You can try JLD.jl.
--Tim
On Saturday, September 26, 2015 05:31:18 PM Marc Stein wrote:
> I'm just starting out with Julia, so please forgive me if this is a
> simplistic question.
>
> I'm using the DecisionTree package which generates an Ensemble of
> DecisionTrees in the code below:
>
>
> ######
>
> using DataFrames
> using DecisionTree
>
> clarity = readtable("/Users/marcstein/Active/julia/clarity.csv");
> head(clarity)
>
> labels = array(clarity[:, 41]);
> features = array(clarity[:, 1:40]);
>
> # Random Forest Classifier
>
> # train random forest classifier
> # using 2 random features, 10 trees, and 0.5 portion of samples per tree
> (optional)
>
> model = build_forest(labels, features, 2, 10, 0.5)
>
> # apply learned model
>
> outcome = apply_forest(model,
> [2,761,0,0,2,1.32,74,0,365,3,2,15,10,1,0,1,24,36,2000,0,1,1,0,0,0,1,0,0,0,1,
> 5,1,0,2,0,2,220,221,220,221])
>
> # # run n-fold cross validation for forests
> # # using 2 random features, 10 trees, 3 folds and 0.5 of samples per tree
> (optional)
>
> accuracy = nfoldCV_forest(labels, features, 2, 10, 3, 0.5)
>
> score = (mean(accuracy[1:3]))
>
> println(outcome)
> println(score)
>
> ######
>
>
> This code works fine. But because the DataFrame that is the training set is
> quite large, I want to build the model and store it in one app and then
> load the model and generate the outcome in a second app.
>
> It seems like this should be simple, just persist the model in a file and
> pass it into the apply_forest method.
>
> I can't find a way, though, to persist the model. If I try
>
> writedlm(outfile,model)
>
> I get:
>
> EnsembleERROR: `start` has no method matching start(::Ensemble)
> in writedlm at datafmt.jl:535
> in writedlm at datafmt.jl:554
>
> If I try:
>
> print(outfile,model)
>
> the output file contains:
>
> Ensemble of Decision Trees
> Trees: 10
> Avg Leaves: 117.8
> Avg Depth: 20.8
>
> which is a summary of the Ensemble, not the individual elements.
>
> I'm clearly missing something, but I haven't been able to figure it out so
> far.
>
> Any suggestions would be greatly appreciated!
