Configuring the Nonlinear Classification Model block
You are able to configure the Nonlinear Classification Model block both preceding and following training of the model.
Preceding training allows you to change the output field(s) name(s) to something more meaningful. This changed output name automatically populates the Offline Trainer block's property page.
Following training, you can enable Input Range Validation on the Input Limits tab, ensuring that the inputs to the model are always within the range on which the model was trained.
We also explain how to enable analysis on the trained model. You are able to view the classification matrix and model audit information of the model, shown on the Classification Matrix and Model Audit Properties tabs.
Models can be imported from XML if the block is untrained. Trained models can be exported.
Return to Overview of the Nonlinear Classification Model block.
Configuring the model block:
Block configuration is specified on the block property page. To open this configuration page, either double click on the block, or right-click on the block and select Block Properties.
Configuration prior to training
Prior to training, there are no configuration options on the Input Limits, Classification Matrix, or Model Audit Properties tabs.
Configuration tab
Prior to training, you can:
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Change the name of the Output field in the Nonlinear Classification Model block by selecting the field and pressing the F2 key, or double-clicking it. Choose to enable case sensitivity on string classes.
Note: The output field name specified in the Nonlinear Classification Model block must be the same as the target data field name in the target data source.
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Select to Import model if you want to import a trained model from an XML file in a location that you specify.
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After import, the property page reflects the information imported from the XML file.
Configuration following training
After training, you can:
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Enable input range validation on the Input Limits tab.
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Export the trained model on the Configuration tab.
All other information is gleaned from the model, and cannot be altered.
Configuration tab
Once a model is trained, you can select to Export model. This produces an XML file, in the location you specify, containing the trained model.
No further configuration is possible on this tab.
Input Limit tab
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Once the model is trained, the input field names are listed with their minimum and maximum values alongside. These are from the database, and cannot be altered.
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Enabled Input Range Validation: By enabling this, the model will check the values of the input fields to validate that they are within the range of the training data set when the model is run.
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The model output is marked as bad if the model inputs are outside of the range of the training data. The model is, in effect, extrapolating results when using input fields that are outside the range of the training data set and the resulting value of the output field could be grossly incorrect.
Classification Matrix
On the Classification Matrix tab, a number of classes are displayed for the training, validation and overall data sets. The Overall data set includes the entire training data set of both the training and validation data sets. The required data set is selected from the drop down list.
A classification percentage is shown which illustrates the percentage of data rows from the selected data which the model has correctly classified in the different data sets. This value therefore changes for each of the data sets.
In this example, there are only two classes present in the target field: high and medium. From the results, we see that the model has correctly classified all medium data as medium, accounting for 86.96% of the data. However, it has incorrectly classed the high data as medium, accounting for the remaining 13.04%.
To increase the accuracy of this classification:
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use a larger database with more rows of data, or
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remove the input fields which are not correlated to the model, as this decreases the amount of "noise" to the model, and therefore increases the accuracy of the model
Model Audit Properties
There are no configuration options on this page, it simply lists information regarding the training data set, including:
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number of records used for training (NumTrainingPatterns),
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number of records used for validation (NumValidationPatterns),
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number of bad quality records (NumBadPatterns),
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the start and end dates for the training data set (StartDataSet and EndDataSet), and
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the start and end dates over which training took place (StartedTraining and EndedTraining).
Enabling analysis on the block
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Right-click on the model block and toggle Analysis on.
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Right-click on the block again (as it closes automatically after the analysis is toggled on or off) and click on Analysis properties to specify certain features of the analysis.
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Features to be specified in the Analysis Properties pop-up box include:
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Display Title: this will be the name on the analysis view tab; it will also be the name of the monitored variable on the analysis view y-axis.
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Smoothing Window Size: a moving average filter is applied to the input tags before they are used in the various analysis calculations. Specify the size of the time window over which the input tags are averaged.
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Setpoint Value: the value of the fixed setpoint where applicable. The Analysis Properties box obtains this value (if it is available) from the Alarming and Analysis block - it may be altered in either of the two blocks and the changes will reflect in both.
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History Buffer Size: this is the number of analysis records Architect will keep in memory at any one time. If you are running 3 days worth of data where the records are 1 minute apart, you will have 4320 records; thus, if you wish to be able to view all of the data once the simulation is complete, you will have to set the history buffer size to 4320.
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