Model Persistence

Fitted linear-regression models can be serialized to disk with model:save(path) and restored into any Lua 5.4 VM with sciencekit.load(path). Both entry points delegate directly to sk_ffi_model_save and sk_ffi_model_load in sciencekit_ffi, so files written by Lua are binary-compatible with C and Go consumers using the same ABI version.

Saving and loading a fitted model

local sciencekit = require("sciencekit")

local features = {
  { 0.0, 0.0 },
  { 1.0, 0.0 },
  { 0.0, 1.0 },
  { 2.0, 3.0 },
}
local targets = { 1.0, 3.0, 4.0, 14.0 }
local path = os.tmpname()

do
  local model <close> = sciencekit.linear_regression():fit(features, targets)
  model:save(path)
end

do
  local loaded <close> = sciencekit.load(path)
  assert(loaded.family == "linear-regression")
  assert(loaded.dtype == "f64")

  local prediction = loaded:predict({ { 2.0, 3.0 } })[1][1]
  assert(math.abs(prediction - 14.0) <= 1e-8)
end

os.remove(path)

Supported estimator scope

In sciencekit_ffi ABI 1.0 (sciencekit_lua 0.5.0), binary model persistence covers fitted f64 linear regression models:

• model:save(path) writes the fitted linear-regression parameters to path and returns nil.
• sciencekit.load(path) reads path and returns a new SKLuaModel userdata with loaded.family == "linear-regression" and loaded.dtype == "f64".
• Like any fitted model userdata, a loaded model owns a live C ABI handle (sciencekit.live_handles() increments by 1) and is released via loaded:close(), local loaded <close>, or garbage collection.

Persistence error conditions

All persistence failures raise structured Lua runtime errors (see Structured Errors):