Welcome to FitBenchmarking’s documentation!¶

FitBenchmarking¶

FitBenchmarking is an open source tool for comparing different minimizers/fitting frameworks. FitBenchmarking is cross platform and we support Windows, Linux and Mac OS. For questions, feature requests or any other inquiries, please open an issue on GitHub, or send us an e-mail at support@fitbenchmarking.com.

Installation Instructions: https://fitbenchmarking.readthedocs.io/en/latest/users/install_instructions/index.html
User Documentation & Example Usage: https://fitbenchmarking.readthedocs.io/en/latest/users/index.html
Community Guidelines: https://fitbenchmarking.readthedocs.io/en/latest/contributors/guidelines.html
Automated Tests: Run via GitHub Actions, https://github.com/fitbenchmarking/fitbenchmarking/actions, and tests are documented at https://fitbenchmarking.readthedocs.io/en/latest/users/tests.html

The package is the result of a collaboration between STFC’s Scientific Computing Department and ISIS Neutron and Muon Facility and the Diamond Light Source. We also would like to acknowledge support from:

EU SINE2020 WP-10, which received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 654000.
EPSRC Grant EP/M025179/1 Least Squares: Fit for the Future.
The Ada Lovelace Centre (ALC). ALC is an integrated, cross-disciplinary data intensive science centre, for better exploitation of research carried out at our large scale National Facilities including the Diamond Light Source (DLS), the ISIS Neutron and Muon Facility, the Central Laser Facility (CLF) and the Culham Centre for Fusion Energy (CCFE).

Table Of Contents¶

FitBenchmarking Concept Documentation¶

Here we outline why we built the fitbenchmarking software, and how the software benchmarks minimizers with the goal of hightlighting the best tool for different types of data.

Why is FitBenchmarking important?¶

Fitting a mathematical model to data is a fundamental task across all scientific disciplines. (At least) three groups of people have an interest in fitting software:

Scientists, who want to know what is the best algorithm for fitting their model to data they might encounter, on their specific hardware;
Scientific software developers, who want to know what is the state-of-the-art in fitting algorithms and implementations, what they should recommend as their default solver, and if they should implement a new method in their software; and
Mathematicians and numerical software developers, who want to understand the types of problems on which current algorithms do not perform well, and to have a route to expose newly developed methods to users.

Representatives of each of these communities have got together to build FitBenchmarking. We hope this tool will help foster fruitful interactions and collaborations across the disciplines.

Example workflow¶

The black crosses on the plot below are data obtained from an experiment at the VESUVIO beamline at ISIS Neutron and Muon source:

VESUVIO experiment data

The scientist needs to interpret this data, and will typically use a data analysis package to help with this. Such packages are written by specialist scientific software developers, who are experts in analysing the kind of data produced by a given experiment; examples include Mantid, SasView, and Horace.

These packages include mathematical models, which depend on parameters, that can describe the data. We need to find values for the parameters in these models which best fit the data – for more background, see this Wikipedia article. The usual way this is done is by finding parameters that minimize the (weighted) squares of the error in the data, or $\chi^2$ value. This is equivalent to formulating a nonlinear least-squares problem; specifically, given $n$ data points $(x_i, y_i)$ (the crosses in the figure above), together with estimates of the errors on the values of $y_i$ , $\sigma_i$ , we solve

${\boldsymbol{\beta}}^* = \arg \min_{{\boldsymbol{\beta}}} \underbrace{\sum_i \left( \frac{y_i - f({\boldsymbol{\beta}};x_i)}{\sigma_i} \right)^2}_{\chi^2({\boldsymbol{\beta}})},\label{eq:chi2}$

where $f({\boldsymbol{\beta}};x)$ is the model we’re trying to fit, and $\boldsymbol{\beta}$ are the parameters we’re trying to find.

Usually the scientist will supply a starting guess, ${\boldsymbol{\beta}}_0$ (the pink curve in the graph above), which describes where they think the solution might be. She then has to choose which algorithm to use to fit the curve from the selection available in the analysis software. Different algorithms may be more or less suited to a problem, depending on factors such as the architecture of the machine, the availability of first and second derivatives, the amount of data, the type of model used, etc.

Below we show the data overlayed by a blue curve, which is a model fitted using the implementation of the Levenberg-Marquardt algorithm from the GNU Scientific Library (lmsder). The algorithm claims to have found a local minimum with a Chi-squared error of 0.4771 in 1.9 seconds.

GSL’s lmsder (Levenberg-Marquardt) algorithm on the data

We also solved the nonlinear least squares problem using GSL’s implementation of a Nedler-Mead simplex algorithm (nmsimplex2), which again claimed to solve the problem, this time in a faster 1.5 seconds. However, this time the Chi-squared error was 0.8505, and we plot the curve obtained in green below. The previous curve is in dotted-blue, for comparison.

VESUVIO experiment data: :code:`nmsimplex2`

GSL’s nmsimplex2 (Nelder-Mead Simplex) algorithm on the data

By eye it is clear that the solution given by lmsder is better. As the volume of data increases, and we do more and more data analysis algorithmically, it is increasingly important that we have the best algorithm without needing to check it by eye.

FitBenchmarking will help the scientist make an informed choice by comparing runtime and accuracy of all available minimizers, on their specific hardware, on problems from their science area, which will ensure they are using the most appropriate minimizer.

FitBenchmarking will help the scientific software developer ensure that the most robust and quickest algorithms for the type of data analysis they support are available in their software.

FitBenchmarking will help mathematicians see what the state of the art is, and what kinds of data are problematic. It will give them access to real data, and will give a route for novel methods to quickly make it into production.

A workflow as described above plays a crucial role in the processing and analysis of data at large research facilities in tasks as diverse as instrument calibration, refinement of structures, and data analysis methods specific to different scientific techniques. FitBenchmarking will ensure that, across all areas that utilise least-squares fitting, scientists can be confident they are using the best tool for the job.

We discuss the specific FitBenchmarking paradigm in the Section How does FitBenchmarking work?

How does FitBenchmarking work?¶

FitBenchmarking takes data and models from real world applications and data analysis packages. It fits the data to the models by casting them as a nonlinear least-squares problem. We fit the data using a range of data fitting and nonlinear optimization software, and present comparisons on the accuracy and timings.

The Benchmarking Paradigm¶

FitBenchmarking can compare against any of the supported minmizers listed in Minimizer Options. We’ve also made it straightforward to add new software by following the instructions in Adding Fitting Software – the software just needs to be callable from Python.

Once you have chosen which minimizers you want to compare for a given problem, running FitBenchmarking will give you a comparison to indicate the minimizer that performs best.

There are a number of options that you can pick to customize what your tests are comparing, or how they are run. A full list of these options, and how to select them, is given in the section FitBenchmarking Options.

FitBenchmarking creates tables, as given in the section FitBenchmarking Output, which show a comparison between the different minimizers available. An example of a table is:

This is the result of FitBenchmarking for a selection of software/minimizers and different problem definition types supported in FitBenchmarking. Both the raw chi squared values, and the values normalised with respect to the best minimizer per problem, are given. The problem names link to html pages that display plots of the data and the fit that was performed, together with initial and final values of the parameters. Here is an example of the final plot fit:

Performance Profile¶

With each test FitBenchmarking also produces a Dolan-Moré performance profile:

The solvers appearing in the top left corner may be considered the best performing on this test set. See Dolan and Moré (2001) for more information.

FitBenchmarking User Documentation¶

In these pages we describe how to install, run, and set options to use the FitBenchmarking software.

Installation¶

Fitbenchmarking will install all packages that can be installed through pip. This includes the minimizers from SciPy, bumps, DFO-GN/LS, Minuit, and also the SASModels package.

To enable Fitbenchmarking with the other supported software, you must install them with the external software instructions.

Installing FitBenchmarking and default fitting packages¶

We recommend using for running/installing Fitbenchmarking. The easiest way to install FitBenchmarking is by using the Python package manager, pip.

Installing via pip¶

FitBenchmarking can be installed via the command line by entering:

python -m pip install fitbenchmarking[bumps,DFO,minuit,SAS]

This will install the latest stable version of FitBenchmarking. For all available versions please visit the FitBenchamarking PyPI project. FitBenchmarking can also use additional software that cannot be installed using pip; please see Installing External Software for details.

Note

This install will include additional optional packages – see Extra dependencies. Any of the dependencies in the square brackets can be omitted, if required, and that package will not be available for Benchmarking, or will use the version of the package already on your system, if appropriate.

Installing from source¶

You may instead wish to install from source, e.g., to get the very latest version of the code that is still in development.

Download this repository or clone it using git: git clone https://github.com/fitbenchmarking/fitbenchmarking.git
Open up a terminal (command prompt) and go into the fitbenchmarking directory.
Once you are in the right directory, we recommend that you type
```
python -m pip install .[bumps,DFO,minuit,SAS]
```
Additional software that cannot be installed via pip can also be used with FitBenchmarking. Follow the instructions at Installing External Software.

Extra dependencies¶

In addition to the external packages described at Installing External Software, some optional dependencies can be installed directly by FitBenchmarking. These are installed by issuing the commands

python -m pip install fitbenchmarking['option-1','option-2',...]

or

python -m pip install .['option-1','option-2',...]

where valid strings option-x are:

bumps– installs the Bumps fitting package.
DFO – installs the DFO-LS and DFO-GN fitting packages.
minuit – installs the Minuit fitting package.
SAS – installs the Sasmodels fitting package.

Installing External Software¶

Fitbenchmarking will install all packages that are available through pip.

To enable Fitbenchmarking with the other supported software, they need to be installed and available on your machine. We give pointers outlining how to do this below, and you can find install scripts for Ubuntu 18.04 in the directory /build/<software>/

CUTEst¶

CUTEst is used to parse SIF files in FitBenchmarking, and is called via the PyCUTEst interface.

Currently this is only supported for Mac and Linux, and can be installed by following the instructions outlined on the pycutest documentation

Please note that the PYCUTEST_CACHE environment variable must be set, and it must be in the PYTHONPATH.

GSL¶

GSL is used as a fitting software in FitBenchmarking, and is called via the pyGSL interface.

Install instructions can be found at the pyGSL docs. This package is also installable via pip, provided GSL is available on your system; see our example build script in build/gsl.

Note: pyGSL may not be installable with the latest versions of pip. We have found that 20.0.2 works for our tests.

Mantid¶

Mantid is used both as fitting software, and to parse data files.

Instructions on how to install Mantid for range of systems are available at https://download.mantidproject.org/.

RALFit¶

RALFit is availble to use as fitting software.

Instructions on how to build the python interface are at https://ralfit.readthedocs.io/projects/Python/en/latest/install.html

Running FitBenchmarking¶

Once installed, issuing the command

fitbenchmarking

will run the NIST test example on SciPy minmizers.

Running alternative problems¶

Other problems written in a supported file format can be analyzed with FitBenchmarking by passing the path using the --problem-sets (or -p) option. Some example problems can be downloaded from Benchmark problems, and they can also be found in the fitbenchmarking/examples directory of the code.

For example, to run the NIST low difficulty set from the base directory of the source, type into the terminal:

fitbenchmarking -p examples/benchmark_problems/NIST/low_difficulty

Changing the options¶

An options file can also be passed with the -o argument. For example, the template file can be run by issuing the command

fitbenchmarking -o examples/options_template.ini \
-p examples/benchmark_problems/NIST/low_difficulty

Details about how the options file must be formatted are given in FitBenchmarking Options.

Running fitbenchmarking -h will give more guidance about available commands, including examples of how to run multiple problem sets.

FitBenchmarking Output¶

FitBenchmarking produces tables as outputs. The links below give descriptions of these tables.

Comparison Table¶

class fitbenchmarking.results_processing.compare_table.CompareTable(results, best_results, options, group_dir, pp_locations, table_name): The combined results show the accuracy in the first line of the cell and the runtime on the second line of the cell.

Accuracy Table¶

class fitbenchmarking.results_processing.acc_table.AccTable(results, best_results, options, group_dir, pp_locations, table_name)

The accuracy results are calculated from the final chi squared value:

$\min_p \sum_{i=1}^n \left( \frac{y_i - f(x_i, p)}{e_i} \right)^2$

where $n$ data points $(x_i,y_i)$ , associated errors $e_i$ , and a model function $f(x,p)$ .

Runtime Table¶

class fitbenchmarking.results_processing.runtime_table.RuntimeTable(results, best_results, options, group_dir, pp_locations, table_name): The timing results are calculated from an average using the timeit module in python. The number of runtimes can be set in FitBenchmarking Options.

Local Minimizer Table¶

class fitbenchmarking.results_processing.local_min_table.LocalMinTable(results, best_results, options, group_dir, pp_locations, table_name)

The local min results shows a True or False value together with $\frac{|| J^T r||}{||r||}$ . The True or False indicates whether the software finds a minimum with respect to the following criteria:

$||r|| \leq$ RES_TOL,
$|| J^T r|| \leq$ GRAD_TOL,
$\frac{|| J^T r||}{||r||} \leq$ GRAD_TOL,

where $J$ and $r$ are the Jacobian and residual of $f(x, p)$ , respectively. The tolerances can be found in the results object.

Table formats¶

The tables for accuracy, runtime and compare have three display modes:

{ 'abs': 'Absolute values are displayed in the table.',
  'both': 'Absolute and relative values are displayed in the table '
          'in the format ``abs (rel)``',
  'rel': 'Relative values are displayed in the table.'}

This can be set in the option file using the Comparison Mode option.

The Local Minimizer Table table is formatted differently, and doesn’t use this convention.

Benchmark problems¶

To help choose between the different minimizers, we have made some curated problems available to use with FitBenchmarking. It is also straightforward to add custom data sets to the benchmark, if that is more appropriate; see Problem Definition Files for specifics of how to add additional problems in a supported file format.

Downloads

You can download a folder containing all examples here: .zip or .tar.gz

Individual problem sets are also available to download below.

We supply some standard nonlinear least-squares test problems in the form of the NIST nonlinear regression set and the relevant problems from the CUTEst problem set, together with some real-world data sets that have been extracted from Mantid and SASView usage examples and system tests. We’ve made it possible to extend this list by following the steps in Adding Fitting Problem Definition Types.

Each of the test problems contain:

a data set consisting of points $(x_i, y_i)$ (with optional errors on $y_i$ , $\sigma_i$ );
a definition of the fitting function, $f({\boldsymbol{\beta}};x)$ ; and
(at least) one set of initial values for the function parameters ${\boldsymbol{\beta}}_0$ .

If a problem doesn’t have observational errors (e.g., the NIST problem set), then FitBenchmarking can approximate errors by taking $\sigma_i = \sqrt{y_i}$ . Alternatively, there is an option to disregard errors and solve the unweighted nonlinear least-squares problem, setting $\sigma_i = 1.0$ irrespective of what has been passed in with the problem data.

As we work with scientists in other areas, we will extend the problem suite to encompass new categories. The FitBenchmarking framework has been designed to make it easy to integrate new problem sets, and any additional data added to the framework can be tested with any and all of the available fitting methods.

Currently FitBenchmarking ships with data from the following sources:

Powder Diffraction Data (SIF files)¶

Download .zip or .tar.gz

These problems (also found in the folder examples/benchmark_problems/DIAMOND_SIF) contain data from powder diffraction experiments. The data supplied comes from the I14 Hard X-Ray Nanoprobe beamline at the Diamond Light source, and has been supplied in the SIF format used by CUTEst.

These problems have either 66 or 99 unknown parameters, and fit around 5,000 data points.

Warning

The external packages CUTEst and pycutest must be installed to run this data set. See Installing External Software for details.

MultiFit Data (Mantid)¶

Download .zip or .tar.gz

These problems (also found in the folder examples/benchmark_problems/MultiFit) contain data for testing the MultFit functionality of Mantid. This contains a simple data set, on which two fits are done, and a calibration dataset from the MuSR spectrometer at ISIS, on which there are four fits available. See The MultiFit documentation for more details.

Warning

The external package Mantid must be installed to run this data set. See Installing External Software for details.

This will also only work using the Mantid Minimizers.

Muon Data (Mantid)¶

Download .zip or .tar.gz

These problems (also found in the folder examples/benchmark_problems/Muon) contain data from Muon spectrometers. The data supplied comes from the HiFi and EMU instruments at STFC’s ISIS Neutron and Muon source, and has been supplied in the format that Mantid uses to process the data.

These problems have between 5 and 13 unknown parameters, and fit around 1,000 data points.

Warning

The external package Mantid must be installed to run this data set. See Installing External Software for details.

NIST¶

Download .zip or .tar.gz

These problems (also found in the folder examples/benchmark_problems/NIST) contain data from the NIST Nonlinear Regression test set.

These problems are split into low, average and high difficulty. They have between 2 and 9 unknown parameters, and fit between 6 and 250 data points.

Neutron Data (Mantid)¶

Download .zip or .tar.gz

These problems (also found in the folder examples/benchmark_problems/Neutron) contain data from Neutron scattering experiments. The data supplied comes from the Engin-X, GEM, eVS, and WISH instruments at STFC’s ISIS Neutron and Muon source, and has been supplied in the format that Mantid uses to process the data.

The size of these problems differ massively. The Engin-X calibration problems find 7 unknown parameters, and fit to 56-67 data points. The Engin-X vanadium problems find 4 unknown parameters, and fit to around 14,168 data points. The eVS problems find 8 unknown parameters, and fit to 1,025 data points. The GEM problem finds 105 unknown parameters, and fits to 1,314 data points. The WISH problems find 5 unknown parameters, and fit to 512 data points.

Warning

The external package Mantid must be installed to run this data set. See Installing External Software for details.

Small Angle Scattering (SASView)¶

Download .zip or .tar.gz

These problems (also found in the folder examples/benchmark_problems/SAS_modelling/1D) are two data sets from small angle scattering experiments. These are from fitting data to a cylinder, and have been supplied in the format that SASView uses to process the data.

These have 6 unknown parameters, and fit to either 20 or 54 data points.

Warning

The external package sasmodels must be installed to run this data set. See Installing External Software for details.

CUTEst (SIF files)¶

Download .zip or .tar.gz

This directory (also found in the folder examples/benchmark_problems/SIF) contain SIF files encoding least squares problems from the CUTEst continuous optimization testing environment.

These are from a wide range of applications. They have between 2 and 9 unknown parameters, and for the most part fit between 6 and 250 data points, although the VESUVIO examples (from the VESUVIO instrument at ISIS) have 1,025 data points (with 8 unknown parameters).

Warning

The external packages CUTEst and pycutest must be installed to run this data set. See Installing External Software for details.

Simple tests¶

Download .zip or .tar.gz

This folder (also found in examples/benchmark_problems/simple_tests) contains a number of simple tests with known, and easy to obtain, answers. We recommend that this is used to test any new minimizers that are added, and also that any new parsers reimplement these data sets and models (if possible).

FitBenchmarking Options¶

The default behaviour of FitBenchmarking can be changed by supplying an options file. The default values of these options, and how to override them, are given in the pages below.

Fitting Options¶

Options that control the benchmarking process are set here.

Software (software)¶

Software is used to select the fitting software to benchmark, this should be a newline-separated list. Available options are:

bumps (default software)
dfo (default software)
gsl (external software – see Installing External Software)
mantid (external software – see Installing External Software)
minuit (default software)
ralfit (external software – see Installing External Software)
scipy (default software)
scipy_ls (default software)

Default are bumps, dfo, minuit, scipy, and scipy_ls

[FITTING]
software: bumps
          dfo
          minuit
          scipy
          scipy_ls

Warning

Software must be listed to be here to be run. Any minimizers set in Minimizer Options will not be run if the software is not also present in this list.

Number of minimizer runs (num_runs)¶

Sets the number of runs to average each fit over.

Default is 5

[FITTING]
num_runs: 5

Algorithm type (algorithm_type)¶

This is used to select what type of algorithm is used within a specific software. The options are:

all - all minimizers
ls - least-squares fitting algorithms
deriv_free - derivative free algorithms (these are algorithms that do not require an information about derivatives. For example, the Simplex method in Mantid does not require derivative information but lm-scipy-no-jac in scipy_ls does but the derivative is handle internally within the software package)
general - minimizers which solve a generic min f(x)

Default is all

[FITTING]
algorithm_type: all

Warning

Choosing an option other than all may deselect certain minimizers set in the options file

Use errors (use_errors)¶

This will switch between weighted and unweighted least squares. If use_errors=True, and no errors are supplied, then e[i] will be set to sqrt(abs(y[i])). Errors below 1.0e-8 will be clipped to that value.

Default is True (yes/no can also be used)

[FITTING]
use_errors: yes

Jacobian method (jac_method)¶

This sets the Jacobian used. Current Jacobian methods are:

SciPyFD - denotes the use of SciPy’s finite difference Jacobian approximations

Default is SciPyFD

[FITTING]
jac_method: SciPyFD

Numerical method (num_method)¶

Sets the numerical method used in conjunction with the Jacobian method. Currently scipy.optimize._numdiff.approx_derivative are the only methods implemented to calculate finite difference Jacobians. Scipy options are given as below:

2point - use the first order accuracy forward or backward difference.
3point - use central difference in interior points and the second order accuracy forward or backward difference near the boundary.
cs - use a complex-step finite difference scheme. This assumes that the user function is real-valued and can be analytically continued to the complex plane. Otherwise, produces bogus results.

Default is 2point

[FITTING]
num_method: 2point

Minimizer Options¶

This section is used to declare the minimizers to use for each fitting software.

Warning

Options set in this section will only have an effect if the related software is also set in Fitting Options (either explicitly, or as a default option).

Bumps (bumps)¶

Bumps is a set of data fitting (and Bayesian uncertainty analysis) routines. It came out of the University of Maryland and NIST as part of the DANSE (Distributed Data Analysis of Neutron Scattering Experiments) project.

FitBenchmarking currently supports the Bumps minimizers:

Nelder-Mead Simplex (amoeba)
Levenberg-Marquardt (lm)
Quasi-Newton BFGS (newton)
Differential Evolution (de)
MINPACK (mp) This is a translation of MINPACK to Python.

Links GitHub - bumps

The Bumps minimizers are set as follows:

[MINIMIZERS]
bumps: amoeba
       lm-bumps
       newton
       de
       mp

Warning

The additional dependency Bumps must be installed for this to be available; See Extra dependencies.

DFO (dfo)¶

There are two Derivative-Free Optimization packages, DFO-LS and DFO-GN. They are derivative free optimization solvers that were developed by Lindon Roberts at the University of Oxford, in conjunction with NAG. They are particularly well suited for solving noisy problems.

FitBenchmarking currently supports the DFO minimizers:

Derivative-Free Optimizer for Least Squares (dfols)
Derivative-Free Gauss-Newton Solver (dfogn)

Links GitHub - DFO-GN GitHub - DFO-LS

The DFO minimizers are set as follows:

[MINIMIZERS]
dfo: dfols
     dfogn

Warning

Additional dependencies DFO-GN and DFO-LS must be installed for these to be available; See Extra dependencies.

GSL (gsl)¶

The GNU Scientific Library is a numerical library that provides a wide range of mathematical routines. We call GSL using the pyGSL Python interface.

The GSL routines have a number of parameters that need to be chosen, often without default suggestions. We have taken the values as used by Mantid.

We provide implementations for the following packages in the multiminimize and multifit sections of the library:

Levenberg-Marquardt (unscaled) (lmder)
Levenberg-Marquardt (scaled) (lmsder)
Nelder-Mead Simplex Algorithm (nmsimplex)
Nelder-Mead Simplex Algorithm (version 2) (nmsimplex2)
Polak-Ribiere Conjugate Gradient Algorithm (conjugate_pr)
Fletcher-Reeves Conjugate-Gradient (conjugate_fr)
The vector quasi-Newton BFGS method (vector_bfgs)
The vector quasi-Newton BFGS method (version 2) (vector_bfgs2)
Steepest Descent (steepest_descent)

Links SourceForge PyGSL

The GSL minimizers are set as follows:

[MINIMIZERS]
gsl: lmsder
     lmder
     nmsimplex
     nmsimplex2
     conjugate_pr
     conjugate_fr
     vector_bfgs
     vector_bfgs2
     steepest_descent

Warning

The external packages GSL and pygsl must be installed to use these minimizers.

Mantid (mantid)¶

Mantid is a framework created to manipulate and analyze neutron scattering and muon spectroscopy data. It has support for a number of minimizers, most of which are from GSL.

BFGS (BFGS)
Conjugate gradient (Fletcher-Reeves) (Conjugate gradient (Fletcher-Reeves imp.))
Conjugate gradient (Polak-Ribiere) (Conjugate gradient (Polak-Ribiere imp.))
Damped GaussNewton (Damped GaussNewton)
Levenberg-Marquardt algorithm (Levenberg-Marquardt)
Levenberg-Marquardt MD (Levenberg-MarquardtMD) - An implementation of Levenberg-Marquardt intended for MD workspaces, where work is divided into chunks to achieve a greater efficiency for a large number of data points.
Simplex (simplex)
SteepestDescent (SteepestDescent)
Trust Region (Trust Region) - An implementation of one of the algorithms available in RALFit.

Links GitHub - Mantid Mantid’s Fitting Docs

The Mantid minimizers are set as follows:

[MINIMIZERS]
mantid: BFGS
        Conjugate gradient (Fletcher-Reeves imp.)
        Conjugate gradient (Polak-Ribiere imp.)
        Damped GaussNewton
        Levenberg-Marquardt
        Levenberg-MarquardtMD
        Simplex
        SteepestDescent
        Trust Region

Warning

The external package Mantid must be installed to use these minimizers.

Minuit (minuit)¶

CERN developed the Minuit package to find the minimum value of a multi-parameter function, and also to compute the uncertainties. We interface via the python interface iminuit with support for the 2.x series.

Minuit’s MIGRAD (minuit)

Links Github - iminuit

The Minuit minimizers are set as follows:

[MINIMIZERS]
minuit: minuit

Warning

The additional dependency Minuit must be installed for this to be available; See Extra dependencies.

RALFit (ralfit)¶

RALFit is a nonlinear least-squares solver, the development of which was funded by the EPSRC grant Least-Squares: Fit for the Future. RALFit is designed to be able to take advantage of higher order derivatives, although only first order derivatives are currently utilized in FitBenchmarking.

Gauss-Newton, trust region method (gn)
Hybrid Newton/Gauss-Newton, trust region method (hybrid)
Gauss-Newton, regularization (gn_reg)
Hybrid Newton/Gauss-Newton, regularization (hybrid_reg)

Links Github - RALFit. RALFit’s Documentation on: Gauss-Newton/Hybrid models, the trust region method and The regularization method

The RALFit minimizers are set as follows:

[MINIMIZERS]
ralfit: gn
        gn_reg
        hybrid
        hybrid_reg

Warning

The external package RALFit must be installed to use these minimizers.

SciPy (scipy)¶

SciPy is the standard python package for mathematical software. In particular, we use the minimize solver for general minimization problems from the optimization chapter the SciPy’s library. Currently we only use the algorithms that do not require Hessian information as inputs.

Nelder-Mead algorithm (Nelder-Mead)
Powell algorithm (Powell)
Conjugate gradient algorithm (CG)
BFGS algorithm (BFGS)
Newton-CG algorithm (Newton-CG)
L-BFGS-B algorithm (L-BFGS-B)
Truncated Newton (TNC) algorithm (TNC)
Sequential Least SQuares Programming (SLSQP)

Links Github - SciPy minimize

The SciPy minimizers are set as follows:

[MINIMIZERS]
scipy: Nelder-Mead
       Powell
       CG
       BFGS
       Newton-CG
       L-BFGS-B
       TNC
       SLSQP

SciPy LS (scipy_ls)¶

SciPy is the standard python package for mathematical software. In particular, we use the least_squares solver for Least-Squares minimization problems from the optimization chapter the SciPy’s library.

Levenberg-Marquardt with supplied Jacobian (lm-scipy) - a wrapper around MINPACK
Levenberg-Marquardt with no Jacobian passed (lm-scipy-no-jac) - as above, but using MINPACK’s approximate Jacobian
The Trust Region Reflective algorithm (trf)
A dogleg algorithm with rectangular trust regions (dogbox)

Links Github - SciPy least_squares

The SciPy least squares minimizers are set as follows:

[MINIMIZERS]
scipy_ls: lm-scipy-no-jac
          lm-scipy
          trf
          dogbox

Plotting Options¶

The plotting section contains options to control how results are presented.

Make plots (make_plots)¶

This allows the user to decide whether or not to create plots during runtime. Toggling this to False will be much faster on large data sets.

Default is True (yes/no can also be used)

[PLOTTING]
make_plots: yes

Colour scale (colour_scale)¶

Lists thresholds for each colour in the html table. In the example below, this means that values less than 1.1 will have the top ranking (brightest) and values over 3 will show as the worst ranking (deep red).

Default thresholds are 1.1, 1.33, 1.75, 3, and inf

[PLOTTING]
colour_scale: 1.1, #fef0d9
              1.33, #fdcc8a
              1.75, #fc8d59
              3, #e34a33
              inf, #b30000

Comparison mode (comparison_mode)¶

This selects the mode for displaying values in the resulting table options are abs, rel, both:

abs indicates that the absolute values should be displayed
rel indicates that the values should all be relative to the best result
both will show data in the form “abs (rel)”

Default is both

[PLOTTING]
comparison_mode: both

Table type (table_type)¶

This selects the types of tables to be produced in FitBenchmarking. Options are:

acc indicates that the resulting table should contain the chi squared values for each of the minimizers.
runtime indicates that the resulting table should contain the runtime values for each of the minimizers.
compare indicates that the resulting table should contain both the chi squared value and runtime values for each of the minimizers. The tables produced have the chi squared values on the top line of the cell and the runtime on the bottom line of the cell.
local_min indicates that the resulting table should return true if a local minimum was found, or false otherwise. The value of $\frac{|| J^T r||}{||r||}$ for those parameters is also returned. The output looks like {bool} (norm_value), and the colouring is red for false and cream for true.

Default is acc, runtime, compare, and local_min.

[PLOTTING]
table_type: acc
            runtime
            compare
            local_min

Results directory (results_dir)¶

This is used to select where the output should be saved

Default is fitbenchmarking_results

[PLOTTING]
results_dir: fitbenchmarking_results

Logging Options¶

The logging section contains options to control how fitbenchmarking logs information.

Logging file name (file_name)¶

This specifies the file path to write the logs to.

Default is fitbenchmarking.log

[LOGGING]
file_name: fitbenchmarking.log

Logging append (append)¶

This specifies whether to log in append mode or not. If append mode is active, the log file will be extended with each subsequent run, otherwise the log will be cleared after each run.

Default is False (yes/no can also be used)

[LOGGING]
append: no

Logging level (level)¶

This specifies the minimum level of logging to display on console during runtime. Options are (from most logging to least):

NOTSET
DEBUG
INFO
WARNING
ERROR
CRITICAL

Default is INFO

[LOGGING]
level: INFO

Logging external output (external_output)¶

This selects the amount of information displayed from third-parties. There are 3 options:

display: Print information from third-parties to the stdout stream

during a run.
log_only: Print information to the log file but not the stdout stream.
debug: Do not intercept third-party use of output streams.

Default is log_only

[LOGGING]
append: log_only

The options file must be a .ini formatted file (see here). Some example files can be found in the examples folder of the source, which is also available to download at Benchmark problems.

Problem Definition Files¶

In FitBenchmarking, problems can be defined using several file formats. The examples/benchmark_problems directory holds a collection of these that can be used for reference.

More information on the supported formats can be found on the following pages.

CUTEst File Format¶

The CUTEst file format in FitBenchmarking is a slight modification of the SIF format. Specifically, the data points, errors, and the number of variables must be defined in such a way to allow FitBenchmarking to access this data; see below. In FitBenchmarking, all SIF files are assumed to be CUTEst problems.

These problems are a subset of the problems in the CUTEr/st Test Problem Set, which may have been adapted to work with FitBenchmarking.

The SIF file format is very powerful, and CUTEst will work with arbitrary variable names, however for FitBenchmarking, these must match a set of expected variable names.

Modifications to the SIF format for FitBenchmarking problems¶

In order for FitBenchmarking to access the data, the SIF files must be written using the following conventions.

Defining Data¶

Data should be defined using the format:

RE X<idx>        <val_x>
RE Y<idx>        <val_y>
RE E<idx>        <val_error>

where <idx> is the index of the data point, and <val_x>, <val_y>, and <val_error> are the values attributed to it.

Usually, <idx> will range from 1 to <num_x>, with that defined as:

IE M             <num_x>

If <idx> does not start at 1, the following lines can be used to specify the range:

IE MLOWER        <min_idx>
IE MUPPER        <max_idx>

Defining Variables¶

For the free variables in functions, we use the convention:

IE N             <num_vars>

This is used to tell FitBenchmarking how many degrees of freedom we need to fit. In some cases variables will be vectors, and the number of degrees of freedom will be greater, most problems use NVEC as a convention to input the number of vectors.

Native File Format¶

In FitBenchmarking, the native file format is used to read Mantid and SASView problems.

In this format, data is separated from the function. This allows running the same dataset against multiple different models to assess which is the most appropriate.

An example of a native problem is:

# FitBenchmark Problem#
software = 'Mantid'
name = 'HIFI 113856'
description = 'An example of (full) detector calibration for the HIFI instrument'
input_file = 'HIFIgrouped_113856.txt'
function = 'name=FlatBackground,A0=0;name=DynamicKuboToyabe,BinWidth=0.050000000000000003,Asym=0.2,Delta=0.2,Field=0,Nu=0.1'
fit_ranges = {'x': [0.1, 16]}

This example shows the basic structure in which the file starts with a comment indicating it is a FitBenchmark problem followed by key-value pairs. Available keys are described below:

software

Either ‘Mantid’ or ‘SasView’ (case insensitive).

This defines whether to use Mantid or SasView to generate the model. The ‘Mantid’ software also supports Mantid’s MultiFit functionality, which requires the parameters listed here to be defined slightly differently. More information can be found in Native File Format (Mantid MultiFit).

name

The name of the problem.

This will be used as a unique reference so should not match other names in the dataset. A sanitised version of this name will also be used in filenames with commas stripped out and spaces replaced by underscores.

description

A description of the dataset.

This is currently unused within FitBenchmarking, but can be useful for explaining problems.

input_file

The name of a file containing the data to fit.

The file must be in a subdirectory named data_files, and should have the form:

header

x1 y1 [e1]
x2 y2 [e2]
...

Mantid uses the convention of # X Y E as the header and SASView uses the convention <X> <Y> <E>, although neither of these are enforced. The error column is optional in this format.

function

This defines the function that will be used as a model for the fitting.

Inside FitBenchmarking, this is passed on to the specified software and, as such, the format is specific to the package we wish to use, as described below.

Mantid

A Mantid function consists of one or more base functions separated by a semicolon. This allows for a powerful way of describing problems, which may have multiple components such as more than one Gaussian and a linear background.

To use one of the base functions in Mantid, please see the list available here.

Note: Any non-standard arguments (e.g. ties, constraints, fixes, …) will only work with Mantid fitting software. Using other minimizers to fit these problems will result in the non-standard arguments being ignored.

SASView

SASView functions can be any of these.

fit_ranges

This specifies the region to be fit.

It takes the form shown in the example, where the first number is the minimum in the range and the second is the maximum.

Native File Format (Mantid MultiFit)¶

As part of the Mantid parsing we also offer limited support for Mantid’s MultiFit functionality.

Here we outline how to use Mantid’s MultiFit with FitBenchmarking, in which some options differ from the standard Native File Format.

Warning

Due to the way Mantid uses ties (a central feature of MultiFit), MultiFit problems can only be used with Mantid minimizers.

In this format, data is separated from the function. This allows running the same dataset against multiple different models to assess which is the most appropriate.

An example of a multifit problem is:

# FitBenchmark Problem
software = 'Mantid'
name = 'MUSR62260'
description = 'Calibration data for mu SR intrument. Run 62260.'
input_file = ['MUSR62260_bkwd.txt','MUSR62260_bottom.txt','MUSR62260_fwd.txt','MUSR62260_top.txt']
function = 'name=FlatBackground,A0=0; name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0'
ties = ['f1.Sigma', 'f1.Frequency']
fit_ranges = [{'x': [0.1, 15.0]}, {'x': [0.1, 15.0]}, {'x': [0.1, 15.0]}, {'x': [0.1, 15.0]}]

Below we outline the differences between this and the Native File Format.

software

Must be Mantid.

name

As in Native File Format.

description

As in Native File Format.

input_file

As in Native File Format, but you must pass in a list of data files (see above example).

function

As in Native File Format.

When fitting, this function will be used for each of the input_files given simultaneously.

ties

This entry is used to define global variables by tieing a variable across input files.

Each string in the list should reference a parameter in the function using Mantid’s convention of f<i>.<name> where i is the position of the function in the function string, and name is the global parameter.

For example to run a fit which has a shared background and peak height, the function and ties fields might look like:

function='name=LinearBackground, A0=0, A1=0; name=Gaussian, Height=0.01, PeakCentre=0.00037, Sigma=1e-05'
ties=['f0.A0', 'f0.A1', 'f1.Height']

fit_ranges

As in Native File Format.

NIST Format¶

The NIST file format is based on the nonlinear regression problems found at the NIST Standard Reference Database. Documentation and background of these problems can be found here.

We note that FitBenchmarking recognizes the NIST file type by checking the first line of the file starts with # NIST/ITL StRD.

Detecting problem file type¶

FitBenchmarking detects which parser to use in two ways:

For the CUTEst file format we check that the extension of the data file is sif

For native and NIST file formats we check the first line of the file

# FitBenchmark Problem corresponds to the native format

# NIST/ITL StRD corresponds to the NIST format

FitBenchmarking Tests¶

The tests for FitBenchmarking require pytest>=3.6. We have split the tests into two categories:

default: denotes tests involving pip installable software packages,
all: in addition to default, also runs tests on external packages.

Unit tests¶

Each module directory in FitBenchmarking (e.g. controllers) contains a test folder which has the unit tests for that module. One can run the tests for a module by:

pytest fitbenchmarking/<MODULE_DIR> --test-type <TEST_TYPE>

where <TEST_TYPE> is either default or all. If --test-type argument is not given the default is all

System tests¶

System tests can be found in the systests directory in FitBenchmarking. As with the unit tests, these can be run via:

pytest fitbenchmarking/systests --test-type <TEST_TYPE>

Warning

The files in the expected results subdirectory of the systests directory are generated to check consistency in our automated tests via GitHub Actions. They might not pass on your local operating system due to, for example, different software package versions being installed.

GitHub Actions tests¶

The scripts that are used for our automated tests via GitHub Actions are located in the ci folder. These give an example of how to run both the unit and system tests within FitBenchmarking.

Known Issues¶

This page is used to detail any known issues or unexpected behaviour within the software.

Problem-Format/Software Combinations¶

When comparing minimizer options from one software package (e.g., comparing all scipy_ls minimizers), we are not aware of any issues. However, the general problem of comparing minimizers from multiple software packages, and with different problem-formats, on truely equal terms is harder to achieve.

The following list details all cases where we are aware of a possible bias:

Using native FitBenchmarking problems with the Mantid software and fitting using Mantid.

With Mantid data, the function evaluation is slightly faster for Mantid minimizers than for all other minimizers. You should account for this when interpreting the results obtained in this case.
Using ties in native FitBenchmarking problems with the Mantid software.

This is not available for non-Mantid fitting software. In these cases, a warning will be generated.

In all cases, the stopping criterion of each minimizer is set to the default value. An experienced user can change this.

Extending FitBenchmarking Documentation¶

FitBenchmarking is designed to be easily extendable to add new features to the software. Below we outline instructions for doing this.

Adding Fitting Problem Definition Types¶

The problem definition types we currently support are listed in the page Problem Definition Files.

To add a new fitting problem type, the parser name must be derived from the file to be parsed. For current file formats by including it as the first line in the file. e.g # Fitbenchmark Problem or NIST/ITL StRD, or by checking the file extension.

To add a new fitting problem definition type, complete the following steps:

Give the format a name (<format_name>). This should be a single word or string of alphanumeric characters, and must be unique ignoring case.
Create a parser in the fitbenchmarking/parsing directory. This parser must satisfy the following:
- The filename should be of the form "<format_name>_parser.py"
- The parser must be a subclass of the base parser, Parser
- The parser must implement parse(self) method which takes only self and returns a populated FittingProblem
Note: File opening and closing is handled automatically.
If the format is unable to accommodate the current convention of starting with the <format_name>, you will need to edit ParserFactory. This should be done in such a way that the type is inferred from the file.
Create the files to test the new parser. Automated tests are run against the parsers in FitBenchmarking, which work by using test files in fitbenchmarking/parsing/tests/<format_name>. In the test_parsers.generate_test_cases() function, one needs to add the new parser’s name to the variable formats, based on whether or not the parser is pip installable. There are 2 types of test files needed:
- Generic tests: fitbenchmarking/parsing/tests/expected/ contains two files, basic.json and start_end_x.json. You must write two input files in the new file format, which will be parsed using the new parser to check that the entries in the generated fitting problem match the values expected. These must be called basic.<ext>, start_end_x.<ext>, where <ext> is the extension of the new file format, and they must be placed in fitbenchmarking/parsing/tests/<format_name>/.
- Function tests: A file named function_evaluations.json must also be provided in fitbenchmarking/parsing/tests/<format_name>/, which tests that the function evaluation behaves as expected. This file must be in json format and contain a string of the form:
```
{"file_name1": [[[x11,x12,...,x1n], [param11, param12,...,param1m], [result11,result12,...,result1n]],
                [[x21,x22,...,x2n], [param21, param22,...,param2m], [result21,result22,...,result2n]],
                ...],
{"file_name2": [...],
 ...}
```
  The test will then parse the files file_name<x> in turn evaluate the function at the given xx values and params. If the result is not suitably close to the specified value the test will fail.
- Integration tests: Add an example to the directory fitbenchmarking/mock_problems/all_parser_set/. This will be used to verify that the problem can be run by scipy, and that accuracy results do not change unexpectedly in future updates. If the software used for the new parser is pip-installable, and the installation is done via FitBenchmarking’s setup.py, then add the same example to fitbenchmarking/mock_problems/default_parsers/.
  As part of this, the systests/expected_results/all_parsers.txt file,
  and if neccessary the systests/expected_results/default_parsers.txt file, will need to be updated. This is done by running the systests:
  pytest fitbenchmarking/systests
  and then checking that the only difference between the results table and the expected value is the new problem, and updating the expected file with the result.
Verify that your tests have been found and are successful by running pytest -vv fitbenchmarking/parsing/tests/test_parsers.py

Adding new Jacobians¶

This section describes how to add further methods to approximate the Jacobian within FitBenchmarking

In order to add a new Jacobian evaluation method, you will need to:

Give the Jacobian a name using the following convention <jac_method> and <num_method>. An example could be scipy_fd for <jac_method> and 2point for <num_method> which would call the SciPy’s 2-point finite difference approximation.
Create fitbenchmarking/jacobian/<jac_method>_<num_method>_jacobian.py, which contains a new subclass of Jacobian. Then implement the method eval(), which evaluates the Jacobian. The numerical method is set sequentially within loop_over_jacobians() by using the method attribute of the class.
Document the available Jacobians by:

updating the docs for Fitting Options

updating options via FitBenchmarking Options and Adding new Options

updating any example files in the example directory

Create tests for the Jacobian evaluation in fitbenchmarking/jacobian/tests/test_jacobians.py.

Adding Fitting Software¶

Controllers are used to interface FitBenchmarking with the various fitting packages. Controllers are responsible for converting the problem into a format that the fitting software can use, and converting the result back to a standardised format (numpy arrays). As well as this, the controller must be written so that the fitting is separated from the preparation wherever possible in order to give accurate timings for the fitting. Supported controllers are found in fitbenchmarking/controllers/.

In order to add a new controller, you will need to:

Give the software a name <software_name>. This will be used by users when selecting this software.
Create fitbenchmarking/controllers/<software_name>_controller.py which contains a new subclass of Controller. This should implement four functions:

Controller.__init__()

Initialise anything that is needed specifically for the software, do any work that can be done without knowledge of the minimizer to use, or function to fit, and call super(<software_name>Controller, self).__init__(problem) (the base class’s __init__ implementation). In this function, you must initialize the a dictionary, self.algorithm_type, such that the keys are given by:

all - all minimizers

ls - least-squares fitting algorithms

deriv_free - derivative free algorithms (these are algorithms that cannot use derivative information. For example, the Simplex method in Mantid does not require Jacobians, and so is derivative free. However, lm-scipy-no-jac in scipy_ls is designed to use derivatives, but calculates an approximation internally if one is not supplied.)

general - minimizers which solve a generic min f(x).

The values of the dictionary are given as a list of minimizers for that specific controller that fit into each of the above categories. See for example the GSL controller.

Parameters: problem (FittingProblem) – The parsed problem

Controller.setup()

Setup the specifics of the fitting.

Anything needed for “fit” that can only be done after knowing the minimizer to use and the function to fit should be done here. Any variables needed should be saved to self (as class attributes).

Controller.fit()

Run the fitting.

This will be timed so should include only what is needed to fit the data.

Controller.cleanup()

Retrieve the result as a numpy array and store results.

Convert the fitted parameters into a numpy array, saved to self.final_params, and store the error flag as self.flag.

The flag corresponds to the following messages:

flag()

0: Successfully converged

1: Software reported maximum number of iterations exceeded

2: Software run but didn’t converge to solution

3: Software raised an exception

Add the new software to the default options, following the instructions in Adding new Options.

Your new software is now fully hooked in with FitBenchmarking, and you can compare it with the current software. You are encouraged to contribute this to the repository so that other can use this package. To do this need to follow our Coding Standards and our Git Workflow, and you’ll also need to

Document the available minimizers (see Fitting Options, Minimizer Options). Note: make sure that you use <software_name> in these places so that the software links in the HTML tables link correctly to the documentation. Add the software to examples/all_software.ini.
Create tests for the software in fitbenchmarking/controllers/tests/test_controllers.py. If the package is pip installable then add the tests to the DefaultControllerTests class and if not add to the ExternalControllerTests class. Unless the new controller is more complicated than the currently available controllers, this can be done by following the example of the others.
If pip installable add to install_requires in setup.py otherwise document the installation procedure in Installing External Software. Update the FullInstall Docker Container – the main developers will help you with this step.

Note

For ease of maintenance, please add new controllers to a list of software in alphabetical order.

The `FittingProblem` and `Jacobian` classes¶

When adding new minimizers, you will find it helpful to make use of the following members of the FittingProblem and subclasses of Jacobian classes:

class fitbenchmarking.jacobian.base_jacobian.Jacobian(problem)

Base class for Jacobian.

eval(params, func=None, **kwargs)

Evaluates Jacobian

Parameters:	params (list) – The parameter values to find the Jacobian at func (Callable, optional) – Function to find the Jacobian for, defaults to problem.eval_r
Returns:	Approximation of the Jacobian
Return type:	numpy array

class fitbenchmarking.parsing.fitting_problem.FittingProblem(options)

Definition of a fitting problem, which will be populated by a parser from a problem definition file.

This defines a fitting problem where, given a set of $n$ data points $(x_i,y_i)$ , associated errors $e_i$ , and a model function $f(x,p)$ , we find the optimal parameters in the least-squares sense by solving:

$\min_p \sum_{i=1}^n \left( \frac{y_i - f(x_i, p)}{e_i} \right)^2$

where $p$ is a vector of length $m$ , and we start from a given intial guess for the optimal parameters.

data_e = None: numpy array The errors

data_x = None: numpy array The x-data

data_y = None: numpy array The y-data

eval_f(params, x=None)

Function evaluation method

Parameters:	params (list) – parameter value(s) x (numpy array) – x data values or None, if None this uses self.data_x
Returns:	y data values evaluated from the function of the problem
Return type:	numpy array

eval_r(params, x=None, y=None, e=None)

Calculate residuals and weight them if using errors

Parameters:	params (list) – The parameters to calculate residuals for x (numpy array, optional) – x data points, defaults to self.data_x y (numpy array, optional) – y data points, defaults to self.data_y e (numpy array, optional) – error at each data point, defaults to self.data_e
Returns:	The residuals for the datapoints at the given parameters
Return type:	numpy array

eval_r_norm(params, x=None, y=None, e=None)

Evaluate the square of the L2 norm of the residuals

Parameters:	params (list) – The parameters to calculate residuals for x (numpy array, optional) – x data points, defaults to self.data_x y (numpy array, optional) – y data points, defaults to self.data_y e (numpy array, optional) – error at each data point, defaults to self.data_e
Returns:	The sum of squares of residuals for the datapoints at the given parameters
Return type:	numpy array

starting_values = None

list of dict Starting values of the fitting parameters

e.g. [{p1_name: p1_val1, p2_name: p2_val1, ...}, {p1_name: p1_val2, ...}, ...]

Adding new Options¶

Default options are set in Options. These can be changed using an .ini formatted file (see here). FitBenchmarking Options gives examples of how this is currently implemented in FitBenchmarking.

To add a new option to one of the five sections FITTING, MINIMIZERS, PLOTTING and LOGGING, follow the steps below. We’ll illustrate the steps using <SECTION>, which could be any of the sections above.

Amend the dictionary DEFAULT_<SECTION> in Options to include any new default options.
If the option amended is to be checked for validity, add accepted option values to the VALID_<SECTION> dictionary in Options.
Using the read_value() function, add your new option to the class, following the examples already in Options. The syntax of this function is:
Options.read_value(func, option)
Helper function which loads in the value
Parameters:

func (callable) – configparser function

option (str) – option to be read for file

Returns:
value of the option

Return type:
list/str/int/bool
Add tests in the following way:
- Each of the sections has it’s own test file, for example, test_option_fitting has tests for the FITTING section.
- Add default tests to the class called <SECTION>OptionTests.
- Add user defined tests to the class called User<SECTION>OptionTests. These should check that the user added option is valid and raise an OptionsError if not.
Add relevant documentation for the new option in FitBenchmarking Options.

Adding new Sections is also possible. To do this you’ll need to extend VALID_SECTIONS with the new section, and follow the same structure as the other SECTIONS.

Amending FitBenchmarking Outputs¶

Here we describe how to add ways of displaying results obtained by FitBenchmarking.

Adding further Tables¶

The tables that are currently supported are listed in FitBenchmarking Output. In order to add a new table, you will need to:

Give the table a name <table_name>. This will be used by users when selecting this output from FitBenchmarking.

Create fitbenchmarking/results_processing/<table_name>_table.py which contains a new subclass of Table. The main functions to change are:

Table.get_values(results_dict)

Gets the main values to be reported in the tables

Parameters:	results_dict (dictionary) – dictionary containing results where the keys are the problem sets and the values are lists of results objects
Returns:	tuple of dictionaries which contain the main values in the tables
Return type:	tuple

Table.display_str(results)

Function which converts the results from get_values() into a string respresentation to be used in the tables. Base class implementation takes the absolute and relative values and uses self.output_string_type as a template for the string format. This can be overwritten to adequately display the results.

Parameters: results (tuple) – tuple containing absolute and relative values

Returns: dictionary containing the string representation of the values in the table.

Return type: dict

Additional functions to be changed are:

Table.get_colour(results)

Converts the result from get_values() into the HTML colours used in the tables. The base class implementation, for example, uses the relative results and colour_scale within Options.

Parameters: results (tuple) – tuple containing absolute and relative values

Returns: dictionary containing HTML colours for the table

Return type: dict

Table.colour_highlight(value, colour)

Takes the HTML colour values from get_colour() and maps it over the HTML table using the Pandas style mapper.

Parameters:	value (pandas.core.series.Series) – Row data from the pandas array colour (dict) – dictionary containing error codes from the minimizers
Returns:	list of HTML colours
Return type:	list

Extend the table_type option in PLOTTING following the instructions in Adding new Options.
Document the new table class is by setting the docstring to be the description of the table, and add to FitBenchmarking Output.
Create tests for the table in fitbenchmarking/results_processing/tests/test_tables.py. This is done by generating, ahead of time using the results problems constructed in fitbenchmarking/results_processing/tests/test_tables.generate_mock_results, both a HTML and text table output as the expected result and adding the new table name to the global variable SORTED_TABLE_NAMES. This will automatically run the comparison tests for the tables.

HTML/CSS Templates¶

In FitBenchmarking, templates are used to generate all html output files, and can be found in the fitbenchmarking/templates directory.

HTML Templates¶

HTML templates allow for easily adaptable outputs. In the simple case of rearranging the page or making static changes (changes that don’t vary with results), this can be done by editing the template, and it will appear in every subsequent HTML page.

For more complicated changes such as adding information that is page dependent, we use jinja. Jinja allows code to be added to templates which can contain conditional blocks, replicated blocks, or substutions for values from python.

Changes to what information is displayed in the page will usually involve editing the python source code to ensure you pass the approprate values to jinja. In practice the amount of effort required to do this can range from one line of code to many depending on the object to be added.

CSS Templates¶

The CSS files contained in the templates directory are used to format the HTML pages.

If you wish to change the style of the output results (e.g. to match a website), this is where they can be changed.

FitBenchmarking Contributor Documentation¶

Thank you for being a contributor to the FitBenchmarking project. Here you will find all you need in order to get started.

Coding Standards¶

All code submitted must meet certain standards, outlined below, before it can be merged into the master branch. It is the contributor’s job to ensure that the following is satisfied, and the reviewer’s role to check that these guidelines have been followed.

The workflow to be used for submitting new code/issues is described in Git Workflow.

Linting¶

All pull requests should be PEP 8 compliant. We suggest running code through flake8 and pylint before submitting to check for this.

Documentation¶

Any new code will be accepted only if the documentation, written in sphinx and found in docs/, has been updated accordingly, and the docstrings in the code have been updated where neccessary.

Testing¶

All tests should pass before submitting code. Tests are written using pytest.

The following should be checked before any code is merged:

Function: Does the change do what it’s supposed to?

Tests: Does it pass? Is there adequate coverage for new code?

Style: Is the coding style consistent? Is anything overly confusing?

Documentation: Is there a suitable change to documentation for this change?

Logging¶

Code should use the logging in utils.log. This uses Python’s built in logging module, and should be used in place of any print statements to ensure that persistent logs are kept after runs.

Git Workflow¶

Issues¶

All new work should start with a new GitHub issue being filed. This should clearly explain what the change to the code will do. There are templates for Bug report, Documentation, Feature request and Test issues on GitHub, and you can also open a blank issue if none of these work.

If issues help meet a piece of work agreed with our funders, it is linked to the appropriate Milestone in GitHub.

Adding new code¶

The first step in adding new code is to create a branch, where the work will be done. Branches should be named according to the convention <nnn>-description_of_work, where <nnn> is the issue number.

Please ensure our Coding Standards are adhered to throughout the branch.

When you think your new code is ready to be merged into the codebase, you should open a pull request to master. The description should contain the words Fixes #<nnn>, where <nnn> is the issue number; this will ensure the issue is closed when the code is merged into master. At this point the automated tests will trigger, and you can see if the code passes on an independent system.

Sometimes it is desirable to open a pull request when the code is not quite ready to be merged. This is a good idea, for example, if you want to get an early opinion on a coding descision. If this is the case, you should mark the pull request as a draft on GitHub.

Once the work is ready to be reviewed, you may want to assign a reviewer, if you think someone would be well suited to review this change. It is worth messaging them on, for example, Slack, as well as requesting their review on GitHub.

Release branches¶

Branches named release-* are protected branches; code must be approved by a reviewer before being added to them, and automated tests will be run on pull requests to these branches. If code is to be included in the release, it must be pulled into this branch from master.

Release branches should have the format release-major.minor.x, starting from release-0.1.x. When the code is released, we will tag that commit with a version number v0.1.0. Any hotfixes will increment x by one, and a new tag will be created accordingly. If at some point we don’t want to provide hot-fixes to a given minor release, then the corresponding release branch may be deleted.

All changes must be initially merged into master. There is a backport-candidate label, which must be put on pull requests that in addition must be merged into the release branch.

The recommended mechanism for merging PRs lablelled with backport-candidate into master is to use the Squash and merge commit option:

After such a PR (with label backport-candidate) has been merged into master, it must then subsequently be merged into the release branch as soon as possible. It is the responsibility of the person merging such PRs to also perform this merge into the release branch.

This can be done using git cherry pick:

git checkout release-x.x.x
git cherry-pick -x <commit-id>
git push

If you didn’t do a squash merge, you will have to cherry pick each commit in the PR that this being backported separately.

If you encounter problems with cherry picking into release branch please don’t hesitate to speak to an experienced member of the FitBenchmarking team.

Creating a release¶

In order to create a new release for FitBenchmarking, there are a few manual steps. These have been streamlined as much as possible.

First checkout the branch to create the release from. Releases should only be made from a release-x-x branch, not a development branch or master.

From the root of the repo run the “ci/prep_and_tag_release.sh” script with the new version number. The version number will be rejected if it is not of the expected form. We expect a “v” followed by the major, minor, and patch numbers, and an optional numbered label to mark the type of release.

Possible labels are:

-beta (release for testing)
-rc (release candidate)

This script will create a new commit with the docs and testing links updated, tag it, and revert the change in a second commit so that the links point back to the latest versions.

These commits will need to be pushed to github.

Finally, you will need to create a release on github. This can be done by navigating to the releases page, selecting new release and typing in the tag that was given to the release (it should tell you the tag exists at this point!).

For example, For a first beta version of release 0.1.0, one would run:

git checkout release-0.1.x
ci/prep_and_tag_release.sh v0.1.0-beta1
git push origin v0.1.0-beta1

<And make the release on GitHub>

Repository Structure¶

At the root of the repository there are six directories:

build

ci

Docker

docs

examples

fitbenchmarking

Build (`build`)¶

This directory contains scripts to allow for installing packages such as Mantid through setuptools.

CI (`ci`)¶

We use GitHub Actions to run our Continuous Integration tests. The specific tests run are defined in a series of Bash scripts, which are stored in this folder.

Docker (`Docker`)¶

The continuous integration process on Github Actions currently run on a Docker container, and this directory holds the Dockerfiles. The Docker containers are hosted on Dockerhub.

BasicInstall holds the Dockerfile that is pushed to the repository fitbenchmarking/fitbenchmarking-deps, the lastest of which should have the tag latest. This contains a basic Ubuntu install, with just the minimal infrastructure needed to run the tests.

FullInstall holds the Dockerfile that is pushed to the repository fitbenchmarking/fitbenchmarking-extras, the lastest of which should have the tag latest. This is built on top of the basic container, and includes optional third party software that FitBenchmarking can work with.

The versions on Docker Hub can be updated from a connected account by issuing the commands:

docker build --tag fitbenchmarking-<type>:<tag>
docker tag fitbenchmarking-<type>:<tag> fitbenchmarking/fitbenchmarking-<type>:<tag>
docker push fitbenchmarking/fitbenchmarking-<type>:<tag>

where <type> is, e.g., deps or extras, and <tag> is, e.g., latest.

Documentation (`docs`)¶

The documentation for FitBenchmarking is stored in this folder under source. A local copy of the documentation can be build using make html in the build directory.

Examples (`examples`)¶

Examples is used to store sample problem files and options files.

A collection of problem files can be found organised into datasets within the examples/benchmark_problems/ directory.

An options template file and a prewritten options file to run a full set of minimizers is also made available in the examples/ directory.

FitBenchmarking Package (`fitbenchmarking`)¶

The main FitBenchmarking package is split across several directories with the intention that it is easily extensible. The majority of these directories are source code, with exceptions being Templates, Mock Problems, and System Tests.

Each file that contains source code will have a directory inside it called tests, which contains all of the tests for that section of the code.

Benchmark Problems (benchmark_problems)¶

This is a copy of the NIST benchmark problems from examples/benchmark_problems. These are the default problems that are run if no problem is passed to the fitbenchmarking command, and is copied here so that it is distributed with the package when installed using, say, pip.

CLI (cli)¶

The CLI directory is used to define all of the entry points into the software. Currently this is just the main fitbenchmarking command.

Controllers (controllers)¶

In FitBenchmarking, controllers are used to interface with third party minimizers.

The controllers directory holds a base controller class (Controller) and all its subclasses, each of which of which interfaces with a different fitting package. The controllers currently implemented are described in Fitting Options and Minimizer Options.

New controllers can be added by following the instructions in Adding Fitting Software.

Core (core)¶

This directory holds all code central to FitBenchmarking. For example, this manages calling the correct parser and controller, as well as compiling the results into a data object.

Jacobian (jacobian)¶

This directory holds the Jacobian class, and subclasses, which are used by the controllers to approximate derivatives. Currenlty available options are described in Fitting Options, and new numerical Jacobians can be added by following the instructions in Adding new Jacobians.

Mock Problems (mock_problems)¶

The mock problems are used in some tests where full problem files are required. These are here so that the examples can be moved without breaking the tests.

Parsing (parsing)¶

The parsers read raw data into a format that FitBenchmarking can use. This directory holds a base parser, Parser and all its subclasses. Each subclass implements a parser for a specific file format. Information about existing parsers can be found in Problem Definition Files, and see Adding Fitting Problem Definition Types for instructions on extending these.

Results Processing (results_processing)¶

All files that are used to generate output are stored here. This includes index pages, text/html tables, plots, and support pages. Information about the tables we provide can be found in FitBenchmarking Output, and instructions on how to add further tables and change the formatting of the displayed information can be found in Amending FitBenchmarking Outputs.

System Tests (systests)¶

FitBenchmarking runs regression tests to check that the accuracy results do not change with updates to the code. These tests run fitbenchmarking against a subset of problems (in subdirectories of /fitbenchmarking/mock_problems/), and compares the text output with that stored in /fitbenchmarking/systests/expected_results/.

Templates (templates)¶

Files in Templates are used to create the resulting html pages, and are a combination of css, html, and python files. The python files in this directory are scripts to update the css and html assets. Instructions on updating these can be found in HTML/CSS Templates.

Utils (utils)¶

This directory contains utility functions that do not fit into the above sections. This includes the Options class (see Adding new Options to extend) and FittingResult class, as well as functions for logging and directory creation.

fitbenchmarking package¶

Subpackages¶

fitbenchmarking.cli package¶

Submodules¶

fitbenchmarking.cli.exception_handler module¶

This file holds an exception handler decorator that should wrap all cli functions to provide cleaner output.

fitbenchmarking.cli.exception_handler.exception_handler(f)¶

Decorator to simplify handling exceptions within FitBenchmarking This will strip off any ‘debug’ inputs.

Parameters:	f (python function) – The function to wrap

fitbenchmarking.cli.main module¶

This is the main entry point into the FitBenchmarking software package. For more information on usage type fitbenchmarking –help or for more general information, see the online docs at docs.fitbenchmarking.com.

fitbenchmarking.cli.main.get_parser()¶

Creates and returns a parser for the args.

Returns:	configured argument parser
Return type:	argparse.ArgParser

fitbenchmarking.cli.main.main()¶: Entry point to be exposed as the fitbenchmarking command.

fitbenchmarking.cli.main.run(problem_sets, options_file='', debug=False)¶

Run benchmarking for the problems sets and options file given. Opens a webbrowser to the results_index after fitting.

Parameters:	problem_sets (list of str) – The paths to directories containing problem_sets options_file (str, optional) – The path to an options file, defaults to ‘’ debug (bool) – Enable debugging output

Module contents¶

fitbenchmarking.controllers package¶

Submodules¶

fitbenchmarking.controllers.base_controller module¶

Implements the base class for the fitting software controllers.

class fitbenchmarking.controllers.base_controller.Controller(problem)¶

Bases: object

Base class for all fitting software controllers. These controllers are intended to be the only interface into the fitting software, and should do so by implementing the abstract classes defined here.

VALID_FLAGS = [0, 1, 2, 3]¶

check_attributes()¶: A helper function which checks all required attributes are set in software controllers

cleanup()¶

Retrieve the result as a numpy array and store results.

Convert the fitted parameters into a numpy array, saved to self.final_params, and store the error flag as self.flag.

The flag corresponds to the following messages:

flag(): 0: Successfully converged

1: Software reported maximum number of iterations exceeded

2: Software run but didn’t converge to solution

3: Software raised an exception

eval_chisq(params, x=None, y=None, e=None)¶

Computes the chisq value

Parameters:	params (list) – The parameters to calculate residuals for x (numpy array, optional) – x data points, defaults to self.data_x y (numpy array, optional) – y data points, defaults to self.data_y e (numpy array, optional) – error at each data point, defaults to self.data_e
Returns:	The sum of squares of residuals for the datapoints at the given parameters
Return type:	numpy array

fit()¶

Run the fitting.

This will be timed so should include only what is needed to fit the data.

flag¶: 0: Successfully converged

1: Software reported maximum number of iterations exceeded

2: Software run but didn’t converge to solution

3: Software raised an exception

prepare()¶: Check that function and minimizer have been set. If both have been set, run self.setup().

setup()¶

Setup the specifics of the fitting.

Anything needed for “fit” that can only be done after knowing the minimizer to use and the function to fit should be done here. Any variables needed should be saved to self (as class attributes).

validate_minimizer(minimizer, algorithm_type)¶

Helper function which checks that the selected minimizer from the options (options.minimizer) exists and whether the minimizer is in self.algorithm_check[options.algorithm_type] (this is a list set in the controller)

Parameters:	minimizer (str) – string of minimizers selected from the options algorithm_type (str) – the algorithm type selected from the options

fitbenchmarking.controllers.bumps_controller module¶

Implements a controller for the Bumps fitting software.

class fitbenchmarking.controllers.bumps_controller.BumpsController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the Bumps fitting software.

Sasview requires a model to fit. Setup creates a model with the correct function.

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from.

fit()¶: Run problem with Bumps.

setup()¶

Setup problem ready to run with Bumps.

Creates a FitProblem for calling in the fit() function of Bumps

fitbenchmarking.controllers.controller_factory module¶

This file contains a factory implementation for the controllers. This is used to manage the imports and reduce effort in adding new controllers.

class fitbenchmarking.controllers.controller_factory.ControllerFactory¶

Bases: object

A factory for creating software controllers. This has the capability to select the correct controller, import it, and generate an instance of it. Controllers generated from this must be a subclass of base_controller.Controller

static create_controller(software)¶

Create a controller that matches the required software.

Parameters:	software (string) – The name of the software to create a controller for
Returns:	Controller class for the problem
Return type:	fitbenchmarking.fitting.base_controller.Controller subclass

fitbenchmarking.controllers.dfo_controller module¶

Implements a controller for DFO-GN http://people.maths.ox.ac.uk/robertsl/dfogn/

class fitbenchmarking.controllers.dfo_controller.DFOController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the DFO-{GN/LS} fitting software.

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from.

fit()¶: Run problem with DFO.

setup()¶: Setup for DFO

fitbenchmarking.controllers.gsl_controller module¶

Implements a controller for GSL https://www.gnu.org/software/gsl/ using the pyGSL python interface https://sourceforge.net/projects/pygsl/

class fitbenchmarking.controllers.gsl_controller.GSLController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the GSL fitting software

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from

fit()¶: Run problem with GSL

setup()¶: Setup for GSL

fitbenchmarking.controllers.mantid_controller module¶

Implements a controller for the Mantid fitting software.

class fitbenchmarking.controllers.mantid_controller.MantidController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the Mantid fitting software.

Mantid requires subscribing a custom function in a predefined format, so this controller creates that in setup.

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from.

eval_chisq(params, x=None, y=None, e=None)¶

Computes the chisq value. If multi-fit inputs will be lists and this will return a list of chi squared of params[i], x[i], y[i], and e[i].

Parameters:	params (list of float or list of list of float) – The parameters to calculate residuals for x (numpy array or list of numpy arrays, optional) – x data points, defaults to self.data_x y (numpy array or list of numpy arrays, optional) – y data points, defaults to self.data_y e (numpy array or list of numpy arrays, optional) – error at each data point, defaults to self.data_e
Returns:	The sum of squares of residuals for the datapoints at the given parameters
Return type:	numpy array

fit()¶: Run problem with Mantid.

setup()¶

Setup problem ready to run with Mantid.

Adds a custom function to Mantid for calling in fit().

fitbenchmarking.controllers.minuit_controller module¶

Implements a controller for the CERN pacakage Minuit https://seal.web.cern.ch/seal/snapshot/work-packages/mathlibs/minuit/ using the iminuit python interface http://iminuit.readthedocs.org

class fitbenchmarking.controllers.minuit_controller.MinuitController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the Minuit fitting software

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from

fit()¶: Run problem with Minuit

setup()¶: Setup for Minuit

fitbenchmarking.controllers.ralfit_controller module¶

Implements a controller for RALFit https://github.com/ralna/RALFit

class fitbenchmarking.controllers.ralfit_controller.RALFitController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the RALFit fitting software.

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from.

fit()¶: Run problem with RALFit.

setup()¶: Setup for RALFit

fitbenchmarking.controllers.scipy_controller module¶

Implements a controller for the scipy fitting software. In particular, here for the scipy minimize solver for general minimization problems

class fitbenchmarking.controllers.scipy_controller.ScipyController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the Scipy fitting software.

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from.

eval_jac(x, *args)¶

Wrapper for problem.eval_j to form the approximate Jacobian for problem.eval_r_norm

Parameters:	x (list) – The parameter values to find the Jacobian at
Returns:	Approximation of the Jacobian
Return type:	numpy array

fit()¶: Run problem with Scipy.

setup()¶: Setup problem ready to be run with SciPy

fitbenchmarking.controllers.scipy_ls_controller module¶

Implements a controller for the scipy ls fitting software. In particular, for the scipy least_squares solver.

class fitbenchmarking.controllers.scipy_ls_controller.ScipyLSController(problem)¶

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the Scipy Least-Squares fitting software.

cleanup()¶: Convert the result to a numpy array and populate the variables results will be read from.

fit()¶: Run problem with Scipy LS.

setup()¶: Setup problem ready to be run with SciPy LS

Module contents¶

fitbenchmarking.core package¶

Submodules¶

fitbenchmarking.core.fitting_benchmarking module¶

Main module of the tool, this holds the master function that calls lower level functions to fit and benchmark a set of problems for a certain fitting software.

fitbenchmarking.core.fitting_benchmarking.benchmark(options, data_dir)¶

Gather the user input and list of paths. Call benchmarking on these. The benchmarking structure is:

loop_over_benchmark_problems()
    loop_over_starting_values()
        loop_over_software()
            loop_over_minimizers()

Parameters:	options (fitbenchmarking.utils.options.Options) – dictionary containing software used in fitting the problem, list of minimizers and location of json file contain minimizers data_dir – full path of a directory that holds a group of problem definition files
Returns:	prob_results array of fitting results for the problem group, list of failed problems and dictionary of unselected minimizers
Return type:	tuple(list, list, dict)

fitbenchmarking.core.fitting_benchmarking.loop_over_benchmark_problems(problem_group, options)¶

Loops over benchmark problems

Parameters:	problem_group (list) – locations of the benchmark problem files options (fitbenchmarking.utils.options.Options) – FitBenchmarking options for current run
Returns:	prob_results array of fitting results for the problem group, list of failed problems and dictionary of unselected minimizers
Return type:	tuple(list, list, dict)

fitbenchmarking.core.fitting_benchmarking.loop_over_fitting_software(problem, options, start_values_index, grabbed_output)¶

Loops over fitting software selected in the options

Parameters:	problem (FittingProblem) – a problem object containing information used in fitting options (fitbenchmarking.utils.options.Options) – FitBenchmarking options for current run start_values_index (int) – Integer that selects the starting values when datasets have multiple ones. grabbed_output (fitbenchmarking.utils.output_grabber.OutputGrabber) – Object that removes third part output from console
Returns:	list of all results, failed problem names and dictionary of unselected minimizers based on algorithm_type
Return type:	tuple(list of fibenchmarking.utils.fitbm_result.FittingResult, list of failed problem names, dictionary of minimizers)

fitbenchmarking.core.fitting_benchmarking.loop_over_minimizers(controller, minimizers, options, grabbed_output)¶

Loops over minimizers in fitting software

Parameters:	controller (Object derived from BaseSoftwareController) – The software controller for the fitting minimizers (list) – array of minimizers used in fitting options (fitbenchmarking.utils.options.Options) – FitBenchmarking options for current run grabbed_output (fitbenchmarking.utils.output_grabber.OutputGrabber) – Object that removes third part output from console
Returns:	list of all results and dictionary of unselected minimizers based on algorithm_type
Return type:	tuple(list of fibenchmarking.utils.fitbm_result.FittingResult, list of failed minimizers)

fitbenchmarking.core.fitting_benchmarking.loop_over_starting_values(problem, options, grabbed_output)¶

Loops over starting values from the fitting problem

Parameters:	problem (FittingProblem) – a problem object containing information used in fitting options (fitbenchmarking.utils.options.Options) – FitBenchmarking options for current run grabbed_output (fitbenchmarking.utils.output_grabber.OutputGrabber) – Object that removes third part output from console
Returns:	prob_results array of fitting results for the problem group, list of failed problems and dictionary of unselected minimizers
Return type:	tuple(list, list, dict)

fitbenchmarking.core.results_output module¶

Functions that create the tables, support pages, figures, and indexes.

fitbenchmarking.core.results_output.create_directories(options, group_name)¶

Create the directory structure ready to store the results

Parameters:	options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting group_name (str) – name of the problem group
Returns:	paths to the top level results, group results, support pages, and figures directories
Return type:	(str, str, str, str)

fitbenchmarking.core.results_output.create_plots(options, results, best_results, figures_dir)¶

Create a plot for each result and store in the figures directory

Parameters:

options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting
results (list of list of fitbenchmarking.utils.fitbm_result.FittingResult) – results nested array of objects
best_results (list of fitbenchmarking.utils.fitbm_result.FittingResult) – best result for each problem
figures_dir (str) – Path to directory to store the figures in

fitbenchmarking.core.results_output.create_problem_level_index(options, table_names, group_name, group_dir, table_descriptions)¶

Generates problem level index page.

Parameters:

options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting
table_names (list) – list of table names
group_name (str) – name of the problem group
group_dir (str) – Path to the directory where the index should be stored
table_descriptions (dict) – dictionary containing descriptions of the tables and the comparison mode

fitbenchmarking.core.results_output.preproccess_data(results_per_test)¶

Preprocess data into the right format for printing and find the best result for each problem

Parameters:	results_per_test (list of list of fitbenchmarking.utils.fitbm_result.FittingResult) – results nested array of objects
Returns:	The best result for each problem
Return type:	list of fitbenchmarking.utils.fitbm_result.FittingResult

fitbenchmarking.core.results_output.save_results(options, results, group_name, failed_problems, unselected_minimzers)¶

Create all results files and store them. Result files are plots, support pages, tables, and index pages.

Params unselected_minimzers:
Parameters:	options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting results (list of list of fitbenchmarking.utils.fitbm_result.FittingResult) – results nested array of objects group_name (str) – name of the problem group failed_problems (list) – list of failed problems to be reported in the html output
	Dictionary containing unselected minimizers based on the algorithm_type option
Returns:	Path to directory of group results
Return type:	str

Module contents¶

fitbenchmarking.jacobian package¶

Submodules¶

fitbenchmarking.jacobian.SciPyFD_2point_jacobian module¶

Module which calculates SciPy 2 point finite difference approximation

class fitbenchmarking.jacobian.SciPyFD_2point_jacobian.ScipyTwoPoint(problem)¶

Bases: fitbenchmarking.jacobian.base_jacobian.Jacobian

Implements SciPy 2 point finite difference approximation to the derivative

eval(params, func=None, **kwargs)¶

Evaluates Jacobian

Parameters:	params (list) – The parameter values to find the Jacobian at func (Callable, optional) – Function to find the Jacobian for, defaults to problem.eval_r
Returns:	Approximation of the Jacobian
Return type:	numpy array

fitbenchmarking.jacobian.SciPyFD_3point_jacobian module¶

Module which calculates SciPy 3 point finite difference approximation

class fitbenchmarking.jacobian.SciPyFD_3point_jacobian.ScipyThreePoint(problem)¶

Bases: fitbenchmarking.jacobian.base_jacobian.Jacobian

Implements SciPy 3 point finite difference approximation to the derivative

eval(params, func=None, **kwargs)¶

Evaluates Jacobian

Parameters:	params (list) – The parameter values to find the Jacobian at func (Callable, optional) – Function to find the Jacobian for, defaults to problem.eval_r
Returns:	Approximation of the Jacobian
Return type:	numpy array

fitbenchmarking.jacobian.SciPyFD_cs_jacobian module¶

Module which calculates SciPy complex steps numerical method

class fitbenchmarking.jacobian.SciPyFD_cs_jacobian.ScipyCS(problem)¶

Bases: fitbenchmarking.jacobian.base_jacobian.Jacobian

Implements SciPy complex steps numerical method for calculating the derivative

eval(params, func=None, **kwargs)¶

Evaluates Jacobian

Parameters:	params (list) – The parameter values to find the Jacobian at func (Callable, optional) – Function to find the Jacobian for, defaults to problem.eval_r
Returns:	Approximation of the Jacobian
Return type:	numpy array

fitbenchmarking.jacobian.base_jacobian module¶

Implements the base class for the Jacobian.

class fitbenchmarking.jacobian.base_jacobian.Jacobian(problem)¶

Bases: object

Base class for Jacobian.

eval(params, func=None, **kwargs)¶

Evaluates Jacobian

Parameters:	params (list) – The parameter values to find the Jacobian at func (Callable, optional) – Function to find the Jacobian for, defaults to problem.eval_r
Returns:	Approximation of the Jacobian
Return type:	numpy array

fitbenchmarking.jacobian.jacobian_factory module¶

This file contains a factory implementation for the Jacobians. This is used to manage the imports and reduce effort in adding new Jacobian methods.

fitbenchmarking.jacobian.jacobian_factory.create_jacobian(options)¶

Create a Jacobian class.

Parameters:	options (fitbenchmarking.utils.Options) – FitBenchmarking options object
Returns:	Controller class for the problem
Return type:	fitbenchmarking.jacobian.base_controller.Jacobian subclass

Module contents¶

fitbenchmarking.parsing package¶

Submodules¶

fitbenchmarking.parsing.base_parser module¶

Implements the base Parser as a Context Manager.

class fitbenchmarking.parsing.base_parser.Parser(filename, options)¶

Bases: object

Base abstract class for a parser. Further parsers should inherit from this and override the abstract parse() method.

parse()¶

Parse the file into a FittingProblem.

Returns:	The parsed problem
Return type:	FittingProblem

fitbenchmarking.parsing.cutest_parser module¶

This file calls the pycutest interface for SIF data

class fitbenchmarking.parsing.cutest_parser.CutestParser(filename, options)¶

Bases: fitbenchmarking.parsing.base_parser.Parser

Use the pycutest interface to parse SIF files

This parser has some quirks. Due to the complex nature of SIF files, we utilise pycutest to generate the function. This function returns the residual r(f,x,y,e) := (f(x) - y)/e for some parameters x. For consistency we require the value to be f(x, p).

To avoid having to convert back from the residual in the evaluation, we store the data separately and set y to 0.0, and e to 1.0 for all datapoints.

We then accomodate for the missing x argument by caching x against an associated function f_x(p).

As such the function is defined by: f(x, p) := r(f_x,p,0,1)

If a new x is passed, we write a new file and parse it to generate a function. We define x to be new if not np.isclose(x, cached_x) for each cached_x that has already been stored.

parse()¶

Get data into a Fitting Problem via cutest.

Returns:	The fully parsed fitting problem
Return type:	fitbenchmarking.parsing.fitting_problem.FittingProblem

fitbenchmarking.parsing.fitbenchmark_parser module¶

This file implements a parser for the Fitbenchmark data format.

class fitbenchmarking.parsing.fitbenchmark_parser.FitbenchmarkParser(filename, options)¶

Bases: fitbenchmarking.parsing.base_parser.Parser

Parser for the native FitBenchmarking problem definition (FitBenchmark) file.

parse()¶

Parse the Fitbenchmark problem file into a Fitting Problem.

Returns:	The fully parsed fitting problem
Return type:	fitbenchmarking.parsing.fitting_problem.FittingProblem

fitbenchmarking.parsing.fitting_problem module¶

Implements the FittingProblem class, this will be the object that inputs are parsed into before being passed to the controllers

class fitbenchmarking.parsing.fitting_problem.FittingProblem(options)¶

Bases: object

Definition of a fitting problem, which will be populated by a parser from a problem definition file.

This defines a fitting problem where, given a set of $n$ data points $(x_i,y_i)$ , associated errors $e_i$ , and a model function $f(x,p)$ , we find the optimal parameters in the least-squares sense by solving:

$\min_p \sum_{i=1}^n \left( \frac{y_i - f(x_i, p)}{e_i} \right)^2$

where $p$ is a vector of length $m$ , and we start from a given intial guess for the optimal parameters.

additional_info = None¶: dict Container for software specific information. This should be avoided if possible.

correct_data()¶: Strip data that overruns the start and end x_range, and approximate errors if not given. Modifications happen on member variables.

data_e = None¶: numpy array The errors

data_x = None¶: numpy array The x-data

data_y = None¶: numpy array The y-data

end_x = None¶: float The end of the range to fit model data over (if different from entire range) (/float/)

equation = None¶: string Equation (function or model) to fit against data

eval_f(params, x=None)¶

Function evaluation method

Parameters:	params (list) – parameter value(s) x (numpy array) – x data values or None, if None this uses self.data_x
Returns:	y data values evaluated from the function of the problem
Return type:	numpy array

eval_r(params, x=None, y=None, e=None)¶

Calculate residuals and weight them if using errors

Parameters:	params (list) – The parameters to calculate residuals for x (numpy array, optional) – x data points, defaults to self.data_x y (numpy array, optional) – y data points, defaults to self.data_y e (numpy array, optional) – error at each data point, defaults to self.data_e
Returns:	The residuals for the datapoints at the given parameters
Return type:	numpy array

eval_r_norm(params, x=None, y=None, e=None)¶

Evaluate the square of the L2 norm of the residuals

Parameters:	params (list) – The parameters to calculate residuals for x (numpy array, optional) – x data points, defaults to self.data_x y (numpy array, optional) – y data points, defaults to self.data_y e (numpy array, optional) – error at each data point, defaults to self.data_e
Returns:	The sum of squares of residuals for the datapoints at the given parameters
Return type:	numpy array

eval_starting_params(param_set)¶

Evaluate the function using the starting parameters.

Parameters:	param_set (int) – The index of the parameter set in starting_params
Returns:	Results from evaluation
Return type:	numpy array

function = None¶: Callable function

get_function_params(params)¶

Return the function definition in a string format for output

Parameters:	params (list) – The parameters to use in the function string
Returns:	Representation of the function example format: ‘b1 * (b2+x) \| b1=-2.0, b2=50.0’
Return type:	string

multifit = None¶: bool Used to check if a problem is using multifit.

name = None¶: string Name (title) of the fitting problem

param_names¶

Utility function to get the parameter names

Returns:	the names of the parameters
Return type:	list of str

sorted_index = None¶: numpy array The index for sorting the data (used in plotting)

start_x = None¶: float The start of the range to fit model data over (if different from entire range)

starting_values = None¶

list of dict Starting values of the fitting parameters

e.g. [{p1_name: p1_val1, p2_name: p2_val1, ...}, {p1_name: p1_val2, ...}, ...]

value_ranges = None¶

dict Smallest and largest values of interest in the data

e.g. {p1_name: [p1_min, p1_max], ...}

verify()¶

Basic check that minimal set of attributes have been set.

Raise FittingProblemError if object is not properly initialised.

fitbenchmarking.parsing.fitting_problem.correct_data(x, y, e, startx, endx, use_errors)¶

Strip data that overruns the start and end x_range, and approximate errors if not given.

Parameters:	x (np.array) – x data y (np.array) – y data e (np.array) – error data startx (float) – minimum x value endx (float) – maximum x value use_errors (bool) – whether errors should be added if not present

fitbenchmarking.parsing.nist_data_functions module¶

Functions that prepare the function specified in NIST problem definition file into the right format for SciPy.

fitbenchmarking.parsing.nist_data_functions.format_function_scipy(function)¶

Formats the function string such that it is scipy-ready.

Parameters:	function (str) – The function to be formatted
Returns:	The formatted function
Return type:	str

fitbenchmarking.parsing.nist_data_functions.is_safe(func_str)¶

Verifies that a string is safe to be passed to exec in the context of an equation.

Parameters:	func_str (string) – The function to be checked
Returns:	Whether the string is of the expected format for an equation
Return type:	bool

fitbenchmarking.parsing.nist_data_functions.nist_func_definition(function, param_names)¶

Processing a function plus different set of starting values as specified in the NIST problem definition file into a callable

Parameters:	function (str) – function string as defined in a NIST problem definition file param_names (list) – names of the parameters in the function
Returns:	callable function
Return type:	callable

fitbenchmarking.parsing.nist_parser module¶

This file implements a parser for the NIST style data format.

class fitbenchmarking.parsing.nist_parser.NISTParser(filename, options)¶

Bases: fitbenchmarking.parsing.base_parser.Parser

Parser for the NIST problem definition file.

parse()¶

Parse the NIST problem file into a Fitting Problem.

Returns:	The fully parsed fitting problem
Return type:	fitbenchmarking.parsing.fitting_problem.FittingProblem

fitbenchmarking.parsing.parser_factory module¶

This file contains a factory implementation for the parsers. This is used to manage the imports and reduce effort in adding new parsers.

class fitbenchmarking.parsing.parser_factory.ParserFactory¶

Bases: object

A factory for creating parsers. This has the capability to select the correct parser, import it, and generate an instance of it. Parsers generated from this must be a subclass of base_parser.Parser

static create_parser(filename)¶

Inspect the input and create a parser that matches the required file.

Parameters:	filename (string) – The path to the file to be parsed
Returns:	Parser for the problem
Return type:	fitbenchmarking.parsing.base_parser.Parser subclass

fitbenchmarking.parsing.parser_factory.parse_problem_file(prob_file, options)¶

Loads the problem file into a fitting problem using the correct parser.

Parameters:	prob_file (string) – path to the problem file options (fitbenchmarking.utils.options.Options) – all the information specified by the user
Returns:	problem object with fitting information
Return type:	fitbenchmarking.parsing.fitting_problem.FittingProblem

Module contents¶

fitbenchmarking.results_processing package¶

Submodules¶

fitbenchmarking.results_processing.acc_table module¶

Accuracy table

class fitbenchmarking.results_processing.acc_table.AccTable(results, best_results, options, group_dir, pp_locations, table_name)¶

Bases: fitbenchmarking.results_processing.base_table.Table

The accuracy results are calculated from the final chi squared value:

$\min_p \sum_{i=1}^n \left( \frac{y_i - f(x_i, p)}{e_i} \right)^2$

where $n$ data points $(x_i,y_i)$ , associated errors $e_i$ , and a model function $f(x,p)$ .

get_values(results_dict)¶

Gets the main values to be reported in the tables

Parameters:	results_dict (dictionary) – dictionary containing results where the keys are the problem sets and the values are lists of results objects
Returns:	two dictionaries containing the absolute chi_sq and the normalised chi_sq with respect to the smallest chi_sq value.
Return type:	tuple(dict, dict)

fitbenchmarking.results_processing.base_table module¶

Implements the base class for the tables.

class fitbenchmarking.results_processing.base_table.Table(results, best_results, options, group_dir, pp_locations, table_name)¶

Bases: object

Base class for the FitBenchmarking HTML and text output tables.

colour_highlight(value, colour)¶

Takes the HTML colour values from get_colour() and maps it over the HTML table using the Pandas style mapper.

Parameters:	value (pandas.core.series.Series) – Row data from the pandas array colour (dict) – dictionary containing error codes from the minimizers
Returns:	list of HTML colours
Return type:	list

create_pandas_data_frame(str_results)¶

Converts dictionary of results into a pandas data frame

Parameters:	str_results (dict) – dictionary containing the string representation of the values in the table.
Returns:	pandas data frame with from results
Return type:	Pandas DataFrame

create_results_dict()¶

Generates a dictionary used to create HTML and txt tables.

Returns:	dictionary containing results where the keys are the problem sets and the values are lists of results objects
Return type:	dictionary

display_str(results)¶

Function which converts the results from get_values() into a string respresentation to be used in the tables. Base class implementation takes the absolute and relative values and uses self.output_string_type as a template for the string format. This can be overwritten to adequately display the results.

Parameters:	results (tuple) – tuple containing absolute and relative values
Returns:	dictionary containing the string representation of the values in the table.
Return type:	dict

enable_error(value, error, template)¶

Enable error codes in table

Parameters:	value (pandas.core.series.Series) – Row data from the pandas array error (dict) – dictionary containing error codes from the minimizers
Returns:	Row data from the pandas array with error codes enabled
Return type:	pandas.core.series.Series

enable_link(value, links)¶

Enable HTML links in values

Parameters:	value (pandas.core.series.Series) – Row data from the pandas array links (dict) – dictionary containing links to the support pages
Returns:	Row data from the pandas array with links enabled
Return type:	pandas.core.series.Series

file_path¶

Getter function for the path to the table

Returns:	path to table
Return type:	str

get_colour(results)¶

Converts the result from get_values() into the HTML colours used in the tables. The base class implementation, for example, uses the relative results and colour_scale within Options.

Parameters:	results (tuple) – tuple containing absolute and relative values
Returns:	dictionary containing HTML colours for the table
Return type:	dict

get_description(html_description)¶

Generates table description from class docstrings and converts them into html

Parameters:	html_description (dict) – Dictionary containing table descriptions
Returns:	Dictionary containing table descriptions
Return type:	dict

get_error(results_dict)¶

Pulls out error code from the results object

Parameters:	results (tuple) – tuple containing absolute and relative values
Returns:	dictionary containing error codes from the minimizers
Return type:	dict

get_links(results_dict)¶

Pulls out links to the individual support pages from the results object

Parameters:	results (tuple) – tuple containing absolute and relative values
Returns:	dictionary containing links to the support pages
Return type:	dict

get_values(results_dict)¶

Gets the main values to be reported in the tables

Parameters:	results_dict (dictionary) – dictionary containing results where the keys are the problem sets and the values are lists of results objects
Returns:	tuple of dictionaries which contain the main values in the tables
Return type:	tuple

table_title¶

Getter function for table name if self._table_title is None

Returns:	name of table
Return type:	str

to_html(table, colour, links, error)¶

Takes Pandas data frame and converts it into the HTML table output

Parameters:	table (Pandas DataFrame) – pandas data frame with from results colour (dict) – dictionary containing error codes from the minimizers links (dict) – dictionary containing links to the support pages error (dict) – dictionary containing error codes from the minimizers
Returns:	HTLM table output
Return type:	str

to_txt(table, error)¶

Takes Pandas data frame and converts it into the plain text table output

Parameters:	table (Pandas DataFrame) – pandas data frame with from results error (dict) – dictionary containing error codes from the minimizers
Returns:	plain text table output
Return type:	str

fitbenchmarking.results_processing.compare_table module¶

compare table

class fitbenchmarking.results_processing.compare_table.CompareTable(results, best_results, options, group_dir, pp_locations, table_name)¶

Bases: fitbenchmarking.results_processing.base_table.Table

The combined results show the accuracy in the first line of the cell and the runtime on the second line of the cell.

colour_highlight(value, colour)¶

Enable HTML colours in the HTML output.

Parameters:	value (pandas.core.series.Series) – Row data from the pandas array colour (dict) – dictionary containing error codes from the minimizers
Returns:	list of HTML colours
Return type:	list

display_str(results)¶

Function that combines the accuracy and runtime results into the string representation.

Parameters:	results (tuple) – tuple containing absolute and relative values
Returns:	dictionary containing the string representation of the values in the table.
Return type:	dict

get_values(results_dict)¶

Gets the main values to be reported in the tables

Parameters:	results_dict (dictionary) – dictionary containing results where the keys are the problem sets and the values are lists of results objects
Returns:	First dictionary containing the absolute chi_sq and runtime results and the second contains the normalised chi_sq and runtime results with respect to the smallest values.
Return type:	tuple(dict, dict)

fitbenchmarking.results_processing.local_min_table module¶

compare table

class fitbenchmarking.results_processing.local_min_table.LocalMinTable(results, best_results, options, group_dir, pp_locations, table_name)¶

Bases: fitbenchmarking.results_processing.base_table.Table

The local min results shows a True or False value together with $\frac{|| J^T r||}{||r||}$ . The True or False indicates whether the software finds a minimum with respect to the following criteria:

$||r|| \leq$ RES_TOL,
$|| J^T r|| \leq$ GRAD_TOL,
$\frac{|| J^T r||}{||r||} \leq$ GRAD_TOL,

where $J$ and $r$ are the Jacobian and residual of $f(x, p)$ , respectively. The tolerances can be found in the results object.

display_str(results)¶

Function that combines the True and False value from variable local_min with the normalised residual

Parameters:	results (tuple) – a dictionary containing true or false values whether the return parameters is a local minimizer and a dictionary containing $\frac{\|\| J^T r\|\|}{\|\|r\|\|}$ values
Returns:	dictionary containing the string representation of the values in the table.
Return type:	dict

get_colour(results)¶

Uses the local minimizer dictionary values to set the HTML colour

Parameters:	results (tuple) – a dictionary containing true or false values whether the return parameters is a local minimizer and a dictionary containing $\frac{\|\| J^T r\|\|}{\|\|r\|\|}$ values
Returns:	dictionary containing error codes from the minimizers
Return type:	dict

get_values(results_dict)¶

Gets the main values to be reported in the tables

Parameters:	results_dict (dictionary) – dictionary containing results where the keys are the problem sets and the values are lists of results objects
Returns:	a dictionary containing true or false values whether the return parameters is a local minimizer and a dictionary containing $\frac{\|\| J^T r\|\|}{\|\|r\|\|}$ values
Return type:	tuple(dict, dict)

fitbenchmarking.results_processing.performance_profiler module¶

Set up performance profiles for both accuracy and runtime tables

fitbenchmarking.results_processing.performance_profiler.create_plot(ax, step_values, solvers)¶

Function to draw the profile on a matplotlib axis

Parameters:	ax (an .axes.SubplotBase subclass of ~.axes.Axes (or a subclass of ~.axes.Axes)) – A matplotlib axis to be filled step_values (list of float) – a sorted list of the values of the metric being profiled solvers (list of strings) – A list of the labels for the different solvers

fitbenchmarking.results_processing.performance_profiler.plot(acc, runtime, fig_dir)¶

Function that generates profiler plots

Parameters:	acc (dict) – acc dictionary containing number of occurrences runtime (dict) – runtime dictionary containing number of occurrences fig_dir (str) – path to directory containing the figures
Returns:	path to acc and runtime profile graphs
Return type:	tuple(str, str)

fitbenchmarking.results_processing.performance_profiler.prepare_profile_data(results)¶

Helper function which generates acc and runtime dictionaries which contain number of occurrences that the minimizer produces a normalised result which is less than the bounds in PROFILER_BOUNDS

Parameters:	results (list of list of fitbenchmarking.utils.fitbm_result.FittingResult) – results nested array of objects
Returns:	dictionary containing number of occurrences
Return type:	tuple(dict, dict)

fitbenchmarking.results_processing.performance_profiler.profile(results, fig_dir)¶

Function that generates profiler plots

Parameters:	results (list of list of fitbenchmarking.utils.fitbm_result.FittingResult) – results nested array of objects fig_dir (str) – path to directory containing the figures
Returns:	path to acc and runtime profile graphs
Return type:	tuple(str, str)

fitbenchmarking.results_processing.plots module¶

Higher level functions that are used for plotting the fit plot and a starting guess plot.

class fitbenchmarking.results_processing.plots.Plot(best_result, options, figures_dir)¶

Bases: object

Class providing plotting functionality.

format_plot()¶: Performs post plot processing to annotate the plot correctly

plot_best(minimizer, params)¶

Plots the fit along with the data using the “best_fit” style and saves to a file

Parameters:	minimizer (str) – name of the best fit minimizer params (list) – fit parameters returned from the best fit minimizer
Returns:	path to the saved file
Return type:	str

plot_data(errors, plot_options, x=None, y=None)¶

Plots the data given

Parameters:	errors (bool) – whether fit minimizer uses errors plot_options (dict) – Values for style of the data to plot, for example color and zorder x (np.array) – x values to be plotted y (np.array) – y values to be plotted

plot_fit(minimizer, params)¶

Updates self.line to show the fit using the passed in params. If self.line is empty it will create a new line. Stores the plot in a file

Parameters:	minimizer (str) – name of the fit minimizer params (list) – fit parameters returned from the best fit minimizer
Returns:	path to the saved file
Return type:	str

plot_initial_guess()¶

Plots the initial guess along with the data and stores in a file

Returns:	path to the saved file
Return type:	str

fitbenchmarking.results_processing.runtime_table module¶

Runtime table

class fitbenchmarking.results_processing.runtime_table.RuntimeTable(results, best_results, options, group_dir, pp_locations, table_name)¶

Bases: fitbenchmarking.results_processing.base_table.Table

The timing results are calculated from an average using the timeit module in python. The number of runtimes can be set in FitBenchmarking Options.

get_values(results_dict)¶

Gets the main values to be reported in the tables

Parameters:	results_dict (dictionary) – dictionary containing results where the keys are the problem sets and the values are lists of results objects
Returns:	two dictionaries containing the absolute runtime and the normalised runtime with respect to the quickest time.
Return type:	tuple(dict, dict)

fitbenchmarking.results_processing.support_page module¶

Set up and build the support pages for various types of problems.

fitbenchmarking.results_processing.support_page.create(results_per_test, group_name, support_pages_dir, options)¶

Iterate through problem results and create a support html page for each.

Parameters:	results_per_test (list[list[list]]) – results object group_name (str) – name of the problem group support_pages_dir (str) – directory in which the results are saved options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting

fitbenchmarking.results_processing.support_page.create_prob_group(prob_results, group_name, support_pages_dir, options)¶

Creates a support page containing figures and other details about the fit for a problem. A link to the support page is stored in the results object.

Parameters:

prob_results (list[fitbenchmarking.utils.fitbm_result.FittingResult]) – problem results objects containing results for each minimizer and a certain fitting function
group_name (str) – name of the problem group
support_pages_dir (str) – directory to store the support pages in
options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting

fitbenchmarking.results_processing.support_page.get_figure_paths(result)¶

Get the paths to the figures used in the support page.

Parameters:	result (fitbenchmarking.utils.fitbm_result.FittingProblem) – The result to get the figures for
Returns:	the paths to the required figures
Return type:	tuple(str, str)

fitbenchmarking.results_processing.tables module¶

Set up and build the results tables.

fitbenchmarking.results_processing.tables.create_results_tables(options, results, best_results, group_name, group_dir, pp_locations, failed_problems, unselected_minimzers)¶

Saves the results of the fitting to html/txt tables.

Params unselected_minimzers:
Parameters:	options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting results (list of list of fitbenchmarking.utils.fitbm_result.FittingResult) – results nested array of objects best_results (list of fitbenchmarking.utils.fitbm_result.FittingResult) – best result for each problem group_name (str) – name of the problem group group_dir (str) – path to the directory where group results should be stored table_descriptions (dict) – dictionary containing descriptions of the tables and the comparison mode pp_locations (tuple(str,str)) – tuple containing the locations of the performance profiles (acc then runtime) failed_problems (list) – list of failed problems to be reported in the html output
	Dictionary containing unselected minimizers based on the algorithm_type option
Returns:	filepaths to each table e.g {‘acc’: <acc-table-filename>, ‘runtime’: …} and dictionary of table descriptions
Return type:	tuple(dict, dict)

fitbenchmarking.results_processing.tables.generate_table(results, best_results, options, group_dir, pp_locations, table_name, suffix)¶

Generate html/txt tables.

Parameters:	results (list of list of fitbenchmarking.utils.fitbm_result.FittingResult) – results nested array of objects best_results (list of fitbenchmarking.utils.fitbm_result.FittingResult) – best result for each problem options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting group_dir (str) – path to the directory where group results should be stored pp_locations (tuple(str,str)) – tuple containing the locations of the performance profiles (acc then runtime) table_name (str) – name of the table suffix (str) – table suffix
Returns:	Table object, HTML string of table and text string of table.
Return type:	tuple(Table object, str, str)

fitbenchmarking.results_processing.tables.load_table(table)¶

Create and return table object.

Parameters:	table (string) – The name of the table to create a table for
Returns:	Table class for the problem
Return type:	fitbenchmarking/results_processing/tables.Table subclass

Module contents¶

fitbenchmarking.utils package¶

Submodules¶

fitbenchmarking.utils.create_dirs module¶

Utility functions for creating/deleting directories.

fitbenchmarking.utils.create_dirs.css(results_dir)¶

Creates a local css directory inside the results directory.

Parameters:	support_pages_dir (str) – path to the results directory
Returns:	path to the local css directory
Return type:	str

fitbenchmarking.utils.create_dirs.del_contents_of_dir(directory)¶

Delete contents of a directory, including other directories.

Parameters:	directory (str) – the target directory

fitbenchmarking.utils.create_dirs.figures(support_pages_dir)¶

Creates the figures directory inside the support_pages directory.

Parameters:	support_pages_dir (str) – path to the support pages directory
Returns:	path to the figures directory
Return type:	str

fitbenchmarking.utils.create_dirs.group_results(results_dir, group_name)¶

Creates the results directory for a specific group. e.g. fitbenchmarking/results/Neutron/

Parameters:	results_dir (str) – path to directory that holds all the results group_name (str) – name of the problem group
Returns:	path to folder group specific results dir
Return type:	str

fitbenchmarking.utils.create_dirs.results(results_dir)¶

Creates the results folder in the working directory.

Parameters:	results_dir (str) – path to the results directory, results dir name
Returns:	proper path to the results directory
Return type:	str

fitbenchmarking.utils.create_dirs.support_pages(group_results_dir)¶

Creates the support_pages directory in the group results directory.

Parameters:	group_results_dir (str) – path to the group results directory
Returns:	path to the figures directory
Return type:	str

fitbenchmarking.utils.exceptions module¶

This file holds all FitBenchmarking exceptions, organised by exception id

exception fitbenchmarking.utils.exceptions.ControllerAttributeError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates an issue with the attributes within a controller

exception fitbenchmarking.utils.exceptions.FitBenchmarkException(message='')¶

Bases: Exception

The base class for all FitBenchmarking exceptions

To define a new exception, inherrit from this and override the _class_message

exception fitbenchmarking.utils.exceptions.FittingProblemError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates a problem with the fitting problem.

exception fitbenchmarking.utils.exceptions.MissingSoftwareError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates that the requirements for a software package are not available.

exception fitbenchmarking.utils.exceptions.NoControllerError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates a controller could not be found

exception fitbenchmarking.utils.exceptions.NoDataError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates that no data could be found.

exception fitbenchmarking.utils.exceptions.NoJacobianError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates a problem with the Jacobian import.

exception fitbenchmarking.utils.exceptions.NoParserError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates a parser could not be found.

exception fitbenchmarking.utils.exceptions.NoResultsError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates a problem with the fitting problem.

exception fitbenchmarking.utils.exceptions.OptionsError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates an error during processing options.

exception fitbenchmarking.utils.exceptions.ParsingError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates an error during parsing.

exception fitbenchmarking.utils.exceptions.UnknownMinimizerError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates that the controller does not support a given minimizer given the current “algorithm_type” option set.

exception fitbenchmarking.utils.exceptions.UnknownTableError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates a problem with the fitting problem.

exception fitbenchmarking.utils.exceptions.UnsupportedMinimizerError(message='')¶

Bases: fitbenchmarking.utils.exceptions.FitBenchmarkException

Indicates that the controller does not support a given minimizer.

fitbenchmarking.utils.fitbm_result module¶

FitBenchmarking results object

class fitbenchmarking.utils.fitbm_result.FittingResult(options, problem, jac, initial_params, params, name=None, chi_sq=None, runtime=None, minimizer=None, error_flag=None, dataset_id=None)¶

Bases: object

Minimal definition of a class to hold results from a fitting problem test.

norm_acc¶

Getting function for norm_acc attribute

Returns:	normalised accuracy value
Return type:	float

norm_runtime¶

Getting function for norm_runtime attribute

Returns:	normalised runtime value
Return type:	float

sanitised_min_name¶

Sanitise the minimizer name into one which can be used as a filename.

Returns:	sanitised name
Return type:	str

sanitised_name¶

Sanitise the problem name into one which can be used as a filename.

Returns:	sanitised name
Return type:	str

fitbenchmarking.utils.log module¶

Utility functions to support logging for the fitbenchmarking project.

fitbenchmarking.utils.log.get_logger(name='fitbenchmarking')¶

Get the unique logger for the given name. This is a straight pass through but will be more intutive for people who have not used python logging.

Parameters:	name (str, optional) – Name of the logger to use, defaults to ‘fitbenchmarking’
Returns:	The named logger
Return type:	logging.Logger

fitbenchmarking.utils.log.setup_logger(log_file='./fitbenchmarking.log', name='fitbenchmarking', append=False, level='INFO')¶

Define the location and style of the log file.

Parameters:	log_file (str, optional) – path to the log file, defaults to ‘./fitbenchmarking.log’ name (str, optional) – The name of the logger to run the setup for, defaults to fitbenchmarking append (bool, optional) – Whether to append to the log or create a new one, defaults to False level (str, optional) – The level of error to print, defaults to ‘INFO’

fitbenchmarking.utils.misc module¶

Miscellaneous functions and utilities used in fitting benchmarking.

fitbenchmarking.utils.misc.get_css(options, working_directory)¶

Returns the path of the local css folder

Parameters:	working_directory (string) – location of current directory
Returns:	A dictionary containing relative links to the local css directory
Return type:	dict of strings

fitbenchmarking.utils.misc.get_js(options, working_directory)¶

Returns the path of the local js folder

Parameters:	working_directory (string) – location of current directory
Returns:	A dictionary containing relative links to the local js directory
Return type:	dict of strings

fitbenchmarking.utils.misc.get_problem_files(data_dir)¶

Gets all the problem definition files from the specified problem set directory.

Parameters:	data_dir (str) – directory containing the problems
Returns:	array containing of paths to the problems e.g. In NIST we would have [low_difficulty/file1.txt, …, …]
Return type:	list of str

fitbenchmarking.utils.options module¶

This file will handle all interaction with the options configuration file.

class fitbenchmarking.utils.options.Options(file_name=None)¶

Bases: object

An options class to store and handle all options for fitbenchmarking

DEFAULTS = {'FITTING': {'algorithm_type': 'all', 'jac_method': 'SciPyFD', 'num_method': '2point', 'num_runs': 5, 'software': ['scipy', 'scipy_ls'], 'use_errors': True}, 'LOGGING': {'append': False, 'external_output': 'log_only', 'file_name': 'fitbenchmarking.log', 'level': 'INFO'}, 'MINIMIZERS': {'bumps': ['amoeba', 'lm-bumps', 'newton', 'mp'], 'dfo': ['dfogn', 'dfols'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region'], 'minuit': ['minuit'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC'], 'scipy_ls': ['lm-scipy-no-jac', 'lm-scipy', 'trf', 'dogbox']}, 'PLOTTING': {'colour_scale': [(1.1, '#fef0d9'), (1.33, '#fdcc8a'), (1.75, '#fc8d59'), (3, '#e34a33'), (inf, '#b30000')], 'comparison_mode': 'both', 'make_plots': True, 'results_dir': 'fitbenchmarking_results', 'table_type': ['acc', 'runtime', 'compare', 'local_min']}}¶

DEFAULT_FITTING = {'algorithm_type': 'all', 'jac_method': 'SciPyFD', 'num_method': '2point', 'num_runs': 5, 'software': ['scipy', 'scipy_ls'], 'use_errors': True}¶

DEFAULT_LOGGING = {'append': False, 'external_output': 'log_only', 'file_name': 'fitbenchmarking.log', 'level': 'INFO'}¶

DEFAULT_MINIMZERS = {'bumps': ['amoeba', 'lm-bumps', 'newton', 'mp'], 'dfo': ['dfogn', 'dfols'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region'], 'minuit': ['minuit'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC'], 'scipy_ls': ['lm-scipy-no-jac', 'lm-scipy', 'trf', 'dogbox']}¶

DEFAULT_PLOTTING = {'colour_scale': [(1.1, '#fef0d9'), (1.33, '#fdcc8a'), (1.75, '#fc8d59'), (3, '#e34a33'), (inf, '#b30000')], 'comparison_mode': 'both', 'make_plots': True, 'results_dir': 'fitbenchmarking_results', 'table_type': ['acc', 'runtime', 'compare', 'local_min']}¶

VALID = {'FITTING': {'algorithm_type': ['all', 'ls', 'deriv_free', 'general'], 'jac_method': ['SciPyFD'], 'num_method': ['2point', '3point', 'cs'], 'software': ['bumps', 'dfo', 'gsl', 'mantid', 'minuit', 'ralfit', 'scipy', 'scipy_ls'], 'use_errors': [True, False]}, 'LOGGING': {'append': [True, False], 'external_output': ['debug', 'display', 'log_only'], 'level': ['NOTSET', 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']}, 'MINIMIZERS': {'bumps': ['amoeba', 'lm-bumps', 'newton', 'de', 'mp'], 'dfo': ['dfogn', 'dfols'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region'], 'minuit': ['minuit'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP'], 'scipy_ls': ['lm-scipy-no-jac', 'lm-scipy', 'trf', 'dogbox']}, 'PLOTTING': {'comparison_mode': ['abs', 'rel', 'both'], 'make_plots': [True, False], 'table_type': ['acc', 'runtime', 'compare', 'local_min']}}¶

VALID_FITTING = {'algorithm_type': ['all', 'ls', 'deriv_free', 'general'], 'jac_method': ['SciPyFD'], 'num_method': ['2point', '3point', 'cs'], 'software': ['bumps', 'dfo', 'gsl', 'mantid', 'minuit', 'ralfit', 'scipy', 'scipy_ls'], 'use_errors': [True, False]}¶

VALID_LOGGING = {'append': [True, False], 'external_output': ['debug', 'display', 'log_only'], 'level': ['NOTSET', 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']}¶

VALID_MINIMIZERS = {'bumps': ['amoeba', 'lm-bumps', 'newton', 'de', 'mp'], 'dfo': ['dfogn', 'dfols'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region'], 'minuit': ['minuit'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP'], 'scipy_ls': ['lm-scipy-no-jac', 'lm-scipy', 'trf', 'dogbox']}¶

VALID_PLOTTING = {'comparison_mode': ['abs', 'rel', 'both'], 'make_plots': [True, False], 'table_type': ['acc', 'runtime', 'compare', 'local_min']}¶

VALID_SECTIONS = ['MINIMIZERS', 'FITTING', 'PLOTTING', 'LOGGING']¶

minimizers¶

read_value(func, option)¶

Helper function which loads in the value

Parameters:	func (callable) – configparser function option (str) – option to be read for file
Returns:	value of the option
Return type:	list/str/int/bool

reset()¶: Resets options object when running multiple problem groups.

results_dir¶

write(file_name)¶

Write the contents of the options object to a new options file.

Parameters:	file_name (str) – The path to the new options file

write_to_stream(file_object)¶

Write the contents of the options object to a file object.

fitbenchmarking.utils.options.read_list(s)¶

Utility function to allow lists to be read by the config parser

Parameters:	s (string) – string to convert to a list
Returns:	list of items
Return type:	list of str

fitbenchmarking.utils.output_grabber module¶

class fitbenchmarking.utils.output_grabber.OutputGrabber(options)¶

Bases: object

Class used to grab outputs for stdout and stderr

class fitbenchmarking.utils.output_grabber.StreamGrabber(stream)¶

Bases: object

Class used to grab an output stream.

escape_char = '\x08'¶

readOutput()¶: Read the stream data (one byte at a time) and save the text in capturedtext.

start()¶: Start capturing the stream data.

stop()¶: Stop capturing the stream data and save the text in capturedtext.

Module contents¶

Welcome to FitBenchmarking’s documentation!¶

FitBenchmarking¶

Table Of Contents¶

FitBenchmarking Concept Documentation¶

Why is FitBenchmarking important?¶

Example workflow¶

How does FitBenchmarking work?¶

The Benchmarking Paradigm¶

FitBenchmarking User Documentation¶

Installation¶

Installing FitBenchmarking and default fitting packages¶

Installing External Software¶

Running FitBenchmarking¶

Running alternative problems¶

Changing the options¶

FitBenchmarking Output¶

Comparison Table¶

Accuracy Table¶

Runtime Table¶

Local Minimizer Table¶

Table formats¶

Benchmark problems¶

Powder Diffraction Data (SIF files)¶

MultiFit Data (Mantid)¶

Muon Data (Mantid)¶

NIST¶

Neutron Data (Mantid)¶

Small Angle Scattering (SASView)¶

CUTEst (SIF files)¶

Simple tests¶

FitBenchmarking Options¶

Fitting Options¶

Minimizer Options¶

Plotting Options¶

Logging Options¶

Problem Definition Files¶

CUTEst File Format¶

Native File Format¶

Native File Format (Mantid MultiFit)¶

NIST Format¶

Detecting problem file type¶

FitBenchmarking Tests¶

Unit tests¶

System tests¶

GitHub Actions tests¶

Known Issues¶

Problem-Format/Software Combinations¶

Extending FitBenchmarking Documentation¶

Adding Fitting Problem Definition Types¶

Adding new Jacobians¶

Adding Fitting Software¶

The FittingProblem and Jacobian classes¶

Adding new Options¶

Amending FitBenchmarking Outputs¶

Adding further Tables¶

HTML/CSS Templates¶

FitBenchmarking Contributor Documentation¶

Coding Standards¶

Linting¶

Documentation¶

Testing¶

Logging¶

Git Workflow¶

Issues¶

Adding new code¶

Release branches¶

Creating a release¶

Repository Structure¶

Build (build)¶

CI (ci)¶

Docker (Docker)¶

Documentation (docs)¶

Examples (examples)¶

FitBenchmarking Package (fitbenchmarking)¶

fitbenchmarking package¶

Subpackages¶

Module contents¶

The `FittingProblem` and `Jacobian` classes¶

Build (`build`)¶

CI (`ci`)¶

Docker (`Docker`)¶

Documentation (`docs`)¶

Examples (`examples`)¶

FitBenchmarking Package (`fitbenchmarking`)¶