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Removed duplicated code.

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master
pbrod 8 years ago
parent afbc852724
commit dcd3db4658
2 changed files with 84 additions and 165 deletions
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97
wafo/stats/_distn_infrastructure.py
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@ -199,11 +199,10 @@ def _penalized_nnlf(self, theta, x):
return _nnlf_and_penalty(self, x, args) + n_log_scale
def _reduce_func(self, args, options):
def _convert_fshapes2num(self, kwds):
# First of all, convert fshapes params to fnum: eg for stats.beta,
# shapes='a, b'. To fix `a`, can specify either `f1` or `fa`.
# Convert the latter into the former.
kwds = options # .copy()
if self.shapes:
shapes = self.shapes.replace(',', ' ').split()
for j, s in enumerate(shapes):
@ -214,11 +213,14 @@ def _reduce_func(self, args, options):
raise ValueError("Duplicate entry for %s." % key)
else:
kwds[key] = val
return kwds
def _unpack_args_kwds(self, args, kwds):
kwds = _convert_fshapes2num(self, kwds)
args = list(args)
Nargs = len(args)
fixedn = []
names = ['f%d' % n for n in range(Nargs - 2)] + ['floc', 'fscale']
names = ['f%d' % n for n in range(len(args) - 2)] + ['floc', 'fscale']
x0 = []
for n, key in enumerate(names):
if key in kwds:
@ -226,17 +228,25 @@ def _reduce_func(self, args, options):
args[n] = kwds.pop(key)
else:
x0.append(args[n])
return x0, args, fixedn
def _reduce_func(self, args, kwds):
method = kwds.pop('method', 'ml').lower()
if method.startswith('mps'):
fitfun = self._penalized_nlogps
else:
fitfun = self._penalized_nnlf
x0, args, fixedn = _unpack_args_kwds(self, args, kwds)
nargs = len(args)
if len(fixedn) == 0:
func = fitfun
restore = None
else:
if len(fixedn) == Nargs:
if len(fixedn) == nargs:
raise ValueError(
"All parameters fixed. There is nothing to optimize.")
@ -245,7 +255,7 @@ def _reduce_func(self, args, options):
# This allows the non-fixed values to vary, but
# we still call self.nnlf with all parameters.
i = 0
for n in range(Nargs):
for n in range(nargs):
if n not in fixedn:
args[n] = theta[i]
i += 1
@ -255,7 +265,7 @@ def _reduce_func(self, args, options):
newtheta = restore(args[:], theta)
return fitfun(newtheta, x)
return x0, func, restore, args
return x0, func, restore, args, fixedn
def _get_optimizer(kwds):
@ -263,16 +273,36 @@ def _get_optimizer(kwds):
# convert string to function in scipy.optimize
if not callable(optimizer) and isinstance(optimizer, string_types):
if not optimizer.startswith('fmin_'):
optimizer = "fmin_" + optimizer
if optimizer == 'fmin_':
optimizer = 'fmin'
optimizer = '_'.join(("fmin", optimizer))
try:
optimizer = getattr(optimize, optimizer)
except AttributeError:
raise ValueError("%s is not a valid optimizer" % optimizer)
raise ValueError("{} is not a valid optimizer".format(optimizer))
return optimizer
def _warn_if_no_success(warnflag):
if warnflag == 1:
warnings.warn("The maximum number of iterations was exceeded.")
elif warnflag == 2:
warnings.warn("Did not converge")
def _fitstart(self, data, args, kwds):
narg = len(args)
if narg > self.numargs:
raise TypeError("Too many input arguments.")
start = [None] * 2
if (narg < self.numargs) or not ('loc' in kwds and 'scale' in kwds):
# get distribution specific starting locations
start = self._fitstart(data)
args += start[narg:-2]
loc = kwds.pop('loc', start[-2])
scale = kwds.pop('scale', start[-1])
args += loc, scale
return args, kwds
def fit(self, data, *args, **kwargs):
"""
Return ML/MPS estimate for shape, location, and scale parameters from data.
@ -311,6 +341,16 @@ def fit(self, data, *args, **kwargs):
- fscale : hold scale parameter fixed to specified value.
- method : of estimation. Options are
'ml' : Maximum Likelihood method (default)
'mps': Maximum Product Spacing method
- alpha : Confidence coefficent (default=0.05)
- search : bool
If true search for best estimator (default),
otherwise return object with initial distribution parameters
- optimizer : The optimizer to use. The optimizer must take ``func``,
and starting position as the first two arguments,
plus ``args`` (for extra arguments to pass to the
@ -361,33 +401,35 @@ def fit(self, data, *args, **kwargs):
1
"""
Narg = len(args)
if Narg > self.numargs:
raise TypeError("Too many input arguments.")
vals, _ = self._fit(data, *args, **kwargs)
return vals
kwds = kwargs.copy()
start = [None]*2
if (Narg < self.numargs) or not ('loc' in kwds and
'scale' in kwds):
# get distribution specific starting locations
start = self._fitstart(data)
args += start[Narg:-2]
loc = kwds.pop('loc', start[-2])
scale = kwds.pop('scale', start[-1])
args += (loc, scale)
x0, func, restore, args = self._reduce_func(args, kwds)
def _fit(self, data, *args, **kwargs):
args, kwds = _fitstart(self, data, args, kwargs.copy())
x0, func, restore, args, fixedn = self._reduce_func(args, kwds)
if kwds.pop('search', True):
optimizer = _get_optimizer(kwds)
# by now kwds must be empty, since everybody took what they needed
if kwds:
raise TypeError("Unknown arguments: %s." % kwds)
vals = optimizer(func, x0, args=(ravel(data),), disp=0)
output = optimizer(func, x0, args=(ravel(data),), full_output=True,
disp=0)
if output[-1] != 0:
output = optimizer(func, output[0], args=(ravel(data),),
full_output=True)
_warn_if_no_success(output[-1])
vals = tuple(output[0])
else:
vals = tuple(x0)
if restore is not None:
vals = restore(args, vals)
vals = tuple(vals)
return vals
return vals, fixedn
def fit2(self, data, *args, **kwds):
@ -460,6 +502,7 @@ rv_continuous._penalized_nlogps = _penalized_nlogps
rv_continuous._penalized_nnlf = _penalized_nnlf
rv_continuous._reduce_func = _reduce_func
rv_continuous.fit = fit
rv_continuous._fit = _fit
rv_continuous.fit2 = fit2
rv_continuous._support_mask = _support_mask
rv_continuous._open_support_mask = _open_support_mask

136
wafo/stats/estimation.py
Unescape Escape View File

@ -1044,8 +1044,7 @@ class FitDistribution(rv_frozen):
>>> sf_ci = profile_logsf.get_bounds(alpha=0.2)
'''
def __init__(self, dist, data, args=(), method='ML', alpha=0.05,
search=True, copydata=True, **kwds):
def __init__(self, dist, data, args=(), **kwds):
extradoc = '''
plotfitsummary()
Plot various diagnostic plots to asses quality of fit.
@ -1096,12 +1095,11 @@ class FitDistribution(rv_frozen):
# '''
self.__doc__ = str(rv_frozen.__doc__) + extradoc
self.dist = dist
self.method = method
self.alpha = alpha
self.par_fix = None
self.search = search
self.copydata = copydata
self.alpha = kwds.pop('alpha', 0.05)
self.copydata = kwds.pop('copydata', True)
self.method = kwds.get('method', 'ml')
self.search = kwds.get('search', True)
self.data = np.ravel(data)
if self.copydata:
self.data = self.data.copy()
@ -1118,7 +1116,7 @@ class FitDistribution(rv_frozen):
self.i_fixed = nonzero(isfinite(self.par_fix))
def fit(self, *args, **kwds):
par, fixedn = self._fit(*args, **kwds.copy())
par, fixedn = self.dist._fit(self.data, *args, **kwds.copy())
super(FitDistribution, self).__init__(self.dist, *par)
self.par = arr(par)
somefixed = len(fixedn) > 0
@ -1158,68 +1156,6 @@ class FitDistribution(rv_frozen):
t.append('%s = %s\n' % (par, str(getattr(self, par))))
return ''.join(t)
def _convert_fshapes2fnum(self, kwds):
# First of all, convert fshapes params to fnum: eg for stats.beta,
# shapes='a, b'. To fix `a`, can specify either `f1` or `fa`.
# Convert the latter into the former.
shapes = self.dist.shapes
if shapes:
shapes = shapes.replace(',', ' ').split()
for j, s in enumerate(shapes):
val = kwds.pop('f' + s, None) or kwds.pop('fix_' + s, None)
if val is not None:
key = 'f%d' % j
if key in kwds:
raise ValueError("Duplicate entry for %s." % key)
else:
kwds[key] = val
return kwds
def _unpack_args_kwds(self, args, kwds):
kwds = self._convert_fshapes2fnum(kwds)
args = list(args)
fixedn = []
names = ['f%d' % n for n in range(len(args) - 2)] + ['floc', 'fscale']
x0 = []
for n, key in enumerate(names):
if key in kwds:
fixedn.append(n)
args[n] = kwds.pop(key)
else:
x0.append(args[n])
return x0, args, fixedn
def _reduce_func(self, args, kwds):
x0, args, fixedn = self._unpack_args_kwds(args, kwds)
nargs = len(args)
fitfun = self._fitfun
if len(fixedn) == 0:
func = fitfun
restore = None
else:
if len(fixedn) == nargs:
raise ValueError("All parameters fixed. " +
"There is nothing to optimize.")
def restore(args, theta):
# Replace with theta for all numbers not in fixedn
# This allows the non-fixed values to vary, but
# we still call self.nnlf with all parameters.
i = 0
for n in range(nargs):
if n not in fixedn:
args[n] = theta[i]
i += 1
return args
def func(theta, x):
newtheta = restore(args[:], theta)
return fitfun(newtheta, x)
return x0, func, restore, args, fixedn
@staticmethod
def _hessian(nnlf, theta, data, eps=None):
''' approximate hessian of nnlf where theta are the parameters
@ -1299,66 +1235,6 @@ class FitDistribution(rv_frozen):
"""
return self.dist._penalized_nlogps(theta, x)
@staticmethod
def _get_optimizer(kwds):
optimizer = kwds.pop('optimizer', optimize.fmin)
# convert string to function in scipy.optimize
if not callable(optimizer) and isinstance(optimizer, string_types):
if not optimizer.startswith('fmin_'):
optimizer = '_'.join(("fmin", optimizer))
try:
optimizer = getattr(optimize, optimizer)
except AttributeError:
raise ValueError("%s is not a valid optimizer" % optimizer)
return optimizer
def _fit_start(self, data, args, kwds):
dist = self.dist
narg = len(args)
if narg > dist.numargs + 2:
raise ValueError("Too many input arguments.")
if (narg < dist.numargs + 2) or not ('loc' in kwds and
'scale' in kwds):
# get distribution specific starting locations
start = dist._fitstart(data)
args += start[narg:]
loc = kwds.pop('loc', args[-2])
scale = kwds.pop('scale', args[-1])
args = args[:-2] + (loc, scale)
return args
@staticmethod
def _warn_if_no_success(warnflag):
if warnflag == 1:
warnings.warn("The maximum number of iterations was exceeded.")
elif warnflag == 2:
warnings.warn("Did not converge")
def _fit(self, *args, **kwds):
data = self.data
args = self._fit_start(data, args, kwds)
x0, func, restore, args, fixedn = self._reduce_func(args, kwds)
if self.search:
optimizer = self._get_optimizer(kwds)
# by now kwds must be empty, since everybody took what they needed
if kwds:
raise TypeError("Unknown arguments: %s." % kwds)
output = optimizer(func, x0, args=(data,), full_output=True,
disp=0)
if output[-1] != 0:
output = optimizer(func, output[0], args=(data,),
full_output=True)
vals = tuple(output[0])
self._warn_if_no_success(output[-1])
else:
vals = tuple(x0)
if restore is not None:
vals = restore(args, vals)
return vals, fixedn
def _compute_cov(self):
'''Compute covariance
'''

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