package com.yahoo.labs.samoa.evaluation;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2014 - 2015 Apache Software Foundation
 * %%
 * 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.
 * #L%
 */

import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.moa.AbstractMOAObject;
import com.yahoo.labs.samoa.moa.core.Measurement;

/**
 * Regression evaluator that performs basic incremental evaluation.
 * 
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @version $Revision: 7 $
 */
public class BasicRegressionPerformanceEvaluator extends AbstractMOAObject
    implements RegressionPerformanceEvaluator {

  private static final long serialVersionUID = 1L;

  protected double weightObserved;

  protected double squareError;

  protected double averageError;

  protected double sumTarget;

  protected double squareTargetError;

  protected double averageTargetError;

  @Override
  public void reset() {
    this.weightObserved = 0.0;
    this.squareError = 0.0;
    this.averageError = 0.0;
    this.sumTarget = 0.0;
    this.averageTargetError = 0.0;
    this.squareTargetError = 0.0;

  }

  @Override
  public void addResult(Instance inst, double[] prediction) {
    double weight = inst.weight();
    double classValue = inst.classValue();
    if (weight > 0.0) {
      if (prediction.length > 0) {
        double meanTarget = this.weightObserved != 0 ?
            this.sumTarget / this.weightObserved : 0.0;
        this.squareError += (classValue - prediction[0]) * (classValue - prediction[0]);
        this.averageError += Math.abs(classValue - prediction[0]);
        this.squareTargetError += (classValue - meanTarget) * (classValue - meanTarget);
        this.averageTargetError += Math.abs(classValue - meanTarget);
        this.sumTarget += classValue;
        this.weightObserved += weight;
      }
    }
  }

  @Override
  public Measurement[] getPerformanceMeasurements() {
    return new Measurement[] {
        new Measurement("classified instances",
            getTotalWeightObserved()),
        new Measurement("mean absolute error",
            getMeanError()),
        new Measurement("root mean squared error",
            getSquareError()),
        new Measurement("relative mean absolute error",
            getRelativeMeanError()),
        new Measurement("relative root mean squared error",
            getRelativeSquareError())
    };
  }

  public double getTotalWeightObserved() {
    return this.weightObserved;
  }

  public double getMeanError() {
    return this.weightObserved > 0.0 ? this.averageError
        / this.weightObserved : 0.0;
  }

  public double getSquareError() {
    return Math.sqrt(this.weightObserved > 0.0 ? this.squareError
        / this.weightObserved : 0.0);
  }

  public double getTargetMeanError() {
    return this.weightObserved > 0.0 ? this.averageTargetError
        / this.weightObserved : 0.0;
  }

  public double getTargetSquareError() {
    return Math.sqrt(this.weightObserved > 0.0 ? this.squareTargetError
        / this.weightObserved : 0.0);
  }

  @Override
  public void getDescription(StringBuilder sb, int indent) {
    Measurement.getMeasurementsDescription(getPerformanceMeasurements(),
        sb, indent);
  }

  private double getRelativeMeanError() {
    return this.averageTargetError > 0 ?
        this.averageError / this.averageTargetError : 0.0;
  }

  private double getRelativeSquareError() {
    return Math.sqrt(this.squareTargetError > 0 ?
        this.squareError / this.squareTargetError : 0.0);
  }
}