# pytorch-widedeep
A flexible package to combine tabular data with text and images using wide and
deep models.
### Introduction
`pytorch-widedeep` is based on Google's Wide and Deep Algorithm. Details of
the original algorithm can be found
[here](https://www.tensorflow.org/tutorials/wide_and_deep), and the nice
research paper can be found [here](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
and images with corresponding tabular data using wide and deep models. With
that in mind there are two architectures that can be implemented with just a
few lines of code.
### Architectures
**Architecture 1**:
Architecture 1 combines the `Wide`, one-hot encoded features with the outputs
from the `DeepDense`, `DeepText` and `DeepImage` components connected to a
final output neuron or neurons, depending on whether we are performing a
binary classification or regression, or a multi-class classification. The
components within the faded-pink rectangles are concatenated.
In math terms, and following the notation in the [paper](https://arxiv.org/abs/1606.07792), Architecture 1 can be formulated as:
Where *'W'* are the weight matrices applied to the wide model and to the final
activations of the deep models, *'a'* are these final activations, and
φ(x) are the cross product transformations of the original features *'x'*.
In case you are wondering what are *"cross product transformations"*, here is
a quote taken directly from the paper: *"For binary features, a cross-product
transformation (e.g., “AND(gender=female, language=en)”) is 1 if and only if
the constituent features (“gender=female” and “language=en”) are all 1, and 0
otherwise".*
**Architecture 2**
Architecture 2 combines the `Wide` one-hot encoded features with the Deep
components of the model connected to the output neuron(s), after the different
Deep components have been themselves combined through a FC-Head (that I refer
as `deephead`).
In math terms, and following the notation in the
[paper](https://arxiv.org/abs/1606.07792), Architecture 2 can be formulated
as:
When using `pytorch-widedeep`, the assumption is that the so called `Wide` and
`DeepDense` components in the figures are **always** present, while `DeepText`
and `DeepImage` are optional. `pytorch-widedeep` includes standard text (stack
of LSTMs) and image (pre-trained ResNets or stack of CNNs) models. However,
the user can use any custom model as long as it has an attribute called
`output_dim` with the size of the last layer of activations, so that
`WideDeep` can be constructed. See the examples folder for more information.
### Installation
Install directly from github
```bash
pip install git+https://github.com/jrzaurin/pytorch-widedeep.git
```
#### Developer Install
```bash
# Clone the repository
git clone https://github.com/jrzaurin/pytorch-widedeep
cd pytorch-widedeep
# Install in dev mode
pip install -e .
```
### Examples
There are 4 main notebooks in the `examples` folder plus some additional
files. These notebooks cover most of the utilities of this package and can
also act as documentation. In the likely case that github does not render the
notebooks, or it renders them missing some parts, they are saved as markdown
files in the `docs` folder.
### Quick start
Binary classification with the [adult
dataset]([adult](https://www.kaggle.com/wenruliu/adult-income-dataset/downloads/adult.csv/2))
using `Wide` and `DeepDense` and defaults settings.
```python
import pandas as pd
from pytorch_widedeep.preprocessing import WidePreprocessor, DeepPreprocessor
from pytorch_widedeep.models import Wide, DeepDense, WideDeep
from pytorch_widedeep.metrics import BinaryAccuracy
# these next 3 lines are not directly related to pytorch-widedeep. I assume
# you have downloaded the dataset and place it in a dir called data/adult/
df = pd.read_csv('data/adult/adult.csv.zip')
df['income_label'] = (df["income"].apply(lambda x: ">50K" in x)).astype(int)
df.drop('income', axis=1, inplace=True)
# prepare wide, crossed, embedding and continuous columns
wide_cols = ['education', 'relationship', 'workclass', 'occupation','native-country', 'gender']
cross_cols = [('education', 'occupation'), ('native-country', 'occupation')]
embed_cols = [('education',16), ('workclass',16), ('occupation',16),('native-country',32)]
cont_cols = ["age", "hours-per-week"]
target_col = 'income_label'
# target
target = df[target_col].values
# wide
preprocess_wide = WidePreprocessor(wide_cols=wide_cols, crossed_cols=cross_cols)
X_wide = preprocess_wide.fit_transform(df)
wide = Wide(wide_dim=X_wide.shape[1], output_dim=1)
# deepdense
preprocess_deep = DeepPreprocessor(embed_cols=embed_cols, continuous_cols=cont_cols)
X_deep = preprocess_deep.fit_transform(df)
deepdense = DeepDense(hidden_layers=[64,32],
deep_column_idx=preprocess_deep.deep_column_idx,
embed_input=preprocess_deep.embeddings_input,
continuous_cols=cont_cols)
# build, compile, fit and predict
model = WideDeep(wide=wide, deepdense=deepdense)
model.compile(method='binary', metrics=[BinaryAccuracy])
model.fit(X_wide=X_wide, X_deep=X_deep, target=target, n_epochs=5, batch_size=256, val_split=0.2)
model.predict(X_wide=X_wide_te, X_deep=X_deep_te)
```
Of course, one can do much more, such as using different initializations,
optimizers or learning rate schedulers for each component of the overall
model. Adding FC-Heads to the Text and Image components, etc. See the
`examples` or the `docs` folders for a better understanding of the content of
the package and its functionalities.
### Testing
```
cd test
pytest --ignore=test_data_utils/test_du_deep_image.py
cd test_data_utils
pytest test_du_deep_image.py
```