pytorch-widedeep

A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch

Documentation: https://pytorch-widedeep.readthedocs.io

Companion posts and tutorials: infinitoml

Experiments and comparison with LightGBM: TabularDL vs LightGBM

Slack: if you want to contribute or just want to chat with us, join slack

The content of this document is organized as follows:

• pytorch-widedeep
  • Introduction
  • Architectures
  • The deeptabular component
  • The rec module
  • Text and Images
  • Installation
    • Developer Install
  • Quick start
  • Testing
  • How to Contribute
  • Acknowledgments
  • License
  • Cite
    • BibTex
    • APA

Introduction

pytorch-widedeep is based on Google's Wide and Deep Algorithm, adjusted for multi-modal datasets.

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 the library. The main components of those architectures are shown in the Figure below:

In math terms, and following the notation in the paper, the expression for the architecture without a deephead component can be formulated as:

Where σ is the sigmoid function, 'W' are the weight matrices applied to the wide model and to the final activations of the deep models, 'a' are these final activations, φ(x) are the cross product transformations of the original features 'x', and , and 'b' is the bias term. 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".

It is perfectly possible to use custom models (and not necessarily those in the library) as long as the the custom models have an property called output_dim with the size of the last layer of activations, so that WideDeep can be constructed. Examples on how to use custom components can be found in the Examples folder and the section below.

Architectures

The pytorch-widedeep library offers a number of different architectures. In this section we will show some of them in their simplest form (i.e. with default param values in most cases) with their corresponding code snippets. Note that all the snippets below shoud run locally. For a more detailed explanation of the different components and their parameters, please refer to the documentation.

For the examples below we will be using a toy dataset generated as follows:

import os
import random

import numpy as np
import pandas as pd
from PIL import Image
from faker import Faker


def create_and_save_random_image(image_number, size=(32, 32)):

    if not os.path.exists("images"):
        os.makedirs("images")

    array = np.random.randint(0, 256, (size[0], size[1], 3), dtype=np.uint8)

    image = Image.fromarray(array)

    image_name = f"image_{image_number}.png"
    image.save(os.path.join("images", image_name))

    return image_name


fake = Faker()

cities = ["New York", "Los Angeles", "Chicago", "Houston"]
names = ["Alice", "Bob", "Charlie", "David", "Eva"]

data = {
    "city": [random.choice(cities) for _ in range(100)],
    "name": [random.choice(names) for _ in range(100)],
    "age": [random.uniform(18, 70) for _ in range(100)],
    "height": [random.uniform(150, 200) for _ in range(100)],
    "sentence": [fake.sentence() for _ in range(100)],
    "other_sentence": [fake.sentence() for _ in range(100)],
    "image_name": [create_and_save_random_image(i) for i in range(100)],
    "target": [random.choice([0, 1]) for _ in range(100)],
}

df = pd.DataFrame(data)

This will create a 100 rows dataframe and a dir in your local folder, called images with 100 random images (or images with just noise).

Perhaps the simplest architecture would be just one component, wide, deeptabular, deeptext or deepimage on their own, which is also possible, but let's start the examples with a standard Wide and Deep architecture. From there, how to build a model comprised only of one component will be straightforward.

Note that the examples shown below would be almost identical using any of the models available in the library. For example, TabMlp can be replaced by TabResnet, TabNet, TabTransformer, etc. Similarly, BasicRNN can be replaced by AttentiveRNN, StackedAttentiveRNN, or HFModel with their corresponding parameters and preprocessor in the case of the Hugging Face models.

1. Wide and Tabular component (aka deeptabular)

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

# Wide
wide_cols = ["city"]
crossed_cols = [("city", "name")]
wide_preprocessor = WidePreprocessor(wide_cols=wide_cols, crossed_cols=crossed_cols)
X_wide = wide_preprocessor.fit_transform(df)
wide = Wide(input_dim=np.unique(X_wide).shape[0])

# Tabular
tab_preprocessor = TabPreprocessor(
    embed_cols=["city", "name"], continuous_cols=["age", "height"]
)
X_tab = tab_preprocessor.fit_transform(df)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[64, 32],
)

# WideDeep
model = WideDeep(wide=wide, deeptabular=tab_mlp)

# Train
trainer = Trainer(model, objective="binary")

trainer.fit(
    X_wide=X_wide,
    X_tab=X_tab,
    target=df["target"].values,
    n_epochs=1,
    batch_size=32,
)

2. Tabular and Text data

from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep
from pytorch_widedeep.training import Trainer

# Tabular
tab_preprocessor = TabPreprocessor(
    embed_cols=["city", "name"], continuous_cols=["age", "height"]
)
X_tab = tab_preprocessor.fit_transform(df)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[64, 32],
)

# Text
text_preprocessor = TextPreprocessor(
    text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text = text_preprocessor.fit_transform(df)
rnn = BasicRNN(
    vocab_size=len(text_preprocessor.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)

# WideDeep
model = WideDeep(deeptabular=tab_mlp, deeptext=rnn)

# Train
trainer = Trainer(model, objective="binary")

trainer.fit(
    X_tab=X_tab,
    X_text=X_text,
    target=df["target"].values,
    n_epochs=1,
    batch_size=32,
)

3. Tabular and text with a FC head on top via the head_hidden_dims param in WideDeep

from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep
from pytorch_widedeep.training import Trainer

# Tabular
tab_preprocessor = TabPreprocessor(
    embed_cols=["city", "name"], continuous_cols=["age", "height"]
)
X_tab = tab_preprocessor.fit_transform(df)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[64, 32],
)

# Text
text_preprocessor = TextPreprocessor(
    text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text = text_preprocessor.fit_transform(df)
rnn = BasicRNN(
    vocab_size=len(text_preprocessor.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)

# WideDeep
model = WideDeep(deeptabular=tab_mlp, deeptext=rnn, head_hidden_dims=[32, 16])

# Train
trainer = Trainer(model, objective="binary")

trainer.fit(
    X_tab=X_tab,
    X_text=X_text,
    target=df["target"].values,
    n_epochs=1,
    batch_size=32,
)

4. Tabular and multiple text columns that are passed directly to WideDeep

from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep
from pytorch_widedeep.training import Trainer


# Tabular
tab_preprocessor = TabPreprocessor(
    embed_cols=["city", "name"], continuous_cols=["age", "height"]
)
X_tab = tab_preprocessor.fit_transform(df)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[64, 32],
)

# Text
text_preprocessor_1 = TextPreprocessor(
    text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text_1 = text_preprocessor_1.fit_transform(df)
text_preprocessor_2 = TextPreprocessor(
    text_col="other_sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text_2 = text_preprocessor_2.fit_transform(df)
rnn_1 = BasicRNN(
    vocab_size=len(text_preprocessor_1.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)
rnn_2 = BasicRNN(
    vocab_size=len(text_preprocessor_2.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)

# WideDeep
model = WideDeep(deeptabular=tab_mlp, deeptext=[rnn_1, rnn_2])

# Train
trainer = Trainer(model, objective="binary")

trainer.fit(
    X_tab=X_tab,
    X_text=[X_text_1, X_text_2],
    target=df["target"].values,
    n_epochs=1,
    batch_size=32,
)

5. Tabular data and multiple text columns that are fused via a the library's ModelFuser class

from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep, ModelFuser
from pytorch_widedeep import Trainer

# Tabular
tab_preprocessor = TabPreprocessor(
    embed_cols=["city", "name"], continuous_cols=["age", "height"]
)
X_tab = tab_preprocessor.fit_transform(df)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[64, 32],
)

# Text
text_preprocessor_1 = TextPreprocessor(
    text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text_1 = text_preprocessor_1.fit_transform(df)
text_preprocessor_2 = TextPreprocessor(
    text_col="other_sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text_2 = text_preprocessor_2.fit_transform(df)

rnn_1 = BasicRNN(
    vocab_size=len(text_preprocessor_1.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)
rnn_2 = BasicRNN(
    vocab_size=len(text_preprocessor_2.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)

models_fuser = ModelFuser(models=[rnn_1, rnn_2], fusion_method="mult")

# WideDeep
model = WideDeep(deeptabular=tab_mlp, deeptext=models_fuser)

# Train
trainer = Trainer(model, objective="binary")

trainer.fit(
    X_tab=X_tab,
    X_text=[X_text_1, X_text_2],
    target=df["target"].values,
    n_epochs=1,
    batch_size=32,
)

6. Tabular and multiple text columns, with an image column. The text columns are fused via the library's ModelFuser and then all fused via the deephead paramenter in WideDeep which is a custom ModelFuser coded by the user

This is perhaps the less elegant solution as it involves a custom component by the user and slicing the 'incoming' tensor. In the future, we will include a TextAndImageModelFuser to make this process more straightforward. Still, is not really complicated and it is a good example of how to use custom components in pytorch-widedeep.

Note that the only requirement for the custom component is that it has a property called output_dim that returns the size of the last layer of activations. In other words, it does not need to inherit from BaseWDModelComponent. This base class simply checks the existence of such property and avoids some typing errors internally.

import torch

from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor, ImagePreprocessor
from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep, ModelFuser, Vision
from pytorch_widedeep.models._base_wd_model_component import BaseWDModelComponent
from pytorch_widedeep import Trainer

# Tabular
tab_preprocessor = TabPreprocessor(
    embed_cols=["city", "name"], continuous_cols=["age", "height"]
)
X_tab = tab_preprocessor.fit_transform(df)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[16, 8],
)

# Text
text_preprocessor_1 = TextPreprocessor(
    text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text_1 = text_preprocessor_1.fit_transform(df)
text_preprocessor_2 = TextPreprocessor(
    text_col="other_sentence", maxlen=20, max_vocab=100, n_cpus=1
)
X_text_2 = text_preprocessor_2.fit_transform(df)
rnn_1 = BasicRNN(
    vocab_size=len(text_preprocessor_1.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)
rnn_2 = BasicRNN(
    vocab_size=len(text_preprocessor_2.vocab.itos),
    embed_dim=16,
    hidden_dim=8,
    n_layers=1,
)
models_fuser = ModelFuser(
    models=[rnn_1, rnn_2],
    fusion_method="mult",
)

# Image
image_preprocessor = ImagePreprocessor(img_col="image_name", img_path="images")
X_img = image_preprocessor.fit_transform(df)
vision = Vision(pretrained_model_setup="resnet18", head_hidden_dims=[16, 8])

# deephead (custom model fuser)
class MyModelFuser(BaseWDModelComponent):
    """
    Simply a Linear + Relu sequence on top of the text + images followed by a
    Linear -> Relu -> Linear for the concatenation of tabular slice of the
    tensor and the output of the text and image sequential model
    """
    def __init__(
        self,
        tab_incoming_dim: int,
        text_incoming_dim: int,
        image_incoming_dim: int,
        output_units: int,
    ):

        super(MyModelFuser, self).__init__()

        self.tab_incoming_dim = tab_incoming_dim
        self.text_incoming_dim = text_incoming_dim
        self.image_incoming_dim = image_incoming_dim
        self.output_units = output_units
        self.text_and_image_fuser = torch.nn.Sequential(
            torch.nn.Linear(text_incoming_dim + image_incoming_dim, output_units),
            torch.nn.ReLU(),
        )
        self.out = torch.nn.Sequential(
            torch.nn.Linear(output_units + tab_incoming_dim, output_units * 4),
            torch.nn.ReLU(),
            torch.nn.Linear(output_units * 4, output_units),
        )

    def forward(self, X: torch.Tensor) -> torch.Tensor:
        tab_slice = slice(0, self.tab_incoming_dim)
        text_slice = slice(
            self.tab_incoming_dim, self.tab_incoming_dim + self.text_incoming_dim
        )
        image_slice = slice(
            self.tab_incoming_dim + self.text_incoming_dim,
            self.tab_incoming_dim + self.text_incoming_dim + self.image_incoming_dim,
        )
        X_tab = X[:, tab_slice]
        X_text = X[:, text_slice]
        X_img = X[:, image_slice]
        X_text_and_image = self.text_and_image_fuser(torch.cat([X_text, X_img], dim=1))
        return self.out(torch.cat([X_tab, X_text_and_image], dim=1))

    @property
    def output_dim(self):
        return self.output_units


deephead = MyModelFuser(
    tab_incoming_dim=tab_mlp.output_dim,
    text_incoming_dim=models_fuser.output_dim,
    image_incoming_dim=vision.output_dim,
    output_units=8,
)

# WideDeep
model = WideDeep(
    deeptabular=tab_mlp,
    deeptext=models_fuser,
    deepimage=vision,
    deephead=deephead,
)

# Train
trainer = Trainer(model, objective="binary")

trainer.fit(
    X_tab=X_tab,
    X_text=[X_text_1, X_text_2],
    X_img=X_img,
    target=df["target"].values,
    n_epochs=1,
    batch_size=32,
)

7. A two-tower model

This is a popular model in the context of recommendation systems. Let's say we have a tabular dataset formed my triples (user features, item features, target). We can create a two-tower model where the user and item features are passed through two separate models and then "fused" via a dot product.

import numpy as np
import pandas as pd

from pytorch_widedeep import Trainer
from pytorch_widedeep.preprocessing import TabPreprocessor
from pytorch_widedeep.models import TabMlp, WideDeep, ModelFuser

# Let's create the interaction dataset
# user_features dataframe
np.random.seed(42)
user_ids = np.arange(1, 101)
ages = np.random.randint(18, 60, size=100)
genders = np.random.choice(["male", "female"], size=100)
locations = np.random.choice(["city_a", "city_b", "city_c", "city_d"], size=100)
user_features = pd.DataFrame(
    {"id": user_ids, "age": ages, "gender": genders, "location": locations}
)

# item_features dataframe
item_ids = np.arange(1, 101)
prices = np.random.uniform(10, 500, size=100).round(2)
colors = np.random.choice(["red", "blue", "green", "black"], size=100)
categories = np.random.choice(["electronics", "clothing", "home", "toys"], size=100)

item_features = pd.DataFrame(
    {"id": item_ids, "price": prices, "color": colors, "category": categories}
)

# Interactions dataframe
interaction_user_ids = np.random.choice(user_ids, size=1000)
interaction_item_ids = np.random.choice(item_ids, size=1000)
purchased = np.random.choice([0, 1], size=1000, p=[0.7, 0.3])
interactions = pd.DataFrame(
    {
        "user_id": interaction_user_ids,
        "item_id": interaction_item_ids,
        "purchased": purchased,
    }
)
user_item_purchased = interactions.merge(
    user_features, left_on="user_id", right_on="id"
).merge(item_features, left_on="item_id", right_on="id")

# Users
tab_preprocessor_user = TabPreprocessor(
    cat_embed_cols=["gender", "location"],
    continuous_cols=["age"],
)
X_user = tab_preprocessor_user.fit_transform(user_item_purchased)
tab_mlp_user = TabMlp(
    column_idx=tab_preprocessor_user.column_idx,
    cat_embed_input=tab_preprocessor_user.cat_embed_input,
    continuous_cols=["age"],
    mlp_hidden_dims=[16, 8],
    mlp_dropout=[0.2, 0.2],
)

# Items
tab_preprocessor_item = TabPreprocessor(
    cat_embed_cols=["color", "category"],
    continuous_cols=["price"],
)
X_item = tab_preprocessor_item.fit_transform(user_item_purchased)
tab_mlp_item = TabMlp(
    column_idx=tab_preprocessor_item.column_idx,
    cat_embed_input=tab_preprocessor_item.cat_embed_input,
    continuous_cols=["price"],
    mlp_hidden_dims=[16, 8],
    mlp_dropout=[0.2, 0.2],
)

two_tower_model = ModelFuser([tab_mlp_user, tab_mlp_item], fusion_method="dot")

model = WideDeep(deeptabular=two_tower_model)

trainer = Trainer(model, objective="binary")

trainer.fit(
    X_tab=[X_user, X_item],
    target=interactions.purchased.values,
    n_epochs=1,
    batch_size=32,
)

8. Tabular with a multi-target loss

This one is "a bonus" to illustrate the use of multi-target losses, more than actually a different architecture.

from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor, ImagePreprocessor
from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep, ModelFuser, Vision
from pytorch_widedeep.losses_multitarget import MultiTargetClassificationLoss
from pytorch_widedeep.models._base_wd_model_component import BaseWDModelComponent
from pytorch_widedeep import Trainer

# let's add a second target to the dataframe
df["target2"] = [random.choice([0, 1]) for _ in range(100)]

# Tabular
tab_preprocessor = TabPreprocessor(
    embed_cols=["city", "name"], continuous_cols=["age", "height"]
)
X_tab = tab_preprocessor.fit_transform(df)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[64, 32],
)

# 'pred_dim=2' because we have two binary targets. For other types of targets,
#  please, see the documentation
model = WideDeep(deeptabular=tab_mlp, pred_dim=2).

loss = MultiTargetClassificationLoss(binary_config=[0, 1], reduction="mean")

# When a multi-target loss is used, 'custom_loss_function' must not be None.
# See the docs
trainer = Trainer(model, objective="multitarget", custom_loss_function=loss)

trainer.fit(
    X_tab=X_tab,
    target=df[["target", "target2"]].values,
    n_epochs=1,
    batch_size=32,
)

The deeptabular component

It is important to emphasize again that each individual component, wide, 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 functionalities inpytorch-widedeep would be the use of the deeptabular component on its own, i.e. what one might normally refer as Deep Learning for Tabular Data. Currently, pytorch-widedeep offers the following different models for that component:

0. Wide: a simple linear model where the nonlinearities are captured via cross-product transformations, as explained before.
1. TabMlp: a simple MLP that receives embeddings representing the categorical features, concatenated with the continuous features, which can also be embedded.
2. TabResnet: similar to the previous model but the embeddings are passed through a series of ResNet blocks built with dense layers.
3. TabNet: details on TabNet can be found in TabNet: Attentive Interpretable Tabular Learning

Two simpler attention based models that we call:

4. ContextAttentionMLP: MLP with at attention mechanism "on top" that is based on Hierarchical Attention Networks for Document Classification
5. SelfAttentionMLP: MLP with an attention mechanism that is a simplified version of a transformer block that we refer as "query-key self-attention".

The Tabformer family, i.e. Transformers for Tabular data:

6. TabTransformer: details on the TabTransformer can be found in TabTransformer: Tabular Data Modeling Using Contextual Embeddings.
7. SAINT: Details on SAINT can be found in SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.
8. FT-Transformer: details on the FT-Transformer can be found in Revisiting Deep Learning Models for Tabular Data.
9. 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
10. TabPerceiver: adaptation of the Perceiver for tabular data. Details on the Perceiver can be found in Perceiver: General Perception with Iterative Attention

And probabilistic DL models for tabular data based on Weight Uncertainty in Neural Networks:

11. BayesianWide: Probabilistic adaptation of the Wide model.
12. BayesianTabMlp: Probabilistic adaptation of the TabMlp model

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.

In addition, Self-Supervised pre-training can be used for all deeptabular models, with the exception of the TabPerceiver. Self-Supervised pre-training can be used via two methods or routines which we refer as: encoder-decoder method and constrastive-denoising method. Please, see the documentation and the examples for details on this functionality, and all other options in the library.

The rec module

This module was introduced as an extension to the existing components in the library, addressing questions and issues related to recommendation systems. While still under active development, it currently includes a select number of powerful recommendation models.

It's worth noting that this library already supported the implementation of various recommendation algorithms using existing components. For example, models like Wide and Deep, Two-Tower, or Neural Collaborative Filtering could be constructed using the library's core functionalities.

The recommendation algorithms in the rec module are:

1. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
2. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
3. (Deep) Field Aware Factorization Machine (FFM): a Deep Learning version of the algorithm presented in Field-aware Factorization Machines in a Real-world Online Advertising System
4. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
5. Deep Interest Network for Click-Through Rate Prediction
6. Deep and Cross Network for Ad Click Predictions
7. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
8. Towards Deeper, Lighter and Interpretable Click-through Rate Prediction
9. A basic Transformer-based model for recommendation where the problem is faced as a sequence.

See the examples for details on how to use these models.

Text and Images

For the text component, deeptext, the library offers the following models:

1. BasicRNN: a simple RNN 2. AttentiveRNN: a RNN with an attention mechanism based on the Hierarchical Attention Networks for DocumentClassification
2. StackedAttentiveRNN: a stack of AttentiveRNNs
3. HFModel: a wrapper around Hugging Face Transfomer-based models. At the moment only models from the families BERT, RoBERTa, DistilBERT, ALBERT and ELECTRA are supported. This is because this library is designed to address classification and regression tasks and these are the most 'popular' encoder-only models, which have proved to be those that work best for these tasks. If there is demand for other models, they will be included in the future.

For the image component, deepimage, the library supports models from the following families: 'resnet', 'shufflenet', 'resnext', 'wide_resnet', 'regnet', 'densenet', 'mobilenetv3', 'mobilenetv2', 'mnasnet', 'efficientnet' and 'squeezenet'. These are offered via torchvision and wrapped up in the Vision class.

Installation

Install using pip:

pip install pytorch-widedeep

Or install directly from github

pip install git+https://github.com/jrzaurin/pytorch-widedeep.git

Developer Install

# Clone the repository
git clone https://github.com/jrzaurin/pytorch-widedeep
cd pytorch-widedeep

# Install in dev mode
pip install -e .

Quick start

Here is an end-to-end example of a binary classification with the adult dataset using Wide and DeepDense and defaults settings.

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

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
from pytorch_widedeep.datasets import load_adult


df = load_adult(as_frame=True)
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)

# Define the 'column set up'
wide_cols = [
    "education",
    "relationship",
    "workclass",
    "occupation",
    "native-country",
    "gender",
]
crossed_cols = [("education", "occupation"), ("native-country", "occupation")]

cat_embed_cols = [
    "workclass",
    "education",
    "marital-status",
    "occupation",
    "relationship",
    "race",
    "gender",
    "capital-gain",
    "capital-loss",
    "native-country",
]
continuous_cols = ["age", "hours-per-week"]
target = "income_label"
target = df_train[target].values

# prepare the data
wide_preprocessor = WidePreprocessor(wide_cols=wide_cols, crossed_cols=crossed_cols)
X_wide = wide_preprocessor.fit_transform(df_train)

tab_preprocessor = TabPreprocessor(
    cat_embed_cols=cat_embed_cols, continuous_cols=continuous_cols  # type: ignore[arg-type]
)
X_tab = tab_preprocessor.fit_transform(df_train)

# build the model
wide = Wide(input_dim=np.unique(X_wide).shape[0], pred_dim=1)
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    continuous_cols=continuous_cols,
)
model = WideDeep(wide=wide, deeptabular=tab_mlp)

# train and validate
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,
)

# predict on test
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=tab_mlp)
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, batch_size=32)

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

How to Contribute

Check CONTRIBUTING page.

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 library, which in itself partially inspired by Keras.

The TextProcessor class in this library uses the fastai'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 (DL4CV) book by Adrian Rosebrock.

License

This work is dual-licensed under Apache 2.0 and MIT (or any later version). You can choose between one of them if you use this work.

SPDX-License-Identifier: Apache-2.0 AND MIT

Cite

BibTex

@article{Zaurin_pytorch-widedeep_A_flexible_2023,
author = {Zaurin, Javier Rodriguez and Mulinka, Pavol},
doi = {10.21105/joss.05027},
journal = {Journal of Open Source Software},
month = jun,
number = {86},
pages = {5027},
title = {{pytorch-widedeep: A flexible package for multimodal deep learning}},
url = {https://joss.theoj.org/papers/10.21105/joss.05027},
volume = {8},
year = {2023}
}

APA

Zaurin, J. R., & Mulinka, P. (2023). pytorch-widedeep: A flexible package for
multimodal deep learning. Journal of Open Source Software, 8(86), 5027.
https://doi.org/10.21105/joss.05027