def make_prior(self):
D = self.D
K = self.K
alpha = self.D / 2.
diriAlpha = 0.1
name = self.name
self.prior = prior = pyutil.util_obj()
try:
tf.get_variable(name + '/prior', [1])
reuse = None
except:
reuse = True
print('reuse', reuse)
with tf.variable_scope(name, reuse=reuse):
uspan = [-1E5, 1E5]
##### Prior
prior.loc = edm.Normal(tf.zeros(D), tf.ones(D), sample_shape=K)
# prior.loc = edm.Uniform(*uspan,sample_shape=(K,D))
prior.scale_diag = edm.Uniform(*[0.001, 10.], sample_shape=(K, D))
prior.scale_perturb_factor = edm.Uniform(*uspan,
sample_shape=(K, D, 1))
# prior.scale_perturb_factor = edm.Normal(tf.zeros([1]), tf.ones([1]),
# sample_shape=(K,D,))
#
prior.concentration = edm.Uniform(*[0.01, 10.], sample_shape=(K, ))
# prior.concentration = edm.Uniform(*uspan,sample_shape=(K,))
prior.rate = edm.Uniform(*[0.01, 10.], sample_shape=(K, ))
prior.weight = pi = edm.Dirichlet(
float(diriAlpha) / K * tf.ones(K))
return prior
def make_post(self):
D = self.D
K = self.K
alpha = self.D / 2.
name = self.name
self.post = post = pyutil.util_obj()
try:
tf.get_variable(name + '/post', [1])
reuse = None
except:
reuse = True
print('reuse', reuse)
with tf.variable_scope(name, reuse=reuse):
uspan = [-1E5, 1E5]
##### Posterior
post.weight = ed.models.PointMass(
tf.nn.softmax(tf.get_variable("q_pi", [K])))
post.mu = ed.models.PointMass(tf.get_variable("q_mu", [K, D]))
post.scale_diag = edm.PointMass(
tf.nn.softplus(tf.get_variable('q_scale_diag', shape=[K,
D])), )
post.scale_perturb_factor = ed.models.PointMass(
(tf.get_variable("q_scale_perturb_factor", [K, D, 1])))
post.concentration = edm.PointMass(
tf.nn.softplus(tf.get_variable('concentration', shape=[K,
1])), )
post.rate = edm.PointMass(
tf.nn.softplus(tf.get_variable('rate', shape=[K, 1])), )
return post
def add_predictProba(glist):
mdl = pyutil.util_obj()
def f(vals):
res = np.nan_to_num(vals).astype(int)
res = pyutil.oneHot(res)
return res
mdl.predict_proba = f
glist.model = mdl
return glist
def fit_PCA(C, n_components=5, **kwargs):
mdl = skpca.PCA(n_components=n_components, **kwargs)
M = mdl.fit_transform(C)
resDict = {
'model': mdl,
'train_data': C,
'trans_data': M,
}
return pyutil.util_obj(**resDict)
def job__cluster__hpm(
tdf,
name='test0',
K=40,
meanNorm=1,
threshold=0.,
batchSize=500,
n_iter=3000,
silent=0,
NCORE=4,
randomState=0,
alpha=None,
weighted=True,
):
import pymisca.tensorflow_extra_.hyper_plane_mixture as hpm
hpm.tf.set_random_seed(randomState)
np.random.seed(randomState)
mdl = hpm.main(K=K,
NCORE=NCORE,
name=name,
meanNorm=meanNorm,
threshold=threshold,
weighted=weighted,
alpha=alpha)
if batchSize == 0 or batchSize is None:
batchMaker = None
# batchMaker = hpm.pytfu.batchMaker__random(batchSize=batchSize)
else:
batchMaker = hpm.pytfu.batchMaker__random(batchSize=batchSize)
res = mdl.fit(
tdf,
batchMaker=batchMaker,
n_iter=n_iter,
autoStop=0,
)
if not silent:
# import matplotlib.pyplot as plt
plt.plot(res)
cdict = pymod.cache__model4data(mdl, tdf)
# assert 0
mdl.post.__dict__.update(cdict)
np.save('params.npy', mdl.params)
res = mdl.params
res['mdl'] = mdl
return pyutil.util_obj(**res)
def worker__fluff(rec, ):
rec = pyutil.util_obj(**rec)
DIR = getattr(rec, 'DIR', '.')
ext = getattr(rec, 'ext', 'svg')
labels = getattr(rec, 'labels', None)
# ofname = rec.acc + '.svg'
ofname = '%s/%s.%s' % (DIR, rec.acc, ext)
interval = rec.interval
tracks = rec.tracks
annotation = rec.annotation
# ofname = bed.acc[i] + '.svg'
# interval = bed.interval[i]
ofname = sjob.fig__fluffProfile(interval,
tracks,
ofname=ofname,
annotation=annotation,
labels=labels)
return ofname
def job__cluster__vmf(
tdf,
K=30,
init_method='kmeans',
weighted=True,
n_iter=3000,
randomState=None,
nStart=15,
min_iters=50,
verbose=1,
callback=None,
silent=0,
sample_weights='sd',
):
import pymisca.model_collection.mixture_vmf as mod
np.random.seed(randomState)
mdl = mod.MixtureVMF(
K=K,
init_method=init_method,
weighted=weighted,
)
histLoss = mdl.fit(
tdf,
verbose=verbose,
callback=callback,
nStart=nStart,
n_iter=n_iter,
min_iters=min_iters,
sample_weights=sample_weights,
)
histLoss = -histLoss
if not silent:
# import matplotlib.pyplot as plt
plt.plot(histLoss)
cdict = pymod.cache__model4data(mdl, tdf)
cdict.update(mdl.params)
np.save('params.npy', cdict)
cdict['mdl'] = mdl
return pyutil.util_obj(**cdict)
def make_prior(self):
D = self.D
K = self.K
alpha = self.D/2.
# diriAlpha = self.K /10.
# diriAlpha = 1.
diriAlpha = 0.001
# diriAlpha = 0.00001
# diriAlpha = 0.0000000000000000000000000000000001
# diriAlpha = 10.
name = self.name
self.prior = prior = pyutil.util_obj()
try:
tf.get_variable(name+'/prior',[1])
reuse = None
except:
reuse = True
print ('reuse',reuse)
with tf.variable_scope(name, reuse=reuse):
uspan = [-1E5,1E5]
##### Prior
# prior.gamma_concentration = edm.Normal(tf.zeros(D), tf.ones(D), sample_shape=K)
# prior.loc = edm.Uniform(*uspan,sample_shape=(K,D))
prior.gamma_concentration = edm.Uniform(*[0.001,1000.],sample_shape=(K,))
prior.gamma_rate = edm.Uniform(*[0.001,100000.],sample_shape=(K,))
prior.vm_concentration = edm.Uniform(*[0.001,100000.],sample_shape=(K,))
prior.vm_direction = edm.Uniform(*[0.001,100000.],sample_shape=(K,D))
# prior.vm_direction = edm.Normal(tf.zeros(D), tf.ones(D), sample_shape=K)
# prior.weight = edm.Uniform(*[0.001,100000.],sample_shape=(K,))
prior.weight = pi = edm.Dirichlet( float(diriAlpha)/K * tf.ones(K) )
# prior.cat = edm.Categorical(weight = post.weight)
return prior
def make_post(self):
D = self.D
K = self.K
alpha = self.D/2.
name = self.name
self.post = post = pyutil.util_obj()
try:
tf.get_variable(name+'/post',[1])
reuse = None
except:
reuse = True
print ('reuse',reuse)
with tf.variable_scope(name, reuse=reuse):
uspan = [-1E5,1E5]
##### Posterior
i = -1
i += 1
# post.weight = edm.PointMass(
# tf.nn.softmax(
# tf.get_variable(str(i), shape=[K]),
# name = 'weight',
# # tf.Variable(name="q_pi",initial_value = self.random([K]) ),
# )
# )
post.weight = edm.PointMass(
tf.square(
tf.nn.l2_normalize(
tf.get_variable(str(i), shape=[K]),
),
name = 'weight',
# tf.Variable(name="q_pi",initial_value = self.random([K]) ),
)
)
# post.cat = edm.PointMass(
# tf.nn.softmax(
# tf.get_variable("cat",[K]),
# # tf.Variable(name="q_pi",initial_value = self.random([K]) ),
# )
# )
i += 1
post.gamma_concentration = edm.PointMass(
tf.nn.softplus(
# tf.Variable(name="concentration",initial_value = self.random([K]) ),
tf.get_variable(str(i),shape=[K,]),
name = 'gamma_concentration',
),
)
i += 1
post.gamma_rate = edm.PointMass(
tf.nn.softplus(
# tf.Variable(name="concentration",initial_value = self.random([K]) ),
tf.get_variable(str(i),shape=[K,]),
name = 'gamma_rate',
),
)
i += 1
post.vm_concentration = edm.PointMass(
0.0 + tf.nn.softplus(
10. - tf.nn.softplus(
# tf.Variable(name="concentration",initial_value = self.random([K]) ),
tf.get_variable(str(i),shape=[K,]),
),
name = 'vm_concentration',)
)
# post.vm_concentration = edm.PointMass(
# 10. * tf.nn.sigmoid(
# # tf.Variable(name="concentration",initial_value = self.random([K]) ),
# tf.get_variable('vm_concentration',shape=[K,])
# ),
# )
i += 1
post.vm_direction = edm.PointMass(
tf.nn.l2_normalize(
tf.get_variable(str(i), [K,D]),
axis = -1,
name = "vm_direction",
),
)
# post.rate = edm.PointMass(
# tf.nn.softplus(
# # tf.Variable(name="rate",initial_value = self.random([K]) ),
# tf.get_variable('rate',shape=[K,])
# ),
# )
return post
def clu2bed(segDF, ofname=None):
'''Must have columns: ('acc','pos','clu')
'''
segDF = segDF.reset_index()
# stdout,isFile = get__stdout(ofname)
stepSize = np.diff(segDF['pos'].values[:2], axis=0)[0]
vals = segDF[['clu', 'acc']].values
isDiff = (vals[1:] != vals[:-1]).any(axis=1)
segDF['isDiff'] = np.concatenate([[True], isDiff], axis=0)
it = (pyutil.util_obj(**vars(x)) for x in segDF.itertuples())
peak = pyutil.collections.OrderedDict((
('chrom', None),
('start', None),
('end', None),
('acc', None),
))
peaks = []
def savePeakStart():
peak['chrom'] = rec.acc
peak['start'] = rec.pos
return
def savePeakEnd():
# kk = loc
peak['end'] = oldPos + stepSize
peak['acc'] = 'summitPos%d' % ((peak['start'] + peak['end']) // 2)
assert peak['end'] > peak['start'], peak
# pyutil.ppJson(locals())
peaks.append(peak.copy())
# line = u'\t'.join(map(unicode,peak.values()))
# stdout.write(u'%s\n'%line)
# print peak
return
def changed():
if idx != 0:
if oldClu == 1:
savePeakEnd()
if rec.clu == 1:
if (oldClu == 0) | (oldAcc != rec.acc):
savePeakStart()
else:
if rec.clu == 1:
savePeakStart()
return
#### Starting the loop
oldClu = 0
for idx, rec in enumerate(it):
if (idx == 0):
changed()
elif (rec.clu != oldClu) or (rec.acc != oldAcc):
changed()
oldClu = rec.clu
oldPos = rec.pos
oldAcc = rec.acc
changed()
resDF = pd.DataFrame(peaks)
if ofname is not None:
try:
pyutil.to_tsv(
resDF,
ofname,
)
return ofname
except Exception as e:
print e
return resDF
def extract_bigwig(
bwFile,
bedFile,
stepSize=1,
mapChunk=None,
# span = None
shift=1,
# outIndex = None,
stranded=1,
):
''' Extracting a signal matrix for each bed region
'''
# assert NCORE == 1,'Multi-thread is slower here..., so dont! '
# assert stepSize == 1,'Not implemented'
with pybw.open(bwFile) as bw:
it = open(bedFile)
worker = pyutil.functools.partial(
extract_bigwig_worker,
bwFile=bwFile,
stepSize=stepSize,
stranded=stranded,
)
if 1 == 1:
res = map(worker, [it])
res = sum(res, [])
# pass
ids, out = zip(*res)
#### Replacing "None" and incomplete intervals
ref = next((item for item in out if item is not None), None)
assert ref is not None, 'Cannot find an reference shape, likely wrong chromosomes.\n\
bigwigFile:"%s" ' % bwFile
# L = len(ref)
# L = len(res) if span is None else span //stepSize
L = max(map(len, out))
lst = []
print '[L]=', L
for x in out:
if x is None:
y = [0.] * L
else:
Lx = len(x)
y = x + [0.] * (L - Lx)
lst += [y]
# out = [[0.]*L if x is None else x for x in out]
out = np.array(lst)
out = np.nan_to_num(out)
# MLEN = np.mean([len(x) for x in out])
MLEN = 'not set'
assert out.dtype != 'O', '''Unable to shape the matrix properly:
%s, %s ''' % (MLEN, [(type(x), x) for x in out if len(x) < MLEN])
out = pd.DataFrame(out).set_index([list(ids)])
cols = stepSize * (np.arange(
0,
out.shape[-1],
))
if shift:
mid = (L * stepSize) // 2
cols += -mid
out.columns = cols
# out.columns = (stepSize * np.arange(0, out.shape[-1], ))
# Do something with the values...
# out = ctMat.countMatrix.from_DataFrame(df=out)
# out.fname = bwFile
out.param = pyutil.util_obj()
out.param['bwFile'] = bwFile
out.param['bedFile'] = bedFile
return out
def init_model(self,D=None,K = None,alpha = 1.0):
self.D = D = self.D if D is None else D
assert D is not None
self.K = K = self.K if K is None else K
assert K is not None
# print (K)
uspan = [-1E5,1E5]
name = self.name
try:
tf.get_variable(name+'/test',[1])
reuse = None
except:
reuse = True
print reuse
prior = pyutil.util_obj()
post = pyutil.util_obj()
with tf.variable_scope(name, reuse=reuse):
##### Prior
prior.mu = edm.Normal(tf.zeros(D), tf.ones(D), sample_shape=K)
prior.scale_diag = edm.Uniform(*uspan,sample_shape=(K,D))
prior.scale_perturb_factor = edm.Uniform(*uspan,sample_shape=(K,D,1))
prior.concentration = edm.Uniform(*uspan,sample_shape=(K,1))
prior.rate = edm.Uniform(*uspan,sample_shape=(K,1))
# prio
# scale_perturb_factor = edm.Normal(
# loc=tf.zeros(1),
# scale=tf.ones(1),
# sample_shape=(K,D)
# )
# prior.weight = edm.Dirichlet(tf.ones(K))
prior.weight = pi = edm.Dirichlet( float(alpha)/K * tf.ones(K) )
##### Posterior
post.weight = ed.models.PointMass(
tf.nn.softmax(
tf.get_variable("q_pi", [K])
)
)
post.mu = ed.models.PointMass(
tf.get_variable("q_mu", [K,D])
)
post.scale_diag = edm.PointMass(
tf.nn.softplus(
tf.get_variable('q_scale_diag',shape=[K,D])
),
)
post.scale_perturb_factor = ed.models.PointMass(
(
tf.get_variable("q_scale_perturb_factor", [K,D,1])
)
)
post.concentration = edm.PointMass(
tf.nn.softplus(
tf.get_variable('concentration',shape=[K,1])
),
)
post.rate = edm.PointMass(
tf.nn.softplus(
tf.get_variable('rate',shape=[K,1])
),
)
self.prior = prior
self.post = post
##### Dictonary for constructing self.emDist(**self.param)
self.em_key =[
'scale_diag',
'scale_perturb_factor',
'concentration',
'rate',
]
self.mix_key = [
'weight',
]
self.param_key = (self.em_key +
self.mix_key)
# self.emKey = ['loc','scale_diag','scale_perturb_factor']
self.paramDict = {getattr(prior,name):
getattr(post,name) for name in self.param_key}
# self.paramDict = {}
# self.priorDict = {v[0]:v[1] for v in self.param.values()}
# self.priorDict.update({self.pi:self.q_pi})
# self.postDict = {k:v[1] for k,v in self.param.items()}
### Prior components
cDicts = [
{key: v[k]
for key,v in prior.__dict__.items()
if key in self.em_key}
for k in range(K)]
self.components = [self.emDist(**d) for d in cDicts]
### Posterior generative
# edm.Mixture
cDicts = [
{key: v[k]
for key,v in post.__dict__.items()
if key in self.em_key}
for k in range(K)]
self.postComponents = [self.emDist(**d) for d in cDicts]
# edm.ParamMixture
# self.x_post = em = self.emDist(**{k:v for k,v in self.post.__dict__.items()
# if k in self.em_key})
self.initialised = True; return self
