Adjusted docs and README. Bump to version 1

.travis.yml

@@ -4,6 +4,7 @@ python:
   - "3.6"
   - "3.7"
   - "3.8"
+  - "3.9"
 matrix:
   fast_finish: true
   include:

README.md

@@ -9,7 +9,7 @@
 [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/jrzaurin/pytorch-widedeep/graphs/commit-activity)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/jrzaurin/pytorch-widedeep/issues)
 [![codecov](https://codecov.io/gh/jrzaurin/pytorch-widedeep/branch/master/graph/badge.svg)](https://codecov.io/gh/jrzaurin/pytorch-widedeep)
-[![Python 3.6 3.7 3.8](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue.svg)](https://www.python.org/)
+[![Python 3.6 3.7 3.8 3.9](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue.svg)](https://www.python.org/)
 
 # pytorch-widedeep
 
@@ -24,8 +24,7 @@ using wide and deep models.
 
 ### Introduction
 
-`pytorch-widedeep` is based on Google's Wide and Deep Algorithm, [Wide & Deep
-Learning for Recommender Systems](https://arxiv.org/abs/1606.07792).
+``pytorch-widedeep`` is based on Google's [Wide and Deep Algorithm](https://arxiv.org/abs/1606.07792)
 
 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
@@ -86,7 +85,7 @@ It is important to emphasize that **each individual component, `wide`,
 isolation. For example, one could use only `wide`, which is in simply a linear
 model. In fact, one of the most interesting functionalities
 in``pytorch-widedeep`` is the ``deeptabular`` component. Currently,
-``pytorch-widedeep`` offers 3 models for that component:
+``pytorch-widedeep`` offers 4 models for that component:
 
 1. ``TabMlp``: this is almost identical to the [tabular
 model](https://docs.fast.ai/tutorial.tabular.html) in the fantastic
@@ -144,20 +143,20 @@ cd pytorch-widedeep
 pip install -e .
 ```
 
-**Important note for Mac users**: at the time of writing (Feb-2021) the latest
-`torch` release is `1.7.1`. This release has some
+**Important note for Mac users**: at the time of writing (June-2021) the
+latest `torch` release is `1.9`. Some past
 [issues](https://stackoverflow.com/questions/64772335/pytorch-w-parallelnative-cpp206)
-when running on Mac and the data-loaders will not run in parallel. In
-addition, since `python 3.8`, [the `multiprocessing` library start method
-changed from `'fork'` to
+when running on Mac, present in previous versions, persist on this release and
+the data-loaders will not run in parallel. In addition, since `python 3.8`,
+[the `multiprocessing` library start method changed from `'fork'` to
 `'spawn'`](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods).
-This also affects the data-loaders (for any `torch` version) and they will not
-run in parallel. Therefore, for Mac users I recommend using `python 3.6` or
-`3.7` and `torch <= 1.6` (with the corresponding, consistent version of
+This also affects the data-loaders (for any `torch` version) and they will
+not run in parallel. Therefore, for Mac users I recommend using `python 3.6`
+or `3.7` and `torch <= 1.6` (with the corresponding, consistent version of
 `torchvision`, e.g. `0.7.0` for `torch 1.6`). I do not want to force this
 versioning in the `setup.py` file since I expect that all these issues are
-fixed in the future. Therefore, after installing `pytorch-widedeep` via pip or
-directly from github, downgrade `torch` and `torchvision` manually:
+fixed in the future. Therefore, after installing `pytorch-widedeep` via pip
+or directly from github, downgrade `torch` and `torchvision` manually:
 
 ```bash
 pip install pytorch-widedeep

VERSION

-0.4.8
+1.0.0
\ No newline at end of file

docs/examples.rst

@@ -13,3 +13,4 @@ them to address different problems
 * `Regression with Images and Text <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/05_Regression_with_Images_and_Text.ipynb>`__
 * `FineTune routines <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/06_FineTune_and_WarmUp_Model_Components.ipynb>`__
 * `Custom Components <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/07_Custom_Components.ipynb>`__
+* `Save and Load Model and Artifacts <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/08_save_and_load_model_and_artifacts.ipynb>`__

docs/index.rst

@@ -90,7 +90,7 @@ deeptabular, deeptext and deepimage, can be used independently** and in
 isolation. For example, one could use only ``wide``, which is in simply a
 linear model. In fact, one of the most interesting offerings of
 ``pytorch-widedeep`` is the ``deeptabular`` component. Currently,
-``pytorch-widedeep`` offers 3 models for that component:
+``pytorch-widedeep`` offers 4 models for that component:
 
 1. ``TabMlp``: this is almost identical to the `tabular
 model <https://docs.fast.ai/tutorial.tabular.html>`_ in the fantastic
@@ -101,12 +101,14 @@ features, and passed then through a MLP.
 2. ``TabRenset``: This is similar to the previous model but the embeddings are
 passed through a series of ResNet blocks built with dense layers.
 
-3. ``TabTransformer``: Details on the TabTransformer can be found in:
-`TabTransformer: Tabular Data Modeling Using Contextual
-Embeddings <https://arxiv.org/pdf/2012.06678.pdf>`_.
+3. ``Tabnet``: Details on TabNet can be found in: `TabNet: Attentive
+Interpretable Tabular Learning <https://arxiv.org/abs/1908.07442>`_.
 
+4. ``TabTransformer``: Details on the TabTransformer can be found in:
+`TabTransformer: Tabular Data Modeling Using Contextual Embeddings
+<https://arxiv.org/pdf/2012.06678.pdf>`_.
 
-For details on these 3 models and their options please see the examples in the
+For details on these 4 models and their options please see the examples in the
 Examples folder and the documentation.
 
 Finally, while I recommend using the ``wide`` and ``deeptabular`` models in

pypi_README.md

@@ -4,7 +4,7 @@
 [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/jrzaurin/pytorch-widedeep/graphs/commit-activity)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/jrzaurin/pytorch-widedeep/issues)
 [![codecov](https://codecov.io/gh/jrzaurin/pytorch-widedeep/branch/master/graph/badge.svg)](https://codecov.io/gh/jrzaurin/pytorch-widedeep)
-[![Python 3.6 3.7 3.8](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue.svg)](https://www.python.org/)
+[![Python 3.6 3.7 3.8 3.9](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue.svg)](https://www.python.org/)
 
 # pytorch-widedeep
 
@@ -19,8 +19,7 @@ using wide and deep models.
 
 ### Introduction
 
-`pytorch-widedeep` is based on Google's Wide and Deep Algorithm, [Wide & Deep
-Learning for Recommender Systems](https://arxiv.org/abs/1606.07792).
+``pytorch-widedeep`` is based on Google's [Wide and Deep Algorithm](https://arxiv.org/abs/1606.07792)
 
 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
@@ -56,20 +55,20 @@ cd pytorch-widedeep
 pip install -e .
 ```
 
-**Important note for Mac users**: at the time of writing (Dec-2020) the latest
-`torch` release is `1.7`. This release has some
+**Important note for Mac users**: at the time of writing (June-2021) the
+latest `torch` release is `1.9`. Some past
 [issues](https://stackoverflow.com/questions/64772335/pytorch-w-parallelnative-cpp206)
-when running on Mac and the data-loaders will not run in parallel. In
-addition, since `python 3.8`, [the `multiprocessing` library start method
-changed from `'fork'` to
+when running on Mac, present in previous versions, persist on this release and
+the data-loaders will not run in parallel. In addition, since `python 3.8`,
+[the `multiprocessing` library start method changed from `'fork'` to
 `'spawn'`](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods).
-This also affects the data-loaders (for any `torch` version) and they will not
-run in parallel. Therefore, for Mac users I recommend using `python 3.6` or
-`3.7` and `torch <= 1.6` (with the corresponding, consistent version of
+This also affects the data-loaders (for any `torch` version) and they will
+not run in parallel. Therefore, for Mac users I recommend using `python 3.6`
+or `3.7` and `torch <= 1.6` (with the corresponding, consistent version of
 `torchvision`, e.g. `0.7.0` for `torch 1.6`). I do not want to force this
 versioning in the `setup.py` file since I expect that all these issues are
-fixed in the future. Therefore, after installing `pytorch-widedeep` via pip or
-directly from github, downgrade `torch` and `torchvision` manually:
+fixed in the future. Therefore, after installing `pytorch-widedeep` via pip
+or directly from github, downgrade `torch` and `torchvision` manually:
 
 ```bash
 pip install pytorch-widedeep

pytorch_widedeep/preprocessing/tab_preprocessor.py

@@ -36,7 +36,7 @@ class TabPreprocessor(BasePreprocessor):
         the possibility of normalising the input continuous features via a
         ``BatchNorm`` or a ``LayerNorm`` layer. see
         :class:`pytorch_widedeep.models`
-    auto_embed_dim: bool
+    auto_embed_dim: bool, default = True
         Boolean indicating whether the embedding dimensions will be
         automatically defined via fastai's rule of thumb':
         :math:`min(600, int(1.6 \times n_{cat}^{0.56}))`

pytorch_widedeep/version.py

-__version__ = "0.4.8"
+__version__ = "1.0.0"