Multinomial Logistic Regression

This repository provides a Multinomial Logistic regression model (a.k.a MNL) for the classification problem of multiple classes.

The novelity of this model is that it is implemented with the deep learning framework 'Pytorch'.

A typical scenario to apply MNL model is to predict the choice of customer in a collection of alternatives, which is often referred as Customer Choice Modeling. As a clarification of terminology, the alternative might also be referred as 'option', and the collection of alternatives might be called as 'session'.

Organization

• MNL.py: this python module contains the implementation of Multinomial Logistic Regression model that is implemented with Pytorch.

• MNL_plus.py: this python module provides a number of auxiliary functions in complement with the MNL.py model. For instance, one can find a train function with the capabililty of early stopping on the predefined threshold of error delta.

• Mint.py: this is a minimized model that is intended for inference only, with on dependency on the Pytorch framework. Once one obtains a model with the MNL module, one could *export" the trained model to Mint and deploy it in the running time with minimal dependencies (panda + numpy).

Usage

One can inspire from the demo notebook on the usage of API. In general, one only needs to provides a dict of parameters for the training, e.g. loss function, optimizer, regularization etc.

As to the input data format, any data source that could be transformed into Python dataframe will do. There is only one requirement on the content of the data, i.e. each data record should contain a session_id attribute that group the records/options into a particular choice session, and a binary choice attribute that indicates whether the option is chosen (value 1) or not (value 0).

session_id	choice	feature_1	feature_2	feature_n
10001	0	0.331	0.587	...
10001	0	0.983	0.223	...
10001	1	0.732	0.945	...