Merge pull request #175 from Evovest/dev

print initial metric only if verbose > 0. Fix for #174

Project.toml

@@ -1,7 +1,7 @@
 authors = ["jeremiedb <[email protected]>"]
 name = "EvoTrees"
 uuid = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
-version = "0.12.2"
+version = "0.12.3"
 
 [deps]
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"

experiments/random.jl

@@ -33,7 +33,7 @@ params1 = EvoTreeRegressor(T=Float32,
 
 # asus laptopt: for 1.25e6 no eval: 9.650007 seconds (893.53 k allocations: 2.391 GiB, 5.52% gc time)
 @time model = fit_evotree(params1; x_train, y_train);
-@time model = fit_evotree(params1; x_train, y_train, metric=:mse, x_eval, y_eval, print_every_n=100);
+@time model = fit_evotree(params1; x_train, y_train, metric=:mse, x_eval, y_eval, print_every_n=50, verbosity=0);
 @btime model = fit_evotree(params1; x_train, y_train);
 @time pred_train = predict(model, x_train);
 @btime pred_train = predict(model, x_train);

src/fit.jl

@@ -350,7 +350,6 @@ function fit_evotree(
             w_eval = isnothing(w_eval) ? CUDA.ones(T, size(y_eval)) : CuArray(T.(w_eval))
             p_eval = predict(model.trees[1], x_eval, model.K)
             !isnothing(offset_eval) && (p_eval .+= CuArray(offset_eval'))
-            eval_vec = CUDA.zeros(T, size(y_eval, 1))
         else # params.device == "cpu"
             x_eval = T.(x_eval)
             y_eval = eltype(cache.y).(y_eval)
@@ -363,9 +362,10 @@ function fit_evotree(
         metric_best = Metric()
         metric_track.metric =
             eval_metric(Val{metric}(), p_eval, y_eval, w_eval, params.alpha)
-        tracker = (iter = [0], metric = [metric_track.metric])
-        @info "Initial tracking info" iter = model.params.nrounds metric =
-            metric_track.metric
+        if verbosity > 0
+            @info "Initial tracking info" iter = model.params.nrounds metric =
+                metric_track.metric
+        end
     end
 
     while model.params.nrounds < nrounds_max && iter_since_best < early_stopping_rounds