RBatchGenerator_PyTorch.py

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### \file
### \ingroup tutorial_ml
### \notebook -nodraw
### Example of getting batches of events from a ROOT dataset into a basic
### PyTorch workflow.
###
### \macro_code
### \macro_output
### \author Dante Niewenhuis
 
import torch
import ROOT
 
tree_name = "sig_tree"
file_name = str(ROOT.gROOT.GetTutorialDir()) + "/machine_learning/data/Higgs_data.root"
 
batch_size = 128
chunk_size = 5000
block_size = 300
 
rdataframe = ROOT.RDataFrame(tree_name, file_name)
 
target = "Type"
 
# Returns two generators that return training and validation batches
# as PyTorch tensors.
gen_train, gen_validation = ROOT.TMVA.Experimental.CreatePyTorchGenerators(
    rdataframe,
    batch_size,    
    chunk_size,
    block_size,        
    target = target,
    validation_split = 0.3,
    shuffle = True,
    drop_remainder=True,
)
 
# Get a list of the columns used for training
input_columns = gen_train.train_columns
num_features = len(input_columns)
 
 
def calc_accuracy(targets, pred):
    return torch.sum(targets == pred.round()) / pred.size(0)
 
 
# Initialize PyTorch model
model = torch.nn.Sequential(
    torch.nn.Linear(num_features, 300),
    torch.nn.Tanh(),
    torch.nn.Linear(300, 300),
    torch.nn.Tanh(),
    torch.nn.Linear(300, 300),
    torch.nn.Tanh(),
    torch.nn.Linear(300, 1),
    torch.nn.Sigmoid(),
)
loss_fn = torch.nn.MSELoss(reduction="mean")
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
 
number_of_epochs = 2
 
for i in range(number_of_epochs):
    print("Epoch ", i)
    model.train()
    # Loop through the training set and train model
    for i, (x_train, y_train) in enumerate(gen_train):
        # Make prediction and calculate loss
        pred = model(x_train)
        loss = loss_fn(pred, y_train)
 
        # improve model
        model.zero_grad()
        loss.backward()
        optimizer.step()
 
        # Calculate accuracy
        accuracy = calc_accuracy(y_train, pred)
 
        print(f"Training => accuracy: {accuracy}")
 
    # #################################################################
    # # Validation
    # #################################################################
 
    model.eval()    
    # Evaluate the model on the validation set
    for i, (x_val, y_val) in enumerate(gen_validation):
        # Make prediction and calculate accuracy
        pred = model(x_val)
        accuracy = calc_accuracy(y_val, pred)
 
        print(f"Validation => accuracy: {accuracy}")