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wagon [options] [-desc ifile] [-data ifile] [-stop int " {50}"] [-test ifile] [-frs float " {10}"] [-dlist ] [-dtree ] [-output ofile] [-o ofile] [-distmatrix ifile] [-quiet ] [-verbose ] [-predictee string] [-ignore string] [-stepwise ] [-swlimit float " {0.0}"] [-swopt string] [-balance float] [-held_out int] [-heap int " {210000}"] [-noprune ]
wagon is used to build CART tress from feature data, its basic features include:
both decisions trees and decision lists are supported
predictees can be discrete or continuous
input features may be discrete or continuous
many options for controling tree building
fixed stop value
balancing
held-out data and pruning
stepwise use of input features
choice of optimization criteria correct/entropy (for classification and rmse/correlation (for regression)
-desc ifile Field description file
-data ifile Datafile, one vector per line
-stop int " {50}" Minimum number of examples for leaf nodes
-test ifile Datafile to test tree on
-frs float " {10}" Float range split, number of partitions to split a float feature range into
-dlist Build a decision list (rather than tree)
-dtree Build a decision tree (rather than list) default
-output ofile
-o ofile File to save output tree in
-distmatrix ifile A distance matrix for clustering
-quiet No questions printed during building
-verbose Lost of information printing during build
-predictee string name of field to predict (default is first field)
-ignore string Filename or bracket list of fields to ignore
-stepwise Incrementally find best features
-swlimit float " {0.0}" Percentage necessary improvement for stepwise
-swopt string Parameter to optimize for stepwise, for classification options are correct or entropy for regression options are rmse or correlation correct and correlation are the defaults
-balance float For derived stop size, if dataset at node, divided by balance is greater than stop it is used as stop if balance is 0 (default) always use stop as is.
-held_out int Percent to hold out for pruning
-heap int " {210000}" Set size of Lisp heap, should not normally need to be changed from its default, only with *very* large description files (> 1M)
-noprune No (same class) pruning required
To build a decision tree (or list) Wagon requires data and a desccription of it. A data file consists a set of samples, one per line each consisting of the same set of features. Features may be categorial or continuous. By default the first feature is the predictee and the others are used as preditors. A typical data file will look like this
| 0.399 pau sh 0 0 0 1 1 0 0 0 0 0 0 0.082 sh iy pau onset 0 1 0 0 1 1 0 0 1 0.074 iy hh sh coda 1 0 1 0 1 1 0 0 1 0.048 hh ae iy onset 0 1 0 1 1 1 0 1 1 0.062 ae d hh coda 1 0 0 1 1 1 0 1 1 0.020 d y ae coda 2 0 1 1 1 1 0 1 1 0.082 y ax d onset 0 1 0 1 1 1 1 1 1 0.082 ax r y coda 1 0 0 1 1 1 1 1 1 0.036 r d ax coda 2 0 1 1 1 1 1 1 1 ... |
The data may come from any source, such as the festival script dumpfeats which allos the creation of such files easily from utetrance files.
In addition to a data file a description file is also require that gives a name and a type to each of the features in the datafile. For the above example it would look like
| ((segment_duration float) ( name aa ae ah ao aw ax ay b ch d dh dx eh el em en er ey f g hh ih iy jh k l m n nx ng ow oy p r s sh t th uh uw v w y z zh pau ) ( n.name 0 aa ae ah ao aw ax ay b ch d dh dx eh el em en er ey f g hh ih iy jh k l m n nx ng ow oy p r s sh t th uh uw v w y z zh pau ) ( p.name 0 aa ae ah ao aw ax ay b ch d dh dx eh el em en er ey f g hh ih iy jh k l m n nx ng ow oy p r s sh t th uh uw v w y z zh pau ) (position_type 0 onset coda) (pos_in_syl float) (syl_initial 0 1) (syl_final 0 1) (R:Sylstructure.parent.R:Syllable.p.syl_break float) (R:Sylstructure.parent.syl_break float) (R:Sylstructure.parent.R:Syllable.n.syl_break float) (R:Sylstructure.parent.R:Syllable.p.stress 0 1) (R:Sylstructure.parent.stress 0 1) (R:Sylstructure.parent.R:Syllable.n.stress 0 1) ) |
The feature names are arbitrary, but as they appear in the generated trees is most useful if the trees are to be used in prediction of an utterance that the names are features and/or pathnames.
Wagon can be used to build a tree with such files with the command
| wagon -data feats.data -desc fest.desc -stop 10 -output feats.tree |