def main_ordinal(fileName, clf, k=3, preproc=False, evalFunc = Util.brierScore):
data = np.loadtxt(fname=fileName, delimiter=",")
hda = HighDimAlg(clf, "ordinal", kTimes=k)
scoreList = []
nRunning = 10
for _ in range(nRunning):
trainX, trainY, testX, testY = Util.readData(data, False, preproc)
hda.fit(trainX, trainY)
s = evalFunc(hda.predict(testX), testY)
print("score = ", s)
scoreList.append(s)
score = sum(scoreList)/nRunning
print("mean score = ", score)
with open("log/HDA_test_log_ordinal.txt", 'a') as f:
log = ','.join([dt.now().strftime("%Y/%m/%d %H:%M"), str(fileName), \
"GLS", str(k), \
str(preproc), evalFunc.__name__, str(score)])
f.write(log + '\n')
def worker(chosenNum):
print("Current chosen number: ", chosenNum)
# global validateX, validateY, trainX, trainY, regs, xis, lock, clfs
clf = GLS()
hda = HighDimAlg(clf, "convolve")
hda.setWidth(28)
hda.setKernels([(2,2), (4,4), (7,7)])
target = validateY.copy()
target[target == chosenNum] = 999
target[target != 999] = 0
target[target == 999] = 1
bestScore, bestXi, bestReg = 1e10, None, None
for xi in xis:
hda.setXi(xi)
score, reg = Util.crossValidate(hda, validateX, target, \
Util.brierScore, 2, "Regular", regs)
if score < bestScore:
bestScore, bestXi, bestReg = score, xi, reg
print("bestScore, bestXi, bestReg = ", bestScore, bestXi, bestReg)
hda.setXi(bestXi)
hda.setRegular(bestReg)
target = trainY.copy()
target[target == chosenNum] = 999
target[target != 999] = 0
target[target == 999] = 1
hda.fit(trainX, target)
res = {}
res['num'] = chosenNum
res['clf'] = hda
res['validateScore'] = bestScore
lock.acquire()
try:
clfs.append(res)
finally:
lock.release()