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Python wrapper for the Weka Machine Learning Workbench

Project description

The python-weka-wrapper package makes it easy to run Weka algorithms and filters from within Python. It offers access to Weka API using thin wrappers around JNI calls using the javabridge package.

Changelog

0.1.17 (2014-12-18)

  • fixed setup.py to download Weka 3.7.12 instead of 3.7.11 (this time correct URL)

0.1.16 (2014-12-18)

  • fixed setup.py to download Weka 3.7.12 instead of 3.7.11

0.1.15 (2014-12-18)

0.1.14 (2014-12-16)

  • fixed setup.py to include the jars again when using eggs (via include_package_data etc)
  • added detailed instructions for installing the library

0.1.13 (2014-11-01)

  • added get_class method to weka.core.utils which returns the Python class object associated with the classname in dot-notation
  • from_commandline method in weka.core.utils now takes an optional classname argument, which is the classname (in dot-notation) of the wrapper class to return - instead of the generic OptionHandler
  • added Kernel and KernelClassifier convenience classes to better handle kernel based classifiers

0.1.12 (2014-10-17)

  • added create_string class method to the Attribute class for creating a string attribute
  • ROC/PRC curves can now consist of multiple plots (ie multiple class labels)
  • switched command-line option handling from getopt to argparse
  • fixed Instance.get_dataset(self) method
  • added iterators for: rows/attributes in dataset, values in dataset row
  • incremental loaders can be iterated now

0.1.11 (2014-09-22)

0.1.10 (2014-08-29)

  • fixed adding custom classpath using jvm.start(class_path=[…])

0.1.9 (2014-08-18)

  • added static methods to Instances class: summary, merge_instances, append_instances
  • added methods to Instances class: delete_with_missing, equal_headers

0.1.8 (2014-06-26)

  • fixed installer: MANIFEST.in now includes CHANGES.rst and DESCRIPTION.rst as well

0.1.7 (2014-06-26)

  • fixed weka/plot/dataset.py imports to avoid error when testing for matplotlib availability
  • Instance.create_instance (weka/core/dataset.py) now accepts Python list and Numpy array

0.1.6 (2014-05-29)

  • added troubleshooting section for Mac OSX to documentation
  • recompiled helper jars with 1.6 rather than 1.7 to make it work on Mac OSX
  • added link to Google Group

0.1.5 (2014-05-23)

  • added CostMatrix support in the classifier evaluation
  • fixed various retrievals of double arrays (accessed them incorrectly as float arrays), like distributionForInstance for a classifier
  • Instances object can now retrieve all (internal) values of an attribute/column as numpy array
  • added plotting of cluster assignments to weka.plot.clusterers
  • fixed weka.core.utils.from_commandline method
  • fixed weka.classifiers.PredictionOutput (get/set_header methods)
  • predictions can be turned into an Instances object now using weka.classifiers.predictions_to_instances

0.1.4 (2014-05-19)

  • dependencies for plotting are now optional (pygraphviz, PIL, matplotlib)
  • plots now support custom titles

0.1.3 (2014-05-17)

  • improved documentation
  • added PRC curve plot
  • aligned to PEP8 style guidelines
  • fixed variety of little bugs (not so commonly used methods)
  • fixed lib directory reference in make files for Java helper classes

0.1.2 (2014-05-13)

  • added matrix plot
  • added scatter plot for two attributes
  • fixes in constructors of classes
  • added MultiFilter convenience class
  • predictions (of classifiers) can now be collected and output using the PredictionOutput class
  • added support for attribute statistics

0.1.1 (2014-05-02)

  • constructors now take list of commandline options as well
  • added Weka package support (list/install/uninstall)
  • ROC plotting for classifiers
  • improved code documentation
  • added more examples
  • added more datasets
  • using javabridge 1.0.1 now

0.1.0 (2014-04-27)

  • Initial release of Python wrapper for Weka, no GUI.

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