adapt MLJ wrapper to Tables API

Project.toml

@@ -1,7 +1,7 @@
-authors = ["jeremiedb <[email protected]>"]
 name = "EvoTrees"
 uuid = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
-version = "0.16.4"
+authors = ["jeremiedb <[email protected]>"]
+version = "0.16.5"
 
 [deps]
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
@@ -16,6 +16,12 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
+[weakdeps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+
+[extensions]
+EvoTreesCUDAExt = "CUDA"
+
 [compat]
 BSON = "0.3"
 CUDA = "3.0, 4.0, 5.0"
@@ -28,9 +34,6 @@ StatsBase = "0.32, 0.33, 0.34"
 Tables = "1.9"
 julia = "1.6"
 
-[extensions]
-EvoTreesCUDAExt = "CUDA"
-
 [extras]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -42,6 +45,3 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 [targets]
 docs = ["Documenter"]
 test = ["CUDA", "DataFrames", "Test", "MLJBase", "MLJTestInterface"]
-
-[weakdeps]
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

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)