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
0.1.14 (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)
moved wekaexamples module to separate github project: https://github.com/fracpete/python-weka-wrapper-examples
added stratify, train_cv and test_cv methods to the Instances class
fixed to_summary method of the Evaluation class: failed when providing a custom title
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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