'''
Train the feed-forward classifier on (de-noised) target.
'''
denoiseTarget, preprocessor = dh.standard_scale(denoiseTarget,
preprocessor = None)
if loadModel:
from keras.models import load_model
cellClassifier = load_model(os.path.join(io.DeepLearningRoot(),
'savemodels/' + dataSet[choice] +
'/cellClassifier.h5'))
else:
print('Train the classifier on de-noised Target')
cellClassifier = net.trainClassifier(denoiseTarget, mode, refSampleInd,
hiddenLayersSizes,
activation,
l2_penalty,
dataSet[choice])
end = tm.time()
print('Training time: ' + str(end - start))
'''
Test the performance with and without calibration.
'''
# Generate the output table.
dim = 2 if isCalibrate else 1
acc = np.zeros((testIndex.size, dim), np.float16)
F1 = np.zeros((testIndex.size, dim), np.float16)
Testing_time = np.zeros((testIndex.size, dim), np.float16)
mmd_before = np.zeros(testIndex.size)
mmd_after = np.zeros(testIndex.size)
skip_header=1)
# 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], ')')