Introduction¶
python-weka-wrapper3 allows you to use Weka from within Python3.
The library uses the jpype library for starting up, communicating with and shutting down the Java Virtual Machine in which the Weka processes get executed.
python-weka-wrapper3 provides a thin wrapper around the basic (non-GUI) functionality of Weka (some plots are available using Python functionality). You can automatically add all your Weka packages to the classpath. Additional jars can be added as well.
Links:
Looking for code?
Questions? You can post them in the project’s Google Group
Requirements¶
The library has the following requirements:
Python 3 (does not work with Python 2)
jpype (required)
pygraphviz (optional)
PIL (optional)
matplotlib (optional)
lxml (optional)
OpenJDK 11 (or later)
Uses:
Weka (3.9.6)
Contents¶
- Installation
- Docker
- Troubleshooting
- Examples
- Start up JVM
- Location of the datasets
- Load dataset and print it
- Create dataset manually
- Create dataset from lists
- Create dataset from matrices
- Dataset subsets
- Data generators
- Filters
- Output help from underlying OptionHandler
- Option handling
- Build classifier on dataset, output predictions
- Build classifier on dataset, print model and draw graph
- Build classifier incrementally with data and print model
- Cross-validate filtered classifier and print evaluation and display ROC
- Cross-validate regressor, display classifier errors and predictions
- Parameter optimization - property names
- Parameter optimization - GridSearch
- Parameter optimization - MultiSearch
- Clustering
- Associations
- Attribute selection
- Timeseries
- Serialization
- Experiments
- Partial classnames
- Packages
- Stop JVM
- Database access
- Recreating environments
- Command-line
- Development
- Flow
API¶
- weka package
- Subpackages
- weka.core package
- weka.core.capabilities module
- weka.core.classes module
- weka.core.converters module
- weka.core.database module
- weka.core.dataset module
- weka.core.distances module
- weka.core.jvm module
- weka.core.packages module
- weka.core.serialization module
- weka.core.stemmers module
- weka.core.stopwords module
- weka.core.tokenizers module
- weka.core.typeconv module
- weka.core.utils module
- weka.core.version module
- Module contents
- weka.flow package
- weka.plot package
- weka.core package
- weka.associations module
AssociationRuleAssociationRule.consequenceAssociationRule.consequence_supportAssociationRule.metric_namesAssociationRule.metric_value()AssociationRule.metric_valuesAssociationRule.premiseAssociationRule.premise_supportAssociationRule.primary_metric_nameAssociationRule.primary_metric_valueAssociationRule.to_dict()AssociationRule.total_supportAssociationRule.total_transactions
AssociationRulesAssociationRulesIteratorAssociatorItemmain()sys_main()
- weka.attribute_selection module
ASEvaluationASSearchAttributeSelectionAttributeSelection.attribute_selection()AttributeSelection.crossvalidation()AttributeSelection.cv_resultsAttributeSelection.evaluator()AttributeSelection.folds()AttributeSelection.number_attributes_selectedAttributeSelection.rank_resultsAttributeSelection.ranked_attributesAttributeSelection.ranking()AttributeSelection.reduce_dimensionality()AttributeSelection.results_stringAttributeSelection.search()AttributeSelection.seed()AttributeSelection.select_attributes()AttributeSelection.select_attributes_cv_split()AttributeSelection.selected_attributesAttributeSelection.subset_results
main()sys_main()
- weka.classifiers module
AttributeSelectedClassifierClassifierClassifier.additional_measure()Classifier.additional_measuresClassifier.batch_sizeClassifier.build_classifier()Classifier.capabilitiesClassifier.classify_instance()Classifier.deserialize()Classifier.distribution_for_instance()Classifier.distributions_for_instances()Classifier.graphClassifier.graph_typeClassifier.has_efficient_batch_prediction()Classifier.headerClassifier.make_copy()Classifier.serialize()Classifier.to_source()Classifier.update_classifier()
CostMatrixCostMatrix.apply_cost_matrix()CostMatrix.expected_costs()CostMatrix.get_cell()CostMatrix.get_element()CostMatrix.get_max_cost()CostMatrix.initialize()CostMatrix.normalize()CostMatrix.num_columnsCostMatrix.num_rowsCostMatrix.parse_matlab()CostMatrix.set_cell()CostMatrix.set_element()CostMatrix.sizeCostMatrix.to_matlab()
EvaluationEvaluation.area_under_prc()Evaluation.area_under_roc()Evaluation.avg_costEvaluation.class_details()Evaluation.class_priorsEvaluation.confusion_matrixEvaluation.correctEvaluation.correlation_coefficientEvaluation.coverage_of_test_cases_by_predicted_regionsEvaluation.crossvalidate_model()Evaluation.cumulative_margin_distribution()Evaluation.discard_predictionsEvaluation.error_rateEvaluation.evaluate_model()Evaluation.evaluate_train_test_split()Evaluation.f_measure()Evaluation.false_negative_rate()Evaluation.false_positive_rate()Evaluation.headerEvaluation.incorrectEvaluation.kappaEvaluation.kb_informationEvaluation.kb_mean_informationEvaluation.kb_relative_informationEvaluation.matrix()Evaluation.matthews_correlation_coefficient()Evaluation.mean_absolute_errorEvaluation.mean_prior_absolute_errorEvaluation.num_false_negatives()Evaluation.num_false_positives()Evaluation.num_instancesEvaluation.num_true_negatives()Evaluation.num_true_positives()Evaluation.percent_correctEvaluation.percent_incorrectEvaluation.percent_unclassifiedEvaluation.precision()Evaluation.predictionsEvaluation.recall()Evaluation.relative_absolute_errorEvaluation.root_mean_prior_squared_errorEvaluation.root_mean_squared_errorEvaluation.root_relative_squared_errorEvaluation.sf_entropy_gainEvaluation.sf_mean_entropy_gainEvaluation.sf_mean_prior_entropyEvaluation.sf_mean_scheme_entropyEvaluation.sf_prior_entropyEvaluation.sf_scheme_entropyEvaluation.size_of_predicted_regionsEvaluation.summary()Evaluation.test_model()Evaluation.test_model_once()Evaluation.total_costEvaluation.true_negative_rate()Evaluation.true_positive_rate()Evaluation.unclassifiedEvaluation.unweighted_macro_f_measureEvaluation.unweighted_micro_f_measureEvaluation.weighted_area_under_prcEvaluation.weighted_area_under_rocEvaluation.weighted_f_measureEvaluation.weighted_false_negative_rateEvaluation.weighted_false_positive_rateEvaluation.weighted_matthews_correlationEvaluation.weighted_precisionEvaluation.weighted_recallEvaluation.weighted_true_negative_rateEvaluation.weighted_true_positive_rate
FilteredClassifierGridSearchKernelKernelClassifierMultiSearchMultipleClassifiersCombinerNominalPredictionNumericPredictionPredictionPredictionOutputSingleClassifierEnhancermain()predictions_to_instances()sys_main()
- weka.clusterers module
ClusterEvaluationClusterEvaluation.classes_to_clustersClusterEvaluation.cluster_assignmentsClusterEvaluation.cluster_resultsClusterEvaluation.crossvalidate_model()ClusterEvaluation.evaluate_clusterer()ClusterEvaluation.log_likelihoodClusterEvaluation.num_clustersClusterEvaluation.set_model()ClusterEvaluation.test_model()
ClustererClusterer.build_clusterer()Clusterer.capabilitiesClusterer.cluster_instance()Clusterer.deserialize()Clusterer.distribution_for_instance()Clusterer.graphClusterer.graph_typeClusterer.headerClusterer.make_copy()Clusterer.number_of_clustersClusterer.serialize()Clusterer.update_clusterer()Clusterer.update_finished()
FilteredClustererSingleClustererEnhanceravg_silhouette_coefficient()main()sys_main()
- weka.datagenerators module
DataGeneratorDataGenerator.dataset_formatDataGenerator.define_data_format()DataGenerator.generate_example()DataGenerator.generate_examples()DataGenerator.generate_finish()DataGenerator.generate_start()DataGenerator.make_copy()DataGenerator.make_data()DataGenerator.num_examples_actDataGenerator.single_mode_flag
main()sys_main()
- weka.experiments module
ExperimentResultMatrixResultMatrix.average()ResultMatrix.columnsResultMatrix.get_col_name()ResultMatrix.get_mean()ResultMatrix.get_row_name()ResultMatrix.get_stdev()ResultMatrix.hide_col()ResultMatrix.hide_row()ResultMatrix.is_col_hidden()ResultMatrix.is_row_hidden()ResultMatrix.rowsResultMatrix.set_col_name()ResultMatrix.set_mean()ResultMatrix.set_row_name()ResultMatrix.set_stdev()ResultMatrix.show_col()ResultMatrix.show_row()ResultMatrix.to_string_header()ResultMatrix.to_string_key()ResultMatrix.to_string_matrix()ResultMatrix.to_string_ranking()ResultMatrix.to_string_summary()
SimpleCrossValidationExperimentSimpleExperimentSimpleRandomSplitExperimentTester
- weka.filters module
- weka.timeseries module
ConfidenceIntervalForecasterCustomPeriodicTestErrorModuleIncrementallyPrimeableOverlayForecasterPeriodicityPeriodicityHandlerTSEvalModuleTSEvaluationTSEvaluation.evaluate()TSEvaluation.evaluate_forecaster()TSEvaluation.evaluate_on_test_dataTSEvaluation.evaluate_on_training_dataTSEvaluation.evaluation_modulesTSEvaluation.forecast_futureTSEvaluation.horizonTSEvaluation.predictions_for_test_data()TSEvaluation.predictions_for_training_data()TSEvaluation.prime_for_test_data_with_test_dataTSEvaluation.prime_window_sizeTSEvaluation.print_future_forecast_on_test_data()TSEvaluation.print_future_forecast_on_training_data()TSEvaluation.print_predictions_for_test_data()TSEvaluation.print_predictions_for_training_data()TSEvaluation.rebuild_model_after_each_test_forecast_stepTSEvaluation.summary()TSEvaluation.test_dataTSEvaluation.training_data
TSForecasterTSForecaster.algorithm_nameTSForecaster.base_model_has_serializerTSForecaster.build_forecaster()TSForecaster.clear_previous_state()TSForecaster.fields_to_forecastTSForecaster.forecast()TSForecaster.headerTSForecaster.load_base_model()TSForecaster.load_serialized_state()TSForecaster.previous_stateTSForecaster.prime_forecaster()TSForecaster.reset()TSForecaster.run_forecaster()TSForecaster.save_base_model()TSForecaster.serialize_state()TSForecaster.uses_state
TSLagMakerTSLagMaker.add_am_indicatorTSLagMaker.add_custom_periodic()TSLagMaker.add_day_of_monthTSLagMaker.add_day_of_weekTSLagMaker.add_month_of_yearTSLagMaker.add_num_days_in_monthTSLagMaker.add_quarter_of_yearTSLagMaker.add_weekend_indicatorTSLagMaker.adjust_for_trendsTSLagMaker.adjust_for_varianceTSLagMaker.artificial_time_start_valueTSLagMaker.average_consecutive_long_lagsTSLagMaker.average_lags_afterTSLagMaker.clear_custom_periodics()TSLagMaker.clear_lag_histories()TSLagMaker.create_time_lag_cross_products()TSLagMaker.current_timestamp_valueTSLagMaker.delta_timeTSLagMaker.fields_to_lagTSLagMaker.fields_to_lag_as_stringTSLagMaker.include_powers_of_timeTSLagMaker.include_timelag_productsTSLagMaker.increment_artificial_time_value()TSLagMaker.is_using_artificial_time_indexTSLagMaker.lag_rangeTSLagMaker.max_lagTSLagMaker.min_lagTSLagMaker.num_consecutive_long_lags_to_averageTSLagMaker.overlay_fieldsTSLagMaker.periodicityTSLagMaker.primary_periodic_field_nameTSLagMaker.remove_leading_instances_with_unknown_lag_valuesTSLagMaker.skip_entriesTSLagMaker.timestamp_fieldTSLagMaker.transformed_data()
TSLagUserTestPartWekaForecaster
- Subpackages
