294 lines
12 KiB
Python
294 lines
12 KiB
Python
import tensorflow as tf
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from tensorflow.contrib.layers import l2_regularizer
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import numpy as np
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import time
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import os
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import pandas as pd
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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#### id of gpu to use
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os.environ['CUDA_VISIBLE_DEVICES'] = "0"
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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#### training data
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#### shape=(n_sample, n_code=854)
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REAL = None #np.load('') #--diagnosis codes (binary)
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#### demographic for training data
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#### shape=(n_sample, 6)
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#### if sample_x is male, then LABEL[x,0]=1, else LABEL[x,1]=1
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#### if sample_x's is within 0-17, then LABEL[x,2]=1
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#### elif sample_x's is within 18-44, then LABEL[x,3]=1
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#### elif sample_x's is within 45-64, then LABEL[x,4]=1
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#### elif sample_x's is within 64-, then LABEL[x,5]=1
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LABEL = None #np.load('') #-- demographics 0,5 set it to 1,0,0,0,0,0
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#### training parameters
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NUM_GPUS = 1
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BATCHSIZE_PER_GPU = 2000
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TOTAL_BATCHSIZE = BATCHSIZE_PER_GPU * NUM_GPUS
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STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)
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g_structure = [128, 128]
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d_structure = [854, 256, 128] #-- change 854 to number of diagnosis
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z_dim = 128
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def _variable_on_cpu(name, shape, initializer=None):
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with tf.device('/cpu:0'):
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var = tf.get_variable(name, shape, initializer=initializer)
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return var
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def batchnorm(inputs, name, labels=None, n_labels=None):
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mean, var = tf.nn.moments(inputs, [0], keep_dims=True)
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shape = mean.shape[1].value
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offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
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initializer=tf.zeros_initializer)
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scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
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initializer=tf.ones_initializer)
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offset = tf.nn.embedding_lookup(offset_m, labels)
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scale = tf.nn.embedding_lookup(scale_m, labels)
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result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
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return result
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def layernorm(inputs, name, labels=None, n_labels=None):
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mean, var = tf.nn.moments(inputs, [1], keep_dims=True)
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shape = inputs.shape[1].value
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offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
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initializer=tf.zeros_initializer)
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scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
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initializer=tf.ones_initializer)
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offset = tf.nn.embedding_lookup(offset_m, labels)
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scale = tf.nn.embedding_lookup(scale_m, labels)
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result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
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return result
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def input_fn():
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features_placeholder = tf.placeholder(shape=REAL.shape, dtype=tf.float32)
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labels_placeholder = tf.placeholder(shape=LABEL.shape, dtype=tf.float32)
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dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
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dataset = dataset.repeat(10000)
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dataset = dataset.batch(batch_size=BATCHSIZE_PER_GPU)
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dataset = dataset.prefetch(1)
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iterator = dataset.make_initializable_iterator()
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# next_element = iterator.get_next()
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# init_op = iterator.initializer
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return iterator, features_placeholder, labels_placeholder
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def generator(z, label):
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x = z
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tmp_dim = z_dim
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with tf.variable_scope('G', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(0.00001)):
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for i, dim in enumerate(g_structure[:-1]):
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kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, dim])
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h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i), labels=label, n_labels=8)
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h2 = tf.nn.relu(h1)
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x = x + h2
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tmp_dim = dim
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i = len(g_structure) - 1
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kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, g_structure[-1]])
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h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i),
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labels=label, n_labels=8)
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h2 = tf.nn.tanh(h1)
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x = x + h2
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kernel = _variable_on_cpu('W_' + str(i+1), shape=[128, 854])
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bias = _variable_on_cpu('b_' + str(i+1), shape=[854])
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x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
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return x
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def discriminator(x, label):
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with tf.variable_scope('D', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(0.00001)):
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for i, dim in enumerate(d_structure[1:]):
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kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[i], dim])
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bias = _variable_on_cpu('b_' + str(i), shape=[dim])
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x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
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x = layernorm(x, name='cln' + str(i), labels=label, n_labels=8)
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i = len(d_structure)
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kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[-1], 1])
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bias = _variable_on_cpu('b_' + str(i), shape=[1])
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y = tf.add(tf.matmul(x, kernel), bias)
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return y
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def compute_dloss(real, fake, label):
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epsilon = tf.random_uniform(
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shape=[BATCHSIZE_PER_GPU, 1],
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minval=0.,
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maxval=1.)
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x_hat = real + epsilon * (fake - real)
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y_hat_fake = discriminator(fake, label)
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y_hat_real = discriminator(real, label)
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y_hat = discriminator(x_hat, label)
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grad = tf.gradients(y_hat, [x_hat])[0]
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slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
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gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
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all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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w_distance = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)
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loss = w_distance + 10 * gradient_penalty + sum(all_regs)
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tf.add_to_collection('dlosses', loss)
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return w_distance, loss
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def compute_gloss(fake, label):
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y_hat_fake = discriminator(fake, label)
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all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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loss = -tf.reduce_mean(y_hat_fake) + sum(all_regs)
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tf.add_to_collection('glosses', loss)
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return loss, loss
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def tower_loss(scope, stage, real, label):
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label = tf.cast(label, tf.int32)
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label = label[:, 1] * 4 + tf.squeeze(
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tf.matmul(label[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
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z = tf.random_normal(shape=[BATCHSIZE_PER_GPU, z_dim])
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fake = generator(z, label)
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if stage == 'D':
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w, loss = compute_dloss(real, fake, label)
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losses = tf.get_collection('dlosses', scope)
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else:
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w, loss = compute_gloss(fake, label)
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losses = tf.get_collection('glosses', scope)
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total_loss = tf.add_n(losses, name='total_loss')
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# loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
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# loss_averages_op = loss_averages.apply(losses + [total_loss])
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#
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# with tf.control_dependencies([loss_averages_op]):
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# total_loss = tf.identity(total_loss)
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return total_loss, w
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def average_gradients(tower_grads):
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average_grads = []
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for grad_and_vars in zip(*tower_grads):
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grads = []
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for g, _ in grad_and_vars:
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expanded_g = tf.expand_dims(g, 0)
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grads.append(expanded_g)
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grad = tf.concat(axis=0, values=grads)
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grad = tf.reduce_mean(grad, 0)
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v = grad_and_vars[0][1]
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grad_and_var = (grad, v)
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average_grads.append(grad_and_var)
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return average_grads
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def graph(stage, opt):
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# global_step = tf.get_variable(stage+'_step', [], initializer=tf.constant_initializer(0), trainable=False)
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tower_grads = []
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per_gpu_w = []
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iterator, features_placeholder, labels_placeholder = input_fn()
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with tf.variable_scope(tf.get_variable_scope()):
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for i in range(NUM_GPUS):
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with tf.device('/gpu:%d' % i):
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with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
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(real, label) = iterator.get_next()
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loss, w = tower_loss(scope, stage, real, label)
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tf.get_variable_scope().reuse_variables()
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vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
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grads = opt.compute_gradients(loss, vars_)
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tower_grads.append(grads)
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per_gpu_w.append(w)
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grads = average_gradients(tower_grads)
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apply_gradient_op = opt.apply_gradients(grads)
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mean_w = tf.reduce_mean(per_gpu_w)
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train_op = apply_gradient_op
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return train_op, mean_w, iterator, features_placeholder, labels_placeholder
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def train(max_epochs, train_dir):
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with tf.device('/cpu:0'):
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opt_d = tf.train.AdamOptimizer(1e-4)
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opt_g = tf.train.AdamOptimizer(1e-4)
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train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = graph('D', opt_d)
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train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = graph('G', opt_g)
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saver = tf.train.Saver()
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init = tf.global_variables_initializer()
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with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
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sess.run(init)
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sess.run(iterator_d.initializer,
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feed_dict={features_placeholder_d: REAL, labels_placeholder_d: LABEL})
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sess.run(iterator_g.initializer,
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feed_dict={features_placeholder_g: REAL, labels_placeholder_g: LABEL})
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for epoch in range(1, max_epochs + 1):
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start_time = time.time()
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w_sum = 0
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for i in range(STEPS_PER_EPOCH):
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for _ in range(2):
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_, w = sess.run([train_d, w_distance])
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w_sum += w
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sess.run(train_g)
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duration = time.time() - start_time
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assert not np.isnan(w_sum), 'Model diverged with loss = NaN'
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format_str = 'epoch: %d, w_distance = %f (%.1f)'
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print(format_str % (epoch, -w_sum/(STEPS_PER_EPOCH*2), duration))
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if epoch % 500 == 0:
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# checkpoint_path = os.path.join(train_dir, 'multi')
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saver.save(sess, train_dir, write_meta_graph=False, global_step=epoch)
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# saver.save(sess, train_dir, global_step=epoch)
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def generate(model_dir, synthetic_dir, demo):
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tf.reset_default_graph()
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z = tf.random_normal(shape=[BATCHSIZE_PER_GPU, z_dim])
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y = tf.placeholder(shape=[BATCHSIZE_PER_GPU, 6], dtype=tf.int32)
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label = y[:, 1] * 4 + tf.squeeze(tf.matmul(y[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
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fake = generator(z, label)
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saver = tf.train.Saver()
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with tf.Session() as sess:
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saver.restore(sess, model_dir)
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for m in range(2):
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for n in range(2, 6):
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idx1 = (demo[:, m] == 1)
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idx2 = (demo[:, n] == 1)
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idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
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num = np.sum(idx)
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nbatch = int(np.ceil(num / BATCHSIZE_PER_GPU))
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label_input = np.zeros((nbatch*BATCHSIZE_PER_GPU, 6))
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label_input[:, n] = 1
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label_input[:, m] = 1
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output = []
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for i in range(nbatch):
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f = sess.run(fake,feed_dict={y: label_input[i*BATCHSIZE_PER_GPU:(i+1)*BATCHSIZE_PER_GPU]})
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output.extend(np.round(f))
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output = np.array(output)[:num]
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np.save(synthetic_dir + str(m) + str(n), output)
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if __name__ == '__main__':
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#### args_1: number of training epochs
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#### args_2: dir to save the trained model
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from bridge import Binary
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df = pd.read_csv('exports/observation.csv')
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cols = 'observation_source_value'
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_map,_df = (Binary()).Export(df)
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i = np.arange(_map[cols]['start'],_map[cols]['end'])
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REAL = _df[:,i]
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LABEL = np.arange(0,_df.shape[0])
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train(500, '')
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#### args_1: dir of trained model
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#### args_2: dir to save synthetic data
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#### args_3, label of data-to-be-generated
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generate('', '', demo=LABEL)
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