Combine tabular data with text and images using Wide and Deep models in Pytorch
Project description
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 comparisson with LightGBM
: TabularDL vs LightGBM
Slack: if you want to contribute or just want to chat with us, join slack
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
just a few lines of code. For details on the main components of those
architectures please visit the
repo.
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
Binary classification with the adult
dataset
using Wide
and DeepDense
and defaults settings.
Building a wide (linear) and deep model with pytorch-widedeep
:
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
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)
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
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.
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