plsregressionmodel.java

化学图形处理软件

     * Loads a PLSRegressionModel object from disk in to the current session.
     *
     * @param fileName The disk file containing the model
     * @throws QSARModelException if the model being loaded is not a PLS regression model
     * object
     */
    public void  loadModel(String fileName) throws QSARModelException {
        // should probably check that the filename does exist
        Object model = (Object)revaluator.call("loadModel", new Object[]{ (Object)fileName });
        String modelName = (String)revaluator.call("loadModel.getName", new Object[] { (Object)fileName });

        if (model.getClass().getName().equals("org.openscience.cdk.qsar.model.R.PLSRegressionModelFit")) {
            this.modelfit = (PLSRegressionModelFit)model;
            this.setModelName(modelName);
        } else throw new QSARModelException("The loaded model was not a PLSRegressionModel");
    }
    /**
     * Loads an PLSRegressionModel 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 QSARModelException if the model being loaded is not a PLS regression model
     * object
     */
    public void  loadModel(String serializedModel, String modelName) throws QSARModelException {
        // should probably check that the fileName does exist
        Object model = (Object)revaluator.call("unserializeModel", new Object[]{ (Object)serializedModel, (Object)modelName });
        String modelname = modelName;

        if (model.getClass().getName().equals("org.openscience.cdk.qsar.model.R.PLSRegressionModelFit")) {
            this.modelfit =(PLSRegressionModelFit)model;
            this.setModelName(modelname);
        } else throw new QSARModelException("The loaded model was not a PLSRegressionModel");
    }



    /**
     * Sets parameters required for building a PLS model or using one for prediction.
     *
     * This function allows the caller to set the various parameters available
     * for the pls()  and predict.mvr() 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[][]");
            }
        }
        if (key.equals("X")) {
            if (!(obj instanceof Double[][])) {
                throw new QSARModelException("The class of the 'X' object must be Double[][]");
            }
        }
        if (key.equals("method")) {
            if (!(obj instanceof String)) {
                throw new QSARModelException("The class of the 'method' object must be String");
            }
            if (!(obj.equals("SIMPLS") || obj.equals("kernelPLS"))) {
                throw new QSARModelException("The value of method must be: SIMPLS or kernelPLS ");
            }
        }
        if (key.equals("validation")) {
            if (!(obj instanceof String)) {
                throw new QSARModelException("The class of the 'validation' object must be String");
            }
            if (!(obj.equals("none") || obj.equals("CV"))) {
                throw new QSARModelException("The value of validation must be: none or CV");
            }
        }
        
        if (key.equals("newX")) {
            if ( !(obj instanceof Double[][])) {
                throw new QSARModelException("The class of the 'newX' object must be Double[][]");
            }
        }
        if (key.equals("grpsize")) {
            if (!(obj instanceof Integer)) {
                throw new QSARModelException("The class of the 'grpsize' object must be Integer");
            }
        }
        if (key.equals("niter")) {
            if (!(obj instanceof Integer)) {
                throw new QSARModelException("The class of the 'niter' object must be Integer");
            }
        }
        if (key.equals("nlv")) {
            if (!(obj instanceof Integer)) {
                throw new QSARModelException("The class of the 'nlv' object must be Integer");
            }
        }
        
        if (key.equals("ncomp")) {
            if (!(obj instanceof Integer[])) {
                throw new QSARModelException("The class of the 'ncomp' object must be Integer[]");
            }
            Integer[] tmp = (Integer[])obj;
            if (tmp.length != 1 && tmp.length != 2) {
                throw new QSARModelException("The 'ncomp' array can have a length of 1 or 2. See documentation");
            }
        }
        
        this.params.put(key,obj);
    }
       
        

    /* interface to fit object */

    /**
     * The method used to build the PLS model.
     *
     * @return String containing 'SIMPLS' or 'kernelPLS'
     */
    public String getFitMethod() {
        return(this.modelfit.getMethod());
    }


    /**
     * Returns the fit NComp value.
     *
     * @return An array of integers indicating the number of components
     * (latent variables)
     */
    public int[] getFitNComp() {
        return(this.modelfit.getNComp());
    }

    /**
     * Gets the coefficents.
     *
     * The return value is a 3D array. The first dimension corresponds
     * to the specific number of LV's (1 or 2 or 3 and so on). The second
     * dimension corresponds to the independent variables and the third
     * dimension corresponds to the Y variables.
     *
     * @return double[][][] containing the coefficients
     */
    public double[][][] getFitB() {
        return(this.modelfit.getB());
    }
    
    /**
     * Get the Root Mean Square (RMS) error for the fit.
     *
     * @return A 2-dimensional array of RMS errors.
     */
    public double[][] getFitRMS() {
        return(this.modelfit.getTrainingRMS());
    }

    /**
     * Get the predicted Y's.
     *
     * Each set of latent variables is used to make predictions for all the
     * Y variables.
     *
     * @return A 3-dimensional array of doubles. The first dimension corresponds
     * to the set of latent variables and the remaining two correspond to the 
     * Y's themselves.
     */
    public double[][][] getFitYPred() {
        return(this.modelfit.getTrainingYPred());
    }

    /**
     * Get the X loadings.
     *
     * @return A 2-dimensional array of doubles containing the X loadings
     */
    public double[][] getFitXLoading() {
        return(this.modelfit.getXLoading());
    }
    /**
     * Get the Y loadings.
     *
     * @return A 2-dimensional array of doubles containing the Y loadings
     */
    public double[][] getFitYLoading() {
        return(this.modelfit.getYLoading());
    }
    /**
     * Get the X scores.
     *
     * @return A 2-dimensional array of doubles containing the X scores
     */
    public double[][] getFitXScores() {
        return(this.modelfit.getXScores());
    }
    /**
     * Get the Y scores.
     *
     * @return A 2-dimensional array of doubles containing the Y scores
     */
    public double[][] getFitYScores() {
        return(this.modelfit.getYScores());
    }
    /**
     * Indicates whether CV was used to build the model.
     *
     * @return A boolean indicating whether CV was used
     */
    public boolean getFitWasValidated() {
        return(this.modelfit.wasValidated());
    }


    /**
     * The number of iterations used during CV.
     *
     * @return An int value indicating the number of iterations in CV
     */
    public int getValidationIter() {
        return(this.modelfit.getValidationIter());
    }
    /**
     * The number of latent variables suggested by CV.
     *
     * @return An int value indicating the number of LV's
     */
    public int getValidationLV() {
        return(this.modelfit.getValidationLV());
    }

    /**
     * Get the R^2 value for validation.
     *
     * @return A 2-dimensional array of doubles
     */
    public double[][] getValidationR2() {
        return(this.modelfit.getValidationR2());
    }
    /**
     * Get the RMS value for validation.
     *
     * @return A 2-dimensional array of doubles
     */
    public double[][] getValidationRMS() {
        return(this.modelfit.getValidationRMS());
    }
    /**
     * Get the standard deviation of the RMS errrors for validation.
     *
     * @return A 2-dimensional array of doubles
     */
    public double[][] getValidationRMSsd() {
        return(this.modelfit.getValidationRMSSD());
    }
    /**
     * Get the predicted Y values from validation.
     *
     * @return A 2-dimensional array of doubles
     */
    public double[][][] getValidationYPred() {
        return(this.modelfit.getValidationYPred());
    }
    



    /* interface to predict object */

    /**
     * Returns the predicted values for the prediction set. 
     *
     * This function only returns meaningful results if the <code>predict</code>
     * method of this class has been called.
     *
     * @return A double[][] containing the predicted values
     */
    public  double[][] getPredictPredicted() {
        return(this.modelpredict.getPredictions());
    }
}