pypi_README.md

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# pytorch-widedeep

A flexible package to use Deep Learning with tabular data, text and images
using wide and deep models.

**Documentation:** [https://pytorch-widedeep.readthedocs.io](https://pytorch-widedeep.readthedocs.io/en/latest/index.html)

**Companion posts and tutorials:** [infinitoml](https://jrzaurin.github.io/infinitoml/)

**Experiments and comparisson with `LightGBM`**: [TabularDL vs LightGBM](https://github.com/jrzaurin/tabulardl-benchmark)

**Slack**: if you want to contribute or just want to chat with us, join [slack](https://join.slack.com/t/pytorch-widedeep/shared_invite/zt-soss7stf-iXpVuLeKZz8lGTnxxtHtTw)

### Introduction

``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
and images with corresponding tabular data using wide and deep models. With
that in mind there are a number of architectures that can be implemented with
just a few lines of code. For details on the main components of those
architectures please visit the
[repo](https://github.com/jrzaurin/pytorch-widedeep).


### Installation

Install using pip:

```bash
pip install pytorch-widedeep
```

Or 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 .
```

**Important note for Mac users**: at the time of writing the latest `torch`
release is `1.9`. Some past [issues](https://stackoverflow.com/questions/64772335/pytorch-w-parallelnative-cpp206)
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 3.7`
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:


```bash
pip install pytorch-widedeep
pip install torch==1.6.0 torchvision==0.7.0
```

None of these issues affect Linux users.

### Quick start

Binary classification with the [adult
dataset]([adult](https://www.kaggle.com/wenruliu/adult-income-dataset))
using `Wide` and `DeepDense` and defaults settings.

Building a wide (linear) and deep model with ``pytorch-widedeep``:

```python

import pandas as pd
import numpy as np
import torch
from sklearn.model_selection import train_test_split

from pytorch_widedeep import Trainer
from pytorch_widedeep.preprocessing import WidePreprocessor, TabPreprocessor
from pytorch_widedeep.models import Wide, TabMlp, WideDeep
from pytorch_widedeep.metrics import Accuracy

# the following 4 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)
df_train, df_test = train_test_split(df, test_size=0.2, stratify=df.income_label)

# 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_train[target_col].values

# wide
wide_preprocessor = WidePreprocessor(wide_cols=wide_cols, crossed_cols=cross_cols)
X_wide = wide_preprocessor.fit_transform(df_train)
wide = Wide(wide_dim=np.unique(X_wide).shape[0], pred_dim=1)

# deeptabular
tab_preprocessor = TabPreprocessor(embed_cols=embed_cols, continuous_cols=cont_cols)
X_tab = tab_preprocessor.fit_transform(df_train)
deeptabular = TabMlp(
    mlp_hidden_dims=[64, 32],
    column_idx=tab_preprocessor.column_idx,
    embed_input=tab_preprocessor.embeddings_input,
    continuous_cols=cont_cols,
)

# wide and deep
model = WideDeep(wide=wide, deeptabular=deeptabular)

# train the model
trainer = Trainer(model, objective="binary", metrics=[Accuracy])
trainer.fit(
    X_wide=X_wide,
    X_tab=X_tab,
    target=target,
    n_epochs=5,
    batch_size=256,
    val_split=0.1,
)

# predict
X_wide_te = wide_preprocessor.transform(df_test)
X_tab_te = tab_preprocessor.transform(df_test)
preds = trainer.predict(X_wide=X_wide_te, X_tab=X_tab_te)

# Save and load

# Option 1: this will also save training history and lr history if the
# LRHistory callback is used
trainer.save(path="model_weights", save_state_dict=True)

# Option 2: save as any other torch model
torch.save(model.state_dict(), "model_weights/wd_model.pt")

# From here in advance, Option 1 or 2 are the same. I assume the user has
# prepared the data and defined the new model components:
# 1. Build the model
model_new = WideDeep(wide=wide, deeptabular=deeptabular)
model_new.load_state_dict(torch.load("model_weights/wd_model.pt"))

# 2. Instantiate the trainer
trainer_new = Trainer(
    model_new,
    objective="binary",
)

# 3. Either start the fit or directly predict
preds = trainer_new.predict(X_wide=X_wide, X_tab=X_tab)
```

Of course, one can do **much more**. See the Examples folder, the
documentation or the companion posts for a better understanding of the content
of the package and its functionalities.

### Testing

```
pytest tests
```

### Acknowledgments

This library takes from a series of other libraries, so I think it is just
fair to mention them here in the README (specific mentions are also included
in the code).

The `Callbacks` and `Initializers` structure and code is inspired by the
[`torchsample`](https://github.com/ncullen93/torchsample) library, which in
itself partially inspired by [`Keras`](https://keras.io/).

The `TextProcessor` class in this library uses the
[`fastai`](https://docs.fast.ai/text.transform.html#BaseTokenizer.tokenizer)'s
`Tokenizer` and `Vocab`. The code at `utils.fastai_transforms` is a minor
adaptation of their code so it functions within this library. To my experience
their `Tokenizer` is the best in class.

The `ImageProcessor` class in this library uses code from the fantastic [Deep
Learning for Computer
Vision](https://www.pyimagesearch.com/deep-learning-computer-vision-python-book/)
(DL4CV) book by Adrian Rosebrock.