Input file

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Domenico Mancuso

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Jul 13, 2015, 7:33:08 AM7/13/15
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buongiorno, sono un tirocinante . Come tesi ho scelto i big data . Il mio professore mi ha detto di capire come dare in ingresso un file pick up con moa. è possibile? Come si fa? Grazie tante

Domenico Mancuso

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Jul 13, 2015, 7:44:37 AM7/13/15
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Good morning, I am a trainee. As thesis I chose the big date. My professor told me to figure out how to input a file pick up with moa. it's possible? How is it done? many thanks

Heitor Murilo Gomes

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Jul 14, 2015, 11:53:26 PM7/14/15
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Hello Domenico,

You can load an arff file. In the GUI window, choose "Configure", then select "stream", in the combo select "moa.streams.ArffFileStream", and finally enter the path to your arff file.
I also recommend the following tutorial to become familiar with MOA: http://moa.cms.waikato.ac.nz/getting-started/

Best Regards,

Heitor

Domenico Mancuso

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Jul 16, 2015, 9:25:55 AM7/16/15
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Thank you very much.  I wanted to know if I can use the same file for clusters and for classificator or need to make changes

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Heitor Murilo Gomes

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Jul 16, 2015, 3:59:17 PM7/16/15
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Hi Domenico,

You can use the same file.

Best Regards,

Heitor

Domenico Mancuso

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Jul 20, 2015, 5:00:51 AM7/20/15
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Thanks. my professor gave me this file, but it does not work, I always fail. Can someone help me?
gruveo1.arff
normal1.arff

Heitor Murilo Gomes

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Jul 21, 2015, 9:36:55 AM7/21/15
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Hello Domenico,

These files are for regression, not classification. You may need to either use the regression tab in MOA or discretize the target attribute (you can do that on WEKA, save the arff and load it back on MOA).

Best Regards,

Heitor

On Monday, July 13, 2015 at 8:33:08 AM UTC-3, Domenico Mancuso wrote:

Domenico Mancuso

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Jul 21, 2015, 10:54:51 AM7/21/15
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Thanks. While this file is suitable for  cluster that classificator?​

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Heitor Murilo Gomes

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Jul 21, 2015, 12:46:18 PM7/21/15
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Hi Domenico,

I am not sure I understood you correctly, but this other file is suitable for classification because its target attribute is nominal (see "@attribute Info" in the arff file).
I suggest that before you move forward on your studies that you review some machine learning concepts. You can attend the Machine learning course on coursera from Professor Andrew Ng, the course has subtitles in italian.

https://www.coursera.org/learn/machine-learning/home/week/1

Best regards,


Heitor

On Monday, July 13, 2015 at 8:33:08 AM UTC-3, Domenico Mancuso wrote:

Domenico Mancuso

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Jul 22, 2015, 5:08:41 AM7/22/15
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thanks very kind


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Domenico Mancuso

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Aug 4, 2015, 5:52:07 AM8/4/15
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Good Morning. I would ask if Moa allows you to analyze a data stream without use data files but real data stream of a node. if you can do it, how you set the stream? Thanks very much

Albert Bifet

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Aug 4, 2015, 8:32:36 PM8/4/15
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To do that, you will need to implement your own stream reader in Java.

Cheers, Albert

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Domenico Mancuso

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Sep 17, 2015, 12:16:01 PM9/17/15
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Thanks for the answer, I downloaded Prequential AUC, but MoaAuc run and I have an error message and opens the panel of moa, the same as I had. What am i wrong? The error displayed is: Problem with options to 'EvaluatePrequentialRegression'.

Valid options for EvaluatePrequentialRegression:
-l learner (default: trees.FIMTDD)
Classifier to train.
-s stream (default: generators.RandomTreeGenerator)
Stream to learn from.
-e evaluator (default: WindowRegressionPerformanceEvaluator)
Classification performance evaluation method.
-i instanceLimit (default: 100000000)
Maximum number of instances to test/train on  (-1 = no limit).
-t timeLimit (default: -1)
Maximum number of seconds to test/train for (-1 = no limit).
-f sampleFrequency (default: 100000)
How many instances between samples of the learning performance.
-q memCheckFrequency (default: 100000)
How many instances between memory bound checks.
-d dumpFile
File to append intermediate csv results to.
-o outputPredictionFile
File to append output predictions to.
-w width (default: 1000)
Size of Window
-a alpha (default: 0.01)
Fading factor or exponential smoothing factor
-O taskResultFile
File to save the final result of the task to.


*** STACK TRACE ***
java.lang.Exception: Problem with options to 'EvaluatePrequentialRegression'.

Valid options for EvaluatePrequentialRegression:
-l learner (default: trees.FIMTDD)
Classifier to train.
-s stream (default: generators.RandomTreeGenerator)
Stream to learn from.
-e evaluator (default: WindowRegressionPerformanceEvaluator)
Classification performance evaluation method.
-i instanceLimit (default: 100000000)
Maximum number of instances to test/train on  (-1 = no limit).
-t timeLimit (default: -1)
Maximum number of seconds to test/train for (-1 = no limit).
-f sampleFrequency (default: 100000)
How many instances between samples of the learning performance.
-q memCheckFrequency (default: 100000)
How many instances between memory bound checks.
-d dumpFile
File to append intermediate csv results to.
-o outputPredictionFile
File to append output predictions to.
-w width (default: 1000)
Size of Window
-a alpha (default: 0.01)
Fading factor or exponential smoothing factor
-O taskResultFile
File to save the final result of the task to.

at moa.options.ClassOption.cliStringToObject(ClassOption.java:156)
at moa.gui.RegressionTaskManagerPanel.setTaskString(RegressionTaskManagerPanel.java:380)
at moa.gui.RegressionTaskManagerPanel.<init>(RegressionTaskManagerPanel.java:223)
at moa.gui.RegressionTabPanel.<init>(RegressionTabPanel.java:40)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Unknown Source)
at java.lang.Class.newInstance(Unknown Source)
at moa.gui.GUI.initGUI(GUI.java:64)
at moa.gui.GUI.<init>(GUI.java:46)
at moa.gui.GUI$1.run(GUI.java:97)
at java.awt.event.InvocationEvent.dispatch(Unknown Source)
at java.awt.EventQueue.dispatchEventImpl(Unknown Source)
at java.awt.EventQueue.access$500(Unknown Source)
at java.awt.EventQueue$3.run(Unknown Source)
at java.awt.EventQueue$3.run(Unknown Source)
at java.security.AccessController.doPrivileged(Native Method)
at java.security.ProtectionDomain$1.doIntersectionPrivilege(Unknown Source)
at java.awt.EventQueue.dispatchEvent(Unknown Source)
at java.awt.EventDispatchThread.pumpOneEventForFilters(Unknown Source)
at java.awt.EventDispatchThread.pumpEventsForFilter(Unknown Source)
at java.awt.EventDispatchThread.pumpEventsForHierarchy(Unknown Source)
at java.awt.EventDispatchThread.pumpEvents(Unknown Source)
at java.awt.EventDispatchThread.pumpEvents(Unknown Source)
at java.awt.EventDispatchThread.run(Unknown Source)
Caused by: java.lang.IllegalArgumentException: Problems with option: learner
at moa.options.ClassOption.setValueViaCLIString(ClassOption.java:69)
at moa.options.Options.setViaCLIString(Options.java:145)
at moa.options.ClassOption.cliStringToObject(ClassOption.java:154)
... 25 more
Caused by: java.lang.Exception: Problem with options to 'trees.FIMTDD'.

Valid options for trees.FIMTDD:
-a PageHinckleyAlpha (default: 0.005)
The alpha value to use in the Page Hinckley change detection tests.
-h PageHinckleyThreshold (default: 50)
The threshold value to be used in the Page Hinckley change detection tests.
-f AlternateTreeFadingFactor (default: 0.995)
The fading factor to use when deciding if an alternate tree should replace an original.
-y AlternateTreeTMin (default: 150)
The Tmin value to use when deciding if an alternate tree should replace an original.
-u AlternateTreeTime (default: 1500)
The 'time' (in terms of number of instances) value to use when deciding if an alternate tree should be discarded.
-w learningRatio (default: 0.01)
Learning ratio to use for training the Perceptrons in the leaves.
-j learningRatio_Decay_or_Const
learning Ratio Decay or const parameter.
-m maxByteSize (default: 33554432)
Maximum memory consumed by the tree.
-n numericEstimator (default: FIMTDDNumericAttributeClassObserver)
Numeric estimator to use.
-d nominalEstimator (default: NominalAttributeClassObserver)
Nominal estimator to use.
-e memoryEstimatePeriod (default: 1000000)
How many instances between memory consumption checks.
-g gracePeriod (default: 200)
The number of instances a leaf should observe between split attempts.
-s splitCriterion (default: SDRSplitCriterion)
Split criterion to use.
-c splitConfidence (default: 1.0E-7)
The allowable error in split decision, values closer to 0 will take longer to decide.
-t tieThreshold (default: 0.05)
Threshold below which a split will be forced to break ties.
-b binarySplits
Only allow binary splits.
-z stopMemManagement
Stop growing as soon as memory limit is hit.
-r removePoorAtts
Disable poor attributes.
-p noPrePrune
Disable pre-pruning.
-l leafprediction (default: NBAdaptive)
Leaf prediction to use.
-q nbThreshold (default: 0)
The number of instances a leaf should observe before permitting Naive Bayes.

at moa.options.ClassOption.cliStringToObject(ClassOption.java:156)
at moa.options.ClassOption.setValueViaCLIString(ClassOption.java:67)
... 27 more
Caused by: java.lang.IllegalArgumentException: Problems with option: splitCriterion
at moa.options.ClassOption.setValueViaCLIString(ClassOption.java:69)
at moa.options.Options.setViaCLIString(Options.java:145)
at moa.options.ClassOption.cliStringToObject(ClassOption.java:154)
... 28 more
Caused by: java.lang.Exception: Class named 'VarianceReductionSplitCriterion' is not an instance of moa.classifiers.core.splitcriteria.SDRSplitCriterion.
at moa.options.ClassOption.cliStringToObject(ClassOption.java:167)
at moa.options.ClassOption.setValueViaCLIString(ClassOption.java:67)
... 30 more
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