def _rm_minos_(self, *variables):
"""Run MINOS for set of parameters
>>> pdf = ...
>>> minuit = pdf.minuit()
>>> minuit.migrad()
>>> minuit.minos ( 'sigma' , 'mean' )
"""
if not variables:
status = self._old_minos_()
if 0 != status:
from ostap.fitting.utils import fit_status
logger.error("MINOS status %s" % fit_status(status))
return status
aset = ROOT.RooArgSet()
res = self.save()
for v in variables:
if isinstance(v, string_types) or isinstance(v, ROOT.RooAbsReal):
par = res.param(v)[1]
if par: aset.add(par)
elif isinstance ( v , ROOT.RooAbsCollection ) or \
isinstance ( v , list_types ) :
for a in v:
if isinstance(a, ROOT.RooAbsReal):
par = res.param(a)[1]
if par: aset.add(par)
del res
if aset: status = self._old_minos_(aset)
else: status = self._old_minos_(aset)
if 0 != status:
from ostap.fitting.utils import fit_status
logger.error("MINOS status %s" % fit_status(status))
return status
def _rm_migrad_(self):
"""Run MIGRAD for RooMinimizer
- see ROOT.RooMinimizer
>>> pdf = ...
>>> minuit = pdf.minuit()
>>> minuit.migrad()
"""
status = self._old_migrad_()
if 0 != status:
from ostap.fitting.utils import fit_status
logger.error("MIGRAD status %s" % fit_status(status))
return status
def _rm_migrad_(self, refit=0, tag="", minos=()):
"""Run MIGRAD for RooMinimizer
- see ROOT.RooMinimizer
>>> pdf = ...
>>> minuit = pdf.minuit()
>>> minuit.migrad()
"""
status = self._old_migrad_()
if 0 != status:
from ostap.fitting.utils import fit_status
message = "MIGRAD %s status %s" % (tag, fit_status(status))
if isinstance(refit, integer_types) and 0 < refit:
logger.error(message + ', refit... ')
return _rm_migrad_(self, refit - 1, tag)
elif isinstance(refit, bool) and refit:
logger.error(message + ', refit... ')
return _rm_migrad_(self, False, tag)
logger.error(message)
elif minos:
return self.minos(*minos)
return status
def _rfr_table_ ( r , title = '' , prefix = '' , more_vars = {} ) :
""" print RooFitResult as a table
>>> result = ...
>>> result.table()
"""
from ostap.fitting.utils import fit_status, cov_qual
rows = []
## 1. fit status
status = r.status()
if status :
row = attention ( 'Status' ) , '' , attention ( fit_status ( status ) ) , ''
rows.append ( row )
else :
row = 'Status' , '' , allright ( fit_status ( status ) ) , ''
rows.append ( row )
## 2. minumum NLL
s , n = pretty_float ( r.minNll() )
if n : n = '[10^%+d]' % n
else : n = ''
rows.append ( ( "Minimized FCN/NLL value" , n , ' ' + s , '' ) )
s , n = pretty_float ( r.edm () )
if n : n = '[10^%+d]' % n
else : n = ''
rows.append ( ( 'Estimated distance to minimum' , n , ' ' + s , '' ) )
cq = r.covQual()
cn = ''
if -1 == cq :
cn = cov_qual ( cq )
elif 3 == cq :
cn = allright ( cov_qual ( cq ) )
elif cq in ( 0 , 1 , 2 ) :
cn = attention ( cov_qual ( cq ) )
else :
cn = cov_qual ( cq )
rows.append ( ( 'Covariance matrix quality' , '' , ' ' + cn , '' ) )
for i in range ( r.numStatusHistory() ) :
label = r.statusLabelHistory ( i )
code = r.statusCodeHistory ( i )
row = 'Status: %s '% label , '' , '%d' % code
if not code in ( 0 , -1 ) :
row = attention ( row [ 0 ] ) , row [ 1 ] , ' ' + attention ( row [ 2 ] ) , ''
else :
row = row [ 0 ] , row [ 1 ] , ' ' + allright ( row [ 2 ] ) , ''
rows.append ( row )
nbadnll = r.numInvalidNLL()
if 0 < nbadnll :
rows.append ( ( 'Invalid FCN/NLL evaluations' , '' , ' %d' % nbadnll , '' ) )
with_globcorr = not ( (6,24) <= root_info < (6,28) )
if with_globcorr : rows = [ ( '', 'Unit', 'Value' , 'Global/max correlation [%]') ] + rows
else : rows = [ ( '', 'Unit', 'Value' , 'Max correlation [%]') ] + rows
pars_all = r.params ( float_only = False )
pars_float = r.params ( float_only = True )
## constant/fix parameters
crows = []
for p in pars_all :
if p in pars_float : continue
v , a = pars_all [ p ]
s , n = pretty_float ( v.value() )
if n : n = '[10^%+d]' % n
else : n = ''
row = p , n , ' ' + s + ' (fix)' , ''
crows.append ( row )
## floating parameters
max_corr = False
frows = []
for p in pars_float :
v , a = pars_float [ p ]
if not a.hasAsymError() :
s , n = pretty_ve ( v )
else :
s , n = pretty_2ve ( a.getVal() , a.getAsymErrorHi() , a.getAsymErrorLo() )
if n : n = '[10^%+d]' % n
else : n = ''
if 0 <= cq and 1 < len ( pars_float ) :
mxr , mxv = r.max_cor ( p )
if with_globcorr :
gc = -1.0
gc = r.globalCorr ( p ) if 3 == cq else -1.00
if 0 <= gc : cc = '% +5.1f/(% +5.1f,%s)' % ( gc*100 , mxr*100 , mxv )
else : cc = '% +5.1f : %-s' % ( mxr * 100 , mxv )
if 0.95 < abs ( mxr ) or 0.95 < gc : cc = attention ( cc )
else :
cc = '% +5.1f : %-s' % ( mxr * 100 , mxv )
if 0.95 < abs ( mxr ) : cc = attention ( cc )
max_corr = True
row = p , n , s , cc
else :
row = p , n , s
frows.append ( row )
## more parameters
mrows = []
for p in sorted ( more_vars ) :
func = more_vars [ p ]
v = func ( r )
s , n = pretty_ve ( v )
if n : n = '[10^%+d]' % n
else : n = ''
cc = 'derived'
row = p , n , s , cc
mrows.append ( row )
crows.sort()
frows.sort()
all = rows + crows + frows + mrows
import ostap.logger.table as T
return T.table ( all , title = title if title else r.GetTitle() , prefix = prefix , alignment = 'llll' )
def _mn_table_ ( self , title = '' , prefix = '' ) :
"""Build parameter table from (T)Minuit
"""
header = ( 'Parameter' , '' , 'Value' )
rows = []
from ostap.fitting.utils import fit_status , cov_qual
from ostap.logger.utils import pretty_float, pretty_ve, pretty_2ve
status = self.GetStatus()
if status :
status = fit_status ( status )
row = ' Status' , '' , status
rows.append ( row )
stat = _mn_stat_ ( self )
istat = stat.pop ( 'ISTAT' , None )
if not istat is None :
cq = ''
if -1 == istat : cq = cov_qual ( istat )
elif 3 == istat : cq = allright ( cov_qual ( istat ) )
elif istat in ( 0 , 1 , 2 ) : cq = attentiont ( cov_qual ( istat ) )
else : cq = cov_qual ( istat )
row = 'Covariance matrix quality' , '' , cq
rows.append ( row )
fmin = stat.pop ( 'FMIN' , None )
if not fmin is None :
s , n = pretty_float ( fmin )
if n : n = '[10^%+d]' % n
else : n = ''
row = 'Minimized FCN value' , n , s
rows.append ( row )
fedm = stat.pop ( 'FEDM' , None )
if not fedm is None :
s , n = pretty_float ( fedm )
if n : n = '[10^%+d]' % n
else : n = ''
row = 'Estimated distance to minimum' , n , s
rows.append ( row )
errdef = stat.pop ( 'ERRDEF' , None )
## needed ?
has_limits = False
has_minos = False
val = ctypes.c_double ( 0 )
err = ctypes.c_double ( 0 )
low = ctypes.c_double ( 0 )
high = ctypes.c_double ( 0 )
idx = ctypes.c_int ( 0 )
dct_pars = {}
## loop over all parameters
for i in self :
name = ROOT.TString()
self.mnpout ( i , name , val , err , low , high , idx )
if not 0 <= idx.value : continue
dct = {}
dct [ 'name' ] = '%-2d: %s' % ( i , str ( name ).strip() )
dct [ 'value'] = val.value
if low.value < high.value :
dct [ 'low' ] = low .value
dct [ 'high'] = high.value
has_limits = True
if 0 <= err.value :
dct [ 'error' ] = err.value
mn_plus , mn_minus = _mn_minerr_ ( self , str ( name ) )
if 0 < mn_plus or mn_minus < 0 :
dct [ 'minos+' ] = mn_plus
dct [ 'minos-' ] = mn_minus
has_minos = True
dct_pars[ i ] = dct
if has_minos :
## some parameters have MINOS errors, add columns
header = ( header ) + ( 'neg-minos' , 'pos-minos' )
rows = [ r + ( '','' ) for r in rows ]
if has_limits :
## some parameters have LIMITS, add columns
header = ( header ) + ( 'low limit' , 'high limit' )
rows = [ r + ( '','' ) for r in rows ]
for p in dct_pars :
pdict = dct_pars [ p ]
row = []
row.append ( pdict.pop ( 'name' ) )
value = pdict.pop ( 'value' )
error = pdict.pop ( 'error', None )
if error is None :
s , n = pretty_float ( value )
s = "%s(fixed)" % s
else :
s , n = pretty_ve ( VE ( value , error * error ) )
if n : row.append ( '[10^%+d]' % n )
else : row.append ( '' )
row.append ( s )
if has_minos :
mn_plus = pdict.pop ( 'minos+' , None )
mn_minus = pdict.pop ( 'minos-' , None )
if mn_plus is None : mn_plus = ''
else : mn_plus = '%8f' % ( mn_plus * 10** n )
if mn_minus is None : mn_minus = ''
else : mn_minus = '%8f' % ( mn_minus * 10** n )
row.append ( mn_minus )
row.append ( mn_plus )
if has_limits :
low = pdict.pop ( 'low' , None )
high = pdict.pop ( 'high' , None )
if low is None : low = ''
else : low = '%8f' % ( low * 10 ** n )
if high is None : high = ''
else : high = '%8f' % ( high * 10 ** n )
row.append ( low )
row.append ( high )
row = tuple ( row )
rows.append ( row )
rows = [ header ] + rows
from ostap.logger.table import table
return table ( rows , title = title , prefix = prefix )
def _fit_table_ ( rfit , title = '' , prefix = '' ) :
"""Print <code>TFitResult</code> as a table
"""
from ostap.fitting.utils import fit_status, cov_qual
from ostap.logger.colorized import attention, allright
from ostap.logger.utils import pretty_float, pretty_ve, pretty_2ve, fmt_pretty_ve
header = ( '', 'Unit' , 'Value' )
rows = []
## 0. minimized type
row = "Minimizer Type" , '' , rfit.MinimizerType()
rows.append ( row )
## 0. minimized type
v = rfit.IsValid()
if v : row = "Valid" , '' , 'True'
else : row = "Valid" , '' , attention ( 'False')
rows.append ( row )
## 1. fit status
status = rfit.Status()
if status :
row = attention ( 'Status' ) , '' , attention ( fit_status ( status ) )
rows.append ( row )
else :
row = 'Status' , '' , allright ( fit_status ( status ) )
rows.append ( row )
## 4. covariance status
cq = rfit.CovMatrixStatus()
cn = ''
if -1 == cq : cn = cov_qual ( cq )
elif 3 == cq : cn = allright ( cov_qual ( cq ) )
elif cq in ( 0 , 1 , 2 ) : cn = attention ( cov_qual ( cq ) )
else : cn = cov_qual ( cq )
rows.append ( ( 'Covariance matrix quality' , '' , ' ' + cn ) )
## 3-6. chi2,nDoF,chi2/nDoF,minFCN
chi2 = rfit.Chi2 ()
s , n = pretty_float ( chi2 )
if n : n = '[10^%+d]' % n
else : n = ''
rows.append ( ( "Chi2" , n , ' ' + s ) )
##
ndf = rfit.Ndf()
rows.append ( ( "nDoF" , '' , ' ' + '%d' % ndf ) )
##
c2ndf = rfit.Chi2 () / ndf
s , n = pretty_float ( c2ndf )
if n : n = '[10^%+d]' % n
else : n = ''
rows.append ( ( "Chi2/nDoF" , n , ' ' + s ) )
##
minfcn = rfit.MinFcnValue()
s , n = pretty_float ( minfcn )
if n : n = '[10^%+d]' % n
else : n = ''
rows.append ( ( "Minimal FCN" , n , ' ' + s ) )
## 7.Probability in %[%]
prob = rfit.Prob() / 100
rows.append ( ( "Probability" , '[%]' , ' %5.3e' % prob ) )
## 8. distrance to minimum
edm = rfit.Edm()
s , n = pretty_float ( edm )
if n : n = '[10^%+d]' % n
else : n = ''
rows.append ( ( "Estimated distance to minimum" , n , ' ' + s ) )
ncalls = rfit.NCalls()
rows.append ( ( "FCN calls" , '' , ' ' + '%d' % ncalls ) )
##
has_minos = False
for i in rfit :
if not rfit.HasMinosError( i ) : continue
has_minos = True
break
if has_minos :
rows = [ row + ('','','') for row in rows ]
header = header + ( 'neg-minos' , 'pos-minos' , 'Global corr.' )
else :
rows = [ row + ('',) for row in rows ]
header = header + ( 'Global corr.' , )
for i in rfit :
pname = rfit.GetParameterName ( i )
value = rfit.Value ( i )
fixed = rfit.IsParameterFixed ( i )
fmte = ''
if fixed :
v = value
s , n = pretty_float ( v )
s = s + '(fixed)'
nv = n
else :
error = rfit.Error ( i )
v = VE ( value , error * error )
##
fmt , fmtv , fmte , n = fmt_pretty_ve ( v )
s = fmt % ( value / 10**n , error / 10**n )
nv = n
if n : n = '[10^%+d]' % n
else : n = ''
pname = "%-2d: %s"% ( i , pname )
row = pname , n , ' ' + s
if not fixed and rfit.HasMinosError( i ) :
if fmte :
error_low = fmte % ( rfit.LowerError ( i ) / 10**nv )
error_up = fmte % ( rfit.UpperError ( i ) / 10**nv )
else :
error_low = "%+8g" % ( rfit.LowerError ( i ) / 10**nv )
error_up = "%+8g" % ( rfit.UpperError ( i ) / 10**nv )
else :
error_low = ''
error_up = ''
if has_minos :
row = row + ( error_low , error_up )
gcc = rfit.GlobalCC ( i ) * 100
gcc = '%+5.1f%%' % gcc
row = row + ( gcc, )
rows.append ( row )
if not title : title = rfit.GetTitle()
import ostap.logger.table as T
rows = [ header ] + rows
return T.table ( rows , title = title , prefix = prefix )
def _rfr_table_(r, title='', prefix=''):
""" print RooFitResult as a table
>>> result = ...
>>> result.table()
"""
from ostap.fitting.utils import fit_status, cov_qual
rows = []
if r.status():
row = attention(' Status'), '', attention(fit_status(r.status())), ''
rows.append(row)
s, n = pretty_float(r.minNll())
if n: n = '[10^%+d]' % n
else: n = ''
rows.append(("Minimized FCN/NLL value", n, ' ' + s, ''))
s, n = pretty_float(r.edm())
if n: n = '[10^%+d]' % n
else: n = ''
rows.append(('Estimated distance to minimum', n, ' ' + s, ''))
cq = r.covQual()
cn = ''
if -1 == cq:
cn = cov_qual(cq)
elif 3 == cq:
cn = allright(cov_qual(cq))
elif cq in (0, 1, 2):
cn = attention(cov_qual(cq))
else:
cn = cov_qual(cq)
rows.append(('Covariance matrix quality', '', ' ' + cn, ''))
for i in range(r.numStatusHistory()):
label = r.statusLabelHistory(i)
code = r.statusCodeHistory(i)
row = 'Status: %s ' % label, '', '%d' % code
if not code in (0, -1):
row = attention(row[0]), row[1], ' ' + attention(row[2]), ''
else:
row = row[0], row[1], ' ' + allright(row[2]), ''
rows.append(row)
nbadnll = r.numInvalidNLL()
if 0 < nbadnll:
rows.append(('Invalid FCN/NLL evaluations', '', ' %d' % nbadnll, ''))
rows = [('', 'Unit', 'Value', 'Global/max correlation')] + rows
pars_all = r.params(float_only=False)
pars_float = r.params(float_only=True)
## constant/fix parameters
crows = []
for p in pars_all:
if p in pars_float: continue
v, a = pars_all[p]
s, n = pretty_float(v.value())
if n: n = '[10^%+d]' % n
else: n = ''
row = p, n, ' ' + s, ''
crows.append(row)
## floating parameters
frows = []
for p in pars_float:
v, a = pars_float[p]
if not a.hasAsymError():
s, n = pretty_ve(v)
else:
s, n = pretty_2ve(a.getVal(), a.getAsymErrorHi(),
a.getAsymErrorLo())
if n: n = '[10^%+d]' % n
else: n = ''
cc = 'Not available'
if 0 <= cq:
mxr, mxv = r.max_cor(p)
gc = r.globalCorr(p)
cc = '%+5.3f/(%+5.3f,%s)' % (gc, mxr, mxv)
if 0.95 < abs(gc) or 0.95 < abs(mxr): cc = attention(cc)
row = p, n, s, cc
frows.append(row)
crows.sort()
frows.sort()
all = rows + crows + frows
import ostap.logger.table as T
all = T.align_column(all, 0, 'left')
all = T.align_column(all, 1, 'left')
all = T.align_column(all, 2, 'left')
all = T.align_column(all, 3, 'left')
for l in range(len(rows), len(all)):
line = all[l]
line = list(line)
line[0] = allright(line[0])
all[l] = tuple(line)
if title:
return T.table(all, title=title, prefix=prefix)
else:
return T.table(all, title=r.GetTitle(), prefix=prefix)