From command-line, python-weka-wrapper behaves similar to Weka itself, i.e., the command-line. Most of the general options are available, as well as the following:
-j for adding additional jars, in the same format as the classpath for the platform. E.g., for Linux, -j /some/where/a.jar:/other/place/b.jar
-X for defining the maximum heap size. E.g., -X 512m for 512 MB of heap size.
The following examples are all for a Linux bash environment. Windows users have to replace forwarding slashes / with backslashes \ and place the command on a single line with the backslashes \ at the end of the lines removed.
Artifical data can be generated using one of Weka’s data generators, e.g., the Agrawal classification generator:
pww-datagenerator \ -o /tmp/out.arff \ weka.datagenerators.classifiers.classification.Agrawal
Filtering a single ARFF dataset, removing the last attribute using the Remove filter:
pww-filter \ -i /my/datasets/iris.arff \ -o /tmp/out.arff \ -c last \ weka.filters.unsupervised.attribute.Remove \ -R last
For batch filtering, you can use the -r and -s options for the input and output for the second file.
Example on how to cross-validate a J48 classifier (with confidence factor 0.3) on the iris UCI dataset:
pww-classifier \ -t /my/datasets/iris.arff \ -c last \ weka.classifiers.trees.J48 -C 0.3
Example on how to perform classes-to-clusters evaluation for SimpleKMeans (with 3 clusters) using the iris UCI dataset:
pww-clusterer \ -t /my/datasets/iris.arff \ -c last \ weka.clusterers.SimpleKMeans -N 3
You can perform attribute selection using BestFirst as search algorithm and CfsSubsetEval as evaluator as follows:
pww-attsel \ -i /my/datasets/iris.arff \ -x 5 \ -n 42 \ -s "weka.attributeSelection.BestFirst -D 1 -N 5" weka.attributeSelection.CfsSubsetEval \ -P 1 \ -E 1
Associators, like Apriori, can be run like this:
pww-associator \ -t /my/datasets/iris.arff \ weka.associations.Apriori \ -N 9 -I