DataSplitting

DataSplitting(split_fit,modelset_fit,device_fit,train_loaderset_fit, test_loader_fit, epochs_fit, criterion_fit, optimizerset_fit, terminate_fit=100, print_fit=100,printchoice=True)

Description: The procedure for prediction and estimation with data-splitting.

Parameters:

  • split_fit : int, the number of splits (= the number of hidden layer)

  • modelset_fit : list, shape = (split_fit), network list

  • device_fit : char, device to use, “cpu” or “cuda”

  • train_loaderset_fit : list, shape = (split_fit x split_fit), training dataloader set

  • test_loader_fit : dataloader for testing data

  • epochs_fit : int, maximum epoch number

  • criterion_fit : chosen criterion

  • optimizerset_fit : list, shape = (split_fit x split_fit), chosen optimizer set

  • terminate_fit : int, terminate parameter, terminate if the loss has no significant improvement over T consecutive epochs, default=100

  • print_fit : int, print parameter for outputting results, print the results after every T epochs, default=100

  • printchoice : bool, choice of print or not, default=True

Methods:

  • fitting(train_x,train_y,test_x,test_y) The procedure for training and testing.
    • Parameters:

      • train_x, test_x : DataFrame, predictors

      • train_y, test_y : Series, responses

    • Returns:

      • trainloss : list, trainning loss (on average)

      • testloss : list, testing loss (on average)

      • prediction : list, model prediction for testing data (on average)

Example:

1from Multi_Layer_Kernel_Machine.GenerateSplit import GenerateSplit
2from Multi_Layer_Kernel_Machine.DataSplitting import DataSplitting
3## Generate Subsamples
4train_loaderset,netset,optimizerset=GenerateSplit(2,device,net,8e-4,0.9,1e-4,train_x,train_y, batch,init_weights)
5## Model Fitting
6splker_trainloss,splker_testloss,splker_prediction = splkermodel.fitting(train_x,train_y,test_x,test_y)