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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.


0.3.4 (2016-01-15)

  • added convenience method create_instances_from_lists to weka.core.dataset module to easily create an Instances object from numeric lists (x and y)

  • added get_object_tags method to Tags class from module weka.core.classes, to allow obtaining weka.core.Tag array from the method of a JavaObject rather than a static field (MultiSearch)

  • updated MultiSearch wrapper in module weka.classifiers to work with the multi-search package version 2016.1.15 or later

0.3.3 (2015-09-26)

  • updated to Weka 3.7.13

  • documentation now covers the API as well

0.3.2 (2015-06-29)

  • The packages parameter of the weka.core.jvm.start() function can be used for specifying an alternative Weka home directory now as well

  • added train_test_split method to weka.core.Instances class to easily create train/test splits

  • evaluate_train_test_split method of weka.classifiers.Evaluation class now uses the train_test_split method

0.3.1 (2015-04-23)

  • added get_tags class method to Tags method for easier instantiation of Tag arrays

  • added find method to Tags class to locate Tag object that matches the string

  • fixed __getitem__ and __setitem__ methods of the Tags class

  • added GridSearch meta-classifier with convenience properties to module weka.classifiers

  • added SetupGenerator and various parameter classes to weka.core.classes

  • added MultiSearch meta-classifier with convenience properties to module weka.classifiers

  • added quote/unquote and backquote/unbackquote methods to weka.core.classes module

  • added main method to weka.core.classes for operations on options: join, split, code

  • added support for option handling to weka.core.classes module

0.3.0 (2015-04-15)

  • added method ndarray_to_instances to weka.converters module for converting Numpy 2-dimensional array into Instances object

  • added method plot_learning_curve to weka.plot.classifiers module for creating learning curves for multiple classifiers for a specific metric

  • added plotting of experiments with plot_experiment methid in weka.plot.experiments module

  • Instance.create_instance method now takes list of tuples (index, internal float value) when generating sparse instances

  • added weka.core.database module for loading data from a database

  • added make_copy class method to Clusterer class

  • added make_copy class method to Associator class

  • added make_copy class method to Filter class

  • added make_copy class method to DataGenerator class

  • most classes (like Classifier and Filter) now have a default classname value in the constructor

  • added TextDirectoryLoader class to weka.core.converters

  • moved all methods from weka.core.utils to weka.core.classes

  • fixed Attribute.index_of method for determining label index

  • fixed Attribute.add_string_value method (used incorrect JNI parameter)

  • create_instance and create_sparse_instance methods of class Instance now ensure that list values are float

  • added to_help method to OptionHandler class which outputs a help string generated from the base class’s globalInfo and listOptions methods

  • fixed test_model method of Evaluation class when supplying a PredictionOutput object (previously generated No dataset structure provided! exception)

  • added batch_finished method to Filter class for incremental filtering

  • added line_plot method to weka.plot.dataset module for plotting dataset using internal format (one line plot per instance)

  • added is_serializable property to JavaObject class

  • added has_class convenience property to Instance class

  • added __repr__ method to JavaObject classes (simply calls toString() method)

  • added Stemmer class in module weka.core.stemmers

  • added Stopwords class in module weka.core.stopwords

  • added Tokenizer class in module weka.core.tokenizers

  • added StringToWordVector filter class in module weka.filters

  • added simple workflow engine (see documentation on Flow)

0.2.2 (2015-01-05)

  • added convenience methods no_class (to unset class) and has_class (class set?) to Instances class

  • switched to using faster method objects for methods classify_instance/distribution_for_instance in Classifier class

  • switched to using faster method objects for methods cluster_instance/distribution_for_instance in Clusterer class

  • switched to using faster method objects for methods class_index, is_missing, get/set_value, get/set_string_value, weight in Instance class

  • switched to using faster method objects for methods input, output, outputformat in Filter class

  • switched to using faster method objects for methods attribute, attribute_by_name, num_attributes, num_instances, class_index, class_attribute, set_instance, get_instance, add_instance in Instances class

0.2.1 (2015-01-05)

  • added unit testing framework

  • added method refresh_cache() to weka/core/ to allow user to refresh local cache

  • method get_classname in weka.core.utils now handles Python objects and class objects as well

  • added convenience method get_jclass to weka.core.utils to instantiate a Java class

  • added a JavaArray wrapper for arrays, which allows getting/setting elements and iterating

  • added property classname to class JavaObject for easy access to classname of underlying object

  • added class method parse_matlab for parsing Matlab matrix strings to CostMatrix class

  • predictions method of Evaluation class now return None if predictions are discarded

  • Associator.get_capabilities() method is now a property: Associator.capabilities

  • added wrapper classes for Java enums: weka.core.classes.Enum

  • fixed retrieval of sumSq in Stats class (used by AttributeStats)

  • fixed cluster_instance method in Clusterer class

  • fixed filter and clusterer properties in clusterer classes (SingleClustererEnhancer, FilteredClusterer)

  • added crossvalidate_model method to ClusterEvaluation

  • added get_prc method to plot/ for calculating the area under the precision-recall curve

  • Filter.filter method now handles list of Instances objects as well, applying the filter sequentially to all the datasets (allows generation of compatible train/test sets)

0.2.0 (2014-12-22)

NB: This release is not backwards compatible!

  • requires JavaBridge 1.0.9 at least

  • moved from Java-like get/set (getIndex() and setIndex(int)) to nicer Python properties

  • using Python properties (also only read-only ones) wherevere possible

  • added weka.core.version for accessing the Weka version currently in use

  • added jwrapper and jclasswrapper methods to JavaObject class (the mother of all objects in python-weka-wrapper) to allow generic access to an object’s methods:

  • added convenience methods class_is_last() and class_is_first() to weka.core.Instances class

  • added convenience methods delete_last_attribute() and delete_first_attribute() to weka.core.Instances class

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