• this tool was developed and tested with jdk1.8.0
• if you copy and paste the configuration of the classifier generate by the weka GUI, please make sure to delete "C:\Program Files\Weka-X-X" and the related flag, or check that the path is correct

Section 3 of my bachelor thesis

So far the tool allows to learn a machine learning model, serialize the model learn, predict the class of the instances contained in a new dataset, apply a cross validation, allow to execute a weka experiment and allow to execute a weka experiment customized to make it faster.

For learning and serialization are avaiable these flags:

• -ser configuration file for serialize the model learn
• -print configuration file for print at command line the human-readable result of the model learn
• -save configuration file for save the human-readable result of the model learn in a file (one for each classifier chosen)

For prediction are avaiable these flags:

• -pred configuration file
• -pred path of the serialized model path of the dataset
• -pred path of the dataset path of the serialized model

N.B. the dataset for the prediction must not contain the class that is supposed to be predected

For cross validation are avaiable these flags:

• -cross configuration file
• -cross -fold number configuration file
• -cross -seed number configuration file
• -cross -fold number -seed number configuration file

N.B. if the fold or the seed are not specified the default numebers will be used, which are respectively 10 and 1

For weka experiment are avaiable these flags:

• -wekaExp -exptype classification -splittype crossvalidation -runs # of runs -folds # of cross-validation folds configuration file
• -wekaExp -exptype regression -splittype crossvalidation -runs # of runs -folds # of cross-validation folds (-randomized) configuration file
• -wekaExp -exptype classification -splittype randomsplit -runs # of runs -percentage percentage for randomsplit configuration file
• -wekaExp -exptype regression -splittype randomsplit -runs # of runs -percentage percentage for randomsplit (-randomized) configuration file

N.B. if one of the flag are not specified or are incorrect the default values will be used, which are: exptype: classification, splittype: crossvalidation, runs: 10, fold: 10, percentage: 66.0, not randomized

For the customized implementation of the weka experiment is avaiable this flag:

• -customExp -runs # of runs -folds # of cross-validation folds

N.B. This implementation allow to execute the weka experiment only with classification as exptype and crossvalidation as splittype. The experiment is performed faster because the classification is executed concurrently, but for this reason if you use too few classifier the execution will result slower (because of the synchronizaton points). So it is therefore advisable to use this flag if there are specified at least ten classifier in the configuration file, plus it It is strongly recommended to have at least 2GB of RAM. If you want to see how it work you can download the sequence diagram

There are two type of .properties configuration file:

Configuration file for serializaton, cross validation and weka experiment

dataset = <path of the dataset>                          (required)
path = <path where all the files will be saved>          (optional)

key = <name of the classifier options of the classifier> (required at least one classifier)

N.B. the names are inteded as the name complete with the path of the weka class that corrispond with the classifier you want to use (see weka documetation). You can also copy and paste the configuration of the classifier generate by the weka GUI. if "path" will be not specified or it is incorrect the models will be saved in the folder "result" in the repository

Configuration file for prediction

dataset = <path dataset that contain the instances that have to be predict> (required!)
labels = <list of class labels>
key =     <path of the serialized file>                                     (required at least one)

Example of a .properties configuration file for serialization:

dataset = C:/Users/uazad/Documents/Progetto/dataset/feature-envy.csv

J48_unpruned = weka.classifiers.meta.AdaBoostM1 -I 2 -W weka.classifiers.trees.J48 -- -U
weka.classifiers.meta.Bagging -W weka.classifiers.trees.J48
weka.classifiers.trees.J48 -R
weka.classifiers.rules.JRip 
weka.classifiers.meta.AdaBoostM1 -W weka.classifiers.rules.JRip
weka.classifiers.functions.LibSVM -P 100 -S 1 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.2 -M 40.0 -C 1.0 -E 0.001 -seed 1

Example of a .properties configuration file for prediction:

dataset: C:/Users/uazad/Documents/Progetto/dataset/feature-envy.csv
labels = true,false
key1 = C:/Users/uazad/Documents/Progetto/result/5_is_feature_envy_JRip.model
key2 = C:/Users/uazad/Documents/Progetto/result/1_is_feature_envy_AdaBoostM1_J48.model

Examples of execution

java -jar OUTLINE_v1.1.jar -ser -save -print .\configuration\try_classification.properties

java -jar OUTLINE_v1.1.jar -pred .\configuration\try_prediction.properties

java -jar OUTLINE_v1.1.jar -pred .\result\5_is_feature_envy_J48.model .\dataset\feature-envy.csv

java -jar OUTLINE_v1.1.jar -pred .\dataset\feature-envy.csv .\result\5_is_feature_envy_J48.model

java -jar OUTLINE_v1.1.jar -cross -seed 2 -fold 8 .\configuration\try_classification.properties

java -jar OUTLINE_v1.1.jar -wekaExp -exptype classification -splittype crossvalidation -runs 8 -folds 8  .\configuration\try_classification.properties

java -jar OUTLINE_v1.1.jar -wekaExp -exptype regression -splittype randomsplit -runs 6 -percentage 80.0 -randomized .\configuration\try_regression.properties

java -jar OUTLINE_v1.1.jar -customExp -fold 10 -runs 10 .\configuration\try_classification.properties