if (PValueTools.getPredictionSetSize(pValues, confidence)==0)
				numEmptyPredictions.put(
						confidence, 
						numEmptyPredictions.getOrDefault(confidence,0)+1);
			
		}
	}


	@Override
	public int getNumExamples() {
		return (int) MathUtils.mean(numExamples.values());
	}

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

	@Override
	public EmptyLabelPredictionsPlotBuilder clone() {
		return new EmptyLabelPredictionsPlotBuilder(new ArrayList<>(numExamples.keySet()));
	}

	@Override
	public void clear() {
		numEmptyPredictions.clear();
		numExamples.clear();
	}
	
	@Override
	public boolean goalIsMinimization() {
		return true;
	}
	
	@Override
	public void setEvaluationPoints(List points) {
		List sortedPoints = PlotMetric.sortAndValidateList(points);
		
		numEmptyPredictions = new LinkedHashMap<>();
		numExamples = new LinkedHashMap<>();
		for (double p : sortedPoints) {
			numEmptyPredictions.put(p,0);
			numExamples.put(p, 0);
		}
	}

	@Override
	public List getEvaluationPoints() {
		return new ArrayList<>(numExamples.keySet());
	}
}