mystic package documentation¶
mystic: highlyconstrained nonconvex optimization and uncertainty quantification¶
About Mystic¶
The mystic
framework provides a collection of optimization algorithms
and tools that allows the user to more robustly (and easily) solve hard
optimization problems. All optimization algorithms included in mystic
provide workflow at the fitting layer, not just access to the algorithms
as function calls. mystic
gives the user finegrained power to both
monitor and steer optimizations as the fit processes are running.
Optimizers can advance one iteration with Step
, or run to completion
with Solve
. Users can customize optimizer stop conditions, where both
compound and userprovided conditions may be used. Optimizers can save
state, can be reconfigured dynamically, and can be restarted from a
saved solver or from a results file. All solvers can also leverage
parallel computing, either within each iteration or as an ensemble of
solvers.
Where possible, mystic
optimizers share a common interface, and thus can
be easily swapped without the user having to write any new code. mystic
solvers all conform to a solver API, thus also have common method calls
to configure and launch an optimization job. For more details, see
mystic.abstract_solver
. The API also makes it easy to bind a favorite
3rd party solver into the mystic
framework.
Optimization algorithms in mystic
can accept parameter constraints,
either in the form of penaties (which “penalize” regions of solution
space that violate the constraints), or as constraints (which “constrain”
the solver to only search in regions of solution space where the
constraints are respected), or both. mystic
provides a large
selection of constraints, including probabistic and dimensionally
reducing constraints. By providing a robust interface designed to
enable the user to easily configure and control solvers, mystic
greatly reduces the barrier to solving hard optimization problems.
mystic
is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated. A list of known issues is maintained
at http://trac.mystic.cacr.caltech.edu/project/mystic/query.html, with a public
ticket list at https://github.com/uqfoundation/mystic/issues.
Major Features¶
mystic
provides a stock set of configurable, controllable solvers with:
 a common interface
 a control handler with: pause, continue, exit, and callback
 ease in selecting initial population conditions: guess, random, etc
 ease in checkpointing and restarting from a log or saved state
 the ability to leverage parallel & distributed computing
 the ability to apply a selection of logging and/or verbose monitors
 the ability to configure solverindependent termination conditions
 the ability to impose custom and userdefined penalties and constraints
To get up and running quickly, mystic
also provides infrastructure to:
 easily generate a model (several standard test models are included)
 configure and autogenerate a cost function from a model
 configure an ensemble of solvers to perform a specific task
Current Release¶
This documentation is for version mystic0.3.3.dev0
.
The latest released version of mystic
is available from:
mystic
is distributed under a 3clause BSD license.
>>> import mystic
>>> print (mystic.license())
Development Version¶
You can get the latest development version with all the shiny new features at:
If you have a new contribution, please submit a pull request.
Installation¶
mystic
is packaged to install from source, so you must
download the tarball, unzip, and run the installer:
[download]
$ tar xvzf mystic0.3.2.tar.gz
$ cd mystic0.3.2
$ python setup py build
$ python setup py install
You will be warned of any missing dependencies and/or settings
after you run the “build” step above. mystic
depends on dill
, numpy
and sympy
, so you should install them first. There are several
functions within mystic
where scipy
is used if it is available;
however, scipy
is an optional dependency. Having matplotlib
installed
is necessary for running several of the examples, and you should
probably go get it even though it’s not required. matplotlib
is required
for results visualization available in the scripts packaged with mystic
.
Alternately, mystic
can be installed with pip
or easy_install
:
$ pip install mystic
Requirements¶
mystic
requires:
python
, version >= 2.6 or version >= 3.1, orpypy
numpy
, version >= 1.0sympy
, version >= 0.6.7dill
, version >= 0.2.8.2klepto
, version >= 0.1.5.2
Optional requirements:
setuptools
, version >= 0.6matplotlib
, version >= 0.91scipy
, version >= 0.6.0mpmath
, version >= 1.0.0pathos
, version >= 0.2.2.1pyina
, version >= 0.2.0
More Information¶
Probably the best way to get started is to look at the documentation at
http://mystic.rtfd.io. Also see mystic.tests
for a set of scripts that
demonstrate several of the many features of the mystic
framework.
You can run the test suite with python m mystic.tests
. There are
several plotting scripts that are installed with mystic
, primary of which
are mystic_log_reader` (also available with python m mystic
) and the
mystic_model_plotter
(also available with python m mystic.models
).
There are several other plotting scripts that come with mystic
, and they
are detailed elsewhere in the documentation. See mystic.examples
for
examples that demonstrate the basic use cases for configuration and launching
of optimization jobs using one of the sample models provided in
mystic.models
. Many of the included examples are standard optimization
test problems. The use of constraints and penalties are detailed in
mystic.examples2
, while more advanced features leveraging ensemble solvers
and dimensional collapse are found in mystic.examples3
. The scripts in
mystic.examples4
demonstrate leveraging pathos
for parallel computing,
as well as demonstrate some autopartitioning schemes. mystic
has the
ability to work in product measure space, and the scripts in
mystic.examples5
show to work with product measures. The source code is
generally well documented, so further questions may be resolved by inspecting
the code itself. Please feel free to submit a ticket on github, or ask a
question on stackoverflow (@Mike McKerns).
If you would like to share how you use mystic
in your work, please send an
email (to mmckerns at uqfoundation dot org).
Instructions on building a new model are in mystic.models.abstract_model
.
mystic
provides base classes for two types of models:
AbstractFunction
[evaluatesf(x)
for given evaluation pointsx
]AbstractModel
[generatesf(x,p)
for given coefficientsp
]
mystic
also provides some convienence functions to help you build a
model instance and a cost function instance onthefly. For more
information, see mystic.forward_model
. It is, however, not necessary
to use base classes or the model builder in building your own model or
cost function, as any standard python function can be used as long as it
meets the basic AbstractFunction
interface of cost = f(x)
.
All mystic
solvers are highly configurable, and provide a robust set of
methods to help customize the solver for your particular optimization
problem. For each solver, a minimal (scipy.optimize
) interface is also
provided for users who prefer to configure and launch their solvers as a
single function call. For more information, see mystic.abstract_solver
for the solver API, and each of the individual solvers for their minimal
functional interface.
mystic
enables solvers to use parallel computing whenever the user provides
a replacement for the (serial) python map
function. mystic
includes a
sample map
in mystic.python_map
that mirrors the behavior of the
builtin python map
, and a pool
in mystic.pools
that provides map
functions using the pathos
(i.e. multiprocessing
) interface. mystic
solvers are designed to utilize distributed and parallel tools provided by
the pathos
package. For more information, see mystic.abstract_map_solver
,
mystic.abstract_ensemble_solver
, and the pathos
documentation at
http://dev.danse.us/trac/pathos.
Important classes and functions are found here:
mystic.solvers
[solver optimization algorithms]mystic.termination
[solver termination conditions]mystic.strategy
[solver population mutation strategies]mystic.monitors
[optimization monitors]mystic.symbolic
[symbolic math in constaints]mystic.constraints
[constraints functions]mystic.penalty
[penalty functions]mystic.collapse
[checks for dimensional collapse]mystic.coupler
[decorators for function coupling]mystic.pools
[parallel worker pool interface]mystic.munge
[file readers and writers]mystic.scripts
[model and convergence plotting]mystic.support
[hypercube measure support plotting]mystic.forward_model
[cost function generator]mystic.tools
[constraints, wrappers, and other tools]mystic.cache
[results caching and archiving]mystic.models
[models and test functions]mystic.math
[mathematical functions and tools]
Important functions within mystic.math
are found here:
mystic.math.Distribution
[a sampling distribution object]mystic.math.legacydata
[classes for legacy data observations]mystic.math.discrete
[classes for discrete measures]mystic.math.measures
[tools to support discrete measures]mystic.math.approx
[tools for measuring equality]mystic.math.grid
[tools for generating points on a grid]mystic.math.distance
[tools for measuring distance and norms]mystic.math.poly
[tools for polynomial functions]mystic.math.samples
[tools related to sampling]mystic.math.integrate
[tools related to integration]mystic.math.stats
[tools related to distributions]
Solver and model API definitions are found here:
mystic.abstract_solver
[the solver API definition]mystic.abstract_map_solver
[the parallel solver API]mystic.abstract_ensemble_solver
[the ensemble solver API]mystic.models.abstract_model
[the model API definition]
mystic
also provides several convience scripts that are used to visualize
models, convergence, and support on the hypercube. These scripts are installed
to a directory on the user’s $PATH
, and thus can be run from anywhere:
mystic_log_reader
[parameter and cost convergence]mystic_collapse_plotter
[convergence and dimensional collapse]mystic_model_plotter
[model surfaces and solver trajectory]support_convergence
[convergence plots for measures]support_hypercube
[parameter support on the hypercube]support_hypercube_measures
[measure support on the hypercube]support_hypercube_scenario
[scenario support on the hypercube]
Typing help
as an argument to any of the above scripts will print out an
instructive help message.
Citation¶
If you use mystic
to do research that leads to publication, we ask that you
acknowledge use of mystic
by citing the following in your publication:
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
"Building a framework for predictive science", Proceedings of
the 10th Python in Science Conference, 2011;
http://arxiv.org/pdf/1202.1056
Michael McKerns, Patrick Hung, and Michael Aivazis,
"mystic: highlyconstrained nonconvex optimization and UQ", 2009 ;
http://trac.mystic.cacr.caltech.edu/project/mystic
Please see http://trac.mystic.cacr.caltech.edu/project/mystic or http://arxiv.org/pdf/1202.1056 for further information.

license
()¶ print license

citation
()¶ print citation

model_plotter
(model, logfile=None, **kwds)¶ generate surface contour plots for model, specified by full import path; and generate model trajectory from logfile (or solver restart file), if provided
Available from the command shell as:
mystic_model_plotter.py model (logfile) [options]
or as a function call:
mystic.model_plotter(model, logfile=None, **options)
Parameters:  model (str) – full import path for the model (e.g.
mystic.models.rosen
)  logfile (str, default=None) – name of convergence logfile (e.g.
log.txt
)
Returns: None
Notes
 The option out takes a string of the filepath for the generated plot.
 The option bounds takes an indicator string, where bounds are given as
commaseparated slices. For example, using
bounds = "1:10, 0:20"
will set lower and upper bounds for x to be (1,10) and y to be (0,20). The “step” can also be given, to control the number of lines plotted in the grid. Thus"1:10:.1, 0:20"
sets the bounds as above, but uses increments of .1 along x and the default step along y. For models > 2D, the bounds can be used to specify 2 dimensions plus fixed values for remaining dimensions. Thus,"1:10, 0:20, 1.0"
plots the 2D surface where the zaxis is fixed at z=1.0. When called from a script, slice objects can be used instead of a string, thus"1:10:.1, 0:20, 1.0"
becomes(slice(1,10,.1), slice(20), 1.0)
.  The option label takes commaseparated strings. For example,
label = "x,y,"
will place ‘x’ on the xaxis, ‘y’ on the yaxis, and nothing on the zaxis. LaTeX is also accepted. For example,label = "$ h $, $ {\alpha}$, $ v$"
will label the axes with standard LaTeX math formatting. Note that the leading space is required, while a trailing space aligns the text with the axis instead of the plot frame.  The option nid takes an integer of the nth simultaneous points to plot.
 The option iter takes an integer of the largest iteration to plot.
 The option reduce can be given to reduce the output of a model to a
scalar, thus converting
model(params)
toreduce(model(params))
. A reducer is given by the import path (e.g.numpy.add
).  The option scale will convert the plot to logscale, and scale the
cost by
z=log(4*z*scale+1)+2
. This is useful for visualizing small contour changes around the minimium.  If using logscale produces negative numbers, the option shift can be
used to shift the cost by
z=z+shift
. Both shift and scale are intended to help visualize contours.  The option fill takes a boolean, to plot using filled contours.
 The option depth takes a boolean, to plot contours in 3D.
 The option dots takes a boolean, to show trajectory points in the plot.
 The option join takes a boolean, to connect trajectory points.
 The option verb takes a boolean, to print the model documentation.
 model (str) – full import path for the model (e.g.

log_reader
(filename, **kwds)¶ plot parameter convergence from file written with
LoggingMonitor
Available from the command shell as:
mystic_log_reader.py filename [options]
or as a function call:
mystic.log_reader(filename, **options)
Parameters: filename (str) – name of the convergence logfile (e.g log.txt
).Returns: None Notes
 The option out takes a string of the filepath for the generated plot.
 The option dots takes a boolean, and will show data points in the plot.
 The option line takes a boolean, and will connect the data with a line.
 The option iter takes an integer of the largest iteration to plot.
 The option legend takes a boolean, and will display the legend.
 The option nid takes an integer of the nth simultaneous points to plot.
 The option param takes an indicator string. The indicator string is
built from commaseparated array slices. For example,
params = ":"
will plot all parameters. Alternatively,params = ":2, 3:"
will plot all parameters except for the third parameter, whileparams = "0"
will only plot the first parameter.

collapse_plotter
(filename, **kwds)¶ generate cost convergence rate plots from file written with
write_support_file
Available from the command shell as:
mystic_collapse_plotter.py filename [options]
or as a function call:
mystic.collapse_plotter(filename, **options)
Parameters: filename (str) – name of the convergence logfile (e.g paramlog.py
).Returns: None Notes
 The option dots takes a boolean, and will show data points in the plot.
 The option linear takes a boolean, and will plot in a linear scale.
 The option out takes a string of the filepath for the generated plot.
 The option iter takes an integer of the largest iteration to plot.
 The option label takes a label string. For example,
label = "y"
will label the plot with a ‘y’, whilelabel = " logcost, $ log_{10}(\hat{P}  \hat{P}_{max})$"
will label the yaxis with standard LaTeX math formatting. Note that the leading space is required, and that the text is aligned along the axis.  The option col takes a string of commaseparated integers indicating iteration numbers where parameter collapse has occurred. If a second set of integers is provided (delineated by a semicolon), the additional set of integers will be plotted with a different linestyle (to indicate a different type of collapse).
 mystic module documentation
 abstract_ensemble_solver module
 abstract_launcher module
 abstract_map_solver module
 abstract_solver module
 cache module
 collapse module
 constraints module
 coupler module
 differential_evolution module
 ensemble module
 filters module
 forward_model module
 helputil module
 linesearch module
 mask module
 math module
 metropolis module
 models module
 monitors module
 munge module
 penalty module
 pools module
 python_map module
 scemtools module
 scipy_optimize module
 search module
 solvers module
 strategy module
 support module
 svc module
 svr module
 symbolic module
 termination module
 tools module
 mystic scripts documentation