heri-eval - evaluate classification algorithm

heri-eval runs training algorithm on dataset and then evaluate it using testing set, specified by option
       -e.  If option -n was applied, cross-validation is used for evaluation, training and testing on different
       folds are run in parallel, thus utilizing available CPUs. If -r is used, the dataset is splitted into
       training and testing datasets randomly with the specified ratio, and then holdout is run.

SVM_TRAIN_CMD
             Training utility, e.g., liblinear-train (the default is svm-train).

       SVM_PREDICT_CMD
             Predicting utility, e.g., liblinear-predict (the default is svm-predict).

       SVM_HERI_STAT_CMD
             Utility for calculating statistics (the default is heri-stat(1)).

       SVM_HERI_STAT_ADDONS_CMD
             Utility for calculating additional statistics (the default is heri-stat-addons(1)).

       SVM_HERI_SPLIT_CMD
             Utility for splitting the dataset (the default is heri-split(1)).

       TMPDIR
             Temporary directory (the default is /tmp).

        heri-eval -e testing_set.libsvm training_set.libsvm -- -s 0 -t 0

        export SVM_TRAIN_CMD='liblinear-train'
        export SVM_PREDICT_CMD='liblinear-predict'
        heri-eval -p '-mr' -n 5 training_set.libsvm -- -s 4 -q
        heri-eval -p '-mr' -n 5 training_set.libsvm -- -s 4 -q

        export SVM_TRAIN_CMD='scikit_rf-train --estimators=400'
        export SVM_PREDICT_CMD='scikit_rf-predict'
        heri-eval -p '-c' -Mt -t 50 -r 70 dataset.libsvm

       <http://github.com/cheusov/herisvm>

       heri-eval - evaluate classification algorithm

-h,--help
             Display help information.

       -f    Enable output of per-fold statistics. See -Mf.

       -nN  Enable T*N-fold cross-validation mode and set the number of folds to N.

       -rratio
             Split  the  dataset  into  training  and  testing parts with the specified ratio of their sizes (in
             percents).

       -tT  Enable T*N-fold cross-validation mode and set the number of runs to T which 1 by default.

       -etesting_dataset
             Enable hold-out mode and set the testing dataset.

       -Tthreshold
             Set the minimum threshold for making a classification decision. If this  flag  is  applied,  micro-
             average precision, recall, and F1 are calculated instead of accuracy.

       -ofilename
             Save predictions from testing sets to the specified file.

             Format: outcome_class prediction_class [score]

       -Ofilename
             Save incorrectly classified objects to the specified file.

             Format: #object_number: outcome_class prediction_class [score])

       -mfilename
             Save confusion matrix to the specified file.

             Format: frequency : outcome_class prediction_class

       -popts
             Pass the specified opts to heri-stat(1).

       -sopts
             Pass the specified opts to heri-split(1).

       -Mchars
             Sets  the  output  mode  where  chars  are:  t  --  output  total  statistics, f -- output per-fold
             statistics, c -- output cross-fold statistics.  The default is "-M tc".

       -Sseed
             Pass the specified seed to heri-split(1).

       -K    Keep temporary directory after exiting.

       -D    Turn on the debugging mode, implies -K.

heri-split(1) heri-stat(1)

                                                   2021-01-25                                       heri-eval(1)

heri-eval [OPTIONS] dataset [-- SVM_TRAIN_OPTIONS]