Installation Guide


Python 3: DT requires Python version 3.6 or 3.7.

Tensorflow >= 2.0.0: DT is based on TensorFlow. Please follow this tutorial to install TensorFlow for python3.

Install DeepTables

pip install deeptables

GPU Setup (Optional): If you have GPUs on your machine and want to use them to accelerate the training, you can use the following command.

pip install deeptables[gpu]

Verify the install:

python -c "from deeptables.utils.quicktest import test; test()”

Getting started: 5 lines to DT

Supported Tasks

DT can be use to solve classification and regression prediction problems on tabular data.

Simple Example

DT supports these tasks with extremely simple interface without dealing with data cleaning and feature engineering. You don’t even specify the task type, DT will automatically infer.

from deeptables.models.deeptable import DeepTable, ModelConfig
from deeptables.models.deepnets import DeepFM

dt = DeepTable(ModelConfig(nets=DeepFM))
dt.fit(X, y)
preds = dt.predict(X_test)


DT has several build-in datasets for the demos or testing which covered binary classification, multi-class classification and regression task. All datasets are accessed through deeptables.datasets.dsutils.


Associated Tasks: Binary Classification

Predict whether income exceeds $50K/yr based on census data. Also known as “Census Income” dataset.

from deeptables.datasets import dsutils
df = dsutils.load_adult()

See: http://archive.ics.uci.edu/ml/datasets/Adult

Glass Identification

Associated Tasks: Multi-class Classification

From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)

from deeptables.datasets import dsutils
df = dsutils.load_glass_uci()

See: http://archive.ics.uci.edu/ml/datasets/Glass+Identification

Boston house-prices

Associated Tasks: Regression

from deeptables.datasets import dsutils
df = dsutils.load_boston()

See: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html


See: Examples