/* $RCSfile$
* $Author$
* $Date$
* $Revision$
*
* Copyright (C) 2004-2008 Rajarshi Guha <rajarshi.guha@gmail.com>
*
* Contact: cdk-devel@lists.sourceforge.net
*
* 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.R2;
import java.io.File;
import java.util.HashMap;
import org.openscience.cdk.qsar.model.QSARModelException;
import org.openscience.cdk.tools.ILoggingTool;
import org.openscience.cdk.tools.LoggingToolFactory;
import org.rosuda.JRI.RBool;
import org.rosuda.JRI.REXP;
import org.rosuda.JRI.RList;
/**
* A modeling class that provides a computational neural network regression model.
* <p/>
* 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;
* double[] y;
* Double[] wts;
* Double[][] newx;
* ...
* try {
* CNNRegressionModel cnnrm = new CNNRegressionModel(x,y,3);
* cnnrm.setParameters("Wts",wts);
* cnnrm.build();
* <p/>
* double fitValue = cnnrm.getFitValue();
* <p/>
* cnnrm.setParameters("newdata", newx);
* cnnrm.setParameters("type", "raw");
* cnnrm.predict();
* <p/>
* 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>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>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>TRUE</td><td>Since this class performs regression this need not be changed</td></tr>
* <tr><td>entropy</td><td>Boolean</td><td>FALSE</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/>
* 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 org.openscience.cdk.qsar.model.R.RModel} for details regarding the R and Java environment.
*
* @author Rajarshi Guha
* @cdk.require r-project
* @cdk.module qsar
* @cdk.githash
* @cdk.keyword neural network
* @cdk.keyword R
*/
public class CNNRegressionModel extends RModel {
public static int globalID = 0;
private int noutput = 0;
private int nvar = 0;
private double[][] modelPredict = null;
private static ILoggingTool logger =
LoggingToolFactory.createLoggingTool(CNNRegressionModel.class);
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", Boolean.FALSE);
this.params.put("mask", Boolean.FALSE);
this.params.put("Wts", Boolean.FALSE);
this.params.put("weights", Boolean.FALSE);
this.params.put("linout", Boolean.TRUE); // we want only regression
this.params.put("entropy", Boolean.FALSE);
this.params.put("softmax", Boolean.FALSE);
this.params.put("censored", Boolean.FALSE);
this.params.put("skip", Boolean.FALSE);
this.params.put("rang", new Double(0.7));
this.params.put("decay", new Double(0.0));
this.params.put("maxit", Integer.valueOf(100));
this.params.put("Hess", Boolean.FALSE);
this.params.put("trace", Boolean.FALSE); // no need to see output
this.params.put("MaxNWts", Integer.valueOf(1000));
this.params.put("abstol", new Double(1.0e-4));
this.params.put("reltol", new Double(1.0e-8));
}
/**
* Constructs a CNNRegressionModel object.
* <p/>
* This constructor allows the user to simply set up an instance of a CNN
* regression modeling class. This constructor simply sets the name for this
* instance. It is expected all the relevent parameters for modeling will be
* set at a later point.
* <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 CNNRegressionModel() throws QSARModelException {
super();
params = new HashMap();
int currentID = CNNRegressionModel.globalID;
CNNRegressionModel.globalID++;
setModelName("cdkCNNModel" + currentID);
setDefaults();
}
/**
* Constructs a CNNRegressionModel object.
* <p/>
* 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 values
* @param size The number of hidden layer neurons
* @throws QSARModelException if the number of observations in x and y do not match
*/
public CNNRegressionModel(double[][] x, double[] y, int size) throws QSARModelException {
super();
params = new HashMap();
int currentID = CNNRegressionModel.globalID;
CNNRegressionModel.globalID++;
setModelName("cdkCNNModel" + 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");
}
nvar = ncol;
noutput = 1;
Double[][] xx = new Double[nrow][ncol];
Double[][] yy = new Double[nrow][1];
for (int i = 0; i < nrow; i++) {
yy[i][0] = new Double(y[i]);
for (int j = 0; j < ncol; j++) {
xx[i][j] = new Double(x[i][j]);
}
}
params.put("x", xx);
params.put("y", yy);
params.put("size", Integer.valueOf(size));
setDefaults();
}
/**
* Constructs a CNNRegressionModel object.
* <p/>
* 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 do not match
*/
public CNNRegressionModel(double[][] x, double[][] y, int size) throws QSARModelException {
super();
params = new HashMap();
int currentID = CNNRegressionModel.globalID;
CNNRegressionModel.globalID++;
setModelName("cdkCNNModel" + 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");
}
nvar = ncol;
noutput = y[0].length;
Double[][] xx = new Double[nrow][ncol];
Double[][] yy = new Double[nrow][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 < noutput; j++) {
yy[i][j] = new Double(y[i][j]);
}
}
params.put("x", xx);
params.put("y", yy);
params.put("size", Integer.valueOf(size));
setDefaults();
}
/**
* Sets parameters required for building a CNN model or using one for prediction.
* <p/>
* This function allows the caller to set the various parameters available
* for 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>
* R routines. See the R help pages for the details of the available
* parameters.
*
* @param key A String containing the name of the parameter as described in the
* R help pages
* @param obj An Object containing the value of the parameter
* @throws QSARModelException if the type of the supplied value does not match the
* expected type
*/
public void setParameters(String key, Object obj) throws QSARModelException {
// since we know the possible values of key we should check the coresponding
// objects and throw errors if required. Note that this checking can't really check
// for values (such as number of variables in the X matrix to build the model and the
// X matrix to make new predictions) - these should be checked in functions that will
// use these parameters. The main checking done here is for the class of obj and
// some cases where the value of obj is not dependent on what is set before it
if (key.equals("y")) {
if (!(obj instanceof Double[][])) {
throw new QSARModelException("The class of the 'y' object must be Double[][]");
} else {
noutput = ((Double[][]) obj)[0].length;
}
}
if (key.equals("x")) {
if (!(obj instanceof Double[][])) {
throw new QSARModelException("The class of the 'x' object must be Double[][]");
} else {
nvar = ((Double[][]) obj)[0].length;
}
}
if (key.equals("weights")) {
if (!(obj instanceof Double[])) {
throw new QSARModelException("The class of the 'weights' object must be Double[]");
}
}
if (key.equals("size")) {
if (!(obj instanceof Integer)) {
throw new QSARModelException("The class of the 'size' object must be Integer");
}
}
if (key.equals("subset")) {
if (!(obj instanceof Integer[])) {
throw new QSARModelException("The class of the 'size' object must be Integer[]");
}
}
if (key.equals("Wts")) {
if (!(obj instanceof Double[])) {
throw new QSARModelException("The class of the 'Wts' object must be Double[]");
}
}
if (key.equals("mask")) {
if (!(obj instanceof Boolean[])) {
throw new QSARModelException("The class of the 'mask' object must be Boolean[]");
}
}
if (key.equals("linout") ||
key.equals("entropy") ||
key.equals("softmax") ||
key.equals("censored") ||
key.equals("skip") ||
key.equals("Hess") ||
key.equals("trace")) {
if (!(obj instanceof Boolean)) {
throw new QSARModelException("The class of the 'trace|skip|Hess|linout|entropy|softmax|censored' object must be Boolean");
}
}
if (key.equals("rang") ||
key.equals("decay") ||
key.equals("abstol") ||
key.equals("reltol")) {
if (!(obj instanceof Double)) {
throw new QSARModelException("The class of the 'reltol|abstol|decay|rang' object must be Double");
}
}
if (key.equals("maxit") ||
key.equals("MaxNWts")) {
if (!(obj instanceof Integer)) {
throw new QSARModelException("The class of the 'maxit|MaxNWts' object must be Integer");
}
}
if (key.equals("newdata")) {
if (!(obj instanceof Double[][])) {
throw new QSARModelException("The class of the 'newdata' object must be Double[][]");
}
}
params.put(key, obj);
}
/**
* Fits a CNN regression model.
* <p/>
* This method calls the R function to fit a CNN regression model
* to the specified dependent and independent variables. If an error
* occurs in the R session, an exception is thrown.
* <p/>
* Note that, this method should be called prior to calling the various get
* methods to obtain information regarding the fit.
*/
public void build() throws QSARModelException {
Double[][] x;
Double[][] y;
x = (Double[][]) this.params.get("x");
y = (Double[][]) this.params.get("y");
if (x.length != y.length)
throw new QSARModelException("Number of observations does not match number of rows in the design matrix");
if (nvar == 0) nvar = x[0].length;
// lets build the model
String paramVarName = loadParametersIntoRSession();
String cmd = "buildCNN(\"" + getModelName() + "\", " + paramVarName + ")";
REXP ret = rengine.eval(cmd);
if (ret == null) {
CNNRegressionModel.logger.debug("Error in buildCNN");
throw new QSARModelException("Error in buildCNN");
}
// remove the parameter list
rengine.eval("rm(" + paramVarName + ")");
// save the model object on the Java side
modelObject = ret.asList();
}
/**
* Uses a fitted model to predict the response for new observations.
* <p/>
* This function uses a previously fitted model to obtain predicted values
* for a new set of observations. If the model has not been fitted prior to this
* call an exception will be thrown. Use <code>setParameters</code>
* to set the values of the independent variable for the new observations and the
* interval type.
*
* @throws org.openscience.cdk.qsar.model.QSARModelException
* if the model has not been built prior to a call
* to this method. Also if the number of independent variables specified for prediction
* is not the same as specified during model building
*/
public void predict() throws QSARModelException {
if (modelObject == null)
throw new QSARModelException("Before calling predict() you must fit the model using build()");
Double[][] newx = (Double[][]) params.get("newdata");
if (newx[0].length != nvar) {
throw new QSARModelException("Number of independent variables used for prediction must match those used for fitting");
}
String pn = loadParametersIntoRSession();
REXP ret = rengine.eval("predicCNN(\"" + getModelName() + "\", " + pn + ")");
if (ret == null) throw new QSARModelException("Error occured in prediction");
// remove the parameter list
rengine.eval("rm(" + pn + ")");
modelPredict = ret.asDoubleMatrix();
}
/**
* Get the matrix of predicted values obtained from <code>predict.nnet<code>.
*
* @return The result of the prediction.
*/
public double[][] getPredictions() {
return modelPredict;
}
/**
* Returns an <code>RList</code> object summarizing the nnet regression model.
* <p/>
* The return object can be queried via the <code>RList</code> methods to extract the
* required components.
*
* @return A summary for the nnet regression model
* @throws org.openscience.cdk.qsar.model.QSARModelException
* if the model has not been built prior to a call
* to this method
*/
public RList summary() throws QSARModelException {
if (modelObject == null)
throw new QSARModelException("Before calling summary() you must fit the model using build()");
REXP ret = rengine.eval("summary(" + getModelName() + ")");
if (ret == null) {
logger.debug("Error in summary()");
throw new QSARModelException("Error in summary()");
}
return ret.asList();
}
/**
* Loads a <code>'nnet'</code> object from disk in to the current session.
*
* @param fileName The disk file containing the model
* @throws org.openscience.cdk.qsar.model.QSARModelException
* if the model being loaded is not a <code>'nnet'</code> model
* object or the file does not exist
*/
public void loadModel(String fileName) throws QSARModelException {
File f = new File(fileName);
if (!f.exists()) throw new QSARModelException(fileName + " does not exist");
rengine.assign("tmpFileName", fileName);
REXP ret = rengine.eval("loadModel(tmpFileName)");
if (ret == null) throw new QSARModelException("Model could not be loaded");
String name = ret.asList().at("name").asString();
if (!isOfClass(name, "nnet")) {
removeObject(name);
throw new QSARModelException("Loaded object was not of class \'nnet\'");
}
modelObject = ret.asList().at("model").asList();
setModelName(name);
nvar = (int) getN()[0];
noutput = (int) getN()[2];
}
/**
* Loads a <code>'nnet'</code> object from a serialized string into the current session.
*
* @param serializedModel A String containing the serialized version of the model
* @param modelName A String indicating the name of the model in the R session
* @throws org.openscience.cdk.qsar.model.QSARModelException
* if the model being loaded is not a <code>'nnet'</code> model
* object
*/
public void loadModel(String serializedModel, String modelName) throws QSARModelException {
rengine.assign("tmpSerializedModel", serializedModel);
rengine.assign("tmpModelName", modelName);
REXP ret = rengine.eval("unserializeModel(tmpSerializedModel, tmpModelName)");
if (ret == null) throw new QSARModelException("Model could not be unserialized");
String name = ret.asList().at("name").asString();
if (!isOfClass(name, "nnet")) {
removeObject(name);
throw new QSARModelException("Loaded object was not of class \'nnet\'");
}
modelObject = ret.asList().at("model").asList();
setModelName(name);
nvar = (int) getN()[0];
noutput = (int) getN()[2];
}
// Autogenerated code: assumes that 'modelObject' is
// a RList object
/**
* Gets the <code>censored</code> field of an <code>'nnet'</code> object.
*
* @return The value of the censored field
*/
public RBool getCensored() {
return modelObject.at("censored").asBool();
}
/**
* Gets the <code>conn</code> field of an <code>'nnet'</code> object.
*
* @return The value of the conn field
*/
public double[] getConn() {
return modelObject.at("conn").asDoubleArray();
}
/**
* Gets the <code>decay</code> field of an <code>'nnet'</code> object.
*
* @return The value of the decay field
*/
public double getDecay() {
return modelObject.at("decay").asDouble();
}
/**
* Gets the <code>entropy</code> field of an <code>'nnet'</code> object.
*
* @return The value of the entropy field
*/
public RBool getEntropy() {
return modelObject.at("entropy").asBool();
}
/**
* Gets the <code>fitted.values</code> field of an <code>'nnet'</code> object.
*
* @return The value of the fitted.values field
*/
public double[][] getFittedValues() {
return modelObject.at("fitted.values").asDoubleMatrix();
}
/**
* Gets the <code>n</code> field of an <code>'nnet'</code> object.
*
* @return The value of the n field
*/
public double[] getN() {
return modelObject.at("n").asDoubleArray();
}
/**
* Gets the <code>nconn</code> field of an <code>'nnet'</code> object.
*
* @return The value of the nconn field
*/
public double[] getNconn() {
return modelObject.at("nconn").asDoubleArray();
}
/**
* Gets the <code>nsunits</code> field of an <code>'nnet'</code> object.
*
* @return The value of the nsunits field
*/
public double getNsunits() {
return modelObject.at("nsunits").asDouble();
}
/**
* Gets the <code>nunits</code> field of an <code>'nnet'</code> object.
*
* @return The value of the nunits field
*/
public double getNunits() {
return modelObject.at("nunits").asDouble();
}
/**
* Gets the <code>residuals</code> field of an <code>'nnet'</code> object.
*
* @return The value of the residuals field
*/
public double[][] getResiduals() {
return modelObject.at("residuals").asDoubleMatrix();
}
/**
* Gets the <code>softmax</code> field of an <code>'nnet'</code> object.
*
* @return The value of the softmax field
*/
public RBool getSoftmax() {
return modelObject.at("softmax").asBool();
}
/**
* Gets the <code>value</code> field of an <code>'nnet'</code> object.
*
* @return The value of the value field
*/
public double getValue() {
return modelObject.at("value").asDouble();
}
/**
* Gets the <code>wts</code> field of an <code>'nnet'</code> object.
*
* @return The value of the wts field
*/
public double[] getWts() {
return modelObject.at("wts").asDoubleArray();
}
}