Command-line¶
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.
Data generators¶
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
Filters¶
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.
Classifiers¶
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
Clusterers¶
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
Attribute selection¶
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¶
Associators, like Apriori, can be run like this:
pww-associator \
-t /my/datasets/iris.arff \
weka.associations.Apriori \
-N 9 -I