# Save and Load Random Forest trained with MLJ/ScikitLearn.jl

**URL:** <https://discourse.julialang.org/t/save-and-load-random-forest-trained-with-mlj-scikitlearn-jl/55798>\
**Category:** Machine Learning\
**Tags:** mlj\
**Created:** [February 22, 2021, 7:56pm UTC](https://discourse.julialang.org/t/save-and-load-random-forest-trained-with-mlj-scikitlearn-jl/55798 "2021-02-22T19:56:08Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![rio](https://avatars.discourse-cdn.com/v4/letter/r/ea666f/32.png) [@rio](https://discourse.julialang.org/u/rio)\
**Post date:** [February 22, 2021, 7:56pm UTC](https://discourse.julialang.org/t/save-and-load-random-forest-trained-with-mlj-scikitlearn-jl/55798/1 "2021-02-22T19:56:08Z")

</div>

I would like to train a Random Forest model (RandomForestRegressor pkg=ScikitLearn) and save the trained model to disk. Then, I would like to load the model and apply it to new data.

After training with success, I tried to save the model using JLD2. Once I read the model and apply it again I get the error

> ERROR: LoadError: ArgumentError: ref of NULL PyObject

Here is my code:

```julia
using MLJ
using ScikitLearn
using MLJScikitLearnInterface
using PyCall
using JLD2

x = rand(Float32, 100, 10) # 100 training samples, 10 predictors
y = x[:,2] + x[:,4]

@MLJ.load RandomForestRegressor pkg=ScikitLearn
clf = RandomForestRegressor()

# Train the model
mach = machine(clf, x, y)
MLJ.fit!(mach, verbosity=2)
yTR_hat = MLJ.predict(mach, x)

# Save the model
@JLD2.save "model.jld2" mach

# Load the saved model and apply to new data
@JLD2.load "model.jld2" mach

# This gives me the ERROR: ArgumentError: ref of NULL PyObject
yTE_hat = MLJ.predict(mach, x)

```

Please, how should I do this?

Thank you very much for the help

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<div class="post-metadata">

**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [February 22, 2021, 8:47pm UTC](https://discourse.julialang.org/t/save-and-load-random-forest-trained-with-mlj-scikitlearn-jl/55798/2 "2021-02-22T20:47:54Z")

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This works using [BetaML](https://github.com/sylvaticus/BetaML.jl):

```julia
using BetaML.Trees
using JLD2
x = rand(Float32, 100, 10) # 100 training samples, 10 predictors
y = x[:,2] + x[:,4]
myForest = buildForest(x,y,100)
yhat = Trees.predict(myForest, x)
# Save the model
@JLD2.save "model.jld2" myForest
# Load the saved model and apply to new data
@JLD2.load "model.jld2" myForest
yhat2 = Trees.predict(myForest, x)

```

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<div class="post-metadata">

**Author:** ![rio](https://avatars.discourse-cdn.com/v4/letter/r/ea666f/32.png) [@rio](https://discourse.julialang.org/u/rio)\
**Post date:** [February 22, 2021, 10:10pm UTC](https://discourse.julialang.org/t/save-and-load-random-forest-trained-with-mlj-scikitlearn-jl/55798/3 "2021-02-22T22:10:53Z")

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thank you very much for the suggestion @sylvaticus ! Your package looks nice, and I will definitely keep it in mind. In my case, I am using other MLJ functionalities as well (ex. parameter optimization of the Random Forest using cross-validation, not shown in the toy example above for the sake of simplicity). All works, expect the saving/loading issue. Therefore, I would love to see a solution to save/load the model created with MLJ and the ScikitLearn packages like in my toy example, if possible. Thank you again.

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<div class="post-metadata">

**Author:** ![rio](https://avatars.discourse-cdn.com/v4/letter/r/ea666f/32.png) [@rio](https://discourse.julialang.org/u/rio)\
**Post date:** [February 22, 2021, 11:31pm UTC](https://discourse.julialang.org/t/save-and-load-random-forest-trained-with-mlj-scikitlearn-jl/55798/4 "2021-02-22T23:31:32Z")

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UPDATE: I found that using `BSON` instead of `JLD2` saves the model ( + `using MLJBase, using ScientificTypes`):

```julia
using MLJ
using ScikitLearn
using MLJScikitLearnInterface
using PyCall
using BSON
using MLJBase
using ScientificTypes

x = rand(Float32, 100, 10) # 100 training samples, 10 predictors
y = x[:,2] + x[:,4]

@MLJ.load RandomForestRegressor pkg=ScikitLearn
clf = RandomForestRegressor()

# Train the model
mach = machine(clf, x, y)
MLJ.fit!(mach, verbosity=2)
yTR_hat = MLJ.predict(mach, x)

# Save the model
@BSON.save "model.bson" mach

# Load the saved model
@BSON.load "model.bson" mach

# and apply to new data. 
yTE_hat = MLJ.predict(mach, x)

```

The above code works fine.

But once I close the Julia section, and start a new one, and load the saved model to apply it to new data:

```julia

using MLJ
using ScikitLearn
using MLJScikitLearnInterface
using PyCall
using BSON
using MLJBase
using ScientificTypes

x = rand(Float32, 100, 10) # 100 training samples, 10 predictors
@BSON.load "model.bson" mach
yTR_hat = MLJ.predict(mach, x)

```

the code crashes with:

> signal (11): Segmentation fault: 11  
> in expression starting at none:1  
> PyObject\_GetAttrString at /Users/rio/.julia/conda/3/lib/libpython3.8.dylib (unknown line)  
> \_getproperty at /Users/rio/.julia/packages/PyCall/tqyST/src/PyCall.jl:300 [inlined]  
> …

I am running Julia 1.5.3 in MacOS

---

<div class="post-metadata">

**Author:** ![rio](https://avatars.discourse-cdn.com/v4/letter/r/ea666f/32.png) [@rio](https://discourse.julialang.org/u/rio)\
**Post date:** [March 1, 2021, 2:08pm UTC](https://discourse.julialang.org/t/save-and-load-random-forest-trained-with-mlj-scikitlearn-jl/55798/5 "2021-03-01T14:08:25Z")

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I found that the answer is in " [Saving machines](https://alan-turing-institute.github.io/MLJ.jl/dev/machines/#Saving-machines-1)" . Instead of using BSON or JLD2 to save the trained model, I should using the specific MLJ.save functionality:

```julia
# Save the model
MLJ.save("my_machine.jlso", mach)

# Load the saved model
mach2 = machine("my_machine.jlso")

# and apply to new data. This works :)
xnew = rand(Float32, 100, 10)
ynew_hat = MLJ.predict(mach2, xnew)

```