krsnewwave

class MVAERecommender(TopNRecommender):
    # TopNRecommender contains methods to predict top k
    def __init__(self, model_conf : Dict, novelty_per_item, num_users, num_items, remove_observed = False, ):
        # ... configuration is skipped
        # # # # Model Structure # # # #
        # this is to handle encoding dimensions as lists
        self.encoder = nn.ModuleList()
        # this enumeration produces dims in pairs, start with 1
        for i, (d_in, d_out) in enumerate(zip(self.enc_dims[:-1], self.enc_dims[1:]), start=1):
            # double d out at last for the mean and variance parameters

krsnewwave

import pytorch_lightning as pl
from nvtabular.framework_utils.torch.layers import ConcatenatedEmbeddings, MultiHotEmbeddings
import torch

class WideAndDeepMultihot(pl.LightningModule):
    def __init__(
        self,
        model_conf,
        cat_names, cont_names, label_names,
        num_continuous,

krsnewwave

class WideAndDeepMultihot(pl.LightningModule):
    # others go here...
    #
    #
    
    def training_step(self, batch, batch_idx):        
        # unpack
        x_cat, x_cont, y = self.transform.transform_with_label(batch)
        # forward
        y_pred = self((x_cat, x_cont))

krsnewwave

# (1) Create loaders
def create_loaders(train_dataset, valid_dataset):
     # dataset and loaders
    train_iter = TorchAsyncItr(
        train_dataset,
        batch_size=BATCH_SIZE,
        cats=CATEGORICAL_COLUMNS + CATEGORICAL_MH_COLUMNS,
        conts=NUMERIC_COLUMNS,
        labels=["rating"],
    )

krsnewwave

import gc

def objective(trial):
    ### Dataset section
    # see https://gist.github.com/krsnewwave/273b9cafa4813771791f076cee32c2e4#file-nvtabular_movielens_main_loop_functions-py-L2
    train_loader, valid_loader = create_loaders(train_dataset, valid_dataset)
    ### Model section
    # see https://gist.github.com/krsnewwave/273b9cafa4813771791f076cee32c2e4#file-nvtabular_movielens_main_loop_functions-py-L29
    epochs = 1
    patience = 3