Extensions for a Python package for GPU-accelerated estimation of mixed logit models.
.. removed travis / etc... # TODO
.. _Mixed Logit: https://xlogit.readthedocs.io/en/latest/api/mixed_logit.html .. _Multinomial Logit: https://xlogit.readthedocs.io/en/latest/api/multinomial_logit.html
.. Examples <https://xlogit.readthedocs.io/en/latest/examples.html>__ | Docs <https://xlogit.readthedocs.io/en/latest/index.html>__ | Installation <https://xlogit.readthedocs.io/en/latest/install.html>__ | API Reference <https://xlogit.readthedocs.io/en/latest/api/index.html>__ | Contributing <https://xlogit.readthedocs.io/en/latest/contributing.html>__ | Contact <https://xlogit.readthedocs.io/en/latest/index.html#contact>__
The following example uses the base searchlogit to estimate a mixed logit model for choices of electricity supplier (See the data here <https://github.com/arteagac/xlogit/blob/master/examples/data/electricity_long.csv>__). The parameters are:
X: 2-D array of input data (in long format) with choice situations as rows, and variables as columnsy: 1-D array of choices (in long format)varnames: List of variable names that matches the number and order of the columns in Xalts: 1-D array of alternative indexes or an alternatives listids: 1-D array of the ids of the choice situationspanels: 1-D array of ids for panel formationrandvars: dictionary of variables and their mixing distributions ("n" normal, "ln" lognormal, "t" triangular, "u" uniform, "tn" truncated normal)transvars: List of variables to apply the specified transformationtransformation: String specifying the transformation to be applied (default is the Box-Cox transformation)correlation: Either a list of variables names that are correlated or a boolean to set all randvars as correlatedThe current version of searchlogit only supports input data in long format.
.. code-block:: python
# Read data from CSV file
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv")
# reverse sign for randvars with a lognormal distribution
df['tod'] = -df['tod']
df['seas'] = -df['seas']
X = df[varnames]
y = df['choice']
# Fit the model with xlogit
from searchlogit import MixedLogit
model = MixedLogit()
model.fit(X, y,
varnames,
alts=df['alt'],
ids=df['chid'],
panels=df['id'],
randvars={'cl':'n','loc':'n','wk':'u','tod':'ln','seas':'n'},
correlation=['loc', 'wk', 'tod', 'seas'],
transvars=['cl'],
n_draws=600
)
model.summary()
::
Optimization terminated successfully.
Current function value: 3942.930393
Iterations: 66
Function evaluations: 77
Gradient evaluations: 77
Estimation time= 51.5 seconds
Frequencies of alternatives: observed choice
[0.22576177 0.26246537 0.23684211 0.26939058]
Frequencies of alternatives: predicted choice
[0.22930773 0.26222102 0.22989339 0.2730377 ]
---------------------------------------------------------------------------
Coefficient Estimate Std.Err. z-val P>|z|
---------------------------------------------------------------------------
pf -0.8844627763 0.0295175526 -29.9639603432 0 ***
loc 2.2458727521 0.1032222566 21.7576405168 0 ***
wk 0.2276055259 0.0685607922 3.3197621949 0.000901 ***
tod 2.1355682749 0.0376005793 56.7961535512 0 ***
seas 8.7966341596 0.2824599096 31.1429475855 0 ***
chol.loc.loc 1.3698552163 0.1301206261 10.5275793490 0 ***
chol.wk.loc 0.5137795414 0.0664560103 7.7311222681 1.07e-14 ***
chol.wk.wk -0.5269078200 0.0465699165 -11.3143389438 0 ***
chol.tod.loc -0.0092950782 0.0277084890 -0.3354595849 0.737
chol.tod.wk 0.0694307288 0.0316747405 2.1919904546 0.0284 *
chol.tod.tod 0.3998943075 0.0283441100 14.1085504981 0 ***
chol.seas.loc 0.2636579056 0.1466998262 1.7972611991 0.0723 .
chol.seas.wk -0.0387176619 0.1862974259 -0.2078271435 0.835
chol.seas.tod 1.0931383738 0.1523794076 7.1737933030 7.29e-13 ***
chol.seas.seas -2.9951794692 0.1818545992 -16.4701881742 0 ***
cl -0.1571373624 0.0234064621 -6.7134179193 1.9e-11 ***
sd.cl 0.2767984154 0.0345402217 8.0137996096 1.11e-15 ***
lambda.cl 1.1525365710 0.1287181869 1.1850428803 0.236
---------------------------------------------------------------------------
Significance: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Log-Likelihood= -3942.930
AIC= 7921.861
BIC= 7991.861
.. For more examples of searchlogit 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 resources for free, which will significantly speed up your model estimation using searchlogit.
Install searchlogit using pip as follows:
.. code-block:: bash
pip install searchlogit
.. hint::
To enable GPU processing, you must install the `CuPy Python library <https://docs.cupy.dev/en/stable/install.html>`__. When ``searchlogit`` detects that CuPy is properly installed, it switches to GPU processing without any additional setup. If you use Google Colab, CuPy is usually installed by default.
.. For additional installation details check xlogit installation instructions at: https://xlogit.readthedocs.io/en/latest/install.html
searchlogit 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 searchlogit 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.see this link <https://colab.research.google.com/github/arteagac/xlogit/blob/master/examples/mixed_logit_model.ipynb>_.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.searchlogit extends xlogit. As shown in the plots below, xlogit is significantly faster than existing estimation packages. Also, xlogit provides convenient scaling when the number of random draws increases. These results were obtained using a modest and low-cost NVIDIA GTX 1060 graphics card. More sophisticated graphics cards are expected to provide even faster estimation times. For additional details about this benchmark and for replication instructions check https://xlogit.readthedocs.io/en/latest/benchmark.html.
.. image:: https://raw.githubusercontent.com/arteagac/xlogit/master/examples/benchmark/results/time_benchmark_artificial.png :width: 300
.. image:: https://raw.githubusercontent.com/arteagac/xlogit/master/examples/benchmark/results/time_benchmark_apollo_biogeme.png :width: 300
The current version allows estimation of:
If you have any questions, ideas to improve searchlogit, or want to report a bug, just open a new issue in xlogit's GitHub repository <https://github.com/RyanJafefKelly/searchlogit/issues>__ .
searchlogitPlease cite searchlogit as follows:
Beeramoole, P. B., Alexander, P., Kelly, R., Arteaga, C.,. (2022). Searchlogit [Computer software]. https://pypi.org/project/searchlogit/
Or using BibTex as follows::
@misc{searchlogit,
author = {Beeramoole, P and Paz, A and Kelly, R and Arteaga, C},
title = "{Searchlogit [Computer software]},
url = {https://pypi.org/project/searchlogit/},
year = {2022}
}
.. .. |Travis| image:: https://travis-ci.com/arteagac/xlogit.svg?branch=master .. :target: https://travis-ci.com/arteagac/xlogit
.. .. |Docs| image:: https://readthedocs.org/projects/xlogit/badge/?version=latest .. :target: https://xlogit.readthedocs.io/en/latest/?badge=latest .. :alt: Documentation Status
.. .. |Coverage| image:: https://coveralls.io/repos/github/arteagac/xlogit/badge.svg?branch=master .. :target: https://coveralls.io/github/arteagac/xlogit?branch=master
.. .. |PyPi| image:: https://badge.fury.io/py/xlogit.svg .. :target: https://badge.fury.io/py/xlogit
.. .. |License| image:: https://img.shields.io/github/license/arteagac/xlogit .. :target: https://github.com/arteagac/xlogit/blob/master/LICENSE
FAQs
Extensions for a Python package for GPU-accelerated estimation of mixed logit models.
We found that searchlogit demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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