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"""
Global minimization using the ``brute`` method (a.k.a. grid search)
===================================================================
"""
###############################################################################
# This notebook shows a simple example of using ``lmfit.minimize.brute`` that
# uses the method with the same name from ``scipy.optimize``.
#
# The method computes the function’s value at each point of a multidimensional
# grid of points, to find the global minimum of the function. It behaves
# identically to ``scipy.optimize.brute`` in case finite bounds are given on
# all varying parameters, but will also deal with non-bounded parameters
# (see below).
import copy
from matplotlib.colors import LogNorm
import matplotlib.pyplot as plt
import numpy as np
from lmfit import Minimizer, create_params, fit_report
###############################################################################
# Let's start with the example given in the documentation of SciPy:
#
# "We illustrate the use of brute to seek the global minimum of a function of
# two variables that is given as the sum of a positive-definite quadratic and
# two deep “Gaussian-shaped” craters. Specifically, define the objective
# function ``f`` as the sum of three other functions, ``f = f1 + f2 + f3``. We
# suppose each of these has a signature ``(z, *params), where z = (x, y)``,
# and params and the functions are as defined below."
#
# First, we create a set of Parameters where all variables except ``x`` and
# ``y`` are given fixed values.
# Just as in the documentation we will do a grid search between ``-4`` and
# ``4`` and use a stepsize of ``0.25``. The bounds can be set as usual with
# the ``min`` and ``max`` attributes, and the stepsize is set using
# ``brute_step``.
params = create_params(a=dict(value=2, vary=False),
b=dict(value=3, vary=False),
c=dict(value=7, vary=False),
d=dict(value=8, vary=False),
e=dict(value=9, vary=False),
f=dict(value=10, vary=False),
g=dict(value=44, vary=False),
h=dict(value=-1, vary=False),
i=dict(value=2, vary=False),
j=dict(value=26, vary=False),
k=dict(value=1, vary=False),
l=dict(value=-2, vary=False),
scale=dict(value=0.5, vary=False),
x=dict(value=0.0, vary=True, min=-4, max=4, brute_step=0.25),
y=dict(value=0.0, vary=True, min=-4, max=4, brute_step=0.25))
###############################################################################
# Second, create the three functions and the objective function:
def f1(p):
par = p.valuesdict()
return (par['a'] * par['x']**2 + par['b'] * par['x'] * par['y'] +
par['c'] * par['y']**2 + par['d']*par['x'] + par['e']*par['y'] +
par['f'])
def f2(p):
par = p.valuesdict()
return (-1.0*par['g']*np.exp(-((par['x']-par['h'])**2 +
(par['y']-par['i'])**2) / par['scale']))
def f3(p):
par = p.valuesdict()
return (-1.0*par['j']*np.exp(-((par['x']-par['k'])**2 +
(par['y']-par['l'])**2) / par['scale']))
def f(params):
return f1(params) + f2(params) + f3(params)
###############################################################################
# Performing the actual grid search is done with:
fitter = Minimizer(f, params)
result = fitter.minimize(method='brute')
###############################################################################
# , which will increment ``x`` and ``y`` between ``-4`` in increments of
# ``0.25`` until ``4`` (not inclusive).
grid_x, grid_y = (np.unique(par.ravel()) for par in result.brute_grid)
print(grid_x)
###############################################################################
# The objective function is evaluated on this grid, and the raw output from
# ``scipy.optimize.brute`` is stored in the MinimizerResult as
# ``brute_<parname>`` attributes. These attributes are:
#
# ``result.brute_x0`` -- A 1-D array containing the coordinates of a point at
# which the objective function had its minimum value.
print(result.brute_x0)
###############################################################################
# ``result.brute_fval`` -- Function value at the point ``x0``.
print(result.brute_fval)
###############################################################################
# ``result.brute_grid`` -- Representation of the evaluation grid. It has the
# same length as ``x0``.
print(result.brute_grid)
###############################################################################
# ``result.brute_Jout`` -- Function values at each point of the evaluation
# grid, i.e., ``Jout = func(*grid)``.
print(result.brute_Jout)
###############################################################################
# **Reassuringly, the obtained results are identical to using the method in
# SciPy directly!**
###############################################################################
# Example 2: fit of a decaying sine wave
#
# In this example, we will explain some of the options of the algorithm.
#
# We start off by generating some synthetic data with noise for a decaying sine
# wave, define an objective function, and create/initialize a Parameter set.
x = np.linspace(0, 15, 301)
np.random.seed(7)
noise = np.random.normal(size=x.size, scale=0.2)
data = (5. * np.sin(2*x - 0.1) * np.exp(-x*x*0.025) + noise)
plt.plot(x, data, 'o')
plt.show()
def fcn2min(params, x, data):
"""Model decaying sine wave, subtract data."""
amp = params['amp']
shift = params['shift']
omega = params['omega']
decay = params['decay']
model = amp * np.sin(x*omega + shift) * np.exp(-x*x*decay)
return model - data
###############################################################################
# In contrast to the implementation in SciPy (as shown in the first example),
# varying parameters do not need to have finite bounds in lmfit. However, if a
# parameter does not have finite bounds, then it does need a ``brute_step``
# attribute specified:
params = create_params(amp=dict(value=7, min=2.5, brute_step=0.25),
decay=dict(value=0.05, brute_step=0.005),
shift=dict(value=0.0, min=-np.pi/2., max=np.pi/2),
omega=dict(value=3, max=5, brute_step=0.25))
###############################################################################
# Our initial parameter set is now defined as shown below and this will
# determine how the grid is set-up.
params.pretty_print()
###############################################################################
# First, we initialize a ``Minimizer`` and perform the grid search:
fitter = Minimizer(fcn2min, params, fcn_args=(x, data))
result_brute = fitter.minimize(method='brute', Ns=25, keep=25)
print(fit_report(result_brute))
###############################################################################
# We used two new parameters here: ``Ns`` and ``keep``. The parameter ``Ns``
# determines the \'number of grid points along the axes\' similarly to its usage
# in SciPy. Together with ``brute_step``, ``min`` and ``max`` for a Parameter
# it will dictate how the grid is set-up:
#
# **(1)** finite bounds are specified ("SciPy implementation"): uses
# ``brute_step`` if present (in the example above) or uses ``Ns`` to generate
# the grid. The latter scenario that interpolates ``Ns`` points from ``min``
# to ``max`` (inclusive), is here shown for the parameter ``shift``:
par_name = 'shift'
indx_shift = result_brute.var_names.index(par_name)
grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")
###############################################################################
# If finite bounds are not set for a certain parameter then the user **must**
# specify ``brute_step`` - three more scenarios are considered here:
#
# **(2)** lower bound ``(min``) and ``brute_step`` are specified:
# ``range = (min, min + Ns * brute_step, brute_step)``
par_name = 'amp'
indx_shift = result_brute.var_names.index(par_name)
grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")
###############################################################################
# **(3)** upper bound (``max``) and ``brute_step`` are specified:
# ``range = (max - Ns * brute_step, max, brute_step)``
par_name = 'omega'
indx_shift = result_brute.var_names.index(par_name)
grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")
###############################################################################
# **(4)** numerical value (``value``) and ``brute_step`` are specified:
# ``range = (value - (Ns//2) * brute_step, value + (Ns//2) * brute_step, brute_step)``
par_name = 'decay'
indx_shift = result_brute.var_names.index(par_name)
grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")
###############################################################################
# The ``MinimizerResult`` contains all the usual best-fit parameters and
# fitting statistics. For example, the optimal solution from the grid search
# is given below together with a plot:
print(fit_report(result_brute))
###############################################################################
plt.plot(x, data, 'o')
plt.plot(x, data + fcn2min(result_brute.params, x, data), '--')
plt.show()
###############################################################################
# We can see that this fit is already very good, which is what we should expect
# since our ``brute`` force grid is sampled rather finely and encompasses the
# "correct" values.
#
# In a more realistic, complicated example the ``brute`` method will be used
# to get reasonable values for the parameters and perform another minimization
# (e.g., using ``leastsq``) using those as starting values. That is where the
# ``keep`` parameter comes into play: it determines the "number of best
# candidates from the brute force method that are stored in the ``candidates``
# attribute". In the example above we store the best-ranking 25 solutions (the
# default value is ``50`` and storing all the grid points can be accomplished
# by choosing ``all``). The ``candidates`` attribute contains the parameters
# and ``chisqr`` from the brute force method as a ``namedtuple``,
# ``(‘Candidate’, [‘params’, ‘score’])``, sorted on the (lowest) ``chisqr``
# value. To access the values for a particular candidate one can use
# ``result.candidate[#].params`` or ``result.candidate[#].score``, where a
# lower # represents a better candidate. The ``show_candidates(#)`` uses the
# ``pretty_print()`` method to show a specific candidate-# or all candidates
# when no number is specified.
#
# The optimal fit is, as usual, stored in the ``MinimizerResult.params``
# attribute and is, therefore, identical to ``result_brute.show_candidates(1)``.
result_brute.show_candidates(1)
###############################################################################
# In this case, the next-best scoring candidate has already a ``chisqr`` that
# increased quite a bit:
result_brute.show_candidates(2)
###############################################################################
# and is, therefore, probably not so likely... However, as said above, in most
# cases you'll want to do another minimization using the solutions from the
# ``brute`` method as starting values. That can be easily accomplished as
# shown in the code below, where we now perform a ``leastsq`` minimization
# starting from the top-25 solutions and accept the solution if the ``chisqr``
# is lower than the previously 'optimal' solution:
best_result = copy.deepcopy(result_brute)
for candidate in result_brute.candidates:
trial = fitter.minimize(method='leastsq', params=candidate.params)
if trial.chisqr < best_result.chisqr:
best_result = trial
###############################################################################
# From the ``leastsq`` minimization we obtain the following parameters for the
# most optimal result:
print(fit_report(best_result))
###############################################################################
# As expected the parameters have not changed significantly as they were
# already very close to the "real" values, which can also be appreciated from
# the plots below.
plt.plot(x, data, 'o')
plt.plot(x, data + fcn2min(result_brute.params, x, data), '-',
label='brute')
plt.plot(x, data + fcn2min(best_result.params, x, data), '--',
label='brute followed by leastsq')
plt.legend()
###############################################################################
# Finally, the results from the ``brute`` force grid-search can be visualized
# using the rather lengthy Python function below (which might get incorporated
# in lmfit at some point).
def plot_results_brute(result, best_vals=True, varlabels=None,
output=None):
"""Visualize the result of the brute force grid search.
The output file will display the chi-square value per parameter and contour
plots for all combination of two parameters.
Inspired by the `corner` package (https://github.com/dfm/corner.py).
Parameters
----------
result : :class:`~lmfit.minimizer.MinimizerResult`
Contains the results from the :meth:`brute` method.
best_vals : bool, optional
Whether to show the best values from the grid search (default is True).
varlabels : list, optional
If None (default), use `result.var_names` as axis labels, otherwise
use the names specified in `varlabels`.
output : str, optional
Name of the output PDF file (default is 'None')
"""
npars = len(result.var_names)
_fig, axes = plt.subplots(npars, npars)
if not varlabels:
varlabels = result.var_names
if best_vals and isinstance(best_vals, bool):
best_vals = result.params
for i, par1 in enumerate(result.var_names):
for j, par2 in enumerate(result.var_names):
# parameter vs chi2 in case of only one parameter
if npars == 1:
axes.plot(result.brute_grid, result.brute_Jout, 'o', ms=3)
axes.set_ylabel(r'$\chi^{2}$')
axes.set_xlabel(varlabels[i])
if best_vals:
axes.axvline(best_vals[par1].value, ls='dashed', color='r')
# parameter vs chi2 profile on top
elif i == j and j < npars-1:
if i == 0:
axes[0, 0].axis('off')
ax = axes[i, j+1]
red_axis = tuple(a for a in range(npars) if a != i)
ax.plot(np.unique(result.brute_grid[i]),
np.minimum.reduce(result.brute_Jout, axis=red_axis),
'o', ms=3)
ax.set_ylabel(r'$\chi^{2}$')
ax.yaxis.set_label_position("right")
ax.yaxis.set_ticks_position('right')
ax.set_xticks([])
if best_vals:
ax.axvline(best_vals[par1].value, ls='dashed', color='r')
# parameter vs chi2 profile on the left
elif j == 0 and i > 0:
ax = axes[i, j]
red_axis = tuple(a for a in range(npars) if a != i)
ax.plot(np.minimum.reduce(result.brute_Jout, axis=red_axis),
np.unique(result.brute_grid[i]), 'o', ms=3)
ax.invert_xaxis()
ax.set_ylabel(varlabels[i])
if i != npars-1:
ax.set_xticks([])
else:
ax.set_xlabel(r'$\chi^{2}$')
if best_vals:
ax.axhline(best_vals[par1].value, ls='dashed', color='r')
# contour plots for all combinations of two parameters
elif j > i:
ax = axes[j, i+1]
red_axis = tuple(a for a in range(npars) if a not in (i, j))
X, Y = np.meshgrid(np.unique(result.brute_grid[i]),
np.unique(result.brute_grid[j]))
lvls1 = np.linspace(result.brute_Jout.min(),
np.median(result.brute_Jout)/2.0, 7, dtype='int')
lvls2 = np.linspace(np.median(result.brute_Jout)/2.0,
np.median(result.brute_Jout), 3, dtype='int')
lvls = np.unique(np.concatenate((lvls1, lvls2)))
ax.contourf(X.T, Y.T, np.minimum.reduce(result.brute_Jout, axis=red_axis),
lvls, norm=LogNorm())
ax.set_yticks([])
if best_vals:
ax.axvline(best_vals[par1].value, ls='dashed', color='r')
ax.axhline(best_vals[par2].value, ls='dashed', color='r')
ax.plot(best_vals[par1].value, best_vals[par2].value, 'rs', ms=3)
if j != npars-1:
ax.set_xticks([])
else:
ax.set_xlabel(varlabels[i])
if j - i >= 2:
axes[i, j].axis('off')
if output is not None:
plt.savefig(output)
###############################################################################
# and finally, to generated the figure:
plot_results_brute(result_brute, best_vals=True, varlabels=None)
plt.show()
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