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improved logging output in command-line scripts (eg pww-classifier)
added sub-command freeze to the pww-packages command-line tool to output the currently installed packages in pip-like format (unofficial packages get URL appended) either to stdout or to text file (eg requirements.txt)
the sub-command install of the pww-packages command-line tool can process a requirements text file now as well
added pww_version() function to weka.core.version to obtain the installed version of python-weka-wrapper3
added with_graph_support() function to weka.core.version to see whether python-weka-wrapper3 has support for graphs using pygraphviz
added with_plot_support() function to weka.core.version to see whether python-weka-wrapper3 has plotting support via matplotlib
added sub-command bootstrap to the pww-packages command-line tool to output a Python script that can reinstall python-weka-wrapper3 and all the packages in another virtual environment or even another machine
added is_official_package function to weka.core.packages module to check whether a package is an official one from the Weka package repository or not

the max_heap_size parameter of the start method from the weka.core.jvm module now has an effect

added is_additional_measure_producer variable, additional_measures property and additional_measure(str) method to the weka.classifiers.Classifier class, giving convenient access to classes implementing weka.core.AdditionalMeasureProducer
replaced javabridge with jpype behind the scenes, simplifying native API calls
removed property JavaObject.jclasswrapper
property JavaObject.jwrapper is now deprecated and JavaObject.jobject should be used instead
renamed JavaArray.new_instance to JavaArray.new_array
now works on M1/M2 Macs
updated doc strings
updated documentation, especially around installation

setup.py no longer lists python-javabridge with github URL, as it fails to install via pip otherwise

the methods create_instances_from_lists and create_instances_from_matrices of the weka.core.dataset module can handle missing values now (None in case of lists, nan in case of matrices), as well as being able to force columns to be nominal
added the method load_csv_file to the module weka.core.converters to provide a more reliable way of loading CSV files compared to Weka's native CSVLoader converter (uses Python's csv module and then calls create_instances_from_lists).
added simple_range library as dependency to make it easier to generate integer list from range strings
added weka.core.utils module with following methods: correlation, variance, normalize
updated requirements (and install instructions) to make pww3 work with Python 3.11+

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

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

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

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

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 (#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)

Releases: fracpete/python-weka-wrapper3