xlogit 0.1.0

A Python package for GPU-accelerated estimation of mixed logit models.

============================================================================== xlogit: A Python package for GPU-accelerated estimation of mixed logit models.

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.. _Mixed Logit: https://xlogit.readthedocs.io/en/latest/api/mixed_logit.html .. _Multinomial Logit: https://xlogit.readthedocs.io/en/latest/api/multinomial_logit.html

Quick start

The following example uses xlogit to estimate a mixed logit model for choices of fishing modes. See the data here <https://github.com/arteagac/xlogit/blob/master/examples/data/fishing_long.csv>__ and more information about the data here <https://doi.org/10.1162/003465399767923827>__. The parameters are:

• X: 2-D array of input data (in long format) with choice situations as rows, and variables as columns
• y: 1-D array of choices (in long format)
• varnames: List of variable names that matches the number and order of the columns in X
• alts: 1-D array of alternative indexes or an alternatives list
• ids: 1-D array of the ids of the choice situations
• randvars: dictionary of variables and their mixing distributions ("n" normal, "ln" lognormal, "t" triangular, "u" uniform, "tn" truncated normal)

The current version of xlogit only supports input data in long format.

.. code-block:: python

# Read data from CSV file
import pandas as pd
df = pd.read_csv("examples/data/fishing_long.csv")

X = df[['price', 'catch']]
y = df['choice']

# Fit the model with xlogit
from xlogit import MixedLogit
model = MixedLogit()
model.fit(X, y,
          varnames=['price', 'catch'],
          ids=df['id'],
          alts=df['alt'],
          randvars={'price': 'n', 'catch': 'n'})
model.summary()

::

Estimation succesfully completed after 21 iterations.
------------------------------------------------------------------------
Coefficient           Estimate      Std.Err.         z-val         P>|z|
------------------------------------------------------------------------
price               -0.0274061     0.0022827   -12.0062499       2.2e-30 ***
catch                1.3345446     0.1735364     7.6902874      2.29e-13 ***
sd.price             0.0104608     0.0020466     5.1113049      1.93e-06 ***
sd.catch             1.5857201     0.3746104     4.2329844      0.000109 ***
------------------------------------------------------------------------
Significance:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Log-Likelihood= -1300.227
AIC= 2608.454
BIC= 2628.754
Estimation time= 0.7 seconds

For more examples of xlogit see this Jupyter Notebook in Google Colab <https://colab.research.google.com/github/arteagac/xlogit/blob/master/examples/mixed_logit_model.ipynb>__. Google Colab provides GPU processing for free, which will help you to significantly speed up your model estimation using xlogit.

Quick install

Install xlogit using pip as follows:

.. code-block:: bash

pip install xlogit

.. hint::

To enable GPU processing, you must install the CuPy Python library <https://docs.cupy.dev/en/stable/install.html>__. When xlogit detects that CuPy is properly installed, it switches to GPU processing without any additional setup.

For additional installation details check xlogit documentation <https://xlogit.readthedocs.io/en/latest/install.html>__.

No GPU? No problem

xlogit can also be used without a GPU. However, if you need to speed up your model estimation, there are several low cost and even free options to access cloud GPU resources. For instance:

• Google Colab <https://colab.research.google.com>_ offers free GPU resources for learning purposes with no setup required, as the service can be accessed using a web browser. Using xlogit in Google Colab is very easy as it runs out of the box without needing to install CUDA or CuPy, which are installed by default. For examples of xlogit running in Google Colab see this link <https://colab.research.google.com/github/arteagac/xlogit/blob/master/examples/mixed_logit_model.ipynb>_.
• The Google Cloud platform <https://cloud.google.com/compute/gpus-pricing>_ offers GPU processing starting at $0.45 USD per hour for a NVIDIA Tesla K80 GPU with 4,992 CUDA cores.
• Amazon Sagemaker <https://aws.amazon.com/ec2/instance-types/p2/>_ offers virtual machine instances with the same TESLA K80 GPU at less than $1 USD per hour.

Notes

The current version allows estimation of:

• Mixed Logit_ with several types of mixing distributions (normal, lognormal, triangular, uniform, and truncated normal)
• Mixed Logit_ with panel data
• Mixed Logit_ with unbalanced panel data
• Mixed Logit_ with Halton draws
• Multinomial Logit_ models
• Conditional logit <https://xlogit.readthedocs.io/en/latest/api/multinomial_logit.html>_ models
• Weighed regression for all of the logit-based models

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