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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
提交
7be73d04
编写于
6月 25, 2021
作者:
J
jrzaurin
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Adjusted docs and README. Bump to version 1
上级
0070c739
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
37 addition
and
35 deletion
+37
-35
.travis.yml
.travis.yml
+1
-0
README.md
README.md
+13
-14
VERSION
VERSION
+1
-1
docs/examples.rst
docs/examples.rst
+1
-0
docs/index.rst
docs/index.rst
+7
-5
pypi_README.md
pypi_README.md
+12
-13
pytorch_widedeep/preprocessing/tab_preprocessor.py
pytorch_widedeep/preprocessing/tab_preprocessor.py
+1
-1
pytorch_widedeep/version.py
pytorch_widedeep/version.py
+1
-1
未找到文件。
.travis.yml
浏览文件 @
7be73d04
...
...
@@ -4,6 +4,7 @@ python:
-
"
3.6"
-
"
3.7"
-
"
3.8"
-
"
3.9"
matrix
:
fast_finish
:
true
include
:
...
...
README.md
浏览文件 @
7be73d04
...
...
@@ -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
浏览文件 @
7be73d04
0.4.8
\ No newline at end of file
1.0.0
\ No newline at end of file
docs/examples.rst
浏览文件 @
7be73d04
...
...
@@ -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
浏览文件 @
7be73d04
...
...
@@ -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
浏览文件 @
7be73d04
...
...
@@ -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
浏览文件 @
7be73d04
...
...
@@ -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
浏览文件 @
7be73d04
__version__
=
"
0.4.8
"
__version__
=
"
1.0.0
"
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