ai.preferred.regression.PlotLinearRegression Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of csvpl Show documentation
Your preferred data manipulation and analysis language for CSV.
The newest version!
/*
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */

package ai.preferred.regression;

import ai.preferred.regression.io.ARFFDataReader;
import ai.preferred.regression.plot.XYChart;
import org.jfree.data.xy.XYSeries;
import org.kohsuke.args4j.Option;
import weka.classifiers.Classifier;
import weka.classifiers.functions.LinearRegression;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SerializationHelper;

import javax.swing.*;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;

public class PlotLinearRegression extends Command {

  @Option(name = "-i", aliases = {"--input"}, usage = "the path to the input CSV file", required = true)
  private File input;

  @Option(name = "-m", aliases = {"--model"}, usage = "the path to the model file", required = true)
  private File model;

  @Option(name = "-n", aliases = {"--name"}, usage = "the name of the plot")
  private String name = "Y = alpha * X + beta";

  @Option(name = "-h", aliases = {"--header"}, usage = "specifies if the input CSV files have headers")
  private boolean header = true;

  @Override
  protected void exec() throws Exception {
    try (final FileInputStream stream = new FileInputStream(model)) {
      final Classifier classifier = (Classifier) SerializationHelper.read(stream);
      if (!(classifier instanceof LinearRegression)) {
        throw new IOException("The model is neither LogisticRegression nor LinearRegression!");
      }
      final double[] w = ((LinearRegression) classifier).coefficients();

      if (w.length != 3) {
        throw new IOException("We can plot only linear functions!");
      }

      final ARFFDataReader reader = new ARFFDataReader(input, false, header);
      final Instances data = reader.read(input);

      final XYSeries dataSeries = new XYSeries("DATA");
      for (final Instance datum : data) {
        dataSeries.add(datum.value(1), datum.value(0));
      }
      final XYSeries regressionSeries = new XYSeries("REGRESSION");
      regressionSeries.add(dataSeries.getMinX(), w[1] * dataSeries.getMinX() + w[2]);
      regressionSeries.add(dataSeries.getMaxX(), w[1] * dataSeries.getMaxX() + w[2]);

      final XYChart chart = new XYChart(name, dataSeries, regressionSeries);
      chart.pack();
      chart.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
      chart.setVisible(true);
    }
  }

  public static void main(String[] args) {
    parseArgsAndRun(PlotLinearRegression.class, args);
  }

}