python-weka-wrapper3 0.2.13

Python3 wrapper for the Weka Machine Learning Workbench

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:

https://groups.google.com/forum/#!forum/python-weka-wrapper

Changelog

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 (https://github.com/fracpete/python-weka-wrapper3/issues/40)

• 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, https://github.com/fracpete/python-weka-wrapper3/pull/39)

• 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)

• 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