Installation

pyStoNED supports Python 3.8 or later versions on Linux, macOS, and Windows. We can install the package using pip or GitHub.

PyPI

pip install pystoned

GitHub [1]

pip install -U git+https://github.com/ds2010/pyStoNED

Solver

All models supported by pyStoNED are either additive or multiplicative models, depending on the specification of the error term. From the perspective of optimization, the additive models are usually stated as QP problems with an exception of the CQR model, which is a linear programming (LP) problem, whereas all multiplicative models are NLP problems.

To solve the relevant QP or NLP problems, external off-the-shelf solvers are required. In our experience, CPLEX or MOSEK provide reliable and convenient platforms for solving the QP and LP problems. The NLP problem can be efficiently solved by MINOS or KNITRO. Note that the tailored algorithm for specific estimators could also be used to solve these models. However, tailored algorithms are not the most convenient choice for our general application purposes. With the help of Pyomo, all models supported by pyStoNED are computable by these off-the-shelf solvers.

  • Remote solver

pyStoNED interfaces with Pyomo to access the network-enabled optimization system (NEOS) server that freely provides a large number of academic solvers for solving the additive or multiplicative models remotely. In this case, the users do not need to install solvers and corresponding licenses on their own computer. Here we have a model estimated by remote solver

model.optimize('email@address')

Replace the argument email@address with your email address. [2] By default, the additive and multiplicative models will be solved by MOSEK and KNITRO, respectively. In addition, the users can freely choose their preferred solver, e.g., MINOS, using

model.optimize(email="email@address", solver='minos')

  • Local solver

Pyomo also provides an application programming interface for pyStoNED to import the local solvers. In the pyStoNED package, MOSEK is attached as the internal dependency to solve the additive model. However, the MOSEK academic license is required to be installed.

## MOSEK Optimizer: License installation ##

1. Request Personal Academic License

   https://www.mosek.com/license/request/personal-academic/

2. Save the license file under the user's home directory
   with a folder named "mosek". e.g.,

   Windows: c:\users\xxxx\mosek\mosek.lic
   Unix/OS X: /home/xxxx/mosek/mosek.lic

   Note: xxxx is the User ID on the computer

Otherwise, the following error message will display on the screen when calculating CNLS/StoNED or other variants.

MOSEK error 1008: License cannot be located. The default search path is ':/home/user/mosek/mosek.lic:'.

After that, we can use the following code to calculate the additive models.

model.optimize(OPT_LOCAL)

The parameter OPT_LOCAL is added in the function .optimize(...) to indicate that the model is computed locally. Similarly, one can use the parameter solver to select another solver when it is available.

Overall, the remote solver through the NEOS server is highly recommended for all light computing jobs, where, in general, the number of observations is no more than 500. The local solver for calculating the multiplicative model will be supported in pyStoNED when a free and stable NLP solver is available.