def regen(genes):
genes = list(genes)
l = len(genes)
random.shuffle(genes)
def r(g):
global i
f.getGene(g)
print("%d/%d" % (i,l))
i += 1
parallel_map_dill(2, r, (g.strip() for g in genes))
def regen(genes):
genes = list(genes)
l = len(genes)
random.shuffle(genes)
def r(g):
global i
f.getGene(g)
print("%d/%d" % (i, l))
i += 1
parallel_map_dill(2, r, (g.strip() for g in genes))
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.")
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.")
