TabTransformer

This is an unofficial TabTransformer Pytorch implementation created by Ignacio Oguiza (oguiza@timeseriesAI.co)

Huang, X., Khetan, A., Cvitkovic, M., & Karnin, Z. (2020). TabTransformer: Tabular Data Modeling Using Contextual Embeddings. arXiv preprint https://arxiv.org/pdf/2012.06678

Official repo: https://github.com/awslabs/autogluon/tree/master/tabular/src/autogluon/tabular/models/tab_transformer

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TabTransformer

 TabTransformer (classes, cont_names, c_out, column_embed=True,
                 add_shared_embed=False, shared_embed_div=8,
                 embed_dropout=0.1, drop_whole_embed=False, d_model=32,
                 n_layers=6, n_heads=8, d_k=None, d_v=None, d_ff=None,
                 res_attention=True, attention_act='gelu',
                 res_dropout=0.1, norm_cont=True, mlp_mults=(4, 2),
                 mlp_dropout=0.0, mlp_act=None, mlp_skip=False,
                 mlp_bn=False, bn_final=False)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

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FullEmbeddingDropout

 FullEmbeddingDropout (dropout:float)

From https://github.com/jrzaurin/pytorch-widedeep/blob/be96b57f115e4a10fde9bb82c35380a3ac523f52/pytorch_widedeep/models/tab_transformer.py#L153

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SharedEmbedding

 SharedEmbedding (num_embeddings, embedding_dim, shared_embed=True,
                  add_shared_embed=False, shared_embed_div=8)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

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ifnone

 ifnone (a, b)

b if a is None else a

from fastai.tabular.all import *

path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
dls = TabularDataLoaders.from_csv(path/'adult.csv', path=path, y_names="salary",
    cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race'],
    cont_names = ['age', 'fnlwgt', 'education-num'],
    procs = [Categorify, FillMissing, Normalize])
x_cat, x_cont, yb = first(dls.train)
model = TabTransformer(dls.classes, dls.cont_names, dls.c)
test_eq(model(x_cat, x_cont).shape, (dls.train.bs, dls.c))