Comments (4)
Also check definitions for attribute selection. Conditional dependencies do not appear to be specified completely there.
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Certain attribute selection search methods disallow certain evaluation methods. There's currently no way to specify these conditional dependencies as each class (i.e. each evaluation/search method) is only considered in isolation.
The proper way to fix this would be to have a unified parameter space for attribute selection where the top-level parameter determines the algorithm to be run and everything else hangs off of that with conditionals. This will require a major redesign of AutoWEKA's internals.
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Upon closer inspection it turns out that there are some such dependencies with respect to classifiers as well, so really the only proper way would be to have a grand unified parameter space definition for everything.
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Examples of things that need to be addressed in the parameter space definition but can't be at the moment:
Training classifier (weka.classifiers.lazy.LWL [-U, 3, -A, weka.core.neighboursearch.LinearNNSearch, -W, weka.classifiers.rules.OneR , --, -B, 3]) failed: Classifier must be a WeightedInstancesHandler!
java.lang.IllegalArgumentException: Classifier must be a WeightedInstancesHandler!
at weka.classifiers.lazy.LWL.buildClassifier(LWL.java:507)
at autoweka.ClassifierRunner$BuilderThread.doWork(ClassifierRunner.java:399)
at autoweka.WorkerThread.run(WorkerThread.java:26)
Training classifier (weka.classifiers.meta.RandomCommittee [-I, 10, -S, 1, -W, weka.classifiers.bayes.BayesNet, --, -Q, weka.classifiers.bayes.net.search.local.TAN]) failed: Base learner must implement Randomizable!
java.lang.IllegalArgumentException: Base learner must implement Randomizable!
at weka.classifiers.meta.RandomCommittee.buildClassifier(RandomCommittee.java:150)
at autoweka.ClassifierRunner$BuilderThread.doWork(ClassifierRunner.java:399)
at autoweka.WorkerThread.run(WorkerThread.java:26)
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Related Issues (20)
- narrow down features
- How to use trained model for inference. (Model created using AutoWEKAClassifier) HOT 1
- Can we set manually the list of algorithms that will run for auto learn. HOT 1
- Is it possible to randomly run Auto-WEKA? HOT 1
- AutoWeka should be updated? HOT 5
- Correctly Classified Instances decreased when more time was given HOT 1
- No results hours after timelimit. HOT 4
- Choose range of attributes number HOT 1
- Select attributes HOT 6
- [WARN ] [PROCESS-ERR]?
- Error running Auto-Weka HOT 3
- Help to a newbie HOT 1
- Error marshalling xml response HOT 6
- Description of Parameter Dialect HOT 1
- Not Serializable error on trying to recall written model from AutoWeka into Weka Classifier HOT 9
- .model files in tmp folder HOT 3
- "Error marshalling XML response" still unresolved HOT 3
- [Request] Provide a permanent link to the latest stable release HOT 13
- Problem running Auto-WEKA HOT 18
- Autoweka: evaluation results too good to be true HOT 5
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