Description:

profit 0.6

Probabilistic Response Model Fitting with Interactive Tools
This is a collection of tools for studying parametric dependencies of
black-box simulation codes or experiments and construction of reduced
order response models over input parameter space.
proFit can be fed with a number of data points consisting of different
input parameter combinations and the resulting output of the simulation under
investigation. It then fits a response-surface through the point cloud
using Gaussian process regression (GPR) models.
This probabilistic response model allows to predict ("interpolate") the output
at yet unexplored parameter combinations including uncertainty estimates.
It can also tell you where to put more training points to gain maximum new
information (experimental design) and automatically generate and start
new simulation runs locally or on a cluster. Results can be explored and checked
visually in a web frontend.
Telling proFit how to interact with your existing simulations is easy
and requires no changes in your existing code. Current functionality covers
starting simulations locally or on a cluster via Slurm, subsequent
surrogate modelling using GPy,
scikit-learn,
as well as an active learning algorithm to iteratively sample at interesting
points and a Markov-Chain-Monte-Carlo (MCMC) algorithm. The web frontend to interactively explore the point cloud
and surrogate is based on plotly/dash.
Features

Compute evaluation points (e.g. from a random distribution) to run simulation
Template replacement and automatic generation of run directories
Starting parallel runs locally or on the cluster (SLURM)
Collection of result output and postprocessing
Response-model fitting using Gaussian Process Regression and Linear Regression
Active learning to reduce number of samples needed
MCMC to find a posterior parameter distribution (similar to active learning)
Graphical user interface to explore the results

Installation
Currently, the code is under heavy development, so it should be cloned
from GitHub via Git and pulled regularly.
Requirements
sudo apt install python3-dev build-essential

To enable compilation of the fortran modules the following is needed:
sudo apt install gfortran

Dependencies

numpy, scipy, matplotlib, sympy, pandas
ChaosPy
GPy
scikit-learn
h5py
plotly/dash - for the UI
ZeroMQ - for messaging
sphinx - for documentation, only needed when docs is specified
torch, GPyTorch - only needed when gpu is specified

All dependencies are configured in setup.cfg and should be installed automatically when using pip.
Automatic tests use pytest.
Windows 10
To install proFit under Windows 10 we recommend using Windows Subsystem
for Linux (WSL2) with the Ubuntu 20.04 LTS distribution (install guide).
After the installation of WSL2 execute the following steps in your Linux terminal (when asked press y to continue):
Make sure you have the right version of Python installed and the basic developer toolset available
sudo apt update
sudo apt install python3 python3-pip python3-dev build-essential

To install proFit from Git (see below), make sure that the project is located in the Linux file system
not the Windows system.
To configure the Python interpreter available in your Linux distribution in pycharm
(tested with professional edition) follow this guide.
Installation from PyPI
To install the latest stable version of proFit, use
pip install profit

For the latest pre-release, use
pip install --pre profit

Installation from Git
To install proFit for the current user (--user) in development-mode (-e) use:
git clone https://github.com/redmod-team/profit.git
cd profit
pip install -e . --user

Fortran
Certain surrogates require a compiled Fortran backend. To enable compilation of the fortran modules during install:
USE_FORTRAN=1 pip install .

Troubleshooting installation problems


Make sure you have all the requirements mentioned above installed.


If pip is not recognized try the following:


python3 -m pip install -e . --user


If pip warns you about PATH or proFit is not found close and reopen the terminal
and type profit --help to check if the installation was successful.

Documentation using Sphinx
Install requirements for building the documentation using sphinx
pip install .[docs]

Additionally pandoc is required on a system level:
sudo apt install pandoc

HowTo
Examples for different model codes are available under examples/:

fit: Simple fit via python interface.
mockup: Simple model called by console command based on template directory.

Also, the integration tests under tests/integration_tests/ may be informative examples:

active_learning:

1D: One dimensional mockup with active learning
2D: Two dimensional mockup with active learning
Log: Active learning with logarithmic search space
MCMC: Markov-Chain-Monte-Carlo application to mockup experimental data


mockup:

1D
2D
Custom postprocessor: Instead of the prebuilt postprocessor, a user-built class is used.
Custom worker: A user-built worker function is used.
Independent: Output with an independent (linear) variable additional to input parameters: f(t; u, v).
KarhunenLoeve: Multi output surrogate model with Karhunen-Loeve encoder.
Multi output: Multi output surrogate with two different output variables.



Steps


Create and enter a directory (e.g. study) containing profit.yaml for your run.
If your code is based on text configuration files for each run, copy the according directory to template and
replace values of parameters to be varied within UQ/surrogate models by placeholders {param}.


Running the simulations:
profit run

to start simulations at all the points. Per default the generated input variables are written to input.txt and the
output data is collected in output.txt.
For each run of the simulation, proFit creates a run directory, fills the templates with the generated input data and
collects the results. Each step can be customized with the
configuration file.


To fit the model:
profit fit

Customization can be done with profit.yaml again.


Explore data graphically:
profit ui

starts a Dash-based browser UI


The figure below gives a graphical representation of the typical profit workflow described above.
The boxes in red describe user actions while the boxes in blue are conducted by profit.

Cluster
proFit supports scheduling the runs on a cluster using slurm. This is done entirely via the configuration files and
the usage doesn't change.
profit ui starts a dash server and it is possible to remotely connect to it (e.g. via ssh port forwarding)
User-supplied files


a configuration file: (default: profit.yaml)

Add parameters and their distributions via variables
Set paths and filenames
Configure the run backend (how to interact with the simulation)
Configure the fit / surrogate model



the template directory

containing everything a simulation run needs (scripts, links to executables, input files, etc)
input files use a template format where {variable_name} is substituted with the generated values



a custom Postprocessor (optional)

if the default postprocessors don't work with the simulation a custom one can be specified using the include parameter in the configuration.



Example directory structure:

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

Share

• Share on Facebook
• Share on Twitter