Package: xlogit
Examples|Docs|Installation|API Reference|Contributing|ContactQuick startThe following example usesxlogitto estimate a mixed logit model for choices of electricity supplier (See the data here). 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 inXalts:  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)The current version ofxlogitonly supports input data in long format.# Read data from CSV fileimportpandasaspddf=pd.read_csv("examples/data/electricity_long.csv")# Fit the model with xlogitfromxlogitimportMixedLogitvarnames=['pf','cl','loc','wk','tod','seas']model=MixedLogit()model.fit(X=df[varnames],y=df['choice'],varnames=varnames,ids=df['chid'],panels=df['id'],alts=df['alt'],n_draws=600,randvars={'pf':'n','cl':'n','loc':'n','wk':'n','tod':'n','seas':'n'})model.summary()GPU processing enabled.
Optimization terminated successfully.
         Current function value: 3888.413414
         Iterations: 46
         Function evaluations: 51
         Gradient evaluations: 51
Estimation time= 2.6 seconds
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Coefficient         Estimate      Std.Err.         z-val         P>|z|
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pf                -0.9996286     0.0331488   -30.1557541     9.98e-100 ***
cl                -0.2355334     0.0220401   -10.6865870      1.97e-22 ***
loc                2.2307891     0.1164263    19.1605300      5.64e-56 ***
wk                 1.6251657     0.0918755    17.6887855      6.85e-50 ***
tod               -9.6067367     0.3112721   -30.8628296     2.36e-102 ***
seas              -9.7892800     0.2913063   -33.6047603     2.81e-112 ***
sd.pf              0.2357813     0.0181892    12.9627201      7.25e-31 ***
sd.cl              0.4025377     0.0220183    18.2819903      2.43e-52 ***
sd.loc             1.9262893     0.1187850    16.2166103      7.67e-44 ***
sd.wk             -1.2192931     0.0944581   -12.9083017      1.17e-30 ***
sd.tod             2.3354462     0.1741859    13.4077786      1.37e-32 ***
sd.seas           -1.4200913     0.2095869    -6.7756668       3.1e-10 ***
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Significance:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Log-Likelihood= -3888.413
AIC= 7800.827
BIC= 7847.493For more examples ofxlogitseethis Jupyter Notebook in Google Colab.Google Colab provides GPU resources for free, which will significantly speed up your model estimation usingxlogit.Quick installInstallxlogitusingpipas follows:pipinstallxlogitHintTo enable GPU processing, you must install theCuPy Python library.  Whenxlogitdetects 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.htmlNo GPU? No problemxlogitcan 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 Colaboffers free GPU resources 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 having to to install CUDA or CuPy, which are installed by default. For examples of xlogit running in Google Colabsee this link.Amazon Sagemaker Studio Laboffers Python runtime environments with free GPUs.Google Cloud platformoffers GPU processing at less than $1 USD per hour for NVIDIA Tesla K80 GPU with 4,992 CUDA cores.Amazon Sagemakeroffers virtual machine instances with the same TESLA K80 GPU at a similar price range of less than $1 USD per hour.BenchmarkAs shown in the plots below,xlogitis significantly faster than existing estimation packages. Also,xlogitprovides 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 checkhttps://xlogit.readthedocs.io/en/latest/benchmark.html.NotesThe current version allows estimation of:Mixed Logitwith several types of mixing distributions (normal, lognormal, triangular, uniform, and truncated normal)Mixed Logitwith panel dataMixed Logitwith unbalanced panel dataMixed Logitwith Halton drawsMultinomial LogitmodelsConditional logitmodelsHandling of unbalanced availability of choice alternatives for all of the supported modelsPost-estimation tools for prediction and specification testingInclusion of sample weights for all of the supported modelsContributorsThe following contributors have tremendously helped in the enhancement and expansion ofxlogit’s features.@crforsytheJohn Helveston (@jhelvy)ContactIf you have any questions, ideas to improvexlogit, or want to report a bug,chat with us on gitteror open anew issue in xlogit’s GitHub repository.CitingxlogitPlease citexlogitas follows:Arteaga, C., Park, J., Beeramoole, P. B., & Paz, A. (2022). xlogit: An open-source Python package for GPU-accelerated estimation of Mixed Logit models. Journal of Choice Modelling, 42, 100339.https://doi.org/10.1016/j.jocm.2021.100339Or using BibTex as follows:@article{xlogit,
    title = {xlogit: An open-source Python package for GPU-accelerated estimation of Mixed Logit models},
    author = {Cristian Arteaga and JeeWoong Park and Prithvi Bhat Beeramoole and Alexander Paz},
    journal = {Journal of Choice Modelling},
    volume = {42},
    pages = {100339},
    year = {2022},
    issn = {1755-5345},
    doi = {https://doi.org/10.1016/j.jocm.2021.100339},
}