PyTorch Tabular aims to make Deep Learning with Tabular data easy and accessible to real-world cases and research alike. The core principles behind the design of the library are:

• Low Resistance Usability
• Easy Customization
• Scalable and Easier to Deploy

It has been built on the shoulders of giants like PyTorch(obviously), and PyTorch Lightning.

Installation

Although the installation includes PyTorch, the best and recommended way is to first install PyTorch from here, picking up the right CUDA version for your machine. (PyTorch Version >1.3)

Once, you have got Pytorch installed, just use:

 pip install pytorch_tabular[all]

to install the complete library with extra dependencies(Weights&Biases).

And :

 pip install pytorch_tabular

for the bare essentials.

The sources for pytorch_tabular can be downloaded from the Github repo.

You can either clone the public repository:

git clone git://github.com/manujosephv/pytorch_tabular

Once you have a copy of the source, you can install it with:

python setup.py install

Usage

from pytorch_tabular import TabularModel
from pytorch_tabular.models import CategoryEmbeddingModelConfig
from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig, ExperimentConfig

data_config = DataConfig(
    target=['target'], #target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented
    continuous_cols=num_col_names,
    categorical_cols=cat_col_names,
)
trainer_config = TrainerConfig(
    auto_lr_find=True, # Runs the LRFinder to automatically derive a learning rate
    batch_size=1024,
    max_epochs=100,
    gpus=1, #index of the GPU to use. 0, means CPU
)
optimizer_config = OptimizerConfig()

model_config = CategoryEmbeddingModelConfig(
    task="classification",
    layers="1024-512-512",  # Number of nodes in each layer
    activation="LeakyReLU", # Activation between each layers
    learning_rate = 1e-3
)

tabular_model = TabularModel(
    data_config=data_config,
    model_config=model_config,
    optimizer_config=optimizer_config,
    trainer_config=trainer_config,
)
tabular_model.fit(train=train, validation=val)
result = tabular_model.evaluate(test)
pred_df = tabular_model.predict(test)
tabular_model.save_model("examples/basic")
loaded_model = TabularModel.load_from_checkpoint("examples/basic")

References and Citations

[1] Sergei Popov, Stanislav Morozov, Artem Babenko. "Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data". arXiv:1909.06312 [cs.LG] (2019)

[2] Sercan O. Arik, Tomas Pfister;. "TabNet: Attentive Interpretable Tabular Learning". arXiv:1908.07442 (2019).