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Python3 wrapper for the Weka Machine Learning Workbench

Project description

The python-weka-wrapper3 package makes it easy to run Weka algorithms and filters from within Python 3. It offers access to Weka API using thin wrappers around JNI calls using the python-javabridge package.

Forum for project at:!forum/python-weka-wrapper


0.2.14 (2023-07-04)

  • extracted arpack_combined.jar, core.jar and mtj.jar from weka.jar to make them available under Java 17

0.2.13 (2023-04-26)

  • moved Configurable and JSONObject into configurable-objects library

  • moved base flow components into simple-data-flow library

  • added methods subset_results, rank_results to class AttributeSelection (module: weka.attribute_selection) to give access to cross-validation output in numeric rather textual form (NB: it has to parse the textual CV output).

  • made the plot_experiments method (module weka.plot.experiments) more customizable

0.2.12 (2022-12-08)

  • method install_packages (module: weka.core.packages) now no longer interprets the installation message returned when installing from URL or local zip file as failure to install; also outputs any installation message now in the console; now has flags for fail_fast mode (first package that fails stops installation process) and whether to return details (dict per package rather than just a bool for all packages)

  • method install_package (module: weka.core.packages) can return either a boolean flag of success or detailed information

0.2.11 (2022-10-11)

  • methods install_packages and install_missing_packages of module weka.core.packages now allow a list of package names instead of tuples (name, version), assuming latest as version

  • method get_jclass in module weka.core.classes can handle primitive classes now as well (eg int -> java.lang.Integer.TYPE)

  • methods get_non_public_field and call_non_public_method in module weka.core.classes allow accessing private/protected fields and calling private/protected methods of Java objects, which avoids having to sub-class classes to get public access to them (NB: only works as long as the security manager allows that)

  • added split_commandline method to module weka.core.classes, which splits a command-line into classname and option list tuple

  • the Instances class (module: weka.core.dataset) now supports slicing

  • added methods plot_xmlbif_graph and xmlbif_to_dot to module weka.plot.graph for plotting XML BIF graphs generated by BayesNet using GraphViz

  • added method plot_graph to module weka.plot.graph to plot dot or XML BIF graphs

0.2.10 (2022-06-27)

  • added logging_level parameter to the start method of the weka.core.jvm module, enabling the user to turn off debugging output in an easy way (

  • added method cv_splits to class Instances from module weka.core.dataset to return a list of train/test tuples as used by cross-validation

  • the Tester class (module: weka.experiments) now has an option to swap columns/rows for comparing datasets rather than classifiers

  • the SimpleExperiment class and derived classes (module: weka.experiments) now have the additional parameters in the constructor: class_for_ir_statistics, attribute_id, pred_target_column

  • the method is_installed (module: weka.core.packages) now can check whether a specific version is installed

  • added pww-packages entry point to allow managing of Weka packges from the command-line (actions: list/info/install/uninstall/suggest/is-installed)

0.2.9 (2022-04-17)

  • method JavaObject.new_instance in module weka.core.classes now automatically installs packages based on suggestions if the JVM was started with the auto_install flag enabled.

  • method test_model_once of class Evaluation (module: weka.classifiers) now has the additional parameter store, which allows the recording of the predictions (necessary for statistics like AUC)

0.2.8 (2022-03-24)

  • methods create_instances_from_lists and create_instances_from_matrices (module weka.core.dataset) now allow the specification of column names, for input and output variables.

0.2.7 (2022-02-22)

  • Added property for attribute indices to DistanceFunction class (module weka.core.distances) (thanks to Martin Trat,

  • improved instantiation of classes, avoiding misleading output of exceptions

  • JavaArray class (module: weka.core.classes) now has __str__ and __repr__ methods that output classname and size

0.2.6 (2022-02-01)

  • upgraded bundled Weka to 3.9.6

0.2.5 (2021-12-17)

  • switched to python-javabridge, the new name (fork?) of the javabridge library

  • Package.__str__ (weka.core.packages module) method now returns a string rather than printing the name/version

  • added to_numpy(…) methods to Instance and Instances classes (module weka.core.dataset) to make it easy to obtain a numpy array from the Weka dataset

0.2.4 (2021-11-25)

  • added method help_for to weka.core.classes module to generate a help screen for an weka.core.OptionHandler class using just the classname.

  • the to_help method of the weka.core.classes.OptionHandler class now allows to tweak the generated output a bit better (e.g., what sections to output).

  • setting window title of Matplotlib is now dependent on version (to avoid deprecation notice being output)

  • plot_classifier_errors (module weka.plot.classifiers) now plots the diagonal after adding all the plot data to get the right limits

0.2.3 (2021-06-09)

  • added weka.core.distances module for distance functions, with DistanceFunction base class

  • added avg_silhouette_coefficient method to weka.clusterers to calculate the average silhouette coefficient

0.2.2 (2021-04-23)

  • the Package class of the weka.core.packages module now has a version property to quickly access the version which is stored in the meta-data; the metadata property now returns a proper Python dictionary

  • added convenience methods to the weka.core.packages module: install_packages to install more than one package, install_missing_package and install_missing_packages to install one or more packages if missing (can automatically stop the JVM and exit the process), uninstall_packages to remove more than one package in one operation

0.2.1 (2021-04-12)

  • the ASEvaluation class in the weka.attribute_selection module now offers the following methods for attribute transformers like PCA: transformed_header, transformed_data, convert_instance

  • classes derived from weka.core.classes.JavaObject are now serializable via pickle

  • added the method copy_structure to the weka.core.dataset.Instances class to quickly get the header of a dataset

  • added the property header to the following classes that returns the training data structure: ASEvaluation, ASSearch, Associator, Classifier, Clusterer, TSForecaster

  • methods from weka.core.serialization have been moved into weka.core.classes, with the following methods getting the serialization_ prefix: write, write_all, read, read_all

0.2.0 (2021-02-21)

  • classes.new_instance method can take an options list now as well

  • added classes.get_enum method to return the instance of a Java enum item

  • added classes.new_instance method to create new instance of Java class

  • added typeconv.jstring_list_to_string_list method to convert a java.util.List containing strings into a Python list

  • added typeconv.jdouble_to_float method to convert a java.lang.Double to a Python float

  • in module typeconv renamed methods: string_array_to_list to jstring_array_to_list, string_list_to_array to string_list_to_jarray, double_matrix_to_ndarray to jdouble_matrix_to_ndarray, enumeration_to_list to jenumeration_to_list, double_to_float to float_to_jfloat

  • added weka.timeseries module that wraps the timeseriesForecasting Weka package

0.1.16 (2020-12-26)

  • upgraded Weka to 3.9.5

0.1.15 (2020-10-25)

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

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 (

0.1.2 (2017-01-04)

  • typeconv.double_matrix_to_ndarray no longer assumes a square matrix (

  • 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

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