# Using a trained MLJ model for prediction on non-Table objects

**URL:** <https://discourse.julialang.org/t/using-a-trained-mlj-model-for-prediction-on-non-table-objects/129071>\
**Category:** Machine Learning\
**Tags:** question\
**Created:** [May 16, 2025, 4:26pm UTC](https://discourse.julialang.org/t/using-a-trained-mlj-model-for-prediction-on-non-table-objects/129071 "2025-05-16T16:26:56Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![elipivo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/elipivo/32/32916_2.png) [@elipivo](https://discourse.julialang.org/u/elipivo)\
**Post date:** [May 16, 2025, 4:26pm UTC](https://discourse.julialang.org/t/using-a-trained-mlj-model-for-prediction-on-non-table-objects/129071/1 "2025-05-16T16:26:57Z")

</div>

After training an MLJ model on data that meets the Tables.jl interface requirements, I’m interested in then deploying these models into an environment where `predict` is called on objects that don’t necessarily meet the Tables.jl interface. For instance, on a single dataframerow, a struct, or a hierarchical struct with a special `getproperty` function:

```julia
using DataFrames
using MLJ
import MLJDecisionTreeInterface.DecisionTreeClassifier as Tree

# Training Data
iris = DataFrame(load_iris());
y, X = unpack(iris, ==(:target); rng=123)

# Model Training
tree = Tree()
mach = machine(tree, X, y)
train, test = partition(eachindex(y), 0.7)
fit!(mach, rows=train)

# Predict on single observation, gives error.
predict(mach, X[test[1],:])

# Predict on a struct, gives error.
struct PlantFlat
    sepal_length::Float64
    sepal_width::Float64
    petal_length::Float64
    petal_width ::Float64
end
plant = PlantFlat(1.0,1.0,1.0,1.0)
predict(mach, plant)

# Predict on a hierarchical struct.
struct PlantStructure
    length::Float64
    width::Float64
end
struct PlantHierarchical
    sepal::PlantStructure
    petal::PlantStructure
end
function Base.getproperty(plant::PlantHierarchical, name::Symbol)
    name_str = String(name)
    if startswith(name_str, "sepal_")
        sepal = getfield(plant, :sepal)
        field = Symbol(chopprefix(name_str, "sepal_"))
        return getproperty(sepal, field)
    elseif startswith(name_str, "petal_")
        petal = getfield(plant, :petal)
        field = Symbol(chopprefix(name_str, "petal_"))
        return getproperty(petal, field)
    else
        throw(ArgumentError("Invalid property name: $name"))
    end
end
plant = PlantHierarchical(PlantStructure(1.0,1.0),PlantStructure(1.0,1.0))
predict(mach, plant)

```

I thought the `getproperty` function might be enough, but will I need to overload all the Tables.jl interface requirements to use a hierarchical object? Thanks for the help!

---

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**Author:** ![ablaom](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ablaom/32/4889_2.png) [@ablaom](https://discourse.julialang.org/u/ablaom)\
**Post date:** [May 18, 2025, 9:42pm UTC](https://discourse.julialang.org/t/using-a-trained-mlj-model-for-prediction-on-non-table-objects/129071/2 "2025-05-18T21:42:09Z")

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MLJ doesn’t really provide tools for you to do this kind of thing.

To predict on a single row of a `DataFrame`, you could try

```julia
predict(mach, X[[test[1]],:])

```

For the other case, you could either implement the Tables.jl interface, or insert a function that converts your custom format to a table, e.g., a named tuple of vectors would do.

Perhaps you could say a little more about your use case, which seems unusual. Why is the from of your production data different from your training data?