def __init__(self, inputFileName, namespace='molecular_function'):
"""Constructor, reads and parses the ontology OBO file."""
debug("Reading ontology file %s... " % inputFileName)
self.root = None
self.namespace = namespace
ontology = defaultdict(lambda: defaultdict(list))
self.inputFileName = Path(inputFileName)
with self.inputFileName.open() as go:
terms = groupby(go.read().splitlines(), lambda x: x != '')
for b, term in terms:
term = list(term)
if not b or term[0] != '[Term]': continue
nonlists = ('id', 'def', 'name', 'namespace', 'is_obsolete')
# Do some deciphering here...
term = defaultdict(
list,
[(a, [y[1]
for y in b][0 if a in nonlists else slice(None)])
for a, b in groupby([x.split(': ', 1)
for x in term[1:]], lambda x: x[0])])
# Filter terms by namespace, discard obsolete terms
if term['namespace'] != namespace or term[
'is_obsolete'] == 'true':
continue
# Decide root node
if term['name'] == namespace:
assert self.root is None
self.root = term['id']
# Save the term to ontology
ontology[term['id']]['name'] = term['name'].replace(
'_', ' ') # FIXME KDYBY BLBLO, ODEBRAT replace
for ref in term['is_a']:
refid, refname = ref.split(' ! ')
ontology[refid]['children'].append(term['id'])
ontology[term['id']]['parents'].append(refid)
# This is used by Bayes nets
ontology[term['id']]['node'] = defaultdict(
dict) # fold : clfName : node
ontology[term['id']]['clf'] = defaultdict(
dict) # fold : clfName : Classifier
self.ontology = {**ontology}
self.associations = None
self.geneFactory = GeneFactory()
debug("Initialized ontology for file %s... " % inputFileName)
def __init__(self, inputFileName, namespace = 'molecular_function'):
"""Constructor, reads and parses the ontology OBO file."""
debug("Reading ontology file %s... " % inputFileName)
self.root = None
self.namespace = namespace
ontology = defaultdict(lambda: defaultdict(list))
self.inputFileName = Path(inputFileName)
with self.inputFileName.open() as go:
terms = groupby(go.read().splitlines(), lambda x: x != '')
for b, term in terms:
term = list(term)
if not b or term[0] != '[Term]': continue
nonlists = ('id', 'def', 'name', 'namespace', 'is_obsolete')
# Do some deciphering here...
term = defaultdict(list, [
(a, [y[1] for y in b][0 if a in nonlists else slice(None)])
for a,b in groupby(
[x.split(': ', 1) for x in term[1:]],
lambda x: x[0])])
# Filter terms by namespace, discard obsolete terms
if term['namespace'] != namespace or term['is_obsolete'] == 'true':
continue
# Decide root node
if term['name'] == namespace:
assert self.root is None
self.root = term['id']
# Save the term to ontology
ontology[term['id']]['name'] = term['name'].replace('_', ' ') # FIXME KDYBY BLBLO, ODEBRAT replace
for ref in term['is_a']:
refid, refname = ref.split(' ! ')
ontology[refid]['children'].append(term['id'])
ontology[term['id']]['parents'].append(refid)
# This is used by Bayes nets
ontology[term['id']]['node'] = defaultdict(dict) # fold : clfName : node
ontology[term['id']]['clf'] = defaultdict(dict) # fold : clfName : Classifier
self.ontology = {**ontology}
self.associations = None
self.geneFactory = GeneFactory()
debug("Initialized ontology for file %s... " % inputFileName)
class Ontology:
""" Class representing the Gene Onotology graph."""
def __init__(self, inputFileName, namespace = 'molecular_function'):
"""Constructor, reads and parses the ontology OBO file."""
debug("Reading ontology file %s... " % inputFileName)
self.root = None
self.namespace = namespace
ontology = defaultdict(lambda: defaultdict(list))
self.inputFileName = Path(inputFileName)
with self.inputFileName.open() as go:
terms = groupby(go.read().splitlines(), lambda x: x != '')
for b, term in terms:
term = list(term)
if not b or term[0] != '[Term]': continue
nonlists = ('id', 'def', 'name', 'namespace', 'is_obsolete')
# Do some deciphering here...
term = defaultdict(list, [
(a, [y[1] for y in b][0 if a in nonlists else slice(None)])
for a,b in groupby(
[x.split(': ', 1) for x in term[1:]],
lambda x: x[0])])
# Filter terms by namespace, discard obsolete terms
if term['namespace'] != namespace or term['is_obsolete'] == 'true':
continue
# Decide root node
if term['name'] == namespace:
assert self.root is None
self.root = term['id']
# Save the term to ontology
ontology[term['id']]['name'] = term['name'].replace('_', ' ') # FIXME KDYBY BLBLO, ODEBRAT replace
for ref in term['is_a']:
refid, refname = ref.split(' ! ')
ontology[refid]['children'].append(term['id'])
ontology[term['id']]['parents'].append(refid)
# This is used by Bayes nets
ontology[term['id']]['node'] = defaultdict(dict) # fold : clfName : node
ontology[term['id']]['clf'] = defaultdict(dict) # fold : clfName : Classifier
self.ontology = {**ontology}
self.associations = None
self.geneFactory = GeneFactory()
debug("Initialized ontology for file %s... " % inputFileName)
def genTranslations(self):
return {self.ontology[term]['name'] : term for term in self.ontology}
def setAssociations(self, associations, attrname = 'associations'):
associations.translations = self.genTranslations()
associations.ontology = self
self.__setattr__(attrname, associations)
associations.transitiveClosure()
def _termDepth(self, term):
#if term == self.root: return 0
parents = self.ontology[term]['parents']
if len(parents) == 0: return 0
return 1 + max(map(self._termDepth, parents))
def deleteSmallTerms(self, lb):
""" Delete terms which don't have enough associations.
Do not call this prior to calling transitiveClosure()"""
notEnough = self.termsByDepth(terms = (term for term in self.ontology if len(self.associations[term]) < lb))
for term in notEnough:
if term in self.associations.associations:
del self.associations.associations[term]
if hasattr(self, 'reserved') and term in self.reserved.associations:
del self.reserved.associations[term]
for d1, d2 in (('parents', 'children'),('children','parents')):
for parent in self.ontology[term][d1]:
if term in self.ontology[parent][d2]:
self.ontology[parent][d2].remove(term)
del self.ontology[term]
#debug("Deleted %d terms with not enough associations. Left %d terms." % (len(notEnough), len(self.ontology)))
def jsonExport(self):
"""Export generated onotology in JSON"""
with (self.inputFileName.parent / (self.inputFileName.stem + '.json')).open('w') as output:
json.dump(self.ontology, output)
def dotExport(self):
""" Export as a graph to the DOT format for graphical presentation."""
debug("Exporting ontology to dot.")
def nodename(t): return t.replace(":", "")
for direction in ("parents", "children"):
dotFile = self.inputFileName.parent / (self.inputFileName.stem +"_"+direction+ '.dot')
with dotFile.open('w') as output:
print("digraph ontology {", file=output)
print('overlap="false";', file=output)
print('root="%s";' % nodename(self.root), file=output)
for term, props in self.ontology.items():
name = props['name']
if not name:
continue
if ',' in name:
name = name.replace(', ', r',\n')
else:
name = name.replace('binding', r'\nbinding').replace('activity',r'\nactivity').replace(r' \n',r'\n')
print('%s [fontsize=8,label="%s"]' % (nodename(term), name), file=output)
for term, props in self.ontology.items():
for related in props[direction]:
print('%s -> %s' % (nodename(term), nodename(related)), file=output)
print("}", file=output)
for fmt in ('ps', 'png'):
outFile = dotFile.parent / (dotFile.stem + '.' + fmt)
#print(" ".join(['dot', '-T'+fmt, str(dotFile), '-o', str(outFile)]))
try:
subprocess.Popen(['dot', '-T'+fmt, str(dotFile), '-o', str(outFile).replace(".","_dot.")])
subprocess.Popen(['twopi', '-T'+fmt, str(dotFile), '-o', str(outFile).replace(".","_twopi.")])
except IOError:
pass
debug("Finished dot export.")
def overView(self):
terms = self.termsByDepth(False)
total = len(self.associations[self.root])
for term in terms:
name = self.ontology[term]['name']
asoc = len(self.associations[term])
ratio = asoc / total
children = ", ".join((repr(self[ch]['name']) for ch in self.ontology[term]['children']))
depth = self._termDepth(term)
print("%s\t%d\t%.2f\t%d\t%s\t%s"% (term, asoc, ratio, depth, name, children), file=sys.stderr)
def __iter__(self):
"""Iterate over the underlying dict."""
return self.ontology.__iter__()
def __getitem__(self, item):
"""An instance of this class can be indexed by GO terms or their names."""
if item in self.ontology:
return self.ontology[item]
term = self.getTermByName(item)
if term in self.ontology:
return self.ontology[term]
raise KeyError(item)
def display(self, lb = 0):
for term in self.ontology:
numTerms = len(self.associations[term])
children = ", ".join(self.ontology[term]['children'])
if numTerms >= lb:
#print(term, self.ontology[term]['name'], numTerms, "->", children)
print(term, self.ontology[term]['name'], numTerms)
def termsByDepth(self, leavesFirst = True, terms = None):
if terms is None:
terms = self.ontology.keys()
m = -1 if leavesFirst else 1
return sorted(terms, key = lambda x: (m*self._termDepth(x), x))
def generateExamplesUnified(self):
#return
debug("Generating unified datasets.")
terms = self.termsByDepth(False)
#rootname = self.ontology[self.root]['name']
with ExitStack() as stack: # Closes all files when exited
files = [(term, stack.enter_context((getTermPath(term) / 'dataset.txt').open('w')))
for term
in (self[t]['name'] for t in self.ontology.keys())
]#if term != rootname]
#for i, geneName in enumerate(self.genes):
for geneName in self.genes:
#debug("%d. Writing gene %s." % (i, geneName))
gene = self.geneFactory.getGene(geneName)
repg = ", ".join(gene.logicalRepresentation())
for term, output in files:
if geneName not in self.associations[term]:
term = '~'+term
e = '"%s" %s' % (term, repg)
print(e, file=output)
def getTermByName(self, name):
"""Returns human-readable name of the given GO term."""
for k,v in self.ontology.items():
if v['name'] == name:
return k
raise KeyError('Name %s is not in the ontology!' % name)
def completeTest(self, treelikerArgs, processes = 1):
self.generateExamplesUnified()
bestClassifiers = []
terms = self.termsByDepth() # This sorting is needed later in bnet learning
treeliker = TreeLikerWrapper(self, *treelikerArgs)
def processTerm(term):
return term, treeliker.runTermTest(term)
nets = defaultdict(dict)
allresults = tuple(parallel_map_dill(processes, processTerm, terms))
combis = set()
for term, learned in allresults:
for clfName, i in learned:
combis.add((term,clfName))
#for clf, X_train, y_train, X_test, y_test, X_validation, y_validation, g_train, g_test, g_validation in folds:
if clfName in nets[i]:
net = nets[i][clfName]
else:
net = BayesNet(i, clfName, self)
nets[i][clfName] = net
net.generateCPD(term)#, clf, X_train, y_train, X_test, y_test, X_validation, y_validation, g_train, g_test, g_validation)
for i, byClf in sorted(nets.items()):
for clfName, net in byClf.items():
net.bake()
net.predict()
debug("Generating plots.")
#for term, learned in allresults:
# for clfName, folds in learned.items():
plt.figure(figsize = (6,12))
for term,clfName in combis:
plt.clf()
termN = self[term]['name']
cvdir = getTermPath(termN)
#folds2 = [(nets[i][clfName].nodeAsClf(term),)+f[1:] for i,f in enumerate(folds)]
s1 = plt.subplot(211, adjustable='box', aspect=1)
s1.axis('equal')
#s1.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plotRoc(termN, clfName, termN)
s2 = plt.subplot(212, adjustable='box', aspect=1)
s2.axis('equal')
#s2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plotRoc(termN, clfName, "Bayes correction",
clfs = (nets[i][clfName].nodeAsClf(term) for i in range(NUM_FOLDS) ))
#plotRoc("Bayes correction", folds2)
print(str(cvdir/(clfName.replace(" ","_")+'_roc.png')))
plt.savefig(str(cvdir/(clfName.replace(" ","_")+'_roc.png')))
plt.savefig(str(cvdir/(clfName.replace(" ","_")+'_roc.ps')))
debug("Finished complete test.")
class Ontology:
""" Class representing the Gene Onotology graph."""
def __init__(self, inputFileName, namespace='molecular_function'):
"""Constructor, reads and parses the ontology OBO file."""
debug("Reading ontology file %s... " % inputFileName)
self.root = None
self.namespace = namespace
ontology = defaultdict(lambda: defaultdict(list))
self.inputFileName = Path(inputFileName)
with self.inputFileName.open() as go:
terms = groupby(go.read().splitlines(), lambda x: x != '')
for b, term in terms:
term = list(term)
if not b or term[0] != '[Term]': continue
nonlists = ('id', 'def', 'name', 'namespace', 'is_obsolete')
# Do some deciphering here...
term = defaultdict(
list,
[(a, [y[1]
for y in b][0 if a in nonlists else slice(None)])
for a, b in groupby([x.split(': ', 1)
for x in term[1:]], lambda x: x[0])])
# Filter terms by namespace, discard obsolete terms
if term['namespace'] != namespace or term[
'is_obsolete'] == 'true':
continue
# Decide root node
if term['name'] == namespace:
assert self.root is None
self.root = term['id']
# Save the term to ontology
ontology[term['id']]['name'] = term['name'].replace(
'_', ' ') # FIXME KDYBY BLBLO, ODEBRAT replace
for ref in term['is_a']:
refid, refname = ref.split(' ! ')
ontology[refid]['children'].append(term['id'])
ontology[term['id']]['parents'].append(refid)
# This is used by Bayes nets
ontology[term['id']]['node'] = defaultdict(
dict) # fold : clfName : node
ontology[term['id']]['clf'] = defaultdict(
dict) # fold : clfName : Classifier
self.ontology = {**ontology}
self.associations = None
self.geneFactory = GeneFactory()
debug("Initialized ontology for file %s... " % inputFileName)
def genTranslations(self):
return {self.ontology[term]['name']: term for term in self.ontology}
def setAssociations(self, associations, attrname='associations'):
associations.translations = self.genTranslations()
associations.ontology = self
self.__setattr__(attrname, associations)
associations.transitiveClosure()
def _termDepth(self, term):
#if term == self.root: return 0
parents = self.ontology[term]['parents']
if len(parents) == 0: return 0
return 1 + max(map(self._termDepth, parents))
def deleteSmallTerms(self, lb):
""" Delete terms which don't have enough associations.
Do not call this prior to calling transitiveClosure()"""
notEnough = self.termsByDepth(
terms=(term for term in self.ontology
if len(self.associations[term]) < lb))
for term in notEnough:
if term in self.associations.associations:
del self.associations.associations[term]
if hasattr(self,
'reserved') and term in self.reserved.associations:
del self.reserved.associations[term]
for d1, d2 in (('parents', 'children'), ('children', 'parents')):
for parent in self.ontology[term][d1]:
if term in self.ontology[parent][d2]:
self.ontology[parent][d2].remove(term)
del self.ontology[term]
#debug("Deleted %d terms with not enough associations. Left %d terms." % (len(notEnough), len(self.ontology)))
def jsonExport(self):
"""Export generated onotology in JSON"""
with (self.inputFileName.parent /
(self.inputFileName.stem + '.json')).open('w') as output:
json.dump(self.ontology, output)
def dotExport(self):
""" Export as a graph to the DOT format for graphical presentation."""
debug("Exporting ontology to dot.")
def nodename(t):
return t.replace(":", "")
for direction in ("parents", "children"):
dotFile = self.inputFileName.parent / (self.inputFileName.stem +
"_" + direction + '.dot')
with dotFile.open('w') as output:
print("digraph ontology {", file=output)
print('overlap="false";', file=output)
print('root="%s";' % nodename(self.root), file=output)
for term, props in self.ontology.items():
name = props['name']
if not name:
continue
if ',' in name:
name = name.replace(', ', r',\n')
else:
name = name.replace('binding', r'\nbinding').replace(
'activity', r'\nactivity').replace(r' \n', r'\n')
print('%s [fontsize=8,label="%s"]' %
(nodename(term), name),
file=output)
for term, props in self.ontology.items():
for related in props[direction]:
print('%s -> %s' % (nodename(term), nodename(related)),
file=output)
print("}", file=output)
for fmt in ('ps', 'png'):
outFile = dotFile.parent / (dotFile.stem + '.' + fmt)
#print(" ".join(['dot', '-T'+fmt, str(dotFile), '-o', str(outFile)]))
try:
subprocess.Popen([
'dot', '-T' + fmt,
str(dotFile), '-o',
str(outFile).replace(".", "_dot.")
])
subprocess.Popen([
'twopi', '-T' + fmt,
str(dotFile), '-o',
str(outFile).replace(".", "_twopi.")
])
except IOError:
pass
debug("Finished dot export.")
def overView(self):
terms = self.termsByDepth(False)
total = len(self.associations[self.root])
for term in terms:
name = self.ontology[term]['name']
asoc = len(self.associations[term])
ratio = asoc / total
children = ", ".join((repr(self[ch]['name'])
for ch in self.ontology[term]['children']))
depth = self._termDepth(term)
print("%s\t%d\t%.2f\t%d\t%s\t%s" %
(term, asoc, ratio, depth, name, children),
file=sys.stderr)
def __iter__(self):
"""Iterate over the underlying dict."""
return self.ontology.__iter__()
def __getitem__(self, item):
"""An instance of this class can be indexed by GO terms or their names."""
if item in self.ontology:
return self.ontology[item]
term = self.getTermByName(item)
if term in self.ontology:
return self.ontology[term]
raise KeyError(item)
def display(self, lb=0):
for term in self.ontology:
numTerms = len(self.associations[term])
children = ", ".join(self.ontology[term]['children'])
if numTerms >= lb:
#print(term, self.ontology[term]['name'], numTerms, "->", children)
print(term, self.ontology[term]['name'], numTerms)
def termsByDepth(self, leavesFirst=True, terms=None):
if terms is None:
terms = self.ontology.keys()
m = -1 if leavesFirst else 1
return sorted(terms, key=lambda x: (m * self._termDepth(x), x))
def generateExamplesUnified(self):
#return
debug("Generating unified datasets.")
terms = self.termsByDepth(False)
#rootname = self.ontology[self.root]['name']
with ExitStack() as stack: # Closes all files when exited
files = [
(term,
stack.enter_context(
(getTermPath(term) / 'dataset.txt').open('w')))
for term in (self[t]['name'] for t in self.ontology.keys())
] #if term != rootname]
#for i, geneName in enumerate(self.genes):
for geneName in self.genes:
#debug("%d. Writing gene %s." % (i, geneName))
gene = self.geneFactory.getGene(geneName)
repg = ", ".join(gene.logicalRepresentation())
for term, output in files:
if geneName not in self.associations[term]:
term = '~' + term
e = '"%s" %s' % (term, repg)
print(e, file=output)
def getTermByName(self, name):
"""Returns human-readable name of the given GO term."""
for k, v in self.ontology.items():
if v['name'] == name:
return k
raise KeyError('Name %s is not in the ontology!' % name)
def completeTest(self, treelikerArgs, processes=1):
self.generateExamplesUnified()
bestClassifiers = []
terms = self.termsByDepth(
) # This sorting is needed later in bnet learning
treeliker = TreeLikerWrapper(self, *treelikerArgs)
def processTerm(term):
return term, treeliker.runTermTest(term)
nets = defaultdict(dict)
allresults = tuple(parallel_map_dill(processes, processTerm, terms))
combis = set()
for term, learned in allresults:
for clfName, i in learned:
combis.add((term, clfName))
#for clf, X_train, y_train, X_test, y_test, X_validation, y_validation, g_train, g_test, g_validation in folds:
if clfName in nets[i]:
net = nets[i][clfName]
else:
net = BayesNet(i, clfName, self)
nets[i][clfName] = net
net.generateCPD(
term
) #, clf, X_train, y_train, X_test, y_test, X_validation, y_validation, g_train, g_test, g_validation)
for i, byClf in sorted(nets.items()):
for clfName, net in byClf.items():
net.bake()
net.predict()
debug("Generating plots.")
#for term, learned in allresults:
# for clfName, folds in learned.items():
plt.figure(figsize=(6, 12))
for term, clfName in combis:
plt.clf()
termN = self[term]['name']
cvdir = getTermPath(termN)
#folds2 = [(nets[i][clfName].nodeAsClf(term),)+f[1:] for i,f in enumerate(folds)]
s1 = plt.subplot(211, adjustable='box', aspect=1)
s1.axis('equal')
#s1.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plotRoc(termN, clfName, termN)
s2 = plt.subplot(212, adjustable='box', aspect=1)
s2.axis('equal')
#s2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plotRoc(termN,
clfName,
"Bayes correction",
clfs=(nets[i][clfName].nodeAsClf(term)
for i in range(NUM_FOLDS)))
#plotRoc("Bayes correction", folds2)
print(str(cvdir / (clfName.replace(" ", "_") + '_roc.png')))
plt.savefig(str(cvdir / (clfName.replace(" ", "_") + '_roc.png')))
plt.savefig(str(cvdir / (clfName.replace(" ", "_") + '_roc.ps')))
debug("Finished complete test.")
