Command-line¶
From command-line, python-weka-wrapper3 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
Command-line help screen:
usage: pww-datagenerator [-h] [-j classpath] [-X heap] datagenerator ...
Executes a data generator from the command-line. Calls JVM start/stop
automatically.
positional arguments:
datagenerator data generator classname, e.g.,
weka.datagenerators.classifiers.classification.LED24
option additional data generator options
optional arguments:
-h, --help show this help message and exit
-j classpath additional classpath, jars/directories
-X heap max heap size for jvm, e.g., 512m
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.
Command-line help screen:
usage: pww-filter [-h] [-j classpath] [-X heap] -i input1 -o output1
[-r input2] [-s output2] [-c classindex]
filter ...
Executes a filter from the command-line. Calls JVM start/stop automatically.
positional arguments:
filter filter classname, e.g., weka.filters.AllFilter
option additional filter options
optional arguments:
-h, --help show this help message and exit
-j classpath additional classpath, jars/directories
-X heap max heap size for jvm, e.g., 512m
-i input1 input file 1
-o output1 output file 1
-r input2 input file 2
-s output2 output file 2
-c classindex 1-based class attribute index
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
Command-line help screen:
usage: pww-classifier [-h] [-j classpath] [-X heap] -t train [-T test]
[-c class index] [-d outmodel] [-l inmodel]
[-x num folds] [-s seed] [-v] [-o] [-i] [-k]
[-m costmatrix] [-g graph]
classifier ...
Performs classification/regression from the command-line. Calls JVM start/stop
automatically.
positional arguments:
classifier classifier classname, e.g., weka.classifiers.trees.J48
option additional classifier options
optional arguments:
-h, --help show this help message and exit
-j classpath additional classpath, jars/directories
-X heap max heap size for jvm, e.g., 512m
-t train Training set file
-T test Test set file
-c class index 1-based class attribute index
-d outmodel model output file name
-l inmodel model input file name
-x num folds number of folds for cross-validation
-s seed seed value for randomization
-v no statistics for training
-o only statistics, don't output model
-i output information retrieval statistics
-k output information theoretic statistics
-m costmatrix cost matrix file
-g graph output file for graph (if supported)
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
Command-line help screen:
usage: pww-clusterer [-h] [-j classpath] [-X heap] -t train [-T test]
[-d outmodel] [-l inmodel] [-p attributes] [-x num folds]
[-s seed] [-c class index] [-g graph]
clusterer ...
Performs clustering from the command-line. Calls JVM start/stop automatically.
positional arguments:
clusterer clusterer classname, e.g., weka.clusterers.SimpleKMeans
option additional clusterer options
optional arguments:
-h, --help show this help message and exit
-j classpath additional classpath, jars/directories
-X heap max heap size for jvm, e.g., 512m
-t train training set file
-T test test set file
-d outmodel model output file name
-l inmodel model input file name
-p attributes attribute range
-x num folds number of folds
-s seed seed value for randomization
-c class index 1-based class attribute index
-g graph graph output file (if supported)
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
Command-line help screen:
usage: pww-attsel [-h] [-j classpath] [-X heap] -i input [-c class index]
[-s search] [-x num folds] [-n seed]
evaluator ...
Performs attribute selection from the command-line. Calls JVM start/stop
automatically.
positional arguments:
evaluator evaluator classname, e.g.,
weka.attributeSelection.CfsSubsetEval
option additional evaluator options
optional arguments:
-h, --help show this help message and exit
-j classpath additional classpath, jars/directories
-X heap max heap size for jvm, e.g., 512m
-i input input file
-c class index 1-based class attribute index
-s search search method, classname and options
-x num folds number of folds
-n seed the seed value for randomization
Associators¶
Associators, like Apriori, can be run like this:
pww-associator \
-t /my/datasets/iris.arff \
weka.associations.Apriori \
-N 9 -I
Command-line help screen:
usage: pww-associator [-h] [-j classpath] [-X heap] -t train associator ...
Executes an associator from the command-line. Calls JVM start/stop
automatically.
positional arguments:
associator associator classname, e.g., weka.associations.Apriori
option additional associator options
optional arguments:
-h, --help show this help message and exit
-j classpath additional classpath, jars/directories
-X heap max heap size for jvm, e.g., 512m
-t train training set file
Package management¶
Versions newer than 0.2.9 also offer package management from the command-line via the pww-packages command. There are several sub-commands available:
usage: pww-packages [-h]
{list,info,install,uninstall,remove,freeze,suggest,is-installed,bootstrap}
...
Manages Weka packages.
positional arguments:
{list,info,install,uninstall,remove,freeze,suggest,is-installed,bootstrap}
list For listing all/installed/available packages
info Outputs information about packages
install For installing one or more packages
uninstall (remove) For uninstalling one or more packages
freeze For outputting list of installed packages
suggest For suggesting packages that contain the specified
class
is-installed Checks whether a package is installed, simply outputs
true/false
bootstrap Generates Python script for recreating current pww3
environment.
optional arguments:
-h, --help show this help message and exit
Listing packages¶
Listing all, available or installed packages can be done using the list sub-command:
usage: pww-packages list [-h] [-f {text,json}] [-o FILE] [-r]
[{all,installed,available}]
positional arguments:
{all,installed,available}
defines what packages to list
optional arguments:
-h, --help show this help message and exit
-f {text,json}, --format {text,json}
the output format to use
-o FILE, --output FILE
the file to store the output in, uses stdout if not
supplied
-r, --refresh-cache whether to refresh the package cache
Info on packages¶
Outputting information on one or more packages is achieved with the list sub-command:
usage: pww-packages info [-h] [-t {brief,full}] [-f {text,json}] [-o FILE]
[-r]
name [name ...]
positional arguments:
name the package(s) to output the information for
optional arguments:
-h, --help show this help message and exit
-t {brief,full}, --type {brief,full}
the type of information to output
-f {text,json}, --format {text,json}
the output format to use
-o FILE, --output FILE
the file to store the output in, uses stdout if not
supplied
-r, --refresh-cache whether to refresh the package cache
Installing/uninstalling/check installed status¶
The install sub-command installs one or more packages:
usage: pww-packages install [-h] [-r FILE] [--refresh-cache]
[packages [packages ...]]
positional arguments:
packages the name of the package(s) to install, append
'==VERSION' to pin to a specific version
optional arguments:
-h, --help show this help message and exit
-r FILE, --requirements FILE
the text file with packages to install (one per line,
format: PKGNAME[==VERSION[|URL]])
--refresh-cache whether to refresh the package cache
The uninstall (or remove) sub-command removes one or more packages:
usage: pww-packages uninstall [-h] packages [packages ...]
positional arguments:
packages the name of the package(s) to uninstall
optional arguments:
-h, --help show this help message and exit
The is-installed sub-command outputs whether a package is installed or not:
usage: pww-packages is-installed [-h] [-f {text,json}] [-o FILE]
name [name ...]
positional arguments:
name the name of the package to check, append '==VERSION'
to pin to a specific version
optional arguments:
-h, --help show this help message and exit
-f {text,json}, --format {text,json}
the output format to use
-o FILE, --output FILE
the file to store the output in, uses stdout if not
supplied
The freeze sub-command outputs all installed packages in the requirements.txt format (PKGNAME==VERSION[|URL], one per line):
usage: pww-packages freeze [-h] [-r FILE] [-u] [-f]
optional arguments:
-h, --help show this help message and exit
-r FILE, --requirements FILE
the text file to store the package/version pairs in
(one per line, format: PKGNAME[==VERSION])
-u, --output_urls whether to output the download URL for unofficial
packages (appends '|URL')
-f, --force_urls forces the output of the URLs for all packages, not
just unofficial ones
Suggest packages¶
If you are not sure which package a certain class is part of, then use the suggest sub-command to help with that (this works only for official packages):
usage: pww-packages suggest [-h] [-e] [-f {text,json}] [-o FILE] classname
positional arguments:
classname the classname to suggest packages for
optional arguments:
-h, --help show this help message and exit
-e, --exact whether to match the name exactly or perform substring
matching
-f {text,json}, --format {text,json}
the output format to use
-o FILE, --output FILE
the file to store the output in, uses stdout if not
supplied
Bootstrapping¶
If you want to recreate your python-weka-wrapper3 installation in another virtual environment or even on another machine, then you can use the bootstrap sub-command to generate a Python script that performs all the necessary steps:
usage: pww-packages bootstrap [-h] [-f] [-o FILE]
optional arguments:
-h, --help show this help message and exit
-f, --force_urls forces the install from URLs, not just unofficial ones
-o FILE, --output FILE
the file to store the Python script in, otherwise
outputs it on stdout
