Mlj table

Project.toml

@@ -1,7 +1,7 @@
 name = "EvoTrees"
 uuid = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
 authors = ["jeremiedb <[email protected]>"]
-version = "0.16.4"
+version = "0.16.5"
 
 [deps]
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"

benchmarks/regressor.jl

@@ -12,9 +12,9 @@ import CUDA
 # desktop | 1e6 | depth 11 | cpu: 37.2s
 # desktop | 10e6 | depth 11 | cpu
 
-### perf depth
-# desktop | 1e6 | depth 11 | cpu: 28s gpu: 73 sec  | xgboost: 26s
-# desktop | 10e6 | depth 11 | cpu 205s gpu: 109 sec | xgboost 260s
+### v0.16.5
+# desktop | 1e6 | depth 11 | cpu: 31s gpu: 50 sec  | xgboost cpu: 26s
+# desktop | 10e6 | depth 11 | cpu 200s gpu: 80 sec | xgboost cpu: 267s
 
 #threads
 # laptop depth 6: 12.717845 seconds (2.08 M allocations: 466.228 MiB)

experiments/MLJ.jl

@@ -68,7 +68,6 @@ config = EvoTreeClassifier(
     gamma = 0.0,
     nbins = 32,
     nrounds = 200,
-    device = "cpu"
 )
 model = fit_evotree(config; x_train, y_train);
 model = fit_evotree(config; x_train, y_train, x_eval = x_train, y_eval = y_train, metric=:mlogloss, print_every_n=10, early_stopping_rounds=25);