Sha256: 1772d3c1fe0808ec09ec651087041989436f5af7bb12817e5bd95591f1102017
Contents?: true
Size: 755 Bytes
Versions: 11
Compression:
Stored size: 755 Bytes
Contents
require 'rbbt/vector/model'
class RFModel < VectorModel
def initialize(dir)
super(dir)
@extract_features = Proc.new{|element|
element
}
@train_model =<<-EOF
rbbt.require("randomForest");
model = randomForest(as.factor(label) ~ ., data = features);
EOF
@eval_model =<<-EOF
rbbt.require("randomForest");
pred = names(model$forest$xlevels)
for (p in pred) {
if (is.factor(features[[p]])) {
features[[p]] = factor(features[[p]], levels=model$forest$xlevels[[p]])
}
}
label = predict(model, features);
EOF
end
def importance
TmpFile.with_file do |tmp|
tsv = R.run <<-EOF
load(file="#{model_file}");
rbbt.tsv.write('#{tmp}', model$importance)
EOF
TSV.open(tmp)
end
end
end
Version data entries
11 entries across 11 versions & 1 rubygems