My tools of the trade for python programming.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 

479 lines
16 KiB

# $Id: fitting.py,v 1.4 2011-04-05 19:20:01 wirawan Exp $
#
# wpylib.math.fitting module
# Created: 20100120
# Wirawan Purwanto
#
# Imported 20100120 from $PWQMC77/expt/Hybrid-proj/analyze-Eh.py
# (dated 20090323).
#
# Some references on fitting:
# * http://stackoverflow.com/questions/529184/simple-multidimensional-curve-fitting
# * http://www.scipy.org/Cookbook/OptimizationDemo1 (not as thorough, but maybe useful)
import numpy
import scipy.optimize
from wpylib.db.result_base import result_base
try:
import lmfit
HAS_LMFIT = True
except ImportError:
HAS_LMFIT = False
last_fit_rslt = None
last_chi_sqr = None
class Poly_base(object):
"""Typical base class for a function to fit a polynomial. (?)
The following members must be defined to use the basic features in
this class---unless the methods are redefined appropriately:
* order = the order (maximum exponent) of the polynomial.
* dim = dimensionality of the function domain (i.e. the "x" coordinate).
A 2-dimensional (y vs x) fitting will have dim==1.
A 3-dimensional (z vs (x,y)) fitting will have dim==2.
And so on.
"""
# Must set the following:
# * order = ?
# * dim = ?
#def __call__(C, x):
# raise NotImplementedError, "must implement __call__"
def __init__(self, xdata=None, ydata=None, ndim=None):
if xdata != None:
self.dim = len(xdata)
elif ndim != None:
self.dim = ndim
else:
raise ValueError, "Either xdata or ndim argument must be supplied"
if ydata: self.guess = [ numpy.mean(ydata) ] + [0.0] * (self.order*self.dim)
def Guess(self, ydata):
"""The simplest guess: set the parameter for the constant term to <y>, and
the rest to zero. In general, this may not be the best."""
return [ numpy.mean(ydata) ] + [0.0] * (self.NParams() - 1)
def NParams(self):
'''Default NParams for polynomial without cross term.'''
return 1 + self.order*self.dim
class Poly_order2(Poly_base):
"""Multidimensional polynomial of order 2 without cross terms."""
order = 2
def __call__(self, C, x):
return C[0] \
+ sum([ C[i*2+1] * x[i] + C[i*2+2] * x[i]**2 \
for i in xrange(len(x)) ])
class Poly_order2_only(Poly_base):
"""Multidimensional polynomial of order 2 without cross terms.
The linear terms are deleted."""
order = 1 # HACK: the linear term is deleted
def __call__(self, C, x):
return C[0] \
+ sum([ C[i+1] * x[i]**2 \
for i in xrange(len(x)) ])
class Poly_order2x_only(Poly_base):
'''Multidimensional order-2-only polynomial with all the cross terms.'''
order = 2 # but not used
def __call__(self, C, x):
ndim = self.dim
# Reorganize the coeffs in the form of symmetric square matrix
# For 4x4 it will become like:
# [ 1, 5, 6, 7]
# [ 5, 2, 8, 9]
# [ 6, 8, 3, 10]
# [ 7, 9, 10, 4]
Cmat = numpy.diag(C[1:ndim+1])
j = ndim+1
for r in xrange(0, ndim-1):
jnew = j + ndim - 1 - r
Cmat[r, r+1:] = C[j:jnew]
Cmat[r+1:, r] = C[j:jnew]
j = jnew
#print Cmat
#print x
nrec = len(x[0]) # assume a 2-D array
rslt = numpy.empty((nrec,), dtype=numpy.float64)
for r in xrange(nrec):
rslt[r] = C[0] \
+ numpy.sum( Cmat * numpy.outer(x[:,r], x[:,r]) )
return rslt
def NParams(self):
# 1 is for the constant term
return 1 + self.dim * (self.dim + 1) / 2
class Poly_order3(Poly_base):
"""Multidimensional polynomial of order 3 without cross terms.
The linear terms are deleted."""
order = 3
def __call__(self, C, x):
return C[0] \
+ sum([ C[i*3+1] * x[i] + C[i*3+2] * x[i]**2 + C[i*3+3] * x[i]**3 \
for i in xrange(len(x)) ])
class Poly_order4(Poly_base):
"""Multidimensional polynomial of order 4 without cross terms.
The linear terms are deleted."""
order = 4
def __call__(self, C, x):
return C[0] \
+ sum([ C[i*4+1] * x[i] + C[i*4+2] * x[i]**2 + C[i*4+3] * x[i]**3 + C[i*4+4] * x[i]**4 \
for i in xrange(len(x)) ])
class fit_result(result_base):
"""The basic values expected in fit_result are:
- xopt
- chi_square
Optional values:
- funcalls
- xerr
"""
pass
def fit_func(Funct, Data=None, Guess=None, Params=None,
x=None, y=None,
w=None, dy=None,
debug=0,
outfmt=1,
Funct_hook=None,
method='leastsq', opts={}):
"""
Performs a function fitting.
The domain of the function is a D-dimensional vector, and the function
yields a scalar.
Funct is a python function (or any callable object) with argument list of
(C, x), where:
* C is the cofficients (parameters) being adjusted by the fitting process
(it is a sequence or a 1-D array)
* x is a 2-D array (or sequence of like nature), say,
of size "N rows times M columns".
N is the dimensionality of the domain, while
M is the number of data points, whose count must be equal to the
size of y data below.
For a 2-D fitting, for example, x should be a column array.
An input guess for the parameters can be specified via Guess argument.
It is an ordered list of scalar values for these parameters.
The Params argument is reserved for lmfit-style fitting.
It is ignored in other cases.
The "y" array is a 1-D array of length M, which contain the "measured"
value of the function at every domain point given in "x".
The "w" or "dy" array (only one of them can be specified in a call),
if given, specifies either the weight or standard error of the y data.
If "dy" is specified, then "w" is defined to be (1.0 / dy**2), per usual
convention.
Inspect Poly_base, Poly_order2, and other similar function classes in this
module to see the example of the Funct function.
The measurement (input) datasets, against which the function is to be fitted,
can be specified in one of two ways:
* via x and y arguments. x is a multi-column dataset, where each row is the
(multidimensional) coordinate of the Funct's domain.
y is a one-dimensional dataset.
Or,
* via Data argument (which is a multi-column dataset, where the first row
is the "y" argument).
OUTPUT
By default, only the fitted parameters are returned.
To return as complete information as possible, use outfmt=0.
The return value will be a struct-like object (class: fit_result).
DEBUGGING
Debugging and other investigations can be done with "Funct_hook", which,
if defined, will be called every time right after "Funct" is called.
It is called with the following signature:
Funct_hook(C, x, y, f, r)
where
f := f(C,x)
r := f(C,x) - y
Note that the reference to the hook object is passed as the first argument
to facilitate object oriented programming.
SUPPORT FOR LMFIT MODULE
This routine also supports the lmfit module.
In this case, the Funct object must supply additional attributes:
* param_names: an ordered list of parameter names.
This is used in the case that Params argument is not defined.
Input parameters can be specified ahead of time in the following ways:
* If Params is None (default), then unconstrained minimization is done
and the necessary Parameter objects are created on-the fly.
* If Params is a Parameters object, then they are used.
Note that these parameters *will* be clobbered!
The input Guess parameter can be set to False, in which case Params *must*
be defined and the initial values will be used as Guess.
"""
global last_fit_rslt, last_chi_sqr
from scipy.optimize import fmin, fmin_bfgs, leastsq, anneal
# We want to minimize this error:
if Data != None:
# an alternative way to specifying x and y
Data = numpy.asarray(Data)
y = Data[0]
x = Data[1:] # possibly multidimensional!
else:
x = numpy.asarray(x)
y = numpy.asarray(y)
if debug >= 1:
print "fit_func: using function=%s, minimizer=%s" \
% (repr(Funct), method)
if debug >= 10:
print "fit routine opts = ", opts
print "Dimensionality of the domain is: ", len(x)
if method.startswith("lmfit:"):
if not HAS_LMFIT:
raise ValueError, \
"Module lmfit is not found, cannot use `%s' minimization method." \
% (method,)
use_lmfit = True
from lmfit import minimize, Parameters, Parameter
param_names = Funct.param_names
if debug >= 10:
print "param names: ", param_names
else:
use_lmfit = False
if Guess != None:
pass
elif hasattr(Funct, "Guess_xy"):
# Try to provide an initial guess
Guess = Funct.Guess_xy(x, y)
elif hasattr(Funct, "Guess"):
# Try to provide an initial guess
# This is an older version with y-only argument
Guess = Funct.Guess(y)
elif Guess == None: # VERY OLD, DO NOT USE ANYMORE!
Guess = [ y.mean() ] + [0.0, 0.0] * len(x)
if use_lmfit:
if Params == None:
if debug >= 10:
print "No input Params given; creating unconstrained params"
# Creates a default list of Parameters for use later
assert Guess != False
Params = Parameters()
for (g,pn) in zip(Guess, param_names):
Params.add(pn, value=g)
else:
if debug >= 10:
print "Input Params specified; use them"
if Guess == None or Guess == False:
# copy the Params' values to Guess
Guess = [ Params[pn].value for pn in param_names ]
else:
# copy the Guess values onto the Params' values
for (g,pn) in zip(Guess, param_names):
Params[pn].value = g
if debug >= 10:
print "lmfit guess parameters:"
for k1 in Params:
print " - ", Params[k1]
if debug >= 5:
print "Guess params:"
print " ", Guess
if Funct_hook != None:
if not hasattr(Funct_hook, "__call__"):
raise TypeError, "Funct_hook argument must be a callable function."
def fun_err(CC, xx, yy, ww):
"""Computes the error of the fitted functional against the
reference data points:
* CC = current function parameters
* xx = domain points of the ("measured") data
* yy = target points of the ("measured") data
* ww = weights of the ("measured") data (usually, 1/error**2 of the data)
"""
ff = Funct(CC,xx)
r = (ff - yy) * ww
Funct_hook(CC, xx, yy, ff, r)
return r
elif debug < 20:
def fun_err(CC, xx, yy, ww):
ff = Funct(CC,xx)
r = (ff - yy) * ww
return r
else:
def fun_err(CC, xx, yy, ww):
ff = Funct(CC,xx)
r = (ff - yy) * ww
print " err: %s << %s << %s, %s, %s" % (r, ff, CC, xx, ww)
return r
fun_err2 = lambda CC, xx, yy, ww: numpy.sum(abs(fun_err(CC, xx, yy, ww))**2)
if w != None and dy != None:
raise TypeError, "Only one of w or dy can be specified."
if dy != None:
sqrtw = 1.0 / numpy.asarray(dy)
elif w != None:
sqrtw = numpy.sqrt(w)
else:
sqrtw = 1.0
# Full result is stored in rec
rec = fit_result()
extra_keys = {}
chi_sqr = None
if method == 'leastsq':
# modified Levenberg-Marquardt algorithm
rslt = leastsq(fun_err,
x0=Guess, # initial coefficient guess
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
keys = ('xopt', 'cov_x', 'infodict', 'mesg', 'ier') # ier = error message code from MINPACK
extra_keys = {
# map the output values to the same keyword as other methods below:
'funcalls': (lambda : rslt[2]['nfev']),
}
# Added estimate of fit parameter uncertainty (matching GNUPLOT parameter
# uncertainty.
# The error is estimated to be the diagonal of cov_x, multiplied by the WSSR
# (chi_square below) and divided by the number of fit degrees of freedom.
# I used newer scipy.optimize.curve_fit() routine as my cheat sheet here.
if outfmt == 0:
if rslt[1] != None and len(y) > len(rslt[0]):
NDF = len(y) - len(rslt[0])
extra_keys['xerr'] = (lambda:
numpy.sqrt(numpy.diagonal(rslt[1]) * rec['chi_square'] / NDF)
)
elif method == 'fmin':
# Nelder-Mead Simplex algorithm
rslt = fmin(fun_err2,
x0=Guess, # initial coefficient guess
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
keys = ('xopt', 'fopt', 'iter', 'funcalls', 'warnflag', 'allvecs')
elif method == 'fmin_bfgs' or method == 'bfgs':
# Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm
rslt = fmin_bfgs(fun_err2,
x0=Guess, # initial coefficient guess
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
keys = ('xopt', 'fopt', 'funcalls', 'gradcalls', 'warnflag', 'allvecs')
elif method == 'anneal':
rslt = anneal(fun_err2,
x0=Guess, # initial coefficient guess
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
keys = ('xopt', 'fopt', 'T', 'funcalls', 'iter', 'accept', 'retval')
elif use_lmfit:
submethod = method.split(":",1)[1]
minrec = minimize(fun_err, Params,
args=(x,y,sqrtw),
method=submethod,
# backend ("real" minimizer) options:
full_output=1,
**opts
)
xopt = [ Params[k1].value for k1 in param_names ]
keys = ('xopt', 'minobj', 'params')
rslt = [ xopt, minrec, Params ]
# map the output values (in the Minimizer instance, minrec), to
# the same keyword as other methods for backward compatiblity.
rec['funcalls'] = minrec.nfev
try:
chi_sqr = minrec.chi_sqr
except:
pass
# These seem to be particular to leastsq:
try:
rec['ier'] = minrec.ier
except:
pass
try:
rec['mesg'] = minrec.lmdif_message
except:
pass
try:
rec['message'] = minrec.message
except:
pass
# Added estimate of fit parameter uncertainty (matching GNUPLOT parameter
# uncertainty.
# The error is estimated to be the diagonal of cov_x, multiplied by the WSSR
# (chi_square below) and divided by the number of fit degrees of freedom.
# I used newer scipy.optimize.curve_fit() routine as my cheat sheet here.
if outfmt == 0:
try:
has_errorbars = minrec.errorbars
except:
has_errorbars = False
if has_errorbars:
try:
rec['xerr'] = [ Params[k1].stderr for k1 in param_names ]
except:
# it shouldn't fail like this!
import warnings
warnings.warn("wpylib.math.fitting.fit_func: Fail to get standard error of the fit parameters")
if debug >= 10:
if 'xerr' in rec:
print "param errorbars are found:"
print " ", tuple(rec['xerr'])
else:
print "param errorbars are NOT found!"
else:
raise ValueError, "Unsupported minimization method: %s" % method
# Final common post-processing:
if chi_sqr == None:
chi_sqr = fun_err2(rslt[0], x, y, sqrtw)
last_chi_sqr = chi_sqr
last_fit_rslt = rslt
if (debug >= 10):
#print "Fit-message: ", rslt[]
print "Fit-result:"
print "\n".join([ "%2d %s" % (ii, rslt[ii]) for ii in xrange(len(rslt)) ])
if debug >= 1:
print "params = ", rslt[0]
print "chi square = ", last_chi_sqr / len(y)
if outfmt == 0: # outfmt == 0 -- full result.
rec.update(dict(zip(keys, rslt)))
rec['chi_square'] = chi_sqr
rec['fit_method'] = method
# If there are extra keys, record them here:
for (k,v) in extra_keys.iteritems():
rec[k] = v()
return rec
elif outfmt == 1:
return rslt[0]
else:
try:
x = str(outfmt)
except:
x = "(?)"
raise ValueError, "Invalid `outfmt' argument = " + x