Save a trained model for future use?

Wow! Great project - thanks for your hard work.

Is there a way to save a trained model, load it into a new notebook, and run inference? Apologies if this is documented somewhere.

e.g.

rf_model = rf_model(X_train, Y_train)
rf_model.train()
rf_model.save("rf_model.pb") # <---- Is there anything like this?

and then in a new notebook

rf_model = esem.open("rf_model.pb") # <---- Is there anything like this?

Thanks!

Currently there isn't a way to do this, no. It seems a very sensible thing to allow though. I think it would require each type of model (GPFlow, sckit-learn and keras) having save and load methods that the Emulator can then just use.

I will flag this as a feature request and try to implement it when I have a chance but would also be very happy to review pull-requests that implement it.

In the meantime, for the random forest model (only) I think you should be able to just use pickle:

import pickle
pickle.dump(rf_model, 'rf_model.pb')
rf_model2 = pickle.load('rf_model.pb')
rf_model2.predict(...)

Thanks for the reply.

That solution doesn't appear to work in my case

pickle.dump(rf_model, 'rf_model.pkl')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_14674/1446057301.py in <module>
----> 1 pickle.dump(rf_model, 'rf_model.pkl')

TypeError: file must have a 'write' attribute
b

Likewise with

with open("rf_model.pkl","wb") as f:
    pickle.dump(rf_model, f)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_14674/851769721.py in <module>
      1 with open("rf_model.pkl","wb") as f:
----> 2     pickle.dump(rf_model, f)

TypeError: can't pickle tensorflow.python._pywrap_tfe.EagerContextThreadLocalData objects

Ah OK, that's because of some of the tensorflow functions on the Emulator... This will need a bit more thought sorry.

@duncanwp

@duncanwp It seems like the esem random forest is a simple implementation of the scikit-learn random forest. In the meantime, would you recommend training my emulator with scikit-learn and saving the model using pickle?

Yes, you're absolutely right.

It's also possible (but a little convoluted currently) to wrap the loaded model back in to ESEm:

# Save the sklearn model held internally in the esem wrapper
with open("rf_model.pkl","wb") as f:
    pickle.dump(esem_rf_model.model.model, f)

# Load it again
with open("rf_model.pkl","rb") as f:
    skmodel=pickle.load(f)

# Wrap the loaded model

from esem.wrappers import wrap_data
from esem.data_processors import Flatten
from esem.model_adaptor import SKLearnModel
from esem.emulator import Emulator
from sklearn.ensemble import RandomForestRegressor

wrapped_skmodel = SKLearnModel(skmodel)

# Note that we need to reload the data seperately (this is used internally for post-processing)
data = wrap_data(y_train, data_processors=[Flatten()])
loaded_esem_rf_model = Emulator(wrapped_skmodel, x_train, data)

loaded_esem_rf_model.predict(...)

I made a full example here: https://gist.github.com/duncanwp/e4b96690da5bb0bf2505bb94d5450001

@duncanwp

Thanks @duncanwp ! I managed to save a RF model. I'll leave this issue open in case you want this as documentation for a feature request. Otherwise, feel free to close. Thanks again!

@duncanwp

Hi there @duncanwp thanks for that work around. Just bumping this thread that this would be great to have! I was able to use your workaround for a GPFlow model, but it would be great to have this as method of an Emulator object too!

@adrifoster

Hi @adrifoster , part of the reason this stalled was because it didn't seem to work for tensor flow based emulators - did you find it worked OK for the GPFLow one though?

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Save a trained model for future use? #24

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Save a trained model for future use? #24

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duncanwp commented on Feb 18, 2022

EricKeenan commented on Feb 18, 2022

duncanwp commented on Feb 18, 2022

EricKeenan commented on Feb 22, 2022

duncanwp commented on Feb 23, 2022

EricKeenan commented on Mar 1, 2022

adrifoster commented on Aug 5, 2024

duncanwp commented on Aug 6, 2024

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