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/v1.0.0-rc1/users/install_instructions/index.html
User Documentation & Example Usage: https://fitbenchmarking.readthedocs.io/en/v1.0.0-rc1/users/index.html
Community Guidelines: https://fitbenchmarking.readthedocs.io/en/v1.0.0-rc1/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/v1.0.0-rc1/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 highlighting 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
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 Nelder-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.
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:
With each test FitBenchmarking also produces a Dolan-Moré performance profile:
Fits are taken from all benchmarks, so if FitBenchmarking is run with
n problems and m cost functions, the resulting profile plots will have
n*m steps on the y-axis.
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.
pipFitBenchmarking can be installed via the command line by entering:
python -m pip install fitbenchmarking[bumps,DFO,gradient_free,minuit,SAS,numdifftools,lmfit,nlopt]
This will install the latest stable version of FitBenchmarking. For all available versions please visit the FitBenchmarking 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.
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.gitOpen up a terminal (command prompt) and go into the
fitbenchmarkingdirectory.Once you are in the right directory, we recommend that you type
python -m pip install .[bumps,DFO,gradient_free,minuit,SAS,numdifftools,lmfit,nlopt]
Additional software that cannot be installed via pip can also be used with FitBenchmarking. Follow the instructions at Installing External Software.
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.gofit– installs the GOFit fitting package.gradient_free– installs the Gradient-Free-Optimizers fitting packagelevmar– installs the levmar fitting package (suitable for Python up to 3.8, see Levmar). Note that the interface we use also requires BLAS and LAPLACK to be installed on the system, and calls to this minimizer will fail if these libraries are not present.matlab– installs the dill module required to run matlab controllers in fitbenchmarkingminuit– installs the Minuit fitting package.SAS– installs the Sasmodels fitting package and the tinycc module.numdifftools– installs the numdifftools numerical differentiation package.nlopt– installs the NLopt 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>/
Ceres Solver is used as a fitting software in FitBenchmarking, and is called via the PyCeres interface.
Install instructions can be found on the PyCeres Github page and Ceres Solver documentation
Please note that the PYCERES_LOCATION environment variable must be set.
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 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.
Horace can be installed by following the instructions on the Horace website. In addition, MATLAB and the MATLAB engine must be installed following the instructions given below.
Levmar is available on pip, however the latest release will only work up to Python 3.8. In order to use Levmar with newer versions of python, you are required to clone the repository and build it locally. This is fast and has been tested on Ubuntu 20.04.
Instructions for linux are below, for other operating systems the process will be the same.
`
git clone git@github.com:bjodah/levmar.git
cd levmar
pip install .
cd ..
rm -rf levmar
`
Mantid is used both as fitting software, and to parse data files.
Instructions on how to install Mantid for a range of systems are available at https://download.mantidproject.org/.
MATLAB is available to use as fitting software in FitBenchmarking, and is called via the MATLAB Engine API for Python.
To use this fitting software, both MATLAB and the MATLAB engine must be installed. Installation instructions for MATLAB are available at https://uk.mathworks.com/help/install/ug/install-products-with-internet-connection.html, and instructions for installing and setting up the MATLAB engine are here: https://uk.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html. Furthermore, Matlab requires additional Python packages to be installed. You can find the instructions on how to install these packages by following the link provided: here.
RALFit is available 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
Theseus is used as a fitting software in FitBenchmarking, and is called via theseus-ai python module which requries pytorch
Install instructions can be found on the Theseus Github page
Running FitBenchmarking
Once installed, issuing the command
fitbenchmarking
will run the NIST average difficulty problem set 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 -p or --problem-sets argument.
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 or --options-file 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.
Changing options via the command line
It is possible to change the following options via the command line rather than from an .ini file or from the default options.
They can be changed using the arguments in the table below.
For example, to change the results directory:
The default directory where the results are saved can be changed using the -r
or --results-dir argument. The results directory option
can also be changed in the options file.
fitbenchmarking -r new_results/
The default results directory is fitbenchmarking_results.
Multiple options
For an option for which you wish to make several choices e.g. table_type, simply use a space to separate your choices:
fitbenchmarking -t acc runtime
If you wish to change several different options, use a space to separate the arguments:
fitbenchmarking -t acc -l WARNING
Help
For more information on changing options via the command line, you can use the -h
or --help argument.
fitbenchmarking -h
Optimization Algorithms
The different minimizers used in Fitbenchmarking implement various numerical optimization algorithms. These are catagorised by the algorithm type list, with options detailed below, along with advantages/disadvantages of each algorithm.
Derivative Free
Derivative Free methods do not compute the gradient of a function and so are often used to minimize problems with nondifferentiable functions. Some derivative free methods will attempt to approximate the gradient using a finite difference approach, another class of methods constructs a linear or quadratic model of the objective functions and uses a trust region approach to find the next iterate. Another widely used derivative free method is the Nelder-Mead simplex method. [Nocedal]
To select all minimizers in fitbenchmarking that use derivative free methods, use the algorithm type deriv_free.
simplex)Nelder-Mead is a simplex based algorithm, with a simplex \(S\) in \({\rm I\!R}\) being defined as the convex hull of \(n+1\) vertices \(\{x_1, ..., x_{n+1}\}\).
In an iteration of the algorithm, the idea is to remove the vertex with the worst function value. It is then replaced with another point with a better value. An iteration consists of the following steps:
Ordering the vertices of \(S\) so that \(f(x_1) \leq f(x_2) \leq ... \leq f(x_{n+1})\)
Calculating the centroid, \(\bar{x}\) of the best \(n\) points \(\bar{x} = \frac{1}{n} \sum_{i=1}^n x_i\)
Carry out a transformation to compute the new simplex. Try to replace only the worst vertex \(x_{n+1}\) with a better point, which then becomes the new vertex. If this fails, the simplex is shrunk towards the best vertex \(x_1\) and \(n\) new vertices are computed. The algorithm terminates when the simplex \(S\) is sufficiently small. [Singer]
- Advantages:
Method only requires 1 or 2 functions evaluations per iteration.
Gives significant improvements in first few iterations - quick to produce satisfactory results.
- Disadvantages:
Stagnation can occur at non-optimal points, with large numbers of iterations leading to negligible improvement even when nowhere near a minimum.
If numerical computation of function derivative can be trusted, then other algorithms are more robust.
Line Search Methods
In line search methods, each iteration is given by \(x_{k+1} = x_k + \alpha_k p_k\), where \(p_k\) is the search direction and \(\alpha_k\) is the step length.
The search direction is often of the form \(p_k = -B_k^{-1} \nabla f_k\) where \(B_k\) is a symmetric and non-singular matrix. The form of \(p_k\) is dependent on algorithm choice.
The ideal step length would be \(min_{\alpha>0} f(x_k + \alpha p_k)\) but this is generally too expensive to calculate. Instead an inexact line search condition such as the Wolfe Conditions can be used:
With \(0<c_1<c_2<1\). Here, the first condition ensures that \(\alpha_k\) gives a sufficient decrease in \(f\), whilst the second condition rules out unacceptably short steps. [Nocedal]
steepest_descent)Simple method where search direction \(p_k\) is set to be \(-\nabla f_k\), i.e. the direction along which \(f\) decreases most rapidly.
- Advantages:
Low storage requirements
Easy to compute
- Disadvantages:
Slow convergence for nonlinear problems
conjugate_gradient)Conjugate Gradient methods have a faster convergence rate than Steepest Descent but avoid the high computational cost of methods where the inverse Hessian is calculated.
Given an iterate \(x_0\), evaluate \(f_0 = f(x_0), \nabla f_0 = \nabla f(x_0)\).
Set \(p_0 \leftarrow - \nabla f_0, k \leftarrow 0\)
Then while \(\nabla f_k \neq 0\):
Carry out a line search to compute the next iterate, then evaluate \(\nabla f_{k+1}\) and use this to determine the subsequent conjugate direction \(p_{k+1} = - \nabla f(x_{k+1}) + \beta_k p_k\)
Different variations of the Conjugate Gradient algorithm use different formulas for \(\beta_k\), for example:
Fletcher-Reeves: \(\beta_{k+1} = \frac{f_{k+1}^T \nabla f_{k+1}}{\nabla f_k^T \nabla f_k}\) Polak-Ribiere: \(\beta_{k+1} = \frac{ \nabla f_{k+1}^T ( \nabla f_{k+1} - \nabla f_k)}{\|\nabla f_k\|^2}\)
- Advantages:
Considered to be one of the best general purpose methods.
Faster convergence rate compared to Steepest Descent and only requires evaluation of objective function and it’s gradient - no matrix operations.
- Disadvantages:
For Fletcher-Reeves method it can be shown that if the method generates a bad direction and step, then the next direction and step are also likely to be bad. However, this is not the case with the Polak Ribiere method.
Generally, the Polak Ribiere method is more efficient that the Fletcher-Reeves method but it has the disadvantage is requiring one more vector of storage.
bfgs)Most popular quasi-Newton method, which uses an approximate Hessian rather than the true Hessian which is used in a Newton line search method.
Starting with an initial Hessian approximation \(H_0\) and starting point \(x_0\):
While \(\| \nabla f_k \| > \epsilon\):
Compute the search direction \(p_k = -H_k \nabla f_k\)
Then find next iterate \(x_{k+1}\) by performing a line search.
Next, define \(s_k = x_{k+1}-x_k\) and \(y_k = \nabla f_{k+1} - \nabla f_k\), then compute
with \(\rho_k = \frac{1}{y_k^T s_k}\)
- Advantages:
Superlinear rate of convergence
Has self-correcting properties - if there is a bad estimate for \(H_k\), then it will tend to correct itself within a few iterations.
No need to compute the Jacobian or Hessian.
- Disadvantages:
Newton’s method has quadratic convergence but this is lost with BFGS.
gauss_newton)Modified Newton’s method with line search. Instead of solving standard Newton equations
solve the system
(where \(J_k\) is the Jacobian) to obtain the search direction \(p_k^{GN}\). The next iterate is then set as \(x_{k+1} = x_k + p_k^{GN}\).
Here, the approximation of the Hessian \(\nabla^2 f_k \approx J_k^T J_k\) has been made, which helps to save on computation time as second derivatives are not calculated.
- Advantages:
Calculation of second derivatives is not required.
If residuals or their second order partial derivatives are small, then \(J_k^T J_k\) is a close approximation to \(\nabla^2 f_k\) and convergence of Gauss-Newton is fast.
The search direction \(p_J^{GN}\) is always a descent direction as long as \(J_k\) has full rank and the gradient \(\nabla f_k\) is nonzero.
- Disadvantages:
Without a good initial guess, or if the matrix \(J_k^T J_k\) is ill-conditioned, the Gauss Newton Algorithm is very slow to converge to a solution.
If relative residuals are large, then large amounts of information will be lost.
\(J_k\) must be full rank.
- Nocedal(1,2,3,4,5,6)
Jorge Nocedal, Stephen J. Wright (2006), Numerical Optimization
- Poczos
Barnabas Poczos, Ryan Tibshirani (2012), Lecture 10: Optimization, School of Computer Science, Carnegie Mellon University
- Floater
Michael S. Floater (2018), Lecture 13: Non-linear least squares and the Gauss-Newton method, University of Oslo
Trust Region
Trust region approach involves constructing a model function \(m_k\) that approximates the function \(f\) in the region, \(\Delta\), near the current point \(x_k\). The model \(m_k\) is often a quadratic obtained by a Taylor Series expansion of the function around \(x_k\).
where \(B_k\) is an approximation of the Hessian.
The subproblem to be solved at each iteration in order to find the step length is \(\min_p m_k(p)\), subject to \(\|p\| \leq \Delta_k\). [Nocedal]
To select all minimizers in fitbenchmarking that use a trust region approach, use the algorithm type trust_region.
levenberg-Marquardt)Most widely used optimization algorithm, which uses the same Hessian approximation as Gauss-Newton but uses a trust region strategy instead of line search. As the Hessian approximation is the same as Gauss-Newton, convergence rate is similar.
For Levenberg-Marquardt, the model function \(m_k\), is chosen to be
So, for a spherical trust region, the subproblem to be solved at each iteration is \(\min_p \frac{1}{2} \|J_k p + r_k\|^2\), subject to \(\|p\| \leq \Delta_k\).
Levenberg-Marquardt uses a combination of gradient descent and Gauss-Newton method. When the solution \(p^{GN}\) lies inside of the trust region \(\Delta\), then \(p^{GN}\) also solves the sub-problem. Otherwise, the current iteration is far from the optimal value and so the search direction is determined using steepest descent, which performs better than Gauss-Newton when far from the minimum.
- Advantages:
Robust (more so than Gauss-Newton).
Avoids the weakness with Gauss-Newton that Jacobian must be full rank.
Fast to converge.
Good initial guess not required.
- Disadvantages:
Similarly to Gauss-Newton, not good for large residual problems.
Can be slow to converge if a problem has many parameters
- Nocedal(1,2)
Jorge Nocedal, Stephen J. Wright (2006), Numerical Optimization
- Ranganathan
Ananth Ranganathan (2004), The Levenberg-Marquardt Algorithm, University of California, Santa Barbara
Algorithm Types of Available Minimizers
Least Squares (ls):
{'bumps': ['lm-bumps', 'scipy-leastsq'],
'ceres': ['Levenberg_Marquardt',
'Dogleg',
'BFGS',
'LBFGS',
'steepest_descent',
'Fletcher_Reeves',
'Polak_Ribiere',
'Hestenes_Stiefel'],
'dfo': ['dfogn', 'dfols'],
'gofit': ['alternating', 'multistart', 'regularisation'],
'gradient_free': [],
'gsl': ['lmsder', 'lmder'],
'horace': ['lm-lsqr'],
'levmar': ['levmar'],
'lmfit': ['least_squares', 'leastsq'],
'mantid': ['Levenberg-Marquardt',
'Levenberg-MarquardtMD',
'Trust Region',
'FABADA'],
'matlab': [],
'matlab_curve': ['Trust-Region', 'Levenberg-Marquardt'],
'matlab_opt': ['levenberg-marquardt', 'trust-region-reflective'],
'matlab_stats': ['Levenberg-Marquardt'],
'minuit': [],
'nlopt': [],
'ralfit': ['gn',
'hybrid',
'newton',
'newton-tensor',
'gn_reg',
'hybrid_reg',
'newton_reg',
'newton-tensor_reg'],
'scipy': [None],
'scipy_go': [None],
'scipy_ls': ['lm-scipy', 'trf', 'dogbox'],
'theseus': ['Levenberg_Marquardt', 'Gauss-Newton']}
Deriv-Free (deriv_free):
{'bumps': ['amoeba', 'de', 'dream'],
'ceres': [],
'dfo': ['dfogn', 'dfols'],
'gofit': [],
'gradient_free': ['HillClimbingOptimizer',
'RepulsingHillClimbingOptimizer',
'SimulatedAnnealingOptimizer',
'RandomSearchOptimizer',
'RandomRestartHillClimbingOptimizer',
'RandomAnnealingOptimizer',
'ParallelTemperingOptimizer',
'ParticleSwarmOptimizer',
'EvolutionStrategyOptimizer',
'BayesianOptimizer',
'TreeStructuredParzenEstimators',
'DecisionTreeOptimizer'],
'gsl': ['nmsimplex', 'nmsimplex2'],
'horace': ['lm-lsqr'],
'levmar': [],
'lmfit': ['powell', 'cobyla', 'emcee', 'nelder', 'differential_evolution'],
'mantid': ['Simplex', 'FABADA'],
'matlab': ['Nelder-Mead Simplex'],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': ['simplex'],
'nlopt': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS'],
'ralfit': [],
'scipy': ['Nelder-Mead', 'Powell', 'COBYLA'],
'scipy_go': ['differential_evolution'],
'scipy_ls': [None],
'theseus': []}
General (general):
{'bumps': ['amoeba', 'newton', 'de', 'dream'],
'ceres': [],
'dfo': [],
'gofit': [],
'gradient_free': ['HillClimbingOptimizer',
'RepulsingHillClimbingOptimizer',
'SimulatedAnnealingOptimizer',
'RandomSearchOptimizer',
'RandomRestartHillClimbingOptimizer',
'RandomAnnealingOptimizer',
'ParallelTemperingOptimizer',
'ParticleSwarmOptimizer',
'EvolutionStrategyOptimizer',
'BayesianOptimizer',
'TreeStructuredParzenEstimators',
'DecisionTreeOptimizer'],
'gsl': ['nmsimplex',
'nmsimplex2',
'conjugate_pr',
'conjugate_fr',
'vector_bfgs',
'vector_bfgs2',
'steepest_descent'],
'horace': [],
'levmar': [],
'lmfit': ['nelder',
'powell',
'cg',
'bfgs',
'newton',
'lbfgs',
'tnc',
'slsqp',
'differential_evolution',
'shgo',
'dual_annealing'],
'mantid': ['BFGS',
'Conjugate gradient (Fletcher-Reeves imp.)',
'Conjugate gradient (Polak-Ribiere imp.)',
'Damped GaussNewton',
'Simplex',
'SteepestDescent'],
'matlab': ['Nelder-Mead Simplex'],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': ['migrad'],
'nlopt': ['LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ'],
'ralfit': [],
'scipy': ['Nelder-Mead',
'Powell',
'CG',
'BFGS',
'Newton-CG',
'L-BFGS-B',
'TNC',
'SLSQP'],
'scipy_go': ['differential_evolution', 'shgo', 'dual_annealing'],
'scipy_ls': [None],
'theseus': []}
Simplex (simplex):
{'bumps': ['amoeba'],
'ceres': [],
'dfo': [],
'gofit': [],
'gradient_free': [],
'gsl': ['nmsimplex', 'nmsimplex2'],
'horace': [],
'levmar': [],
'lmfit': ['nelder'],
'mantid': ['Simplex'],
'matlab': ['Nelder-Mead Simplex'],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': ['simplex'],
'nlopt': ['LN_NELDERMEAD', 'LN_SBPLX'],
'ralfit': [],
'scipy': ['Nelder-Mead'],
'scipy_go': [],
'scipy_ls': [],
'theseus': []}
Trust Region (trust_region):
{'bumps': ['lm-bumps', 'scipy-leastsq'],
'ceres': ['Levenberg_Marquardt', 'Dogleg'],
'dfo': ['dfols', 'dfogn'],
'gofit': [],
'gradient_free': [],
'gsl': ['lmder', 'lmsder'],
'horace': [],
'levmar': ['levmar'],
'lmfit': ['least_squares',
'trust-ncg',
'trust-exact',
'trust-krylov',
'trust-constr',
'dogleg'],
'mantid': ['Trust Region', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD'],
'matlab': [],
'matlab_curve': ['Trust-Region', 'Levenberg-Marquardt'],
'matlab_opt': ['levenberg-marquardt', 'trust-region-reflective'],
'matlab_stats': ['Levenberg-Marquardt'],
'minuit': [],
'nlopt': ['LN_COBYLA', 'LD_CCSAQ', 'LD_MMA'],
'ralfit': ['gn', 'hybrid', 'newton', 'newton-tensor'],
'scipy': ['trust-ncg',
'trust-exact',
'trust-krylov',
'trust-constr',
'dogleg'],
'scipy_go': [],
'scipy_ls': ['lm-scipy', 'trf', 'dogbox'],
'theseus': []}
Levenberg-Marquardt (levenberg-marquardt):
{'bumps': ['lm-bumps', 'scipy-leastsq'],
'ceres': [],
'dfo': [],
'gofit': ['regularisation'],
'gradient_free': [],
'gsl': ['lmder', 'lmsder'],
'horace': ['lm-lsqr'],
'levmar': ['levmar'],
'lmfit': ['leastsq'],
'mantid': ['Levenberg-Marquardt', 'Levenberg-MarquardtMD'],
'matlab': [],
'matlab_curve': ['Levenberg-Marquardt'],
'matlab_opt': ['levenberg-marquardt'],
'matlab_stats': ['Levenberg-Marquardt'],
'minuit': [],
'nlopt': [],
'ralfit': ['gn', 'gn_reg'],
'scipy': [],
'scipy_go': [],
'scipy_ls': ['lm-scipy'],
'theseus': ['Levenberg_Marquardt']}
Gauss Newton (gauss_newton):
{'bumps': [],
'ceres': [],
'dfo': ['dfogn'],
'gofit': ['regularisation'],
'gradient_free': [],
'gsl': [],
'horace': [],
'levmar': [],
'lmfit': ['newton', 'tnc'],
'mantid': ['Damped GaussNewton'],
'matlab': [],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': [],
'nlopt': ['LD_TNEWTON_PRECOND_RESTART',
'LD_TNEWTON_PRECOND',
'LD_TNEWTON_RESTART',
'LD_TNEWTON'],
'ralfit': ['gn', 'gn_reg'],
'scipy': [],
'scipy_go': [],
'scipy_ls': [],
'theseus': ['Gauss-Newton']}
BFGS (bfgs):
{'bumps': ['newton'],
'ceres': ['BFGS', 'LBFGS'],
'dfo': [],
'gofit': [],
'gradient_free': [],
'gsl': ['vector_bfgs', 'vector_bfgs2'],
'horace': [],
'levmar': [],
'lmfit': ['lbfgsb', 'bfgs'],
'mantid': ['BFGS'],
'matlab': [],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': [],
'nlopt': ['LD_LBFGS'],
'ralfit': [],
'scipy': ['BFGS', 'L-BFGS-B'],
'scipy_go': [],
'scipy_ls': [],
'theseus': []}
Conjugate Gradient (conjugate_gradient):
{'bumps': [],
'ceres': ['Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'],
'dfo': [],
'gofit': [],
'gradient_free': [],
'gsl': ['conjugate_fr', 'conjugate_pr'],
'horace': [],
'levmar': [],
'lmfit': ['cg', 'newton', 'powell'],
'mantid': ['Conjugate gradient (Fletcher-Reeves imp.)',
'Conjugate gradient (Polak-Ribiere imp.)'],
'matlab': [],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': [],
'nlopt': ['LN_COBYLA'],
'ralfit': [],
'scipy': ['CG', 'Newton-CG', 'Powell'],
'scipy_go': [],
'scipy_ls': [],
'theseus': []}
Steepest Descent (steepest_descent):
{'bumps': [],
'ceres': ['steepest_descent'],
'dfo': [],
'gofit': [],
'gradient_free': [],
'gsl': ['steepest_descent'],
'horace': [],
'levmar': [],
'lmfit': [],
'mantid': ['SteepestDescent'],
'matlab': [],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': [],
'nlopt': [],
'ralfit': [],
'scipy': [],
'scipy_go': [],
'scipy_ls': [],
'theseus': []}
Global Optimization (global_optimization):
{'bumps': ['de', 'dream'],
'ceres': [],
'dfo': [],
'gofit': ['alternating', 'multistart'],
'gradient_free': ['HillClimbingOptimizer',
'RepulsingHillClimbingOptimizer',
'SimulatedAnnealingOptimizer',
'RandomSearchOptimizer',
'RandomRestartHillClimbingOptimizer',
'RandomAnnealingOptimizer',
'ParallelTemperingOptimizer',
'ParticleSwarmOptimizer',
'EvolutionStrategyOptimizer',
'BayesianOptimizer',
'TreeStructuredParzenEstimators',
'DecisionTreeOptimizer'],
'gsl': [],
'horace': [],
'levmar': [],
'lmfit': ['differential_evolution', 'ampgo', 'shgo', 'dual_annealing'],
'mantid': ['FABADA'],
'matlab': [],
'matlab_curve': [],
'matlab_opt': [],
'matlab_stats': [],
'minuit': [],
'nlopt': ['GN_DIRECT',
'GN_DIRECT_L',
'GN_DIRECT_L_RAND',
'GNL_DIRECT_NOSCAL',
'GN_DIRECT_L_NOSCAL',
'GN_DIRECT_L_RAND_NOSCAL',
'GN_ORIG_DIRECT',
'GN_ORIG_DIRECT_L',
'GN_CRS2_LM',
'G_MLSL_LDS',
'G_MLSL',
'GD_STOGO',
'GD_STOGO_RAND',
'GN_AGS',
'GN_ISRES'],
'ralfit': [],
'scipy': [],
'scipy_go': ['differential_evolution', 'shgo', 'dual_annealing'],
'scipy_ls': [],
'theseus': []}
Cost functions
Fitbenchmarking supports multiple cost functions. These can be set via the cost_func_type option in Fitting Options.
Fitbenchmarking is designed to work with problems that have the form
The function \(F(\cdot)\) is known as the cost function, while the function \(r(x,u,p)\) is known as the residual of the cost function. The residual will generally be zero if the fit was perfect. Both of these quantities together define a cost function in FitBenchmarking.
The cost functions that are currently supported are:
Non-linear least squares cost function
- class fitbenchmarking.cost_func.nlls_cost_func.NLLSCostFunc(problem)
This defines the non-linear least squares cost function 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 root least-squares sense by solving:
\[\min_p \sum_{i=1}^n \left(y_i - f(x_i, p)\right)^2\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters. More information on non-linear least squares cost functions can be found here.
Weighted non-linear least squares cost function
- class fitbenchmarking.cost_func.weighted_nlls_cost_func.WeightedNLLSCostFunc(problem)
This defines the weighted non-linear least squares cost function 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 root 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 initial guess for the optimal parameters. More information on non-linear least squares cost functions can be found here.
Hellinger non-linear least squares cost function
- class fitbenchmarking.cost_func.hellinger_nlls_cost_func.HellingerNLLSCostFunc(problem)
This defines the Hellinger non-linear least squares cost function 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 Hellinger least-squares sense by solving:
\[\min_p \sum_{i=1}^n \left(\sqrt{y_i} - \sqrt{f(x_i, p})\right)^2\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters. More information on non-linear least squares cost functions can be found here and for the Hellinger distance measure see here.
Poisson deviance cost function
- class fitbenchmarking.cost_func.poisson_cost_func.PoissonCostFunc(problem)
This defines the Poisson deviance cost-function where, given the set of \(n\) data points \((x_i, y_i)\), and a model function \(f(x,p)\), we find the optimal parameters in the Poisson deviance sense by solving:
\[\min_p \sum_{i=1}^n \left( y_i \left(\log{y_i} - \log{f(x_i, p)} \right) - \left( y_i - f(x_i, p) \right) \right)\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters.
This cost function is intended for positive values.
This cost function is not a least squares problem and as such will not work with least squares minimizers. Please use algorithm_type to select general solvers. See options docs (Fitting Options) for information on how to do this.
FitBenchmarking Output
FitBenchmarking produces tables and reports called support pages as outputs. The links below give descriptions of these outputs.
Tables
- 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.
- class fitbenchmarking.results_processing.acc_table.AccTable(results, best_results, options, group_dir, pp_locations, table_name)
The accuracy results are calculated by evaluating the cost function with the fitted parameters.
- 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 (over num_runs) using the timeit module in python. num_runs is set in FitBenchmarking Options.
- class fitbenchmarking.results_processing.local_min_table.LocalMinTable(results, best_results, options, group_dir, pp_locations, table_name)
The local min results shows a
TrueorFalsevalue together with \(\frac{|| J^T r||}{||r||}\). TheTrueorFalseindicates 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.
- class fitbenchmarking.results_processing.emissions_table.EmissionsTable(results, best_results, options, group_dir, pp_locations, table_name)
The emissions (kg CO2eq) results are calculated from an average (over num_runs) using the codecarbon module. num_runs is set in FitBenchmarking Options.
Configuration for codecarbon is set in
.codecarbon.config.Please note that for tracking CPU power usage on Windows or Mac,
Intel Power Gadgetshoud also be installed. For more information, see the Methodology section of the codecarbon docs.
The tables for accuracy, runtime, emissions, 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.
Below the table there is a Performance Profile.
Support Pages
In each of the tables, a fitting_report for an individual result can be accessed by clicking on the associated table cell. Clicking the problem name at the start of a row will open a Problem Summary Page for the problem as a whole.
The fitting report pages can be used to see more information about the problem and a given fit.
Each page represents a single fitting combination and is split into 2 sections.
First is the initial problem. Here you will see information about the function being fit and the set of initial parameters used for the fitting. If plots are enabled (see Make plots (make_plots)), you will also see a scatter plot of the data to fit with a line of the initial fit given to the minimizer.
The second section focusses on the results of the fitting. Here you will find the minimizer name and the final parameters for the fit found by the minimizer. A plot of the fit is also shown with an overlaid best fit from whichever minimizer was found to produce the smallest error.
The problem summary page can be used to give an overview of the problem and solutions obtained.
First is the initial problem. Here you will see information about the function being fit and the set of initial parameters used for the fitting. If plots are enabled (see Make plots (make_plots)), you will also see a scatter plot of the data to fit with a line of the initial fit given to the minimizer.
The main plot on the page shows a comparison of all fits at once. This can be used to compare how cost functions perform for a problem accross all minimizers.
This uses colours to identify the cost function for each fit and shows all fits on a single graph. The best minimizer for each cost function is more pronounced on the plot.
This should not be used to identify the best individual fit, but can be a good indication of whether cost functions are biased to certain datapoints in the input.
The page ends with an expandable section for each cost function tested, which gives the parameter values and plot of the best fit obtained for that cost function.
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:
CrystalField Data (Mantid)
This folder (also found in examples/benchmark_problems/CrystalField) contains a test set for inelastic neutron scattering measurements of transitions between crystal field energy levels.
This problem has 8 parameters, and fits around 200 data points.
Warning
The external package Mantid must be installed to run this data set. See Installing External Software for details.
CUTEst (NIST files)
This folder (also found in examples/benchmark_problems/CUTEst) contains several problems from the CUTEst continuous optimization testing environment which have been converted to the NIST format.
These problems all have 8 unknown parameters, and fit around 15 data points
with the exception of VESUVIOLS which fits around 1000.
Data Assimilation
This folder (also found in examples/benchmark_problems/Data_Assimilation) contains two examples using the data assimilation problem definition in fitbenchmarking. These examples follow the method set out in this paper.
These data files are synthetic and have been generated as an initial test of the minimizers. We plan to extend this with time series data which is more representative of the expectations for data assimilation in future updates.
These problems have either 2 or 3 unknown parameters, and fit either 100 or
1000 data points for Simplified ANAC and Lorentz problems respectively.
Powder Diffraction Data (SIF files)
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)
These problems (also found in the folder examples/benchmark_problems/MultiFit) contain data for testing the MultiFit 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.
Basic Multifit has 3 unknown parameters, and fits 40 data points. MUSR62260 has 18 unknown parameters, and fits around 8000 data points.
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)
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.
Neutron Data (Mantid)
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.
NIST
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.
Poisson Data
These problems (also found in the folder examples/benchmark_problems/Poisson) contain both simulated and real data measuring particle counts. The real data is ISIS muon data, and the simulated datasets have been made to represent counts using models provided by both Mantid and Bumps.
These problems have between 4 and 6 unknown parameters, and around 350, 800, and 2000 data points for simulated bumps, HIFI_160973, and simulated mantid respectively.
Warning
The external package Mantid must be installed to run this data set. See Installing External Software for details.
Small Angle Scattering (SASView)
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)
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.
SIF_GO
This directory (also found in the folder examples/benchmark_problems/SIF_GO) contains SIF files encoding least squares problems from the CUTEst continuous optimization testing environment.
All of these problems have been modified, with finite bounds added for all parameters, making the problems appropriate for testing global optimization solvers. The bounds that have been added to each problem are the same as those used in SciPy’s global optimization benchmark functions.
These problems have between 3 and 7 unknown parameters, and fit between 9 and 37 data points.
Warning
The external packages CUTEst and pycutest must be installed to run this data set. See Installing External Software for details.
Simple tests
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).
These problems have 3 or 4 unknown parameters, and around 100 data points.
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 is used to select the fitting software to benchmark, this should be a newline-separated list. Available options are:
bumps(default software)ceres(external software – see Installing External Software)CUTEst(external software – see Installing External Software)dfo(external software – see Extra dependencies)gofit(external software – see Extra dependencies)gradient_free(external software – see Extra dependencies)gsl(external software – see Installing External Software)horace(external software – see Installing External Software)levmar(external software – see Extra dependencies)lmfit(external software – see Extra dependencies)mantid(external software – see Installing External Software)matlab(external software – see Installing External Software)matlab_curve(external software – see Installing External Software)matlab_opt(external software – see Installing External Software)matlab_stats(external software – see Installing External Software)minuit(external software – see Extra dependencies)nlopt(external software – see Extra dependencies)ralfit(external software – see Installing External Software)scipy(default software)scipy_ls(default software)scipy_gotheseus(external software – see Installing External Software)
Default software options are scipy and scipy_ls
[FITTING]
software: bumps
dfo
minuit
scipy
scipy_ls
scipy_go
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.
num_runs)Sets the number of runs to average each fit over.
Default is 5
[FITTING]
num_runs: 5
algorithm_type)This is used to select what type of algorithm is used within a specific software. For a full list of available minimizers for each algorithm type, see Algorithm Types of Available Minimizers. The options are:
all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid), see Derivative Free.general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Mead, see Simplextrust_region- algorithms which employ a trust region approach, see Trust Regionlevenberg-marquardt- minimizers that use the Levenberg Marquardt algorithm, see Levenberg-Marquardt.gauss_newton- minimizers that use the Gauss Newton algorithm, see Gauss-Newtonbfgs- minimizers that use the BFGS algorithm, see BFGSconjugate_gradient- Conjugate Gradient algorithms, see Conjugate Gradientsteepest_descent- Steepest Descent algorithms, see Steepest Descentglobal_optimization- Global Optimization algorithms
Default is all
[FITTING]
algorithm_type: all
Warning
Choosing an option other than all may deselect certain
minimizers set in the options file
jac_method)This sets the Jacobian used. Choosing multiple options via a new line seperated list will result in all combinations being benchmarked. Current Jacobian methods are:
analytic- uses the analytic Jacobian extracted from the fitting problem.scipy- uses SciPy’s finite difference Jacobian approximations.default- uses the default derivative approximation implemented in the minimizer.numdifftools- uses the python package numdifftools.
Default is default
[FITTING]
jac_method: scipy
Warning
Currently analytic Jacobians are only available for problems that use the cutest and NIST parsers.
hes_method)This sets the Hessian used. Choosing multiple options via a new line seperated list will result in all combinations being benchmarked. Current Hessian methods are:
default- Hessian information is not passed to minimizersanalytic- uses the analytic Hessian extracted from the fitting problem.scipy- uses SciPy’s finite difference approximations.numdifftools- uses the python package numdifftools.
Default is default
[FITTING]
hes_method: default
Warning
Currently analytic Hessians are only available for problems that use the cutest and NIST parsers.
cost_func_type)This sets the cost functions to be used for the given data. Choosing multiple options via a new line seperated list will result in all combinations being benchmarked. Currently supported cost functions are:
nlls- This sets the cost function to be non-weighted non-linear least squares,NLLSCostFunc.weighted_nlls- This sets the cost function to be weighted non-linear least squares,WeightedNLLSCostFunc.hellinger_nlls- This sets the cost function to be the Hellinger cost function,HellingerNLLSCostFunc.poisson- This sets the cost function to be the Poisson Deviation cost function,PoissonCostFunc.
Default is weighted_nlls
[FITTING]
cost_func_type: weighted_nlls
max_runtime)This sets the maximum runtime a minimizer has to solve one benchmark problem num_runs number of times, where num_runs is another option a user can set. If the minimizer is still running after the maximum time has elapsed, then this result will be skipped and FitBenchmarking will move on to the next minimizer / benchmark dataset combination. The main purpose of this option is to get to result tables quicker by limit the runtime.
max_runtime is set by specifying a number in unit of seconds. Please note that depending on platform the time specified with max_runtime may not match entirely with the absolute run-times specified in tables. Hence you may have to experiment a bit with this option to get the cutoff you want.
Default is 600 seconds
[FITTING]
max_runtime: 600
Minimizer Options
This section is used to declare the minimizers to use for each fitting software. If a fitting software has been selected in Fitting Options then a default set of minimizers for that solver will be run unless alternative minimizer options have been set. All minimizers for a software are included on the default list of minimizers unless otherwise stated.
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 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-bumps) This is mpfit, a translation of MINPACK to Python.Quasi-Newton BFGS (
newton)scipy’s leastsq (
scipy-leastsq) This calls scipy’s Levenberg-Marquardt method. Note that this was the default method for lm prior to Bumps v0.8.2.
Licence The main licence file for Bumps is here. Individual files have their own copyright and licence – if you plan to incorporate this in your own software you should first check that the licences used are compatible.
Links GitHub - bumps
The Bumps minimizers are set as follows:
[MINIMIZERS]
bumps: amoeba
lm-bumps
newton
de
scipy-leastsq
dream
Warning
The additional dependency Bumps must be installed for this to be available; See Extra dependencies.
Note
de is not included in the default list of minimizers for bumps. To run this solver, you must explicitly set the minimizer as seen above.
ceres)Ceres Solver is an open source C++ library for modeling and solving large, complicated optimization problems. It can be used to solve Non-linear Least Squares problems with bounds constraints and general unconstrained optimization problems.
FitBenchmarking currently supports the Ceres Solver minimizers:
Levenberg-Marquardt (
Levenberg-Marquardt)Dogleg (
Dogleg)Steepest Descent (
steepest_descent)BFGS algorithm (
BFGS)LBFGS algorithm (
LBFGS)Fletcher-Reeves Non Linear Conjugate-Gradient (
Fletcher_Reeves)Polak-Ribiere Non Linear Conjugate-Gradient (
Polak_Ribiere)Hestenes-Stiefel Non Linear Conjugate-Gradient (
Hestenes_Stiefel)
Licence Ceres Solver is available under the new BSD licence – details can be found here
Links Ceres Solver PyCeres - Ceres Python Bindings
The Ceres Solver minimizers are set as follows:
[MINIMIZERS]
ceres: Levenberg_Marquardt
Dogleg
BFGS
LBFGS
steepest_descent
Fletcher_Reeves
Polak_Ribiere
Hestenes_Stiefel
Warning
The additional dependency Ceres Solver must be installed for this to be available; See Extra dependencies.
Note
The PyCeres currently only works with Ceres Solver versions 2.0.0
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:
Licence Both DFO-GN and DFO-LS are available under the GPL-3 licence. A proprietary licence is also available from NAG .
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.
gofit)GOFit is a package of C++ algorithms with Python interfaces designed for the global optimization of parameters in curve fitting, i.e. for nonlinear least-squares problems arising from curve fitting. It is also included with Mantid since release 6.5.
FitBenchmarking currently supports the GOFit minimizers:
Multistart Global Minimizer (
multistart)Alternating Multistart Global Minimizer (
alternating)Quadratic Regularisation Local Minimizer (
regularisation)
Links Documentation
Licence GOFit is available under a 3-clause BSD Licence
The GOFit minimizers are set as follows:
[MINIMIZERS]
gofit: multistart
alternating
regularisation
Note
The alternating minimizer currently only supports Crystal Field problems.
Warning
The additional dependency GOFit must be installed to use these minimizers. See Extra dependencies.
gradient_free)Gradient-Free-Optimizers are a collection of gradient-free methods capable of solving various optimization problems. Please note that Gradient-Free-Optimizers must be run with problems that have finite bounds on all parameters.
Hill Climbing (
HillClimbingOptimizer)Repulsing Hill Climbing (
RepulsingHillClimbingOptimizer)Simulated Annealing (
SimulatedAnnealingOptimizer)Random Search (
RandomSearchOptimizer)Random Restart Hill Climbing (
RandomRestartHillClimbingOptimizer)Random Annealing (
RandomAnnealingOptimizer)Parallel Tempering (
ParallelTemperingOptimizer)Particle Swarm (
ParticleSwarmOptimizer)Evolution Strategy (
EvolutionStrategyOptimizer)Bayesian (
BayesianOptimizer)Tree Structured Parzen Estimators (
TreeStructuredParzenEstimators)Decision Tree (
DecisionTreeOptimizer)
Licence The Gradient-Free-Optimizers package is available under an MIT Licence .
The gradient_free minimizers are set as follows:
[MINIMIZERS]
gradient_free: HillClimbingOptimizer
RepulsingHillClimbingOptimizer
SimulatedAnnealingOptimizer
RandomSearchOptimizer
RandomRestartHillClimbingOptimizer
RandomAnnealingOptimizer
ParallelTemperingOptimizer
ParticleSwarmOptimizer
EvolutionStrategyOptimizer
BayesianOptimizer
TreeStructuredParzenEstimators
DecisionTreeOptimizer
Warning
The additional dependency Gradient-Free-Optimizers must be installed for this to be available; See Extra dependencies.
Note
BayesianOptimizer, TreeStructuredParzenEstimators and DecisionTreeOptimizer may be slow running and so are not run by default when gradient_free software is selected. To run these minimizers you must explicity set them as seen above.
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
Licence The GNU Scientific Library is available under the GPL-3 licence .
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.
horace)Horace is described as a suite of programs for the visiualization and analysis from time-of-flight neutron inelastic scattering spectrometers. We currently support:
Levenberg-Marquardt (
lm-lsqr)
Licence Matlab must be installed to use Horace within FitBenchmarking, which is a proprietary product. Horace is made available under the the GPL-3 licence.
[MINIMIZERS]
horace: lm-lsqr
Note
If you have a non standard installation of Horace please set the HORACE_LOCATION (e.g on IDAaaS).
Warning
The Horace Toolbox and MATLAB must be installed for this to be available; see Installing External Software.
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)FABADA (
FABADA)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.
Licence Mantid is available under the GPL-3 licence .
The Mantid minimizers are set as follows:
[MINIMIZERS]
mantid: BFGS
Conjugate gradient (Fletcher-Reeves imp.)
Conjugate gradient (Polak-Ribiere imp.)
Damped GaussNewton
FABADA
Levenberg-Marquardt
Levenberg-MarquardtMD
Simplex
SteepestDescent
Trust Region
Warning
The external package Mantid must be installed to use these minimizers.
levmar)The levmar package which implements the Levenberg-Marquardt method for nonlinear least-squares. We interface via the python interface available on PyPI.
Levenberg-Marquardt with supplied Jacobian (
levmar) - the Levenberg-Marquardt method
Licence Levmar is available under the GPL-3 licence . A paid licence for proprietary commerical use is available from the author .
The levmar minimizer is set as follows:
[MINIMIZERS]
levmar: levmar
Warning
The additional dependency levmar must be installed for this to be available; See Extra dependencies. This package also requires the BLAS and LAPACK libraries to be present on the system.
lmfit)The lmfit package provides simple tools to help you build complex fitting models for non-linear least-squares problems and apply these models to real data. Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. It builds on and extends many of the optimization methods of scipy.optimize.
Levenberg-Marquardt (
leastsq)Least-Squares minimization, using Trust Region Reflective method (
least_squares)Differential evolution (
differential_evolution)Adaptive Memory Programming for Global Optimization (
ampgo)Nelder-Mead (
nelder)L-BFGS-B (
lbfgsb)Powell (
powell)Conjugate-Gradient (
cg)Newton-CG (
newton)Cobyla (
cobyla)BFGS (
bfgs)Truncated Newton (
tnc)Newton-CG trust-region (
trust-ncg)Nearly exact trust-region (
trust-exact)Newton GLTR trust-region (
trust-krylov)Trust-region for constrained optimization (
trust-constr)Dog-leg trust-region (
dogleg)Sequential Linear Squares Programming (
slsqp)Simplicial Homology Global Optimization (
shgo)Dual Annealing optimization (
dual_annealing)
Licence LMFIT is available the new BSD-3 licence – details can be found here
The lmfit minimizer is set as follows:
[MINIMIZERS]
lmfit: differential_evolution
powell
cobyla
slsqp
emcee
nelder
least_squares
trust-ncg
trust-exact
trust-krylov
trust-constr
dogleg
leastsq
newton
tnc
lbfgsb
bfgs
cg
ampgo
shgo
dual_annealing
Note
The shgo solver is particularly slow running and should generally be avoided. As a result, this solver is not run by default when lmfit software is selected. In order to run this minimizer, you must explicitly set it as above.
Warning
emcee uses a Markov Chain Monte Carlo package and assumes that the prior is Uniform. This may not perform well for certain fitting problems.
matlab)We call the fminsearch function from MATLAB, using the MATLAB Engine API for Python.
Nelder-Mead Simplex (
Nelder-Mead Simplex)
Licence Matlab is a proprietary product .
The matlab minimizer is set as follows:
[MINIMIZERS]
matlab: Nelder-Mead Simplex
Warning
MATLAB must be installed for this to be available; See Installing External Software.
matlab_curve)We call the fit function from the MATLAB Curve Fitting Toolbox, using the MATLAB Engine API for Python.
Levenberg-Marquardt (
Levenberg-Marquardt)Trust-Region (
Trust-Region)
Licence Matlab and the Curve Fitting Toolbox are both proprietary products .
The matlab_curve minimizers are set as follows:
[MINIMIZERS]
matlab_curve: Levenberg-Marquardt
Trust-Region
Warning
MATLAB Curve Fitting Toolbox must be installed for this to be available; See Installing External Software.
matlab_opt)We call the lsqcurvefit function from the MATLAB Optimization Toolbox, using the MATLAB Engine API for Python.
Levenberg-Marquardt (
levenberg-marquardt)Trust-Region-Reflective (
trust-region-reflective)
Licence Matlab and the Optimization Toolbox are both proprietary products .
The matlab_opt minimizers are set as follows:
[MINIMIZERS]
matlab_opt: levenberg-marquardt
trust-region-reflective
Warning
MATLAB Optimization Toolbox must be installed for this to be available; See Installing External Software.
matlab_stats)We call the nlinfit function from the MATLAB Statistics Toolbox, using the MATLAB Engine API for Python.
Levenberg-Marquardt (
Levenberg-Marquardt)
Licence Matlab and the Statistics Toolbox are both proprietary products .
The matlab_stats minimizer is set as follows:
[MINIMIZERS]
matlab_stats: Levenberg-Marquardt
Warning
MATLAB Statistics Toolbox must be installed for this to be available; See Installing External Software.
minuit)CERN developed the Minuit 2 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 (
migrad)Minuit’s SIMPLEX (
simplex)
Links Github - iminuit
Licence iminuit is released under the MIT licence, while Minuit 2 is LGPL v2 .
The Minuit minimizers are set as follows:
[MINIMIZERS]
minuit: migrad
simplex
Warning
The additional dependency Minuit must be installed for this to be available; See Extra dependencies.
nlopt)NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms
Bound Optimization BY Quadratic Approximation (
LN_BOBYQA)NEW Unconstrained Optimization Algorithm (
LN_NEWUOA)NEW Unconstrained Optimization Algorithm, bound algorithm (
LN_NEWUOA_BOUND)Principal-axis method (
LN_PRAXIS)Sequential Least Squares Programming (
LD_SLSQP)Shifted limited-memory variable-metric (rank 1) (
LD_VAR1)Shifted limited-memory variable-metric (rank 2) (
LD_VAR2)Augmented Lagrangian local (
AUGLAG)Augmented Lagrangian local (equality constraints) (
AUGLAG_EQ)Nelder-Mead Simplex (
LN_NELDERMEAD)Subplex (
LN_SBPLX)Constrained Optimization BY Linear Approximations (
LN_COBYLA)Conservative convex separable approximation (
LD_CCSAQ)Method of Moving Asymptotes (
LD_MMA)Newton (
LD_TNEWTON)Newton preconditioned by the low-storage BFGS algorithm (
LD_TNEWTON_PRECOND)Newton with steepest-descent restarting (
LD_TNEWTON_RESTART)Newton preconditioned by the low-storage BFGS algorithm with steepest-descent restarting (
LD_TNEWTON_PRECOND_RESTART)LBFGS, and derivative-free algorithm (
LD_LBFGS)DIviding RECTangles (
GN_DIRECT)DIviding RECTangles (locally biased) (
GN_DIRECT_L)DIviding RECTangles (locally biased which uses some randomization) (
GN_DIRECT_L_RAND)DIviding RECTangles (unscaled variant) (
GNL_DIRECT_NOSCAL)DIviding RECTangles (locally biased and unscaled variant) (
GN_DIRECT_L_NOSCAL)DIviding RECTangles (locally biased, unscaled variant which uses some randomization) (
GN_DIRECT_L_RAND_NOSCAL)DIviding RECTangles (based on the original Fortran code by Gablonsky et al. (1998-2001)) (
GN_ORIG_DIRECT)DIviding RECTangles (based on the original Fortran code by Gablonsky et al. (1998-2001) and locally biased ) (
GN_ORIG_DIRECT_L)Controlled Random Search (
GN_CRS2_LM)Multi-Level Single-Linkage, low-discrepancy sequence (
G_MLSL_LDS)Multi-Level Single-Linkage (
G_MLSL)Stochastic Global Optimization (
GD_STOGO)Stochastic Global Optimizatiom, randomized variant (
GD_STOGO_RAND)AGS (
GN_AGS)Improved Stochastic Ranking Evolution Strategy (
GN_ISRES)
The Nlopt minimizers are set as follows:
[MINIMIZERS]
nlopt: LN_BOBYQA
LN_NEWUOA
LN_NEWUOA_BOUND
LN_PRAXIS
LD_SLSQP
LD_VAR2
LD_VAR1
AUGLAG
AUGLAG_EQ
LN_NELDERMEAD
LN_SBPLX
LN_COBYLA
LD_CCSAQ
LD_MMA
LD_TNEWTON_PRECOND_RESTART
LD_TNEWTON_PRECOND
LD_TNEWTON_RESTART
LD_TNEWTON
LD_LBFGS
GN_DIRECT
GN_DIRECT_L
GN_DIRECT_L_RAND
GNL_DIRECT_NOSCAL
GN_DIRECT_L_NOSCAL
GN_DIRECT_L_RAND_NOSCAL
GN_ORIG_DIRECT
GN_ORIG_DIRECT_L
GN_CRS2_LM
G_MLSL_LDS
G_MLSL
GD_STOGO
GD_STOGO_RAND
GN_AGS
GN_ISRES
Note
The global optimization solvers are not run by default when nlopt software is selected. In order to run these minimizers, you must explicitly set them as above.
Note
The following 4 minimizers need a local optimizer selected to run. This has been set to use LD_LBFGS.
[MINIMIZERS]
nlopt: AUGLAG
AUGLAG_EQ
G_MLSL_LDS
G_MLSL
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)Newton, trust region method (
newton)Newton-tensor, trust region method (
newton-tensor)Gauss-Newton, regularization (
gn_reg)Hybrid Newton/Gauss-Newton, regularization (
hybrid_reg)Newton, regularization (
newton_reg)Newton-tensor, regularization (
newton-tensor_reg)
Note that the Newton-tensor methods take significantly longer than the other options to run (but may give a better solution in some cases). For this reason, they are not included in the default minimizers for RALFit, but must be turned on in the options file.
Links Github - RALFit. RALFit’s Documentation on: Gauss-Newton/Hybrid models, the trust region method and The regularization method
Licence RALFit is available under a 3-clause BSD Licence
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 is the standard python package for mathematical software. In particular, we use the minimize solver for general minimization problems from the optimization chapter of 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)BFGS algorithm (
BFGS)Newton-CG algorithm (
Newton-CG)L-BFGS-B algorithm (
L-BFGS-B)Newton-CG trust-region (
trust-ncg)Nearly exact trust-region (
trust-exact)Newton Generalized Lanczos Trust Region (
trust-krylov)Trust-region for constrained optimization (
trust-constr)Dog-leg trust-region (
dogleg)
Links Github - SciPy minimize
Licence Scipy is available under a 3-clause BSD Licence. Individual packages may have their own (compatible) licences, as listed here.
The SciPy minimizers are set as follows:
[MINIMIZERS]
scipy: Nelder-Mead
Powell
CG
BFGS
Newton-CG
L-BFGS-B
TNC
SLSQP
COBYLA
trust-ncg
trust-exact
trust-krylov
trust-constr
dogleg
Note
The Hessian enabled solvers are not run by default when scipy software is selected. In order to run these minimizers, you must explicitly set them as above.
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 of SciPy’s library.
Levenberg-Marquardt with supplied Jacobian (
lm-scipy) - a wrapper around MINPACKThe Trust Region Reflective algorithm (
trf)A dogleg algorithm with rectangular trust regions (
dogbox)
Links Github - SciPy least_squares
Licence Scipy is available under a 3-clause BSD Licence. Individual packages many have their own (compatible) licences, as listed here.
The SciPy least squares minimizers are set as follows:
[MINIMIZERS]
scipy_ls: lm-scipy
trf
dogbox
scipy_go)SciPy is the standard python package for mathematical software. In particular, we use the Global Optimization solvers for global optimization problems from the optimization chapter of SciPy’s library.
Differential Evolution (derivative-free) (
differential_evolution)Dual Annealing (
dual_annealing)
Links Github - SciPy optimization
Licence Scipy is available under a 3-clause BSD Licence. Individual packages may have their own (compatible) licences, as listed here.
The SciPy global optimization minimizers are set as follows:
[MINIMIZERS]
scipy_go: differential_evolution
shgo
dual_annealing
Note
The shgo solver is particularly slow running and should generally be avoided. As a result, this solver is not run by default when scipy_go software is selected. In order to run this minimizer, you must explicitly set it as above.
theseus)Theseus is an efficient application-agnostic library for building custom nonlinear optimization layers in PyTorch to support constructing various problems in robotics and vision as end-to-end differentiable architectures.
Levenberg Marquardt (
Levenberg_Marquardt)Gauss Newton (
Gauss-Newton)
Links Paper- Theseus optimization
Licence Theseus is available under a MIT licence.
The theseus minimizers are set as follows:
[MINIMIZERS]
theseus: Levenberg_Marquardt
Gauss-Newton
Note
We strongly recommend you install Theseus in a venv or conda environment with Python 3.7-3.9
Jacobian Options
The Jacobian section allows you to control which methods for computing Jacobians the software uses.
analytic)Analytic Jacobians can only be used for specific Problem Definition Files. Currently the supported formats are cutest and NIST. The only option is:
default- use the analytic derivative provided by a supported format.
Default is default
[JACOBIAN]
analytic: default
scipy)Calculates the Jacobian using the numerical Jacobian in
SciPy, this uses scipy.optimize._numdiff.approx_derivative. The supported
options are:
2-point- use the first order accuracy forward or backward difference.3-point- 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 2-point
Licence SciPy is available under a 3-clause BSD Licence. Individual packages may have their own (compatible) licences, as listed here.
[JACOBIAN]
scipy: 2-point
default)This uses the approximation of the Jacobian that is used by default in the minimizer, and will vary between solvers. If the minimizer requires the user to pass a Jacobian, a warning will be printed to the screen and the SciPy (scipy) 2-point approximation will be used. The only option is:
default- use the default derivative approximation provided by the software.
Default is default
[JACOBIAN]
default: default
numdifftools)Calculates the Jacobian using the python package numdifftools.
We allow the user to change the method used, but other options
(e.g, the step size generator and the order of the approximation) are set the defaults.
The supported options are:
central- central differencing. Almost as accurate as complex, but with no restriction on the type of function.forward- forward differencing.backward- backward differencing.complex- based on the complex-step derivative method of Lyness and Moler. Usually the most accurate, provided the function is analytic.multicomplex- extends complex method using multicomplex numbers. (see, e.g., Lantoine, Russell, Dargent (2012)).
Default is central.
Licence numdifftools is available under a 3-clause BSD Licence.
[JACOBIAN]
numdifftools: central
Hessian Options
The Hessian section allows you to control which methods for computing Hessians the software uses.
analytic)Analytic Hessians can only be used for specific Problem Definition Files. Currently the supported formats are cutest and NIST. The only option is:
default- use the analytic derivative provided by a supported format.
Default is default
[HESSIAN]
analytic: default
scipy)Calculates the Hessian from the Jacobian using the finite differencing in
SciPy, this uses scipy.optimize._numdiff.approx_derivative. The supported
options are:
2-point- use the first order accuracy forward or backward difference.3-point- 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 2-point
Licence SciPy is available under a 3-clause BSD Licence. Individual packages may have their own (compatible) licences, as listed here.
[HESSIAN]
scipy: 2-point
default)Hessian information is not passed to minimizers. The only option is:
default- don’t pass Hessian information to minimizers.
Default is default
[HESSIAN]
default: default
numdifftools)Calculates the Hessian from the Jacobian using the python package numdifftools.
We allow the user to change the method used, but other options
(e.g, the step size generator and the order of the approximation) are set to the defaults.
The supported options are:
central- central differencing. Almost as accurate as complex, but with no restriction on the type of function.forward- forward differencing.backward- backward differencing.complex- based on the complex-step derivative method of Lyness and Moler. Usually the most accurate, provided the function is analytic.multicomplex- extends complex method using multicomplex numbers. (see, e.g., Lantoine, Russell, Dargent (2012)).
Default is central.
Licence numdifftools is available under a 3-clause BSD Licence.
[HESSIAN]
numdifftools: central
Output Options
The output section contains options to control how results are outputted and presented.
results_dir)This is used to select where the output should be saved. If the results directory command line argument is provided, this option is overridden.
Default is fitbenchmarking_results
[OUTPUT]
results_dir: fitbenchmarking_results
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)
[OUTPUT]
make_plots: yes
colour_map)Specifies the name of the colourmap the user wishes to use, e.g. magma, viridis, OrRd. Options are:
Any colourmap from the library in
matplotlib, see the complete library here.Appending
_rto the end of the name will reverse the colourmap.The following sequential colourmaps are recommended:
Default colourmap is magma_r
[OUTPUT]
colour_map: magma_r
cmap_range)A two-element list used to specify the lower and upper limit of the chosen colourmap. Options are:
[lower_limit, upper_limit]where limits consider the full colourscale limits to be 0 and 1, so any pair of values must fall within this range.Limits should be introduced to make the white text readable, see the following example.
Default for magma is [0.2, 0.8] (suitability depends on colourmap)
[OUTPUT]
colour_map: magma_r
cmap_range: [0.2, 0.8]
colour_ulim)Controls how relatively poorly a minimizer has to perform in order to receive the worst colour. For example, a value of 100 would mean that any performance greater than or equal to 100 times worse than the best minimizer would receive the worst colour. This ensures that colour scale is not compromised by especially poor relative results. Options are:
Any float between
1andnp.infRecommended value
100
Default is 100
[OUTPUT]
colour_map: magma_r
cmap_range: [0.2, 0.8]
colour_ulim: 100
comparison_mode)This selects the mode for displaying values in the resulting table
options are abs, rel, both:
absindicates that the absolute values should be displayedrelindicates that the values should all be relative to the best resultbothwill show data in the form “abs (rel)”
Default is both
[OUTPUT]
comparison_mode: both
table_type)This selects the types of tables to be produced in FitBenchmarking. Options are:
accindicates that the resulting table should contain the chi squared values for each of the minimizers.runtimeindicates that the resulting table should contain the runtime values for each of the minimizers.compareindicates that the resulting table should contain both the chi squared value and runtime value 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_minindicates 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. This option is only meaningful for least-squares cost functions.emissionsindicates that the resulting table should contain the CO2 emissions for each of the minimizers.
Default is acc, runtime, compare, local_min, and emissions.
[OUTPUT]
table_type: acc
runtime
compare
local_min
emissions
Logging Options
The logging section contains options to control how fitbenchmarking logs information.
file_name)This specifies the file path to write the logs to.
Default is fitbenchmarking.log
[LOGGING]
file_name: fitbenchmarking.log
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
level)This specifies the minimum level of logging to display on console during runtime. Options are (from most logging to least):
NOTSETDEBUGINFOWARNINGERRORCRITICAL
Default is INFO
[LOGGING]
level: INFO
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.
Licence This file format needs PyCUTEst and the packages ARCHDefs, CUTEst and SIFDECODE to be installed. PyCUTEst is available under the GPL-3 licence. ARCHDEFS, CUTEst and SIFDECODE are available under an LGPL (v2.1 or later) licence.
In order for FitBenchmarking to access the data, the SIF files must be written using the following conventions.
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>
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.
Parameter ranges can be added to SIF files using the BOUNDS indicator card.
Currently in Fitbenchmarking, problems with parameter ranges can be handled by SciPy, Bumps, Minuit, Mantid, Matlab Optimization Toolbox, DFO, Levmar and RALFit fitting software. Please note that the following Mantid minimizers currently throw an exception when parameter ranges are used: BFGS, Conjugate gradient (Fletcher-Reeves imp.), Conjugate gradient (Polak-Ribiere imp.) and SteepestDescent.
Native File Format
In FitBenchmarking, the native file format is used to read IVP, 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.
Examples of native problems are:
# Fitbenchmark Problem
software = 'ivp'
name = 'Lorentz'
description = 'A simple lorentz system for testing the ivp parser. Exact results should be 10, 28, 8/3.'
input_file = 'lorentz3d.txt'
function = 'module=functions/lorentz,func=lorentz3d,step=0.1,sigma=11,r=30,b=3'
plot_scale = 'linear'
# 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]}
# FitBenchmark Problem
software = 'SASView'
name = '1D cylinder (synthetic neutron) IV0'
description = 'A first iteration synthetic dataset generated for the 1D cylinder SASView model in the fashion of neutron small angle scattering experiments. Generated on Fri May 28 10:31:19 2021.'
input_file = '1D_cylinder_20_400_nosmearing_neutron_synth.txt'
function = 'name=cylinder,radius=35.0,length=350.0,background=0.0,scale=1.0,sld=4.0,sld_solvent=1.0'
plot_scale = 'loglog'
These examples show 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 ‘IVP’, ‘Mantid’, ‘SasView’, or ‘Horace’ (case insensitive).
This defines whether to use an IVP format, 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).
For information on the ‘Horace’ format, see Horace File Format.
Licence Mantid is available under a GPL-3 Licence. The component of SasView we use is SasModels, which is available under a BSD 3-clause licence.
- 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 will be displayed in the Problem Summary Pages and Fitting Reports produced by a benchmark.
- 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 x11 [x12 [x13 ...]] y11 [y12 [y13 ...]] [e11 [e12 ...]] x21 [x22 [x23 ...]] y21 [y22 [y23 ...]] [e21 [e22 ...]] ...
Mantid uses the convention of
# X Y Eas the header and SASView uses the convention<X> <Y> <E>, although neither of these are enforced. The error column is optional in this format.If the data contains multiple inputs or outputs, the header must be written in one of the above conventions with the labels as “x”, “y”, or “e” followed by a number. An example of this can be seen in
examples/benchmark_problems/Data_Assimilation/data_files/lorentz.txt- plot_scale
The scale of the x and y axis for the plots. The options are ‘loglog’, ‘logy’, ‘logx’ and ‘linear’. If this is not set it will default to ‘linear’.
- 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.
IVP
The IVP parser allows a user to define
fin the following equation:\[x' = f(t, x, *args)\]To do this we use a python module to define the function. As in the above formula, the function can take the following arguments:
t (float): The time to evaluate at
x (np.array): A value for x to evaluate at
*args (floats): The parameters to fit
To link to this function we use a function string with the following parameters:
module: The path to the module
func: The name of the function within the module
step: The time step that the input data uses (currently only fixed steps are supported - if you need varying time steps please raise an issue on our GitHub)
*args: Starting values for the parameters
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.
Horace
The Horace functions are defined here Horace File Format
- 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.
- parameter_ranges
An optional setting which specifies upper and lower bounds for parameters in the problem.
Similarly to
fit_ranges, it takes the form where the first number is the minimum in the range and the second is the maximum.Currently in Fitbenchmarking, problems with parameter_ranges can be handled by SciPy, Bumps, Minuit, Mantid, Matlab Optimization Toolbox, DFO, Levmar and RALFit fitting software. Please note that the following Mantid minimizers currently throw an exception when parameter_ranges are used: BFGS, Conjugate gradient (Fletcher-Reeves imp.), Conjugate gradient (Polak-Ribiere imp.) and SteepestDescent.
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]}]
plot_scale = 'linear'
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_filesgiven 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>whereiis the position of the function in the function string, andnameis 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.
Horace File Format
The Horace file format is based on Native File Format, this page is intended to demonstrate where the format differs.
Examples of horace problems are:
# FitBenchmark Problem
software = 'Horace'
name = '1D Gaussian 1'
description = '1D Gaussian (Test example from test_multifit_herbert)'
input_file = 'testdata_multifit_1.mat'
function = 'foreground=m_scripts/functions/mftest_gauss_bkgd_fb_test.m ,height=100,centre=50,sigma=7,const=0,grad=0'
wye_function = 'matlab_script=m_scripts/wye_functions/fb_wye_IX_1D_test1.m'
simulate_function = 'matlab_script=m_scripts/simulate_functions/fb_simulate_IX_1D_test1.m'
# FitBenchmark Problem
software = 'Horace'
name = '3D_Gaussian'
description = '3D Gaussian (Horace\documentation\herbert\example_codes). The data in this example is generated in the wye_function'
input_file = 'dummy.txt'
function = 'foreground=m_scripts/functions/gauss3d_fb_test.m ,height=1100,centre_p1=66 ,centre_p2=1055,centre_p3=117,covmat_p1=15,covmat_p2=3,covmat_p3=5,covmat_p4=30,covmat_p5=-3,covmat_p6=20; background=m_scripts/functions/linear3D_bg_fb_test.m,bkgd_const=15'
wye_function = 'matlab_script=m_scripts/wye_functions/fb_wye_IX_1D_test6.m'
simulate_function = 'matlab_script=m_scripts/simulate_functions/fb_simulate_IX_1D_test6.m'
# FitBenchmark Problem
software = 'Horace'
name = 'PCSMO_at_001_data'
description = 'PCSMO data at 0.0001 data'
input_file = 'pcsmo_ei70_base_bkd.sqw'
function = 'foreground=m_scripts/functions/testfunc_nb_sqw_db_test.m ,JF1=-11.39,JA=1.5,JF2=-1.35,JF3=1.5,Jperp=0.88,D=0.074,en_width=0.1'
wye_function = 'matlab_script=m_scripts/wye_functions/fb_wye_pcsmo_test.m'
simulate_function = 'matlab_script=m_scripts/simulate_functions/fb_simulate_pcsmo_test.m'
The Horace file format requires you to have run the benchmark problem in Horace
using fit() and simulate() successfully. Relevant links on
how to run this are: Multifit ,
Advanced Multifit
and Tobyfit problems as well as
Running Horace in Parallel.
As in the native format, an input file must start with a comment indicating that it is a FitBenchmarking problem followed by a number of key value pairs. Available keys can be seen in Native File Format and below:
- software, name, description
As described in the native format.
- input_file
For Horace we require a
.sqwor.matfile containing preprocessed, Horace-compatible data.
Note
The .mat file is the result of using save(file, sqw_objects)
and should be used if you are loading in multiple sqw objects. Make sure
to load the data in appropriately in the wye_function.
- function
The function is defined by one or more matlab script (
.m) files which return a model of the foreground or foreground and background respectively.The format for defining the function is based on comma-separated key-value pairs, where the Matlab script files are defined by the variables “foreground” and “background”. The remaining pairs define the starting values for each of the models, respectively. It’s important to note that these pairs must be defined after their respective models.
If both the foreground and background models are defined, they should be given as a semicolon-separated list. In this case, there would be two comma-separated key-value pairs, one for the foreground and foreground parameters, and another for the background and background parameters, separated by a semicolon.
Examples:
Only foreground:
function = 'foreground=m_scripts/functions/mftest_gauss_bkgd.m ,height=100,centre=50,sigma=7,const=0,grad=0'
Foreground and background:
function = 'foreground=m_scripts/functions/gauss.m ,height=1100,centre=66 ,stdev=13; background=m_scripts/functions/linear_bg.m ,bkgd_const=15'
Note
All parameters must have unique names e.g.
function = 'foreground=gauss.m ,height=100,centre=50,sigma=7' ; background=gauss.m ,height_bkgd=100,centre_bkgd=50,sigma_bkgd=7'
- wye_function
The wye_function is defined by a matlab file which returns the:
- w
an
sqw,dnd,ix_datasetobject orxyestruct. (see Multifit)- e
standard deviation data
- y
intensity data
- msk
logical mask array of which elements are to be retained
This function takes the path to the datafile and the path to the matlab functions as arguments.
Explained example of the wye_function:
The first three lines add the path to matlab functions needed for fitting and loads the w object.
addpath(genpath(path)); source_data = load(datafile); w = source_data.w1 ;The next line gets the y and e from the w object. These are the true y, e and msk values from the experiment.
[y, e, msk] = sigvar_get(w);Any elements in msk that have a corresponding element in y that is equal to zero will be set to zero. The purpose of this is to exclude these elements from subsequent calculations, since they are not informative.
msk(y==0) = 0;The last two lines of the wye_function applies the msk to the y and e data. As the e from retrieved above is the variance we have taken the square root of the value to get the standard deviation.
y = y(msk); e = sqrt(e(msk));Examples of the wye_function:
function [w, y, e, msk] = fb_wye_IX_1D_test(datafile, path) % Gets the w , y, e and msk from the sqw object addpath(genpath(path)); source_data = load(datafile); w = source_data.w1 ; [y, e, msk] = sigvar_get(w); msk(y==0) = 0; y = y(msk); e = sqrt(e(msk)); endfunction [w, y, e, msk] = fb_wye_pcsmo_test(datafile, path) % Gets the w , x, y ,e and msk from the sqw object sqw_file = datafile addpath(genpath(path)); ne = 10; frac = 1.e-6; ei = [25, 35, 50, 70, 100, 140]; freq = [300, 200, 200, 250, 300, 400]; proj = ortho_proj([1, 0, 0], [0, 1, 0], 'type', 'rrr'); sample = IX_sample(true,[0,0,1],[0,1,0],'cuboid',[0.01,0.05,0.01]); sample.angdeg = [90 90 90]; sample.alatt = [3.4 3.4 3.4]; maps = maps_instrument(ei(4), freq(4), 'S'); lower_e = ei(4)*0.2; upper_e = ei(4)*0.7; ebin = [lower_e, ei(4)/25, upper_e]; w1 = cut_sqw(sqw_file, proj, [-1, 2/39, 1], [-1, 2/39, 1], [-10, 10], ebin); w1 = set_sample(w1,sample); w1 = set_instrument(w1,maps); w1 = mask_random_fraction_pixels(w1, 0.1); [y, e, msk] = sigvar_get(w1); msk(y==0) = 0; y = y(msk); e = sqrt(e(msk)); w = w1;
- simulate_function
The simulate_function is defined by a matlab file which returns the derived y values from simulate() for the fitting function. This matlab file takes in the w, fitpars (fitting parameters) and msk. The w and msk are the same as the wye_function. The fitpars are determined by the current minimizer.
Explained Example of the simulate_function:
forefunc = @mftest_gauss_bkgd;
mf = multifit(w);
mf = mf.set_fun(forefunc);
mf = mf.set_pin(fitpars);
[wout,fitpar] = mf.simulate();
[y, e] = sigvar_get(wout);
y=y(msk);
Note
If the benchmark problem uses random numbers in any way (e.g. tobyfit).
A persisent seed needs to be set before simulate is run.
This makes sure that it uses the same seed everytime simulate() is ran.
persistent seed
if isempty (seed)
rng(3,"twister");
seed = rng();
else
rng(seed);
end
Examples of the simulate_function:
function y = fb_simulate_IX_1D_test1(w,fitpars,msk)
% simulate loop to solve for the parameters
forefunc = @mftest_gauss_bkgd_fb_test;
mf = multifit(w);
mf = mf.set_fun(forefunc);
mf = mf.set_pin(fitpars);
[wout,fitpar] = mf.simulate();
[y, e] = sigvar_get(wout);
y=y(msk);
end
function y = fb_simulate_pcsmo_test(w,fitpars,msk)
%simulate loop to solve for the parameters
persistent seed
if isempty (seed)
rng(3,"twister");
seed = rng();
else
rng(seed);
end
JF1 = -11.39; JA = 1.5; JF2 = -1.35; JF3 = 1.5; Jperp = 0.88; D = 0.074; en_width = 0.1;
frac = 1.e-6;
sw_obj = pcsmo_fb_test(JF1, JA, JF2, JF3, Jperp, D);
%fitpars = [JF1 JA JF2 JF3 Jperp D en_width];
cpars = {fitpars 'mat', {'JF1', 'JA', 'JF2', 'JF3', 'Jperp', 'D(3,3)'}, ...
'hermit', false, 'optmem', 0, 'useFast', false, 'formfact', ...
true,'resfun', 'gauss', 'coordtrans', diag([2 2 1 1]), ...
'use_brille', true, 'node_volume_fraction', frac, ...
'use_vectors', false, 'Qtrans', diag([1./4 1./4 1.])};
tbf = tobyfit(w);
tbf = tbf.set_fun(@sw_obj.horace_sqw, {cpars{:}});
tbf = tbf.set_mc_points(5);
[fit_data , fit_pars] = tbf.simulate();
[y, e, ~] = sigvar_get(fit_data);
y=y(msk);
end
Note
All the functions needed in the fitting must be in the subdirectory of the benchmark problem.
Note
If you have a non standard installation of Horace please set the HORACE_LOCATION and the SPINW_LOCATION as environment variables(e.g on IDAaaS).
Note
Horace Problems currently does not support plotting. Please set make_plots: no in the options file.
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
Checkpointing in FitBenchmarking
In some cases, fitting can take a long time and rerunning the fits to change output options is inefficient. For this situation we provide a checkpointing file.
Using the checkpointing feature
As indicated above this feature is currently only for rendering changes to the presentation of runs although we plan to extend it to combining and filtering runs in the future.
By default, when running FitBenchmarking it will create a checkpoint file in
the results directory which will contain all the information required to create
output tables and plots.
To generate new reports for an existing checkpoint, use the
--load_checkpoint option:
fitbenchmarking --load_checkpoint
This can use the same options file as in the original run but with the output changed, or seperate one as long as the results directory and checkpointing file are the same.
There is also a seperate tool for working with checkpoint files
fitbenchmarking-cp that can be used to regenerate the reports.
fitbenchmarking-cp report --help
Warnings
Using --load_checkpoint will not re-run any results or run any new
combinations that have been added to the options file.
This command also does not check that the checkpoint file has not been edited manually. Manual editing may be desired for removing or combining data while these features are developed but should be done with caution to ensure results are still comparable.
FitBenchmarking Tests
The tests for FitBenchmarking require pytest>=3.6. We have split the tests
into three categories:
default: denotes tests involvingpipinstallable software packages,all: in addition todefault, also runs tests on external packages, with the exception of matlab.matlab: Runs tests for matlab fitting software. Please note that these tests can currently only be run locally through pytest.
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 truly 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 non-scalar ties in native FitBenchmarking problems with the Mantid software.
Mantid allows parameters to be tied to expressions - e.g. X0=5.0 or X0=X1*2. While scalar ties are now supported for all minimizers the more complicated expressions are not supported. If you need this feature please get in touch with the development team with your use case.
Running Mantid problems with Matlab fitting software.
To run problems with Matlab fitting software through FitBenchmarking, within the Matlab Controller the dynamically created cost_func.eval_model function is serialized and then loaded in the Matlab Engine workspace. However for Mantid problems, this function is not picklable resulting in the problem being skipped over.
In all cases, the stopping criterion of each minimizer is set to the default value. An experienced user can change this.
Specific Problem/Minimizer Combinations
CrystalField Example with Mantid - DampedGaussNewton Minimizer.
With this combination, GSL is known to crash during Mantid’s fitting. This causes python to exit without completing any remaining runs or generating output files. More information may be available via the issue on Mantid’s github page.
FitBenchmarking tutorial videos
On this page you will find some short tutorial videos on how to use FitBenchmarking.
Interpreting FitBenchmarking results
The following video explains how to interpret FitBenchmarking results.
Running FitBenchmarking
The following video explains how to run FitBenchmarking.
python -m pip install fitbenchmarking[bumps,DFO,gradient_free,minuit,SAS,numdifftools]
fitbenchmarking
fitbenchmarking -p examples/benchmark_problems/NIST/low_difficulty
fitbenchmarking -o examples/options_template.ini
fitbenchmarking -r new_results/
fitbenchmarking -t acc runtime
fitbenchmarking -t acc -l WARNING
Choosing your options
The following video explains how to choose the best cost function / software / minimizer / Jacobian / Hessian for your data.
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/parsingdirectory. 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,
ParserThe parser must implement
parse(self)method which takes onlyselfand returns a populatedFittingProblem
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 editParserFactory. This should be done in such a way that the type is inferred from the file.Document the parser (see Problem Definition Files), being sure to include any licencing information.
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 thetest_parsers.generate_test_cases()function, one needs to add the new parser’s name to the variableformats, based on whether or not the parser ispipinstallable. There are 2 types of test files needed:Generic tests:
fitbenchmarking/parsing/tests/expected/contains two files,basic.jsonandstart_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 calledbasic.<ext>,start_end_x.<ext>, where<ext>is the extension of the new file format, and they must be placed infitbenchmarking/parsing/tests/<format_name>/.Function tests: A file named
function_evaluations.jsonmust also be provided infitbenchmarking/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 givenxxvalues andparams. If the result is not suitably close to the specified value the test will fail.Jacobian tests: If the parser you add has analytic Jacobian information, then in
test_parsers.pyadd<format_name>to theJACOBIAN_ENABLED_PARSERSglobal variable. Then add a filejacobian_evaluations.jsontofitbenchmarking/parsing/tests/<format_name>/, which tests that the Jacobian evaluation behaves as expected. This file should have the same file structure as function_evaluations.json, and works in a similar way.Hessian tests: If the parser you add has analytic Hessian information, then in
test_parsers.pyadd<format_name>to theHESSIAN_ENABLED_PARSERSglobal variable. Then add a filehessian_evaluations.jsontofitbenchmarking/parsing/tests/<format_name>/, which tests that the Hessian evaluation behaves as expected. This file should have the same file structure as function_evaluations.json, and works in a similar way.Integration tests: Add an example to the directory
fitbenchmarking/test_files/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’ssetup.py, then add the same example tofitbenchmarking/test_files/default_parsers_set/.- As part of this, the
systests/expected_results/all_parsers.csvfile, and if necessary the
systests/expected_results/default_parsers_set.csvfile, 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.
- As part of this, the
Verify that your tests have been found and are successful by running pytest -vv fitbenchmarking/parsing/tests/test_parsers.py
Once the new parser is added, please add some examples that use this problem definition type following the instructions at Adding More Data.
Adding More Data
We encourage users to contribute more data sets to FitBenchmarking; the more data we have available to test against, the more useful FitBenchmarking is. First, please ensure that there is a parser available that can read your dataset, and if necessary follow the instructions to add this functionality to FitBenchmarking. Once this is done, follow the steps below and add to a pull request to make the data available to others.
Create a directory that contains:
the data sets to be included
a file META.txt containing metadata about the dataset.
a subfolder data_files which contains any supplemental data
needed by the data parser. We particularly encourage analytic derivative information, if available.
Update the Benchmark problems page to include a description of the dataset. As well as information about the source of the data, this should include:
information about how many parameters and how many data points
are to be fitted in the dataset
details of any external software that needs to be installed to load these
datasets.
Create zip and tar.gz archives of these directories, and pass along to one of the core developers to put on the webspace. They will pass you a location of the dataset to include in the description page, and update the folder containing all examples to contain your data set.
If the data is to be distributed with the GitHub source, add the directory to the examples/benchmark_problems folder and commit to the repository. Please note that the maximum size of a file supported by GitHub is 100MB, and so datasets with files larger than this cannot be added to the repository. Also, GitHub recommends that the size of a Git repository is not more than 1GB.
Adding new cost functions
This section describes how to add cost functions to benchmarking in FitBenchmarking
In order to add a new cost function, <cost_func>,
you will need to:
Create
fitbenchmarking/cost_func/<cost_func>_cost_func.py, which contains a new subclass ofCostFunc. Then implement the methods:- abstract CostFunc.eval_cost()
Evaluate the cost function
- Parameters
params (list) – The parameters to calculate residuals for
- Returns
evaluated cost function
- Return type
float
- abstract CostFunc.jac_res()
Uses the Jacobian of the model to evaluate the Jacobian of the cost function residual, \(\nabla_p r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the residual at each x, y pair
- Return type
a list of 1D numpy arrays
- abstract CostFunc.jac_cost()
Uses the Jacobian of the model to evaluate the Jacobian of the cost function, \(\nabla_p F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the cost function
- Return type
1D numpy array
- abstract CostFunc.hes_res()
Uses the Hessian of the model to evaluate the Hessian of the cost function residual, \(\nabla_p^2 r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian and Jacobian of the residual at each x, y pair
- Return type
tuple (list of 2D numpy arrays, list of 1D numpy arrays)
- abstract CostFunc.hes_cost()
Uses the Hessian of the model to evaluate the Hessian of the cost function, \(\nabla_p^2 F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian of the cost function
- Return type
2D numpy array
Document the available cost functions by:
adding
<cost_func>to thecost_func_typeoption in Fitting Options.updating any example files in the
examplesdirectoryadding the new cost function to the Cost functions user docs.
Create tests for the cost function in
fitbenchmarking/cost_func/tests/test_cost_func.py.
The FittingProblem and CostFunc classes
When adding new cost functions, you will find it helpful to make use of the
following members of the FittingProblem
class.
- class fitbenchmarking.parsing.fitting_problem.FittingProblem(options)
Definition of a fitting problem, which will be populated by a parser from a problem definition file.
Onces populated, this should include the data, the function and any other additional requirements from the data.
- data_e
numpy array The errors or weights
- data_x
numpy array The x-data
- data_y
numpy array The y-data
- eval_model(params, **kwargs)
Function evaluation method
- Parameters
params (list) – parameter value(s)
- Returns
data values evaluated from the function of the problem
- Return type
numpy array
You will also find it useful to implement the subclass members of
CostFunc,
Jacobian and
Hessian.
- class fitbenchmarking.cost_func.base_cost_func.CostFunc(problem)
Base class for the cost functions.
- abstract eval_cost(params, **kwargs)
Evaluate the cost function
- Parameters
params (list) – The parameters to calculate residuals for
- Returns
evaluated cost function
- Return type
float
- abstract hes_cost(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function, \(\nabla_p^2 F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian of the cost function
- Return type
2D numpy array
- abstract hes_res(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function residual, \(\nabla_p^2 r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian and Jacobian of the residual at each x, y pair
- Return type
tuple (list of 2D numpy arrays, list of 1D numpy arrays)
- abstract jac_cost(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function, \(\nabla_p F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the cost function
- Return type
1D numpy array
- abstract jac_res(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function residual, \(\nabla_p r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the residual at each x, y pair
- Return type
a list of 1D numpy arrays
- class fitbenchmarking.jacobian.base_jacobian.Jacobian(problem)
Base class for Jacobian.
- abstract eval(params, **kwargs)
Evaluates Jacobian of the model, \(\nabla_p f(x,p)\), at the point given by the parameters.
- Parameters
params (list) – The parameter values at which to evaluate the Jacobian
- Returns
Computed Jacobian
- Return type
numpy array
- class fitbenchmarking.hessian.base_hessian.Hessian(problem, jacobian)
Base class for Hessian.
- abstract eval(params, **kwargs)
Evaluates Hessian of the model
- Parameters
params (list) – The parameter values to find the Hessian at
- Returns
Computed Hessian
- Return type
3D numpy array
Adding new Hessians
This section describes how to add further methods to approximate the Hessian within FitBenchmarking
In order to add a new Hessian evaluation method, <hes_method>,
you will need to:
Create
fitbenchmarking/hessian/<hes_method>_hessian.py, which contains a new subclass ofhessian. Then implement the method:- abstract Hessian.eval()
Evaluates Hessian of the model
- Parameters
params (list) – The parameter values to find the Hessian at
- Returns
Computed Hessian
- Return type
3D numpy array
The method is set sequentially within
loop_over_hessians()by using themethodattribute of the class.Enable the new method as an option in Fitting Options, following the instructions in Adding new Options. Specifically:
Amend the
VALID_FITTINGdictionary so that the element associated with thehes_methodkey contains the new<hes_method>.
Document the available Hessians by:
adding to the list of available
methodoptions underhes_methodin Fitting Options.updating any example files in the
examplesdirectory
Create tests for the Hessian evaluation in
fitbenchmarking/hessian/tests/test_hessians.py.
The FittingProblem and Jacobian
When adding new Hessian, you will find it helpful to make use of the
following members of the FittingProblem
and subclasses of Jacobian:
- class fitbenchmarking.parsing.fitting_problem.FittingProblem(options)
Definition of a fitting problem, which will be populated by a parser from a problem definition file.
Onces populated, this should include the data, the function and any other additional requirements from the data.
- data_e
numpy array The errors or weights
- data_x
numpy array The x-data
- data_y
numpy array The y-data
- eval_model(params, **kwargs)
Function evaluation method
- Parameters
params (list) – parameter value(s)
- Returns
data values evaluated from the function of the problem
- Return type
numpy array
- class fitbenchmarking.jacobian.base_jacobian.Jacobian(problem)
Base class for Jacobian.
- abstract eval(params, **kwargs)
Evaluates Jacobian of the model, \(\nabla_p f(x,p)\), at the point given by the parameters.
- Parameters
params (list) – The parameter values at which to evaluate the Jacobian
- Returns
Computed Jacobian
- Return type
numpy array
Adding new Jacobians
FitBenchmarking allows the use of custom methods for evaluating the Jacobian of the model. This section describes how to add further methods within FitBenchmarking
In order to add a new Jacobian evaluation method, <jac_method>,
you will need to:
Create
fitbenchmarking/jacobian/<jac_method>_jacobian.py, which contains a new subclass ofJacobian. Then implement the method:- abstract Jacobian.eval()
Evaluates Jacobian of the model, \(\nabla_p f(x,p)\), at the point given by the parameters.
- Parameters
params (list) – The parameter values at which to evaluate the Jacobian
- Returns
Computed Jacobian
- Return type
numpy array
The numerical method is set sequentially within
loop_over_jacobians()by using themethodattribute of the class.Enable the new method as an option in Fitting Options, following the instructions in Adding new Options. Specifically:
Amend the
VALID_FITTINGdictionary so that the element associated with thejac_methodkey contains the new<jac_method>.Extend the
VALID_JACOBIANdictionary to have a new key<jac_method>, with the associated element being a list of valid options for this Jacobian.Extend the
DEFAULT_JACOBIANdictionary to have a new key<jac_method>, with the associated element being a subset of the valid options added inVALID_JACOBIANin the previous step.Amend the file
fitbenchmarking/utils/tests/test_options_jacobian.pyto include tests for the new options.
Document the available Jacobians by:
adding a list of available
methodoptions to the docs for Jacobian Options, and including licencing information, if appropriate.updating any example files in the
examplesdirectory
Create tests for the Jacobian evaluation in
fitbenchmarking/jacobian/tests/test_jacobians.py.
The FittingProblem class
When adding new Jacobian, you will find it helpful to make use of the
following members of the FittingProblem
class:
- class fitbenchmarking.parsing.fitting_problem.FittingProblem(options)
Definition of a fitting problem, which will be populated by a parser from a problem definition file.
Onces populated, this should include the data, the function and any other additional requirements from the data.
- data_e
numpy array The errors or weights
- data_x
numpy array The x-data
- data_y
numpy array The y-data
- eval_model(params, **kwargs)
Function evaluation method
- Parameters
params (list) – parameter value(s)
- Returns
data values evaluated from the function of the problem
- Return type
numpy array
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.pywhich contains a new subclass ofController.Note
Please note that if the fitting package being added uses Matlab, then the new controller should also inherit from the mixin class
MatlabMixin.- class fitbenchmarking.controllers.matlab_mixin.MatlabMixin(cost_func)
Mixin class for matlab fitting software controllers
- py_to_mat(func)
Get the named function from the matlab version of the cost function
- Parameters
func (str) – The name of the function to retrieve
The new controller should implement four functions, as well as initializing the dictionary
algorithm_check:
- Controller.algorithm_check = {'all': [], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': [], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, 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 information about derivatives – e.g., theSimplexmethod inMantid)
general- minimizers which solve a generic min f(x)
simplex- derivative free simplex based algorithms e.g. Nelder-Mead
trust_region- algorithms which emply a trust region approach
levenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithm
gauss_newton- minimizers that use the Gauss Newton algorithm
bfgs- minimizers that use the BFGS algorithm
conjugate_gradient- Conjugate Gradient algorithms
steepest_descent- Steepest Descent algorithms
global_optimization- Global Optimization algorithmsThe 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
GSLcontroller.
- 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).
- Parameters
cost_func (subclass of
CostFunc) – Cost function object selected from options.
- abstract 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).
If a solver supports bounded problems, then this is where value_ranges should be set up for that specific solver. The default format is a list of tuples containing the lower and upper bounds for each parameter e.g. [(p1_lb, p2_ub), (p2_lb, p2_ub),…]
- abstract Controller.fit()
Run the fitting.
This will be timed so should include only what is needed to fit the data.
- abstract 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 asself.flag.The flag corresponds to the following messages:
- flag()
By default, a controller does not accept Jacobian or Hessian information. If the controller being added can use hessians and/or jacobians, then the following controller attributes should be set:
- Controller.jacobian_enabled_solvers = []
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.
jacobian_enabled_solvers: a list of minimizers in a specificsoftware that allow Jacobian information to be passed into the fitting algorithm
- Controller.hessian_enabled_solvers = []
Within the controller class, you must define the list
hessian_enabled_solversif any of the minimizers for the specific software are able to use hessian information.
hessian_enabled_solvers: a list of minimizers in a specificsoftware that allow Hessian information to be passed into the fitting algorithm
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), including licencing information, if appropriate. 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 toexamples/all_software.ini.You should also ensure that the available minimizers are catagorised correctly in
self.algorithm_checkusing the algorithm type options. Please refer to the Optimization Algorithms page for more information about each algorithm type.Create tests for the software in
fitbenchmarking/controllers/tests/test_controllers.py. If the package ispipinstallable then add the tests to theDefaultControllerTestsclass and if not add to theExternalControllerTestsclass. Unless the new controller is more complicated than the currently available controllers, this can be done by following the example of the others.If the software is deterministic, add the software to the regression tests in
fitbenchmarking/systests/test_regression.py.If pip installable add to
install_requiresinsetup.pyand add to the installation step in.github/workflows/release.yml. If not, document the installation procedure in Installing External Software and update theFullInstallDocker Container – the main developers will help you with this.
Note
For ease of maintenance, please add new controllers to a list of software in alphabetical order.
The FittingProblem, CostFunc and Jacobian classes
When adding new minimizers, you will find it helpful to make use of the
following members of the
FittingProblem, subclasses of
CostFunc and subclasses of
Jacobian classes:
- class fitbenchmarking.parsing.fitting_problem.FittingProblem(options)
Definition of a fitting problem, which will be populated by a parser from a problem definition file.
Onces populated, this should include the data, the function and any other additional requirements from the data.
- data_e
numpy array The errors or weights
- data_x
numpy array The x-data
- data_y
numpy array The y-data
- eval_model(params, **kwargs)
Function evaluation method
- Parameters
params (list) – parameter value(s)
- Returns
data values evaluated from the function of the problem
- Return type
numpy array
- set_value_ranges(value_ranges)
Function to format parameter bounds before passing to controllers, so self.value_ranges is a list of tuples, which contain lower and upper bounds (lb,ub) for each parameter in the problem
- Parameters
value_ranges (dict) –
- dictionary of bounded parameter names with
lower and upper bound values e.g.
{p1_name: [p1_min, p1_max], ...}
- class fitbenchmarking.cost_func.base_cost_func.CostFunc(problem)
Base class for the cost functions.
- abstract eval_cost(params, **kwargs)
Evaluate the cost function
- Parameters
params (list) – The parameters to calculate residuals for
- Returns
evaluated cost function
- Return type
float
- class fitbenchmarking.jacobian.base_jacobian.Jacobian(problem)
Base class for Jacobian.
- abstract eval(params, **kwargs)
Evaluates Jacobian of the model, \(\nabla_p f(x,p)\), at the point given by the parameters.
- Parameters
params (list) – The parameter values at which to evaluate the Jacobian
- Returns
Computed Jacobian
- Return type
numpy array
Adding new Options
Default options are set by the class
Options, which is defined in
the file fitbenchmarking/utils/options.py.
The options used 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,
JACOBIAN, OUTPUT 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>inOptionsto include any new default options.If the option amended is to be checked for validity, add accepted option values to the
VALID_<SECTION>dictionary inOptions.Using the
read_value()function, add your new option to the class, following the examples already inOptions. The syntax of this function is:- Options.read_value(func, option, additional_options)
Helper function which loads in the value
- Parameters
func (callable) – configparser function
option (str) – option to be read for file
additional_options – A dictionary of options
input by the user into the command line. :type additional_options: dict
- 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_fittinghas tests for theFITTINGsection.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 anOptionsErrorif 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.pywhich contains a new subclass ofTable. The main functions to change are:- abstract Table.get_value(result)
Gets the main value to be reported in the tables for a given result
If more than one value is returned please note that the first value will be used in the default colour handling.
- Parameters
result (FittingResult) – The result to generate the values for.
- Returns
The value to convert to a string for the tables
- Return type
tuple(float)
- Table.display_str(value)
Converts a value generated by
get_value()into a string respresentation to be used in the tables. Base class implementation takes the relative and absolute values and usesself.output_string_typeas a template for the string format. This can be overridden to adequately display the results.- Parameters
value (tuple) – Relative and absolute values
- Returns
string representation of the value for display in the table.
- Return type
str
Additional functions that may need to be overridden are:
- static Table.get_error_str(result, error_template='[{}]')
Get the error string for a result based on error_template This can be overridden if tables require different error formatting.
- Parameters
result (FittingResult) – The result to get the error string for
- Returns
A string representation of the error
- Return type
str
- Table.get_link_str(result)
Get the link as a string for the result. This can be overridden if tables require different links.
- Parameters
result (FittingResult) – The result to get the link for
- Returns
The link to go to when the cell is selected
- Return type
string
- static Table.vals_to_colour(vals, cmap, cmap_range, log_ulim)
Converts an array of values to a list of hexadecimal colour strings using logarithmic sampling from a matplotlib colourmap according to relative value.
- Parameters
vals (list[float]) – values in the range [0, 1] to convert to colour strings
cmap (matplotlib colourmap object) – matplotlib colourmap
cmap_range (list[float], 2 elements) – values in range [0, 1] for colourmap cropping
log_ulim (float) – log10 of worst shading cutoff value
- Returns
Colours as hex strings for each input value and Foreground colours for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
- Return type
tuple[list[str], list[str]]
Extend the
table_typeoption inOUTPUTfollowing 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 infitbenchmarking/results_processing/tests/test_tables.generate_test_files, both a HTML and text table output as the expected result and adding the new table name to the global variableSORTED_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 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 substitutions 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 appropriate 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.
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.
Install Instructions for Contributors
Please see below for the recommended install instructions for new contributors:
Before installing, it is recommended that you create an empty virtual environment. For more information about installing packages using virtual environments please see here.
To ensure that you are working with the latest version of the code, you should install Fitbenchmarking from source. For details on how to do this, please see Installing from source.
Note
The editable flag should be used when installing from source, like
pip install -e ..., so that changes made to the cloned code are immediately reflected in the imported package.So that you are able to run all tests locally, all extra dependencies and external software should be installed. Please see Extra dependencies and Installing External Software for instructions on how to do this.
Note
Please note that if you are using WSL, Matlab is likely to be difficult to install so it is recommended that you use an alternative operating system if you are working on Matlab related issues.
Additionally, the tests run through Github do not currently include Matlab fitting software, so please ensure tests are run locally before submitting code.
You should check that the versions of packages such as pylint are up to date with those used listed in requirements.txt. This can be done by running the command
pip install -r requirements.txtfrom within thefitbenchmarkingdirectory.
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 necessary.
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 (PR) 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 PR 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 decision. If this is the case, you should mark the PR 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 PRs 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 PRs that in addition must be merged into the release branch.
The recommended mechanism for merging PRs labeled 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 and the tag will need to be pushed to github.
If the release is a final release (i.e., it has no numbered label) and it is the highest numbered release, the tag should be cherry-picked onto the master branch. Instructions for this should be output after running the script.
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 release-0.1.x
git push origin v0.1.0-beta1
<And make the release on GitHub>
And for after the version is tested and ready for a final release, one would run:
git checkout release-0.1.x
ci/prep_and_tag_release.sh v0.1.0
git push origin release-0.1.x
git push origin v0.1.0
git switch master
git cherry-pick v0.1.0
git push
<And make the release on GitHub>
Adding New Datasets
Users or developers are encouraged to add new data sets following the instructions here. Someone in SCD’s Computational Mathematics Group must make this publically available by:
adding the zip and tar.gz archives to powell:/var/www/html/numerical-www/fitbenchmarking/
adding the datasets to the master examples.zip and examples.tar.gz folders, and updating the versions on powell
Please note that the maximum file size allowed by GitHub is 100MB, and the total repository size is recommended to be kept below 1GB. Please bear this in mind when advising users whether or not they should also add their data to the examples/benchmark_problems directory of FitBenchmarking.
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 latest 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 latest 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, Test Files, 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)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)The CLI directory is used to define all of the entry points into the software. Currently this is just the main fitbenchmarking command.
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)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.
Hessian)This directory holds the Hessian class,
and subclasses, which are used by the controllers to approximate second derivatives.
Currently available options are described in Fitting Options, and new
Hessians can be added by following the instructions in Adding new Hessians.
jacobian)This directory holds the Jacobian class,
and subclasses, which are used by the controllers to approximate derivatives.
Currently available options are described in Jacobian Options, and new
numerical Jacobians can be added by following the instructions in
Adding new Jacobians.
test_files)The test files 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)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)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.
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/test_files/), and compares the text output with that stored in /fitbenchmarking/systests/expected_results/.
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)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
This is a command line tool for handling checkpoint files for 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.checkpoint_handler.generate_report(options_file='', additional_options=None, debug=False)
Generate the fitting reports and tables for a checkpoint file.
- Parameters
options_file (str, optional) – Path to an options file, defaults to ‘’
additional_options (dict, optional) –
Extra options for the reporting. Available keys are:
filename (str): The checkpoint file to use.
- fitbenchmarking.cli.checkpoint_handler.get_parser() argparse.ArgumentParser
Creates and returns a parser for the args.
- Returns
Configured argument parser
- Return type
ArgumentParser
- fitbenchmarking.cli.checkpoint_handler.main()
Entry point exposed as the fitbenchmarking-cp command.
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
Implements the base class for the fitting software controllers.
- class fitbenchmarking.controllers.base_controller.Controller(cost_func)
Bases:
objectBase 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, 4, 5, 6, 7]
- algorithm_check = {'all': [], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': [], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- check_attributes()
A helper function which checks all required attributes are set in software controllers
- check_bounds_respected()
Check whether the selected minimizer has respected parameter bounds
- check_minimizer_bounds(minimizer)
Helper function which checks whether the selected minimizer from the options (options.minimizer) supports problems with parameter bounds
- Parameters
minimizer (str) – string of minimizers selected from the options
- abstract 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 asself.flag.The flag corresponds to the following messages:
- flag()
- 0: Successfully converged1: Software reported maximum number of iterations exceeded2: Software run but didn’t converge to solution3: Software raised an exception4: Solver doesn’t support bounded problems5: Solution doesn’t respect parameter bounds6: Solver has exceeded maximum allowed runtime7: Validation of the provided options failed
- controller_name = None
A name to be used in tables. If this is set to None it will be inferred from the class name.
- 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
- execute()
Starts and stops the timer used to check if the fit reaches the ‘max_runtime’. In the middle, it calls self.fit().
- abstract fit()
Run the fitting.
This will be timed so should include only what is needed to fit the data.
- property flag
- 0: Successfully converged1: Software reported maximum number of iterations exceeded2: Software run but didn’t converge to solution3: Software raised an exception4: Solver doesn’t support bounded problems5: Solution doesn’t respect parameter bounds6: Solver has exceeded maximum allowed runtime7: Validation of the provided options failed
- hessian_enabled_solvers = []
Within the controller class, you must define the list
hessian_enabled_solversif any of the minimizers for the specific software are able to use hessian information.hessian_enabled_solvers: a list of minimizers in a specific
software that allow Hessian information to be passed into the fitting algorithm
- incompatible_problems = []
A list of incompatible problem formats for this controller.
- jacobian_enabled_solvers = []
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- prepare()
Check that function and minimizer have been set. If both have been set, run self.setup().
- record_alg_type(minimizer, algorithm_type)
Helper function which records the algorithm types of the selected minimizer that match those chosen in options
- Parameters
minimizer (str) – string of minimizers selected from the options
algorithm_type (list) – the algorithm type selected from the options
- abstract 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).
If a solver supports bounded problems, then this is where value_ranges should be set up for that specific solver. The default format is a list of tuples containing the lower and upper bounds for each parameter e.g. [(p1_lb, p2_ub), (p2_lb, p2_ub),…]
- property software
Return the name of the software.
This assumes the class is named ‘<software>Controller’
- validate() None
Validates that the provided options are compatible with each other. If there are some invalid options, the relevant exception is raised.
- 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 (list) – the algorithm type selected from the options
Implements a controller for the Bumps fitting software.
- class fitbenchmarking.controllers.bumps_controller.BumpsController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the Bumps fitting software.
Sasview requires a model to fit. Setup creates a model with the correct function.
- algorithm_check = {'all': ['amoeba', 'lm-bumps', 'newton', 'de', 'scipy-leastsq', 'dream'], 'bfgs': ['newton'], 'conjugate_gradient': [], 'deriv_free': ['amoeba', 'de', 'dream'], 'gauss_newton': [], 'general': ['amoeba', 'newton', 'de', 'dream'], 'global_optimization': ['de', 'dream'], 'levenberg-marquardt': ['lm-bumps', 'scipy-leastsq'], 'ls': ['lm-bumps', 'scipy-leastsq'], 'simplex': ['amoeba'], 'steepest_descent': [], 'trust_region': ['lm-bumps', 'scipy-leastsq']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- 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
Implements a controller for the Ceres fitting software.
- class fitbenchmarking.controllers.ceres_controller.CeresController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for Ceres Solver
- algorithm_check = {'all': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'bfgs': ['BFGS', 'LBFGS'], 'conjugate_gradient': ['Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'simplex': [], 'steepest_descent': ['steepest_descent'], 'trust_region': ['Levenberg_Marquardt', 'Dogleg']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with Ceres solver
- jacobian_enabled_solvers = ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with Ceres solver
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:
objectA 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
Implements a controller for DFO-GN http://people.maths.ox.ac.uk/robertsl/dfogn/
- class fitbenchmarking.controllers.dfo_controller.DFOController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the DFO-{GN/LS} fitting software.
- algorithm_check = {'all': ['dfogn', 'dfols'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['dfogn', 'dfols'], 'gauss_newton': ['dfogn'], 'general': [], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': ['dfogn', 'dfols'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['dfols', 'dfogn']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- 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
Implements a controller for global optimization algorithms.
- class fitbenchmarking.controllers.gofit_controller.GOFitController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for GOFit multistart global optimisation algorithms
- algorithm_check = {'all': ['alternating', 'multistart', 'regularisation'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': ['regularisation'], 'general': [], 'global_optimization': ['alternating', 'multistart'], 'levenberg-marquardt': ['regularisation'], 'ls': ['alternating', 'multistart', 'regularisation'], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with GoFit
- jacobian_enabled_solvers = ['multistart', 'regularisation']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with GoFit
Implements a controller for Gradient Free Optimizers
- class fitbenchmarking.controllers.gradient_free_controller.GradientFreeController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the Gradient Free Optimizers fitting software.
- algorithm_check = {'all': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer', 'BayesianOptimizer', 'TreeStructuredParzenEstimators', 'DecisionTreeOptimizer'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer', 'BayesianOptimizer', 'TreeStructuredParzenEstimators', 'DecisionTreeOptimizer'], 'gauss_newton': [], 'general': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer', 'BayesianOptimizer', 'TreeStructuredParzenEstimators', 'DecisionTreeOptimizer'], 'global_optimization': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer', 'BayesianOptimizer', 'TreeStructuredParzenEstimators', 'DecisionTreeOptimizer'], 'levenberg-marquardt': [], 'ls': [], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- controller_name = 'gradient_free'
A name to be used in tables. If this is set to None it will be inferred from the class name.
- fit()
Run problem with Gradient Free Optimizers
- setup()
Setup for Gradient Free Optimizers
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(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the GSL fitting software
- algorithm_check = {'all': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'bfgs': ['vector_bfgs', 'vector_bfgs2'], 'conjugate_gradient': ['conjugate_fr', 'conjugate_pr'], 'deriv_free': ['nmsimplex', 'nmsimplex2'], 'gauss_newton': [], 'general': ['nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'global_optimization': [], 'levenberg-marquardt': ['lmder', 'lmsder'], 'ls': ['lmsder', 'lmder'], 'simplex': ['nmsimplex', 'nmsimplex2'], 'steepest_descent': ['steepest_descent'], 'trust_region': ['lmder', 'lmsder']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with GSL
- jacobian_enabled_solvers = ['lmsder', 'lmder', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup for GSL
Implements a controller to the lm implementation in Herbert/Horace
- class fitbenchmarking.controllers.horace_controller.HoraceController(cost_func)
Bases:
fitbenchmarking.controllers.matlab_mixin.MatlabMixin,fitbenchmarking.controllers.base_controller.ControllerController for fit in Herbert
- algorithm_check = {'all': ['lm-lsqr'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['lm-lsqr'], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['lm-lsqr'], 'ls': ['lm-lsqr'], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- fit()
Run problem with Horace
- horace_on()
Turning Horace on in matlab
- incompatible_problems = ['mantid']
A list of incompatible problem formats for this controller.
- setup()
Setup for Matlab fitting
Implements a controller for the levmar fitting software. http://www.ics.forth.gr/~lourakis/levmar/ via the python interface https://pypi.org/project/levmar/
- class fitbenchmarking.controllers.levmar_controller.LevmarController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the levmar fitting software
- algorithm_check = {'all': ['levmar'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['levmar'], 'ls': ['levmar'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['levmar']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- fit()
run problem with levmar
- jacobian_enabled_solvers = ['levmar']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with levmar
Implements a controller for the lmfit fitting software.
- class fitbenchmarking.controllers.lmfit_controller.LmfitController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for lmfit
- algorithm_check = {'all': ['differential_evolution', 'powell', 'cobyla', 'slsqp', 'emcee', 'nelder', 'least_squares', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg', 'leastsq', 'newton', 'tnc', 'lbfgsb', 'bfgs', 'cg', 'ampgo', 'shgo', 'dual_annealing'], 'bfgs': ['lbfgsb', 'bfgs'], 'conjugate_gradient': ['cg', 'newton', 'powell'], 'deriv_free': ['powell', 'cobyla', 'emcee', 'nelder', 'differential_evolution'], 'gauss_newton': ['newton', 'tnc'], 'general': ['nelder', 'powell', 'cg', 'bfgs', 'newton', 'lbfgs', 'tnc', 'slsqp', 'differential_evolution', 'shgo', 'dual_annealing'], 'global_optimization': ['differential_evolution', 'ampgo', 'shgo', 'dual_annealing'], 'levenberg-marquardt': ['leastsq'], 'ls': ['least_squares', 'leastsq'], 'simplex': ['nelder'], 'steepest_descent': [], 'trust_region': ['least_squares', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with lmfit
- hessian_enabled_solvers = ['newton', 'dogleg', 'trust-constr', 'trust-ncg', 'trust-krylov', 'trust-exact']
Within the controller class, you must define the list
hessian_enabled_solversif any of the minimizers for the specific software are able to use hessian information.hessian_enabled_solvers: a list of minimizers in a specific
software that allow Hessian information to be passed into the fitting algorithm
- jacobian_enabled_solvers = ['cg', 'bfgs', 'newton', 'lbfgsb', 'tnc', 'slsqp', 'dogleg', 'trust-ncg', 'trust-krylov', 'trust-exact']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- lmfit_jacobians(params)
lmfit jacobians
- lmfit_resdiuals(params)
lmfit resdiuals
- setup()
Setup problem ready to be run with lmfit
Implements a controller for the Mantid fitting software.
- class fitbenchmarking.controllers.mantid_controller.MantidController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the Mantid fitting software.
Mantid requires subscribing a custom function in a predefined format, so this controller creates that in setup.
- COST_FUNCTION_MAP = {'nlls': 'Unweighted least squares', 'poisson': 'Poisson', 'weighted_nlls': 'Least squares'}
A map from fitbenchmarking cost functions to mantid ones.
- algorithm_check = {'all': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region', 'FABADA'], 'bfgs': ['BFGS'], 'conjugate_gradient': ['Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)'], 'deriv_free': ['Simplex', 'FABADA'], 'gauss_newton': ['Damped GaussNewton'], 'general': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Simplex', 'SteepestDescent'], 'global_optimization': ['FABADA'], 'levenberg-marquardt': ['Levenberg-Marquardt', 'Levenberg-MarquardtMD'], 'ls': ['Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Trust Region', 'FABADA'], 'simplex': ['Simplex'], 'steepest_descent': ['SteepestDescent'], 'trust_region': ['Trust Region', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- 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.
- jacobian_enabled_solvers = ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'SteepestDescent', 'Trust Region']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to run with Mantid.
Adds a custom function to Mantid for calling in fit().
Implements a controller for MATLAB
- class fitbenchmarking.controllers.matlab_controller.MatlabController(cost_func)
Bases:
fitbenchmarking.controllers.matlab_mixin.MatlabMixin,fitbenchmarking.controllers.base_controller.ControllerController for MATLAB fitting (fminsearch)
- algorithm_check = {'all': ['Nelder-Mead Simplex'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['Nelder-Mead Simplex'], 'gauss_newton': [], 'general': ['Nelder-Mead Simplex'], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': [], 'simplex': ['Nelder-Mead Simplex'], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- fit()
Run problem with Matlab
- incompatible_problems = ['mantid']
A list of incompatible problem formats for this controller.
- setup()
Setup for Matlab fitting
Implements a controller for MATLAB Curve Fitting Toolbox
- class fitbenchmarking.controllers.matlab_curve_controller.MatlabCurveController(cost_func)
Bases:
fitbenchmarking.controllers.matlab_mixin.MatlabMixin,fitbenchmarking.controllers.base_controller.ControllerController for MATLAB Curve Fitting Toolbox fitting (fit)
- algorithm_check = {'all': ['Trust-Region', 'Levenberg-Marquardt'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['Levenberg-Marquardt'], 'ls': ['Trust-Region', 'Levenberg-Marquardt'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['Trust-Region', 'Levenberg-Marquardt']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- controller_name = 'matlab_curve'
A name to be used in tables. If this is set to None it will be inferred from the class name.
- fit()
Run problem with Matlab
- incompatible_problems = ['mantid', 'horace']
A list of incompatible problem formats for this controller.
- setup()
Setup for Matlab fitting
Implements mixin class for the matlab fitting software controllers.
- class fitbenchmarking.controllers.matlab_mixin.MatlabMixin(cost_func)
Bases:
objectMixin class for matlab fitting software controllers
- clear_matlab()
Clear the matlab instance, ready for the next setup.
- py_to_mat(func)
Get the named function from the matlab version of the cost function
- Parameters
func (str) – The name of the function to retrieve
- setup_timer()
Create an interface into the timer associated with the cost function. The timer will be created in the matlab engine as “timer” and must not be overridden, although this can be changed in this function if there’s a conflict.
This overrides the controller’s timer so that it can be controlled from other parts of the code.
- class fitbenchmarking.controllers.matlab_mixin.MatlabTimerInterface(timer)
Bases:
objectA timer to convert from matlab to the python timer.
Implements a controller for MATLAB Optimization Toolbox
- class fitbenchmarking.controllers.matlab_opt_controller.MatlabOptController(cost_func)
Bases:
fitbenchmarking.controllers.matlab_mixin.MatlabMixin,fitbenchmarking.controllers.base_controller.ControllerController for MATLAB Optimization Toolbox, implementing lsqcurvefit
- algorithm_check = {'all': ['levenberg-marquardt', 'trust-region-reflective'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['levenberg-marquardt'], 'ls': ['levenberg-marquardt', 'trust-region-reflective'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['levenberg-marquardt', 'trust-region-reflective']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- controller_name = 'matlab_opt'
A name to be used in tables. If this is set to None it will be inferred from the class name.
- fit()
Run problem with Matlab Optimization Toolbox
- incompatible_problems = ['mantid']
A list of incompatible problem formats for this controller.
- jacobian_enabled_solvers = ['levenberg-marquardt', 'trust-region-reflective']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup for Matlab Optimization Toolbox fitting
Implements a controller for MATLAB Statistics Toolbox
- class fitbenchmarking.controllers.matlab_stats_controller.MatlabStatsController(cost_func)
Bases:
fitbenchmarking.controllers.matlab_mixin.MatlabMixin,fitbenchmarking.controllers.base_controller.ControllerController for MATLAB Statistics Toolbox fitting (nlinfit)
- algorithm_check = {'all': ['Levenberg-Marquardt'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['Levenberg-Marquardt'], 'ls': ['Levenberg-Marquardt'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['Levenberg-Marquardt']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- controller_name = 'matlab_stats'
A name to be used in tables. If this is set to None it will be inferred from the class name.
- fit()
Run problem with Matlab Statistics Toolbox
- incompatible_problems = ['mantid']
A list of incompatible problem formats for this controller.
- setup()
Setup for Matlab fitting
Implements a controller for the CERN package 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(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the Minuit fitting software
- algorithm_check = {'all': ['migrad', 'simplex'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['simplex'], 'gauss_newton': [], 'general': ['migrad'], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': [], 'simplex': ['simplex'], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- 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
Implements a controller for the NLOPT software.
- class fitbenchmarking.controllers.nlopt_controller.NLoptController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for NLOPT
- algorithm_check = {'all': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS', 'LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ', 'LN_NELDERMEAD', 'LN_SBPLX', 'LN_COBYLA', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'LD_LBFGS', 'GN_DIRECT', 'GN_DIRECT_L', 'GN_DIRECT_L_RAND', 'GNL_DIRECT_NOSCAL', 'GN_DIRECT_L_NOSCAL', 'GN_DIRECT_L_RAND_NOSCAL', 'GN_ORIG_DIRECT', 'GN_ORIG_DIRECT_L', 'GN_CRS2_LM', 'G_MLSL_LDS', 'G_MLSL', 'GD_STOGO', 'GD_STOGO_RAND', 'GN_AGS', 'GN_ISRES'], 'bfgs': ['LD_LBFGS'], 'conjugate_gradient': ['LN_COBYLA'], 'deriv_free': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS'], 'gauss_newton': ['LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON'], 'general': ['LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ'], 'global_optimization': ['GN_DIRECT', 'GN_DIRECT_L', 'GN_DIRECT_L_RAND', 'GNL_DIRECT_NOSCAL', 'GN_DIRECT_L_NOSCAL', 'GN_DIRECT_L_RAND_NOSCAL', 'GN_ORIG_DIRECT', 'GN_ORIG_DIRECT_L', 'GN_CRS2_LM', 'G_MLSL_LDS', 'G_MLSL', 'GD_STOGO', 'GD_STOGO_RAND', 'GN_AGS', 'GN_ISRES'], 'levenberg-marquardt': [], 'ls': [], 'simplex': ['LN_NELDERMEAD', 'LN_SBPLX'], 'steepest_descent': [], 'trust_region': ['LN_COBYLA', 'LD_CCSAQ', 'LD_MMA']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with NLOPT
- jacobian_enabled_solvers = ['LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'GD_STOGO', 'GD_STOGO_RAND']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- objective_master_nlopt(x, grad)
NLOPT objective function
- setup()
Setup problem ready to be run with NLOPT
Implements a controller for RALFit https://github.com/ralna/RALFit
- class fitbenchmarking.controllers.ralfit_controller.RALFitController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the RALFit fitting software.
- algorithm_check = {'all': ['gn', 'hybrid', 'newton', 'newton-tensor', 'gn_reg', 'hybrid_reg', 'newton_reg', 'newton-tensor_reg'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': ['gn', 'gn_reg'], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['gn', 'gn_reg'], 'ls': ['gn', 'hybrid', 'newton', 'newton-tensor', 'gn_reg', 'hybrid_reg', 'newton_reg', 'newton-tensor_reg'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['gn', 'hybrid', 'newton', 'newton-tensor']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- fit()
Run problem with RALFit.
- hes_eval(params, r)
Function to ensure correct inputs and outputs are used for the RALFit hessian evaluation
- param params
parameters
- type params
numpy array
- param r
residuals, required by RALFit to be passed for hessian evaluation
- type r
numpy array
- return
hessian 2nd order term: sum_{i=1}^m r_i
- abla^2 r_i
- rtype
numpy array
- hessian_enabled_solvers = ['hybrid', 'newton', 'newton-tensor', 'hybrid_reg', 'newton_reg', 'newton-tensor_reg']
Within the controller class, you must define the list
hessian_enabled_solversif any of the minimizers for the specific software are able to use hessian information.hessian_enabled_solvers: a list of minimizers in a specific
software that allow Hessian information to be passed into the fitting algorithm
- jacobian_enabled_solvers = ['gn', 'hybrid', 'newton', 'newton-tensor', 'gn_reg', 'hybrid_reg', 'newton_reg', 'newton-tensor_reg']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup for RALFit
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(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the Scipy fitting software.
- algorithm_check = {'all': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP', 'COBYLA', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg'], 'bfgs': ['BFGS', 'L-BFGS-B'], 'conjugate_gradient': ['CG', 'Newton-CG', 'Powell'], 'deriv_free': ['Nelder-Mead', 'Powell', 'COBYLA'], 'gauss_newton': [], 'general': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP'], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': [None], 'simplex': ['Nelder-Mead'], 'steepest_descent': [], 'trust_region': ['trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- fit()
Run problem with Scipy.
- hessian_enabled_solvers = ['Newton-CG', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg']
Within the controller class, you must define the list
hessian_enabled_solversif any of the minimizers for the specific software are able to use hessian information.hessian_enabled_solvers: a list of minimizers in a specific
software that allow Hessian information to be passed into the fitting algorithm
- jacobian_enabled_solvers = ['CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with SciPy
Implements a controller for the scipy fitting software. In particular, here for the scipy minimize solver for general minimization problems.
- class fitbenchmarking.controllers.scipy_go_controller.ScipyGOController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the Scipy fitting software.
- algorithm_check = {'all': ['differential_evolution', 'shgo', 'dual_annealing'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['differential_evolution'], 'gauss_newton': [], 'general': ['differential_evolution', 'shgo', 'dual_annealing'], 'global_optimization': ['differential_evolution', 'shgo', 'dual_annealing'], 'levenberg-marquardt': [], 'ls': [None], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- controller_name = 'scipy_go'
A name to be used in tables. If this is set to None it will be inferred from the class name.
- fit()
Run problem with Scipy GO.
- jacobian_enabled_solvers = ['shgo', 'dual_annealing']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with SciPy GO
Implements a controller for the scipy ls fitting software. In particular, for the scipy least_squares solver.
- class fitbenchmarking.controllers.scipy_ls_controller.ScipyLSController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for the Scipy Least-Squares fitting software.
- algorithm_check = {'all': ['lm-scipy', 'trf', 'dogbox'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [None], 'gauss_newton': [], 'general': [None], 'global_optimization': [], 'levenberg-marquardt': ['lm-scipy'], 'ls': ['lm-scipy', 'trf', 'dogbox'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['lm-scipy', 'trf', 'dogbox']}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- controller_name = 'scipy_ls'
A name to be used in tables. If this is set to None it will be inferred from the class name.
- fit()
Run problem with Scipy LS.
- jacobian_enabled_solvers = ['lm-scipy', 'trf', 'dogbox']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with SciPy LS
Implements a controller for the Theseus fitting software.
- class fitbenchmarking.controllers.theseus_controller.TheseusController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.ControllerController for Theseus
- algorithm_check = {'all': ['Levenberg_Marquardt', 'Gauss-Newton'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': ['Gauss-Newton'], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['Levenberg_Marquardt'], 'ls': ['Levenberg_Marquardt', 'Gauss-Newton'], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check, such that the keys are given by:all- all minimizersls- least-squares fitting algorithmsderiv_free- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplexmethod inMantid)general- minimizers which solve a generic min f(x)simplex- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region- algorithms which emply a trust region approachlevenberg-marquardt- minimizers that use the Levenberg-Marquardt algorithmgauss_newton- minimizers that use the Gauss Newton algorithmbfgs- minimizers that use the BFGS algorithmconjugate_gradient- Conjugate Gradient algorithmssteepest_descent- Steepest Descent algorithmsglobal_optimization- Global Optimization algorithms
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
GSLcontroller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with Theseus
- jacobian_enabled_solvers = ['Levenberg_Marquardt', 'Gauss-Newton']
Within the controller class, you must define the list
jacobian_enabled_solversif any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with Theseus
- class fitbenchmarking.controllers.theseus_controller.TheseusCostFunction(*args: Any, **kwargs: Any)
Bases:
theseus.CostFunctionCost function for Theseus ai
- dim() int
Lenght of x data
- error() Tuple[List[torch.Tensor], List[float]]
Resdiuals in pytorch tensor form for Theseus ai
- jacobians() Tuple[List[torch.Tensor], torch.Tensor]
Jacobians in pytorch tensor form for Theseus ai
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 group results, support pages, and figures directories
- Return type
(str, str, str)
- fitbenchmarking.core.results_output.create_index_page(options: Options, groups: list[str], result_directories: list[str]) str
Creates the results index page for the benchmark, and copies the fonts and js directories to the correct location.
- Parameters
options (fitbenchmarking.utils.options.Options) – The user options for the benchmark.
groups (A list of strings.) – Names for each of the problem set groups.
result_directories – Result directory paths for each
problem set group. :type result_directories: A list of strings. :return: The filepath of the results_index.html file. :rtype: 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[dict[str: fitbenchmarking.utils.fitbm_result.FittingResult]]) – best result for each problem seperated by cost function
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.open_browser(output_file: str, options) None
Opens a browser window to show the results of a fit benchmark.
- Parameters
output_file (str) – The absolute path to the results index file.
- fitbenchmarking.core.results_output.preprocess_data(results: list[FittingResult])
Generate a dictionary of results lists sorted into the correct order with rows and columns as the key and list elements respectively.
This is used to create HTML and txt tables. This is stored in self.sorted_results
- Parameters
results (list[fitbenchmarking.utils.fitbm_result.FittingResult]) – The results to process
- Returns
The best result grouped by row and category (cost function), The sorted results grouped by row and category
- Return type
dict[str, dict[str, utils.fitbm_result.FittingResult]], dict[str, dict[str, list[utils.fitbm_result.FittingResult]]]
- fitbenchmarking.core.results_output.save_results(options, results, group_name, failed_problems, unselected_minimizers)
Create all results files and store them. Result files are plots, support pages, tables, and index pages.
- Parameters
options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting
results (list[fitbenchmarking.utils.fitbm_result.FittingResult]) – The results from fitting
group_name (str) – name of the problem group
failed_problems (list) – list of failed problems to be reported in the html output
- Params unselected_minimizers
Dictionary containing unselected minimizers based on the algorithm_type option
- Returns
Path to directory of group results
- Return type
str
Implements the base class for the cost function class.
- class fitbenchmarking.cost_func.base_cost_func.CostFunc(problem)
Bases:
objectBase class for the cost functions.
- abstract eval_cost(params, **kwargs)
Evaluate the cost function
- Parameters
params (list) – The parameters to calculate residuals for
- Returns
evaluated cost function
- Return type
float
- abstract hes_cost(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function, \(\nabla_p^2 F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian of the cost function
- Return type
2D numpy array
- abstract hes_res(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function residual, \(\nabla_p^2 r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian and Jacobian of the residual at each x, y pair
- Return type
tuple (list of 2D numpy arrays, list of 1D numpy arrays)
- abstract jac_cost(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function, \(\nabla_p F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the cost function
- Return type
1D numpy array
- abstract jac_res(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function residual, \(\nabla_p r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the residual at each x, y pair
- Return type
a list of 1D numpy arrays
- validate_algorithm_type(algorithm_check, minimizer)
Helper function which checks that the algorithm type of the selected minimizer from the options (options.minimizer) is incompatible with the selected cost function
- Parameters
algorithm_check – dictionary object containing algorithm
types and minimizers for selected software :type algorithm_check: dict :param minimizer: string of minimizers selected from the options :type minimizer: str
- validate_problem()
Helper function to check if the problem definition is compatible with the cost function.
By default do not raise an error.
This file contains a factory implementation for the available cost functions within FitBenchmarking.
- fitbenchmarking.cost_func.cost_func_factory.create_cost_func(cost_func_type)
Create a cost function class class.
- Parameters
cost_func_type (str) – Type of cost function selected from options
- Returns
Cost function class for the problem
- Return type
fitbenchmarking.cost_func.base_cost_func.CostFunc subclass
Implements the root non-linear least squares cost function
- class fitbenchmarking.cost_func.hellinger_nlls_cost_func.HellingerNLLSCostFunc(problem)
Bases:
fitbenchmarking.cost_func.nlls_base_cost_func.BaseNLLSCostFuncThis defines the Hellinger non-linear least squares cost function 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 Hellinger least-squares sense by solving:
\[\min_p \sum_{i=1}^n \left(\sqrt{y_i} - \sqrt{f(x_i, p})\right)^2\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters. More information on non-linear least squares cost functions can be found here and for the Hellinger distance measure see here.
- eval_r(params, **kwargs)
Calculate the residuals, \(\sqrt{y_i} - \sqrt{f(x_i, p)}\)
- Parameters
params (list) – The parameters, \(p\), to calculate residuals for
- Returns
The residuals for the datapoints at the given parameters
- Return type
numpy array
- hes_res(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function residual, \(\nabla_p^2 r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian and Jacobian of the residual at each x, y pair
- Return type
tuple (list of 2D numpy arrays, list of 1D numpy arrays)
- jac_res(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function residual, \(\nabla_p r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the residual at each x, y pair
- Return type
a list of 1D numpy arrays
- validate_problem()
Validate the problem for the Hellinger Cost Function. Hellinger involves a square root so will fail on negative inputs.
- Raises:
- IncompatibleCostFunctionError: When the problem has negative
values.
Implements the base non-linear least squares cost function
- class fitbenchmarking.cost_func.nlls_base_cost_func.BaseNLLSCostFunc(problem)
Bases:
fitbenchmarking.cost_func.base_cost_func.CostFuncThis defines a base cost function for objectives of the type
\[\min_p \sum_{i=1}^n r(y_i, x_i, p)^2\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters.
- eval_cost(params, **kwargs)
Evaluate the square of the L2 norm of the residuals, \(\sum_i r(x_i,y_i,p)^2\) at the given parameters
- Parameters
params (list) – The parameters, \(p\), to calculate residuals for
- Returns
The sum of squares of residuals for the datapoints at the given parameters
- Return type
numpy array
- abstract eval_r(params, **kwargs)
Calculate residuals used in Least-Squares problems
- Parameters
params (list) – The parameters to calculate residuals for
- Returns
The residuals for the datapoints at the given parameters
- Return type
numpy array
- hes_cost(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function, \(\nabla_p^2 F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian of the cost function
- Return type
2D numpy array
- jac_cost(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function, \(\nabla_p F(r(x,y,p))\), at the given parameters. :param params: The parameters at which to calculate Jacobians :type params: list :return: evaluated Jacobian of the cost function :rtype: 1D numpy array
Implements the non-weighted non-linear least squares cost function
- class fitbenchmarking.cost_func.nlls_cost_func.NLLSCostFunc(problem)
Bases:
fitbenchmarking.cost_func.nlls_base_cost_func.BaseNLLSCostFuncThis defines the non-linear least squares cost function 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 root least-squares sense by solving:
\[\min_p \sum_{i=1}^n \left(y_i - f(x_i, p)\right)^2\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters. More information on non-linear least squares cost functions can be found here.
- eval_r(params, **kwargs)
Calculate the residuals, \(y_i - f(x_i, p)\)
- Parameters
params (list) – The parameters, \(p\), to calculate residuals for
- Returns
The residuals for the datapoints at the given parameters
- Return type
numpy array
- hes_res(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function residual, \(\nabla_p^2 r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian and Jacobian of the residual at each x, y pair
- Return type
tuple (list of 2D numpy arrays, list of 1D numpy arrays)
- jac_res(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function residual, \(\nabla_p r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the residual at each x, y pair
- Return type
a list of 1D numpy arrays
Implements a Poisson deviance cost function based on Mantid’s: https://docs.mantidproject.org/nightly/fitting/fitcostfunctions/Poisson.html
- class fitbenchmarking.cost_func.poisson_cost_func.PoissonCostFunc(problem)
Bases:
fitbenchmarking.cost_func.base_cost_func.CostFuncThis defines the Poisson deviance cost-function where, given the set of \(n\) data points \((x_i, y_i)\), and a model function \(f(x,p)\), we find the optimal parameters in the Poisson deviance sense by solving:
\[\min_p \sum_{i=1}^n \left( y_i \left(\log{y_i} - \log{f(x_i, p)} \right) - \left( y_i - f(x_i, p) \right) \right)\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters.
This cost function is intended for positive values.
This cost function is not a least squares problem and as such will not work with least squares minimizers. Please use algorithm_type to select general solvers. See options docs (Fitting Options) for information on how to do this.
- eval_cost(params, **kwargs)
Evaluate the Poisson deviance cost function
- Parameters
params (list) – The parameters to calculate residuals for
x (np.array (optional)) – The x values to evaluate at. Default is self.problem.data_x
y (np.array (optional)) – The y values to evaluate at. Default is self.problem.data_y
- Returns
evaluated cost function
- Return type
float
- hes_cost(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function, \(\nabla_p^2 F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian of the cost function
- Return type
2D numpy array
- hes_res(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function residual, \(\nabla_p^2 r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian and Jacobian of the residual at each x, y pair
- Return type
tuple (list of 2D numpy arrays, list of 1D numpy arrays)
- jac_cost(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function, \(\nabla_p F(r(x,y,p))\), at the given parameters. :param params: The parameters at which to calculate Jacobians :type params: list :return: evaluated Jacobian of the cost function :rtype: 1D numpy array
- jac_res(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function residual, \(\nabla_p r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the residual at each x, y pair
- Return type
a list of 1D numpy arrays
- validate_problem()
Validate the problem for the Poisson Cost Function. Poisson involves a log so will fail on negative inputs.
- Raises:
- IncompatibleCostFunctionError: When the problem has negative
values.
Implements the weighted non-linear least squares cost function
- class fitbenchmarking.cost_func.weighted_nlls_cost_func.WeightedNLLSCostFunc(problem)
Bases:
fitbenchmarking.cost_func.nlls_base_cost_func.BaseNLLSCostFuncThis defines the weighted non-linear least squares cost function 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 root 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 initial guess for the optimal parameters. More information on non-linear least squares cost functions can be found here.
- eval_r(params, **kwargs)
Calculate the residuals, \(\frac{y_i - f(x_i, p)}{e_i}\)
- Parameters
params (list) – The parameters, \(p\), to calculate residuals for
- Returns
The residuals for the data points at the given parameters
- Return type
numpy array
- hes_res(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function residual, \(\nabla_p^2 r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian and Jacobian of the residual at each x, y pair
- Return type
tuple (list of 2D numpy arrays, list of 1D numpy arrays)
- jac_res(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function residual, \(\nabla_p r(x,y,p)\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Jacobians
- Returns
evaluated Jacobian of the residual at each x, y pair
- Return type
a list of 1D numpy arrays
Module which acts as an analytic Hessian calculator
- class fitbenchmarking.hessian.analytic_hessian.Analytic(problem, jacobian)
Bases:
fitbenchmarking.hessian.base_hessian.HessianClass to apply an analytic Hessian
- eval(params, **kwargs)
Evaluates Hessian of problem.eval_model, returning the value
abla^2_p f(x, p)
- param params
The parameter values to find the Hessian at
- type params
list
- return
Approximation of the Hessian
- rtype
3D numpy array
- name() str
Get a name for the current status of the jacobian.
- Returns
A unique name for this jacobian/method combination
- Return type
str
Implements the base class for the Hessian.
- class fitbenchmarking.hessian.base_hessian.Hessian(problem, jacobian)
Bases:
objectBase class for Hessian.
- INCOMPATIBLE_PROBLEMS = {}
- abstract eval(params, **kwargs)
Evaluates Hessian of the model
- Parameters
params (list) – The parameter values to find the Hessian at
- Returns
Computed Hessian
- Return type
3D numpy array
- property method
Utility function to get the numerical method
- Returns
the names of the parameters
- Return type
list of str
- name() str
Get a name for the current status of the hessian.
- Returns
A unique name for this hessian/method combination
- Return type
str
This file contains a factory implementation for the Hessians. This is used to manage the imports and reduce effort in adding new Hessian methods.
- fitbenchmarking.hessian.hessian_factory.create_hessian(hes_method)
Create a Hessian class.
- Parameters
hes_method (str) – Type of Hessian selected from options
- Returns
Controller class for the problem
- Return type
fitbenchmarking.hessian.base_controller.Hessian subclass
Module which calculates SciPy finite difference approximations
- class fitbenchmarking.hessian.scipy_hessian.Scipy(problem, jacobian)
Bases:
fitbenchmarking.hessian.base_hessian.HessianImplements SciPy finite difference approximations to the derivative
- INCOMPATIBLE_PROBLEMS = {'cs': ['mantid']}
- eval(params, **kwargs)
Evaluates Hessian of problem.eval_model, returning the value
abla^2_p f(x, p)
- param params
The parameter values to find the Hessian at
- type params
list
- return
Approximation of the Hessian
- rtype
3D numpy array
Module which acts as a analytic Jacobian calculator
- class fitbenchmarking.jacobian.analytic_jacobian.Analytic(problem)
Bases:
fitbenchmarking.jacobian.base_jacobian.JacobianClass to apply an analytical Jacobian
- eval(params, **kwargs)
Evaluates Jacobian of problem.eval_model
- Parameters
params (list) – The parameter values to find the Jacobian at
- Returns
Approximation of the Jacobian
- Return type
numpy array
- name() str
Get a name for the current status of the jacobian.
- Returns
A unique name for this jacobian/method combination
- Return type
str
Implements the base class for the Jacobian.
- class fitbenchmarking.jacobian.base_jacobian.Jacobian(problem)
Bases:
objectBase class for Jacobian.
- INCOMPATIBLE_PROBLEMS = {}
- abstract eval(params, **kwargs)
Evaluates Jacobian of the model, \(\nabla_p f(x,p)\), at the point given by the parameters.
- Parameters
params (list) – The parameter values at which to evaluate the Jacobian
- Returns
Computed Jacobian
- Return type
numpy array
- property method
Utility function to get the numerical method
- Returns
the names of the parameters
- Return type
list of str
- name() str
Get a name for the current status of the jacobian.
- Returns
A unique name for this jacobian/method combination
- Return type
str
Module which uses the minimizer’s default jacobian
- class fitbenchmarking.jacobian.default_jacobian.Default(problem)
Bases:
fitbenchmarking.jacobian.scipy_jacobian.ScipyUse the minimizer’s jacobian/derivative approximation
- eval(params, **kwargs)
Evaluates Jacobian of problem.eval_model
- Parameters
params (list) – The parameter values to find the Jacobian at
- Returns
Approximation of the Jacobian
- Return type
numpy array
- name() str
Get a name for the current status of the jacobian.
- Returns
A unique name for this jacobian/method combination
- Return type
str
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(jac_method)
Create a Jacobian class.
- Parameters
jac_method (str) – Type of Jacobian selected from options
- Returns
Controller class for the problem
- Return type
fitbenchmarking.jacobian.base_controller.Jacobian subclass
Module which calculates SciPy finite difference approximations
- class fitbenchmarking.jacobian.scipy_jacobian.Scipy(problem)
Bases:
fitbenchmarking.jacobian.base_jacobian.JacobianImplements SciPy finite difference approximations to the derivative
- INCOMPATIBLE_PROBLEMS = {'cs': ['mantid']}
- eval(params, **kwargs)
Evaluates Jacobian of problem.eval_model
- Parameters
params (list) – The parameter values to find the Jacobian at
- Returns
Approximation of the Jacobian
- Return type
numpy array
Implements the base Parser as a Context Manager.
This file calls the pycutest interface for SIF data
- class fitbenchmarking.parsing.cutest_parser.CutestParser(filename, options)
Bases:
fitbenchmarking.parsing.base_parser.ParserUse 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 eachcached_xthat has already been stored.- parse()
Get data into a Fitting Problem via cutest.
- Returns
The fully parsed fitting problem
- Return type
This file implements a parser for the Fitbenchmark data format.
- class fitbenchmarking.parsing.fitbenchmark_parser.FitbenchmarkParser(filename, options)
Bases:
fitbenchmarking.parsing.base_parser.ParserParser for the native FitBenchmarking problem definition (FitBenchmark) file.
- parse() fitbenchmarking.parsing.fitting_problem.FittingProblem
Parse the Fitbenchmark problem file into a Fitting Problem.
- Returns
The fully parsed fitting problem
- Return type
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:
objectDefinition of a fitting problem, which will be populated by a parser from a problem definition file.
Onces populated, this should include the data, the function and any other additional requirements from the data.
- additional_info
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
numpy array The errors or weights
- data_x
numpy array The x-data
- data_y
numpy array The y-data
- description
string Description of the fitting problem
- end_x
float The end of the range to fit model data over (if different from entire range) (/float/)
- equation
string Equation (function or model) to fit against data
- eval_model(params, **kwargs)
Function evaluation method
- Parameters
params (list) – parameter value(s)
- Returns
data values evaluated from the function of the problem
- Return type
numpy array
- format
string Name of the problem definition type (e.g., ‘cutest’)
- function
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
- hessian
Callable function for the Hessian
- ini_y(parameter_set=0)
Return the result of evaluating the problem at the initial parameters for plotting.
- Parameters
parameter_set (int, optional) – The initial parameters to use, defaults to 0
- Returns
The initial estimates
- Return type
numpy.ndarray
- jacobian
Callable function for the Jacobian
- multifit
bool Used to check if a problem is using multifit.
- multivariate
bool Whether the function has been wrapped to reduce the dimension of x on function calls
- name
string Name (title) of the fitting problem
- property param_names
Utility function to get the parameter names
- Returns
the names of the parameters
- Return type
list of str
- plot_scale
string The plot scale for the y and x data
- set_value_ranges(value_ranges)
Function to format parameter bounds before passing to controllers, so self.value_ranges is a list of tuples, which contain lower and upper bounds (lb,ub) for each parameter in the problem
- Parameters
value_ranges (dict) –
- dictionary of bounded parameter names with
lower and upper bound values e.g.
{p1_name: [p1_min, p1_max], ...}
- sorted_index
numpy array The index for sorting the data (used in plotting)
- start_x
float The start of the range to fit model data over (if different from entire range)
- starting_values: list
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
list Smallest and largest values of interest in the data
e.g.
[(p1_min, p1_max), (p2_min, p2_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
This file implements a parser for Horace problem sets.
- class fitbenchmarking.parsing.horace_parser.HoraceParser(*args, **kwargs)
Bases:
fitbenchmarking.parsing.fitbenchmark_parser.FitbenchmarkParserParser for a Horace problem definition file.
- fitbenchmarking.parsing.horace_parser.horace_on()
Turning Horace and SpinW on in matlab
This file implements a parser for the IVP data format.
- class fitbenchmarking.parsing.ivp_parser.IVPParser(filename, options)
Bases:
fitbenchmarking.parsing.fitbenchmark_parser.FitbenchmarkParserParser for a IVP problem definition file.
This file implements a parser for the Mantid data format.
- class fitbenchmarking.parsing.mantid_parser.MantidParser(filename, options)
Bases:
fitbenchmarking.parsing.fitbenchmark_parser.FitbenchmarkParserParser for a Mantid problem definition file.
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_int(value)
Checks to see if a value is an integer or not
- Parameters
value (str) – String representation of an equation
- Returns
Whether or not value is an int
- Return type
bool
- 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_data_functions.nist_hessian_definition(hessian, param_names)
Processing a Hessian into a callable
- Parameters
hessian (str) – Hessian string as defined in the data files for the corresponding NIST problem definition file
param_names (list) – names of the parameters in the function
- Returns
callable function
- Return type
callable
- fitbenchmarking.parsing.nist_data_functions.nist_jacobian_definition(jacobian, param_names)
Processing a Jacobian plus different set of starting values as specified in the NIST problem definition file into a callable
- Parameters
jacobian (str) – Jacobian string as defined in the data files for the corresponding NIST problem definition file
param_names (list) – names of the parameters in the function
- Returns
callable function
- Return type
callable
This file implements a parser for the NIST style data format.
- class fitbenchmarking.parsing.nist_parser.NISTParser(filename, options)
Bases:
fitbenchmarking.parsing.base_parser.ParserParser for the NIST problem definition file.
- parse()
Parse the NIST problem file into a Fitting Problem.
- Returns
The fully parsed fitting problem
- Return type
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:
objectA 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
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.TableThe accuracy results are calculated by evaluating the cost function with the fitted parameters.
- get_value(result)
Gets the main value to be reported in the tables for a given result
Note that the first value (relative accuracy) will be used in the default colour handling.
- Parameters
result (FittingResult) – The result to generate the values for.
- Returns
The normalised chi sq with respect to the smallest accuracy value and absolute accuracy for the result.
- Return type
tuple(float, float)
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:
objectBase class for the FitBenchmarking HTML and text output tables.
When inheriting from this, it may be useful to override the following functions as required:
get_value
display_str
get_error_str
get_link_str
- create_pandas_data_frame(html=False)
Creates a pandas data frame of results
- Parameters
html (bool. defaults to False) – Whether to make the dataframe for html or plain text
- Returns
DataFrame with string representations of results
- Return type
pandas.DataFrame
- create_results_dict()
Generate a dictionary of results lists with rows and columns as the key and list elements respectively. This is used to create HTML and csv tables. This is stored in self.sorted_results
- display_str(value)
Converts a value generated by
get_value()into a string respresentation to be used in the tables. Base class implementation takes the relative and absolute values and usesself.output_string_typeas a template for the string format. This can be overridden to adequately display the results.- Parameters
value (tuple) – Relative and absolute values
- Returns
string representation of the value for display in the table.
- Return type
str
- property file_path
Getter function for the path to the table
- Returns
path to table
- Return type
str
- get_colour_df(like_df=None)
Generate a dataframe of colours to add to the html rendering.
If like_df is passed this will use the column and row indexes of that dataframe.
- Parameters
like_df (pandas.DataFrame) – The dataframe to copy headings from. Defaults to None.
- Returns
A dataframe with colourings as strings
- Return type
pandas.DataFrame
- get_colours_for_row(results)
Get the colours as strings for the given results in the table. The base class implementation, for example, uses the first value from self.get_value and
colour_map,colour_ulimandcmap_rangewithinOptions.- Parameters
result (list[fitbenchmarking.utils.fitbm_result.FittingResult]) – Results to get the colours for.
- Returns
The colour to use for each cell in the list and Foreground colours for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
- Return type
tuple[list[str], list[str]]
- get_description()
Generates table description from class docstrings and converts them into html
- Returns
Dictionary containing table descriptions
- Return type
dict
- static get_error_str(result, error_template='[{}]')
Get the error string for a result based on error_template This can be overridden if tables require different error formatting.
- Parameters
result (FittingResult) – The result to get the error string for
- Returns
A string representation of the error
- Return type
str
- get_hyperlink(result, val_str, text_col)
Generates the hyperlink for a given result
- Parameters
result (fitbenchmarking.utils.ftibm_result.FittingResult) – The result to generate a string for
val_str (str) – Preprocessed val_str to display
text_col (str) – Foreground colour for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
- Returns
The hyperlink representation.
- Return type
str
- get_link_str(result)
Get the link as a string for the result. This can be overridden if tables require different links.
- Parameters
result (FittingResult) – The result to get the link for
- Returns
The link to go to when the cell is selected
- Return type
string
- get_str_dict(html=False)
Create a dictionary with the table values as strings for display.
- Returns
The dictionary of strings for the table
- Return type
dict[list[str]]
- get_str_result(result, text_col=None, html=False)
Given a single result, generate the string to display in this table. The html flag can be used to switch between a plain text and html format.
This is intended to be easily extensible by overriding the following functions:
get_value
display_str
get_error_str
get_link_str
If you find yourself overriding this, please consider if changes could be made to allow future tables to benefit.
- Parameters
result (fitbenchmarking.utils.ftibm_result.FittingResult) – The result to generate a string for
text_col (list[str]) – Foreground colours for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
html (bool) – Flag to control whether to generate a html string or plain text. Defaults to False.
- Returns
The string representation.
- Return type
str
- abstract get_value(result)
Gets the main value to be reported in the tables for a given result
If more than one value is returned please note that the first value will be used in the default colour handling.
- Parameters
result (FittingResult) – The result to generate the values for.
- Returns
The value to convert to a string for the tables
- Return type
tuple(float)
- minimizer_dropdown_html() str
Generates the HTML for a dropdown checklist of minimizers.
- Returns
HTML for a dropdown checklist of minimizers.
- Return type
str
- problem_dropdown_html() str
Generates the HTML for a dropdown checklist of problem sets.
- Returns
HTML for a dropdown checklist of problem sets.
- Return type
str
- save_colourbar(fig_dir, n_divs=100, sz_in=(3, 0.8)) str
Generates a png of a labelled colourbar using matplotlib.
- Parameters
fig_dir (str) – path to figures directory
n_divs (int) – number of divisions of shading in colourbar
sz_in (list[float] - 2 elements) – dimensions of png in inches [width, height]
- Returns
The relative path to the colourbar image.
- Return type
str
- property table_title
Getter function for table name if self._table_title is None
- Returns
name of table
- Return type
str
- to_csv_file()
Generate a plain text version of the table
- Returns
Plain text table output
- Return type
str
- to_html()
Generate a html version of the table.
- Returns
HTML table output
- Return type
str
- static vals_to_colour(vals, cmap, cmap_range, log_ulim)
Converts an array of values to a list of hexadecimal colour strings using logarithmic sampling from a matplotlib colourmap according to relative value.
- Parameters
vals (list[float]) – values in the range [0, 1] to convert to colour strings
cmap (matplotlib colourmap object) – matplotlib colourmap
cmap_range (list[float], 2 elements) – values in range [0, 1] for colourmap cropping
log_ulim (float) – log10 of worst shading cutoff value
- Returns
Colours as hex strings for each input value and Foreground colours for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
- Return type
tuple[list[str], list[str]]
- fitbenchmarking.results_processing.base_table.background_to_text(background_col, contrast_threshold)
Determines the foreground color for the table elements. The optimum color is selected from two options - white and black. White is selected if its contrast ratio is greater than the contrast threshold. However, If the contrast ratio of white text does not meet the requirement, then the text color which provides the greatest contrast ratio is selected.
- Parameters
background_col – a list of r,g,b values [0, 255]
contrast_threshold (float) – the threshold value [0, 21]
- Returns
Foreground colour for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
- Return type
str
- fitbenchmarking.results_processing.base_table.calculate_contrast(background, foreground)
Calculates the contrast ratio between the background and foreground colors. Visit link for more info: https://www.w3.org/WAI/WCAG21/Understanding/contrast-minimum.html#dfn-contrast-ratio
- Parameters
background – list of r, g, b values representing background [0, 255]
foreground (list[int]) – list of r, g, b values representing foreground [0, 255]
- Returns
the contrast ratio [0, 21]
- Return type
float
- fitbenchmarking.results_processing.base_table.calculate_luminance(rgb)
Calculates the relative luminance. Visit link for more info: https://www.w3.org/WAI/WCAG21/Understanding/contrast-minimum.html#dfn-relative-luminance
- Parameters
rgb – a list containing r, g, b values [0, 255]
- Returns
the luminance [0, 1]
- Return type
float
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.TableThe combined results show the accuracy in the first line of the cell and the runtime on the second line of the cell.
- display_str(value)
Combine the accuracy and runtime values into a string representation.
- Parameters
value (list[list[float]]) – Relative and absolute values for accuracy and runtime [[acc_rel, runtime_rel], [acc_abs, runtime_abs]]
- Returns
string representation of the value for display in the table.
- Return type
str
- get_hyperlink(result, val_str, text_col)
Generates the hyperlink for a given result
- Parameters
result (fitbenchmarking.utils.ftibm_result.FittingResult) – The result to generate a string for
val_str (str) – Preprocessed val_str to display
text_col (str) – Foreground colour for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
- Returns
The hyperlink representation.
- Return type
str
- get_value(result)
Gets the main value to be reported in the tables for a given result
Note that the first value (relative chi_sq) will be used in the default colour handling.
- Parameters
result (FittingResult) – The result to generate the values for.
- Returns
The normalised chi sq and runtime with respect to the smallest chi_sq and runtime respectively as well as the absolute values for both chi_sq and runtime for the result. [[acc_rel, runtime_rel], [acc_abs, runtime_abs]]
- Return type
list[list[float]]
- vals_to_colour(vals, *args)
Override vals_to_colour to allow it to run for both accuracy and runtime.
- Parameters
vals (list[list[float, float]]) – The relative values to get the colours for
- Returns
The background colours for the acc and runtime values and The text colours for the acc and runtime values
- Return type
tuple[zip[list[str], list[str]], zip[list[str], list[str]]]
Emissions table
- class fitbenchmarking.results_processing.emissions_table.EmissionsTable(results, best_results, options, group_dir, pp_locations, table_name)
Bases:
fitbenchmarking.results_processing.base_table.TableThe emissions (kg CO2eq) results are calculated from an average (over num_runs) using the codecarbon module. num_runs is set in FitBenchmarking Options.
Configuration for codecarbon is set in
.codecarbon.config.Please note that for tracking CPU power usage on Windows or Mac,
Intel Power Gadgetshoud also be installed. For more information, see the Methodology section of the codecarbon docs.- get_value(result)
Gets the main value to be reported in the tables for a given result
Note that the first value (relative emissions) will be used in the default colour handling.
- Parameters
result (FittingResult) – The result to generate the values for.
- Returns
The normalised emissions with respect to the smallest emissions value and absolute emissions for the result.
- Return type
tuple(float, float)
Set up and build the fitting reports for various types of problems.
- fitbenchmarking.results_processing.fitting_report.create(results, support_pages_dir, options)
Iterate through problem results and create a fitting report html page for each.
- Parameters
results (list[FittingResult]) – results object
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.fitting_report.create_prob_group(result, support_pages_dir, options)
Creates a fitting report containing figures and other details about the fit for a problem. A link to the fitting report is stored in the results object.
- Parameters
result (fitbenchmarking.utils.fitbm_result.FittingResult) – The result for a specific benchmark problem-minimizer-etc combination
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.fitting_report.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)
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.TableThe local min results shows a
TrueorFalsevalue together with \(\frac{|| J^T r||}{||r||}\). TheTrueorFalseindicates 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(value)
Combine the boolean value from variable local_min with the normalised residual
- Parameters
value (bool, float) – Whether the minimizer found a local minimizer and the \(\frac{|| J^T r||}{||r||}\) value
- Returns
string representation of the value for display in the table.
- Return type
str
- get_description()
Generates table description from class docstrings and converts them into html
- Returns
Dictionary containing table descriptions
- Return type
dict
- classmethod get_error_str(result, *args, **kwargs)
Get the error string for a result based on error_template This can be overridden if tables require different error formatting.
- Parameters
result (FittingResult) – The result to get the error string for
- Returns
A string representation of the error
- Return type
str
- get_value(result)
Gets the main value to be reported in the tables for a given result
Note that the first value (relative accuracy) will be used in the default colour handling.
- Parameters
result (FittingResult) – The result to generate the values for.
- Returns
Whether the minimizer found a local minimizer (under the tests specified above) and \(\frac{|| J^T r||}{||r||}\)
- Return type
bool, float
- save_colourbar(fig_dir, n_divs=2, sz_in=None) str
Override default save_colourbar as there are only 2 possible divisions of the colour map (true or false).
- Parameters
fig_dir (str) – path to figures directory
n_divs (int, Fixed to 2) – Unused number of divisions of shading in colourbar
sz_in (list[float] - 2 elements) – dimensions of png in inches [width, height]
- Returns
The relative path to the colourbar image.
- Return type
str
- static vals_to_colour(vals, cmap, cmap_range, log_ulim)
Converts an array of values to a list of hexadecimal colour strings using sampling from a matplotlib colourmap according to whether a minimum was found.
Set to the bottom of the range if minimum was found, otherwise set to the top of the range.
- Parameters
vals (list[float]) – values in the range [0, 1] to convert to colour strings
cmap (matplotlib colourmap object) – matplotlib colourmap
cmap_range (list[float], 2 elements) – values in range [0, 1] for colourmap cropping
log_ulim (float) – Unused log10 of worst shading cutoff value
- Returns
Colours as hex strings for each input value and Foreground colours for the text as html rgb strings e.g. ‘rgb(255, 255, 255)’
- Return type
tuple[list[str], list[str]]
Set up performance profiles for both accuracy and runtime tables
- fitbenchmarking.results_processing.performance_profiler.create_plot(ax, step_values: list[np.ndarray], solvers: list[str])
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 np.array[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 the values for each minimizer.
- Parameters
results (dict[str, dict[str, list[utils.fitbm_result.FittingResult]]]) – The sorted results grouped by row and category
- 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 (dict[str, dict[str, list[utils.fitbm_result.FittingResult]]]) – The sorted results grouped by row and category
fig_dir (str) – path to directory containing the figures
- Returns
path to acc and runtime profile graphs
- Return type
tuple(str, str)
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:
objectClass providing plotting functionality.
- best_fit_plot_options = {'color': '#6699ff', 'linestyle': ':', 'linewidth': 3, 'marker': '', 'zorder': 3}
- data_plot_options = {'color': 'black', 'label': 'Data', 'linestyle': '', 'linewidth': 1, 'marker': 'x', 'zorder': 0}
- fit_plot_options = {'color': '#99ff66', 'linestyle': '-', 'linewidth': 3, 'marker': '', 'zorder': 2}
- format_plot()
Performs post plot processing to annotate the plot correctly
- ini_guess_plot_options = {'color': '#ff6699', 'label': 'Starting Guess', 'linestyle': '-', 'linewidth': 3, 'marker': '', 'zorder': 1}
- plot_best(result)
Plots the fit along with the data using the “best_fit” style and saves to a file
- Parameters
result (FittingResult) – The result to plot
- 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(result)
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
result (FittingResult) – The result to plot
- 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
- classmethod plot_summary(categories, title, options, figures_dir)
Create a comparison plot showing all fits from the results with the best for each category highlighted.
- Parameters
categories (dict[str, list[FittingResults]]) – The results to plot sorted into colour groups
title (str) – A title for the graph
options (utils.options.Options) – The options for the run
figures_dir (str) – The directory to save the figures in
- Returns
The path to the new plot
- Return type
str
- summary_best_plot_options = {'linestyle': '-', 'linewidth': 2, 'marker': '', 'zorder': 2}
- summary_plot_options = {'alpha': 0.5, 'linestyle': '-', 'linewidth': 1, 'marker': '', 'zorder': 1}
Create the summary pages for the best minimizers.
- fitbenchmarking.results_processing.problem_summary_page.create(results, best_results, support_pages_dir, figures_dir, options)
Create the problem summary pages.
- Parameters
results (dict[str, dict[str, list[FittingResult]]]) – The results to create summary pages for
best_results (dict[str, dict[str, FittingResult]]) – The best result from each row and category
support_pages_dir (str) – directory in which the results are to be saved
figures_dir (str) – The directory where figures are stored.
options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting
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.TableThe timing results are calculated from an average (over num_runs) using the timeit module in python. num_runs is set in FitBenchmarking Options.
- get_value(result)
Gets the main value to be reported in the tables for a given result
Note that the first value (relative runtime) will be used in the default colour handling.
- Parameters
result (FittingResult) – The result to generate the values for.
- Returns
The normalised runtime with respect to the smallest runtime and absolute runtime for the result.
- Return type
tuple(float, float)
Set up and build the results tables.
- fitbenchmarking.results_processing.tables.create_results_tables(options, results, best_results, group_dir, fig_dir, pp_locations, failed_problems, unselected_minimzers)
Saves the results of the fitting to html/csv tables.
- Parameters
options (fitbenchmarking.utils.options.Options) – The options used in the fitting problem and plotting
results (dict[str, dict[str, list[utils.fitbm_result.FittingResult]]]) – Results grouped by row and category (for colouring)
best_results (dict[str, dict[str, utils.fitbm_result.FittingResult]]) – The best results from each row/category
group_dir (str) – path to the directory where group results should be stored
fig_dir (str) – path to the directory where figures should be stored
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
- Params unselected_minimzers
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, fig_dir, pp_locations, table_name, suffix)
Generate html/csv tables.
- Parameters
results (dict[str, dict[str, list[utils.fitbm_result.FittingResult]]]) – Results grouped by row and category (for colouring)
best_results (dict[str, dict[str, utils.fitbm_result.FittingResult]]) – The best results from each row/category
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
fig_dir (str) – path to the directory where figures 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, Dict of HTML strings for table and dropdowns,
text string of table, path to colourbar) :rtype: tuple(Table object, dict{str: str}, 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
This file implements a checkpointclass to save results as they are generated. This is also used to read in checkpointed data.
- class fitbenchmarking.utils.checkpoint.Checkpoint(options: Options)
Bases:
objectA class to build a checkpoint file. Each run can be added to the file as they finish. This class must be finalised to create the checkpoint.
- add_result(result: fitbenchmarking.utils.fitbm_result.FittingResult)
Add the result to the checkpoint file.
- Parameters
result (FittingResult) – The result to add.
- finalise()
Add the trailing bracket to validate the json.
- finalise_group(label='benchmark', failed_problems=None, unselected_minimizers=None)
Combine the problem and results file into the main checkpoint file.
- load()
Load fitting results from a checkpoint file along with failed problems and unselected minimizers.
- Returns
Instantiated fitting results, unselected minimisers, failed problems
- Return type
Tuple[ dict[str, list[FittingResult]], dict, dict[str, list[str]]]
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
Functions used for debugging and printing information in a readable format.
- fitbenchmarking.utils.debug.get_printable_table(class_name: str, class_info: dict) str
Creates and returns a string displaying the class info in a format that is easily read in a table.
- Parameters
class_name (str) – The name of a class owning the data.
class_info (dict{str: variant}) – The data in the class to display.
- Returns
The class info in a readable format.
- Return type
str
This file holds all FitBenchmarking exceptions, organised by exception id
- exception fitbenchmarking.utils.exceptions.CheckpointError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates an error occured during checkpointing.
- class_message = 'An error occured during handling the checkpoint file.'
- error_code = 28
- exception fitbenchmarking.utils.exceptions.ControllerAttributeError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates an issue with the attributes within a controller
- class_message = 'Error in the controller attributes.'
- error_code = 7
- exception fitbenchmarking.utils.exceptions.CostFuncError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a problem with the cost function class.
- class_message = 'FitBenchmarking ran with no results'
- error_code = 16
- exception fitbenchmarking.utils.exceptions.FilepathTooLongError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates the filepath to save a file to is too long.
- class_message = 'The filepath for saving a file is too long.'
- error_code = 25
- exception fitbenchmarking.utils.exceptions.FitBenchmarkException(message='')
Bases:
ExceptionThe base class for all FitBenchmarking exceptions
To define a new exception, inherrit from this and override the _class_message
- class_message = 'An unknown exception occurred.'
- error_code = 1
- exception fitbenchmarking.utils.exceptions.FittingProblemError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a problem with the fitting problem.
- class_message = 'Fitting Problem raised and exception.'
- error_code = 10
- exception fitbenchmarking.utils.exceptions.IncompatibleCostFunctionError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that the cost function is not compatible with the given problem.
- class_message = 'The selected cost function cannot be used with the given problem'
- error_code = 29
- exception fitbenchmarking.utils.exceptions.IncompatibleHessianError(message='')
Bases:
fitbenchmarking.utils.exceptions.ValidationExceptionIndicates that the selected Hessian method is not compatible with selected options/problem set
- class_message = 'The provided Hessian method cannot be used with the selected options or problem set.'
- error_code = 26
- exception fitbenchmarking.utils.exceptions.IncompatibleJacobianError(message='')
Bases:
fitbenchmarking.utils.exceptions.ValidationExceptionIndicates that the selected jacobian method is not compatible with selected options/problem set
- class_message = 'The provided Jacobian method cannot be used with the selected options or problem set.'
- error_code = 24
- exception fitbenchmarking.utils.exceptions.IncompatibleMinimizerError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that the selected minimizer is not compatible with selected options/problem set
- class_message = 'Minimizer cannot be used with selected options/problem set'
- error_code = 17
- exception fitbenchmarking.utils.exceptions.IncompatibleProblemError(message='')
Bases:
fitbenchmarking.utils.exceptions.ValidationExceptionIndicates that the selected problem is not compatible with the selected options.
- class_message = 'The selected software can not be used with the given problem.'
- error_code = 27
- exception fitbenchmarking.utils.exceptions.IncompatibleTableError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that selected cost function and table are not compatible
- class_message = 'The table type selected is not compatible with the selected cost function'
- error_code = 18
- exception fitbenchmarking.utils.exceptions.IncorrectBoundsError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that parameter_ranges have been set incorrectly
- class_message = 'Bounds for this problem are unable to be set, so this problem will be skipped.'
- error_code = 19
- exception fitbenchmarking.utils.exceptions.MaxRuntimeError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a minimizer has taken too long to run
- class_message = 'Minimizer runtime exceeded maximum runtime'
- error_code = 23
- exception fitbenchmarking.utils.exceptions.MissingBoundsError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that parameter_ranges have not been set but are required
- class_message = 'Bounds on all parameters are required to use this software.'
- error_code = 20
- exception fitbenchmarking.utils.exceptions.MissingSoftwareError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that the requirements for a software package are not available.
- class_message = 'Missing dependencies for fit.'
- error_code = 5
- exception fitbenchmarking.utils.exceptions.NoAnalyticJacobian(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates when no Jacobian data files can be found
- class_message = 'Could not find Jacobian data files'
- error_code = 12
- exception fitbenchmarking.utils.exceptions.NoControllerError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a controller could not be found
- class_message = 'Could not find controller.'
- error_code = 6
- exception fitbenchmarking.utils.exceptions.NoDataError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that no data could be found.
- class_message = 'No data found.'
- error_code = 8
- exception fitbenchmarking.utils.exceptions.NoHessianError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicated a problem with Hessian import
- class_message = 'Could not find Hessian class'
- error_code = 22
- exception fitbenchmarking.utils.exceptions.NoJacobianError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a problem with the Jacobian import.
- class_message = 'Could not find Jacobian class'
- error_code = 11
- exception fitbenchmarking.utils.exceptions.NoParserError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a parser could not be found.
- class_message = 'Could not find parser.'
- error_code = 4
- exception fitbenchmarking.utils.exceptions.NoResultsError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a problem with the fitting problem.
- class_message = 'FitBenchmarking ran with no results'
- error_code = 14
- exception fitbenchmarking.utils.exceptions.OptionsError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates an error during processing options.
- class_message = 'Failed to process options.'
- error_code = 2
- exception fitbenchmarking.utils.exceptions.ParsingError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates an error during parsing.
- class_message = 'Could not parse problem.'
- error_code = 3
- exception fitbenchmarking.utils.exceptions.PlottingError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates an error during plotting results
- class_message = 'An error occurred during plotting.'
- error_code = 21
- exception fitbenchmarking.utils.exceptions.UnknownMinimizerError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that the controller does not support a given minimizer given the current “algorithm_type” option set.
- class_message = 'Minimizer cannot be run with Controller with current "algorithm_type" option set.'
- error_code = 9
- exception fitbenchmarking.utils.exceptions.UnknownTableError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates a problem with the fitting problem.
- class_message = 'Set table option could not be found'
- error_code = 13
- exception fitbenchmarking.utils.exceptions.UnsupportedMinimizerError(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionIndicates that the controller does not support a given minimizer.
- class_message = 'FitBenchmarking ran with no results'
- error_code = 15
- exception fitbenchmarking.utils.exceptions.ValidationException(message='')
Bases:
fitbenchmarking.utils.exceptions.FitBenchmarkExceptionThis should be subclassed to indicate any validation errors that preclude a combination from running.
- class_message = 'An error occured while verifying controller.'
FitBenchmarking results object
- class fitbenchmarking.utils.fitbm_result.FittingResult(controller: Controller, accuracy: float | list[float] = inf, runtimes: float | list[float] = inf, emissions: float = inf, dataset: Optional[int] = None)
Bases:
objectMinimal definition of a class to hold results from a fitting problem test.
- init_blank()
Initialise a new blank version of the class with the required placeholder values not set during standard initialisation.
- modified_minimizer_name(with_software: bool = False) str
Get a minimizer name which contains jacobian and hessian information. Optionally also include the software.
- Parameters
with_software (bool, optional) – Add software to the name, defaults to False
- Returns
A name for the result combination
- Return type
str
- property norm_acc
Getting function for norm_acc attribute
- Returns
normalised accuracy value
- Return type
float
- property norm_emissions
Getting function for norm_emissions attribute
- Returns
normalised emissions value
- Return type
float
- property norm_runtime
Getting function for norm_runtime attribute
- Returns
normalised runtime value
- Return type
float
- sanitised_min_name(with_software=False)
Sanitise the modified minimizer name into one which can be used as a filename.
- Returns
sanitised name
- Return type
str
- property sanitised_name
Sanitise the problem name into one which can be used as a filename.
- Returns
sanitised name
- Return type
str
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’
Manage the persistent variables withing the matlab engine
- fitbenchmarking.utils.matlab_engine.add_persistent_matlab_var(name: str) None
Manage a list of variables to keep when clearing memory.
- Parameters
name (str) – The variable to add to the persistent list
- fitbenchmarking.utils.matlab_engine.clear_non_persistent_matlab_vars() None
Clear any non-persistent variables. That is any variable that hasn’t been added via the above method.
- fitbenchmarking.utils.matlab_engine.list_persistent_matlab_vars() list[str]
Return a list of all persistent variables
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
This file will handle all interaction with the options configuration file.
- class fitbenchmarking.utils.options.Options(file_name=None, additional_options=None)
Bases:
objectAn options class to store and handle all options for fitbenchmarking
- DEFAULTS = {'FITTING': {'algorithm_type': ['all'], 'cost_func_type': ['weighted_nlls'], 'hes_method': ['default'], 'jac_method': ['scipy'], 'max_runtime': 600, 'num_runs': 5, 'software': ['scipy', 'scipy_ls']}, 'HESSIAN': {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central'], 'scipy': ['2-point']}, 'JACOBIAN': {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central'], 'scipy': ['2-point']}, 'LOGGING': {'append': False, 'external_output': 'log_only', 'file_name': 'fitbenchmarking.log', 'level': 'INFO'}, 'MINIMIZERS': {'bumps': ['amoeba', 'lm-bumps', 'newton', 'scipy-leastsq'], 'ceres': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'dfo': ['dfogn', 'dfols'], 'gofit': ['multistart'], 'gradient_free': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'horace': ['lm-lsqr'], 'levmar': ['levmar'], 'lmfit': ['powell', 'cobyla', 'slsqp', 'nelder', 'least_squares', 'leastsq', 'newton', 'tnc', 'lbfgsb', 'bfgs', 'cg', 'ampgo'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region'], 'matlab': ['Nelder-Mead Simplex'], 'matlab_curve': ['Levenberg-Marquardt', 'Trust-Region'], 'matlab_opt': ['levenberg-marquardt', 'trust-region-reflective'], 'matlab_stats': ['Levenberg-Marquardt'], 'minuit': ['migrad', 'simplex'], 'nlopt': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS', 'LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ', 'LN_NELDERMEAD', 'LN_SBPLX', 'LN_COBYLA', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'LD_LBFGS'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg', 'newton', 'newton_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP', 'COBYLA'], 'scipy_go': ['differential_evolution', 'dual_annealing'], 'scipy_ls': ['lm-scipy', 'trf', 'dogbox'], 'theseus': ['Levenberg_Marquardt', 'Gauss-Newton']}, 'OUTPUT': {'checkpoint_filename': 'checkpoint.json', 'cmap_range': [0.2, 0.8], 'colour_map': 'magma_r', 'colour_ulim': 100, 'comparison_mode': 'both', 'make_plots': True, 'pbar': True, 'results_browser': True, 'results_dir': 'fitbenchmarking_results', 'run_name': '', 'table_type': ['acc', 'runtime', 'compare', 'local_min', 'emissions']}}
- DEFAULT_FITTING = {'algorithm_type': ['all'], 'cost_func_type': ['weighted_nlls'], 'hes_method': ['default'], 'jac_method': ['scipy'], 'max_runtime': 600, 'num_runs': 5, 'software': ['scipy', 'scipy_ls']}
- DEFAULT_HESSIAN = {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central'], 'scipy': ['2-point']}
- DEFAULT_JACOBIAN = {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central'], 'scipy': ['2-point']}
- DEFAULT_LOGGING = {'append': False, 'external_output': 'log_only', 'file_name': 'fitbenchmarking.log', 'level': 'INFO'}
- DEFAULT_MINIMZERS = {'bumps': ['amoeba', 'lm-bumps', 'newton', 'scipy-leastsq'], 'ceres': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'dfo': ['dfogn', 'dfols'], 'gofit': ['multistart'], 'gradient_free': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'horace': ['lm-lsqr'], 'levmar': ['levmar'], 'lmfit': ['powell', 'cobyla', 'slsqp', 'nelder', 'least_squares', 'leastsq', 'newton', 'tnc', 'lbfgsb', 'bfgs', 'cg', 'ampgo'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region'], 'matlab': ['Nelder-Mead Simplex'], 'matlab_curve': ['Levenberg-Marquardt', 'Trust-Region'], 'matlab_opt': ['levenberg-marquardt', 'trust-region-reflective'], 'matlab_stats': ['Levenberg-Marquardt'], 'minuit': ['migrad', 'simplex'], 'nlopt': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS', 'LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ', 'LN_NELDERMEAD', 'LN_SBPLX', 'LN_COBYLA', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'LD_LBFGS'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg', 'newton', 'newton_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP', 'COBYLA'], 'scipy_go': ['differential_evolution', 'dual_annealing'], 'scipy_ls': ['lm-scipy', 'trf', 'dogbox'], 'theseus': ['Levenberg_Marquardt', 'Gauss-Newton']}
- DEFAULT_OUTPUT = {'checkpoint_filename': 'checkpoint.json', 'cmap_range': [0.2, 0.8], 'colour_map': 'magma_r', 'colour_ulim': 100, 'comparison_mode': 'both', 'make_plots': True, 'pbar': True, 'results_browser': True, 'results_dir': 'fitbenchmarking_results', 'run_name': '', 'table_type': ['acc', 'runtime', 'compare', 'local_min', 'emissions']}
- VALID = {'FITTING': {'algorithm_type': ['all', 'ls', 'deriv_free', 'general', 'simplex', 'trust_region', 'levenberg-marquardt', 'gauss_newton', 'bfgs', 'conjugate_gradient', 'steepest_descent', 'global_optimization'], 'cost_func_type': ['nlls', 'weighted_nlls', 'hellinger_nlls', 'poisson'], 'hes_method': ['scipy', 'analytic', 'default', 'numdifftools'], 'jac_method': ['scipy', 'analytic', 'default', 'numdifftools'], 'software': ['bumps', 'ceres', 'dfo', 'gofit', 'gradient_free', 'gsl', 'horace', 'levmar', 'lmfit', 'mantid', 'matlab', 'matlab_curve', 'matlab_opt', 'matlab_stats', 'minuit', 'nlopt', 'ralfit', 'scipy', 'scipy_ls', 'scipy_go', 'theseus']}, 'HESSIAN': {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central', 'complex', 'multicomplex', 'forward', 'backward'], 'scipy': ['2-point', '3-point', 'cs']}, 'JACOBIAN': {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central', 'complex', 'multicomplex', 'forward', 'backward'], 'scipy': ['2-point', '3-point', 'cs']}, 'LOGGING': {'append': [True, False], 'external_output': ['debug', 'display', 'log_only'], 'level': ['NOTSET', 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']}, 'MINIMIZERS': {'bumps': ['amoeba', 'lm-bumps', 'newton', 'de', 'scipy-leastsq', 'dream'], 'ceres': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'dfo': ['dfogn', 'dfols'], 'gofit': ['alternating', 'multistart', 'regularisation'], 'gradient_free': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer', 'BayesianOptimizer', 'TreeStructuredParzenEstimators', 'DecisionTreeOptimizer'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'horace': ['lm-lsqr'], 'levmar': ['levmar'], 'lmfit': ['differential_evolution', 'brute', 'basinhopping', 'powell', 'cobyla', 'slsqp', 'emcee', 'nelder', 'least_squares', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg', 'leastsq', 'newton', 'tnc', 'lbfgsb', 'bfgs', 'cg', 'ampgo', 'shgo', 'dual_annealing'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region', 'FABADA'], 'matlab': ['Nelder-Mead Simplex'], 'matlab_curve': ['Levenberg-Marquardt', 'Trust-Region'], 'matlab_opt': ['levenberg-marquardt', 'trust-region-reflective'], 'matlab_stats': ['Levenberg-Marquardt'], 'minuit': ['migrad', 'simplex'], 'nlopt': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS', 'LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ', 'LN_NELDERMEAD', 'LN_SBPLX', 'LN_COBYLA', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'LD_LBFGS', 'GN_DIRECT', 'GN_DIRECT_L', 'GN_DIRECT_L_RAND', 'GNL_DIRECT_NOSCAL', 'GN_DIRECT_L_NOSCAL', 'GN_DIRECT_L_RAND_NOSCAL', 'GN_ORIG_DIRECT', 'GN_ORIG_DIRECT_L', 'GN_CRS2_LM', 'G_MLSL_LDS', 'G_MLSL', 'GD_STOGO', 'GD_STOGO_RAND', 'GN_AGS', 'GN_ISRES'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg', 'newton', 'newton_reg', 'newton-tensor', 'newton-tensor_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP', 'COBYLA', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg'], 'scipy_go': ['differential_evolution', 'shgo', 'dual_annealing'], 'scipy_ls': ['lm-scipy', 'trf', 'dogbox'], 'theseus': ['Levenberg_Marquardt', 'Gauss-Newton']}, 'OUTPUT': {'append': [True, False], 'colour_map': ['magma', 'inferno', 'plasma', 'viridis', 'cividis', 'twilight', 'twilight_shifted', 'turbo', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'CMRmap', 'GnBu', 'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr', 'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn', 'Reds', 'Spectral', 'Wistia', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary', 'bone', 'brg', 'bwr', 'cool', 'coolwarm', 'copper', 'cubehelix', 'flag', 'gist_earth', 'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow', 'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2', 'gray', 'hot', 'hsv', 'jet', 'nipy_spectral', 'ocean', 'pink', 'prism', 'rainbow', 'seismic', 'spring', 'summer', 'terrain', 'winter', 'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c', 'magma_r', 'inferno_r', 'plasma_r', 'viridis_r', 'cividis_r', 'twilight_r', 'twilight_shifted_r', 'turbo_r', 'Blues_r', 'BrBG_r', 'BuGn_r', 'BuPu_r', 'CMRmap_r', 'GnBu_r', 'Greens_r', 'Greys_r', 'OrRd_r', 'Oranges_r', 'PRGn_r', 'PiYG_r', 'PuBu_r', 'PuBuGn_r', 'PuOr_r', 'PuRd_r', 'Purples_r', 'RdBu_r', 'RdGy_r', 'RdPu_r', 'RdYlBu_r', 'RdYlGn_r', 'Reds_r', 'Spectral_r', 'Wistia_r', 'YlGn_r', 'YlGnBu_r', 'YlOrBr_r', 'YlOrRd_r', 'afmhot_r', 'autumn_r', 'binary_r', 'bone_r', 'brg_r', 'bwr_r', 'cool_r', 'coolwarm_r', 'copper_r', 'cubehelix_r', 'flag_r', 'gist_earth_r', 'gist_gray_r', 'gist_heat_r', 'gist_ncar_r', 'gist_rainbow_r', 'gist_stern_r', 'gist_yarg_r', 'gnuplot_r', 'gnuplot2_r', 'gray_r', 'hot_r', 'hsv_r', 'jet_r', 'nipy_spectral_r', 'ocean_r', 'pink_r', 'prism_r', 'rainbow_r', 'seismic_r', 'spring_r', 'summer_r', 'terrain_r', 'winter_r', 'Accent_r', 'Dark2_r', 'Paired_r', 'Pastel1_r', 'Pastel2_r', 'Set1_r', 'Set2_r', 'Set3_r', 'tab10_r', 'tab20_r', 'tab20b_r', 'tab20c_r'], 'comparison_mode': ['abs', 'rel', 'both'], 'external_output': ['debug', 'display', 'log_only'], 'level': ['NOTSET', 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], 'make_plots': [True, False], 'pbar': [True, False], 'results_browser': [True, False], 'table_type': ['acc', 'runtime', 'compare', 'local_min', 'emissions']}}
- VALID_FITTING = {'algorithm_type': ['all', 'ls', 'deriv_free', 'general', 'simplex', 'trust_region', 'levenberg-marquardt', 'gauss_newton', 'bfgs', 'conjugate_gradient', 'steepest_descent', 'global_optimization'], 'cost_func_type': ['nlls', 'weighted_nlls', 'hellinger_nlls', 'poisson'], 'hes_method': ['scipy', 'analytic', 'default', 'numdifftools'], 'jac_method': ['scipy', 'analytic', 'default', 'numdifftools'], 'software': ['bumps', 'ceres', 'dfo', 'gofit', 'gradient_free', 'gsl', 'horace', 'levmar', 'lmfit', 'mantid', 'matlab', 'matlab_curve', 'matlab_opt', 'matlab_stats', 'minuit', 'nlopt', 'ralfit', 'scipy', 'scipy_ls', 'scipy_go', 'theseus']}
- VALID_HESSIAN = {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central', 'complex', 'multicomplex', 'forward', 'backward'], 'scipy': ['2-point', '3-point', 'cs']}
- VALID_JACOBIAN = {'analytic': ['default'], 'default': ['default'], 'numdifftools': ['central', 'complex', 'multicomplex', 'forward', 'backward'], 'scipy': ['2-point', '3-point', 'cs']}
- 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', 'scipy-leastsq', 'dream'], 'ceres': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'dfo': ['dfogn', 'dfols'], 'gofit': ['alternating', 'multistart', 'regularisation'], 'gradient_free': ['HillClimbingOptimizer', 'RepulsingHillClimbingOptimizer', 'SimulatedAnnealingOptimizer', 'RandomSearchOptimizer', 'RandomRestartHillClimbingOptimizer', 'RandomAnnealingOptimizer', 'ParallelTemperingOptimizer', 'ParticleSwarmOptimizer', 'EvolutionStrategyOptimizer', 'BayesianOptimizer', 'TreeStructuredParzenEstimators', 'DecisionTreeOptimizer'], 'gsl': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'horace': ['lm-lsqr'], 'levmar': ['levmar'], 'lmfit': ['differential_evolution', 'brute', 'basinhopping', 'powell', 'cobyla', 'slsqp', 'emcee', 'nelder', 'least_squares', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg', 'leastsq', 'newton', 'tnc', 'lbfgsb', 'bfgs', 'cg', 'ampgo', 'shgo', 'dual_annealing'], 'mantid': ['BFGS', 'Conjugate gradient (Fletcher-Reeves imp.)', 'Conjugate gradient (Polak-Ribiere imp.)', 'Damped GaussNewton', 'Levenberg-Marquardt', 'Levenberg-MarquardtMD', 'Simplex', 'SteepestDescent', 'Trust Region', 'FABADA'], 'matlab': ['Nelder-Mead Simplex'], 'matlab_curve': ['Levenberg-Marquardt', 'Trust-Region'], 'matlab_opt': ['levenberg-marquardt', 'trust-region-reflective'], 'matlab_stats': ['Levenberg-Marquardt'], 'minuit': ['migrad', 'simplex'], 'nlopt': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS', 'LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ', 'LN_NELDERMEAD', 'LN_SBPLX', 'LN_COBYLA', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'LD_LBFGS', 'GN_DIRECT', 'GN_DIRECT_L', 'GN_DIRECT_L_RAND', 'GNL_DIRECT_NOSCAL', 'GN_DIRECT_L_NOSCAL', 'GN_DIRECT_L_RAND_NOSCAL', 'GN_ORIG_DIRECT', 'GN_ORIG_DIRECT_L', 'GN_CRS2_LM', 'G_MLSL_LDS', 'G_MLSL', 'GD_STOGO', 'GD_STOGO_RAND', 'GN_AGS', 'GN_ISRES'], 'ralfit': ['gn', 'gn_reg', 'hybrid', 'hybrid_reg', 'newton', 'newton_reg', 'newton-tensor', 'newton-tensor_reg'], 'scipy': ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'SLSQP', 'COBYLA', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg'], 'scipy_go': ['differential_evolution', 'shgo', 'dual_annealing'], 'scipy_ls': ['lm-scipy', 'trf', 'dogbox'], 'theseus': ['Levenberg_Marquardt', 'Gauss-Newton']}
- VALID_OUTPUT = {'append': [True, False], 'colour_map': ['magma', 'inferno', 'plasma', 'viridis', 'cividis', 'twilight', 'twilight_shifted', 'turbo', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'CMRmap', 'GnBu', 'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr', 'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn', 'Reds', 'Spectral', 'Wistia', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary', 'bone', 'brg', 'bwr', 'cool', 'coolwarm', 'copper', 'cubehelix', 'flag', 'gist_earth', 'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow', 'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2', 'gray', 'hot', 'hsv', 'jet', 'nipy_spectral', 'ocean', 'pink', 'prism', 'rainbow', 'seismic', 'spring', 'summer', 'terrain', 'winter', 'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c', 'magma_r', 'inferno_r', 'plasma_r', 'viridis_r', 'cividis_r', 'twilight_r', 'twilight_shifted_r', 'turbo_r', 'Blues_r', 'BrBG_r', 'BuGn_r', 'BuPu_r', 'CMRmap_r', 'GnBu_r', 'Greens_r', 'Greys_r', 'OrRd_r', 'Oranges_r', 'PRGn_r', 'PiYG_r', 'PuBu_r', 'PuBuGn_r', 'PuOr_r', 'PuRd_r', 'Purples_r', 'RdBu_r', 'RdGy_r', 'RdPu_r', 'RdYlBu_r', 'RdYlGn_r', 'Reds_r', 'Spectral_r', 'Wistia_r', 'YlGn_r', 'YlGnBu_r', 'YlOrBr_r', 'YlOrRd_r', 'afmhot_r', 'autumn_r', 'binary_r', 'bone_r', 'brg_r', 'bwr_r', 'cool_r', 'coolwarm_r', 'copper_r', 'cubehelix_r', 'flag_r', 'gist_earth_r', 'gist_gray_r', 'gist_heat_r', 'gist_ncar_r', 'gist_rainbow_r', 'gist_stern_r', 'gist_yarg_r', 'gnuplot_r', 'gnuplot2_r', 'gray_r', 'hot_r', 'hsv_r', 'jet_r', 'nipy_spectral_r', 'ocean_r', 'pink_r', 'prism_r', 'rainbow_r', 'seismic_r', 'spring_r', 'summer_r', 'terrain_r', 'winter_r', 'Accent_r', 'Dark2_r', 'Paired_r', 'Pastel1_r', 'Pastel2_r', 'Set1_r', 'Set2_r', 'Set3_r', 'tab10_r', 'tab20_r', 'tab20b_r', 'tab20c_r'], 'comparison_mode': ['abs', 'rel', 'both'], 'external_output': ['debug', 'display', 'log_only'], 'level': ['NOTSET', 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], 'make_plots': [True, False], 'pbar': [True, False], 'results_browser': [True, False], 'table_type': ['acc', 'runtime', 'compare', 'local_min', 'emissions']}
- VALID_SECTIONS = ['MINIMIZERS', 'FITTING', 'JACOBIAN', 'HESSIAN', 'OUTPUT', 'LOGGING']
- property minimizers
Returns the minimizers in a software package
- read_value(func, option, additional_options)
Helper function which loads in the value
- Parameters
func (callable) – configparser function
option (str) – option to be read for file
additional_options – A dictionary of options
input by the user into the command line. :type additional_options: dict
- Returns
value of the option
- Return type
list/str/int/bool
- property results_dir: str
Returns the directory to store the results in.
- 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.find_options_file(options_file: str, additional_options: dict) fitbenchmarking.utils.options.Options
Attempts to find the options file and creates an Options object for it. Wildcards are accepted in the parameters of this function.
- Parameters
options_file (str) – The path or glob pattern for an options file.
additional_options (dict) – A dictionary of options input by the user into the command line.
- Returns
An Options object.
- Return type
- 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.options.read_range(s)
Utility function to allow ranges to be read by the config parser
- Parameters
s (string) – string to convert to a list
- Returns
two element list [lower_lim, upper lim]
- Return type
list
This file will capture output that would be printed to the terminal
- class fitbenchmarking.utils.output_grabber.OutputGrabber(options)
Bases:
objectClass used to grab outputs for stdout and stderr
- class fitbenchmarking.utils.output_grabber.StreamGrabber(stream)
Bases:
objectClass 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.
Implements the TimerWithMaxTime class used for checking the ‘max_runtime’ is not exceeded.
- class fitbenchmarking.utils.timer.TimerWithMaxTime(max_runtime: float)
Bases:
objectA timer class used for checking if the ‘max_runtime’ is exceeded when executing the fits.
- check_elapsed_time() None
Checks whether the max runtime has been exceeded. Raises a MaxRuntimeError exception if it has been exceeded. Otherwise, it carries on.
- reset() None
Resets the timer so it can be used for timing a different fit combination.
- start() None
Starts the timer by recording the current time.
- stop() None
Stops the timer if it is timing something. The elapsed time since starting the timer is added onto the total elapsed time.
Utility decorator function for saving and writing files.
- fitbenchmarking.utils.write_files.write_file(function)
A decorator function used for catching exceptions which can happen specifically when writing files. It will log a useful error message if the problem is identified.
- Parameters
function (A callable function.) – A callable function which writes files.
- Returns
A callable wrapped function which writes files.
- Return type
A callable function.