cnnclassificationmodel.java

化学图形处理软件

/*
 *  Copyright (C) 2004-2007  The Chemistry Development Kit (CDK) project
 *
 *  Contact: [e-mail]
 *
 *  This program is free software; you can redistribute it and/or
 *  modify it under the terms of the GNU Lesser General Public License
 *  as published by the Free Software Foundation; either version 2.1
 *  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 Lesser General Public License for more details.
 *
 *  You should have received a copy of the GNU Lesser General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
 */

package org.openscience.cdk.qsar.model.R;

import org.openscience.cdk.qsar.model.QSARModelException;

import java.util.HashMap;

/** 
 * A modeling class that provides a computational neural network classification model.
 *
 * When instantiated this class ensures that the R/Java interface has been 
 * initialized. The response and independent variables can be specified at construction
 * time or via the <code>setParameters</code> method. 
 * The actual fitting procedure is carried out by <code>build</code> after which 
 * the model may be used to make predictions, via <code>predict</code>. An example of the use
 * of this class is shown below:
 * <pre>
 * double[][] x;
 * String[] y;
 * Double[] wts;
 * Double[][] newx;
 * ...
 * try {
 *     CNNClassificationModel cnnrm = new CNNClassificationModel(x,y,3);
 *     cnnrm.setParameters("Wts",wts);
 *     cnnrm.build();
 *     
 *     double fitValue = cnnrm.getFitValue();
 *     
 *     cnnrm.setParameters("newdata", newx);
 *     cnnrm.setParameters("type", "raw");
 *     cnnrm.predict();
 *
 *     double[][] preds = cnnrm.getPredictPredicted();
 * } catch (QSARModelException qme) {
 *     System.out.println(qme.toString());
 * }
 * </pre>
 * The above code snippet builds a 3-3-1 CNN model.
 * Multiple output neurons are easily
 * specified by supplying a matrix for y (i.e., double[][]) with the output variables
 * in the columns. 
 * <p>
 * Nearly all the arguments to 
 * <a href="http://www.maths.lth.se/help/R/.R/library/nnet/html/nnet.html" target="_top">nnet()</a> are
 * supported via the <code>setParameters</code> method. The table below lists the names of the arguments,
 * the expected type of the argument and the default setting for the arguments supported by this wrapper class.
 * <center>
 * <table border=1 cellpadding=5>
 * <THEAD>
 * <tr>
 * <th>Name</th><th>Java Type</th><th>Default</th><th>Notes</th>
 * </tr>
 * </thead>
 * <tbody>
 * <tr><td>x</td><td>Double[][]</td><td>None</td><td>This must be set by the caller via the constructors or via <code>setParameters</code></td></tr>
 * <tr><td>y</td><td>String[][]</td><td>None</td><td>This must be set by the caller via the constructors or via <code>setParameters</code></td></tr>
 * <tr><td>weights</td><td>Double[]</td><td>rep(1,nobs)</td><td>The default case weights is a vector of 1's equal in length to the number of observations, nobs</td></tr>
 * <tr><td>size</td><td>Integer</td><td>None</td><td>This must be set by the caller via the constructors or via <code>setParameters</code></td></tr>
 * <tr><td>subset</td><td>Integer[]</td><td>1:nobs</td><td>This is supposed to be an index vector specifying which observations are to be used in building the model. The default indicates that all should be used</td></tr>
 * <tr><td>Wts</td><td>Double[]</td><td>runif(1,nwt)</td><td>The initial weight vector is set to a random vector of length equal to the number of weights if not set by the user</td></tr>
 * <tr><td>mask</td><td>Boolean[]</td><td>rep(TRUE,nwt)</td><td>All weights are to be optimized unless otherwise specified by the user</td></tr>
 * <tr><td>linout</td><td>Boolean</td><td>FALSE</td><td>Since this class performs classification this need not be changed</td></tr>
 * <tr><td>entropy</td><td>Boolean</td><td>TRUE</td><td></td></tr>
 * <tr><td>softmax</td><td>Boolean</td><td>FALSE</td><td></td></tr>
 * <tr><td>censored</td><td>Boolean</td><td>FALSE</td><td></td></tr>
 * <tr><td>skip</td><td>Boolean</td><td>FALSE</td><td></td></tr>
 * <tr><td>rang</td><td>Double</td><td>0.7</td><td></td></tr>
 * <tr><td>decay</td><td>Double</td><td>0.0</td><td></td></tr>
 * <tr><td>maxit</td><td>Integer</td><td>100</td><td></td></tr>
 * <tr><td>Hess</td><td>Boolean</td><td>FALSE</td><td></td></tr>
 * <tr><td>trace</td><td>Boolean</td><td>TRUE</td><td></td></tr>
 * <tr><td>MaxNWts</td><td>Integer</td><td>1000</td><td></td></tr>
 * <tr><td>abstol</td><td>Double</td><td>1.0e-4</td><td></td></tr>
 * <tr><td>reltol</td><td>Double</td><td>1.0e-8</td><td></td></tr>
 * </tbody>
 * </table>
 * </center>
 * <p>
 * In general the <code>getFit*</code> methods provide access to results from the fit
 * and <code>getPredict*</code> methods provide access to results from the prediction (i.e.,
 * prediction using the model on new data). The values returned correspond to the various 
 * values returned by the <a href="http://www.maths.lth.se/help/R/.R/library/nnet/html/nnet.html" target="_top">nnet</a> and
 * <a href="http://www.maths.lth.se/help/R/.R/library/nnet/html/predict.nnet.html" target="_top">predict.nnet</a> functions
 * in R
 * <p>
 * See {@link RModel} for details regarding the R and SJava environment.
 * 
 * @author Rajarshi Guha
 * @cdk.require r-project
 * @cdk.module qsar
 * 
 * @cdk.keyword neural network
 * @cdk.keyword classification
 * @deprecated
 */
public class CNNClassificationModel extends RModel {
    
    static int globalID = 0;
    private int currentID;
    private CNNClassificationModelFit modelfit = null;
    private CNNClassificationModelPredict modelpredict = null;

    private HashMap params = null;
    private int noutput = 0;
    private int nvar = 0;

    private void setDefaults() {
        // lets set the default values of the arguments that are specified
        // to have default values in ?nnet

        // these params are vectors that depend on user defined stuff
        // so as a default we set them to FALSE so R can check if these
        // were not set
        this.params.put("subset", new Boolean(false));
        this.params.put("mask", new Boolean(false) );
        this.params.put("Wts", new Boolean(false));
        this.params.put("weights", new Boolean(false));

        this.params.put("linout", new Boolean(false)); // we want only classification
        this.params.put("entropy", new Boolean(true));
        this.params.put("softmax",new Boolean(false));
        this.params.put("censored", new Boolean(false));
        this.params.put("skip", new Boolean(false));
        this.params.put("rang", new Double(0.7));
        this.params.put("decay", new Double(0.0));
        this.params.put("maxit", new Integer(100));
        this.params.put("Hess", new Boolean(false));
        this.params.put("trace", new Boolean(false)); // no need to see output
        this.params.put("MaxNWts", new Integer(1000));
        this.params.put("abstol", new Double(1.0e-4));
        this.params.put("reltol", new Double(1.0e-8));
    }        
    
    /**
     * Constructs a CNNClassificationModel object.
     *
     * This constructor allows the user to simply set up the modeling class. It is
     * expected that parameters such as training data, architecture will be set at a 
     * later point. The result of this constructor is to simply create a name for the 
     * current instance of the modeling object.
     * <p>
     * Other parameters that are required to be set should be done via
     * calls to <code>setParameters</code>. A number of parameters are set to the
     * defaults as specified in the manpage for 
     * <a href="http://www.maths.lth.se/help/R/.R/library/nnet/html/nnet.html" target="_top">nnet</a>.
     *
     */
    public CNNClassificationModel() {
        super();
        this.params = new HashMap();
        this.currentID = CNNClassificationModel.globalID;
        CNNClassificationModel.globalID++;
        this.setModelName("cdkCNNCModel"+this.currentID);
        this.setDefaults();
    }

    /**
     * Constructs a CNNClassificationModel object.
     *
     * This constructor allows the user to specify the dependent and
     * independent variables along with the number of hidden layer neurons.
     * This constructor is suitable for cases when there is a single output 
     * neuron. If the number of rows of the design matrix is not equal to 
     * the number of observations in y an exception will be thrown.
     * <p>
     * Other parameters that are required to be set should be done via
     * calls to <code>setParameters</code>. A number of parameters are set to the
     * defaults as specified in the manpage for 
     * <a href="http://www.maths.lth.se/help/R/.R/library/nnet/html/nnet.html" target="_top">nnet</a>.
     *
     * @param x An array of independent variables. Observations should be in
     * the rows and variables in the columns.
     * @param y An array (single column) of observed class assignments
     * @param size The number of hidden layer neurons
     * @throws QSARModelException if the number of observations in x and y are not the same
     */
    public CNNClassificationModel(double[][] x, String[] y, int size) throws QSARModelException {
        super();
        this.params = new HashMap();
        this.currentID = CNNClassificationModel.globalID;
        CNNClassificationModel.globalID++;
        this.setModelName("cdkCNNCModel"+this.currentID);

        int nrow = y.length;
        int ncol = x[0].length;
        
        if (nrow != x.length) {
            throw new QSARModelException("The number of values for the dependent variable does not match the number of rows of the design matrix");
        }

        this.nvar = ncol;
        this.noutput = 1;
        
        Double[][] xx = new Double[nrow][ncol];
        String[][] yy = new String[nrow][1];

        for (int i = 0; i < nrow; i++) {
            yy[i][0] = new String(y[i]);
            for (int j = 0; j < ncol; j++) {
                xx[i][j] = new Double(x[i][j]);
            }
        }
        this.params.put("x", xx);
        this.params.put("y", yy);
        this.params.put("size", new Integer(size));
        this.setDefaults();
    }
    
    /**
     * Constructs a CNNClassificationModel object.
     *
     * This constructor allows the user to specify the dependent and
     * independent variables along with the number of hidden layer neurons.
     * This constructor is suitable for cases when there are multiple output 
     * neuron. If the number of rows of the design matrix is not equal to 
     * the number of observations in y an exception will be thrown.
     * <p>
     * Other parameters that are required to be set should be done via
     * calls to <code>setParameters</code>. A number of parameters are set to the
     * defaults as specified in the manpage for 
     * <a href="http://www.maths.lth.se/help/R/.R/library/nnet/html/nnet.html" target="_top">nnet</a>.
     *
     * @param x An array of independent variables. Observations should be in
     * the rows and variables in the columns.
     * @param y An array (multiple columns) of observed values
     * @param size The number of hidden layer neurons
     * @throws QSARModelException if the number of observations in x and y are not the same
     */
    public CNNClassificationModel(double[][] x, String[][] y, int size) throws QSARModelException{
        super();
        this.params = new HashMap();
        this.currentID = CNNClassificationModel.globalID;
        CNNClassificationModel.globalID++;
        this.setModelName("cdkCNNCModel"+this.currentID);

        int nrow = y.length;
        int ncol = x[0].length;
        
        if (nrow != x.length) {
            throw new QSARModelException("The number of values for the dependent variable does not match the number of rows of the design matrix");
        }

        this.nvar = ncol;
        this.noutput = y[0].length;
        
        Double[][] xx = new Double[nrow][ncol];
        String[][] yy = new String[nrow][this.noutput];

        for (int i = 0; i < nrow; i++) {
            for (int j = 0; j < ncol; j++) {
                xx[i][j] = new Double(x[i][j]);
            }
        }
        for (int i = 0; i < nrow; i++) {
            for (int j = 0; j < this.noutput; j++) {
                yy[i][j] = new String(y[i][j]);
            }
        }
        this.params.put("x", xx);
        this.params.put("y", yy);
        this.params.put("size", new Integer(size));
        this.setDefaults();
    }

    
    /**
     * Sets parameters required for building a linear model or using one for prediction.