def main():
NCORE = 3
# NJOB = 10 ### number of parallel jobs
import pymisca.header as pyhead
pyhead.mpl__setBackend('agg')
# execfile(pyhead.base__file('headers/header__import.py'))
import pymisca.jobs as pyjob
import pymisca.callbacks as pycbk
import pymisca.util as pyutil
pd = pyutil.pd
import pymisca.vis_util as pyvis
import matplotlib.pyplot as plt
figs = pyutil.collections.OrderedDict()
import pymisca.ext as pyext
pyext.base__check()
# pyutil.shellexec('mkdir -p results/')
# pyhead.check__base()
def loadData():
import sklearn.datasets as skdat
data_digit = data = skdat.load_digits()
din = data['data']
data_digit.keys()
y_true = data_digit['target']
return din
tdf = loadData()
tdf = pd.DataFrame(tdf)
# assert 0
# ##### debugging!!!!!!!!!!!!
# tdf = pyutil.readData('http://172.26.114.34:81/static/results/0129__cluster__Brachy-RNA-all/mdl.npy').tolist().data
# ##### debugging!!!!!!!!!!!!
# lst = [75434668]
# betas = np.linspace(0.001,1.52, 25).tolist() + [1.52] * 25
# for i,r in enumerate(lst):
# def getBeta(i):
# betas = [_betas[i]] * 100
# return betas
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)
res = [worker((0, 1))]
# figs.update(res)
# N = 5
# _betas = np.linspace(0, 2.0, N)
# np.random.seed(0)
# lst = np.random.randint(100000000,size=(N))
# it = enumerate(lst)
# res = pyutil.mp_map(worker,it,n_cpu=NJOB)
# res = res[::60//5]
# figs.update(res)
pyutil.render__images(figs, )
import pymisca.ext as pyext
pyext.base__check()
suc, res = pyext.job__script(
pyext.base__file('BrachyPhoton/0131__dumpDataRNA__Brachy.py'),
# env=env,
)
assert suc, res
suc, res = pyext.job__script(
pyext.base__file('BrachyPhoton/0130__makeTracks-Brachy.py'),
# env=env,
)
assert suc, res