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Integrating KFold with layer?
Help · 25 Sep 2022, 16:24 · 4

Hello @Layer team. The examples shown use train test split. Anyone who has succeeded in using KFold cv plus layer?

Discussion 4 answers

Yeah, I wrapped the entire cross validation loop in a function and added the decorator normally. Right now I'm only returning the model fitted on the last iteration. But I guess it's not much of a problem right now since I'm more concerned with logging the experiments and saving the predictions.

25 Sep 2022, 21:33
Upvotes 0

Can you please send a sample of the loop using

from layer.decorators import model

def cross_validate_predict(params, X, y, cv, scoring, seed=0):
    params: model parameters
    X: training features
    y: training labels
    cv: validation strategy
    scoring: scoring function for evaluating each 
             fold's prediction
    returns model:
    # Define base model, and pass params
    model = ....
    layer.log(params) # log parameters

    # Cross validation loop
    for fold, (train_index, val_index) in enumerate(cv.split(X, y)):
          # Fit model, evaluate and generate predictions, etc. You can also log 
          the results 
# Save predictions to submission file, you also log the dataframe

return model
# To run locally
model = cross_validate_predict(best_params, X_train, y_train, cv, scoring)
# or use[  ])