continue
source = dh.preProcessSamplesCyTOFData(source)
# De-noising the source.
denoiseSource = dae.predictDAE(source, DAE, denoise)
denoiseSource, _ = dh.standard_scale(denoiseSource,
preprocessor=preprocessor)
# Predict the cell type of the source.
print('Run the classifier on source ', str(sourceIndex),
'without calibration')
start = time.time()
acc[i,
0], F1[i,
0], predLabel = net.prediction(denoiseSource, mode, i,
cellClassifier)
end = time.time()
print(end - start)
if not os.path.isdir(os.path.dirname(fname)):
os.mkdir(os.path.dirname(fname))
np.savetxt(fname, predLabel, delimiter=',', fmt='%i')
'''
sourceInds = np.random.randint(low=0, high=source.X.shape[0], size=1000)
targetInds = np.random.randint(low=0, high=target.X.shape[0], size=1000)
mmd_before[i] = K.eval(cf.MMD(denoiseSource.X, denoiseTarget.X).cost(
K.variable(value=denoiseSource.X[sourceInds]),
K.variable(value=denoiseTarget.X[targetInds])) )
# f = open(dataPath + "/predlabel_nocal" + str(sourceIndex) + ".csv", 'w')
# for item in predLabel:
sourceIndex = testIndex[i]
source = dh.loadDeepCyTOFData(dataPath, sourceIndex, relevantMarkers, mode)
if choice!=5:
source = dh.preProcessSamplesCyTOFData(source)
# De-noising the source.
denoiseSource = dae.predictDAE(source, DAE, denoise)
denoiseSource, _ = dh.standard_scale(denoiseSource,
preprocessor = preprocessor)
# Predict the cell type of the source.
print('Run the classifier on source ', str(sourceIndex),
'without calibration')
start = tm.time()
acc[i,0], F1[i,0], predLabel = net.prediction(denoiseSource,
mode, i,
cellClassifier)
Testing_time[i,0] = tm.time()-start
sourceInds = np.random.randint(low=0, high = source.X.shape[0], size = 1000)
targetInds = np.random.randint(low=0, high = target.X.shape[0], size = 1000)
mmd_before[i] = K.eval(cf.MMD(denoiseSource.X, denoiseTarget.X).cost(
K.variable(value=denoiseSource.X[sourceInds]),
K.variable(value=denoiseTarget.X[targetInds])))
print('MMD before: ', str(mmd_before[i]))
if isCalibrate:
if loadModel:
calibMMDNet = mmd.loadModel(denoiseTarget, denoiseSource,
sourceIndex, predLabel, dataSet[choice])
calibrateSource = Sample(calibMMDNet.predict(denoiseSource.X),
denoiseSource.y)
calibMMDNet = None
# Pre-process sample.
print('Pre-process sample ', str(i + 1))
sample = dh.preProcessSamplesCyTOFData(sample)
sample, preprocessor = dh.standard_scale(sample, preprocessor=None)
# Split data into training and testing.
print('Split data into training and testing.')
trainSample, testSample = dh.splitData(sample, test_size=.75)
# Train a feed-forward neural net classifier on the training data.
print('Train a feed-forward neural net classifier on the training data.')
classifier = net.trainClassifier(trainSample,
dataSet[choice],
i,
hiddenLayersSizes,
activation=activation,
l2_penalty=l2_penalty)
# Run the classifier on the testing data.
print('Run the classifier on the testing data.')
acc[i - 1], F1[i - 1], _ = net.prediction(testSample, dataSet[choice], i,
classifier)
'''
Output the overall results.
'''
CI = np.zeros(10000)
for i in range(10000):
CI[i] = np.mean(np.random.choice(F1, size=30))
CI = np.sort(CI)
print(dataSet[choice], ', ', np.mean(CI), ' (', CI[250], CI[9750], ')')