Metadata-Version: 2.1
Name: python-weka-wrapper3
Version: 0.1.16
Summary: Python3 wrapper for the Weka Machine Learning Workbench
Home-page: https://github.com/fracpete/python-weka-wrapper3
Author: Peter "fracpete" Reutemann
Author-email: pythonwekawrapper@gmail.com
License: GNU General Public License version 3.0 (GPLv3)
Description: The **python-weka-wrapper3** package makes it easy to run
        `Weka <http://www.cs.waikato.ac.nz/~ml/weka/>`_ algorithms and filters from 
        within Python 3. It offers access to Weka API using thin wrappers around JNI 
        calls using the `javabridge <https://pypi.python.org/pypi/javabridge>`_ package.
        
        Forum for project at:
        
        https://groups.google.com/forum/#!forum/python-weka-wrapper
        
        Changelog
        =========
        
        0.1.16 (2020-12-26)
        -------------------
        
        - upgraded Weka to 3.9.5
        
        
        0.1.15 (2020-10-25)
        -------------------
        
        - added `weka.core.systeminfo` module for obtaining output from `weka.core.SystemInfo`
        - added `system_info` parameter to `weka.core.jvm.start()` method
        - merged PR #33 (https://github.com/fracpete/python-weka-wrapper3/pull/33) to better handle
          associator output
        
        
        0.1.14 (2020-05-26)
        -------------------
        
        - added `AttributeSelectedClassifier` meta-classifier to module `weka.classifiers`
        - added `AttributeSelection` meta-filter to module `weka.filters`
        
        
        0.1.13 (2020-05-06)
        -------------------
        
        - added `class_index` parameter to `weka.core.converters.load_any_file`
          and `weka.core.converters.Loader.load_file`, which allows specifying of
          index while loading it (`first`, `second`, `third`, `last-2`, `last-1`,
          `last` or 1-based index).
        - added `append` and `clear` methods to `weka.filters.MultiFilter` and
          `weka.classifiers.MultipleClassifiersCombiner` to make adding of
          filters/classifiers easier.
        - added `attribute_names()` method to `weka.core.dataset.Instances` class
        - added `subset` method to `weka.core.dataset.Instances` class, which returns
          a subset of columns and/or rows.
        
        
        0.1.12 (2020-01-10)
        -------------------
        
        - added method `list_property_names` to `weka.core.classes` module to allow listing of Bean property names
          (which are used by `GridSearch` and `MultiSearch`) for a Java object.
        
        
        0.1.11 (2020-01-04)
        -------------------
        
        - Upgraded Weka to 3.9.4
        - added method `suggest_package` to the `weka.core.packages` module for suggesting packages for partial class
          names/package names (`NNge` or `.ft.`) or exact class names (`weka.classifiers.meta.StackingC`)
        - the `JavaObject.new_instance` method now suggests packages (if possible) in case the instantiation fails
          due to package not installed or JVM not started with package support
        
        
        0.1.10 (2019-12-02)
        -------------------
        
        - method `train_test_split` of the `weka.dataset.Instances` class now creates a copy of itself before
          applying randomization, to avoid changing the order of data for subsequent calls.
        
        
        0.1.9 (2019-11-19)
        ------------------
        
        - method `create_instances_from_matrices` from module `weka.core.dataset` now works with pure numeric data again
        - added sections for creating datasets (manual, lists, matrices) to examples documentation
        
        
        0.1.8 (2019-11-11)
        ------------------
        
        - added console scripts: `pww-associator`, `pww-attsel`, `pww-classifier`, `pww-clusterer`, `pww-datagenerator`, `pww-filter`
        - added `serialize`, `deserialize` methods to `weka.classifiers.Classifier` to simplify loading/saving model
        - added `serialize`, `deserialize` methods to `weka.clusterers.Clusterer` to simplify loading/saving model
        - added `serialize`, `deserialize` methods to `weka.filters.Filter` to simplify loading/saving filter
        - added methods `plot_rocs` and `plot_prcs` to `weka.plot.classifiers` module to plot ROC/PRC curve on same dataset
          for multiple classifiers
        - method `plot_classifier_errors` of `weka.plot.classifiers` module now allows plotting predictions of multiple
          classifiers by providing a dictionary
        - method `create_instances_from_matrices` from module `weka.core.dataset` now allows string and bytes as well
        - method `create_instances_from_lists` from module `weka.core.dataset` now allows string and bytes as well
        
        
        0.1.7 (2019-01-11)
        ------------------
        
        - added wrapper classes for association classes that implement `AssociationRuleProducer`
          (package `weka.associations`): `AssociationRules`, `AssociationRule`, `item`
        - added `to_source` method to `weka.classifiers.Classifier` and `weka.filters.Filter`
          (underlying Java classes must implement the respective `Sourcable` interface)
        
        
        0.1.6 (2018-10-28)
        ------------------
        
        - fixed logging setup in `weka.core.jvm` to avoid global setting global logging
          setup to `DEBUG` (thanks to https://github.com/Arnie97)
        
        
        0.1.5 (2018-09-16)
        ------------------
        
        - upgraded to Weka 3.9.3
        - `weka.jar` now included in PyPi package
        - exposed the following methods in `weka.classifiers.Evaluation`:
          `cumulative_margin_distribution`, `sf_prior_entropy`, `sf_scheme_entropy`
        
        
        0.1.4 (2018-02-18)
        ------------------
        
        - upgraded to Weka 3.9.2
        - properly initializing package support now, rather than adding package jars to classpath
        - added `weka.core.ClassHelper` Java class for obtaining classes and static fields, as
          javabridge only uses the system class loader
        
        
        0.1.3 (2017-08-23)
        ------------------
        
        - added `check_for_modified_class_attribute` method to `FilterClassifier` class
        - added `complete_classname` method to `weka.core.classes` module, which allows
          completion of partial classnames like `.J48` to `weka.classifiers.trees.J48`
          if there is a unique match; `JavaObject.new_instance` and `JavaObject.check_type`
          now make use of this functionality, allowing for instantiations like
          `Classifier(cls=".J48")`
        - `jvm.start(system_cp=True)` no longer fails with a `KeyError: 'CLASSPATH'` if
          there is no `CLASSPATH` environment variable defined
        - Libraries `mtl.jar`, `core.jar` and `arpack_combined_all.jar` were added as is
          to the `weka.jar` in the 3.9.1 release instead of adding their content to it.
          Repackaged `weka.jar` to fix this issue (https://github.com/fracpete/python-weka-wrapper3/issues/5)
        
        
        0.1.2 (2017-01-04)
        ------------------
        
        - `typeconv.double_matrix_to_ndarray` no longer assumes a square matrix
          (https://github.com/fracpete/python-weka-wrapper3/issues/4)
        - `len(Instances)` now returns the number of rows in the dataset (module `weka.core.dataset`)
        - added method `insert_attribute` to the `Instances` class
        - added class method `create_relational` to the `Attribute` class
        - upgraded Weka to 3.9.1
        
        
        0.1.1 (2016-10-19)
        ------------------
        
        - `plot_learning_curve` method of module `weka.plot.classifiers` now accepts a list of test sets;
          `*` is index of test set in label template string
        - added `missing_value()` methods to `weka.core.dataset` module and `Instance` class
        - output variable `y` for convenience method `create_instances_from_lists` in module
          `weka.core.dataset` is now optional
        - added convenience method `create_instances_from_matrices` to `weka.core.dataset` module to easily create
          an `Instances` object from numpy matrices (x and y)
        
        
        0.1.0 (2016-05-09)
        ------------------
        
        - initial release of Python3 port
        
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Provides-Extra: plots
Provides-Extra: graphs
