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imported>Lyle |
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| ==Introduction== | | __TOC___ |
| Eigenevctor Research's [[Function_Reference_Manual|PLS_Toolbox]] and [http://www.siemens.com/ Siemens' SiPAT product] can be used together for deployment of PLS_Toolbox or Solo multivariate analysis models in process control applications. This integration utilizes Mathworks' [http://www.mathworks.com/ Matlab] and functionality built into SiPAT to run custom Matlab functions. These functions can include calls to PLS_Toolbox functions.
| | ==Importing / Exporting== |
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| The following discusses the PLS_Toolbox-specific configuration and provides example m-files which can be used to quickly get SiPAT and PLS_Toolbox integrated.
| | [[faq_concatenate_multiple_files|How do I concatenate multiple files into a single DataSet?]] |
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| ====Licensing====
| | [[faq_create_multivariate_image_from_separate_images|How do I create a multivariate image from separate images?]] |
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| Note that the [http://www.eigenvector.com/software/license_evri.html PLS_Toolbox license] requires that, unless special site licensing arrangements are made, you must obtain a separate PLS_Toolbox license for each instance of any PLS_Toolbox code you wish to deploy. Special licensing options are available. See the [http://www.eigenvector.com Eigenvector Website] for more information.
| | [[faq_export_PCA_scores_and_loadings_to_text_file|How do I export PCA scores and loadings to a text file (to read into MS Excel, for example)?]] |
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| ==Installation and Basic Configuration==
| | [[faq_import_three-way_data|How do I import three-way data into Solo or PLS_Toolbox?]] |
| ===SiPAT Configuration===
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| The user is directed to the Siemens documentation for details on configuration of SiPAT for use with Matlab. The discussion below describes some of the PLS_Toolbox-specific considerations of this configuration.
| | [[faq_import_horiba_NGC_64bit |Why can't I import a Horiba NGC file on my 64-bit computer?]] |
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| ===Matlab and PLS_Toolbox Configuration===
| | [[faq_SPCREADR_cant_read_multiple_files |Why can't SPCREADR read multiple files I've selected?]] |
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| With Matlab already installed (as per [http://www.mathworks.com The Mathworks installation instructions]), PLS_Toolbox can be copied onto the target computer using any of the available file types as described in the [[Installation|standard installation instructions]]. The primary differences from the standard installation will be that you will create two special files and place these files into the PLS_Toolbox/utilities folder on the target computer:
| | [[faq_some_EXCEL_files_fail_to_import |Why do some Excel files fail to import?]] |
| # Create a text file named '''evrilicense.lic''' and put your license code (available from the [http://download.eigenvector.com Eigenvector Research download page]) as a single line in that file. You can also obtain a evrilicense.lic file from the [mailto:helpdesk@eigenvector.com Eigenvector Helpdesk].
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| # Create an ''empty'' text file named '''evrinetwork.lic'''. This file will instruct PLS_Toolbox to ignore installation errors and run without warnings (necessary when calling PLS_Toolbox functions from within SiPAT.)
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| ==Specific Model and M-file Configuration== | | ==General== |
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| The SiPAT interface into Matlab provides for the specification of a data file to load and a Matlab function to execute (along with specific input and output configuration information.)
| | [[faq_PARALIND_in_PLS_Toolbox |Can I do PARALIND in PLS_Toolbox?]] |
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| In the examples given here, the data file will be in the Matlab MAT format and will contain the model to apply. The Matlab function will be given in the Matlab m-file format and will contain the specific instructions for applying the model and returning the results to SiPAT. In these examples, it is assumed there is only one model that is being applied per each method (no special calibration transfer or other pre-transformation steps being used).
| | [[faq_install_on_more_than_one_PC | Can I install PLS_Toolbox (or Solo) on more than one PC, such as on my desktop and laptop computer?]] |
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| ===Creation of the Model MAT File===
| | [[faq_multiple_class_sets_together_in_SIMCA_PLSDA_LDA | Can I use multiple class sets (categorical variables) together in a SIMCA, PLSDA, or LDA model?]] |
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| The model you wish to apply to new data should be saved from Matlab or Solo into a MAT file as the one and only item stored in the MAT file. In the [[Analysis Window]], this is done by selecting the menu item: File > Save Model and using the [[WorkspaceBrowser_ImportingData#To_save_imported_data_to_a_.mat_file save dialog]] to specify a filename and an item name. This will save the model into the given filename with the specified item name. Although the MAT file name can be any standard filename, it will make m-file construction easier if the item name used is always the same in all saved models. In the example here, we will assume that the item is called "model". If a different name is used, the SiPAT-specific configuration comments in the m-file (see below) will need to be modified to indicate to SiPAT the name used for the model.
| | [[faq_more_info_on_R_Squared_statistic | Can you give me more information on the R-Squared statistic?]] |
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| If saving the model from the Matlab command line, the following command can be used (assuming the model to use is currently named "model" in your Matlab workspace) :
| | [[faq_how_RMSEC_and_RMSECV_related to R2Y_and_Q2Y_seen_other_software | How are RMSEC and RMSECV related to R2Y and Q2Y I see in other software?]] |
| save myfile.mat model
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| ===Creation of the Function M-file===
| | [[ faq_convergence_of_PARAFAC| Convergence of PARAFAC. How much variation between models is expected a particular PARAFAC is fit multiple times with the same settings?]] |
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| The second part to the SiPAT/PLS_Toolbox interface is a Matlab m-file which contains a function definition (Note: The contents of this m-file must actually be a Matlab function, meaning it must contain a function header line as shown in the scripts below. It cannot be a "script" in the strict Matlab definition of that term which implies code that is not wrapped inside a function definition.)
| | [[ faq_does_software_stop_working_if_maintenance_expires | Does the software stop working if my maintenance expires?]] |
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| Three example m-files are given below: one for use with any of the regression model types (PLS, PCR, MLR, CLS, SVM, LWR, etc), one for use with classification model types (PLSDA, KNN, SIMCA, SVMDA/SVM-C), and one for use with principal component analysis (PCA) models. Each of these m-files assumes that the input is a single vector of values (passed by SiPAT) and each returns two or three values which correspond to the predictions from the model.
| | ==Command Line== |
| | ==Manual== |
| | ==GUI== |
| | ==Installation== |
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| These functions all also assume that the input data will be two columns where the first column contains axis scale information for the variables (such as wavelength, m/z, time, etc) and the second column is the actual measured data. If this does not fit the type of data being passed by SiPAT (e.g. no axisscale information), the initial lines:
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| <pre>
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| %convert second column of input data into a dataset (if not appropriate column, change next line)
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| x = data(:,2);
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| x = dataset(double(x'));
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| %Assume first column is axisscale information. If not true, comment out the next line
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| x.axisscale{2} = double(data(:,1));
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| </pre>
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| should be converted as necessary to handle the input data. For example, if only a single column of values is being passed, the following can be substituted in for the above code:
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| <pre>
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| %convert column of input data into a dataset
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| x = dataset(double(data'));
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| </pre>
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| ====Regression Model Predictions====
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| The following m-file contents are appropriate for use with regression model types:
| | [[Category:FAQ]] |
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| <pre>
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| %START SIPAT
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| %<CONFIG>
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| % <MODEL>REGRESSION</MODEL>
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| % <PREFIX></PREFIX>
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| % <SUFFIX></SUFFIX>
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| % <INPUTS>
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| % <INPUT Name="data" XDataType="MultiValue" YDataType="Single" />
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| % </INPUTS>
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| % <OUTPUTS>
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| % <OUTPUT Name="y" XDataType="SingleValue" YDataType="Double" />
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| % <OUTPUT Name="q_x" XDataType="SingleValue" YDataType="Double" />
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| % <OUTPUT Name="H_x" XDataType="SingleValue" YDataType="Double" />
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| % </OUTPUTS>
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| % <FUNCTION>[y,q_x,H_x]=regpred(data,model)</FUNCTION>
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| %</CONFIG>
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| %END SIPAT
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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| % Regression using PLS_toobox from Eigenvector
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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| %This function calculates the responses y and the Q-residuals
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| %(q_x) and Hotellings T2 (H_x) corresponding to the input variables data
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| %(data) using a standard model structure.
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| %
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| %Before using this function, make sure you loaded the following data into
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| %the workspace:
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| % model: Model structure used in PLS_Toolbox/Solo including all pretreatment.
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| function [y,q_x,H_x] = regpred(data,model)
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| try
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| %convert second column of input data into a dataset (if not appropriate column, change next line)
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| x = data(:,2);
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| x = dataset(double(x'));
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| %Assume first column is axisscale information. If not true, comment out the next line
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| x.axisscale{2} = double(data(:,1));
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| %make a prediction
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| opts =[];
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| opts.plots='none';
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| opts.display='off';
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| pred_x = feval(lower(model.modeltype),x,model,opts);
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| %return prediction in y
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| y = double(pred_x.pred{2});
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| if isfield(pred_x,'ssqresiduals')
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| q_x = double(pred_x.ssqresiduals{1,1}./pred_x.detail.reslim{1});
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| H_x = double(pred_x.tsqs{1}./pred_x.detail.tsqlim{1});
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| else
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| q_x = 0;
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| H_x = 0;
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| end
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| catch
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| %errors are saved to the following file (change location as desired)
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| fid=fopen('C:\sipat_error_sout.txt','w');
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| fwrite(fid,encode(lasterror));
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| fwrite(fid,encode(evalin('base','whos')));
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| fclose(fid);
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| end
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| </pre>
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| ====Classification Model Predictions====
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| The following m-file contents are appropriate for use with classification model types:
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| <pre>
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| %START SIPAT
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| %<CONFIG>
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| % <MODEL>CLASSIFICATION</MODEL>
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| % <PREFIX></PREFIX>
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| % <SUFFIX></SUFFIX>
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| % <INPUTS>
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| % <INPUT Name="data" XDataType="MultiValue" YDataType="Single" />
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| % </INPUTS>
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| % <OUTPUTS>
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| % <OUTPUT Name="y" XDataType="SingleValue" YDataType="Double" />
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| % <OUTPUT Name="q_x" XDataType="SingleValue" YDataType="Double" />
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| % <OUTPUT Name="H_x" XDataType="SingleValue" YDataType="Double" />
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| % </OUTPUTS>
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| % <FUNCTION>[y,q_x,H_x]=classpred(data,model)</FUNCTION>
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| %</CONFIG>
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| %END SIPAT
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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| % Classification using PLS_toobox from Eigenvector
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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| %This function calculates the numerical class assignment, the Q-residuals
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| %(q_x) and Hotellings T2 (H_x) corresponding to the input variables data
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| %(data) using a standard model structure.
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| %
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| %Before using this function, make sure you loaded the following data into
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| %the workspace:
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| % model: Model structure used in PLS_Toolbox/Solo including all pretreatment.
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| function [y,q_x,H_x] = classpred(data,model)
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| try
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| %convert second column of input data into a dataset (if not appropriate column, change next line)
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| x = data(:,2);
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| x = dataset(double(x'));
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| %Assume first column is axisscale information. If not true, comment out the next line
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| x.axisscale{2} = double(data(:,1));
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| %make a prediction
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| opts =[];
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| opts.plots='none';
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| opts.display='off';
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| pred_x = feval(lower(model.modeltype),x,model,opts);
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| %return prediction in y
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| y = double(pred_x.classification.mostprobable);
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| if isfield(pred_x,'ssqresiduals')
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| q_x = double(pred_x.ssqresiduals{1,1}./pred_x.detail.reslim{1});
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| H_x = double(pred_x.tsqs{1}./pred_x.detail.tsqlim{1});
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| else
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| q_x = 0;
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| H_x = 0;
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| end
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| catch
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| %errors are saved to the following file (change location as desired)
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| fid=fopen('C:\sipat_error_sout.txt','w');
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| fwrite(fid,encode(lasterror));
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| fwrite(fid,encode(evalin('base','whos')));
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| fclose(fid);
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| end
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| </pre>
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