def test_separation():
tube = Tube()
chemComp = {"a/D": 3, "b/T": 2, "c/B": 1, "d/D": 5}
tube.chemComp = chemComp
r = Operator.separation(tube, 0.5)
print r
def test(self, cyc, HN, tst, tsExpt, ftest):
tsPerCyc = self.pm.get("tsPerCyc")
cls = self.pm.get("cls")
dvol = self.pm.get("dvol")
tag = self.pm.get("tag")
rTime = self.pm.get("rTime")
sepY = self.pm.get("sepY")
dThres = self.pm.get("dThres")
isRecordReact = self.pm.get("isRecordReact")
isDNATube = self.pm.get("isDNATube")
clsNum = len(cls)
# Prepare test Tubes
# print("\t\t\t\tPreparing test tubes...")
tsTubes = list()
for i in range(clsNum) :
c = self.testData[i][1][0][0]
lblPrf = "ts_cyc" + str(cyc)
if isDNATube :
tsTubes.extend(self.makeDNATubesFromData(lblPrf, "B", self.testData[i][0], c, tsPerCyc/2, tst, tsExpt))
else :
tsTubes.extend(self.makeTubesFromData(lblPrf, "B", self.testData[i][0], c, tsPerCyc/2, tst, tsExpt))
random.shuffle(tsTubes)
# Run Test
# print("\t\t\t\tRunning test reaction...")
testSummary = np.zeros((clsNum,clsNum))
for i in range(tsPerCyc) :
print("\t\t\t\t%dth test" % (i+1))
subHN = [None]*clsNum
DList = [None]*clsNum
for j in range(clsNum) :
ts = tsTubes[i]
subHN[j] = HN[j].copyTube(dvol)
subHN[j].setLabel("HN" + str(cls[j]) + "_Cycle" + str(cyc+1) + "_test" + str(i+1))
# print "\t\t\t\tPour test tube..."
subHN[j].addTube(ts)
# print "\t\t\t\tReacting..."
Operator.reactionSSA(subHN[j], rTime, tag, isRecordReact)
# print "\t\t\t\tElectrophoresis..."
DList[j] = Operator.separation(subHN[j], sepY)
csf = Classifier()
if isDNATube :
score, predict = csf.thresholdClassifyOnDNATube(DList, cls, dThres)
else :
score, predict = csf.thresholdClassifyOnTube(DList, cls, dThres)
testSummary[cls.index(predict)][cls.index(ts.cls)] += 1
tsTubes[i] = None
# Save Result
dm.saveTestSummaryLine(ftest, cyc, testSummary)
def run(self, trTubes, cyc, tst, tsExpt):
cls = self.pm.get("cls")
dim = self.pm.get("dim")
conc = self.pm.get("conc")
vol = self.pm.get("vol")
dvol = self.pm.get("dvol")
dThres = self.pm.get("dThres")
tag = self.pm.get("tag")
sepY = self.pm.get("sepY")
rThres = self.pm.get("rThres")
rTime = self.pm.get("rTime")
isDNATube = self.pm.get("isDNATube")
isRecordReact = self.pm.get("isRecordReact")
clsNum = len(cls)
dm = DataModule()
ftrain = dm.makeTrainSummaryFile(tag)
ftest = dm.makeTestSummaryFile(tag)
HN = [None] * clsNum
print("\t\tMaking Initial Random Hypernetworks...")
for i in range(clsNum) :
if isDNATube :
HN[i] = Operator.makeRandomLibraryOnDNATube(dim, conc, vol, rThres, "T")
else :
HN[i] = Operator.makeRandomLibraryOnTube(dim, conc, vol, rThres)
HN[i].setLabel("Hypernetworks" + str(cls[i]))
print("\t\tStart Learning Cycle...")
for i in range(cyc) :
start = timer()
summaryCycle = list()
summaryCycle.append(i+1)
for j in range(clsNum) :
if isDNATube :
summaryCycle.append(HN[j].getSpcNum("T"))
summaryCycle.append(HN[j].getConcSum("T"))
else :
summaryCycle.append(HN[j].getSpcNum())
summaryCycle.append(HN[j].getConcSum())
print("\t\t%dth Cycle" % (i+1))
subHN = [None] * clsNum
DList = [None] * clsNum
for j in range(clsNum) :
print("\t\t\tFor Class %d Hypernetwork" % cls[j])
tr = trTubes[i]
subHN[j] = HN[j].divideTube(dvol)
subHN[j].setLabel("HN" + str(cls[j]) + "_learningcycle" + str(i+1))
print "\t\t\tPour training tube..."
subHN[j].addTube(tr)
print "\t\t\tReacting..."
rTimeEnd = Operator.reactionSSA(subHN[j], rTime, tag, isRecordReact)
summaryCycle.append(rTimeEnd)
print "\t\t\tElectrophoresis..."
DList[j] = Operator.separation(subHN[j], sepY)
csf = Classifier()
if isDNATube :
score, predict = csf.thresholdClassifyOnDNATube(DList, cls, dThres)
else :
score, predict = csf.thresholdClassifyOnTube(DList, cls, dThres)
for j in range(clsNum) :
summaryCycle.append(score[j])
summaryCycle.append(predict)
summaryCycle.append(tr.cls)
print("\t\t\tFeedback...")
update = [None] * clsNum
for j in range(clsNum) :
if isDNATube :
DList[j] = Operator.PCR_DNA(DList[j])
update[j] = Operator.denaturationOnDNATube(DList[j], "T", dvol)
else :
DList[j] = Operator.PCR(DList[j])
update[j] = Operator.denaturationOnTube(DList[j], "T", dvol)
if predict == tr.cls :
for j in range(clsNum) :
if cls[j] == predict :
Operator.amplification(update[j], 8) #TODO: Tune multiply number
HN[j].addTube(update[j])
else :
Operator.amplification(update[j], 1)
HN[j].addTube(update[j])
else :
for j in range(clsNum) :
if cls[j] == predict :
Operator.amplification(update[j], 0)
HN[j].addTube(update[j])
else :
Operator.amplification(update[j], 1)
HN[j].addTube(update[j])
# Test with test data
# don't update Hypernetwork
print("\t\t\tTesting...")
self.test(i, HN, tst, tsExpt, ftest)
trTubes[i] = None # Discard used tube
end = timer()
dt = end-start
print("\t\t\ttime elapsed %f s" % dt)
summaryCycle.append(dt)
dm.saveTrainSummaryLine(ftrain, summaryCycle)
ftrain.close()
ftest.close()
