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Introduction to Lightwood

Lightwood is a Pytorch based framework with two objectives:

  • Make it so simple that you can build predictive models with a line of code.
  • Make it so flexible that you can change and customize everything.

Lightwood was inspired on Keras+Ludwig but runs on Pytorch and gives you full control of what you can do.

Installing Lightwood

On Linux, OSX, Windows and all other operating systems

pip3 install lightwood

If this fails, please report the bug on github and try installing the current master branch:

git clone;
cd lightwood;
pip install --no-cache-dir -e .

Please note that, depending on your os and python setup, you might want to use pip instead of pip3, so please try the commands with pip if the ones above fail

You need python 3.6 or higher.

Note on MacOS, you need to install libomp:

brew install libomp

Quick example

Assume that you have a training file (sensor_data.csv) such as this one.

sensor1 sensor2 sensor3
1 -1 -1
0 1 0
-1 -1 1
1 0 0
0 1 0
-1 1 -1
0 0 0
-1 -1 1
1 0 0

And you would like to learn to predict the values of sensor3 given the readings in sensor1 and sensor2.


You can train a Predictor as follows:

from lightwood import Predictor
import pandas

sensor3_predictor = Predictor(output=['sensor3'])


You can now be given new readings from sensor1 and sensor2 predict what sensor3 will be.

prediction = sensor3_predictor.predict(when={'sensor1':1, 'sensor2':-1})

Of course, that example was just the tip of the iceberg, please read about the main concepts of lightwood, the API and then jump into examples.