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c287c870
编写于
10月 20, 2021
作者:
J
jrzaurin
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added ray to the docs dependencies which hopefully will fix docs issues
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docs/callbacks.rst
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Callbacks
Callbacks
=========
=========
Here are the
4
callbacks available in ``pytorch-widedepp``: ``History``,
Here are the
5
callbacks available in ``pytorch-widedepp``: ``History``,
``LRHistory``, ``ModelCheckpoint``
and ``EarlyStopping
``.
``LRHistory``, ``ModelCheckpoint``
, ``EarlyStopping`` and ``RayTuneReporter
``.
.. note:: ``History`` runs by default, so it should not be passed
.. note:: ``History`` runs by default, so it should not be passed
to the ``Trainer``
to the ``Trainer``
...
...
docs/examples.rst
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...
@@ -17,3 +17,4 @@ them to address different problems
...
@@ -17,3 +17,4 @@ them to address different problems
* `Using Custom DataLoaders and Torchmetrics <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/09_Custom_DataLoader_Imbalanced_dataset.ipynb>`__
* `Using Custom DataLoaders and Torchmetrics <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/09_Custom_DataLoader_Imbalanced_dataset.ipynb>`__
* `The Transformer Family <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/10_The_Transformer_Family.ipynb>`__
* `The Transformer Family <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/10_The_Transformer_Family.ipynb>`__
* `Extracting Embeddings <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/11_Extracting_Embeddings.ipynb>`__
* `Extracting Embeddings <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/11_Extracting_Embeddings.ipynb>`__
* `HyperParameter Tuning With RayTune <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/12_HyperParameter_tuning_w_RayTune.ipynb>`__
docs/index.rst
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...
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...
@@ -33,11 +33,11 @@ Introduction
<https://arxiv.org/abs/1606.07792>`_.
<https://arxiv.org/abs/1606.07792>`_.
In general terms, ``pytorch-widedeep`` is a package to use deep learning with
In general terms, ``pytorch-widedeep`` is a package to use deep learning with
tabular
data. In particular, is intended to facilitate the combination of text
tabular
and multimodal data. In particular, is intended to facilitate the
and images with corresponding tabular data using wide and deep models. With
combination of text and images with corresponding tabular data using wide and
that in mind there are a number of architectures that can be implemented with
deep models. With that in mind there are a number of architectures that can
just a few lines of code. The main components of those architectures are shown
be implemented with just a few lines of code. The main components of those
in the Figure below:
architectures are shown
in the Figure below:
.. image:: figures/widedeep_arch.png
.. image:: figures/widedeep_arch.png
:width: 700px
:width: 700px
...
@@ -88,29 +88,52 @@ into:
...
@@ -88,29 +88,52 @@ into:
It is important to emphasize that **each individual component, wide,
It is important to emphasize that **each individual component, wide,
deeptabular, deeptext and deepimage, can be used independently
**
and in
deeptabular, deeptext and deepimage, can be used independently and in
isolation. For example, one could use only ``wide``, which is in simply a
isolation
**
. For example, one could use only ``wide``, which is in simply a
linear model. In fact, one of the most interesting
offerings of
linear model. In fact, one of the most interesting
functionalities in
``pytorch-widedeep``
is the ``deeptabular`` component. Currently,
``pytorch-widedeep``
would be the use of the ``deeptabular`` component on its
``pytorch-widedeep`` offers 4 models for that component:
own, i.e. what one might normally refer as Deep Learning for Tabular Data.
Currently, ``pytorch-widedeep`` offers the following different models for
1. ``TabMlp``: this is almost identical to the `tabular
that component:
model <https://docs.fast.ai/tutorial.tabular.html>`_ in the fantastic
`fastai <https://docs.fast.ai/>`_ library, and consists simply in embeddings
representing the categorical features, concatenated with the continuous
1. **TabMlp**: a simple MLP that receives embeddings representing the
features, and passed then through a MLP
.
categorical features, concatenated with the continuous features
.
2.
``TabRenset``: This is
similar to the previous model but the embeddings are
2.
**TabResnet**:
similar to the previous model but the embeddings are
passed through a series of ResNet blocks built with dense layers.
passed through a series of ResNet blocks built with dense layers.
3.
``Tabnet``: Details on TabNet can be found in:
`TabNet: Attentive
3.
**TabNet**: details on TabNet can be found in
`TabNet: Attentive
Interpretable Tabular Learning <https://arxiv.org/abs/1908.07442>`_
.
Interpretable Tabular Learning <https://arxiv.org/abs/1908.07442>`_
4. ``TabTransformer``: Details on the TabTransformer can be found in:
And the ``Tabformer`` family, i.e. Transformers for Tabular data:
4. **TabTransformer**: details on the TabTransformer can be found in
`TabTransformer: Tabular Data Modeling Using Contextual Embeddings
`TabTransformer: Tabular Data Modeling Using Contextual Embeddings
<https://arxiv.org/pdf/2012.06678.pdf>`_.
<https://arxiv.org/pdf/2012.06678.pdf>`_.
For details on these 4 models and their options please see the examples in the
5. **SAINT**: Details on SAINT can be found in `SAINT: Improved Neural
Networks for Tabular Data via Row Attention and Contrastive Pre-Training
<https://arxiv.org/abs/2106.01342>`_.
6. **FT-Transformer**: details on the FT-Transformer can be found in
`Revisiting Deep Learning Models for Tabular Data
<https://arxiv.org/abs/2106.11959>`_.
7. **TabFastFormer**: adaptation of the FastFormer for tabular data. Details
on the Fasformer can be found in `FastFormers: Highly Efficient Transformer
Models for Natural Language Understanding
<https://arxiv.org/abs/2010.13382>`_
8. **TabPerceiver**: adaptation of the Perceiver for tabular data. Details on
the Perceiver can be found in `Perceiver: General Perception with Iterative
Attention <https://arxiv.org/abs/2103.03206>`_
Note that while there are scientific publications for the TabTransformer,
SAINT and FT-Transformer, the TabFasfFormer and TabPerceiver are our own
adaptation of those algorithms for tabular data.
For details on these models and their options please see the examples in the
Examples folder and the documentation.
Examples folder and the documentation.
Finally, while I recommend using the ``wide`` and ``deeptabular`` models in
Finally, while I recommend using the ``wide`` and ``deeptabular`` models in
...
@@ -120,13 +143,8 @@ possible as long as the the custom models have an attribute called
...
@@ -120,13 +143,8 @@ possible as long as the the custom models have an attribute called
``output_dim`` with the size of the last layer of activations, so that
``output_dim`` with the size of the last layer of activations, so that
``WideDeep`` can be constructed. Again, examples on how to use custom
``WideDeep`` can be constructed. Again, examples on how to use custom
components can be found in the Examples folder. Just in case
components can be found in the Examples folder. Just in case
``pytorch-widedeep`` includes standard text (stack of LSTMs) and image
``pytorch-widedeep`` includes standard text (stack of LSTMs or GRUs) and
(pre-trained ResNets or stack of CNNs) models.
image(pre-trained ResNets or stack of CNNs) models.
References
----------
[1] Heng-Tze Cheng, et al. 2016. Wide & Deep Learning for Recommender Systems.
`arXiv:1606.07792 <https://arxiv.org/abs/1606.07792>`_.
Indices and tables
Indices and tables
==================
==================
...
...
docs/installation.rst
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...
@@ -42,3 +42,4 @@ Dependencies
...
@@ -42,3 +42,4 @@ Dependencies
* einops
* einops
* wrapt
* wrapt
* torchmetrics
* torchmetrics
* ray[tune]
docs/requirements.txt
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...
@@ -18,3 +18,4 @@ torchvision
...
@@ -18,3 +18,4 @@ torchvision
einops
einops
wrapt
wrapt
torchmetrics
torchmetrics
ray[tune]
\ No newline at end of file
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