def worker((i, r)):
# betas = [3.0] * 25
betas = getBeta(i)
nIter = 100
alias = 'i-%d_r-%d' % (i, r)
mdl0 = pyjob.job__cluster__mixtureVMF__incr(
# normalizeSample=1,
tdf=tdf,
meanNorm=1,
weighted=True,
init_method='random',
nIter=nIter,
start=0.001,
end=4.0,
betas=betas,
randomState=r,
alias='mdl_' + alias,
verbose=2,
K=60,
)
axs = pycbk.qc__vmf__speed(mdl0)
fig = plt.gcf()
ax = fig.axes[0]
pyvis.abline(y0=3.7, k=0, ax=ax)
# alias = 'qcVMF__i-%d_r-%d'%(i,r)
figs[alias] = fig
return (alias, fig)
def worker((i, r)):
# betas = [3.0] * 25
# betas = getBeta(i)
nIter = 100
alias = 'i-%d_r-%d' % (i, r)
mdl0 = pyjob.job__cluster__mixtureVMF__incr(
normalizeSample=0, #### set to 1 to normalize the vector lenght
tdf=tdf,
meanNorm=1, ##### perform X = X-E(X)_
weighted=True,
init_method='random',
nIter=nIter,
# start=0.001, #### specify temperature range
# end=2.0,
# end=0.7,
start=0.2, #### specify temperature range
# end=2.0,
end=0.7,
# betas = betas, #### alternatively, pass a callable for temperature
randomState=r,
alias='mdl_' + alias, #### filename of cache produced
verbose=2,
K=60,
)
##### produce diagnostic plot
YCUT = entropy_cutoff = 2.5
XCUT = step = 30
axs = pycbk.qc__vmf__speed(
mdl0,
# XCUT=step,YCUT=entropy_cutoff ### not working yet
)
fig = plt.gcf()
ax = fig.axes[0]
# pyvis.abline(y0=3.7,k=0,ax=ax)
pyvis.abline(y0=YCUT, k=0, ax=ax)
pyvis.abline(x0=XCUT, k=0, ax=ax)
figs['diagnostic-plot'] = plt.gcf()
#### using the last model to predict cluster
mdls = mdl0.callback.mdls #### models is recorded for each point
mdl = mdls[step][-1] #### getting the model at step
clu = mdl.predictClu(tdf, entropy_cutoff=entropy_cutoff)
clu.to_csv('cluster.csv') ### getting cluster assignment
pyvis.heatmap(tdf.reindex(clu.sort_values('clu').index),
figsize=[14, 7])
figs['clustered-heatmap'] = plt.gcf()
return (alias, fig)
# ##### debugging!!!!!!!!!!!!
# tdf = pyutil.readData('http://172.26.114.34:81/static/results/0129__cluster__Brachy-RNA-all/mdl.npy').tolist().data
# ##### debugging!!!!!!!!!!!!
N = 5
np.random.seed(0)
lst = np.random.randint(100000000, size=(N))
for i, r in enumerate(lst):
mdl0 = pyjob.job__cluster__mixtureVMF__incr(
# normalizeSample=1,
tdf=tdf,
meanNorm=0,
init_method='random',
nIter=50,
start=0.001,
end=4.0,
randomState=r,
alias='mdl-%d' % r,
verbose=2,
K=60,
)
# axs = pyjob._mod1.qc__vmf(mdl0)
axs = pycbk.qc__vmf__speed(mdl0)
fig = plt.gcf()
ax = fig.axes[0]
pyvis.abline(y0=3.7, k=0, ax=ax)
figs['qcVMF__r-%d' % r] = fig
N = 5
np.random.seed(0)
lst = np.random.randint(100000000, size=(N))
lst = [75434668]
betas = np.linspace(0.001, 1.52, 25).tolist() + [1.52] * 25
nIter = 100
for i, r in enumerate(lst):
mdl0 = pyjob.job__cluster__mixtureVMF__incr(
normalizeSample=True,
# normalizeSample=1,
tdf=tdf,
meanNorm=0,
weighted=False,
init_method='random',
nIter=nIter,
start=0.001,
end=4.0,
betas=betas,
randomState=r,
alias='mdl-%d' % r,
verbose=2,
K=60,
)
axs = pycbk.qc__vmf__speed(mdl0)
fig = plt.gcf()
ax = fig.axes[0]
pyvis.abline(y0=3.7, k=0, ax=ax)
figs['qcVMF__r-%d' % r] = fig
tks.rnaseq_wk2ld_phycko,
tks.rnaseq_wk3ldppd1_wk3ldwt,
],
axis=1)
##### debugging
tdf = pyutil.readData(
'http://172.26.114.34:81/static/results/0129__cluster__Brachy-RNA-all/mdl.npy'
).tolist().data
##### debugging
mdl0 = pyjob.job__cluster__mixtureVMF__incr(
# normalizeSample=1,
tdf=tdf,
meanNorm=0,
init_method='random',
nIter=250,
start=0.001,
end=7.0,
verbose=2,
K=80,
)
axs = pyjob._mod1.qc__vmf(mdl0)
fig = plt.gcf()
ax = fig.axes[0]
pyvis.abline(y0=3.7, k=0, ax=ax)
figs['qcVMF'] = fig
pyutil.render__images(figs, )