def check_feature(self, vec):
vec_type = get_vec_type(vec)
if vec_type == 'boolean':
return vec.value_counts()[1] > 10
elif vec_type == 'real':
return vec.count() > 50
else:
return False
def check_feature(self, vec):
vec_type = get_vec_type(vec)
if vec_type == 'boolean':
return vec.value_counts()[1] > 10
elif vec_type == 'real':
return vec.count() > 50
else:
return False
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data), lty=1, col=ls, lwd=4, cex=1.25,
xlab='Years to Event', ylab='Survival');
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data),
lty=1,
col=ls,
lwd=4,
cex=1.25,
xlab='Years to Event',
ylab='Survival')
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
def draw_survival_curves(feature,
surv,
assignment=None,
filename='tmp.png',
show=False,
title=True,
labels=None,
colors=['blue', 'red'],
ann=None,
show_legend=True,
q=.25,
std=None):
if assignment is None:
num_panels = 1
assignment = feature.map(lambda s: 1)
name = lambda v: str(feature.name) if feature.name != None else ''
else:
num_panels = len(assignment.unique())
name = lambda v: str(assignment.name) + ' = ' + str(v)
if (labels is None) and ((len(feature) / feature.nunique()) > 10):
labels = r.sort(r.c(*feature.unique())) # R sorts bad
colors = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black']
if feature.dtype == 'bool':
feature = feature.map({True: 'True', False: 'False'})
r.png(filename=filename, width=200 * (num_panels + 1), height=300, res=75)
fmla = robjects.Formula('Surv(days, event) ~ feature')
r.par(mfrow=r.c(1, num_panels))
r.par(mar=r.c(4, 5, 4, 1))
r.par(xpd=True)
if (get_vec_type(feature) == 'real') and (len(feature.unique()) > 10):
colors = ['blue', 'orange', 'red']
if q == .5:
labels = ['Bottom 50%', 'Top 50%']
else:
labels = [
'Bottom {}%'.format(int(q * 100)), 'Normal',
'Top {}%'.format(int(q * 100))
]
ls = r.c(*colors)
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data),
lty=1,
col=ls,
lwd=4,
cex=1.25,
xlab='Years to Event',
ylab='Survival')
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
if show_legend == 'out':
r.par(xpd=True, mar=r.c(4, 5, 5, 8))
for value in sorted(assignment.ix[feature.index].dropna().unique()):
f = feature.ix[assignment[assignment == value].index]
if len(f.unique()) > 1:
plot_me(f, name(value))
if show_legend == True:
mean_s = surv.ix[:, 'event'].ix[assignment[assignment ==
value].index].mean()
if mean_s < .5:
r.legend(surv.ix[:, 'days'].max() * .05 / 365.,
.45,
labels,
lty=1,
col=ls,
lwd=3,
bty='o')
else:
r.legend(surv.ix[:, 'days'].max() * .4 / 365,
.9,
labels,
lty=1,
col=ls,
lwd=3,
bty='o')
elif show_legend == 'out':
r.legend(surv.ix[:, 'days'].max() * 1.1 / 365,
.9,
labels,
lty=1,
col=ls,
lwd=3,
bty='o')
r('dev.off()')
if show:
return Show(filename)
def draw_survival_curves(feature, surv, assignment=None, filename='tmp.png', show=False,
title=True, labels=None, colors=['blue', 'red'], ann=None,
show_legend=True, q=.25, std=None):
if assignment is None:
num_panels = 1
assignment = feature.map(lambda s: 1)
name = lambda v: str(feature.name) if feature.name != None else ''
else:
num_panels = len(assignment.unique())
name = lambda v: str(assignment.name) + ' = ' + str(v)
if (labels is None) and ((len(feature) / feature.nunique()) > 10):
labels = r.sort(r.c(*feature.unique())) # R sorts bad
colors = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black']
if feature.dtype == 'bool':
feature = feature.map({True: 'True', False: 'False'})
r.png(filename=filename, width=200 * (num_panels + 1), height=300, res=75)
fmla = robjects.Formula('Surv(days, event) ~ feature')
r.par(mfrow=r.c(1, num_panels))
r.par(mar=r.c(4, 5, 4, 1))
r.par(xpd=True)
if (get_vec_type(feature) == 'real') and (len(feature.unique()) > 10):
colors = ['blue', 'orange', 'red']
if q == .5:
labels = ['Bottom 50%', 'Top 50%']
else:
labels = ['Bottom {}%'.format(int(q * 100)), 'Normal', 'Top {}%'.format(int(q * 100))]
ls = r.c(*colors)
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data), lty=1, col=ls, lwd=4, cex=1.25,
xlab='Years to Event', ylab='Survival');
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
if show_legend == 'out':
r.par(xpd=True, mar=r.c(4, 5, 5, 8))
for value in sorted(assignment.ix[feature.index].dropna().unique()):
f = feature.ix[assignment[assignment == value].index]
if len(f.unique()) > 1:
plot_me(f, name(value))
if show_legend == True:
mean_s = surv.ix[:, 'event'].ix[assignment[assignment == value].index].mean()
if mean_s < .5:
r.legend(surv.ix[:, 'days'].max() * .05 / 365., .45, labels,
lty=1, col=ls, lwd=3, bty='o')
else:
r.legend(surv.ix[:, 'days'].max() * .4 / 365, .9, labels,
lty=1, col=ls, lwd=3, bty='o')
elif show_legend == 'out':
r.legend(surv.ix[:, 'days'].max() * 1.1 / 365, .9, labels,
lty=1, col=ls, lwd=3, bty='o')
r('dev.off()')
if show:
return Show(filename)
def process_feature(feature, q, std):
if (get_vec_type(feature) == 'real') and (len(feature.unique()) > 10):
feature = to_quants(feature, q=q, std=std, labels=True)
return feature
