From bf7b3e7a635932bd919f0bfc432873bc94c99a0b Mon Sep 17 00:00:00 2001 From: changhee Date: Wed, 10 Jun 2020 01:21:07 -0700 Subject: [PATCH] first commit --- class_AC_TPC.py | 396 ++++ .../data_ADNI_preprocess-checkpoint.ipynb | 441 +++++ .../data_CF_label_test-checkpoint.ipynb | 1661 +++++++++++++++++ .../data_CF_preprocess-checkpoint.ipynb | 875 +++++++++ data/sample/data.npz | Bin 0 -> 9411970 bytes data_loader.py | 19 + main.py | 392 ++++ tutorial.ipynb | 535 ++++++ utils_log.py | 48 + utils_network.py | 133 ++ 10 files changed, 4500 insertions(+) create mode 100644 class_AC_TPC.py create mode 100644 data/.ipynb_checkpoints/data_ADNI_preprocess-checkpoint.ipynb create mode 100644 data/.ipynb_checkpoints/data_CF_label_test-checkpoint.ipynb create mode 100644 data/.ipynb_checkpoints/data_CF_preprocess-checkpoint.ipynb create mode 100644 data/sample/data.npz create mode 100755 data_loader.py create mode 100644 main.py create mode 100644 tutorial.ipynb create mode 100755 utils_log.py create mode 100755 utils_network.py diff --git a/class_AC_TPC.py b/class_AC_TPC.py new file mode 100644 index 0000000..c3d3278 --- /dev/null +++ b/class_AC_TPC.py @@ -0,0 +1,396 @@ +import numpy as np +import tensorflow as tf + +import random +import os,sys + +from tensorflow.python.ops.rnn import _transpose_batch_time + +#user defined +import utils_network as utils + + + +def log(x): + return tf.log(x + 1e-8) + +def div(x, y): + return tf.div(x, (y + 1e-8)) + +def get_seq_length(sequence): + used = tf.sign(tf.reduce_max(tf.abs(sequence), 2)) + tmp_length = tf.reduce_sum(used, 1) + tmp_length = tf.cast(tmp_length, tf.int32) + return tmp_length + + + +class AC_TPC: + def __init__(self, sess, name, input_dims, network_settings): + self.sess = sess + self.name = name + + # INPUT/OUTPUT DIMENSIONS + self.x_dim = input_dims['x_dim'] #features + delta + self.y_dim = input_dims['y_dim'] + self.y_type = input_dims['y_type'] + self.K = input_dims['max_cluster'] + self.max_length = input_dims['max_length'] + + # Encoder + self.h_dim_f = network_settings['h_dim_encoder'] #encoder nodes + self.num_layers_f = network_settings['num_layers_encoder'] #encoder layers + self.rnn_type = network_settings['rnn_type'] + self.rnn_activate_fn = network_settings['rnn_activate_fn'] + + # Selector + self.h_dim_h = network_settings['h_dim_selector'] #selector nodes + self.num_layers_h = network_settings['num_layers_selector'] #selector layers + + # Predictor + self.h_dim_g = network_settings['h_dim_predictor'] #predictor nodes + self.num_layers_g = network_settings['num_layers_predictor'] #predictor layers + + self.fc_activate_fn = network_settings['fc_activate_fn'] #selector & predictor + + # Latent Space + self.z_dim = self.h_dim_f * self.num_layers_f + + self._build_net() + + + def _build_net(self): + with tf.variable_scope(self.name): + + self.mb_size = tf.placeholder(tf.int32, [], name='batch_size') + self.lr_rate1 = tf.placeholder(tf.float32, name='learning_rate1') + self.lr_rate2 = tf.placeholder(tf.float32, name='learning_rate2') + self.keep_prob = tf.placeholder(tf.float32, name='keep_probability') + + # Input and Output + self.x = tf.placeholder(tf.float32, [None, self.max_length, self.x_dim], name='inputs') + self.y = tf.placeholder(tf.float32, [None, self.max_length, self.y_dim], name='labels_onehot') + + # Embedding + self.E = tf.placeholder(tf.float32, [self.K, self.z_dim], name='embeddings_input') + self.EE = tf.Variable(self.E, name='embeddings_var') + self.embeddings = tf.nn.tanh(self.EE) + + # self.embde = tf.nn.tanh(self.EE) + # self.EE = tf.Variable(self.E, name='embeddings_var') + + self.s = tf.placeholder(tf.int32, [None], name='cluster_label') + self.s_onehot = tf.one_hot(self.s, self.K) + + # LOSS PARAMETERS + self.alpha = tf.placeholder(tf.float32, name = 'alpha') #For sample-wise entropy + self.beta = tf.placeholder(tf.float32, name = 'beta') #For prediction loss (i.e., mle) + self.gamma = tf.placeholder(tf.float32, name = 'gamma') #For batch-wise entropy + self.delta = tf.placeholder(tf.float32, name = 'delta') #For embedding + + + ''' + ### CREATE RNN MASK + - This is to flexibly handle sequences with different length + - rnn_mask1: last observation; [mb_size, max_length] + - rnn_mask2: all available observations; [mb_size, max_length] + ''' + # CREATE RNN MASK: + seq_length = get_seq_length(self.x) + tmp_range = tf.expand_dims(tf.range(0, self.max_length, 1), axis=0) + self.rnn_mask1 = tf.cast(tf.equal(tmp_range, tf.expand_dims(seq_length-1, axis=1)), tf.float32) #last observation + self.rnn_mask2 = tf.cast(tf.less_equal(tmp_range, tf.expand_dims(seq_length-1, axis=1)), tf.float32) #all available observation + + + ### DEFINE SELECTOR + def selector(x_, o_dim_=self.K, num_layers_=2, h_dim_=self.h_dim_h, activation_fn=self.fc_activate_fn, reuse=tf.AUTO_REUSE): + out_fn = tf.nn.softmax + with tf.variable_scope('selector', reuse=reuse): + if num_layers_ == 1: + out = tf.contrib.layers.fully_connected(inputs=x_, num_outputs=o_dim_, activation_fn=out_fn, scope='selector_out') + else: #num_layers > 1 + for tmp_layer in range(num_layers_-1): + if tmp_layer == 0: + net = x_ + net = tf.contrib.layers.fully_connected(inputs=net, num_outputs=h_dim_, activation_fn=activation_fn, scope='selector_'+str(tmp_layer)) + net = tf.nn.dropout(net, keep_prob=self.keep_prob) + out = tf.contrib.layers.fully_connected(inputs=net, num_outputs=o_dim_, activation_fn=out_fn, scope='selector_out') + return out + + + ### DEFINE PREDICTOR + def predictor(x_, o_dim_=self.y_dim, o_type_=self.y_type, num_layers_=1, h_dim_=self.h_dim_g, activation_fn=self.fc_activate_fn, reuse=tf.AUTO_REUSE): + if o_type_ == 'continuous': + out_fn = None + elif o_type_ == 'categorical': + out_fn = tf.nn.softmax #for classification task + elif o_type_ == 'binary': + out_fn = tf.nn.sigmoid + else: + raise Exception('Wrong output type. The value {}!!'.format(o_type_)) + + with tf.variable_scope('predictor', reuse=reuse): + if num_layers_ == 1: + out = tf.contrib.layers.fully_connected(inputs=x_, num_outputs=o_dim_, activation_fn=out_fn, scope='predictor_out') + else: #num_layers > 1 + for tmp_layer in range(num_layers_-1): + if tmp_layer == 0: + net = x_ + net = tf.contrib.layers.fully_connected(inputs=net, num_outputs=h_dim_, activation_fn=activation_fn, scope='predictor_'+str(tmp_layer)) + net = tf.nn.dropout(net, keep_prob=self.keep_prob) + out = tf.contrib.layers.fully_connected(inputs=net, num_outputs=o_dim_, activation_fn=out_fn, scope='predictor_out') + return out + + + ### DEFINE LOOP FUNCTION FOR ENCODRER (f-g, f-h relations are created here) + def loop_fn(time, cell_output, cell_state, loop_state): + + emit_output = cell_output + + if cell_output is None: # time == 0 + next_cell_state = cell.zero_state(self.mb_size, tf.float32) + next_loop_state = loop_state_ta + else: + next_cell_state = cell_state + tmp_z = utils.create_concat_state_h(next_cell_state, self.num_layers_f, self.rnn_type) + tmp_y = predictor(tmp_z, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn) + tmp_pi = selector(tmp_z, self.K, self.num_layers_h, self.h_dim_h, self.fc_activate_fn) + + next_loop_state = (loop_state[0].write(time-1, tmp_z), # save all the hidden states + loop_state[1].write(time-1, tmp_y), # save all the output + loop_state[2].write(time-1, tmp_pi)) # save all the selector_net output (i.e., pi) + + elements_finished = (time >= self.max_length) + + #this gives the break-point (no more recurrence after the max_length) + finished = tf.reduce_all(elements_finished) + next_input = tf.cond(finished, + lambda: tf.zeros([self.mb_size, self.x_dim], dtype=tf.float32), + lambda: inputs_ta.read(time)) + return (elements_finished, next_input, next_cell_state, emit_output, next_loop_state) + + + ''' + ##### CREATE RNN NETWORK + - (INPUT) inputs_ta: TensorArray with [max_length, mb_size, x_dim] #x_dim included delta + - (OUTPUT) + . zs = rnn states (h) in LSTM/GRU ; [mb_size, max_length z_dim] + . y_hats = output of predictor taking zs as inputs; [mb_size, max_length, y_dim] + . pis = output of selector ; [mb_size, max_length, K] + + ''' + inputs = self.x + inputs_ta = tf.TensorArray( + dtype=tf.float32, size=self.max_length + ).unstack(_transpose_batch_time(inputs), name = 'rnn_input') + + + cell = utils.create_rnn_cell( + self.h_dim_f, self.num_layers_f, + self.keep_prob, self.rnn_type, self.rnn_activate_fn + ) + + #define the loop_state TensorArray for information from rnn time steps + loop_state_ta = ( + tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False), #zs (j=1,...,J) + tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False), #y_hats (j=1,...,J) + tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False) #pis (j=1,...,J) + ) + + _, _, loop_state_ta = tf.nn.raw_rnn(cell, loop_fn) #, parallel_iterations=1) + + + self.zs = _transpose_batch_time(loop_state_ta[0].stack()) + self.y_hats = _transpose_batch_time(loop_state_ta[1].stack()) + self.pis = _transpose_batch_time(loop_state_ta[2].stack()) + + ### SAMPLING PROCESS + s_dist = tf.distributions.Categorical(probs=tf.reshape(self.pis, [-1, self.K])) #define the categorical dist. + s_sample = s_dist.sample() + + mask_e = tf.cast(tf.equal(tf.expand_dims(tf.range(0, self.K, 1), axis=0), tf.expand_dims(s_sample, axis=1)), tf.float32) + z_bars = tf.matmul(mask_e, self.embeddings) + pi_sample = tf.reduce_sum(mask_e * tf.reshape(log(self.pis), [-1, self.K]), axis=1) + + with tf.variable_scope('rnn', reuse=True): + y_bars = predictor(z_bars, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn) + + self.z_bars = tf.reshape(z_bars, [-1, self.max_length, self.z_dim]) + self.y_bars = tf.reshape(y_bars, [-1, self.max_length, self.y_dim]) + self.pi_sample = tf.reshape(pi_sample, [-1, self.max_length]) + self.s_sample = tf.reshape(s_sample, [-1, self.max_length]) + + + ### DEFINE LOSS FUNCTIONS + #\ell_{1}: KL divergence loss for regression and binary/categorical-classification task + def loss_1(y_true_, y_pred_, y_type_ = self.y_type): + if y_type_ == 'continuous': + tmp_loss = tf.reduce_sum((y_true_ - y_pred_)**2, axis=-1) + elif y_type_ == 'categorical': + tmp_loss = - tf.reduce_sum(y_true_ * log(y_pred_), axis=-1) + elif y_type_ == 'binary': + tmp_loss = - tf.reduce_sum(y_true_ * log(y_pred_) + (1.-y_true_) * log(1.-y_pred_), axis=-1) + else: + raise Exception('Wrong output type. The value {}!!'.format(y_type_)) + return tmp_loss + + #batch-wise entropy + tmp_pis = tf.tile(tf.expand_dims(self.rnn_mask2, axis=2), [1,1,self.K]) * self.pis + mean_pis = tf.reduce_sum(tf.reduce_sum(tmp_pis, axis=1), axis=0) / tf.reduce_sum(tf.reduce_sum(self.rnn_mask2, axis=1), axis=0, keepdims=True) + + ## LOSS_MLE: MLE prediction loss (for initalization) + self.LOSS_MLE = tf.reduce_mean(tf.reduce_sum(self.rnn_mask2 * loss_1(self.y, self.y_hats, self.y_type), axis=1)) + + + ## LOSS1: predictive clustering loss + self.LOSS_1 = tf.reduce_mean(tf.reduce_sum(self.rnn_mask2 * loss_1(self.y, self.y_bars, self.y_type), axis=1)) + self.LOSS_1_AC = tf.reduce_mean(tf.reduce_sum(self.rnn_mask2 * self.pi_sample * loss_1(self.y, self.y_bars, self.y_type), axis=1)) + + ## LOSS2: sample-wise entropy loss + self.LOSS_2 = tf.reduce_mean(-tf.reduce_sum(self.rnn_mask2 * tf.reduce_sum(self.pis * log(self.pis), axis=2), axis=1)) + + + predictor_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope= self.name+'/rnn/predictor') + selecter_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope= self.name+'/rnn/selector') + embedding_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope= self.name+'/embeddings_var') + encoder_vars = [vars_ for vars_ in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) + if vars_ not in predictor_vars+selecter_vars+embedding_vars] + + ### EMBEDDING TRAINING + with tf.variable_scope('rnn', reuse=True): + Ey = predictor(self.embeddings, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn) + # Ey = predictor(self.EE, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn) + + ## LOSS3: embedding separation loss (prevents embedding from collapsing) + self.LOSS_3 = 0 + for i in range(self.K): + for j in range(i+1, self.K): + self.LOSS_3 += - loss_1(Ey[i, :], Ey[j, :], y_type_ = self.y_type) / ((self.K-1)*(self.K-2)) # negative because we want to increase this; + + + ### DEFINE OPTIMIZATION SOLVERS + self.solver_MLE = tf.train.AdamOptimizer(self.lr_rate1).minimize( + self.LOSS_MLE, var_list=encoder_vars+predictor_vars + ) + self.solver_L1_critic = tf.train.AdamOptimizer(self.lr_rate1).minimize( + self.LOSS_1, + var_list=encoder_vars+predictor_vars + ) + self.solver_L1_actor = tf.train.AdamOptimizer(self.lr_rate2).minimize( + self.LOSS_1_AC + self.alpha*self.LOSS_2, + var_list=encoder_vars + selecter_vars + ) + self.solver_E = tf.train.AdamOptimizer(self.lr_rate1).minimize( + self.LOSS_1 + self.beta*self.LOSS_3, + var_list=embedding_vars + ) + + ### INITIALIZE SELECTOR + self.zz = tf.placeholder(tf.float32, [None, self.z_dim]) + with tf.variable_scope('rnn', reuse=True): + self.yy = predictor(self.zz, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn) #to check the predictor output given z + self.s_out = selector(self.zz, self.K, self.num_layers_h, self.h_dim_h, self.fc_activate_fn) + + ## LOSS_S: selector initialization (cross-entropy wrt initialized class) + self.LOSS_S = tf.reduce_mean(- tf.reduce_sum(self.s_onehot*log(self.s_out), axis=1)) + self.solver_S = tf.train.AdamOptimizer(self.lr_rate1).minimize( + self.LOSS_S, var_list=selecter_vars + ) + + ### TRAINING FUNCTIONS + def train_mle(self, x_, y_, lr_train, k_prob): + return self.sess.run([self.solver_MLE, self.LOSS_MLE], + feed_dict={self.x: x_, self.y: y_, + self.mb_size:np.shape(x_)[0], self.lr_rate1: lr_train, self.keep_prob: k_prob}) + + def train_critic(self, x_, y_, lr_train, k_prob): + return self.sess.run([self.solver_L1_critic, self.LOSS_1], + feed_dict={self.x: x_, self.y: y_, + self.mb_size:np.shape(x_)[0], self.lr_rate1: lr_train, self.keep_prob: k_prob}) + + def train_actor(self, x_, y_, alpha_, lr_train, k_prob): + return self.sess.run([self.solver_L1_actor, self.LOSS_1, self.LOSS_2], + feed_dict={self.x: x_, self.y: y_, + self.alpha: alpha_, + self.mb_size:np.shape(x_)[0], self.lr_rate2: lr_train, self.keep_prob: k_prob}) + + def train_selector(self, z_, s_, lr_train, k_prob): + return self.sess.run([self.solver_S, self.LOSS_S], + feed_dict={self.zz: z_, self.s: s_, + self.lr_rate1: lr_train, self.keep_prob: k_prob}) + + def train_embedding(self, x_, y_, beta_, lr_train, k_prob): + return self.sess.run([self.solver_E, self.LOSS_1, self.LOSS_3], + feed_dict={self.x:x_, self.y:y_, + self.beta:beta_, + self.mb_size:np.shape(x_)[0], + self.lr_rate1:lr_train, self.keep_prob:k_prob}) + + def get_losses(self, x_, y_): + return self.sess.run([self.LOSS_1, self.LOSS_2, self.LOSS_3], + feed_dict={self.x:x_, self.y:y_, + self.mb_size:np.shape(x_)[0], + self.keep_prob:1.0}) + + ### PREDICTION FUNCTIONS + def predict_y_hats(self, x_): + return self.sess.run([self.y_hats, self.rnn_mask2], + feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) + + def predict_y_bars(self, x_): + return self.sess.run([self.y_bars, self.rnn_mask2], + feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) + + def predict_yy(self, z_): + return self.sess.run(self.yy, + feed_dict={self.zz:z_, self.mb_size:np.shape(z_)[0], self.keep_prob:1.0}) + + def predict_zs_and_pis_m2(self, x_): + return self.sess.run([self.zs, self.pis, self.rnn_mask2], + feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) + + def predict_s_sample(self, x_): + return self.sess.run([self.s_sample, self.rnn_mask2], + feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) + + def predict_zbars_and_pis_m1(self, x_): + return self.sess.run([self.z_bars, self.pis, self.rnn_mask1], + feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) + + def predict_zs_and_pis_m1(self, x_): + return self.sess.run([self.zs, self.pis, self.rnn_mask1], + feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) + + def predict_zbars_and_pis_m2(self, x_): + return self.sess.run([self.z_bars, self.pis, self.rnn_mask2], + feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) + + + +### INITIALIZE EMBEDDING AND SELECTOR +from sklearn.cluster import MiniBatchKMeans, KMeans + +def initialize_embedding(model, x, K): + tmp_z, _, _ = model.predict_zs_and_pis_m2(x) + tmp_y, tmp_m = model.predict_y_hats(x) + + z_dim = np.shape(tmp_z)[-1] + y_dim = np.shape(tmp_y)[-1] + + tmp_z = (tmp_z * np.tile(np.expand_dims(tmp_m, axis=2), [1,1,z_dim])).reshape([-1, z_dim]) + tmp_z = tmp_z[np.sum(np.abs(tmp_z), axis=1) != 0] + + tmp_y = (tmp_y * np.tile(np.expand_dims(tmp_m, axis=2), [1,1,y_dim])).reshape([-1, y_dim]) + tmp_y = tmp_y[np.sum(np.abs(tmp_y), axis=1) != 0] + + km = KMeans(n_clusters = K, init='k-means++') + _ = km.fit(tmp_y) + tmp_ey = km.cluster_centers_ + tmp_s = km.predict(tmp_y) + + tmp_e = np.zeros([K, z_dim]) + for k in range(K): + # tmp_e[k, :] = np.mean(tmp_z[tmp_s == k]) + tmp_e[k,:] = tmp_z[np.argmin(np.sum(np.abs(tmp_y - tmp_ey[k, :]),axis=1)), :] + + return tmp_e, tmp_s, tmp_z \ No newline at end of file diff --git a/data/.ipynb_checkpoints/data_ADNI_preprocess-checkpoint.ipynb b/data/.ipynb_checkpoints/data_ADNI_preprocess-checkpoint.ipynb new file mode 100644 index 0000000..590d6e8 --- /dev/null +++ b/data/.ipynb_checkpoints/data_ADNI_preprocess-checkpoint.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def f_get_Normalization(X, norm_mode):\n", + " num_Patient, num_Feature = np.shape(X)\n", + "\n", + " if norm_mode == 'standard': #zero mean unit variance\n", + " for j in range(num_Feature):\n", + " if np.std(X[:,j]) != 0:\n", + " X[:,j] = (X[:,j] - np.mean(X[:, j]))/np.std(X[:,j])\n", + " else:\n", + " X[:,j] = (X[:,j] - np.mean(X[:, j]))\n", + " elif norm_mode == 'normal': #min-max normalization\n", + " for j in range(num_Feature):\n", + " X[:,j] = (X[:,j] - np.min(X[:,j]))/(np.max(X[:,j]) - np.min(X[:,j]))\n", + " else:\n", + " print(\"INPUT MODE ERROR!\")\n", + "\n", + " return X" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MAKE ADNI TIME-SERIES DATASET" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "filename= '/mnt/85c3b84c-90ae-4506-81f1-4ff88bb29110/[ DATASET ]/[ TIME-SERIES DATA ]/ADNI/cleaned_Byran/adni_cleaned_changhee.csv'\n", + "\n", + "df = pd.read_csv(filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[:, 'DX_Dementia'] = 0\n", + "df.loc[(df['DX_MCI'] == 0) & (df['DX_NL'] == 0), 'DX_Dementia'] = 1\n", + "\n", + "feat_list = ['AGE', 'PTEDUCAT', 'APOE4', 'CDRSB', 'MMSE', 'ADAS11', 'RAVLT_immediate', 'RAVLT_learning',\n", + " 'ADAS13', 'RAVLT_forgetting', 'RAVLT_perc_forgetting', 'ICV', 'WholeBrain', 'Ventricles',\n", + " 'Hippocampus', 'Fusiform', 'MidTemp', 'Entorhinal', 'serial', 'PTMARRY_Divorced', 'PTMARRY_Married',\n", + " 'PTMARRY_Never married', 'PTMARRY_Unknown', 'PTMARRY_Widowed', 'PTRACCAT_Am Indian/Alaskan',\n", + " 'PTRACCAT_Asian', 'PTRACCAT_Black', 'PTRACCAT_Hawaiian/Other PI', 'PTRACCAT_More than one',\n", + " 'PTRACCAT_Unknown', 'PTRACCAT_White', 'PTETHCAT_Hisp/Latino', 'PTETHCAT_Not Hisp/Latino', 'PTETHCAT_Unknown']\n", + "\n", + "\n", + "label_list = ['DX_Dementia', 'DX_MCI', 'DX_NL']\n", + "\n", + "tmp_df = df[['ID']+feat_list + label_list]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "['AGE', 'PTEDUCAT', 'APOE4', '', '', , '', '',\n", + " '', '', '', '', '',\n", + " '', '', '', '', '', 'PTMARRY_Divorced', 'PTMARRY_Married',\n", + " 'PTMARRY_Never married', 'PTMARRY_Unknown', 'PTMARRY_Widowed', 'PTRACCAT_Am Indian/Alaskan',\n", + " 'PTRACCAT_Asian', 'PTRACCAT_Black', 'PTRACCAT_Hawaiian/Other PI', 'PTRACCAT_More than one',\n", + " 'PTRACCAT_Unknown', 'PTRACCAT_White', 'PTETHCAT_Hisp/Latino', 'PTETHCAT_Not Hisp/Latino', 'PTETHCAT_Unknown']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntorhinalFusiformHippocampusICVMidTempVentriclesWholeBrainserial
count11663.00000011663.00000011663.0000001.166300e+0411663.00000011663.0000001.166300e+0411663.000000
mean3575.01805417473.0908926880.4822341.533785e+0619664.33913040966.1986871.020341e+066770.129898
std735.3327952553.3754721119.7599741.619768e+052833.00237822924.6045441.085778e+053792.804599
min1041.0000009019.0000002822.0000002.918840e+028876.0000005737.0000006.516860e+050.000000
25%3131.00000015875.0000006124.0000001.422150e+0617915.00000024521.0000009.458080e+053354.500000
50%3566.00000017294.0000006902.0000001.525030e+0619446.00000036022.0000001.021180e+067000.000000
75%4081.00000019084.0000007644.0000001.639195e+0621550.50000051357.5000001.092100e+0610181.500000
max6711.00000028878.00000011207.0000002.110290e+0632189.000000158508.0000001.486040e+0612740.000000
\n", + "
" + ], + "text/plain": [ + " Entorhinal Fusiform Hippocampus ICV MidTemp \\\n", + "count 11663.000000 11663.000000 11663.000000 1.166300e+04 11663.000000 \n", + "mean 3575.018054 17473.090892 6880.482234 1.533785e+06 19664.339130 \n", + "std 735.332795 2553.375472 1119.759974 1.619768e+05 2833.002378 \n", + "min 1041.000000 9019.000000 2822.000000 2.918840e+02 8876.000000 \n", + "25% 3131.000000 15875.000000 6124.000000 1.422150e+06 17915.000000 \n", + "50% 3566.000000 17294.000000 6902.000000 1.525030e+06 19446.000000 \n", + "75% 4081.000000 19084.000000 7644.000000 1.639195e+06 21550.500000 \n", + "max 6711.000000 28878.000000 11207.000000 2.110290e+06 32189.000000 \n", + "\n", + " Ventricles WholeBrain serial \n", + "count 11663.000000 1.166300e+04 11663.000000 \n", + "mean 40966.198687 1.020341e+06 6770.129898 \n", + "std 22924.604544 1.085778e+05 3792.804599 \n", + "min 5737.000000 6.516860e+05 0.000000 \n", + "25% 24521.000000 9.458080e+05 3354.500000 \n", + "50% 36022.000000 1.021180e+06 7000.000000 \n", + "75% 51357.500000 1.092100e+06 10181.500000 \n", + "max 158508.000000 1.486040e+06 12740.000000 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp_df[['Entorhinal', 'Fusiform', 'Hippocampus', 'ICV', 'MidTemp', 'Ventricles', 'WholeBrain', 'serial']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/changhee/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self.obj[key] = _infer_fill_value(value)\n", + "/home/changhee/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self.obj[item] = s\n", + "/home/changhee/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + } + ], + "source": [ + "tmp_df.loc[:, 'Delta'] = 0.5 #all 6 month interval\n", + "\n", + "tmp_df[feat_list] = f_get_Normalization(np.asarray(tmp_df[feat_list]), norm_mode='standard')\n", + "\n", + "id_list = pd.unique(tmp_df['ID'])\n", + "grouped = tmp_df.groupby(by=['ID'])\n", + "\n", + "max_length = 0\n", + "\n", + "remove_list = [162, 167] #these patients have error in the label.. starts with Dementia\n", + "for p_idx in range(len(id_list)): \n", + " tmp_length = grouped.get_group(id_list[p_idx]).shape[0]\n", + " \n", + " if tmp_length == 1:\n", + " remove_list.append(id_list[p_idx])\n", + " \n", + " if max_length <= tmp_length:\n", + " max_length = tmp_length" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "new_id_list = [p_id for p_id in id_list if p_id not in remove_list]\n", + "\n", + "data_x = np.zeros([len(new_id_list), max_length, len(feat_list)+1])\n", + "data_y = np.zeros([len(new_id_list), max_length, 3])\n", + "\n", + "for p_idx in range(len(new_id_list)):\n", + " tmp = grouped.get_group(new_id_list[p_idx])\n", + " tmp_length = tmp.shape[0]\n", + "\n", + " data_x[p_idx, :tmp_length, :] = np.asarray(tmp[['Delta']+feat_list])\n", + " data_y[p_idx, :tmp_length, :] = np.asarray(tmp[label_list])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "np.savez('./ADNI/data.npz', \n", + " data_x = data_x,\n", + " data_y = data_y, \n", + " feat_list = ['Delta'] + feat_list,\n", + " label_list = label_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Jinsung's preprocessed one does not contain last event: dementia\n", + "# filename= './ADNI/ADNI_Data.csv'\n", + "\n", + "# df = pd.read_csv(filename)\n", + "# tmp_list = ['ID', 'AGE', 'PTEDUCAT', 'PTGENDER', 'APOE4', 'CDRSB', 'MMSE', 'ADAS11',\n", + "# 'RAVLT_immediate', 'RAVLT_learning', 'ADAS13', 'RAVLT_forgetting', 'RAVLT_perc_forgetting',\n", + "# 'ICV', 'WholeBrain', 'Ventricles', 'Hippocampus', 'Fusiform', 'MidTemp', 'Entorhinal',\n", + "# 'PTMARRY_Divorced', 'PTMARRY_Married', 'PTMARRY_Never.married', 'PTMARRY_Unknown', 'PTMARRY_Widowed',\n", + "# 'PTRACCAT_Am.Indian.Alaskan', 'PTRACCAT_Asian', 'PTRACCAT_Black', 'PTRACCAT_Hawaiian.Other.PI',\n", + "# 'PTRACCAT_More.than.one', 'PTRACCAT_Unknown', 'PTRACCAT_White', 'PTETHCAT_Hisp.Latino',\n", + "# 'PTETHCAT_Not.Hisp.Latino', 'PTETHCAT_Unknown',\n", + "# 'DX_Dementia', 'DX_MCI', 'DX_NL']\n", + "\n", + "# tmp_df = df[tmp_list]\n", + "\n", + "# feat_list = ['AGE', 'PTEDUCAT', 'PTGENDER', 'APOE4', 'CDRSB', 'MMSE', 'ADAS11',\n", + "# 'RAVLT_immediate', 'RAVLT_learning', 'ADAS13', 'RAVLT_forgetting', 'RAVLT_perc_forgetting',\n", + "# 'ICV', 'WholeBrain', 'Ventricles', 'Hippocampus', 'Fusiform', 'MidTemp', 'Entorhinal',\n", + "# 'PTMARRY_Divorced', 'PTMARRY_Married', 'PTMARRY_Never.married', 'PTMARRY_Unknown', 'PTMARRY_Widowed',\n", + "# 'PTRACCAT_Am.Indian.Alaskan', 'PTRACCAT_Asian', 'PTRACCAT_Black', 'PTRACCAT_Hawaiian.Other.PI',\n", + "# 'PTRACCAT_More.than.one', 'PTRACCAT_Unknown', 'PTRACCAT_White', 'PTETHCAT_Hisp.Latino',\n", + "# 'PTETHCAT_Not.Hisp.Latino', 'PTETHCAT_Unknown']\n", + "\n", + "# label_list = ['DX_Dementia', 'DX_MCI', 'DX_NL']\n", + "\n", + "# tmp_df['Delta'] = 0.5 #all 6 month interval\n", + "\n", + "# tmp_df[feat_list] = f_get_Normalization(np.asarray(tmp_df[feat_list]), norm_mode='standard')\n", + "\n", + "# id_list = pd.unique(tmp_df['ID'])\n", + "# grouped = tmp_df.groupby(by=['ID'])\n", + "\n", + "# max_length = 0\n", + "# remove_list = []\n", + "\n", + "# for p_idx in range(len(id_list)): \n", + "# tmp_length = grouped.get_group(id_list[p_idx]).shape[0]\n", + " \n", + "# if tmp_length == 1:\n", + "# remove_list.append(id_list[p_idx])\n", + " \n", + "# if max_length <= tmp_length:\n", + "# max_length = tmp_length\n", + " \n", + " \n", + "# new_id_list = [p_id for p_id in id_list if p_id not in remove_list]\n", + "\n", + "# data_x = np.zeros([len(new_id_list), max_length, len(feat_list)+1])\n", + "# data_y = np.zeros([len(new_id_list), max_length, 3])\n", + "\n", + "# for p_idx in range(len(new_id_list)):\n", + "# tmp = grouped.get_group(new_id_list[p_idx])\n", + "# tmp_length = tmp.shape[0]\n", + "\n", + "# data_x[p_idx, :tmp_length, :] = np.asarray(tmp[['Delta']+feat_list])\n", + "# data_y[p_idx, :tmp_length, :] = np.asarray(tmp[label_list])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data/.ipynb_checkpoints/data_CF_label_test-checkpoint.ipynb b/data/.ipynb_checkpoints/data_CF_label_test-checkpoint.ipynb new file mode 100644 index 0000000..e092404 --- /dev/null +++ b/data/.ipynb_checkpoints/data_CF_label_test-checkpoint.ipynb @@ -0,0 +1,1661 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/changhee/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "\n", + "import random\n", + "import os\n", + "\n", + "from tensorflow.python.ops.rnn import _transpose_batch_time\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "#performance metrics\n", + "from sklearn.cluster import MiniBatchKMeans, KMeans\n", + "from sklearn.metrics import roc_auc_score, average_precision_score\n", + "from sklearn.metrics import normalized_mutual_info_score, homogeneity_score, adjusted_rand_score\n", + "from sklearn.metrics.cluster import contingency_matrix\n", + "\n", + "#user defined\n", + "import utils_network as utils" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "data_mode = 'CF_comorbidity_select'" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORT DATASET\n", + "\n", + "if data_mode == 'CF':\n", + " npz = np.load('./data/CF/data.npz')\n", + "\n", + " data_x = npz['data_x']\n", + " data_y = npz['data_y']\n", + " data_y_onehot = npz['data_y_onehot']\n", + " feat_list = npz['feat_list']\n", + " \n", + "elif data_mode == 'CF_comorbidity':\n", + " npz = np.load('./data/CF_comorbidity/data_como.npz')\n", + " \n", + " data_x = npz['data_x']\n", + " data_y = npz['data_y']\n", + " feat_list = npz['feat_list']\n", + " label_list = npz['label_list']\n", + " selected_list = npz['selected_list']\n", + " \n", + " data_y_selected = data_y[:, :, np.where([f in selected_list for f in label_list])[0]]\n", + " \n", + "elif data_mode == 'CF_comorbidity_select':\n", + " npz = np.load('./data/CF_comorbidity/data_como.npz')\n", + " \n", + " data_x = npz['data_x']\n", + " data_y = npz['data_y']\n", + " feat_list = npz['feat_list']\n", + " label_list = npz['label_list']\n", + " selected_list = npz['selected_list']\n", + " \n", + " data_y = data_y[:, :, np.where([f in selected_list for f in label_list])[0]]\n", + " label_list = selected_list\n", + " \n", + " tmp_onehot = np.zeros([np.shape(data_y)[0], np.shape(data_y)[1], 8])\n", + "\n", + " tmp_onehot[np.sum(data_y == [0,0,0], axis=2) == 3] = [1, 0, 0, 0, 0, 0, 0, 0]\n", + " tmp_onehot[np.sum(data_y == [0,0,1], axis=2) == 3] = [0, 1, 0, 0, 0, 0, 0, 0]\n", + " tmp_onehot[np.sum(data_y == [0,1,0], axis=2) == 3] = [0, 0, 1, 0, 0, 0, 0, 0]\n", + " tmp_onehot[np.sum(data_y == [0,1,1], axis=2) == 3] = [0, 0, 0, 1, 0, 0, 0, 0]\n", + " tmp_onehot[np.sum(data_y == [1,0,0], axis=2) == 3] = [0, 0, 0, 0, 1, 0, 0, 0]\n", + " tmp_onehot[np.sum(data_y == [1,0,1], axis=2) == 3] = [0, 0, 0, 0, 0, 1, 0, 0]\n", + " tmp_onehot[np.sum(data_y == [1,1,0], axis=2) == 3] = [0, 0, 0, 0, 0, 0, 1, 0]\n", + " tmp_onehot[np.sum(data_y == [1,1,1], axis=2) == 3] = [0, 0, 0, 0, 0, 0, 0, 1]\n", + "\n", + " tmp_onehot[np.sum(np.abs(data_x), axis=2) == 0] = [0, 0, 0, 0, 0, 0, 0, 0] #put all 0's for not selected ones..\n", + "\n", + " data_y = tmp_onehot" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "### PARAMETER LOGGING\n", + "def save_logging(dictionary, log_name):\n", + " with open(log_name, 'w') as f:\n", + " for key, value in dictionary.items():\n", + " if 'activate_fn' in key:\n", + " value = str(value).split(' ')[1]\n", + " \n", + " f.write('%s:%s\\n' % (key, value))\n", + "\n", + "\n", + "def load_logging(filename):\n", + " data = dict()\n", + " with open(filename) as f:\n", + " def is_float(input):\n", + " try:\n", + " num = float(input)\n", + " except ValueError:\n", + " return False\n", + " return True\n", + "\n", + " for line in f.readlines():\n", + " if ':' in line:\n", + " key,value = line.strip().split(':', 1)\n", + " \n", + " if 'activate_fn' in key:\n", + " if value == 'relu':\n", + " value = tf.nn.relu\n", + " elif value == 'elu':\n", + " value = tf.nn.elu\n", + " elif value == 'tanh':\n", + " value = tf.nn.tanh\n", + " else:\n", + " raise ValueError('ERROR: wrong choice of activation function!')\n", + " data[key] = value\n", + " else:\n", + " if value.isdigit():\n", + " data[key] = int(value)\n", + " elif is_float(value):\n", + " data[key] = float(value)\n", + " elif value == 'None':\n", + " data[key] = None\n", + " else:\n", + " data[key] = value\n", + " else:\n", + " pass # deal with bad lines of text here \n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "def log(x): \n", + " return tf.log(x + 1e-8)\n", + "\n", + "def div(x, y):\n", + " return tf.div(x, (y + 1e-8))\n", + "\n", + "def get_seq_length(sequence):\n", + " used = tf.sign(tf.reduce_max(tf.abs(sequence), 2))\n", + " tmp_length = tf.reduce_sum(used, 1)\n", + " tmp_length = tf.cast(tmp_length, tf.int32)\n", + " return tmp_length\n", + "\n", + "\n", + "def f_get_minibatch(mb_size, x, y):\n", + " idx = range(np.shape(x)[0])\n", + " idx = random.sample(idx, mb_size)\n", + "\n", + " x_mb = x[idx, :, :].astype(float) \n", + " y_mb = y[idx, :, :].astype(float) \n", + "\n", + " return x_mb, y_mb" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "### PERFORMANCE METRICS:\n", + "def f_get_prediction_scores(y_true_, y_pred_):\n", + " if np.sum(y_true_) == 0: #no label for running roc_auc_curves\n", + " auroc_ = -1.\n", + " auprc_ = -1.\n", + " else:\n", + " auroc_ = roc_auc_score(y_true_, y_pred_)\n", + " auprc_ = average_precision_score(y_true_, y_pred_)\n", + " return (auroc_, auprc_)\n", + "\n", + "def purity_score(y_true, y_pred):\n", + " # compute contingency matrix (also called confusion matrix)\n", + " c_matrix = contingency_matrix(y_true, y_pred)\n", + " # return purity\n", + " return np.sum(np.amax(c_matrix, axis=0)) / np.sum(c_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "class DCN_E2P:\n", + " def __init__(self, sess, name, input_dims, network_settings):\n", + " self.sess = sess\n", + " self.name = name\n", + " \n", + " # INPUT/OUTPUT DIMENSIONS\n", + " self.x_dim = input_dims['x_dim'] #features + delta\n", + " self.max_length = input_dims['max_length']\n", + " \n", + " self.y_dim = input_dims['y_dim']\n", + " self.y_type = input_dims['y_type'] #categorical, binary, continuous\n", + " \n", + "\n", + " # Encoder\n", + " self.h_dim_f = network_settings['h_dim_encoder'] #encoder nodes\n", + " self.num_layers_f = network_settings['num_layers_encoder'] #encoder layers\n", + " self.rnn_type = network_settings['rnn_type']\n", + " self.rnn_activate_fn = network_settings['rnn_activate_fn']\n", + "\n", + " # Predictor\n", + " self.h_dim_g = network_settings['h_dim_predictor'] #predictor nodes\n", + " self.num_layers_g = network_settings['num_layers_predictor'] #predictor layers\n", + " \n", + " self.fc_activate_fn = network_settings['fc_activate_fn'] #selector & predictor\n", + " \n", + " # Latent Space\n", + " self.z_dim = self.h_dim_f * self.num_layers_f\n", + "\n", + " \n", + " self._build_net()\n", + "\n", + " \n", + " def _build_net(self):\n", + " with tf.variable_scope(self.name):\n", + " \n", + " self.mb_size = tf.placeholder(tf.int32, [], name='batch_size')\n", + " self.lr_rate = tf.placeholder(tf.float32, name='learning_rate')\n", + " self.keep_prob = tf.placeholder(tf.float32, name='keep_probability')\n", + "\n", + " self.x = tf.placeholder(tf.float32, [None, self.max_length, self.x_dim], name='inputs')\n", + " self.y = tf.placeholder(tf.float32, [None, self.max_length, self.y_dim], name='labels')\n", + "# self.y_onehot = tf.placeholder(tf.float32, [None, self.max_length, self.y_dim], name='labels_onehot')\n", + " \n", + " \n", + " #FOR CLUSTERING\n", + " self.E = tf.placeholder(tf.float32, [None, self.z_dim], name='embeddings') #mu\n", + " self.K = tf.placeholder(tf.int32, [], name='num_Cluster')\n", + " self.s = tf.placeholder(tf.int32, [None], name='s')\n", + " s_one_hot = tf.one_hot(self.s, self.K, name='s_one_hot')\n", + "\n", + " \n", + " # LOSS PARAMETERS\n", + " self.alpha = tf.placeholder(tf.float32, name = 'alpha')\n", + "\n", + " \n", + " '''\n", + " ### CREATE RNN MASK\n", + " - This is to flexibly handle sequences with different length\n", + " - rnn_mask1: last observation; [mb_size, max_length]\n", + " - rnn_mask2: all available observations; [mb_size, max_length]\n", + " '''\n", + " # CREATE RNN MASK: \n", + " seq_length = get_seq_length(self.x)\n", + " tmp_range = tf.expand_dims(tf.range(0, self.max_length, 1), axis=0)\n", + " self.rnn_mask1 = tf.cast(tf.equal(tmp_range, tf.expand_dims(seq_length-1, axis=1)), tf.float32) #last observation\n", + " self.rnn_mask2 = tf.cast(tf.less_equal(tmp_range, tf.expand_dims(seq_length-1, axis=1)), tf.float32) #all available observation\n", + " \n", + " \n", + " ### DEFINE PREDICTOR\n", + " def predictor(x_, o_dim_=self.y_dim, o_type_=self.y_type, num_layers_=1, h_dim_=self.h_dim_g, activation_fn=self.fc_activate_fn, reuse=tf.AUTO_REUSE):\n", + " if o_type_ == 'continuous':\n", + " out_fn = None\n", + " elif o_type_ == 'categorical':\n", + " out_fn = tf.nn.softmax #for classification task\n", + " elif o_type_ == 'binary':\n", + " out_fn = tf.nn.sigmoid\n", + " else:\n", + " raise Exception('Wrong output type. The value {}!!'.format(o_type_))\n", + " \n", + " with tf.variable_scope('predictor', reuse=reuse):\n", + " if num_layers_ == 1:\n", + " out = tf.contrib.layers.fully_connected(inputs=x_, num_outputs=o_dim_, activation_fn=out_fn, scope='predictor_out')\n", + " else: #num_layers > 1\n", + " for tmp_layer in range(num_layers_-1):\n", + " if tmp_layer == 0:\n", + " net = x_\n", + " net = tf.contrib.layers.fully_connected(inputs=net, num_outputs=h_dim_, activation_fn=activation_fn, scope='predictor_'+str(tmp_layer))\n", + " net = tf.nn.dropout(net, keep_prob=self.keep_prob)\n", + " out = tf.contrib.layers.fully_connected(inputs=net, num_outputs=o_dim_, activation_fn=out_fn, scope='predictor_out') \n", + " return out\n", + "\n", + " \n", + " ### DEFINE LOOP FUNCTION FOR ENCODRER (f-g, f-h relations are created here)\n", + " def loop_fn(time, cell_output, cell_state, loop_state):\n", + " \n", + " emit_output = cell_output \n", + "\n", + " if cell_output is None: # time == 0\n", + " next_cell_state = cell.zero_state(self.mb_size, tf.float32)\n", + " next_loop_state = loop_state_ta\n", + " else:\n", + " next_cell_state = cell_state\n", + " tmp_z = utils.create_concat_state_h(next_cell_state, self.num_layers_f, self.rnn_type) \n", + " tmp_y = predictor(tmp_z, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn) \n", + "\n", + " next_loop_state = (loop_state[0].write(time-1, tmp_z), # save all the hidden states\n", + " loop_state[1].write(time-1, tmp_y)) # save all the selector_net output (i.e., pi)\n", + "\n", + " elements_finished = (time >= max_length)\n", + "\n", + " #this gives the break-point (no more recurrence after the max_length)\n", + " finished = tf.reduce_all(elements_finished) \n", + " next_input = tf.cond(finished, \n", + " lambda: tf.zeros([self.mb_size, self.x_dim], dtype=tf.float32), \n", + " lambda: inputs_ta.read(time))\n", + " return (elements_finished, next_input, next_cell_state, emit_output, next_loop_state)\n", + "\n", + " \n", + " '''\n", + " ##### CREATE RNN NETWORK\n", + " - (INPUT) inputs_ta: TensorArray with [max_length, mb_size, x_dim] #x_dim included delta\n", + " - (OUTPUT) \n", + " . zs = rnn states (h) in LSTM/GRU ; [mb_size, max_length z_dim]\n", + " . y_hats = output of predictor taking zs as inputs; [mb_size, max_length, y_dim]\n", + "\n", + " '''\n", + " inputs = self.x\n", + " inputs_ta = tf.TensorArray(\n", + " dtype=tf.float32, size=self.max_length\n", + " ).unstack(_transpose_batch_time(inputs), name = 'rnn_input')\n", + "\n", + "\n", + " cell = utils.create_rnn_cell(\n", + " self.h_dim_f, self.num_layers_f, \n", + " self.keep_prob, self.rnn_type, self.rnn_activate_fn\n", + " )\n", + "\n", + " #define the loop_state TensorArray for information from rnn time steps\n", + " loop_state_ta = (\n", + " tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False), #zs (j=1,...,J)\n", + " tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False) #y_hats (j=1,...,J)\n", + " ) \n", + "\n", + " _, _, loop_state_ta = tf.nn.raw_rnn(cell, loop_fn) #, parallel_iterations=1)\n", + "\n", + "\n", + " self.zs = _transpose_batch_time(loop_state_ta[0].stack())\n", + " self.y_hats = _transpose_batch_time(loop_state_ta[1].stack())\n", + " \n", + " #Last latent representation\n", + " self. Z = tf.reduce_sum(tf.tile(tf.expand_dims(self.rnn_mask1, axis=2), [1,1,self.z_dim]) * self.zs, axis=1)\n", + " \n", + " \n", + " ### DEFINE LOSS FUNCTIONS\n", + " #\\ell_{1}: KL divergence loss for regression and binary/categorical-classification task\n", + " def loss_1(y_true_, y_pred_, y_type_ = self.y_type): \n", + " if y_type_ == 'continuous':\n", + " tmp_loss = tf.reduce_sum((y_true_ - y_pred_)**2, axis=2)\n", + " elif y_type_ == 'categorical':\n", + " tmp_loss = - tf.reduce_sum(y_true_ * log(y_pred_), axis=2)\n", + " elif y_type_ == 'binary':\n", + " tmp_loss = - tf.reduce_sum(y_true_ * log(y_pred_) + (1.-y_true_) * log(1.-y_pred_), axis=2)\n", + " else:\n", + " raise Exception('Wrong output type. The value {}!!'.format(y_type_)) \n", + " return tmp_loss\n", + "\n", + "\n", + " ## LOSS_MLE: MLE loss\n", + " self.LOSS_MLE = tf.reduce_mean(tf.reduce_sum(self.rnn_mask2 * loss_1(self.y, self.y_hats, self.y_type), axis=1))\n", + "\n", + " \n", + " ### CLUSTER LOSS\n", + " Z_expanded = tf.tile(tf.expand_dims(self.Z, axis=1), [1, self.K, 1]) #[None, num_Cluster, 2]\n", + " MU_expanded = tf.tile(tf.expand_dims(self.E, axis=0), [self.mb_size, 1, 1]) #[None, num_Cluster, 2]\n", + " dist_z_expanded = tf.reduce_sum((Z_expanded - MU_expanded)**2, axis=2) #[None, num_Cluster]\n", + "\n", + " dist_z_homo = tf.reduce_sum(dist_z_expanded * s_one_hot, axis=1) #[None]\n", + " dist_z_hetero = tf.reduce_sum(dist_z_expanded * (1. -s_one_hot), axis=1) #[None]\n", + "\n", + " dist_z_homo = tf.reduce_mean(dist_z_homo)\n", + " dist_z_hetero = tf.reduce_mean(dist_z_hetero)\n", + "\n", + " self.LOSS_CLU = dist_z_homo\n", + "\n", + " \n", + " self.LOSS_TOTAL = self.LOSS_MLE + self.alpha*self.LOSS_CLU\n", + "\n", + "\n", + " predictor_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='rnn/predictor')\n", + " encoder_vars = [vars_ for vars_ in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) \n", + " if vars_ not in predictor_vars]\n", + " \n", + " \n", + " self.solver_MLE = tf.train.AdamOptimizer(self.lr_rate).minimize(self.LOSS_MLE)#, var_list=encoder_vars+predictor_vars)\n", + " self.solver_TOTAL = tf.train.AdamOptimizer(self.lr_rate).minimize(self.LOSS_TOTAL)\n", + "\n", + "\n", + " #to check the predictor output given z\n", + " self.zz = tf.placeholder(tf.float32, [None, self.z_dim])\n", + " with tf.variable_scope('rnn', reuse=True):\n", + " self.yy = predictor(self.zz, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn)\n", + " \n", + "\n", + " def train_mle(self, x_, y_, lr_train, k_prob):\n", + " return self.sess.run([self.solver_MLE, self.LOSS_MLE],\n", + " feed_dict={self.x: x_, self.y: y_, \n", + " self.mb_size:np.shape(x_)[0], self.lr_rate: lr_train, self.keep_prob: k_prob})\n", + " \n", + " def train_total(self, x_, y_, s_, E_, K_, a, lr_train, k_prob):\n", + " return self.sess.run([self.solver_TOTAL, self.LOSS_TOTAL, self.LOSS_MLE, self.LOSS_CLU],\n", + " feed_dict={self.x: x_, self.y: y_, self.s: s_, self.E: E_, self.K: K_,\n", + " self.mb_size:np.shape(x_)[0], self.alpha:a,\n", + " self.lr_rate: lr_train, self.keep_prob: k_prob})\n", + " \n", + " def predict_y_hats(self, x_):\n", + " return self.sess.run([self.y_hats, self.rnn_mask2], \n", + " feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})\n", + " \n", + " def predict_zs(self, x_):\n", + " return self.sess.run([self.zs, self.rnn_mask2], \n", + " feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})\n", + " \n", + " def predict_Z(self, x_): #get the last latent encoding\n", + " return self.sess.run(self.Z, \n", + " feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0}) " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "h_dim_FC = 50 #for fully_connected layers\n", + "h_dim_RNN = 50\n", + "\n", + "x_dim = np.shape(data_x)[2]\n", + "y_dim = np.shape(data_y)[2]\n", + "\n", + "if data_mode == 'CF':\n", + " y_type = 'categorical'\n", + "elif data_mode == 'CF_comorbidity':\n", + " y_type = 'binary'\n", + "elif data_mode == 'CF_comorbidity_select':\n", + " y_type = 'categorical'\n", + " \n", + "num_layer_encoder = 1\n", + "num_layer_predictor = 2\n", + "\n", + "z_dim = h_dim_RNN * num_layer_encoder\n", + "\n", + "max_length = np.shape(data_x)[1]\n", + "\n", + "rnn_type = 'LSTM' #GRU, LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "input_dims ={\n", + " 'x_dim': x_dim,\n", + " 'y_dim': y_dim,\n", + " 'max_length': max_length,\n", + " 'y_type': y_type\n", + "}\n", + "\n", + "network_settings ={\n", + " 'h_dim_encoder': h_dim_RNN,\n", + " 'num_layers_encoder': num_layer_encoder,\n", + " 'rnn_type': rnn_type,\n", + " 'rnn_activate_fn': tf.nn.tanh,\n", + " \n", + " 'h_dim_predictor': h_dim_FC,\n", + " 'num_layers_predictor': num_layer_predictor,\n", + " \n", + " 'fc_activate_fn': tf.nn.relu\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "OUT_ITERATION = 5\n", + "\n", + "RESULT_NMI = np.zeros([OUT_ITERATION, 1])\n", + "RESULT_RI = np.zeros([OUT_ITERATION, 1])\n", + "RESULT_PURITY = np.zeros([OUT_ITERATION, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "# Turn on xla optimization\n", + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "sess = tf.Session(config=config)\n", + "\n", + "model = DCN_E2P(sess, \"dcn_E2P\", input_dims, network_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "seed = 1234\n", + "\n", + "out_itr = 0\n", + "\n", + "if data_mode == 'CF':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y, tr_data_y_onehot,te_data_y_onehot = train_test_split(\n", + " data_x, data_y, data_y_onehot, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y, tr_data_y_onehot,va_data_y_onehot = train_test_split(\n", + " tr_data_x, tr_data_y, tr_data_y_onehot, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "elif data_mode == 'CF_comorbidity':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y = train_test_split(\n", + " data_x, data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y = train_test_split(\n", + " tr_data_x, tr_data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "elif data_mode == 'CF_comorbidity_select':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y = train_test_split(\n", + " data_x, data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y = train_test_split(\n", + " tr_data_x, tr_data_y, test_size=0.2, random_state=seed+out_itr\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "save_path = './{}/dcn_E2P/init/itr{}/'.format(data_mode, out_itr)\n", + "\n", + "if not os.path.exists(save_path + '/models/'):\n", + " os.makedirs(save_path + '/models/')\n", + "\n", + "if not os.path.exists(save_path + '/results/'):\n", + " os.makedirs(save_path + '/results/')" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "saver = tf.train.Saver()\n", + "sess.run(tf.global_variables_initializer())\n", + "\n", + "lr_rate = 1e-3\n", + "keep_prob = 0.7\n", + "mb_size = 128" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ITR 1000: loss_2=3.5328 | va_auroc:0.9279, va_auprc:0.6123\n", + "ITR 2000: loss_2=2.6224 | va_auroc:0.9166, va_auprc:0.5837\n", + "ITR 3000: loss_2=2.3386 | va_auroc:0.9043, va_auprc:0.5518\n", + "ITR 4000: loss_2=2.1690 | va_auroc:0.9009, va_auprc:0.5197\n", + "ITR 5000: loss_2=2.0748 | va_auroc:0.8936, va_auprc:0.5056\n", + "ITR 6000: loss_2=1.9900 | va_auroc:0.8936, va_auprc:0.5074\n", + "ITR 7000: loss_2=1.9322 | va_auroc:0.8847, va_auprc:0.4933\n", + "ITR 8000: loss_2=1.8913 | va_auroc:0.8867, va_auprc:0.4971\n", + "ITR 9000: loss_2=1.8549 | va_auroc:0.8805, va_auprc:0.4870\n", + "ITR 10000: loss_2=1.8282 | va_auroc:0.8835, va_auprc:0.5035\n", + "ITR 11000: loss_2=1.7946 | va_auroc:0.8823, va_auprc:0.4938\n", + "ITR 12000: loss_2=1.7767 | va_auroc:0.8761, va_auprc:0.4856\n", + "ITR 13000: loss_2=1.7539 | va_auroc:0.8762, va_auprc:0.4638\n", + "ITR 14000: loss_2=1.7354 | va_auroc:0.8791, va_auprc:0.4774\n", + "ITR 15000: loss_2=1.7241 | va_auroc:0.8754, va_auprc:0.4709\n", + "ITR 16000: loss_2=1.6905 | va_auroc:0.8758, va_auprc:0.4707\n", + "ITR 17000: loss_2=1.6871 | va_auroc:0.8796, va_auprc:0.4719\n", + "ITR 18000: loss_2=1.6852 | va_auroc:0.8741, va_auprc:0.4515\n", + "ITR 19000: loss_2=1.6575 | va_auroc:0.8732, va_auprc:0.4719\n", + "ITR 20000: loss_2=1.6598 | va_auroc:0.8746, va_auprc:0.4625\n", + "ITR 21000: loss_2=1.6511 | va_auroc:0.8707, va_auprc:0.4541\n", + "ITR 22000: loss_2=1.6342 | va_auroc:0.8735, va_auprc:0.4585\n", + "ITR 23000: loss_2=1.6347 | va_auroc:0.8770, va_auprc:0.4648\n", + "ITR 24000: loss_2=1.6347 | va_auroc:0.8744, va_auprc:0.4617\n", + "ITR 25000: loss_2=1.6184 | va_auroc:0.8693, va_auprc:0.4490\n", + "ITR 26000: loss_2=1.6181 | va_auroc:0.8764, va_auprc:0.4507\n", + "ITR 27000: loss_2=1.6066 | va_auroc:0.8744, va_auprc:0.4605\n", + "ITR 28000: loss_2=1.5930 | va_auroc:0.8729, va_auprc:0.4562\n", + "ITR 29000: loss_2=1.6006 | va_auroc:0.8705, va_auprc:0.4469\n", + "ITR 30000: loss_2=1.5888 | va_auroc:0.8750, va_auprc:0.4492\n", + "ITR 31000: loss_2=1.5846 | va_auroc:0.8721, va_auprc:0.4579\n", + "ITR 32000: loss_2=1.5676 | va_auroc:0.8753, va_auprc:0.4453\n", + "ITR 33000: loss_2=1.5869 | va_auroc:0.8737, va_auprc:0.4565\n", + "ITR 34000: loss_2=1.5643 | va_auroc:0.8724, va_auprc:0.4536\n", + "ITR 35000: loss_2=1.5635 | va_auroc:0.8689, va_auprc:0.4464\n", + "ITR 36000: loss_2=1.5698 | va_auroc:0.8747, va_auprc:0.4577\n", + "ITR 37000: loss_2=1.5441 | va_auroc:0.8728, va_auprc:0.4470\n", + "ITR 38000: loss_2=1.5563 | va_auroc:0.8710, va_auprc:0.4571\n", + "ITR 39000: loss_2=1.5515 | va_auroc:0.8713, va_auprc:0.4518\n", + "ITR 40000: loss_2=1.5482 | va_auroc:0.8683, va_auprc:0.4541\n", + "ITR 41000: loss_2=1.5550 | va_auroc:0.8655, va_auprc:0.4526\n", + "ITR 42000: loss_2=1.5374 | va_auroc:0.8684, va_auprc:0.4468\n", + "ITR 43000: loss_2=1.5344 | va_auroc:0.8692, va_auprc:0.4556\n", + "ITR 44000: loss_2=1.5276 | va_auroc:0.8694, va_auprc:0.4450\n", + "ITR 45000: loss_2=1.5276 | va_auroc:0.8631, va_auprc:0.4472\n", + "ITR 46000: loss_2=1.5258 | va_auroc:0.8711, va_auprc:0.4713\n", + "ITR 47000: loss_2=1.5191 | va_auroc:0.8708, va_auprc:0.4389\n", + "ITR 48000: loss_2=1.5156 | va_auroc:0.8719, va_auprc:0.4431\n", + "ITR 49000: loss_2=1.5157 | va_auroc:0.8702, va_auprc:0.4353\n", + "ITR 50000: loss_2=1.5166 | va_auroc:0.8742, va_auprc:0.4408\n" + ] + } + ], + "source": [ + "ITERATION = 50000\n", + "check_step = 1000\n", + "\n", + "avg_loss_mle = 0\n", + "for itr in range(ITERATION):\n", + " x_mb, y_mb = f_get_minibatch(mb_size, tr_data_x, tr_data_y)\n", + " \n", + " _, tmp_loss_mle= model.train_mle(x_mb, y_mb, lr_rate, keep_prob)\n", + " avg_loss_mle += tmp_loss_mle/check_step\n", + " \n", + " if (itr+1)%check_step == 0: \n", + " tmp_y, tmp_m = model.predict_y_hats(va_data_x)\n", + "\n", + " y_pred = tmp_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1]\n", + " y_true = va_data_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1]\n", + " \n", + " AUROC = np.zeros([y_dim])\n", + " AUPRC = np.zeros([y_dim])\n", + " for y_idx in range(y_dim):\n", + " auroc, auprc = f_get_prediction_scores(y_true[:, y_idx], y_pred[:, y_idx])\n", + " AUROC[y_idx] = auroc\n", + " AUPRC[y_idx] = auprc\n", + " \n", + " print (\"ITR {}: loss_2={:.4f} | va_auroc:{:.4f}, va_auprc:{:.4f}\".format(\n", + " itr+1, avg_loss_mle, np.mean(AUROC), np.mean(AUPRC)))\n", + " \n", + " avg_loss_mle = 0\n", + " \n", + "saver.save(sess, save_path + 'models/dcn_E2P_v4_init')\n", + "save_logging(network_settings, save_path + 'models/network_settings_v4.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "saver.save(sess, save_path + 'models/dcn_E2P_v4_init')\n", + "save_logging(network_settings, save_path + 'models/network_settings_v4.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ITR 1000: loss_2=3.5245 | va_auroc:0.9311, va_auprc:0.6494\n", + "ITR 2000: loss_2=2.6116 | va_auroc:0.9184, va_auprc:0.5977\n", + "ITR 3000: loss_2=2.3233 | va_auroc:0.9109, va_auprc:0.5550\n", + "ITR 4000: loss_2=2.1541 | va_auroc:0.9054, va_auprc:0.5346\n", + "ITR 5000: loss_2=2.0763 | va_auroc:0.9026, va_auprc:0.5227\n", + "ITR 6000: loss_2=1.9935 | va_auroc:0.9002, va_auprc:0.5143\n", + "ITR 7000: loss_2=1.9401 | va_auroc:0.8964, va_auprc:0.5248\n", + "ITR 8000: loss_2=1.8791 | va_auroc:0.8955, va_auprc:0.5254\n", + "ITR 9000: loss_2=1.8576 | va_auroc:0.8891, va_auprc:0.5125\n", + "ITR 10000: loss_2=1.8172 | va_auroc:0.8964, va_auprc:0.5218\n", + "ITR 11000: loss_2=1.7970 | va_auroc:0.8939, va_auprc:0.5229\n", + "ITR 12000: loss_2=1.7709 | va_auroc:0.8880, va_auprc:0.5121\n", + "ITR 13000: loss_2=1.7556 | va_auroc:0.8853, va_auprc:0.5068\n", + "ITR 14000: loss_2=1.7356 | va_auroc:0.8873, va_auprc:0.5095\n", + "ITR 15000: loss_2=1.7163 | va_auroc:0.8852, va_auprc:0.5078\n", + "ITR 16000: loss_2=1.7007 | va_auroc:0.8852, va_auprc:0.5111\n", + "ITR 17000: loss_2=1.7022 | va_auroc:0.8896, va_auprc:0.5107\n", + "ITR 18000: loss_2=1.6834 | va_auroc:0.8828, va_auprc:0.4972\n", + "ITR 19000: loss_2=1.6780 | va_auroc:0.8856, va_auprc:0.5098\n", + "ITR 20000: loss_2=1.6549 | va_auroc:0.8851, va_auprc:0.4990\n", + "ITR 21000: loss_2=1.6408 | va_auroc:0.8816, va_auprc:0.4817\n", + "ITR 22000: loss_2=1.6410 | va_auroc:0.8834, va_auprc:0.4856\n", + "ITR 23000: loss_2=1.6340 | va_auroc:0.8811, va_auprc:0.4889\n", + "ITR 24000: loss_2=1.6170 | va_auroc:0.8830, va_auprc:0.4829\n", + "ITR 25000: loss_2=1.6113 | va_auroc:0.8834, va_auprc:0.4995\n", + "ITR 26000: loss_2=1.6056 | va_auroc:0.8790, va_auprc:0.4820\n", + "ITR 27000: loss_2=1.6063 | va_auroc:0.8828, va_auprc:0.4912\n", + "ITR 28000: loss_2=1.6019 | va_auroc:0.8809, va_auprc:0.4821\n", + "ITR 29000: loss_2=1.5866 | va_auroc:0.8768, va_auprc:0.4759\n", + "ITR 30000: loss_2=1.5858 | va_auroc:0.8807, va_auprc:0.4895\n", + "ITR 31000: loss_2=1.5838 | va_auroc:0.8782, va_auprc:0.4767\n", + "ITR 32000: loss_2=1.5748 | va_auroc:0.8825, va_auprc:0.4899\n", + "ITR 33000: loss_2=1.5683 | va_auroc:0.8799, va_auprc:0.4902\n", + "ITR 34000: loss_2=1.5599 | va_auroc:0.8817, va_auprc:0.4912\n", + "ITR 35000: loss_2=1.5589 | va_auroc:0.8821, va_auprc:0.4910\n", + "ITR 36000: loss_2=1.5601 | va_auroc:0.8766, va_auprc:0.4963\n", + "ITR 37000: loss_2=1.5551 | va_auroc:0.8843, va_auprc:0.4889\n", + "ITR 38000: loss_2=1.5559 | va_auroc:0.8834, va_auprc:0.4984\n", + "ITR 39000: loss_2=1.5486 | va_auroc:0.8790, va_auprc:0.4892\n", + "ITR 40000: loss_2=1.5447 | va_auroc:0.8828, va_auprc:0.4879\n", + "ITR 41000: loss_2=1.5327 | va_auroc:0.8802, va_auprc:0.4906\n", + "ITR 42000: loss_2=1.5294 | va_auroc:0.8830, va_auprc:0.4913\n", + "ITR 43000: loss_2=1.5213 | va_auroc:0.8799, va_auprc:0.4933\n", + "ITR 44000: loss_2=1.5217 | va_auroc:0.8827, va_auprc:0.4931\n", + "ITR 45000: loss_2=1.5290 | va_auroc:0.8793, va_auprc:0.4770\n", + "ITR 46000: loss_2=1.5256 | va_auroc:0.8826, va_auprc:0.5004\n", + "ITR 47000: loss_2=1.5184 | va_auroc:0.8804, va_auprc:0.5064\n", + "ITR 48000: loss_2=1.5211 | va_auroc:0.8816, va_auprc:0.5102\n", + "ITR 49000: loss_2=1.5114 | va_auroc:0.8769, va_auprc:0.5071\n", + "ITR 50000: loss_2=1.5110 | va_auroc:0.8810, va_auprc:0.4960\n", + "ITR 1000: loss_2=3.4658 | va_auroc:0.9241, va_auprc:0.6109\n", + "ITR 2000: loss_2=2.6006 | va_auroc:0.9063, va_auprc:0.5904\n", + "ITR 3000: loss_2=2.3193 | va_auroc:0.8947, va_auprc:0.5615\n", + "ITR 4000: loss_2=2.1569 | va_auroc:0.8891, va_auprc:0.5437\n", + "ITR 5000: loss_2=2.0446 | va_auroc:0.8818, va_auprc:0.5324\n", + "ITR 6000: loss_2=1.9652 | va_auroc:0.8813, va_auprc:0.5220\n", + "ITR 7000: loss_2=1.9079 | va_auroc:0.8790, va_auprc:0.5098\n", + "ITR 8000: loss_2=1.8619 | va_auroc:0.8733, va_auprc:0.5200\n", + "ITR 9000: loss_2=1.8345 | va_auroc:0.8792, va_auprc:0.5103\n", + "ITR 10000: loss_2=1.8015 | va_auroc:0.8762, va_auprc:0.5093\n", + "ITR 11000: loss_2=1.7780 | va_auroc:0.8777, va_auprc:0.5032\n", + "ITR 12000: loss_2=1.7572 | va_auroc:0.8772, va_auprc:0.5113\n", + "ITR 13000: loss_2=1.7303 | va_auroc:0.8756, va_auprc:0.5057\n", + "ITR 14000: loss_2=1.7152 | va_auroc:0.8783, va_auprc:0.5128\n", + "ITR 15000: loss_2=1.7008 | va_auroc:0.8740, va_auprc:0.5142\n", + "ITR 16000: loss_2=1.6972 | va_auroc:0.8730, va_auprc:0.4910\n", + "ITR 17000: loss_2=1.6738 | va_auroc:0.8758, va_auprc:0.4978\n", + "ITR 18000: loss_2=1.6645 | va_auroc:0.8736, va_auprc:0.4979\n", + "ITR 19000: loss_2=1.6589 | va_auroc:0.8706, va_auprc:0.4934\n", + "ITR 20000: loss_2=1.6325 | va_auroc:0.8686, va_auprc:0.4873\n", + "ITR 21000: loss_2=1.6200 | va_auroc:0.8715, va_auprc:0.4902\n", + "ITR 22000: loss_2=1.6203 | va_auroc:0.8684, va_auprc:0.4981\n", + "ITR 23000: loss_2=1.6138 | va_auroc:0.8674, va_auprc:0.4931\n", + "ITR 24000: loss_2=1.5979 | va_auroc:0.8746, va_auprc:0.4923\n", + "ITR 25000: loss_2=1.6038 | va_auroc:0.8706, va_auprc:0.4839\n", + "ITR 26000: loss_2=1.5728 | va_auroc:0.8715, va_auprc:0.4920\n", + "ITR 27000: loss_2=1.5782 | va_auroc:0.8695, va_auprc:0.4984\n", + "ITR 28000: loss_2=1.5828 | va_auroc:0.8723, va_auprc:0.4887\n", + "ITR 29000: loss_2=1.5794 | va_auroc:0.8722, va_auprc:0.4981\n", + "ITR 30000: loss_2=1.5725 | va_auroc:0.8691, va_auprc:0.4866\n", + "ITR 31000: loss_2=1.5688 | va_auroc:0.8715, va_auprc:0.4862\n", + "ITR 32000: loss_2=1.5574 | va_auroc:0.8661, va_auprc:0.4813\n", + "ITR 33000: loss_2=1.5462 | va_auroc:0.8704, va_auprc:0.4822\n", + "ITR 34000: loss_2=1.5564 | va_auroc:0.8687, va_auprc:0.4649\n", + "ITR 35000: loss_2=1.5571 | va_auroc:0.8743, va_auprc:0.4935\n", + "ITR 36000: loss_2=1.5345 | va_auroc:0.8685, va_auprc:0.4770\n", + "ITR 37000: loss_2=1.5438 | va_auroc:0.8681, va_auprc:0.4761\n", + "ITR 38000: loss_2=1.5357 | va_auroc:0.8722, va_auprc:0.4795\n", + "ITR 39000: loss_2=1.5393 | va_auroc:0.8676, va_auprc:0.4699\n", + "ITR 40000: loss_2=1.5255 | va_auroc:0.8657, va_auprc:0.4659\n", + "ITR 41000: loss_2=1.5277 | va_auroc:0.8660, va_auprc:0.4723\n", + "ITR 42000: loss_2=1.5172 | va_auroc:0.8697, va_auprc:0.4717\n", + "ITR 43000: loss_2=1.5105 | va_auroc:0.8741, va_auprc:0.4755\n", + "ITR 44000: loss_2=1.5113 | va_auroc:0.8719, va_auprc:0.4668\n", + "ITR 45000: loss_2=1.5234 | va_auroc:0.8684, va_auprc:0.4663\n", + "ITR 46000: loss_2=1.4995 | va_auroc:0.8663, va_auprc:0.4624\n", + "ITR 47000: loss_2=1.5016 | va_auroc:0.8676, va_auprc:0.4654\n", + "ITR 48000: loss_2=1.5019 | va_auroc:0.8668, va_auprc:0.4664\n", + "ITR 49000: loss_2=1.5129 | va_auroc:0.8637, va_auprc:0.4680\n", + "ITR 50000: loss_2=1.4870 | va_auroc:0.8662, va_auprc:0.4724\n", + "ITR 1000: loss_2=3.5118 | va_auroc:0.9310, va_auprc:0.6386\n", + "ITR 2000: loss_2=2.6017 | va_auroc:0.9168, va_auprc:0.6050\n", + "ITR 3000: loss_2=2.3373 | va_auroc:0.8979, va_auprc:0.5698\n", + "ITR 4000: loss_2=2.1746 | va_auroc:0.8895, va_auprc:0.5385\n", + "ITR 5000: loss_2=2.0619 | va_auroc:0.8908, va_auprc:0.5349\n", + "ITR 6000: loss_2=1.9888 | va_auroc:0.8893, va_auprc:0.5185\n", + "ITR 7000: loss_2=1.9373 | va_auroc:0.8840, va_auprc:0.5141\n", + "ITR 8000: loss_2=1.8846 | va_auroc:0.8835, va_auprc:0.5088\n", + "ITR 9000: loss_2=1.8518 | va_auroc:0.8819, va_auprc:0.5050\n", + "ITR 10000: loss_2=1.8139 | va_auroc:0.8826, va_auprc:0.4974\n", + "ITR 11000: loss_2=1.7914 | va_auroc:0.8834, va_auprc:0.5079\n", + "ITR 12000: loss_2=1.7672 | va_auroc:0.8793, va_auprc:0.4972\n", + "ITR 13000: loss_2=1.7509 | va_auroc:0.8775, va_auprc:0.4934\n", + "ITR 14000: loss_2=1.7149 | va_auroc:0.8789, va_auprc:0.4959\n", + "ITR 15000: loss_2=1.7047 | va_auroc:0.8789, va_auprc:0.5020\n", + "ITR 16000: loss_2=1.6890 | va_auroc:0.8788, va_auprc:0.4926\n", + "ITR 17000: loss_2=1.6679 | va_auroc:0.8790, va_auprc:0.5085\n", + "ITR 18000: loss_2=1.6608 | va_auroc:0.8798, va_auprc:0.4931\n", + "ITR 19000: loss_2=1.6507 | va_auroc:0.8795, va_auprc:0.4962\n", + "ITR 20000: loss_2=1.6408 | va_auroc:0.8802, va_auprc:0.4918\n", + "ITR 21000: loss_2=1.6402 | va_auroc:0.8820, va_auprc:0.4910\n", + "ITR 22000: loss_2=1.6303 | va_auroc:0.8788, va_auprc:0.4987\n", + "ITR 23000: loss_2=1.6231 | va_auroc:0.8800, va_auprc:0.4923\n", + "ITR 24000: loss_2=1.6168 | va_auroc:0.8809, va_auprc:0.4933\n", + "ITR 25000: loss_2=1.6040 | va_auroc:0.8812, va_auprc:0.4962\n", + "ITR 26000: loss_2=1.5982 | va_auroc:0.8762, va_auprc:0.4902\n", + "ITR 27000: loss_2=1.5889 | va_auroc:0.8788, va_auprc:0.4916\n", + "ITR 28000: loss_2=1.5876 | va_auroc:0.8750, va_auprc:0.4827\n", + "ITR 29000: loss_2=1.5744 | va_auroc:0.8804, va_auprc:0.4904\n", + "ITR 30000: loss_2=1.5681 | va_auroc:0.8775, va_auprc:0.4807\n", + "ITR 31000: loss_2=1.5686 | va_auroc:0.8764, va_auprc:0.4893\n", + "ITR 32000: loss_2=1.5761 | va_auroc:0.8790, va_auprc:0.4850\n", + "ITR 33000: loss_2=1.5661 | va_auroc:0.8766, va_auprc:0.4842\n", + "ITR 34000: loss_2=1.5493 | va_auroc:0.8740, va_auprc:0.4839\n", + "ITR 35000: loss_2=1.5572 | va_auroc:0.8751, va_auprc:0.4843\n", + "ITR 36000: loss_2=1.5449 | va_auroc:0.8759, va_auprc:0.4814\n", + "ITR 37000: loss_2=1.5390 | va_auroc:0.8815, va_auprc:0.4840\n", + "ITR 38000: loss_2=1.5395 | va_auroc:0.8786, va_auprc:0.4838\n", + "ITR 39000: loss_2=1.5346 | va_auroc:0.8741, va_auprc:0.4908\n", + "ITR 40000: loss_2=1.5251 | va_auroc:0.8768, va_auprc:0.4750\n", + "ITR 41000: loss_2=1.5179 | va_auroc:0.8770, va_auprc:0.4772\n", + "ITR 42000: loss_2=1.5127 | va_auroc:0.8735, va_auprc:0.4757\n", + "ITR 43000: loss_2=1.5080 | va_auroc:0.8732, va_auprc:0.4699\n", + "ITR 44000: loss_2=1.5154 | va_auroc:0.8781, va_auprc:0.4531\n", + "ITR 45000: loss_2=1.5186 | va_auroc:0.8768, va_auprc:0.4750\n", + "ITR 46000: loss_2=1.5105 | va_auroc:0.8767, va_auprc:0.4666\n", + "ITR 47000: loss_2=1.5053 | va_auroc:0.8770, va_auprc:0.4630\n", + "ITR 48000: loss_2=1.4967 | va_auroc:0.8739, va_auprc:0.4555\n", + "ITR 49000: loss_2=1.5011 | va_auroc:0.8746, va_auprc:0.4678\n", + "ITR 50000: loss_2=1.4941 | va_auroc:0.8750, va_auprc:0.4746\n", + "ITR 1000: loss_2=3.5160 | va_auroc:0.9221, va_auprc:0.6304\n", + "ITR 2000: loss_2=2.6026 | va_auroc:0.9088, va_auprc:0.6007\n", + "ITR 3000: loss_2=2.3290 | va_auroc:0.8953, va_auprc:0.5442\n", + "ITR 4000: loss_2=2.1684 | va_auroc:0.8865, va_auprc:0.5193\n", + "ITR 5000: loss_2=2.0688 | va_auroc:0.8785, va_auprc:0.5072\n", + "ITR 6000: loss_2=1.9846 | va_auroc:0.8815, va_auprc:0.5051\n", + "ITR 7000: loss_2=1.9214 | va_auroc:0.8764, va_auprc:0.4876\n", + "ITR 8000: loss_2=1.8809 | va_auroc:0.8725, va_auprc:0.4908\n", + "ITR 9000: loss_2=1.8416 | va_auroc:0.8687, va_auprc:0.4773\n", + "ITR 10000: loss_2=1.8065 | va_auroc:0.8717, va_auprc:0.4846\n", + "ITR 11000: loss_2=1.7732 | va_auroc:0.8664, va_auprc:0.4786\n", + "ITR 12000: loss_2=1.7691 | va_auroc:0.8677, va_auprc:0.4613\n", + "ITR 13000: loss_2=1.7464 | va_auroc:0.8643, va_auprc:0.4740\n", + "ITR 14000: loss_2=1.7205 | va_auroc:0.8618, va_auprc:0.4594\n", + "ITR 15000: loss_2=1.7090 | va_auroc:0.8566, va_auprc:0.4508\n", + "ITR 16000: loss_2=1.6951 | va_auroc:0.8628, va_auprc:0.4688\n", + "ITR 17000: loss_2=1.6730 | va_auroc:0.8686, va_auprc:0.4687\n", + "ITR 18000: loss_2=1.6770 | va_auroc:0.8629, va_auprc:0.4654\n", + "ITR 19000: loss_2=1.6637 | va_auroc:0.8640, va_auprc:0.4749\n", + "ITR 20000: loss_2=1.6473 | va_auroc:0.8630, va_auprc:0.4570\n", + "ITR 21000: loss_2=1.6310 | va_auroc:0.8624, va_auprc:0.4485\n", + "ITR 22000: loss_2=1.6275 | va_auroc:0.8624, va_auprc:0.4604\n", + "ITR 23000: loss_2=1.6234 | va_auroc:0.8567, va_auprc:0.4475\n", + "ITR 24000: loss_2=1.6138 | va_auroc:0.8624, va_auprc:0.4557\n", + "ITR 25000: loss_2=1.6149 | va_auroc:0.8577, va_auprc:0.4467\n", + "ITR 26000: loss_2=1.6001 | va_auroc:0.8572, va_auprc:0.4500\n", + "ITR 27000: loss_2=1.5999 | va_auroc:0.8575, va_auprc:0.4404\n", + "ITR 28000: loss_2=1.5946 | va_auroc:0.8550, va_auprc:0.4498\n", + "ITR 29000: loss_2=1.5891 | va_auroc:0.8562, va_auprc:0.4601\n", + "ITR 30000: loss_2=1.5811 | va_auroc:0.8548, va_auprc:0.4616\n", + "ITR 31000: loss_2=1.5746 | va_auroc:0.8576, va_auprc:0.4523\n", + "ITR 32000: loss_2=1.5697 | va_auroc:0.8547, va_auprc:0.4610\n", + "ITR 33000: loss_2=1.5716 | va_auroc:0.8567, va_auprc:0.4579\n", + "ITR 34000: loss_2=1.5626 | va_auroc:0.8590, va_auprc:0.4603\n", + "ITR 35000: loss_2=1.5521 | va_auroc:0.8549, va_auprc:0.4623\n", + "ITR 36000: loss_2=1.5513 | va_auroc:0.8536, va_auprc:0.4623\n", + "ITR 37000: loss_2=1.5433 | va_auroc:0.8587, va_auprc:0.4517\n", + "ITR 38000: loss_2=1.5367 | va_auroc:0.8594, va_auprc:0.4452\n", + "ITR 39000: loss_2=1.5408 | va_auroc:0.8602, va_auprc:0.4615\n", + "ITR 40000: loss_2=1.5303 | va_auroc:0.8559, va_auprc:0.4609\n", + "ITR 41000: loss_2=1.5236 | va_auroc:0.8548, va_auprc:0.4618\n", + "ITR 42000: loss_2=1.5283 | va_auroc:0.8619, va_auprc:0.4504\n", + "ITR 43000: loss_2=1.5246 | va_auroc:0.8546, va_auprc:0.4739\n", + "ITR 44000: loss_2=1.5137 | va_auroc:0.8588, va_auprc:0.4611\n", + "ITR 45000: loss_2=1.5179 | va_auroc:0.8611, va_auprc:0.4679\n", + "ITR 46000: loss_2=1.5101 | va_auroc:0.8590, va_auprc:0.4520\n", + "ITR 47000: loss_2=1.5140 | va_auroc:0.8621, va_auprc:0.4667\n", + "ITR 48000: loss_2=1.5159 | va_auroc:0.8531, va_auprc:0.4598\n", + "ITR 49000: loss_2=1.5199 | va_auroc:0.8588, va_auprc:0.4743\n", + "ITR 50000: loss_2=1.5099 | va_auroc:0.8614, va_auprc:0.4657\n" + ] + } + ], + "source": [ + "seed = 1234\n", + "\n", + "for out_itr in [1,2,3,4]:\n", + "\n", + " if data_mode == 'CF':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y, tr_data_y_onehot,te_data_y_onehot = train_test_split(\n", + " data_x, data_y, data_y_onehot, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y, tr_data_y_onehot,va_data_y_onehot = train_test_split(\n", + " tr_data_x, tr_data_y, tr_data_y_onehot, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + " elif data_mode == 'CF_comorbidity':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y = train_test_split(\n", + " data_x, data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y = train_test_split(\n", + " tr_data_x, tr_data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + " elif data_mode == 'CF_comorbidity_select':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y = train_test_split(\n", + " data_x, data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y = train_test_split(\n", + " tr_data_x, tr_data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " save_path = './{}/dcn_E2P/init/itr{}/'.format(data_mode, out_itr)\n", + "\n", + " if not os.path.exists(save_path + '/models/'):\n", + " os.makedirs(save_path + '/models/')\n", + "\n", + " if not os.path.exists(save_path + '/results/'):\n", + " os.makedirs(save_path + '/results/')\n", + "\n", + "\n", + " saver = tf.train.Saver()\n", + " sess.run(tf.global_variables_initializer())\n", + "\n", + " lr_rate = 1e-3\n", + " keep_prob = 0.7\n", + " mb_size = 128\n", + "\n", + "\n", + " ITERATION = 50000\n", + " check_step = 1000\n", + "\n", + " avg_loss_mle = 0\n", + " for itr in range(ITERATION):\n", + " x_mb, y_mb = f_get_minibatch(mb_size, tr_data_x, tr_data_y)\n", + "\n", + " _, tmp_loss_mle= model.train_mle(x_mb, y_mb, lr_rate, keep_prob)\n", + " avg_loss_mle += tmp_loss_mle/check_step\n", + "\n", + " if (itr+1)%check_step == 0: \n", + " tmp_y, tmp_m = model.predict_y_hats(va_data_x)\n", + "\n", + " y_pred = tmp_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1]\n", + " y_true = va_data_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1]\n", + "\n", + " AUROC = np.zeros([y_dim])\n", + " AUPRC = np.zeros([y_dim])\n", + " for y_idx in range(y_dim):\n", + " auroc, auprc = f_get_prediction_scores(y_true[:, y_idx], y_pred[:, y_idx])\n", + " AUROC[y_idx] = auroc\n", + " AUPRC[y_idx] = auprc\n", + "\n", + " print (\"ITR {}: loss_2={:.4f} | va_auroc:{:.4f}, va_auprc:{:.4f}\".format(\n", + " itr+1, avg_loss_mle, np.mean(AUROC), np.mean(AUPRC)))\n", + "\n", + " avg_loss_mle = 0\n", + "\n", + " saver.save(sess, save_path + 'models/dcn_E2P_v4_init')\n", + " save_logging(network_settings, save_path + 'models/network_settings_v4.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n For quantifying Purity, NMI, RI -> 8 true classes!\\n - next_ABPA\\n - next_Diabetes\\n - next_Intestinal Obstruction\\n \\n For qualitative results\\n - First, use all the available commorbidities!\\n'" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''\n", + " For quantifying Purity, NMI, RI -> 8 true classes!\n", + " - next_ABPA\n", + " - next_Diabetes\n", + " - next_Intestinal Obstruction\n", + " \n", + " For qualitative results\n", + " - First, use all the available commorbidities!\n", + "'''# " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.8727475 0.77051879 0.88274351 0.81488763 0.87738161 0.89441366\n", + " 0.89372434 0.93625356]\n", + "[0.87966504 0.29058946 0.71388751 0.23338525 0.52075964 0.34831282\n", + " 0.39325973 0.06607209]\n" + ] + } + ], + "source": [ + "tmp_y, tmp_m = model.predict_y_hats(te_data_x)\n", + "\n", + "y_pred = tmp_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1]\n", + "y_true = te_data_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1]\n", + "\n", + "AUROC = np.zeros([y_dim])\n", + "AUPRC = np.zeros([y_dim])\n", + "for y_idx in range(y_dim):\n", + " auroc, auprc = f_get_prediction_scores(y_true[:, y_idx], y_pred[:, y_idx])\n", + " AUROC[y_idx] = auroc\n", + " AUPRC[y_idx] = auprc\n", + "\n", + "# df_result1 = pd.DataFrame(np.asarray([AUROC, AUPRC]).reshape([-1,2]),\n", + "# columns=['AUROC', 'AURPC'], index=label_list)\n", + "# df_result1\n", + "\n", + "print(AUROC)\n", + "print(AUPRC)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "load_path = './{}/dcn_E2P/init/itr{}/'.format(data_mode, out_itr)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./CF_comorbidity_select/dcn_E2P/init/itr0/models/dcn_E2P_v4_init\n" + ] + } + ], + "source": [ + "input_dims ={\n", + " 'x_dim': x_dim,\n", + " 'y_dim': y_dim,\n", + " 'y_type': y_type,\n", + " 'max_length': max_length \n", + "}\n", + "\n", + "tf.reset_default_graph()\n", + "\n", + "# Turn on xla optimization\n", + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "sess = tf.Session(config=config)\n", + "\n", + "network_settings = load_logging(load_path + 'models/network_settings_v4.txt')\n", + "\n", + "model = DCN_E2P(sess, \"dcn_E2P\", input_dims, network_settings)\n", + "\n", + "saver = tf.train.Saver()\n", + "sess.run(tf.global_variables_initializer())\n", + "\n", + "saver.restore(sess, load_path + 'models/dcn_E2P_v4_init')" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "K = 8\n", + "# for num_Cluster in NUM_CLUSTER:\n", + "lr_rate = 1e-4\n", + "keep_prob = 0.7\n", + "mb_size = 128\n", + "\n", + "alpha = 0.1 #L_CLUSTER" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "### CLUSTER INITIALIZATION\n", + "num_Cluster = K\n", + "\n", + "km = MiniBatchKMeans(n_clusters = num_Cluster, batch_size=mb_size)\n", + "tr_z = model.predict_Z(tr_data_x)\n", + "\n", + "_ = km.fit(tr_z)\n", + "mu = km.cluster_centers_" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/changhee/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:26: RuntimeWarning: Explicit initial center position passed: performing only one init in MiniBatchKMeans instead of n_init=3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ITR 100: loss_total=2.1159\t loss_mle=1.4786 \t loss_clue=6.3726\n", + "ITR 200: loss_total=2.1269\t loss_mle=1.5113 \t loss_clue=6.1558\n", + "ITR 300: loss_total=2.1182\t loss_mle=1.5151 \t loss_clue=6.0307\n", + "ITR 400: loss_total=2.0631\t loss_mle=1.4759 \t loss_clue=5.8722\n", + "ITR 500: loss_total=2.0529\t loss_mle=1.4732 \t loss_clue=5.7971\n", + "ITR 600: loss_total=2.0309\t loss_mle=1.4601 \t loss_clue=5.7072\n", + "ITR 700: loss_total=2.0790\t loss_mle=1.5206 \t loss_clue=5.5837\n", + "ITR 800: loss_total=2.0520\t loss_mle=1.4982 \t loss_clue=5.5378\n", + "ITR 900: loss_total=2.0384\t loss_mle=1.4891 \t loss_clue=5.4933\n", + "ITR 1000: loss_total=2.0119\t loss_mle=1.4703 \t loss_clue=5.4158\n", + "ITR 1100: loss_total=1.9909\t loss_mle=1.4541 \t loss_clue=5.3688\n", + "ITR 1200: loss_total=2.0245\t loss_mle=1.4945 \t loss_clue=5.2999\n", + "ITR 1300: loss_total=2.0368\t loss_mle=1.5097 \t loss_clue=5.2710\n", + "ITR 1400: loss_total=1.9674\t loss_mle=1.4486 \t loss_clue=5.1879\n", + "ITR 1500: loss_total=1.9949\t loss_mle=1.4766 \t loss_clue=5.1829\n", + "ITR 1600: loss_total=2.0017\t loss_mle=1.4904 \t loss_clue=5.1133\n", + "ITR 1700: loss_total=2.0309\t loss_mle=1.5202 \t loss_clue=5.1062\n", + "ITR 1800: loss_total=1.9939\t loss_mle=1.4879 \t loss_clue=5.0603\n", + "ITR 1900: loss_total=1.9895\t loss_mle=1.4891 \t loss_clue=5.0045\n", + "ITR 2000: loss_total=2.0161\t loss_mle=1.5181 \t loss_clue=4.9803\n", + "ITR 2100: loss_total=1.9830\t loss_mle=1.4865 \t loss_clue=4.9649\n", + "ITR 2200: loss_total=1.9886\t loss_mle=1.4941 \t loss_clue=4.9453\n", + "ITR 2300: loss_total=1.9677\t loss_mle=1.4761 \t loss_clue=4.9160\n", + "ITR 2400: loss_total=1.9932\t loss_mle=1.5052 \t loss_clue=4.8800\n", + "ITR 2500: loss_total=1.9789\t loss_mle=1.4967 \t loss_clue=4.8218\n", + "ITR 2600: loss_total=1.9716\t loss_mle=1.4932 \t loss_clue=4.7850\n", + "ITR 2700: loss_total=1.9509\t loss_mle=1.4737 \t loss_clue=4.7718\n", + "ITR 2800: loss_total=1.9370\t loss_mle=1.4620 \t loss_clue=4.7496\n", + "ITR 2900: loss_total=1.9651\t loss_mle=1.4888 \t loss_clue=4.7632\n", + "ITR 3000: loss_total=1.9536\t loss_mle=1.4831 \t loss_clue=4.7054\n", + "ITR 3100: loss_total=1.9619\t loss_mle=1.4882 \t loss_clue=4.7366\n", + "ITR 3200: loss_total=1.9625\t loss_mle=1.4897 \t loss_clue=4.7282\n", + "ITR 3300: loss_total=1.9826\t loss_mle=1.5147 \t loss_clue=4.6785\n", + "ITR 3400: loss_total=1.9299\t loss_mle=1.4662 \t loss_clue=4.6367\n", + "ITR 3500: loss_total=1.9344\t loss_mle=1.4714 \t loss_clue=4.6305\n", + "ITR 3600: loss_total=1.9343\t loss_mle=1.4724 \t loss_clue=4.6194\n", + "ITR 3700: loss_total=1.9405\t loss_mle=1.4822 \t loss_clue=4.5824\n", + "ITR 3800: loss_total=1.9323\t loss_mle=1.4762 \t loss_clue=4.5618\n", + "ITR 3900: loss_total=1.9237\t loss_mle=1.4698 \t loss_clue=4.5394\n", + "ITR 4000: loss_total=1.9420\t loss_mle=1.4903 \t loss_clue=4.5170\n", + "ITR 4100: loss_total=1.9494\t loss_mle=1.4969 \t loss_clue=4.5247\n", + "ITR 4200: loss_total=1.9583\t loss_mle=1.5074 \t loss_clue=4.5090\n", + "ITR 4300: loss_total=1.9415\t loss_mle=1.4929 \t loss_clue=4.4861\n", + "ITR 4400: loss_total=1.9351\t loss_mle=1.4872 \t loss_clue=4.4790\n", + "ITR 4500: loss_total=1.9183\t loss_mle=1.4708 \t loss_clue=4.4758\n", + "ITR 4600: loss_total=1.8838\t loss_mle=1.4405 \t loss_clue=4.4326\n", + "ITR 4700: loss_total=1.8888\t loss_mle=1.4468 \t loss_clue=4.4206\n", + "ITR 4800: loss_total=1.9066\t loss_mle=1.4693 \t loss_clue=4.3727\n", + "ITR 4900: loss_total=1.8891\t loss_mle=1.4505 \t loss_clue=4.3863\n", + "ITR 5000: loss_total=1.8915\t loss_mle=1.4524 \t loss_clue=4.3917\n", + "ITR 5100: loss_total=1.9144\t loss_mle=1.4779 \t loss_clue=4.3648\n", + "ITR 5200: loss_total=1.9270\t loss_mle=1.4962 \t loss_clue=4.3078\n", + "ITR 5300: loss_total=1.9191\t loss_mle=1.4854 \t loss_clue=4.3377\n", + "ITR 5400: loss_total=1.8822\t loss_mle=1.4515 \t loss_clue=4.3071\n", + "ITR 5500: loss_total=1.8962\t loss_mle=1.4656 \t loss_clue=4.3058\n", + "ITR 5600: loss_total=1.9037\t loss_mle=1.4782 \t loss_clue=4.2548\n", + "ITR 5700: loss_total=1.9377\t loss_mle=1.5139 \t loss_clue=4.2385\n", + "ITR 5800: loss_total=1.9240\t loss_mle=1.4971 \t loss_clue=4.2698\n", + "ITR 5900: loss_total=1.8771\t loss_mle=1.4491 \t loss_clue=4.2801\n", + "ITR 6000: loss_total=1.9000\t loss_mle=1.4772 \t loss_clue=4.2277\n", + "ITR 6100: loss_total=1.8962\t loss_mle=1.4763 \t loss_clue=4.1988\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0ms_mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_Z\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m _, tmp_loss_total, tmp_loss_mle, tmp_loss_clu = model.train_total(\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mx_mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms_mb\u001b[0m\u001b[0;34m,\u001b[0m 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run_metadata_ptr)\n\u001b[0m\u001b[1;32m 888\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 889\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1108\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1109\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1110\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1111\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1112\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1285\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1286\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1287\u001b[0m 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run_metadata)\u001b[0m\n\u001b[1;32m 1275\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1276\u001b[0m return self._call_tf_sessionrun(\n\u001b[0;32m-> 1277\u001b[0;31m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m 1278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1279\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m 1365\u001b[0m return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m 1366\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1369\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "ITERATION = 10000\n", + "\n", + "check_step = 100\n", + "\n", + "avg_loss_total = 0\n", + "avg_loss_mle = 0\n", + "avg_loss_clu = 0\n", + "\n", + "for itr in range(ITERATION):\n", + " x_mb, y_mb = f_get_minibatch(mb_size, tr_data_x, tr_data_y)\n", + " \n", + " s_mb = km.predict(model.predict_Z(x_mb))\n", + " _, tmp_loss_total, tmp_loss_mle, tmp_loss_clu = model.train_total(\n", + " x_mb, y_mb, s_mb, mu, num_Cluster, alpha, lr_rate, keep_prob\n", + " ) \n", + " \n", + " avg_loss_total += tmp_loss_total/check_step\n", + " avg_loss_mle += tmp_loss_mle/check_step\n", + " avg_loss_clu += tmp_loss_clu/check_step\n", + " \n", + " \n", + " if (itr+1)%check_step == 0:\n", + " km = MiniBatchKMeans(n_clusters = num_Cluster, batch_size=mb_size, init=mu)\n", + " tr_z = model.predict_Z(tr_data_x)\n", + "\n", + " _ = km.fit(tr_z)\n", + " mu = km.cluster_centers_ \n", + " \n", + " print (\"ITR {}: loss_total={:.4f}\\t loss_mle={:.4f} \\t loss_clue={:.4f}\".format(\n", + " itr+1, avg_loss_total, avg_loss_mle, avg_loss_clu))\n", + " avg_loss_total = 0\n", + " avg_loss_mle = 0\n", + " avg_loss_clu = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "save_path = './{}/dcn_E2P/K{}/itr{}/'.format(data_mode, K, out_itr)\n", + "\n", + "if not os.path.exists(save_path + '/models/'):\n", + " os.makedirs(save_path + '/models/')\n", + "\n", + "if not os.path.exists(save_path + '/results/'):\n", + " os.makedirs(save_path + '/results/')\n", + " \n", + "saver.save(sess, save_path + 'models/dcn_E2P_clustered_v3')\n", + "\n", + "save_logging(network_settings, save_path + 'models/network_settings.txt')\n", + "np.savez(save_path + 'models/embeddings.npz', km=km, mu=mu)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "tmp_z, tmp_m = model.predict_zs(te_data_x)\n", + "\n", + "tmp_z = tmp_z.reshape([-1, z_dim])[tmp_m.reshape([-1]) == 1]\n", + "pred_y = km.predict(tmp_z)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "yyy = sess.run(model.yy, feed_dict={model.zz:km.cluster_centers_, model.mb_size:num_Cluster, model.keep_prob:1.0})" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9.9901032e-01, 9.9985099e-01, 7.3584542e-04, 4.2787267e-04,\n", + " 9.9994671e-01, 9.9652326e-01, 5.7657436e-03, 3.6333574e-04],\n", + " dtype=float32)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yyy[:, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ITR0 - K8 | NMI:0.2823, RI:0.1859, PURITY:0.7359\n" + ] + } + ], + "source": [ + "tmp_z, tmp_m = model.predict_zs(te_data_x)\n", + "\n", + "tmp_z = tmp_z.reshape([-1, z_dim])[tmp_m.reshape([-1]) == 1]\n", + "pred_y = km.predict(tmp_z)\n", + "\n", + "true_y = (te_data_y * np.tile(np.expand_dims(tmp_m, axis=2), [1,1,y_dim])).reshape([-1, y_dim])\n", + "true_y = true_y[(tmp_m.reshape([-1]) == 1)]\n", + "true_y = np.argmax(true_y, axis=1)\n", + "\n", + "tmp_nmi = normalized_mutual_info_score(true_y, pred_y)\n", + "tmp_ri = adjusted_rand_score(true_y, pred_y)\n", + "tmp_purity = purity_score(true_y, pred_y)\n", + "\n", + "# pd.DataFrame([[tmp_nmi, tmp_ri, tmp_purity]], \n", + "# columns=['NMI', 'RI', 'PURITY'], \n", + "# index=['itr'+str(out_itr)]).to_csv(save_path + 'results/nmi_ir_purity.csv')\n", + "\n", + "print('ITR{} - K{} | NMI:{:.4f}, RI:{:.4f}, PURITY:{:.4f}'.format(out_itr, K, tmp_nmi, tmp_ri, tmp_purity))\n", + "\n", + "RESULT_NMI[out_itr, 0] = tmp_nmi\n", + "RESULT_RI[out_itr, 0] = tmp_ri\n", + "RESULT_PURITY[out_itr, 0] = tmp_purity\n", + "\n", + "\n", + "# pd.DataFrame(RESULT_NMI, \n", + "# columns=['NMI'], \n", + "# index=['itr'+str(out_itr) for out_itr in range(OUT_ITERATION)]).to_csv('./{}/dcn_E2P/K{}/'.format(data_mode, K) + 'results_nmi.csv')\n", + "\n", + "# pd.DataFrame(RESULT_RI, \n", + "# columns=['RI'], \n", + "# index=['itr'+str(out_itr) for out_itr in range(OUT_ITERATION)]).to_csv('./{}/dcn_E2P/K{}/'.format(data_mode, K) + 'results_ri.csv')\n", + "\n", + "# pd.DataFrame(RESULT_PURITY, \n", + "# columns=['PURITY'], \n", + "# index=['itr'+str(out_itr) for out_itr in range(OUT_ITERATION)]).to_csv('./{}/dcn_E2P/K{}/'.format(data_mode, K) + 'results_purity.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "### KMEANS:\n", + "def get_predictions_for_kmeans(x_):\n", + " tmp_z, _ = model.predict_zs(x_)\n", + " tmp_y, tmp_m = model.predict_y_hats(x_)\n", + " return tmp_z, tmp_y, tmp_m\n", + "\n", + "def get_kmeans(input_, K_):\n", + " km_ = KMeans(n_clusters = K_, init='k-means++')\n", + " _ = km_.fit(input_)\n", + " tmp_c = km_.cluster_centers_\n", + " return km_" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "ITERATION = 5\n", + "NUM_CLUSTERS = [4, 8, 16]\n", + "\n", + "RESULTS_Z_NMI = np.zeros([ITERATION, len(NUM_CLUSTERS)])\n", + "RESULTS_Z_PURITY = np.zeros([ITERATION, len(NUM_CLUSTERS)])\n", + "RESULTS_Z_RI = np.zeros([ITERATION, len(NUM_CLUSTERS)])\n", + "\n", + "RESULTS_Y_NMI = np.zeros([ITERATION, len(NUM_CLUSTERS)])\n", + "RESULTS_Y_PURITY = np.zeros([ITERATION, len(NUM_CLUSTERS)])\n", + "RESULTS_Y_RI = np.zeros([ITERATION, len(NUM_CLUSTERS)])" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./CF_comorbidity_select/dcn_E2P/init/itr0/models/dcn_E2P_v4_init\n", + "ITR0 - K4 | NMI:0.2773, RI:0.2577, PURITY:0.7228\n", + "ITR0 - K8 | NMI:0.2092, RI:0.1036, PURITY:0.7108\n", + "ITR0 - K16 | NMI:0.2062, RI:0.0611, PURITY:0.7158\n", + "ITR0 - K4 | NMI:0.3256, RI:0.4854, PURITY:0.7196\n", + "ITR0 - K8 | NMI:0.3201, RI:0.4327, PURITY:0.7443\n", + "ITR0 - K16 | NMI:0.2894, RI:0.3195, PURITY:0.7477\n", + "INFO:tensorflow:Restoring parameters from ./CF_comorbidity_select/dcn_E2P/init/itr1/models/dcn_E2P_v4_init\n", + "ITR1 - K4 | NMI:0.1913, RI:0.1496, PURITY:0.7146\n", + "ITR1 - K8 | NMI:0.1961, RI:0.1045, PURITY:0.7179\n", + "ITR1 - K16 | NMI:0.2002, RI:0.0626, PURITY:0.7268\n", + "ITR1 - K4 | NMI:0.3298, RI:0.4863, PURITY:0.7385\n", + "ITR1 - K8 | NMI:0.3274, RI:0.4495, PURITY:0.7565\n", + "ITR1 - K16 | NMI:0.2943, RI:0.3310, PURITY:0.7644\n", + "INFO:tensorflow:Restoring parameters from ./CF_comorbidity_select/dcn_E2P/init/itr2/models/dcn_E2P_v4_init\n", + "ITR2 - K4 | NMI:0.2727, RI:0.2459, PURITY:0.7261\n", + "ITR2 - K8 | NMI:0.2351, RI:0.1217, PURITY:0.7368\n", + "ITR2 - K16 | NMI:0.2405, RI:0.0812, PURITY:0.7574\n", + "ITR2 - K4 | NMI:0.3475, RI:0.5120, PURITY:0.7460\n", + "ITR2 - K8 | NMI:0.3470, RI:0.4697, PURITY:0.7678\n", + "ITR2 - K16 | NMI:0.3041, RI:0.3313, PURITY:0.7690\n", + "INFO:tensorflow:Restoring parameters from ./CF_comorbidity_select/dcn_E2P/init/itr3/models/dcn_E2P_v4_init\n", + "ITR3 - K4 | NMI:0.2104, RI:0.1484, PURITY:0.7145\n", + "ITR3 - K8 | NMI:0.2065, RI:0.1022, PURITY:0.7147\n", + "ITR3 - K16 | NMI:0.2187, RI:0.0654, PURITY:0.7293\n", + "ITR3 - K4 | NMI:0.3068, RI:0.4636, PURITY:0.7143\n", + "ITR3 - K8 | NMI:0.3094, RI:0.4181, PURITY:0.7394\n", + "ITR3 - K16 | NMI:0.2764, RI:0.3124, PURITY:0.7404\n", + "INFO:tensorflow:Restoring parameters from ./CF_comorbidity_select/dcn_E2P/init/itr4/models/dcn_E2P_v4_init\n", + "ITR4 - K4 | NMI:0.1862, RI:0.1490, PURITY:0.7029\n", + "ITR4 - K8 | NMI:0.2093, RI:0.1022, PURITY:0.7163\n", + "ITR4 - K16 | NMI:0.2123, RI:0.0680, PURITY:0.7298\n", + "ITR4 - K4 | NMI:0.3135, RI:0.4754, PURITY:0.7220\n", + "ITR4 - K8 | NMI:0.3209, RI:0.4303, PURITY:0.7490\n", + "ITR4 - K16 | NMI:0.2879, RI:0.3056, PURITY:0.7505\n" + ] + } + ], + "source": [ + "seed = 1234\n", + "\n", + "for out_itr in [0,1,2,3,4]:\n", + "\n", + " if data_mode == 'CF':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y, tr_data_y_onehot,te_data_y_onehot = train_test_split(\n", + " data_x, data_y, data_y_onehot, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y, tr_data_y_onehot,va_data_y_onehot = train_test_split(\n", + " tr_data_x, tr_data_y, tr_data_y_onehot, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + " elif data_mode == 'CF_comorbidity':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y = train_test_split(\n", + " data_x, data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y = train_test_split(\n", + " tr_data_x, tr_data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + " elif data_mode == 'CF_comorbidity_select':\n", + " tr_data_x,te_data_x, tr_data_y,te_data_y = train_test_split(\n", + " data_x, data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + " tr_data_x,va_data_x, tr_data_y,va_data_y = train_test_split(\n", + " tr_data_x, tr_data_y, test_size=0.2, random_state=seed+out_itr\n", + " )\n", + "\n", + "\n", + " load_path = './{}/dcn_E2P/init/itr{}/'.format(data_mode, out_itr)\n", + "\n", + " input_dims ={\n", + " 'x_dim': x_dim,\n", + " 'y_dim': y_dim,\n", + " 'y_type': y_type,\n", + " 'max_length': max_length \n", + " }\n", + "\n", + " tf.reset_default_graph()\n", + "\n", + " # Turn on xla optimization\n", + " config = tf.ConfigProto()\n", + " config.gpu_options.allow_growth = True\n", + " sess = tf.Session(config=config)\n", + "\n", + " network_settings = load_logging(load_path + 'models/network_settings_v4.txt')\n", + "\n", + " model = DCN_E2P(sess, \"dcn_E2P\", input_dims, network_settings)\n", + "\n", + " saver = tf.train.Saver()\n", + " sess.run(tf.global_variables_initializer())\n", + "\n", + " saver.restore(sess, load_path + 'models/dcn_E2P_v4_init')\n", + "\n", + "\n", + " for c_idx, K_kmeans in enumerate(NUM_CLUSTERS):\n", + " tmp_z, tmp_y, tmp_m = get_predictions_for_kmeans(tr_data_x)\n", + " tmp_z = tmp_z.reshape([-1, z_dim])[tmp_m.reshape([-1]) == 1]\n", + "\n", + " km = get_kmeans(tmp_z, K_kmeans)\n", + "\n", + " tmp_z, tmp_y, tmp_m = get_predictions_for_kmeans(te_data_x)\n", + "\n", + " pred_y = km.predict(tmp_z.reshape([-1, z_dim])[tmp_m.reshape([-1]) == 1])\n", + "\n", + " true_y = (te_data_y * np.tile(np.expand_dims(tmp_m, axis=2), [1,1,y_dim])).reshape([-1, y_dim])\n", + " true_y = true_y[(tmp_m.reshape([-1]) == 1)]\n", + " true_y = np.argmax(true_y, axis=1)\n", + "\n", + " tmp_nmi = normalized_mutual_info_score(true_y, pred_y)\n", + " tmp_ri = adjusted_rand_score(true_y, pred_y)\n", + " tmp_purity = purity_score(true_y, pred_y)\n", + "\n", + " print('ITR{} - K{} | NMI:{:.4f}, RI:{:.4f}, PURITY:{:.4f}'.format(out_itr, K_kmeans, tmp_nmi, tmp_ri, tmp_purity))\n", + "\n", + " RESULTS_Z_NMI[out_itr, c_idx] = tmp_nmi\n", + " RESULTS_Z_RI[out_itr, c_idx] = tmp_ri\n", + " RESULTS_Z_PURITY[out_itr, c_idx] = tmp_purity\n", + "\n", + "\n", + " for c_idx, K_kmeans in enumerate(NUM_CLUSTERS):\n", + " tmp_z, tmp_y, tmp_m = get_predictions_for_kmeans(tr_data_x)\n", + " tmp_y = tmp_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1]\n", + "\n", + " km = get_kmeans(tmp_y, K_kmeans)\n", + "\n", + " tmp_z, tmp_y, tmp_m = get_predictions_for_kmeans(te_data_x)\n", + "\n", + " pred_y = km.predict(tmp_y.reshape([-1, y_dim])[tmp_m.reshape([-1]) == 1])\n", + "\n", + " true_y = (te_data_y * np.tile(np.expand_dims(tmp_m, axis=2), [1,1,y_dim])).reshape([-1, y_dim])\n", + " true_y = true_y[(tmp_m.reshape([-1]) == 1)]\n", + " true_y = np.argmax(true_y, axis=1)\n", + "\n", + " tmp_nmi = normalized_mutual_info_score(true_y, pred_y)\n", + " tmp_ri = adjusted_rand_score(true_y, pred_y)\n", + " tmp_purity = purity_score(true_y, pred_y)\n", + "\n", + " print('ITR{} - K{} | NMI:{:.4f}, RI:{:.4f}, PURITY:{:.4f}'.format(out_itr, K_kmeans, tmp_nmi, tmp_ri, tmp_purity))\n", + "\n", + " RESULTS_Y_NMI[out_itr, c_idx] = tmp_nmi\n", + " RESULTS_Y_RI[out_itr, c_idx] = tmp_ri\n", + " RESULTS_Y_PURITY[out_itr, c_idx] = tmp_purity" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "save_path_z = './{}/RESULTS/kmeans_z/'.format(data_mode)\n", + "save_path_y = './{}/RESULTS/kmeans_y/'.format(data_mode)\n", + "\n", + "if not os.path.exists(save_path_z):\n", + " os.makedirs(save_path_z)\n", + "\n", + "if not os.path.exists(save_path_y):\n", + " os.makedirs(save_path_y)\n", + " \n", + " \n", + "tmp_column = ['K'+str(k) for k in NUM_CLUSTERS]\n", + "tmp_index = ['itr'+str(out_itr) for out_itr in range(ITERATION)]\n", + "\n", + "pd.DataFrame(RESULTS_Z_NMI, columns=tmp_column, index=tmp_index).to_csv(save_path_z + 'results_nmi.csv')\n", + "pd.DataFrame(RESULTS_Z_RI, columns=tmp_column, index=tmp_index).to_csv(save_path_z + 'results_ri.csv')\n", + "pd.DataFrame(RESULTS_Z_PURITY, columns=tmp_column, index=tmp_index).to_csv(save_path_z + 'results_purity.csv')\n", + "\n", + "pd.DataFrame(RESULTS_Y_NMI, columns=tmp_column, index=tmp_index).to_csv(save_path_y + 'results_nmi.csv')\n", + "pd.DataFrame(RESULTS_Y_RI, columns=tmp_column, index=tmp_index).to_csv(save_path_y + 'results_ri.csv')\n", + "pd.DataFrame(RESULTS_Y_PURITY, columns=tmp_column, index=tmp_index).to_csv(save_path_y + 'results_purity.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data/.ipynb_checkpoints/data_CF_preprocess-checkpoint.ipynb b/data/.ipynb_checkpoints/data_CF_preprocess-checkpoint.ipynb new file mode 100644 index 0000000..4648c5f --- /dev/null +++ b/data/.ipynb_checkpoints/data_CF_preprocess-checkpoint.ipynb @@ -0,0 +1,875 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def f_get_Normalization(X, norm_mode):\n", + " num_Patient, num_Feature = np.shape(X)\n", + "\n", + " if norm_mode == 'standard': #zero mean unit variance\n", + " for j in range(num_Feature):\n", + " if np.std(X[:,j]) != 0:\n", + " X[:,j] = (X[:,j] - np.mean(X[:, j]))/np.std(X[:,j])\n", + " else:\n", + " X[:,j] = (X[:,j] - np.mean(X[:, j]))\n", + " elif norm_mode == 'normal': #min-max normalization\n", + " for j in range(num_Feature):\n", + " X[:,j] = (X[:,j] - np.min(X[:,j]))/(np.max(X[:,j]) - np.min(X[:,j]))\n", + " else:\n", + " print(\"INPUT MODE ERROR!\")\n", + "\n", + " return X" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MAKE CF TIME-SERIES DATASET" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "filename= '/mnt/85c3b84c-90ae-4506-81f1-4ff88bb29110/[ DATASET ]/[ TIME-SERIES DATA ]/CYSTIC FIBROSIS/Preprocessed/Longitudinal/version 5/stacked_Data_imputed_v5.csv'\n", + "\n", + "df = pd.read_csv(filename)\n", + "tmp_df = df.copy(deep=True)\n", + "\n", + "feat_list = ['Age', 'Weight', 'Height', 'BMI', 'Gender', 'Smoking Status',\n", + " 'Class I Mutation', 'Class II Mutation', 'Class III Mutation', 'Class IV Mutation', 'Class V Mutation',\n", + " 'Class VI Mutation', 'DF508 Mutation', 'G551D Mutation',\n", + " 'FEV1', 'FEV1 Predicted', 'Best FEV1', 'Best FEV1 Predicted',\n", + " 'Pseudomonas Aeruginosa', 'Haemophilus Influenza', 'Klebsiella Pneumoniae', 'Ecoli', 'ALCA',\n", + " 'Aspergillus', 'NTM', 'Gram-Negative', 'Xanthomonas', 'Staphylococcus Aureus', 'Liver Disease',\n", + " 'Asthma', 'ABPA', 'Hypertension', 'Diabetes', 'Arthropathy', 'Bone fracture', 'Osteoporosis',\n", + " 'Osteopenia', 'Cancer', 'Cirrhosis', 'Kidney Stones', 'Depression', 'Hemoptysis', 'Pancreatitus',\n", + " 'Hearing Loss', 'Homozygous', 'Heterozygous', 'Gall bladder', 'Colonic structure',\n", + " 'Intestinal Obstruction', 'GI bleeding non-var source', 'GI bleeding var source', \n", + " 'Non-IV Hospital Admission', 'IV Antibiotic Days Hosp', 'IV Antibiotic Days Home', \n", + " 'Dornase Alpha', 'Anti-fungals', 'Liver Enzymes', 'Lab Liver Enzymes', 'HyperSaline', 'HypertonicSaline',\n", + " 'Tobi Solution', 'Cortico Combo', 'Noninvasive Ventilation', 'Acetylcysteine', 'Aminoglycoside',\n", + " 'iBuprofen', 'Drug Dornase', 'HDI Buprofen', 'Tobramycin', 'Leukotriene', 'Colistin',\n", + " 'Diabetes Inter Insulin', 'Macrolida Antibiotics', 'Inhaled Broncho BAAC', 'Inhaled Broncho LAAC',\n", + " 'Inhaled Broncho SAAC', 'Inhaled Broncho LABA', 'Inhaled Bronchodilators', 'Cortico Inhaled',\n", + " 'Oral Broncho THEOPH', 'Oral Broncho BA', 'Oral Hypoglycemic Agents', 'Chronic Oral Antibiotic',\n", + " 'Cortico Oral', 'O2 Prn', 'O2 Exc', 'O2 Noct', 'O2 Cont', 'Oxygen Therapy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# UKCF - MORTALITY" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label:0 -- 0.9229030717809963\n", + "label:1 -- 0.06175393920101862\n", + "label:2 -- 0.008594620404265478\n", + "label:3 -- 0.006748368613719561\n" + ] + } + ], + "source": [ + "for c in [0, 1, 2, 3]:\n", + " print('label:{} -- {}'.format(c, np.sum(tmp_df['cause'] == c)/np.shape(tmp_df)[0]) )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "tmp_df['new_time_to_event'] = np.nan\n", + "tmp_df['label'] = np.nan #this is 1-yr mortality label\n", + "# if time-to-event > 12 -> label 0\n", + "# if time-to-event <= 12 & cause !=0 -> label 1\n", + "# if time-to-event <= 12 & cause == 0 -> label -1 (these will be removed) \n", + "\n", + "grouped = tmp_df.groupby('ID')\n", + "id_list = pd.unique(tmp_df['ID'])\n", + "\n", + "tmp_df['new_time_to_event'] = tmp_df['time_to_event'] - tmp_df['Age']\n", + "tmp_df['new_time_to_event'] = tmp_df['time_to_event'] - tmp_df['Age']\n", + "\n", + "condition1 = (tmp_df['new_time_to_event'] > 12)\n", + "condition2 = (tmp_df['new_time_to_event'] <= 12) & (tmp_df['cause'] != 0)\n", + "condition3 = (tmp_df['new_time_to_event'] <= 12) & (tmp_df['cause'] == 0)\n", + "\n", + "tmp_df.loc[condition1, 'label'] = 0\n", + "tmp_df.loc[condition2, 'label'] = 1\n", + "tmp_df.loc[condition3, 'label'] = -1\n", + "\n", + "\n", + "new_df = tmp_df[tmp_df['label'] != -1].reset_index(drop=True)\n", + "new_df = new_df.drop(columns=['cause', 'time_to_event', 'new_time_to_event'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#make Delta_{j} = t_{j} - t_{j-1}\n", + "new_df['Delta'] = new_df['Age'].diff()\n", + "new_df.loc[new_df.drop_duplicates('ID').index, 'Delta'] = 0\n", + "\n", + "new_df[feat_list] = f_get_Normalization(np.asarray(new_df[feat_list]), norm_mode='standard')\n", + "\n", + "id_list = pd.unique(new_df['ID'])\n", + "grouped = new_df.groupby(by='ID')\n", + "\n", + "max_length = 0\n", + "remove_list = []\n", + "\n", + "for p_idx in range(len(id_list)): \n", + " tmp_length = grouped.get_group(id_list[p_idx]).shape[0]\n", + " \n", + " if tmp_length == 1:\n", + " remove_list.append(id_list[p_idx])\n", + " \n", + " if max_length <= tmp_length:\n", + " max_length = tmp_length" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "new_id_list = [p_id for p_id in id_list if p_id not in remove_list]\n", + "\n", + "data_x = np.zeros([len(new_id_list), max_length, len(feat_list)+1])\n", + "data_y = np.zeros([len(new_id_list), max_length, 1])\n", + "data_y_onehot = np.zeros([len(new_id_list), max_length, 2])\n", + "\n", + "for p_idx in range(len(new_id_list)):\n", + " tmp = grouped.get_group(new_id_list[p_idx])\n", + " tmp_length = tmp.shape[0]\n", + "\n", + " data_x[p_idx, :tmp_length, :] = np.asarray(tmp[['Delta']+feat_list])\n", + " data_y[p_idx, :tmp_length, :] = np.asarray(tmp[['label']])\n", + " data_y_onehot[p_idx, np.where(np.asarray(tmp['label']) == 0)[0], 0] = 1\n", + " data_y_onehot[p_idx, np.where(np.asarray(tmp['label']) == 1)[0], 1] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "np.savez('./data/CF/data.npz', \n", + " data_x=data_x,\n", + " data_y=data_y, \n", + " data_y_onehot=data_y_onehot, \n", + " feat_list= ['Delta'] + feat_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(label_candidate_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# UKCF - COMORBIDITY" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "filename= '/mnt/85c3b84c-90ae-4506-81f1-4ff88bb29110/[ DATASET ]/[ TIME-SERIES DATA ]/CYSTIC FIBROSIS/Preprocessed/Longitudinal/version 5/stacked_Data_imputed_v5.csv'\n", + "\n", + "df = pd.read_csv(filename)\n", + "tmp_df = df.copy(deep=True)\n", + "\n", + "feat_list = ['Age', 'Weight', 'Height', 'BMI', 'Gender', 'Smoking Status',\n", + " 'Class I Mutation', 'Class II Mutation', 'Class III Mutation', 'Class IV Mutation', 'Class V Mutation',\n", + " 'Class VI Mutation', 'DF508 Mutation', 'G551D Mutation',\n", + " 'FEV1', 'FEV1 Predicted', 'Best FEV1', 'Best FEV1 Predicted',\n", + " 'Pseudomonas Aeruginosa', 'Haemophilus Influenza', 'Klebsiella Pneumoniae', 'Ecoli', 'ALCA',\n", + " 'Aspergillus', 'NTM', 'Gram-Negative', 'Xanthomonas', 'Staphylococcus Aureus', 'Liver Disease',\n", + " 'Asthma', 'ABPA', 'Hypertension', 'Diabetes', 'Arthropathy', 'Bone fracture', 'Osteoporosis',\n", + " 'Osteopenia', 'Cancer', 'Cirrhosis', 'Kidney Stones', 'Depression', 'Hemoptysis', 'Pancreatitus',\n", + " 'Hearing Loss', 'Homozygous', 'Heterozygous', 'Gall bladder', 'Colonic structure',\n", + " 'Intestinal Obstruction', 'GI bleeding non-var source', 'GI bleeding var source', \n", + " 'Non-IV Hospital Admission', 'IV Antibiotic Days Hosp', 'IV Antibiotic Days Home', \n", + " 'Dornase Alpha', 'Anti-fungals', 'Liver Enzymes', 'HyperSaline', 'HypertonicSaline',\n", + " 'Tobi Solution', 'Cortico Combo', 'Noninvasive Ventilation', 'Acetylcysteine', 'Aminoglycoside',\n", + " 'iBuprofen', 'Drug Dornase', 'HDI Buprofen', 'Tobramycin', 'Leukotriene', 'Colistin',\n", + " 'Diabetes Inter Insulin', 'Macrolida Antibiotics', 'Inhaled Broncho BAAC', 'Inhaled Broncho LAAC',\n", + " 'Inhaled Broncho SAAC', 'Inhaled Broncho LABA', 'Inhaled Bronchodilators', 'Cortico Inhaled',\n", + " 'Oral Broncho THEOPH', 'Oral Broncho BA', 'Oral Hypoglycemic Agents', 'Chronic Oral Antibiotic',\n", + " 'Cortico Oral', 'O2 Prn', 'O2 Exc', 'O2 Noct', 'O2 Cont', 'Oxygen Therapy']\n", + "\n", + "# removed ['Lab Liver Enzymes']\n", + "label_candidate_list = ['Liver Disease', 'Asthma', 'ABPA', 'Hypertension', 'Diabetes', 'Arthropathy', 'Bone fracture', 'Osteoporosis',\n", + " 'Osteopenia', 'Cancer', 'Cirrhosis', 'Kidney Stones', 'Depression', 'Hemoptysis', 'Pancreatitus',\n", + " 'Hearing Loss', 'Gall bladder', 'Colonic structure', 'Intestinal Obstruction', \n", + " 'GI bleeding non-var source', 'GI bleeding var source', 'Liver Enzymes']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "tmp_df = df.copy(deep=True)\n", + "\n", + "label_list = ['next_'+f for f in label_candidate_list]\n", + "selected_list = ['next_Diabetes', 'next_ABPA', 'next_Intestinal Obstruction'] #3 selected comorbidities for creating " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/changhee/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self._setitem_with_indexer(indexer, value)\n" + ] + } + ], + "source": [ + "### assign the next comorbidity value to f in label_list\n", + "for f in label_list:\n", + " tmp_df[f] = np.nan\n", + " \n", + "for f_idx in range(len(label_list)):\n", + " tmp_df[label_list[f_idx]].iloc[0:-1] = np.asarray(tmp_df[label_candidate_list[f_idx]].iloc[1:]) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "grouped = tmp_df.groupby('ID')\n", + "id_list = pd.unique(tmp_df['ID'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "new_df = tmp_df.groupby('ID', as_index=False, group_keys=False).apply(lambda x: x.iloc[:-1])\n", + "new_df = new_df.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#make Delta_{j} = t_{j} - t_{j-1}\n", + "new_df['Delta'] = new_df['Age'].diff()\n", + "new_df.loc[new_df.drop_duplicates('ID').index, 'Delta'] = 0\n", + "\n", + "new_df2 = new_df.copy(deep=True) #for original values\n", + "new_df[feat_list] = f_get_Normalization(np.asarray(new_df[feat_list]), norm_mode='standard')\n", + "\n", + "id_list = pd.unique(new_df['ID'])\n", + "grouped = new_df.groupby(by='ID')\n", + "grouped2 = new_df2.groupby(by='ID')\n", + "\n", + "max_length = 0\n", + "remove_list = []\n", + "\n", + "for p_idx in range(len(id_list)): \n", + " tmp_length = grouped.get_group(id_list[p_idx]).shape[0]\n", + " \n", + " if tmp_length == 1:\n", + " remove_list.append(id_list[p_idx])\n", + " \n", + " if max_length <= tmp_length:\n", + " max_length = tmp_length" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "new_id_list = [p_id for p_id in id_list if p_id not in remove_list]\n", + "\n", + "data_x = np.zeros([len(new_id_list), max_length, len(feat_list)+1])\n", + "data_x_org = np.zeros([len(new_id_list), max_length, len(feat_list)+1])\n", + "data_y = np.zeros([len(new_id_list), max_length, len(label_list)])\n", + "\n", + "# data_y_onehot = np.zeros([len(new_id_list), max_length, 2])\n", + "\n", + "for p_idx in range(len(new_id_list)):\n", + " tmp = grouped.get_group(new_id_list[p_idx])\n", + " tmp2 = grouped2.get_group(new_id_list[p_idx])\n", + " tmp_length = tmp.shape[0]\n", + "\n", + " data_x[p_idx, :tmp_length, :] = np.asarray(tmp[['Delta']+feat_list])\n", + " data_x_org[p_idx, :tmp_length, :] = np.asarray(tmp2[['Delta']+feat_list])\n", + " data_y[p_idx, :tmp_length, :] = np.asarray(tmp[label_list])\n", + "# data_new_y[p_idx, np.where(np.asarray(tmp['label']) == 0)[0], 0] = 1\n", + "# data_new_y[p_idx, np.where(np.asarray(tmp['label']) == 1)[0], 1] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "np.savez('./CF_comorbidity/data_como2.npz', \n", + " data_x=data_x,\n", + " data_x_org=data_x_org,\n", + " data_y=data_y, \n", + " feat_list= ['Delta'] + feat_list,\n", + " label_list = label_list,\n", + " selected_list = selected_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [], + "source": [ + "def print_full(x):\n", + " pd.set_option('display.max_rows', len(x))\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 2000)\n", + " pd.set_option('display.float_format', '{:20,.2f}'.format)\n", + " pd.set_option('display.max_colwidth', -1)\n", + " print(x)\n", + " pd.reset_option('display.max_rows')\n", + " pd.reset_option('display.max_columns')\n", + " pd.reset_option('display.width')\n", + " pd.reset_option('display.float_format')\n", + " pd.reset_option('display.max_colwidth')" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " next_Asthma\n", + "count 25532.000000\n", + "mean 0.157802\n", + "std 0.364562\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_ABPA\n", + "count 25532.000000\n", + "mean 0.121260\n", + "std 0.326435\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Hypertension\n", + "count 25532.000000\n", + "mean 0.042457\n", + "std 0.201632\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Diabetes\n", + "count 25532.000000\n", + "mean 0.292848\n", + "std 0.455078\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 1.000000\n", + "max 1.000000\n", + " next_Arthropathy\n", + "count 25532.000000\n", + "mean 0.095684\n", + "std 0.294163\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Bone fracture\n", + "count 25532.000000\n", + "mean 0.009478\n", + "std 0.096896\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Osteoporosis\n", + "count 25532.000000\n", + "mean 0.099953\n", + "std 0.299943\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Osteopenia\n", + "count 25532.000000\n", + "mean 0.225560\n", + "std 0.417959\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Cancer\n", + "count 25532.000000\n", + "mean 0.004504\n", + "std 0.066963\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Cirrhosis\n", + "count 25532.000000\n", + "mean 0.026829\n", + "std 0.161587\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Kidney Stones\n", + "count 25532.000000\n", + "mean 0.016019\n", + "std 0.125551\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Depression\n", + "count 25532.000000\n", + "mean 0.079743\n", + "std 0.270900\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Hemoptysis\n", + "count 25532.000000\n", + "mean 0.009752\n", + "std 0.098274\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Pancreatitus\n", + "count 25532.000000\n", + "mean 0.013356\n", + "std 0.114795\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Hearing Loss\n", + "count 25532.000000\n", + "mean 0.031177\n", + "std 0.173798\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Gall bladder\n", + "count 25532.000000\n", + "mean 0.007677\n", + "std 0.087281\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Colonic structure\n", + "count 25532.000000\n", + "mean 0.000196\n", + "std 0.013993\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Intestinal Obstruction\n", + "count 25532.000000\n", + "mean 0.082563\n", + "std 0.275226\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_GI bleeding non-var source\n", + "count 25532.000000\n", + "mean 0.001097\n", + "std 0.033098\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_GI bleeding var source\n", + "count 25532.000000\n", + "mean 0.001371\n", + "std 0.037000\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + " next_Liver Enzymes\n", + "count 25532.000000\n", + "mean 0.161014\n", + "std 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