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1601 lines
54 KiB
Python
1601 lines
54 KiB
Python
'''
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Contains FitDistribution and Profile class, which are
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important classes for fitting to various Continous and Discrete Probability
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Distributions
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Author: Per A. Brodtkorb 2008
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'''
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from __future__ import division, absolute_import
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import warnings
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from wafo.plotbackend import plotbackend as plt
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from wafo.misc import ecross, findcross
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from wafo.stats._constants import _EPS
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from wafo.stats._distn_infrastructure import rv_frozen, rv_continuous
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from scipy._lib.six import string_types
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import numdifftools as nd # @UnresolvedImport
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from scipy import special
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from scipy.linalg import pinv2
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from scipy import optimize
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import numpy as np
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from scipy.special import expm1, log1p
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from numpy import (arange, zeros, log, sqrt, exp,
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asarray, nan, pi, isfinite)
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from numpy import flatnonzero as nonzero
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__all__ = ['Profile', 'FitDistribution']
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floatinfo = np.finfo(float)
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arr = asarray
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# all = alltrue # @ReservedAssignment
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def _burr_link(x, logsf, phat, ix):
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c, d, loc, scale = phat
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logp = log(-expm1(logsf))
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xn = (x - loc) / scale
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if ix == 1:
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return -logp / log1p(xn**(-c))
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if ix == 0:
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return log1p(-exp(-logp / d)) / log(xn)
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if ix == 2:
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return x - scale * exp(log1p(-exp(-logp / d)) / c)
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if ix == 3:
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return (x - loc) / exp(log1p(-exp(-logp / d)) / c)
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raise IndexError('Index to the fixed parameter is out of bounds')
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def _expon_link(x, logsf, phat, ix):
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if ix == 1:
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return - (x - phat[0]) / logsf
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if ix == 0:
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return x + phat[1] * logsf
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raise IndexError('Index to the fixed parameter is out of bounds')
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def _weibull_min_link(x, logsf, phat, ix):
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c, loc, scale = phat
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if ix == 0:
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return log(-logsf) / log((x - loc) / scale)
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if ix == 1:
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return x - scale * (-logsf) ** (1. / c)
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if ix == 2:
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return (x - loc) / (-logsf) ** (1. / c)
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raise IndexError('Index to the fixed parameter is out of bounds')
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def _exponweib_link(x, logsf, phat, ix):
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a, c, loc, scale = phat
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logP = -log(-expm1(logsf))
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xn = (x - loc) / scale
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if ix == 0:
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return - logP / log(-expm1(-xn**c))
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if ix == 1:
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return log(-log1p(- logP**(1.0 / a))) / log(xn)
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if ix == 2:
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return x - (-log1p(- logP**(1.0/a))) ** (1.0 / c) * scale
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if ix == 3:
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return (x - loc) / (-log1p(- logP**(1.0/a))) ** (1.0 / c)
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raise IndexError('Index to the fixed parameter is out of bounds')
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def _genpareto_link(x, logsf, phat, ix):
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# Reference
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# Stuart Coles (2004)
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# "An introduction to statistical modelling of extreme values".
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# Springer series in statistics
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c, loc, scale = phat
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if ix == 2:
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# Reorganizing w.r.t.scale, Eq. 4.13 and 4.14, pp 81 in
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# Coles (2004) gives
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# link = -(x-loc)*c/expm1(-c*logsf)
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if c != 0.0:
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phati = (x - loc) * c / expm1(-c * logsf)
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else:
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phati = -(x - loc) / logsf
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elif ix == 1:
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if c != 0:
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phati = x + scale * expm1(c * logsf) / c
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else:
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phati = x + scale * logsf
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elif ix == 0:
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raise NotImplementedError(
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'link(x,logsf,phat,i) where i=0 is not implemented!')
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else:
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raise IndexError('Index to the fixed parameter is out of bounds')
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return phati
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def _genextreme_link(x, logsf, phat, ix):
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c, loc, scale = phat
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loglogP = log(-log(-expm1(logsf)))
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if ix == 2:
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# link = -(x-loc)*c/expm1(c*log(-logP))
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if c != 0.0:
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return -(x - loc) * c / expm1(c * loglogP)
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return -(x - loc) / loglogP
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if ix == 1:
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if c != 0:
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return x + scale * expm1(c * loglogP) / c
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return x + scale * loglogP
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if ix == 0:
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raise NotImplementedError(
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'link(x,logsf,phat,i) where i=0 is not implemented!')
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raise IndexError('Index to the fixed parameter is out of bounds')
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def _genexpon_link(x, logsf, phat, ix):
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a, b, c, loc, scale = phat
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xn = (x - loc) / scale
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fact1 = (xn + expm1(-c * xn) / c)
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if ix == 0:
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return b * fact1 + logsf # a
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if ix == 1:
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return (a - logsf) / fact1 # b
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if ix in [2, 3, 4]:
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raise NotImplementedError('Only implemented for index in [0,1]!')
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raise IndexError('Index to the fixed parameter is out of bounds')
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def _rayleigh_link(x, logsf, phat, ix):
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if ix == 1:
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return x - phat[0] / sqrt(-2.0 * logsf)
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if ix == 0:
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return x - phat[1] * sqrt(-2.0 * logsf)
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raise IndexError('Index to the fixed parameter is out of bounds')
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def _trunclayleigh_link(x, logsf, phat, ix):
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c, loc, scale = phat
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if ix == 0:
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xn = (x - loc) / scale
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return - 2 * logsf / xn - xn / 2.0
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if ix == 2:
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return x - loc / (sqrt(c * c - 2 * logsf) - c)
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if ix == 1:
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return x - scale * (sqrt(c * c - 2 * logsf) - c)
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raise IndexError('Index to the fixed parameter is out of bounds')
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LINKS = dict(expon=_expon_link,
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weibull_min=_weibull_min_link,
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frechet_r=_weibull_min_link,
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genpareto=_genpareto_link,
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genexpon=_genexpon_link,
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rayleigh=_rayleigh_link,
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trunclayleigh=_trunclayleigh_link,
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genextreme=_genextreme_link,
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exponweib=_exponweib_link,
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burr=_burr_link)
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def chi2isf(p, df):
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return special.chdtri(df, p)
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def chi2sf(x, df):
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return special.chdtrc(df, x)
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def norm_ppf(q):
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return special.ndtri(q)
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class Profile(object):
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'''
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Profile Log- likelihood or Product Spacing-function for phat[i].
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Parameters
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----------
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fit_dist : FitDistribution object
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with ML or MPS estimated distribution parameters.
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i : scalar integer
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defining which distribution parameter to keep fixed in the
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profiling process (default first non-fixed parameter)
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pmin, pmax : real scalars
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Interval for the parameter, phat[i] used in the optimization of the
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profile function (default is based on the 100*(1-alpha)% confidence
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interval computed with the delta method.)
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n : scalar integer
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Max number of points used in Lp (default 100)
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alpha : real scalar
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confidence coefficent (default 0.05)
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Returns
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-------
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Lp : Profile log-likelihood function with parameters phat given
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the data and phat[i], i.e.,
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Lp = max(log(f(phat| data, phat[i])))
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Member methods
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-------------
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plot() : Plot profile function with 100(1-alpha)% confidence interval
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get_bounds() : Return 100(1-alpha)% confidence interval
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Member variables
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----------------
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fit_dist : FitDistribution data object.
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data : profile function values
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args : profile function arguments
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alpha : confidence coefficient
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Lmax : Maximum value of profile function
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alpha_cross_level :
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PROFILE is a utility function for making inferences on a particular
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component of the vector phat.
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This is usually more accurate than using the delta method assuming
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asymptotic normality of the ML estimator or the MPS estimator.
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Examples
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--------
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# MLE
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>>> import wafo.stats as ws
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>>> R = ws.weibull_min.rvs(1,size=100);
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>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
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# Better 90% CI for phat.par[i=0]
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>>> profile_phat_i = Profile(phat, i=0)
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>>> profile_phat_i.plot()
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>>> phat_ci = profile_phat_i.get_bounds(alpha=0.1)
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'''
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def __init__(self, fit_dist, i=None, pmin=None, pmax=None, n=100,
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alpha=0.05):
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self.fit_dist = fit_dist
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self.pmin = pmin
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self.pmax = pmax
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self.n = n
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self.alpha = alpha
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self.data = None
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self.args = None
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self._set_indexes(fit_dist, i)
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method = fit_dist.method.lower()
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self._set_plot_labels(fit_dist, method)
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Lmax = self._loglike_max(fit_dist, method)
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self.Lmax = Lmax
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self.alpha_Lrange = 0.5 * chi2isf(self.alpha, 1)
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self.alpha_cross_level = Lmax - self.alpha_Lrange
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self._set_profile()
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def _set_plot_labels(self, fit_dist, method):
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percent = 100 * (1.0 - self.alpha)
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self.title = '{:g}% CI for {:s} params'.format(percent,
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fit_dist.dist.name)
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like_txt = 'likelihood' if method == 'ml' else 'product spacing'
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self.ylabel = 'Profile log' + like_txt
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self.xlabel = 'phat[{}]'.format(np.ravel(self.i_fixed)[0])
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@staticmethod
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def _loglike_max(fit_dist, method):
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if method.startswith('ml'):
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Lmax = fit_dist.LLmax
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elif method.startswith('mps'):
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Lmax = fit_dist.LPSmax
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else:
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raise ValueError(
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"PROFILE is only valid for ML- or MPS- estimators")
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return Lmax
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@staticmethod
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def _default_i_fixed(fit_dist):
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try:
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i0 = 1 - np.isfinite(fit_dist.par_fix).argmax()
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except Exception:
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i0 = 0
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return i0
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@staticmethod
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def _get_not_fixed_mask(fit_dist):
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if fit_dist.par_fix is None:
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isnotfixed = np.ones(fit_dist.par.shape, dtype=bool)
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else:
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isnotfixed = 1 - np.isfinite(fit_dist.par_fix)
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return isnotfixed
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def _check_i_fixed(self):
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if self.i_fixed not in self.i_notfixed:
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raise IndexError("Index i must be equal to an index to one of " +
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"the free parameters.")
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def _set_indexes(self, fit_dist, i):
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if i is None:
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i = self._default_i_fixed(fit_dist)
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self.i_fixed = np.atleast_1d(i)
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isnotfixed = self._get_not_fixed_mask(fit_dist)
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self.i_notfixed = nonzero(isnotfixed)
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self._check_i_fixed()
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isfree = isnotfixed
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isfree[self.i_fixed] = False
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self.i_free = nonzero(isfree)
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@staticmethod
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def _local_link(fix_par, par):
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"""
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Return fixed distribution parameter
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"""
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return fix_par
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def _correct_Lmax(self, Lmax, free_par, fix_par):
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if Lmax > self.Lmax: # foundNewphat = True
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par_old = str(self._par)
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dL = Lmax - self.Lmax
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self.alpha_cross_level += dL
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self.Lmax = Lmax
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par = self._par.copy()
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par[self.i_free] = free_par
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par[self.i_fixed] = fix_par
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self.best_par = par
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self._par = par
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warnings.warn(
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'The fitted parameters does not provide the optimum fit. ' +
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'Something wrong with fit ' +
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'(par = {}, par_old= {}, dl = {})'.format(str(par), par_old,
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dL))
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def _profile_optimum(self, phatfree0, p_opt):
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phatfree = optimize.fmin(self._profile_fun, phatfree0, args=(p_opt,),
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disp=0)
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Lmax = -self._profile_fun(phatfree, p_opt)
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self._correct_Lmax(Lmax, phatfree, p_opt)
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return Lmax, phatfree
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def _get_p_opt(self):
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return self._par[self.i_fixed]
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def _set_profile(self):
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self._par = self.fit_dist.par.copy()
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# Set up variable to profile and _local_link function
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p_opt = self._get_p_opt()
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phatfree = self._par[self.i_free].copy()
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pvec = self._get_pvec(phatfree, p_opt)
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self.data = np.ones_like(pvec) * nan
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k1 = (pvec >= p_opt).argmax()
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for size, step in ((-1, -1), (np.size(pvec), 1)):
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phatfree = self._par[self.i_free].copy()
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for ix in range(k1, size, step):
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Lmax, phatfree = self._profile_optimum(phatfree, pvec[ix])
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self.data[ix] = Lmax
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if ix != k1 and Lmax < self.alpha_cross_level:
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break
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np.putmask(pvec, np.isnan(self.data), nan)
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self.args = pvec
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self._prettify_profile()
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def _prettify_profile(self):
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pvec = self.args
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ix = nonzero(np.isfinite(pvec))
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self.data = self.data[ix]
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self.args = pvec[ix]
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cond = self.data == -np.inf
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if np.any(cond):
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ind, = cond.nonzero()
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self.data.put(ind, floatinfo.min / 2.0)
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ind1 = np.where(ind == 0, ind, ind - 1)
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cl = self.alpha_cross_level - self.alpha_Lrange / 2.0
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try:
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t0 = ecross(self.args, self.data, ind1, cl)
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self.data.put(ind, cl)
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self.args.put(ind, t0)
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except IndexError as err:
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warnings.warn(str(err))
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def _get_variance(self):
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return self.fit_dist.par_cov[self.i_fixed, :][:, self.i_fixed]
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def _approx_p_min_max(self, p_opt):
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pvar = self._get_variance()
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if pvar <= 1e-5 or np.isnan(pvar):
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pvar = max(abs(p_opt) * 0.5, 0.2)
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pvar = max(pvar, 0.1)
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p_crit = -norm_ppf(self.alpha / 2.0) * sqrt(np.ravel(pvar)) * 1.5
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return p_opt - p_crit * 5, p_opt + p_crit * 5
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def _p_min_max(self, phatfree0, p_opt):
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p_low, p_up = self._approx_p_min_max(p_opt)
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pmin, pmax = self.pmin, self.pmax
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if pmin is None:
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pmin = self._search_pmin(phatfree0, p_low, p_opt)
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if pmax is None:
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pmax = self._search_pmax(phatfree0, p_up, p_opt)
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return pmin, pmax
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def _adaptive_pvec(self, p_opt, pmin, pmax):
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p_crit_low = (p_opt - pmin) / 5
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p_crit_up = (pmax - p_opt) / 5
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n4 = np.floor(self.n / 4.0)
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a, b = p_opt - p_crit_low, p_opt + p_crit_up
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pvec1 = np.linspace(pmin, a, n4 + 1)
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pvec2 = np.linspace(a, b, self.n - 2 * n4)
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pvec3 = np.linspace(b, pmax, n4 + 1)
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pvec = np.unique(np.hstack((pvec1, p_opt, pvec2, pvec3)))
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return pvec
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def _get_pvec(self, phatfree0, p_opt):
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''' return proper interval for the variable to profile
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'''
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if self.pmin is None or self.pmax is None:
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pmin, pmax = self._p_min_max(phatfree0, p_opt)
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return self._adaptive_pvec(p_opt, pmin, pmax)
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return np.linspace(self.pmin, self.pmax, self.n)
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def _search_pmin(self, phatfree0, p_min0, p_opt):
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phatfree = phatfree0.copy()
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dp = np.maximum((p_opt - p_min0)/40, 0.01)*10
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Lmax, phatfree = self._profile_optimum(phatfree, p_opt)
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p_min_opt = p_min0
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for j in range(51):
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p_min = p_opt - dp
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Lmax, phatfree = self._profile_optimum(phatfree, p_min)
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# print((dp, p_min, p_min_opt, Lmax))
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if np.isnan(Lmax):
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dp *= 0.33
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elif Lmax < self.alpha_cross_level - self.alpha_Lrange*5*(j+1):
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p_min_opt = p_min
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dp *= 0.33
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elif Lmax < self.alpha_cross_level:
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p_min_opt = p_min
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break
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else:
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dp *= 1.67
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else:
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msg = 'Exceeded max iterations. (p_min0={}, p_min={}, p={})'
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warnings.warn(msg.format(p_min0, p_min_opt, p_opt))
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# print('search_pmin iterations={}'.format(j))
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return p_min_opt
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def _search_pmax(self, phatfree0, p_max0, p_opt):
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phatfree = phatfree0.copy()
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dp = (p_max0 - p_opt)/40
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if dp < 1e-2:
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dp = 0.1
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Lmax, phatfree = self._profile_optimum(phatfree, p_opt)
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p_max_opt = p_opt
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for j in range(51):
|
|
p_max = p_opt + dp
|
|
Lmax, phatfree = self._profile_optimum(phatfree, p_max)
|
|
if np.isnan(Lmax):
|
|
dp *= 0.33
|
|
elif Lmax < self.alpha_cross_level - self.alpha_Lrange*5*(j+1):
|
|
p_max_opt = p_max
|
|
dp *= 0.33
|
|
elif Lmax < self.alpha_cross_level:
|
|
p_max_opt = p_max
|
|
break
|
|
else:
|
|
dp *= 1.67
|
|
else:
|
|
msg = 'Exceeded max iterations. (p={}, p_max={}, p_max0 = {})'
|
|
warnings.warn(msg.format(p_opt, p_max_opt, p_max0))
|
|
# print('search_pmax iterations={}'.format(j))
|
|
return p_max_opt
|
|
|
|
def _profile_fun(self, free_par, fix_par):
|
|
''' Return negative of loglike or logps function
|
|
|
|
free_par - vector of free parameters
|
|
fix_par - fixed parameter, i.e., either quantile (return level),
|
|
probability (return period) or distribution parameter
|
|
'''
|
|
par = self._par.copy()
|
|
par[self.i_free] = free_par
|
|
|
|
par[self.i_fixed] = self._local_link(fix_par, par)
|
|
return self.fit_dist.fitfun(par)
|
|
|
|
def get_bounds(self, alpha=0.05):
|
|
'''Return confidence interval for profiled parameter
|
|
'''
|
|
if alpha < self.alpha:
|
|
msg = 'Might not be able to return bounds with alpha less than {}'
|
|
warnings.warn(msg.format(self.alpha))
|
|
cross_level = self.Lmax - 0.5 * chi2isf(alpha, 1)
|
|
ind = findcross(self.data, cross_level)
|
|
n = len(ind)
|
|
if n == 0:
|
|
warnings.warn('''Number of crossings is zero, i.e.,
|
|
upper and lower bound is not found!''')
|
|
bounds = (self.pmin, self.pmax)
|
|
|
|
elif n == 1:
|
|
x0 = ecross(self.args, self.data, ind, cross_level)
|
|
is_upcrossing = self.data[ind] < self.data[ind + 1]
|
|
if is_upcrossing:
|
|
bounds = (x0, self.pmax)
|
|
warnings.warn('Upper bound is larger')
|
|
else:
|
|
bounds = (self.pmin, x0)
|
|
warnings.warn('Lower bound is smaller')
|
|
|
|
elif n == 2:
|
|
bounds = ecross(self.args, self.data, ind, cross_level)
|
|
else:
|
|
warnings.warn('Number of crossings too large! Something is wrong!')
|
|
bounds = ecross(self.args, self.data, ind[[0, -1]], cross_level)
|
|
return bounds
|
|
|
|
def plot(self, axis=None):
|
|
'''
|
|
Plot profile function for p_opt with 100(1-alpha)% confidence interval.
|
|
'''
|
|
if axis is None:
|
|
axis = plt.gca()
|
|
|
|
p_ci = self.get_bounds(self.alpha)
|
|
axis.plot(
|
|
self.args, self.data,
|
|
p_ci, [self.Lmax, ] * 2, 'r--',
|
|
p_ci, [self.alpha_cross_level, ] * 2, 'r--')
|
|
ymax = self.Lmax + self.alpha_Lrange/10
|
|
ymin = self.alpha_cross_level - self.alpha_Lrange/10
|
|
axis.vlines(p_ci, ymin=ymin, ymax=self.Lmax,
|
|
color='r', linestyles='--')
|
|
p_opt = self._get_p_opt()
|
|
axis.vlines(p_opt, ymin=ymin, ymax=self.Lmax,
|
|
color='g', linestyles='--')
|
|
axis.set_title(self.title)
|
|
axis.set_ylabel(self.ylabel)
|
|
axis.set_xlabel(self.xlabel)
|
|
axis.set_ylim(ymin=ymin, ymax=ymax)
|
|
|
|
|
|
def plot_all_profiles(phats, plot=None):
|
|
if plot is not None:
|
|
plt = plot
|
|
|
|
if phats.par_fix:
|
|
indices = phats.i_notfixed
|
|
else:
|
|
indices = np.arange(len(phats.par))
|
|
n = len(indices)
|
|
for j, k in enumerate(indices):
|
|
plt.subplot(n, 1, j+1)
|
|
profile_phat_k = Profile(phats, i=k)
|
|
m = 0
|
|
while hasattr(profile_phat_k, 'best_par') and m < 7:
|
|
phats.fit(*profile_phat_k.best_par)
|
|
profile_phat_k = Profile(phats, i=k)
|
|
m += 1
|
|
profile_phat_k.plot()
|
|
if j != 0:
|
|
plt.title('')
|
|
if j != n//2:
|
|
plt.ylabel('')
|
|
plt.subplots_adjust(hspace=0.5)
|
|
par_txt = ('{:1.2g}, '*len(phats.par))[:-2].format(*phats.par)
|
|
plt.suptitle('phat = [{}] (fit metod: {})'.format(par_txt, phats.method))
|
|
return phats
|
|
|
|
|
|
class ProfileQuantile(Profile):
|
|
'''
|
|
Profile Log- likelihood or Product Spacing-function for quantile.
|
|
|
|
Parameters
|
|
----------
|
|
fit_dist : FitDistribution object
|
|
with ML or MPS estimated distribution parameters.
|
|
x : real scalar
|
|
Quantile (return value)
|
|
i : scalar integer
|
|
defining which distribution parameter to keep fixed in the
|
|
profiling process (default first non-fixed parameter)
|
|
pmin, pmax : real scalars
|
|
Interval for the parameter, x, used in the
|
|
optimization of the profile function (default is based on the
|
|
100*(1-alpha)% confidence interval computed with the delta method.)
|
|
n : scalar integer
|
|
Max number of points used in Lp (default 100)
|
|
alpha : real scalar
|
|
confidence coefficent (default 0.05)
|
|
link : function connecting the x-quantile and the survival probability
|
|
(sf) with the fixed distribution parameter, i.e.:
|
|
self.par[i] = link(x, logsf, self.par, i), where
|
|
logsf = log(Prob(X>x;phat)).
|
|
(default is fetched from the LINKS dictionary)
|
|
|
|
Returns
|
|
-------
|
|
Lp : Profile log-likelihood function with parameters phat given
|
|
the data, phat[i] and quantile (x) i.e.,
|
|
Lp = max(log(f(phat|data,phat(i),x)))
|
|
|
|
Member methods
|
|
-------------
|
|
plot() : Plot profile function with 100(1-alpha)% confidence interval
|
|
get_bounds() : Return 100(1-alpha)% confidence interval
|
|
|
|
Member variables
|
|
----------------
|
|
fit_dist : FitDistribution data object.
|
|
data : profile function values
|
|
args : profile function arguments
|
|
alpha : confidence coefficient
|
|
Lmax : Maximum value of profile function
|
|
alpha_cross_level :
|
|
|
|
ProfileQuantile is a utility function for making inferences on the
|
|
quantile, x.
|
|
This is usually more accurate than using the delta method assuming
|
|
asymptotic normality of the ML estimator or the MPS estimator.
|
|
|
|
Examples
|
|
--------
|
|
# MLE
|
|
>>> import wafo.stats as ws
|
|
>>> R = ws.weibull_min.rvs(1,size=100);
|
|
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
|
|
|
|
>>> sf = 1./990
|
|
>>> x = phat.isf(sf)
|
|
|
|
# 80% CI for x
|
|
>>> profile_x = ProfileQuantile(phat, x)
|
|
>>> profile_x.plot()
|
|
>>> x_ci = profile_x.get_bounds(alpha=0.2)
|
|
'''
|
|
def __init__(self, fit_dist, x, i=None, pmin=None, pmax=None, n=100,
|
|
alpha=0.05, link=None):
|
|
self.x = x
|
|
self.log_sf = fit_dist.logsf(x)
|
|
if link is None:
|
|
link = LINKS.get(fit_dist.dist.name)
|
|
self.link = link
|
|
super(ProfileQuantile, self).__init__(fit_dist, i=i, pmin=pmin,
|
|
pmax=pmax, n=100, alpha=alpha)
|
|
|
|
def _get_p_opt(self):
|
|
return self.x
|
|
|
|
def _local_link(self, fixed_x, par):
|
|
"""
|
|
Return fixed distribution parameter from fixed quantile
|
|
"""
|
|
fix_par = self.link(fixed_x, self.log_sf, par, self.i_fixed)
|
|
return fix_par
|
|
|
|
def _myinvfun(self, phatnotfixed):
|
|
mphat = self._par.copy()
|
|
mphat[self.i_notfixed] = phatnotfixed
|
|
prb = exp(self.log_sf)
|
|
return self.fit_dist.dist.isf(prb, *mphat)
|
|
|
|
def _get_variance(self):
|
|
i_notfixed = self.i_notfixed
|
|
phatv = self._par
|
|
gradfun = nd.Gradient(self._myinvfun)
|
|
drl = gradfun(phatv[self.i_notfixed])
|
|
pcov = self.fit_dist.par_cov[i_notfixed, :][:, i_notfixed]
|
|
pvar = np.sum(np.dot(drl, pcov) * drl)
|
|
return pvar
|
|
|
|
def _set_plot_labels(self, fit_dist, method):
|
|
super(ProfileQuantile, self)._set_plot_labels(fit_dist, method)
|
|
percent = 100 * (1.0 - self.alpha)
|
|
self.title = '{:g}% CI for {:s} quantile'.format(percent,
|
|
fit_dist.dist.name)
|
|
self.xlabel = 'x'
|
|
|
|
|
|
class ProfileProbability(Profile):
|
|
''' Profile Log- likelihood or Product Spacing-function probability.
|
|
|
|
Parameters
|
|
----------
|
|
fit_dist : FitDistribution object
|
|
with ML or MPS estimated distribution parameters.
|
|
logsf : real scalar
|
|
logarithm of survival probability
|
|
i : scalar integer
|
|
defining which distribution parameter to keep fixed in the
|
|
profiling process (default first non-fixed parameter)
|
|
pmin, pmax : real scalars
|
|
Interval for the parameter, log_sf, used in the
|
|
optimization of the profile function (default is based on the
|
|
100*(1-alpha)% confidence interval computed with the delta method.)
|
|
n : scalar integer
|
|
Max number of points used in Lp (default 100)
|
|
alpha : real scalar
|
|
confidence coefficent (default 0.05)
|
|
link : function connecting the x-quantile and the survival probability
|
|
(sf) with the fixed distribution parameter, i.e.:
|
|
self.par[i] = link(x, logsf, self.par, i), where
|
|
logsf = log(Prob(X>x;phat)).
|
|
(default is fetched from the LINKS dictionary)
|
|
|
|
Returns
|
|
-------
|
|
Lp : Profile log-likelihood function with parameters phat given
|
|
the data, phat[i] and quantile (x) i.e.,
|
|
Lp = max(log(f(phat|data,phat(i),x)))
|
|
|
|
Member methods
|
|
-------------
|
|
plot() : Plot profile function with 100(1-alpha)% confidence interval
|
|
get_bounds() : Return 100(1-alpha)% confidence interval
|
|
|
|
Member variables
|
|
----------------
|
|
fit_dist : FitDistribution data object.
|
|
data : profile function values
|
|
args : profile function arguments
|
|
alpha : confidence coefficient
|
|
Lmax : Maximum value of profile function
|
|
alpha_cross_level :
|
|
|
|
ProfileProbability is a utility function for making inferences the survival
|
|
probability (sf).
|
|
This is usually more accurate than using the delta method assuming
|
|
asymptotic normality of the ML estimator or the MPS estimator.
|
|
|
|
Examples
|
|
--------
|
|
# MLE
|
|
>>> import wafo.stats as ws
|
|
>>> R = ws.weibull_min.rvs(1,size=100);
|
|
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
|
|
|
|
>>> sf = 1./990
|
|
|
|
# 80% CI for sf
|
|
>>> profile_logsf = ProfileProbability(phat, np.log(sf))
|
|
>>> profile_logsf.plot()
|
|
>>> logsf_ci = profile_logsf.get_bounds(alpha=0.2)
|
|
'''
|
|
def __init__(self, fit_dist, logsf, i=None, pmin=None, pmax=None, n=100,
|
|
alpha=0.05, link=None):
|
|
self.x = fit_dist.isf(np.exp(logsf))
|
|
self.log_sf = logsf
|
|
if link is None:
|
|
link = LINKS.get(fit_dist.dist.name)
|
|
self.link = link
|
|
super(ProfileProbability, self).__init__(fit_dist, i=i, pmin=pmin,
|
|
pmax=pmax, n=100, alpha=alpha)
|
|
|
|
def _get_p_opt(self):
|
|
return self.log_sf
|
|
|
|
def _local_link(self, fixed_log_sf, par):
|
|
"""
|
|
Return fixed distribution parameter from fixed log_sf
|
|
"""
|
|
fix_par = self.link(self.x, fixed_log_sf, par, self.i_fixed)
|
|
return fix_par
|
|
|
|
def _myprbfun(self, phatnotfixed):
|
|
mphat = self._par.copy()
|
|
mphat[self.i_notfixed] = phatnotfixed
|
|
logsf = self.fit_dist.dist.logsf(self.x, *mphat)
|
|
return np.where(np.isfinite(logsf), logsf, np.nan)
|
|
|
|
def _get_variance(self):
|
|
i_notfixed = self.i_notfixed
|
|
phatv = self._par
|
|
gradfun = nd.Gradient(self._myprbfun)
|
|
drl = gradfun(phatv[self.i_notfixed])
|
|
pcov = self.fit_dist.par_cov[i_notfixed, :][:, i_notfixed]
|
|
pvar = np.sum(np.dot(drl, pcov) * drl)
|
|
return pvar
|
|
|
|
def _set_plot_labels(self, fit_dist, method):
|
|
super(ProfileProbability, self)._set_plot_labels(fit_dist, method)
|
|
percent = 100 * (1.0 - self.alpha)
|
|
self.title = '{:g}% CI for {:s} probability'.format(percent,
|
|
fit_dist.dist.name)
|
|
self.xlabel = 'log(sf)'
|
|
|
|
|
|
class FitDistribution(rv_frozen):
|
|
'''
|
|
Return estimators to shape, location, and scale from data
|
|
|
|
Starting points for the fit are given by input arguments. For any
|
|
arguments not given starting points, dist._fitstart(data) is called
|
|
to get the starting estimates.
|
|
|
|
You can hold some parameters fixed to specific values by passing in
|
|
keyword arguments f0..fn for shape paramters and floc, fscale for
|
|
location and scale parameters.
|
|
|
|
Parameters
|
|
----------
|
|
dist : scipy distribution object
|
|
distribution to fit to data
|
|
data : array-like
|
|
Data to use in calculating the ML or MPS estimators
|
|
args : optional
|
|
Starting values for any shape arguments (those not specified
|
|
will be determined by dist._fitstart(data))
|
|
kwds : loc, scale
|
|
Starting values for the location and scale parameters
|
|
Special keyword arguments are recognized as holding certain
|
|
parameters fixed:
|
|
f0..fn : hold respective shape paramters fixed
|
|
floc : hold location parameter fixed to specified value
|
|
fscale : hold scale parameter fixed to specified value
|
|
method : of estimation. Options are
|
|
'ml' : Maximum Likelihood method (default)
|
|
'mps': Maximum Product Spacing method
|
|
alpha : scalar, optional
|
|
Confidence coefficent (default=0.05)
|
|
search : bool
|
|
If true search for best estimator (default),
|
|
otherwise return object with initial distribution parameters
|
|
copydata : bool
|
|
If true copydata (default)
|
|
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
|
|
function to be optimized) and disp=0 to suppress
|
|
output as keyword arguments.
|
|
|
|
Return
|
|
------
|
|
phat : FitDistribution object
|
|
Fitted distribution object with following member variables:
|
|
LLmax : loglikelihood function evaluated using par
|
|
LPSmax : log product spacing function evaluated using par
|
|
pvalue : p-value for the fit
|
|
par : distribution parameters (fixed and fitted)
|
|
par_cov : covariance of distribution parameters
|
|
par_fix : fixed distribution parameters
|
|
par_lower : lower (1-alpha)% confidence bound for the parameters
|
|
par_upper : upper (1-alpha)% confidence bound for the parameters
|
|
|
|
Note
|
|
----
|
|
`data` is sorted using this function, so if `copydata`==False the data
|
|
in your namespace will be sorted as well.
|
|
|
|
Examples
|
|
--------
|
|
Estimate distribution parameters for weibull_min distribution.
|
|
>>> import wafo.stats as ws
|
|
>>> R = ws.weibull_min.rvs(1,size=100);
|
|
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
|
|
|
|
# Plot various diagnostic plots to asses quality of fit.
|
|
>>> phat.plotfitsummary()
|
|
|
|
# phat.par holds the estimated parameters
|
|
# phat.par_upper upper CI for parameters
|
|
# phat.par_lower lower CI for parameters
|
|
|
|
# Better 90% CI for phat.par[0]
|
|
>>> profile_phat_i = phat.profile(i=0)
|
|
>>> profile_phat_i.plot()
|
|
>>> p_ci = profile_phat_i.get_bounds(alpha=0.1)
|
|
|
|
>>> sf = 1./990
|
|
>>> x = phat.isf(sf)
|
|
|
|
# 80% CI for x
|
|
>>> profile_x = phat.profile_quantile(x=x)
|
|
>>> profile_x.plot()
|
|
>>> x_ci = profile_x.get_bounds(alpha=0.2)
|
|
|
|
# 80% CI for logsf=log(sf)
|
|
>>> profile_logsf = phat.profile_probability(log(sf))
|
|
>>> profile_logsf.plot()
|
|
>>> 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):
|
|
extradoc = '''
|
|
plotfitsummary()
|
|
Plot various diagnostic plots to asses quality of fit.
|
|
plotecdf()
|
|
Plot Empirical and fitted Cumulative Distribution Function
|
|
plotesf()
|
|
Plot Empirical and fitted Survival Function
|
|
plotepdf()
|
|
Plot Empirical and fitted Probability Distribution Function
|
|
plotresq()
|
|
Displays a residual quantile plot.
|
|
plotresprb()
|
|
Displays a residual probability plot.
|
|
|
|
profile()
|
|
Return Profile Log- likelihood or Product Spacing-function.
|
|
|
|
Parameters
|
|
----------
|
|
x : array-like
|
|
quantiles
|
|
q : array-like
|
|
lower or upper tail probability
|
|
size : int or tuple of ints, optional
|
|
shape of random variates (default computed from input arguments )
|
|
moments : str, optional
|
|
composed of letters ['mvsk'] specifying which moments to compute where
|
|
'm' = mean, 'v' = variance, 's' = (Fisher's) skew and
|
|
'k' = (Fisher's) kurtosis. (default='mv')
|
|
'''
|
|
# Member variables
|
|
# ----------------
|
|
# data - data used in fitting
|
|
# alpha - confidence coefficient
|
|
# method - method used
|
|
# LLmax - loglikelihood function evaluated using par
|
|
# LPSmax - log product spacing function evaluated using par
|
|
# pvalue - p-value for the fit
|
|
# search - True if search for distribution parameters (default)
|
|
# copydata - True if copy input data (default)
|
|
#
|
|
# par - parameters (fixed and fitted)
|
|
# par_cov - covariance of parameters
|
|
# par_fix - fixed parameters
|
|
# par_lower - lower (1-alpha)% confidence bound for the parameters
|
|
# par_upper - upper (1-alpha)% confidence bound for the parameters
|
|
#
|
|
# '''
|
|
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.data = np.ravel(data)
|
|
if self.copydata:
|
|
self.data = self.data.copy()
|
|
self.data.sort()
|
|
if isinstance(args, (float, int)):
|
|
args = (args, )
|
|
self.fit(*args, **kwds)
|
|
|
|
def _set_fixed_par(self, fixedn):
|
|
self.par_fix = [nan] * len(self.par)
|
|
for i in fixedn:
|
|
self.par_fix[i] = self.par[i]
|
|
self.i_notfixed = nonzero(1 - isfinite(self.par_fix))
|
|
self.i_fixed = nonzero(isfinite(self.par_fix))
|
|
|
|
def fit(self, *args, **kwds):
|
|
par, fixedn = self._fit(*args, **kwds.copy())
|
|
super(FitDistribution, self).__init__(self.dist, *par)
|
|
self.par = arr(par)
|
|
somefixed = len(fixedn) > 0
|
|
if somefixed:
|
|
self._set_fixed_par(fixedn)
|
|
|
|
self.par_cov = self._compute_cov()
|
|
|
|
# Set confidence interval for parameters
|
|
pvar = np.diag(self.par_cov)
|
|
zcrit = -norm_ppf(self.alpha / 2.0)
|
|
self.par_lower = self.par - zcrit * sqrt(pvar)
|
|
self.par_upper = self.par + zcrit * sqrt(pvar)
|
|
|
|
self.LLmax = -self._nnlf(self.par, self.data)
|
|
self.LPSmax = -self._nlogps(self.par, self.data)
|
|
self.pvalue = self._pvalue(self.par, self.data,
|
|
unknown_numpar=len(par)-len(fixedn))
|
|
|
|
@property
|
|
def method(self):
|
|
return self._method
|
|
|
|
@method.setter
|
|
def method(self, method):
|
|
self._method = method.lower()
|
|
if self._method.startswith('mps'):
|
|
self._fitfun = self._nlogps
|
|
else:
|
|
self._fitfun = self._nnlf
|
|
|
|
def __repr__(self):
|
|
params = ['alpha', 'method', 'LLmax', 'LPSmax', 'pvalue',
|
|
'par', 'par_lower', 'par_upper', 'par_fix', 'par_cov']
|
|
t = ['%s:\n' % self.__class__.__name__]
|
|
for par in params:
|
|
t.append('%s = %s\n' % (par, str(getattr(self, par))))
|
|
return ''.join(t)
|
|
|
|
def _reduce_func(self, args, 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
|
|
args = list(args)
|
|
nargs = len(args)
|
|
fixedn = []
|
|
names = ['f%d' % n for n in range(nargs - 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])
|
|
|
|
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
|
|
(including loc and scale)
|
|
'''
|
|
if eps is None:
|
|
eps = (_EPS) ** 0.25
|
|
num_par = len(theta)
|
|
# pab 07.01.2001: Always choose the stepsize h so that
|
|
# it is an exactly representable number.
|
|
# This is important when calculating numerical derivatives and is
|
|
# accomplished by the following.
|
|
delta = (eps + 2.0) - 2.0
|
|
delta2 = delta ** 2.0
|
|
# Approximate 1/(nE( (d L(x|theta)/dtheta)^2)) with
|
|
# 1/(d^2 L(theta|x)/dtheta^2)
|
|
# using central differences
|
|
|
|
LL = nnlf(theta, data)
|
|
H = zeros((num_par, num_par)) # Hessian matrix
|
|
theta = tuple(theta)
|
|
for ix in range(num_par):
|
|
sparam = list(theta)
|
|
sparam[ix] = theta[ix] + delta
|
|
fp = nnlf(sparam, data)
|
|
|
|
sparam[ix] = theta[ix] - delta
|
|
fm = nnlf(sparam, data)
|
|
|
|
H[ix, ix] = (fp - 2 * LL + fm) / delta2
|
|
for iy in range(ix + 1, num_par):
|
|
sparam[ix] = theta[ix] + delta
|
|
sparam[iy] = theta[iy] + delta
|
|
fpp = nnlf(sparam, data)
|
|
|
|
sparam[iy] = theta[iy] - delta
|
|
fpm = nnlf(sparam, data)
|
|
|
|
sparam[ix] = theta[ix] - delta
|
|
fmm = nnlf(sparam, data)
|
|
|
|
sparam[iy] = theta[iy] + delta
|
|
fmp = nnlf(sparam, data)
|
|
|
|
H[ix, iy] = ((fpp + fmm) - (fmp + fpm)) / (4. * delta2)
|
|
H[iy, ix] = H[ix, iy]
|
|
sparam[iy] = theta[iy]
|
|
return -H
|
|
|
|
def _nnlf(self, theta, x):
|
|
return self.dist._penalized_nnlf(theta, x)
|
|
|
|
def _nlogps(self, theta, x):
|
|
""" Moran's negative log Product Spacings statistic
|
|
|
|
where theta are the parameters (including loc and scale)
|
|
|
|
Note the data in x must be sorted
|
|
|
|
References
|
|
-----------
|
|
|
|
R. C. H. Cheng; N. A. K. Amin (1983)
|
|
"Estimating Parameters in Continuous Univariate Distributions with a
|
|
Shifted Origin.",
|
|
Journal of the Royal Statistical Society. Series B (Methodological),
|
|
Vol. 45, No. 3. (1983), pp. 394-403.
|
|
|
|
R. C. H. Cheng; M. A. Stephens (1989)
|
|
"A Goodness-Of-Fit Test Using Moran's Statistic with Estimated
|
|
Parameters", Biometrika, 76, 2, pp 385-392
|
|
|
|
Wong, T.S.T. and Li, W.K. (2006)
|
|
"A note on the estimation of extreme value distributions using maximum
|
|
product of spacings.",
|
|
IMS Lecture Notes Monograph Series 2006, Vol. 52, pp. 272-283
|
|
"""
|
|
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
|
|
'''
|
|
numpar = self.dist.numargs + 2
|
|
par_cov = zeros((numpar, numpar))
|
|
|
|
somefixed = ((self.par_fix is not None) and
|
|
np.any(isfinite(self.par_fix)))
|
|
|
|
H = np.asmatrix(self._hessian(self._fitfun, self.par, self.data))
|
|
# H = -nd.Hessian(lambda par: self._fitfun(par, self.data),
|
|
# method='forward')(self.par)
|
|
self.H = H
|
|
try:
|
|
if somefixed:
|
|
allfixed = np.all(isfinite(self.par_fix))
|
|
if allfixed:
|
|
self.par_cov[:, :] = 0
|
|
else:
|
|
pcov = -pinv2(H[self.i_notfixed, :][..., self.i_notfixed])
|
|
for row, ix in enumerate(list(self.i_notfixed)):
|
|
par_cov[ix, self.i_notfixed] = pcov[row, :]
|
|
else:
|
|
par_cov = -pinv2(H)
|
|
except:
|
|
par_cov[:, :] = nan
|
|
return par_cov
|
|
|
|
def fitfun(self, phat):
|
|
return self._fitfun(phat, self.data)
|
|
|
|
def profile(self, **kwds):
|
|
'''
|
|
Profile Log- likelihood or Log Product Spacing- function for phat[i]
|
|
|
|
Examples
|
|
--------
|
|
# MLE
|
|
>>> import wafo.stats as ws
|
|
>>> R = ws.weibull_min.rvs(1,size=100);
|
|
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
|
|
|
|
# Better CI for phat.par[i=0]
|
|
>>> Lp = phat.profile(i=0)
|
|
>>> Lp.plot()
|
|
>>> phat_ci = Lp.get_bounds(alpha=0.1)
|
|
|
|
See also
|
|
--------
|
|
Profile
|
|
'''
|
|
return Profile(self, **kwds)
|
|
|
|
def profile_quantile(self, x, **kwds):
|
|
'''
|
|
Profile Log- likelihood or Product Spacing-function for quantile.
|
|
|
|
Examples
|
|
--------
|
|
# MLE
|
|
>>> import wafo.stats as ws
|
|
>>> R = ws.weibull_min.rvs(1,size=100);
|
|
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
|
|
|
|
>>> sf = 1./990
|
|
>>> x = phat.isf(sf)
|
|
|
|
# 80% CI for x
|
|
>>> profile_x = phat.profile_quantile(x)
|
|
>>> profile_x.plot()
|
|
>>> x_ci = profile_x.get_bounds(alpha=0.2)
|
|
'''
|
|
return ProfileQuantile(self, x, **kwds)
|
|
|
|
def profile_probability(self, log_sf, **kwds):
|
|
'''
|
|
Profile Log- likelihood or Product Spacing-function for probability.
|
|
|
|
Examples
|
|
--------
|
|
# MLE
|
|
>>> import wafo.stats as ws
|
|
>>> R = ws.weibull_min.rvs(1,size=100);
|
|
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
|
|
|
|
>>> log_sf = np.log(1./990)
|
|
|
|
# 80% CI for log_sf
|
|
>>> profile_logsf = phat.profile_probability(log_sf)
|
|
>>> profile_logsf.plot()
|
|
>>> log_sf_ci = profile_logsf.get_bounds(alpha=0.2)
|
|
'''
|
|
return ProfileProbability(self, log_sf, **kwds)
|
|
|
|
def ci_sf(self, sf, alpha=0.05, i=2):
|
|
ci = []
|
|
for log_sfi in np.atleast_1d(np.log(sf)).ravel():
|
|
try:
|
|
Lp = self.profile_probability(log_sfi, i=i)
|
|
ci.append(np.exp(Lp.get_bounds(alpha=alpha)))
|
|
except Exception:
|
|
ci.append((np.nan, np.nan))
|
|
return np.array(ci)
|
|
|
|
def ci_quantile(self, x, alpha=0.05, i=2):
|
|
ci = []
|
|
for xi in np.atleast_1d(x).ravel():
|
|
try:
|
|
Lx = self.profile_quantile(xi, i=2)
|
|
ci.append(Lx.get_bounds(alpha=alpha))
|
|
except Exception:
|
|
ci.append((np.nan, np.nan))
|
|
return np.array(ci)
|
|
|
|
def plotfitsummary(self, axes=None, fig=None):
|
|
''' Plot various diagnostic plots to asses the quality of the fit.
|
|
|
|
PLOTFITSUMMARY displays probability plot, density plot, residual
|
|
quantile plot and residual probability plot.
|
|
The purpose of these plots is to graphically assess whether the data
|
|
could come from the fitted distribution. If so the empirical- CDF and
|
|
PDF should follow the model and the residual plots will be linear.
|
|
Other distribution types will introduce curvature in the residual
|
|
plots.
|
|
'''
|
|
if axes is None:
|
|
fig, axes = plt.subplots(2, 2, figsize=(11, 8))
|
|
fig.subplots_adjust(hspace=0.4, wspace=0.4)
|
|
# plt.subplots_adjust(hspace=0.4, wspace=0.4)
|
|
# self.plotecdf()
|
|
plot_funs = (self.plotesf, self.plotepdf,
|
|
self.plotresq, self.plotresprb)
|
|
for axis, plot in zip(axes.ravel(), plot_funs):
|
|
plot(axis=axis)
|
|
|
|
if fig is None:
|
|
fig = plt.gcf()
|
|
fixstr = ''
|
|
if self.par_fix is not None:
|
|
numfix = len(self.i_fixed)
|
|
if numfix > 0:
|
|
format0 = ', '.join(['%d'] * numfix)
|
|
format1 = ', '.join(['%g'] * numfix)
|
|
phatistr = format0 % tuple(self.i_fixed)
|
|
phatvstr = format1 % tuple(self.par[self.i_fixed])
|
|
fixstr = 'Fixed: phat[{0:s}] = {1:s} '.format(phatistr,
|
|
phatvstr)
|
|
|
|
subtxt = ('Fit method: {0:s}, Fit p-value: {1:2.2f} {2:s}, ' +
|
|
'phat=[{3:s}], {4:s}')
|
|
par_txt = ('{:1.2g}, '*len(self.par))[:-2].format(*self.par)
|
|
try:
|
|
LL_txt = 'Lps_max={:2.2g}, Ll_max={:2.2g}'.format(self.LPSmax,
|
|
self.LLmax)
|
|
except Exception:
|
|
LL_txt = 'Lps_max={}, Ll_max={}'.format(self.LPSmax, self.LLmax)
|
|
try:
|
|
fig.text(0.05, 0.01, subtxt.format(self.method.upper(),
|
|
self.pvalue, fixstr, par_txt,
|
|
LL_txt))
|
|
except AttributeError:
|
|
pass
|
|
|
|
def plotesf(self, symb1='r-', symb2='b.', axis=None, plot_ci=False):
|
|
''' Plot Empirical and fitted Survival Function
|
|
|
|
The purpose of the plot is to graphically assess whether
|
|
the data could come from the fitted distribution.
|
|
If so the empirical CDF should resemble the model CDF.
|
|
Other distribution types will introduce deviations in the plot.
|
|
'''
|
|
if axis is None:
|
|
axis = plt.gca()
|
|
n = len(self.data)
|
|
sf = (arange(n, 0, -1)) / n
|
|
axis.semilogy(self.data, sf, symb2,
|
|
self.data, self.sf(self.data), symb1)
|
|
|
|
if plot_ci:
|
|
low = int(np.log10(1.0/n)-0.7) - 1
|
|
sf1 = np.logspace(low, -0.5, 7)[::-1]
|
|
ci1 = self.ci_sf(sf, alpha=0.05, i=2)
|
|
axis.semilogy(self.isf(sf1), ci1, 'r--')
|
|
axis.set_xlabel('x')
|
|
axis.set_ylabel('F(x) (%s)' % self.dist.name)
|
|
axis.set_title('Empirical SF plot')
|
|
|
|
def plotecdf(self, symb1='r-', symb2='b.', axis=None):
|
|
''' Plot Empirical and fitted Cumulative Distribution Function
|
|
|
|
The purpose of the plot is to graphically assess whether
|
|
the data could come from the fitted distribution.
|
|
If so the empirical CDF should resemble the model CDF.
|
|
Other distribution types will introduce deviations in the plot.
|
|
'''
|
|
if axis is None:
|
|
axis = plt.gca()
|
|
n = len(self.data)
|
|
F = (arange(1, n + 1)) / n
|
|
axis.plot(self.data, F, symb2,
|
|
self.data, self.cdf(self.data), symb1)
|
|
axis.set_xlabel('x')
|
|
axis.set_ylabel('F(x) ({})'.format(self.dist.name))
|
|
axis.set_title('Empirical CDF plot')
|
|
|
|
def _get_grid(self, odd=False):
|
|
x = np.atleast_1d(self.data)
|
|
n = np.ceil(4 * np.sqrt(np.sqrt(len(x))))
|
|
mn = x.min()
|
|
mx = x.max()
|
|
d = (mx - mn) / n * 2
|
|
e = np.floor(np.log(d) / np.log(10))
|
|
m = np.floor(d / 10 ** e)
|
|
if m > 5:
|
|
m = 5
|
|
elif m > 2:
|
|
m = 2
|
|
d = m * 10 ** e
|
|
mn = (np.floor(mn / d) - 1) * d - odd * d / 2
|
|
mx = (np.ceil(mx / d) + 1) * d + odd * d / 2
|
|
limits = np.arange(mn, mx, d)
|
|
return limits
|
|
|
|
@staticmethod
|
|
def _staircase(x, y):
|
|
xx = x.reshape(-1, 1).repeat(3, axis=1).ravel()[1:-1]
|
|
yy = y.reshape(-1, 1).repeat(3, axis=1)
|
|
# yy[0,0] = 0.0 # pdf
|
|
yy[:, 0] = 0.0 # histogram
|
|
yy.shape = (-1,)
|
|
yy = np.hstack((yy, 0.0))
|
|
return xx, yy
|
|
|
|
def _get_empirical_pdf(self):
|
|
limits = self._get_grid()
|
|
pdf, x = np.histogram(self.data, bins=limits, normed=True)
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return self._staircase(x, pdf)
|
|
|
|
def plotepdf(self, symb1='r-', symb2='b-', axis=None):
|
|
'''Plot Empirical and fitted Probability Density Function
|
|
|
|
The purpose of the plot is to graphically assess whether
|
|
the data could come from the fitted distribution.
|
|
If so the histogram should resemble the model density.
|
|
Other distribution types will introduce deviations in the plot.
|
|
'''
|
|
if axis is None:
|
|
axis = plt.gca()
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|
x, pdf = self._get_empirical_pdf()
|
|
ymax = pdf.max()
|
|
# axis.hist(self.data,normed=True,fill=False)
|
|
axis.plot(self.data, self.pdf(self.data), symb1,
|
|
x, pdf, symb2)
|
|
axis1 = list(axis.axis())
|
|
axis1[3] = min(ymax * 1.3, axis1[3])
|
|
axis.axis(axis1)
|
|
axis.set_xlabel('x')
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|
axis.set_ylabel('f(x) (%s)' % self.dist.name)
|
|
axis.set_title('Density plot')
|
|
|
|
def plotresq(self, symb1='r-', symb2='b.', axis=None):
|
|
'''PLOTRESQ displays a residual quantile plot.
|
|
|
|
The purpose of the plot is to graphically assess whether
|
|
the data could come from the fitted distribution. If so the
|
|
plot will be linear. Other distribution types will introduce
|
|
curvature in the plot.
|
|
'''
|
|
if axis is None:
|
|
axis = plt.gca()
|
|
n = len(self.data)
|
|
eprob = (arange(1, n + 1) - 0.5) / n
|
|
y = self.ppf(eprob)
|
|
y1 = self.data[[0, -1]]
|
|
axis.plot(self.data, y, symb2, y1, y1, symb1)
|
|
axis.set_xlabel('Empirical')
|
|
axis.set_ylabel('Model (%s)' % self.dist.name)
|
|
axis.set_title('Residual Quantile Plot')
|
|
axis.axis('tight')
|
|
axis.axis('equal')
|
|
|
|
def plotresprb(self, symb1='r-', symb2='b.', axis=None):
|
|
''' PLOTRESPRB displays a residual probability plot.
|
|
|
|
The purpose of the plot is to graphically assess whether
|
|
the data could come from the fitted distribution. If so the
|
|
plot will be linear. Other distribution types will introduce curvature
|
|
in the plot.
|
|
'''
|
|
if axis is None:
|
|
axis = plt.gca()
|
|
n = len(self.data)
|
|
# ecdf = (0.5:n-0.5)/n;
|
|
ecdf = arange(1, n + 1) / (n + 1)
|
|
mcdf = self.cdf(self.data)
|
|
p1 = [0, 1]
|
|
axis.plot(ecdf, mcdf, symb2, p1, p1, symb1)
|
|
axis.set_xlabel('Empirical')
|
|
axis.set_ylabel('Model (%s)' % self.dist.name)
|
|
axis.set_title('Residual Probability Plot')
|
|
axis.axis('equal')
|
|
axis.axis([0, 1, 0, 1])
|
|
|
|
def _pvalue(self, theta, x, unknown_numpar=None):
|
|
''' Return P-value for the fit using Moran's negative log Product
|
|
Spacings statistic
|
|
|
|
where theta are the parameters (including loc and scale)
|
|
|
|
Note: the data in x must be sorted
|
|
'''
|
|
dx = np.diff(x, axis=0)
|
|
tie = (dx == 0)
|
|
if np.any(tie):
|
|
warnings.warn(
|
|
'P-value is on the conservative side (i.e. too large) due to' +
|
|
' ties in the data!')
|
|
|
|
T = self._nlogps(theta, x)
|
|
|
|
n = len(x)
|
|
np1 = n + 1
|
|
if unknown_numpar is None:
|
|
k = len(theta)
|
|
else:
|
|
k = unknown_numpar
|
|
|
|
isParUnKnown = True
|
|
m = (np1) * (log(np1) + 0.57722) - 0.5 - 1.0 / (12. * (np1))
|
|
v = (np1) * (pi ** 2. / 6.0 - 1.0) - 0.5 - 1.0 / (6. * (np1))
|
|
C1 = m - sqrt(0.5 * n * v)
|
|
C2 = sqrt(v / (2.0 * n))
|
|
# chi2 with n degrees of freedom
|
|
Tn = (T + 0.5 * k * isParUnKnown - C1) / C2
|
|
pvalue = chi2sf(Tn, n) # _WAFODIST.chi2.sf(Tn, n)
|
|
return pvalue
|
|
|
|
|
|
def test_doctstrings():
|
|
import doctest
|
|
doctest.testmod()
|
|
|
|
|
|
def test1():
|
|
import wafo.stats as ws
|
|
dist = ws.weibull_min
|
|
# dist = ws.bradford
|
|
# dist = ws.gengamma
|
|
R = dist.rvs(2, .5, size=500)
|
|
phat = FitDistribution(dist, R, floc=0.5, method='ml')
|
|
phats = FitDistribution(dist, R, floc=0.5, method='mps')
|
|
# import matplotlib.pyplot as plt
|
|
# plt.figure(0)
|
|
# plot_all_profiles(phat, plot=plt)
|
|
|
|
plt.figure(1)
|
|
phats.plotfitsummary()
|
|
|
|
# plt.figure(2)
|
|
# plot_all_profiles(phat, plot=plt)
|
|
|
|
|
|
# plt.figure(3)
|
|
# phat.plotfitsummary()
|
|
|
|
plt.figure(4)
|
|
|
|
sf = 1./990
|
|
x = phat.isf(sf)
|
|
|
|
# 80% CI for x
|
|
profile_x = ProfileQuantile(phat, x)
|
|
profile_x.plot()
|
|
# x_ci = profile_x.get_bounds(alpha=0.2)
|
|
|
|
plt.figure(5)
|
|
|
|
sf = 1./990
|
|
x = phat.isf(sf)
|
|
|
|
# 80% CI for x
|
|
profile_logsf = ProfileProbability(phat, np.log(sf))
|
|
profile_logsf.plot()
|
|
# logsf_ci = profile_logsf.get_bounds(alpha=0.2)
|
|
plt.show('hold')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test1()
|
|
# test_doctstrings()
|