The MLJ example in README does not work

[liuyxpp]

Julia version:

Julia Version 1.10.1
Commit 7790d6f0641 (2024-02-13 20:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 128 × Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, icelake-server)
Threads: 1 default, 0 interactive, 1 GC (on 128 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_PKG_SERVER = https://mirrors.tuna.tsinghua.edu.cn/julia
  JULIA_PKG_USE_CLI_GIT = true

CatBoost version: v0.3.3
MLJBase version: v1.1.1

The following line errored:

julia> mach = machine(model, train_data, train_labels)
ERROR: MethodError: no method matching machine(::PythonCall.Py, ::DataFrame, ::Vector{Float64})

Closest candidates are:
  machine(::Type{<:Model}, ::Any...; kwargs...)
   @ MLJBase ~/.julia/packages/MLJBase/mIaqI/src/machines.jl:336
  machine(::Static, ::Any...; cache, kwargs...)
   @ MLJBase ~/.julia/packages/MLJBase/mIaqI/src/machines.jl:340
  machine(::Union{Symbol, Model}, ::Any, ::Any...; scitype_check_level, kwargs...)
   @ MLJBase ~/.julia/packages/MLJBase/mIaqI/src/machines.jl:364
  ...

Stacktrace:
 [1] top-level scope
   @ REPL[7]:1

Do I have to install any other packages?

[tylerjthomas9]

I forgot to update the readme example when I split up the MLJ interface into its own module. Here is the working version of this example. I will update the readme. Thank you for bringing this to my attention. Let me know if this does not work for you.

module Regression

using CatBoost.MLJCatBoostInterface
using DataFrames
using MLJBase
using PythonCall

# Initialize data
train_data = DataFrame([[1, 4, 30], [4, 5, 40], [5, 6, 50], [6, 7, 60]], :auto)
train_labels = [10.0, 20.0, 30.0]
eval_data = DataFrame([[2, 1], [4, 4], [6, 50], [8, 60]], :auto)

# Initialize CatBoostClassifier
model = CatBoostRegressor(; iterations=2, learning_rate=1.0, depth=2)
mach = machine(model, train_data, train_labels)

# Fit model
MLJBase.fit!(mach)

# Get predictions
preds_class = MLJBase.predict(mach, eval_data)

end # module

[liuyxpp]

Great! It now works like a charm. Thank you!

[tylerjthomas9]

Closed with #31