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Rename eval to evaluate and updated compat #18

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fe4501e
rename eval function to evaluate to avoid error on latest Julia versi…
pat-alt Feb 4, 2026
163f4a4
downgraded CategoricalArrays again
pat-alt Feb 4, 2026
68b4f7f
applied linter (JuliaFormatter.format(.))
pat-alt Feb 4, 2026
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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
authors = ["jeremiedb <jeremie.db@evovest.com>"]
name = "NeuroTreeModels"
uuid = "1db4e0a5-a364-4b0c-897c-2bd5a4a3a1f2"
version = "1.5.0"
version = "1.5.1"

[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
Expand Down
47 changes: 28 additions & 19 deletions benchmarks/Higgs-logloss.jl
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Expand Up @@ -10,11 +10,11 @@ using AWS: AWSCredentials, AWSConfig, @service

@service S3
aws_creds = AWSCredentials(ENV["AWS_ACCESS_KEY_ID_JDB"], ENV["AWS_SECRET_ACCESS_KEY_JDB"])
aws_config = AWSConfig(; creds=aws_creds, region="ca-central-1")
aws_config = AWSConfig(; creds = aws_creds, region = "ca-central-1")
bucket = "jeremiedb"

path = "share/data/higgs/HIGGS.arrow"
df_tot = Connectors.read_arrow_aws(path; bucket="jeremiedb", aws_config)
df_tot = Connectors.read_arrow_aws(path; bucket = "jeremiedb", aws_config)

rename!(df_tot, "Column1" => "y")
feature_names = setdiff(names(df_tot), ["y"])
Expand All @@ -26,21 +26,21 @@ target_name = "y"

# transform!(df_tot, feature_names .=> percent_rank .=> feature_names)

dtrain = df_tot[1:end-1_000_000, :];
deval = df_tot[end-1_000_000+1:end-500_000, :];
dtest = df_tot[end-500_000+1:end, :];
dtrain = df_tot[1:(end-1_000_000), :];
deval = df_tot[(end-1_000_000+1):(end-500_000), :];
dtest = df_tot[(end-500_000+1):end, :];

config = NeuroTreeRegressor(
loss=:logloss,
nrounds=200,
depth=4,
lr=2e-3,
ntrees=128,
stack_size=2,
hidden_size=16,
batchsize=8092,
early_stopping_rounds=2,
device=:gpu,
loss = :logloss,
nrounds = 200,
depth = 4,
lr = 2e-3,
ntrees = 128,
stack_size = 2,
hidden_size = 16,
batchsize = 8092,
early_stopping_rounds = 2,
device = :gpu,
)

@time m = NeuroTreeModels.fit(
Expand All @@ -49,15 +49,25 @@ config = NeuroTreeRegressor(
deval,
target_name,
feature_names,
print_every_n=1
print_every_n = 1,
);

dinfer_eval = NeuroTreeModels.get_df_loader_infer(deval; feature_names, batchsize=config.batchsize, device=config.device);
dinfer_eval = NeuroTreeModels.get_df_loader_infer(
deval;
feature_names,
batchsize = config.batchsize,
device = config.device,
);
p_eval = m(dinfer_eval);
error_eval = 1 - mean(round.(Int, p_eval) .== deval.y)
@info "ERROR - deval" error_eval

dinfer_test = NeuroTreeModels.get_df_loader_infer(dtest; feature_names, batchsize=config.batchsize, device=config.device);
dinfer_test = NeuroTreeModels.get_df_loader_infer(
dtest;
feature_names,
batchsize = config.batchsize,
device = config.device,
);
p_test = m(dinfer_test);
error_test = 1 - mean(round.(Int, p_test) .== dtest.y)
@info "ERROR - dtest" error_test
Expand All @@ -82,4 +92,3 @@ error_test = 1 - mean(round.(Int, p_test) .== dtest.y)
# 34568.885039 seconds (7.51 G allocations: 1.109 TiB, 1.01% gc time)
# ┌ Info: ERROR - dtest
# └ error_test = 0.22153599999999996

42 changes: 24 additions & 18 deletions benchmarks/MSRank.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -13,18 +13,24 @@ using AWS: AWSCredentials, AWSConfig, @service

@service S3
aws_creds = AWSCredentials(ENV["AWS_ACCESS_KEY_ID_JDB"], ENV["AWS_SECRET_ACCESS_KEY_JDB"])
aws_config = AWSConfig(; creds=aws_creds, region="ca-central-1")
aws_config = AWSConfig(; creds = aws_creds, region = "ca-central-1")
bucket = "jeremiedb"

# initial prep
function read_libsvm_aws(file::String; has_query=false, aws_config=AWSConfig())
raw = S3.get_object("jeremiedb", file, Dict("response-content-type" => "application/octet-stream"); aws_config)
function read_libsvm_aws(file::String; has_query = false, aws_config = AWSConfig())
raw = S3.get_object(
"jeremiedb",
file,
Dict("response-content-type" => "application/octet-stream");
aws_config,
)
return read_libsvm(raw; has_query)
end

@time train_raw = read_libsvm_aws("share/data/msrank/train.txt"; has_query=true, aws_config);
@time eval_raw = read_libsvm_aws("share/data/msrank/vali.txt"; has_query=true, aws_config);
@time test_raw = read_libsvm_aws("share/data/msrank/test.txt"; has_query=true, aws_config);
@time train_raw =
read_libsvm_aws("share/data/msrank/train.txt"; has_query = true, aws_config);
@time eval_raw = read_libsvm_aws("share/data/msrank/vali.txt"; has_query = true, aws_config);
@time test_raw = read_libsvm_aws("share/data/msrank/test.txt"; has_query = true, aws_config);

dtrain = DataFrame(train_raw[:x], :auto)
dtrain.y_raw = train_raw[:y]
Expand Down Expand Up @@ -53,17 +59,17 @@ transform!(deval, feature_names .=> percent_rank .=> feature_names)
transform!(dtest, feature_names .=> percent_rank .=> feature_names)

config = NeuroTreeRegressor(
loss=:mse,
nrounds=2,
actA=:tanh,
depth=4,
ntrees=64,
stack_size=2,
hidden_size=16,
batchsize=4096,
lr=3e-4,
early_stopping_rounds=3,
device=:gpu,
loss = :mse,
nrounds = 2,
actA = :tanh,
depth = 4,
ntrees = 64,
stack_size = 2,
hidden_size = 16,
batchsize = 4096,
lr = 3e-4,
early_stopping_rounds = 3,
device = :gpu,
)

@time m = NeuroTreeModels.fit(
Expand All @@ -72,7 +78,7 @@ config = NeuroTreeRegressor(
deval,
target_name,
feature_names,
print_every_n=1,
print_every_n = 1,
);

p_eval = m(deval);
Expand Down
57 changes: 36 additions & 21 deletions benchmarks/YEAR-gaussian.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -14,20 +14,35 @@ using NeuroTreeModels
using AWS: AWSCredentials, AWSConfig, @service
@service S3
aws_creds = AWSCredentials(ENV["AWS_ACCESS_KEY_ID_JDB"], ENV["AWS_SECRET_ACCESS_KEY_JDB"])
aws_config = AWSConfig(; creds=aws_creds, region="ca-central-1")
aws_config = AWSConfig(; creds = aws_creds, region = "ca-central-1")

path = "share/data/year/year.csv"
raw = S3.get_object("jeremiedb", path, Dict("response-content-type" => "application/octet-stream"); aws_config)
df = DataFrame(CSV.File(raw, header=false))
raw = S3.get_object(
"jeremiedb",
path,
Dict("response-content-type" => "application/octet-stream");
aws_config,
)
df = DataFrame(CSV.File(raw, header = false))
df_tot = copy(df)

path = "share/data/year/year-train-idx.txt"
raw = S3.get_object("jeremiedb", path, Dict("response-content-type" => "application/octet-stream"); aws_config)
train_idx = DataFrame(CSV.File(raw, header=false))[:, 1] .+ 1
raw = S3.get_object(
"jeremiedb",
path,
Dict("response-content-type" => "application/octet-stream");
aws_config,
)
train_idx = DataFrame(CSV.File(raw, header = false))[:, 1] .+ 1

path = "share/data/year/year-eval-idx.txt"
raw = S3.get_object("jeremiedb", path, Dict("response-content-type" => "application/octet-stream"); aws_config)
eval_idx = DataFrame(CSV.File(raw, header=false))[:, 1] .+ 1
raw = S3.get_object(
"jeremiedb",
path,
Dict("response-content-type" => "application/octet-stream");
aws_config,
)
eval_idx = DataFrame(CSV.File(raw, header = false))[:, 1] .+ 1

transform!(df_tot, "Column1" => identity => "y_raw")
transform!(df_tot, "y_raw" => (x -> (x .- mean(x)) ./ std(x)) => "y_norm")
Expand All @@ -43,19 +58,19 @@ dtest = df_tot[(end-51630+1):end, :];
device = :gpu

config = NeuroTreeRegressor(;
loss=:gaussian_mle,
actA=:identity,
init_scale=0.0,
nrounds=200,
depth=4,
ntrees=32,
hidden_size=8,
stack_size=1,
MLE_tree_split=true,
batchsize=2048,
lr=1e-3,
early_stopping_rounds=2,
device
loss = :gaussian_mle,
actA = :identity,
init_scale = 0.0,
nrounds = 200,
depth = 4,
ntrees = 32,
hidden_size = 8,
stack_size = 1,
MLE_tree_split = true,
batchsize = 2048,
lr = 1e-3,
early_stopping_rounds = 2,
device,
)

@time m = NeuroTreeModels.fit(
Expand All @@ -64,7 +79,7 @@ config = NeuroTreeRegressor(;
deval,
target_name,
feature_names,
print_every_n=5
print_every_n = 5,
);

@time p_eval = m(deval; device);
Expand Down
57 changes: 36 additions & 21 deletions benchmarks/YEAR-mse.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -9,20 +9,35 @@ using NeuroTreeModels
using AWS: AWSCredentials, AWSConfig, @service
@service S3
aws_creds = AWSCredentials(ENV["AWS_ACCESS_KEY_ID_JDB"], ENV["AWS_SECRET_ACCESS_KEY_JDB"])
aws_config = AWSConfig(; creds=aws_creds, region="ca-central-1")
aws_config = AWSConfig(; creds = aws_creds, region = "ca-central-1")

path = "share/data/year/year.csv"
raw = S3.get_object("jeremiedb", path, Dict("response-content-type" => "application/octet-stream"); aws_config)
df = DataFrame(CSV.File(raw, header=false))
raw = S3.get_object(
"jeremiedb",
path,
Dict("response-content-type" => "application/octet-stream");
aws_config,
)
df = DataFrame(CSV.File(raw, header = false))
df_tot = copy(df)

path = "share/data/year/year-train-idx.txt"
raw = S3.get_object("jeremiedb", path, Dict("response-content-type" => "application/octet-stream"); aws_config)
train_idx = DataFrame(CSV.File(raw, header=false))[:, 1] .+ 1
raw = S3.get_object(
"jeremiedb",
path,
Dict("response-content-type" => "application/octet-stream");
aws_config,
)
train_idx = DataFrame(CSV.File(raw, header = false))[:, 1] .+ 1

path = "share/data/year/year-eval-idx.txt"
raw = S3.get_object("jeremiedb", path, Dict("response-content-type" => "application/octet-stream"); aws_config)
eval_idx = DataFrame(CSV.File(raw, header=false))[:, 1] .+ 1
raw = S3.get_object(
"jeremiedb",
path,
Dict("response-content-type" => "application/octet-stream");
aws_config,
)
eval_idx = DataFrame(CSV.File(raw, header = false))[:, 1] .+ 1

transform!(df_tot, "Column1" => identity => "y_raw")
transform!(df_tot, "y_raw" => (x -> (x .- mean(x)) ./ std(x)) => "y_norm")
Expand All @@ -38,19 +53,19 @@ dtest = df_tot[(end-51630+1):end, :];
device = :gpu

config = NeuroTreeRegressor(;
loss=:mse,
actA=:identity,
scaler=true,
init_scale=0.1,
nrounds=200,
depth=4,
ntrees=32,
stack_size=1,
hidden_size=8,
batchsize=2048,
lr=1e-3,
early_stopping_rounds=2,
device
loss = :mse,
actA = :identity,
scaler = true,
init_scale = 0.1,
nrounds = 200,
depth = 4,
ntrees = 32,
stack_size = 1,
hidden_size = 8,
batchsize = 2048,
lr = 1e-3,
early_stopping_rounds = 2,
device,
)

@time m = NeuroTreeModels.fit(
Expand All @@ -59,7 +74,7 @@ config = NeuroTreeRegressor(;
deval,
target_name,
feature_names,
print_every_n=5
print_every_n = 5,
);

@time p_eval = m(deval; device);
Expand Down
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