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Releases: fracpete/sklearn-weka-plugin

Release v0.0.8

07 Apr 23:03
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  • using scikit-learn instead of deprecated sklearn dependency for scikit-learn (#10)

Release v0.0.7

06 Jul 21:00
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  • WekaEstimator (module sklweka.classifiers) now has a custom score method that distinguishes between classification and regression to return the correct score.
  • renamed data to X and targets to y, since some sklearn schemes use named arguments
  • added dummy argument sample_weight=None to fit, score and fit_predict methods
  • fixed: when supplying Classifier or JBObject instead of classname/options, classname/options now get determined automatically
  • method to_instance (module: sklweka.dataset) now performs correct missing value check
  • method to_nominal_labels (module: sklweka.dataset) generates nicer labels now

Release v0.0.6

25 Apr 22:34
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  • WekaEstimator (module sklweka.classifiers) and WekaCluster (module sklweka.clusters) now allow specifying how many labels a particular nominal attribute or class attribute has (to avoid error message like Cannot handle unary class attribute! if there is only one label present in a particular split)

Release v0.0.5

31 Mar 22:21
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  • the to_nominal_attributes method in the sklearn.dataset module requires now the indices parameter (incorrectly declared as optional); can parse a range string now as well
  • fixed the fit, set_params and __str__ methods fo the MakeNominal transformer (module sklweka.preprocessing)
  • WekaEstimator (module sklweka.classifiers) and WekaCluster (module sklweka.clusters) now allow specifying which attributes to turn into nominal ones, which avoids having to manually convert the data (either as list with 0-based indices or range string with 1-based indices)
  • set_params methods now ignore empty dictionaries

Release v0.0.4

17 Dec 03:58
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  • fixed sorting of labels in to_instances method in module sklweka.dataset
  • redoing X when no class present in load_arff method (module sklweka.dataset)
  • added load_dataset method in module sklweka.dataset that uses Weka to load the data before converting it into sklearn data structures (slower, but more flexible)

Release v0.0.3

26 Oct 03:51
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  • added support for generating data via Weka's data generators

Release v0.0.2

11 Apr 23:49
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  • requiring python-weka-wrapper3 version 0.2.1 at least in order to offer pickle support

Release v0.0.1

28 Mar 02:59
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  • initial release