In this data mining competition I was trying out, they give you a final test set without the actual answers and your job is to predict that the actual values are, and well, send them in. I did manage to get it figured out after a lot of searching. So I’m back on my path to winning

So here’s the easiest way to use a model to simply get its predictions without training it again:

If you right-click on the result list you’ll get a popup
with options including “Load model” which allows you to load
some saved model; and there is another option “Re-evaluate
model on the current test-set”; obviously before you can
do that, you will have to load some test-set: tick “Supplied
test set” and then the “Set” button and select your file …

I found that answer here, and later in the thread they also mention a way to do it with the command line interface (CLI).

If that quoted answer above still isn’t clear, allow me to summarize:

1. You open Weka and get that little window with the four buttons.
2. Click on Explorer.
3. load any old bogus data set you’re not going to need just so it allows you to get to the classifier tab.
4. Then under test options you choose “supplied test set” and point it to the data you want it to make predictions on. (Note: this 5. must have all of the same attributes, in the same order that you trained your model on.).
6. Finally right click in the result list area and select load model.
7. Point it to your saved model.
8. Next right click to model you just loaded and tell it to re-evaluate on current test set.

Here’s what your output might look like:

You know, all frusteration aside, Weka really is an incredibly useful, free program, and perhaps I should be glad the developers are spending time on the machine learning side of development instead of my personal needs…

My search terms (see the frusteration build):

1. weka run a model on test data
2. weka use a model
3. weka + “use a model”
4. weka + (get OR output) + predictions + without + training
5. weka + “without training”
6. weka don’t have actual
7. weka cli
8. weka + sucks
9. “weka sucks”

Here is more information about saving models in Weka.

[tags]Weka, machine learning, data mining, predictions[/tags]

Just follow these steps to fix it:

1. Create a “LookAndFeel.props” file in your home directory:
   Type:touch ~/LookAndFeel.props into system command line.
2. Type this into the first line of the newly created LookAndFeel.props file:
   Theme=javax.swing.plaf.metal.MetalLookAndFeel
3. Save the file and exit.
4. Start WEKA and everything should now work.

Here’s a detailed version of the answer and explains a little bit of the why of this problem.

Here’s the full text of the original error:

chiefinnovator@MAIN1:~$ cd /home/chiefinnovator/weka/weka-3-4-8a/
chiefinnovator@MAIN1:~/weka/weka-3-4-8a$ java -jar weka.jar
 /usr/share/themes/Simple/gtk-2.0/gtkrc:46: Engine "thinice" is unsupported, ignoring
/usr/share/themes/Simple/gtk-2.0/gtkrc:53: Engine "redmond95" is unsupported, ignoring
/usr/share/themes/Simple/gtk-2.0/gtkrc:57: Engine "redmond95" is unsupported, ignoring
---Registering Weka Editors---
Exception in thread "AWT-EventQueue-0" java.lang.NullPointerException
        at weka.gui.explorer.PreprocessPanel.addPropertyChangeListener(Unknown Source)
        at javax.swing.plaf.synth.SynthPanelUI.installListeners(SynthPanelUI.java:49)
        at javax.swing.plaf.synth.SynthPanelUI.installUI(SynthPanelUI.java:38)
        at javax.swing.JComponent.setUI(JComponent.java:652)
        at javax.swing.JPanel.setUI(JPanel.java:131)
        at javax.swing.JPanel.updateUI(JPanel.java:104)
        at javax.swing.JPanel.(JPanel.java:64)
        at javax.swing.JPanel.(JPanel.java:87)
        at javax.swing.JPanel.(JPanel.java:95)
        at weka.gui.explorer.PreprocessPanel.(Unknown Source)
        at weka.gui.explorer.Explorer.(Unknown Source)
        at weka.gui.GUIChooser$3.actionPerformed(Unknown Source)
        at java.awt.Button.processActionEvent(Button.java:388)
        at java.awt.Button.processEvent(Button.java:356)
        at java.awt.Component.dispatchEventImpl(Component.java:3955)
        at java.awt.Component.dispatchEvent(Component.java:3803)
        at java.awt.EventQueue.dispatchEvent(EventQueue.java:463)
        at java.awt.EventDispatchThread.pumpOneEventForHierarchy(EventDispatchThread.java:242)
        at java.awt.EventDispatchThread.pumpEventsForHierarchy(EventDispatchThread.java:163)
        at java.awt.EventDispatchThread.pumpEvents(EventDispatchThread.java:157)        at java.awt.EventDispatchThread.pumpEvents(EventDispatchThread.java:149)        at java.awt.EventDispatchThread.run(EventDispatchThread.java:110)

[tags]weka, ubuntu, java, weka Exception, AWT-EventQueue-0, java.lang.NullPointerException[/tags]

Image from Knight[1]

The CLN learns by being having each record (stimuli) fed into the input layer and having the output nodes compete for control of that input. It may sound complicated but it’s not. In this sense “competing” just means that the output node with the highest weighted sum of its inputs “wins”.
The winner gets all of his incoming weights from active nodes increased while the losers do not get their weights changed. The effect of updating the weights like this is that the winner then becomes more likely to win a future input with similar attributes.
There are a few different methods for increasing the weights and distributing the weights. The most popular method is to increase each of the winner’s weights by following this formula :

weight_value = (learning_rate)*(input_value/number_non_zero_input_nodes)-(learning_rate*previous_weight_value)

The learning rate should be a small constant between 0 and 1. I used 0.25 in my experiments.

————————————

This is from a portion of a paper I wrote for a Neural Networks class I recently took. Read the full paper here.

I know this material is tough when you’re starting out, so if you’re still confused just leave a comment and I’ll try to help you out.

References:
1. K. Knight, “Connectionist ideas and algorithms,” Communications of the ACM, vol. 33, no. 11, pp. 59–74, 1990.

[tags]Knight, Connectionist ideas, ACM, Neural Networks, Competitive Learning Network, Competitive Learning Networks, Cluster, Clustering, Unsupervised Learning[/tags]

I wrote to gmelli_sigkdd@predictionworks.com to find out. Hopefully it will be announced on their website soon.

What is the KDD Cup? It’s an annual data mining competition that my company Blended Technologies will be participating in this year.

[tags]Data Mining, Data Mining Competition, KDD Cup[/tags]

This was a question from my neural networks class and I couldn’t find any help on the internet. I hope I’m not spoiling anyone’s homework by writing it here.