/**
 * Copyright (C) 2013-2017 Vasilis Vryniotis 
 *
 * 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.modelselection.metrics;

import com.datumbox.framework.core.common.dataobjects.Dataframe;
import com.datumbox.framework.core.common.dataobjects.Record;
import com.datumbox.framework.core.machinelearning.common.abstracts.modelselection.AbstractMetrics;

import java.util.*;

/**
 * Estimates validation metrics for Classifiers.
 *
 * @author Vasilis Vryniotis 
 */
public class ClassificationMetrics extends AbstractMetrics {
    private static final long serialVersionUID = 1L;

    /**
     * Enum that stores the 4 possible Sensitivity Rates.
     */
    public enum SensitivityRates {
        /**
         * True Positive.
         */
        TRUE_POSITIVE,

        /**
         * True Negative.
         */
        TRUE_NEGATIVE,

        /**
         * False Positive.
         */
        FALSE_POSITIVE,

        /**
         * False Negative.
         */
        FALSE_NEGATIVE;
    }

    //validation metrics
    private double accuracy = 0.0;

    private double macroPrecision = 0.0;
    private double macroRecall = 0.0;
    private double macroF1 = 0.0;

    private final Map microPrecision = new HashMap<>(); //this is small. Size equal to 4*class numbers

    private final Map microRecall = new HashMap<>(); //this is small. Size equal to 4*class numbers

    private final Map microF1 = new HashMap<>(); //this is small. Size equal to 4*class numbers

    private final Map, Double> contingencyTable = new HashMap<>(); //this is small. Size equal to 4*class numbers

    /**
     * Getter for Accuracy.
     *
     * @return
     */
    public double getAccuracy() {
        return accuracy;
    }

    /**
     * Getter for Macro Precision.
     *
     * @return
     */
    public double getMacroPrecision() {
        return macroPrecision;
    }

    /**
     * Getter for Macro Recall.
     *
     * @return
     */
    public double getMacroRecall() {
        return macroRecall;
    }

    /**
     * Getter for Macro F1.
     *
     * @return
     */
    public double getMacroF1() {
        return macroF1;
    }

    /**
     * Getter for Micro Precision.
     *
     * @return
     */
    public Map getMicroPrecision() {
        return microPrecision;
    }

    /**
     * Getter for Micro Recall.
     *
     * @return
     */
    public Map getMicroRecall() {
        return microRecall;
    }

    /**
     * Getter for Micro F1.
     *
     * @return
     */
    public Map getMicroF1() {
        return microF1;
    }

    /**
     * Getter for Contingency Table.
     *
     * @return
     */
    public Map, Double> getContingencyTable() {
        return contingencyTable;
    }

    /**
     * @param predictedData
     * @see AbstractMetrics#AbstractMetrics(Dataframe)
     */
    public ClassificationMetrics(Dataframe predictedData) {
        super(predictedData);

        //retrieve the classes from the dataset
        Set