def glmnetPlot(x, xvar = 'norm', label = False, ptype = 'coef', **options):
# process inputs
xvar = getFromList(xvar, ['norm', 'lambda', 'dev'], 'xvar should be one of ''norm'', ''lambda'', ''dev'' ')
ptype = getFromList(ptype, ['coef', '2norm'], 'ptype should be one of ''coef'', ''2norm'' ')
if x['class'] in ['elnet', 'lognet', 'coxnet', 'fishnet']:
handle = plotCoef(x['beta'], [], x['lambdau'], x['df'], x['dev'],
label, xvar, '', 'Coefficients', **options)
elif x['class'] in ['multnet', 'mrelnet']:
beta = x['beta']
if xvar == 'norm':
norm = 0
nzbeta = beta
for i in range(len(beta)):
which = nonzeroCoef(beta[i])
nzbeta[i] = beta[i][which, :]
norm = norm + scipy.sum(scipy.absolute(nzbeta[i]), axis = 0)
else:
norm = 0
if ptype == 'coef':
ncl = x['dfmat'].shape[0]
if x['class'] == 'multnet':
for i in range(ncl):
str = 'Coefficients: Class %d' % (i)
handle = plotCoef(beta[i], norm, x['lambdau'], x['dfmat'][i,:],
x['dev'], label, xvar, '', str, **options)
if i < ncl - 1:
plt.figure()
else:
str = 'Coefficients: Response %d' % (i)
handle = plotCoef(beta[i], norm, x['lambdau'], x['dfmat'][i,:],
x['dev'], label, xvar, '', str, **options)
else:
dfseq = scipy.round_(scipy.mean(x['dfmat'], axis = 0))
coefnorm = beta[1]*0
for i in range(len(beta)):
coefnorm = coefnorm + scipy.absolute(beta[i])**2
coefnorm = scipy.sqrt(coefnorm)
if x['class'] == 'multnet':
str = 'Coefficient 2Norms'
handle = plotCoef(coefnorm, norm, x['lambdau'], dfseq, x['dev'],
label, xvar, '',str, **options);
if i < ncl - 1:
plt.figure()
else:
str = 'Coefficient 2Norms'
handle = plotCoef(coefnorm, norm, x['lambdau'], x['dfmat'][0,:], x['dev'],
label, xvar, '', str, **options);
return(handle)
# end of glmnetplot
# =========================================
#
# =========================================
# helper functions
# =========================================
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