medgan.py

import sys, time, argparse
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from tensorflow.contrib.layers import l2_regularizer
from tensorflow.contrib.layers import batch_norm

_VALIDATION_RATIO = 0.1


class Medgan(object):
    def __init__(self,
                 dataType='binary',
                 inputDim=615,
                 embeddingDim=128,
                 randomDim=128,
                 generatorDims=(128, 128),
                 discriminatorDims=(256, 128, 1),
                 compressDims=(),
                 decompressDims=(),
                 bnDecay=0.99,
                 l2scale=0.001):
        self.inputDim = inputDim
        self.embeddingDim = embeddingDim
        self.generatorDims = list(generatorDims) + [embeddingDim]
        self.randomDim = randomDim
        self.dataType = dataType

        if dataType == 'binary':
            self.aeActivation = tf.nn.tanh
        else:
            self.aeActivation = tf.nn.relus

        self.generatorActivation = tf.nn.relu
        self.discriminatorActivation = tf.nn.relu
        self.discriminatorDims = discriminatorDims
        self.compressDims = list(compressDims) + [embeddingDim]
        self.decompressDims = list(decompressDims) + [inputDim]
        self.bnDecay = bnDecay
        self.l2scale = l2scale

    def loadData(self, dataPath=''):
        data = np.load(dataPath)

        if self.dataType == 'binary':
            data = np.clip(data, 0, 1)

        trainX, validX = train_test_split(data, test_size=_VALIDATION_RATIO, random_state=0)
        return trainX, validX

    def buildAutoencoder(self, x_input):
        decodeVariables = {}
        with tf.variable_scope('autoencoder', regularizer=l2_regularizer(self.l2scale)):
            tempVec = x_input
            tempDim = self.inputDim
            i = 0
            for compressDim in self.compressDims:
                W = tf.get_variable('aee_W_'+str(i), shape=[tempDim, compressDim])
                b = tf.get_variable('aee_b_'+str(i), shape=[compressDim])
                tempVec = self.aeActivation(tf.add(tf.matmul(tempVec, W), b))
                tempDim = compressDim
                i += 1
    
            i = 0
            for decompressDim in self.decompressDims[:-1]:
                W = tf.get_variable('aed_W_'+str(i), shape=[tempDim, decompressDim])
                b = tf.get_variable('aed_b_'+str(i), shape=[decompressDim])
                tempVec = self.aeActivation(tf.add(tf.matmul(tempVec, W), b))
                tempDim = decompressDim
                decodeVariables['aed_W_'+str(i)] = W
                decodeVariables['aed_b_'+str(i)] = b
                i += 1
            W = tf.get_variable('aed_W_'+str(i), shape=[tempDim, self.decompressDims[-1]])
            b = tf.get_variable('aed_b_'+str(i), shape=[self.decompressDims[-1]])
            decodeVariables['aed_W_'+str(i)] = W
            decodeVariables['aed_b_'+str(i)] = b

            if self.dataType == 'binary':
                x_reconst = tf.nn.sigmoid(tf.add(tf.matmul(tempVec,W),b))
                loss = tf.reduce_mean(-tf.reduce_sum(x_input * tf.log(x_reconst + 1e-12) + (1. - x_input) * tf.log(1. - x_reconst + 1e-12), 1), 0)
            else:
                x_reconst = tf.nn.relu(tf.add(tf.matmul(tempVec,W),b))
                loss = tf.reduce_mean((x_input - x_reconst)**2)
            
        return loss, decodeVariables

    def buildGenerator(self, x_input, bn_train):
        tempVec = x_input
        tempDim = self.randomDim
        with tf.variable_scope('generator', regularizer=l2_regularizer(self.l2scale)):
            for i, genDim in enumerate(self.generatorDims[:-1]):
                W = tf.get_variable('W_'+str(i), shape=[tempDim, genDim])
                h = tf.matmul(tempVec,W)
                h2 = batch_norm(h, decay=self.bnDecay, scale=True, is_training=bn_train, updates_collections=None)
                h3 = self.generatorActivation(h2)
                tempVec = h3 + tempVec # Shortcut connection
                tempDim = genDim
            # There is no shortcut connection in the last layer
            W = tf.get_variable('W'+str(i), shape=[tempDim, self.generatorDims[-1]])
            h = tf.matmul(tempVec,W)
            h2 = batch_norm(h, decay=self.bnDecay, scale=True, is_training=bn_train, updates_collections=None)

            if self.dataType == 'binary':
                h3 = tf.nn.tanh(h2)
            else:
                h3 = tf.nn.relu(h2)

            output = h3 + tempVec
        return output
    
    def buildGeneratorTest(self, x_input, bn_train):
        tempVec = x_input
        tempDim = self.randomDim
        with tf.variable_scope('generator', regularizer=l2_regularizer(self.l2scale)):
            for i, genDim in enumerate(self.generatorDims[:-1]):
                W = tf.get_variable('W_'+str(i), shape=[tempDim, genDim])
                h = tf.matmul(tempVec,W)
                h2 = batch_norm(h, decay=self.bnDecay, scale=True, is_training=bn_train, updates_collections=None, trainable=False)
                h3 = self.generatorActivation(h2)
                tempVec = h3 + tempVec
                tempDim = genDim
            W = tf.get_variable('W'+str(i), shape=[tempDim, self.generatorDims[-1]])
            h = tf.matmul(tempVec,W)
            h2 = batch_norm(h, decay=self.bnDecay, scale=True, is_training=bn_train, updates_collections=None, trainable=False)

            if self.dataType == 'binary':
                h3 = tf.nn.tanh(h2)
            else:
                h3 = tf.nn.relu(h2)

            output = h3 + tempVec
        return output
    
    def getDiscriminatorResults(self, x_input, keepRate, reuse=False):
        batchSize = tf.shape(x_input)[0]
        inputMean = tf.reshape(tf.tile(tf.reduce_mean(x_input,0), [batchSize]), (batchSize, self.inputDim))
        tempVec = tf.concat([x_input, inputMean], 1)
        tempDim = self.inputDim * 2
        with tf.variable_scope('discriminator', reuse=reuse, regularizer=l2_regularizer(self.l2scale)):
            for i, discDim in enumerate(self.discriminatorDims[:-1]):
                W = tf.get_variable('W_'+str(i), shape=[tempDim, discDim])
                b = tf.get_variable('b_'+str(i), shape=[discDim])
                h = self.discriminatorActivation(tf.add(tf.matmul(tempVec,W),b))
                h = tf.nn.dropout(h, keepRate)
                tempVec = h
                tempDim = discDim
            W = tf.get_variable('W', shape=[tempDim, 1])
            b = tf.get_variable('b', shape=[1])
            y_hat = tf.squeeze(tf.nn.sigmoid(tf.add(tf.matmul(tempVec, W), b)))
        return y_hat
    
    def buildDiscriminator(self, x_real, x_fake, keepRate, decodeVariables, bn_train):
        #Discriminate for real samples
        y_hat_real = self.getDiscriminatorResults(x_real, keepRate, reuse=False)

        #Decompress, then discriminate for real samples
        tempVec = x_fake
        i = 0
        for _ in self.decompressDims[:-1]:
            tempVec = self.aeActivation(tf.add(tf.matmul(tempVec, decodeVariables['aed_W_'+str(i)]), decodeVariables['aed_b_'+str(i)]))
            i += 1

        if self.dataType == 'binary':
            x_decoded = tf.nn.sigmoid(tf.add(tf.matmul(tempVec, decodeVariables['aed_W_'+str(i)]), decodeVariables['aed_b_'+str(i)]))
        else:
            x_decoded = tf.nn.relu(tf.add(tf.matmul(tempVec, decodeVariables['aed_W_'+str(i)]), decodeVariables['aed_b_'+str(i)]))

        y_hat_fake = self.getDiscriminatorResults(x_decoded, keepRate, reuse=True)

        loss_d = -tf.reduce_mean(tf.log(y_hat_real + 1e-12)) - tf.reduce_mean(tf.log(1. - y_hat_fake + 1e-12))
        loss_g = -tf.reduce_mean(tf.log(y_hat_fake + 1e-12))

        return loss_d, loss_g, y_hat_real, y_hat_fake

    def print2file(self, buf, outFile):
        outfd = open(outFile, 'a')
        outfd.write(buf + '\n')
        outfd.close()
    
    def generateData(self,
                     nSamples=100,
                     modelFile='model',
                     batchSize=100,
                     outFile='out'):
        x_dummy = tf.placeholder('float', [None, self.inputDim])
        _, decodeVariables = self.buildAutoencoder(x_dummy)
        x_random = tf.placeholder('float', [None, self.randomDim])
        bn_train = tf.placeholder('bool')
        x_emb = self.buildGeneratorTest(x_random, bn_train)
        tempVec = x_emb
        i = 0
        for _ in self.decompressDims[:-1]:
            tempVec = self.aeActivation(tf.add(tf.matmul(tempVec, decodeVariables['aed_W_'+str(i)]), decodeVariables['aed_b_'+str(i)]))
            i += 1

        if self.dataType == 'binary':
            x_reconst = tf.nn.sigmoid(tf.add(tf.matmul(tempVec, decodeVariables['aed_W_'+str(i)]), decodeVariables['aed_b_'+str(i)]))
        else:
            x_reconst = tf.nn.relu(tf.add(tf.matmul(tempVec, decodeVariables['aed_W_'+str(i)]), decodeVariables['aed_b_'+str(i)]))

        np.random.seed(1234)
        saver = tf.train.Saver()
        outputVec = []
        burn_in = 1000
        with tf.Session() as sess:
            saver.restore(sess, modelFile)
            print('burning in')
            for i in range(burn_in):
                randomX = np.random.normal(size=(batchSize, self.randomDim))
                output = sess.run(x_reconst, feed_dict={x_random:randomX, bn_train:True})

            print('generating')
            nBatches = int(np.ceil(float(nSamples)) / float(batchSize))
            for i in range(nBatches):
                randomX = np.random.normal(size=(batchSize, self.randomDim))
                output = sess.run(x_reconst, feed_dict={x_random:randomX, bn_train:False})
                outputVec.extend(output)

        outputMat = np.array(outputVec)
        np.save(outFile, outputMat)
    
    def calculateDiscAuc(self, preds_real, preds_fake):
        preds = np.concatenate([preds_real, preds_fake], axis=0)
        labels = np.concatenate([np.ones((len(preds_real))), np.zeros((len(preds_fake)))], axis=0)
        auc = roc_auc_score(labels, preds)
        return auc
    
    def calculateDiscAccuracy(self, preds_real, preds_fake):
        total = len(preds_real) + len(preds_fake)
        hit = 0
        for pred in preds_real: 
            if pred > 0.5: hit += 1
        for pred in preds_fake: 
            if pred < 0.5: hit += 1
        acc = float(hit) / float(total)
        return acc

    def train(self,
              dataPath='data',
              modelPath='',
              outPath='out',
              nEpochs=500,
              discriminatorTrainPeriod=2,
              generatorTrainPeriod=1,
              pretrainBatchSize=100,
              batchSize=1000,
              pretrainEpochs=100,
              saveMaxKeep=0):

        # placeholders
        x_raw = tf.placeholder('float', [None, self.inputDim])  # Place holder for real input data
        x_random= tf.placeholder('float', [None, self.randomDim])   # place holder for random noise
        keep_prob = tf.placeholder('float')
        bn_train = tf.placeholder('bool')

        # Buiding autoencoder and generators
        # Autoencoder task: Variable transformation of discrete->continuous->discrete
        loss_ae, decodeVariables = self.buildAutoencoder(x_raw)
        x_fake = self.buildGenerator(x_random, bn_train)
        loss_d, loss_g, y_hat_real, y_hat_fake = self.buildDiscriminator(x_raw, x_fake, keep_prob, decodeVariables, bn_train)
        trainX, validX = self.loadData(dataPath)

        t_vars = tf.trainable_variables()
        ae_vars = [var for var in t_vars if 'autoencoder' in var.name]
        d_vars = [var for var in t_vars if 'discriminator' in var.name]
        g_vars = [var for var in t_vars if 'generator' in var.name]

        all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)

        optimize_ae = tf.train.AdamOptimizer().minimize(loss_ae + sum(all_regs), var_list=ae_vars)
        optimize_d = tf.train.AdamOptimizer().minimize(loss_d + sum(all_regs), var_list=d_vars)
        # list(decodeVariables.values()) is used to create a list to be concatenated with g_vars list.
        optimize_g = tf.train.AdamOptimizer().minimize(loss_g + sum(all_regs), var_list=g_vars+list(decodeVariables.values()))

        initOp = tf.global_variables_initializer()

        nBatches = int(np.ceil(float(trainX.shape[0]) / float(batchSize)))
        saver = tf.train.Saver(max_to_keep=saveMaxKeep)
        logFile = outPath + '.log'

        with tf.Session() as sess:
            if modelPath == '': sess.run(initOp)
            else: saver.restore(sess, modelPath)
            nTrainBatches = int(np.ceil(float(trainX.shape[0])) / float(pretrainBatchSize))
            nValidBatches = int(np.ceil(float(validX.shape[0])) / float(pretrainBatchSize))

            if modelPath== '':
                for epoch in range(pretrainEpochs):
                    idx = np.random.permutation(trainX.shape[0])
                    trainLossVec = []
                    for i in range(nTrainBatches):
                        batchX = trainX[idx[i*pretrainBatchSize:(i+1)*pretrainBatchSize]]
                        _, loss = sess.run([optimize_ae, loss_ae], feed_dict={x_raw:batchX})
                        trainLossVec.append(loss)
                    idx = np.random.permutation(validX.shape[0])
                    validLossVec = []
                    for i in range(nValidBatches):
                        batchX = validX[idx[i*pretrainBatchSize:(i+1)*pretrainBatchSize]]
                        loss = sess.run(loss_ae, feed_dict={x_raw:batchX})
                        validLossVec.append(loss)
                    validReverseLoss = 0.
                    buf = 'Pretrain_Epoch:%d, trainLoss:%f, validLoss:%f, validReverseLoss:%f' % (epoch, np.mean(trainLossVec), np.mean(validLossVec), validReverseLoss)
                    print(buf)
                    self.print2file(buf, logFile)

            idx = np.arange(trainX.shape[0])
            for epoch in range(nEpochs):
                d_loss_vec= []
                g_loss_vec = []
                for i in range(nBatches):
                    for _ in range(discriminatorTrainPeriod):
                        batchIdx = np.random.choice(idx, size=batchSize, replace=False)
                        batchX = trainX[batchIdx]
                        randomX = np.random.normal(size=(batchSize, self.randomDim))
                        _, discLoss = sess.run([optimize_d, loss_d], feed_dict={x_raw:batchX, x_random:randomX, keep_prob:1.0, bn_train:False})
                        d_loss_vec.append(discLoss)
                    for _ in range(generatorTrainPeriod):
                        randomX = np.random.normal(size=(batchSize, self.randomDim))
                        _, generatorLoss = sess.run([optimize_g, loss_g], feed_dict={x_raw:batchX, x_random:randomX, keep_prob:1.0, bn_train:True})
                        g_loss_vec.append(generatorLoss)

                idx = np.arange(len(validX))
                nValidBatches = int(np.ceil(float(len(validX)) / float(batchSize)))
                validAccVec = []
                validAucVec = []
                for i in range(nBatches):
                    batchIdx = np.random.choice(idx, size=batchSize, replace=False)
                    batchX = validX[batchIdx]
                    randomX = np.random.normal(size=(batchSize, self.randomDim))
                    preds_real, preds_fake, = sess.run([y_hat_real, y_hat_fake], feed_dict={x_raw:batchX, x_random:randomX, keep_prob:1.0, bn_train:False})
                    validAcc = self.calculateDiscAccuracy(preds_real, preds_fake)
                    validAuc = self.calculateDiscAuc(preds_real, preds_fake)
                    validAccVec.append(validAcc)
                    validAucVec.append(validAuc)
                buf = 'Epoch:%d, d_loss:%f, g_loss:%f, accuracy:%f, AUC:%f' % (epoch, np.mean(d_loss_vec), np.mean(g_loss_vec), np.mean(validAccVec), np.mean(validAucVec))
                print(buf)
                self.print2file(buf, logFile)

                # Path checking
                import os
                try:
                    os.stat(outPath)
                except:
                    os.mkdir(outPath)

                savePath = saver.save(sess, outPath, global_step=epoch)
        print(savePath)

def str2bool(v):
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')

def parse_arguments(parser):
    parser.add_argument('--embed_size', type=int, default=128, help='The dimension size of the embedding, which will be generated by the generator. (default value: 128)')
    parser.add_argument('--noise_size', type=int, default=128, help='The dimension size of the random noise, on which the generator is conditioned. (default value: 128)')
    parser.add_argument('--generator_size', type=tuple, default=(128, 128), help='The dimension size of the generator. Note that another layer of size "--embed_size" is always added. (default value: (128, 128))')
    parser.add_argument('--discriminator_size', type=tuple, default=(256, 128, 1), help='The dimension size of the discriminator. (default value: (256, 128, 1))')
    parser.add_argument('--compressor_size', type=tuple, default=(), help='The dimension size of the encoder of the autoencoder. Note that another layer of size "--embed_size" is always added. Therefore this can be a blank tuple. (default value: ())')
    parser.add_argument('--decompressor_size', type=tuple, default=(), help='The dimension size of the decoder of the autoencoder. Note that another layer, whose size is equal to the dimension of the <patient_matrix>, is always added. Therefore this can be a blank tuple. (default value: ())')
    parser.add_argument('--data_type', type=str, default='binary', choices=['binary', 'count'], help='The input data type. The <patient matrix> could either contain binary values or count values. (default value: "binary")')
    parser.add_argument('--batchnorm_decay', type=float, default=0.99, help='Decay value for the moving average used in Batch Normalization. (default value: 0.99)')
    parser.add_argument('--L2', type=float, default=0.001, help='L2 regularization coefficient for all weights. (default value: 0.001)')

    import os
    parser.add_argument('--data_file', type=str, metavar='<patient_matrix>', default=os.path.expanduser('~/data/PhisioNet/MIMIC/processed/out_binary.matrix'), help='The path to the numpy matrix containing aggregated patient records.')
    parser.add_argument('--out_file', type=str, metavar='<out_file>', default=os.path.expanduser('~/medgan/out_gan_binary'), help='The path to the output models.')
    parser.add_argument('--model_file', type=str, metavar='<model_file>', default='', help='The path to the model file, in case you want to continue training. (default value: '')')
    parser.add_argument('--n_pretrain_epoch', type=int, default=100, help='The number of epochs to pre-train the autoencoder. (default value: 100)')
    parser.add_argument('--n_epoch', type=int, default=1000, help='The number of epochs to train medGAN. (default value: 1000)')
    parser.add_argument('--n_discriminator_update', type=int, default=2, help='The number of times to update the discriminator per epoch. (default value: 2)')
    parser.add_argument('--n_generator_update', type=int, default=1, help='The number of times to update the generator per epoch. (default value: 1)')
    parser.add_argument('--pretrain_batch_size', type=int, default=100, help='The size of a single mini-batch for pre-training the autoencoder. (default value: 100)')
    parser.add_argument('--batch_size', type=int, default=1000, help='The size of a single mini-batch for training medGAN. (default value: 1000)')
    parser.add_argument('--save_max_keep', type=int, default=0, help='The number of models to keep. Setting this to 0 will save models for every epoch. (default value: 0)')
    parser.add_argument('--generate_data', type=str2bool, default=False, help='If True the model generates data, if False the model is trained (default value: False)')
    args = parser.parse_args()
    return args


if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    args = parse_arguments(parser)

    data = np.load(args.data_file)
    inputDim = data.shape[1]

    mg = Medgan(dataType=args.data_type,
                inputDim=inputDim,
                embeddingDim=args.embed_size,
                randomDim=args.noise_size,
                generatorDims=args.generator_size,
                discriminatorDims=args.discriminator_size,
                compressDims=args.compressor_size,
                decompressDims=args.decompressor_size,
                bnDecay=args.batchnorm_decay,
                l2scale=args.L2)

    # True for generation, False for training
    if not args.generate_data:
    # Training
        mg.train(dataPath=args.data_file,
                 modelPath=args.model_file,
                 outPath=args.out_file,
                 pretrainEpochs=args.n_pretrain_epoch,
                 nEpochs=args.n_epoch,
                 discriminatorTrainPeriod=args.n_discriminator_update,
                 generatorTrainPeriod=args.n_generator_update,
                 pretrainBatchSize=args.pretrain_batch_size,
                 batchSize=args.batch_size,
                 saveMaxKeep=args.save_max_keep)
    else:
    # Generate synthetic data using a trained model
    # You must specify "--model_file" and "<out_file>" to generate synthetic data.
        mg.generateData(nSamples=10000,
                        modelFile=args.model_file,
                        batchSize=args.batch_size,
                        outFile=args.out_file)