final_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow.python.keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Gordon-2002_LungCancer.csv')\n",
    "df = df.transpose()\n",
    "\n",
    "mpm_df = df[df[0]=='MPM']\n",
    "ad_df = df[df[0]=='AD']\n",
    "\n",
    "\n",
    "\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "df = df.drop(0,axis=1)\n",
    "\n",
    "df.columns = df.iloc[0]\n",
    "\n",
    "df = df.drop(0)\n",
    "df = df.reset_index(drop=True)\n",
    "df = df.apply(pd.to_numeric)\n",
    "\n",
    "df[df.columns] = StandardScaler().fit_transform(df[df.columns])\n",
    "df.to_csv('patient_removed_type.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameters for neural network\n",
    "learning_param = 0.005\n",
    "epochs = 5000\n",
    "batch_size = 32\n",
    "beta = 5\n",
    "\n",
    "# network parameters\n",
    "input_dimension = 1626\n",
    "nn_dimension = 512\n",
    "\n",
    "# how many latent variables do we want?\n",
    "latent_var_dimension = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialization\n",
    "def xavier (in_shape):\n",
    "    val = tf.random_normal(shape=in_shape, stddev= 1./tf.sqrt(in_shape[0]/2.))\n",
    "    return(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Weight = { \"weight_matrix_encoder_hidden\": tf.Variable(xavier([input_dimension,nn_dimension])),\n",
    "            \"weight_mean_hidden\": tf.Variable(xavier([nn_dimension,latent_var_dimension])),\n",
    "            \"weight_std_hidden\": tf.Variable(xavier([nn_dimension,latent_var_dimension])),\n",
    "            \"weight_matrix_decoder_hidden\": tf.Variable(xavier([latent_var_dimension,nn_dimension])),\n",
    "            \"weight_decoder\": tf.Variable(xavier([nn_dimension, input_dimension]))\n",
    "        }\n",
    "\n",
    "Bias = {    \"bias_matrix_encoder_hidden\": tf.Variable(xavier([nn_dimension])),\n",
    "            \"bias_mean_hidden\": tf.Variable(xavier([latent_var_dimension])),\n",
    "            \"bias_std_hidden\": tf.Variable(xavier([latent_var_dimension])),\n",
    "            \"bias_matrix_decoder_hidden\": tf.Variable(xavier([nn_dimension])),\n",
    "            \"bias_decoder\": tf.Variable(xavier([input_dimension]))\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building variational autoencoder\n",
    "\n",
    "# Encoder\n",
    "patient_X = tf.placeholder(tf.float32, shape= [None, input_dimension])\n",
    "Encoder_layer = tf.add(tf.matmul(patient_X, Weight[\"weight_matrix_encoder_hidden\"]), Bias[\"bias_matrix_encoder_hidden\"])\n",
    "Encoder_layer = tf.nn.tanh(Encoder_layer)\n",
    "\n",
    "Mean_layer = tf.add(tf.matmul(Encoder_layer, Weight[\"weight_mean_hidden\"]), Bias[\"bias_mean_hidden\"])\n",
    "SD_layer = tf.add(tf.matmul(Encoder_layer, Weight[\"weight_std_hidden\"]), Bias[\"bias_std_hidden\"])\n",
    "\n",
    "# reparameterize\n",
    "epsilon = tf.random_normal(tf.shape(SD_layer),dtype = tf.float32, mean = 0.0, stddev = 1.0)\n",
    "\n",
    "latent_layer = Mean_layer + tf.exp(0.5*SD_layer)*epsilon\n",
    "\n",
    "# Decoder\n",
    "Decoder_hidden = tf.add(tf.matmul(latent_layer, Weight[\"weight_matrix_decoder_hidden\"]), Bias[\"bias_matrix_decoder_hidden\"])\n",
    "Decoder_hidden = tf.nn.tanh(Decoder_hidden)\n",
    "\n",
    "Decoder_output_layer = tf.add(tf.matmul(Decoder_hidden, Weight[\"weight_decoder\"]), Bias[\"bias_decoder\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def next_batch(num, data):\n",
    "    '''\n",
    "    Return a total of `num` random samples and labels. \n",
    "    '''\n",
    "    idx = np.arange(0 , len(data))\n",
    "    np.random.shuffle(idx)\n",
    "    idx = idx[:num]\n",
    "    data_shuffle = [data.loc[i] for i in idx]\n",
    "    return np.asarray(data_shuffle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-21f9fe568fe5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mlatent_loss\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mloss_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloss_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpatient_X\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDecoder_output_layer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRMSPropOptimizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlearning_param\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-30-21f9fe568fe5>\u001b[0m in \u001b[0;36mloss_function\u001b[1;34m(original_data, reconstructed_data)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mloss_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreconstructed_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;31m#network_loss = tf.sqrt(tf.reduce_mean((original_data - reconstructed_data)**2))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mmean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mreconstructed_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mreconstructed_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36mvar\u001b[1;34m(a, axis, dtype, out, ddof, keepdims)\u001b[0m\n\u001b[0;32m   3192\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3193\u001b[0m     return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n\u001b[1;32m-> 3194\u001b[1;33m                          **kwargs)\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\numpy\\core\\_methods.py\u001b[0m in \u001b[0;36m_var\u001b[1;34m(a, axis, dtype, out, ddof, keepdims)\u001b[0m\n\u001b[0;32m    100\u001b[0m     \u001b[1;31m# Note that if dtype is not of inexact type then arraymean will\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# not be either.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[0marrmean\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrmean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m         arrmean = um.true_divide(\n",
      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "# loss function\n",
    "def loss_function(original_data, reconstructed_data):\n",
    "    network_loss = tf.sqrt(tf.reduce_mean((original_data - reconstructed_data)**2))\n",
    "    #mean, var = tf.reduce_mean(original_data-reconstructed_data), np.var(original_data-reconstructed_data, dtype= np.float64)\n",
    "    #print(mean, var)\n",
    "    #x = [mean, tf.log(var)]\n",
    "    #latent_loss = -0.5 * (1 + x[1] - K.square(x[0]) - K.exp(x[1]))\n",
    "    #latent_loss = tf.reduce_sum(latent_loss, axis=-1) # sum over latent dimension\n",
    "    #latent_loss = tf.reduce_mean(latent_loss, axis=0) # avg over batch\n",
    "\n",
    "    # use beta to force less usage of vector space:\n",
    "    #latent_loss = beta * latent_loss\n",
    "    #latent_loss = latent_loss + x\n",
    "    return network_loss\n",
    "\n",
    "loss_value = loss_function(patient_X, Decoder_output_layer)\n",
    "optimizer = tf.train.RMSPropOptimizer(learning_param).minimize(loss_value)\n",
    "\n",
    "# initialize variables\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss is [[ 1.3973942e+00 -1.5154779e-03  2.8845720e+00 ... -3.3387318e-02\n",
      "   6.6457062e+00  3.9273143e+00]\n",
      " [ 2.8213658e+00  5.2856565e-01  4.7262254e+00 ...  3.8485530e-01\n",
      "   6.5292249e+00  4.3811979e+00]] at iteration 0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-6e5b13c3c549>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m         \u001b[0mx_batch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m         \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mWeight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss_value\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mpatient_X\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mx_batch\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-2785cb73c4ee>\u001b[0m in \u001b[0;36mnext_batch\u001b[1;34m(num, data)\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mdata_shuffle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_shuffle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    490\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m     \"\"\"\n\u001b[1;32m--> 492\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\base.py\u001b[0m in \u001b[0;36m__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    918\u001b[0m         \u001b[1;33m(\u001b[0m\u001b[1;32mfor\u001b[0m \u001b[0mTimestamp\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mTimedelta\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mInterval\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mPeriod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m         \"\"\"\n\u001b[1;32m--> 920\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    921\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    922\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# executing graph\n",
    "\n",
    "# start session\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "weight_matrix = {}\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for i in range(epochs):\n",
    "        x_batch = next_batch(batch_size, df)\n",
    "        _, weight, loss = sess.run([optimizer,Weight, loss_value], feed_dict = {patient_X : x_batch})\n",
    "\n",
    "        if i % 500 ==0:\n",
    "            print(\"Loss is {0} at iteration {1}\".format(loss, i))\n",
    "        \n",
    "        if i % 4999 ==0:\n",
    "            weight_matrix = weight\n",
    "    save_path = saver.save(sess, \"./model.ckpt\")    \n",
    "    print(\"Model saved in path: %s\" % save_path)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reconstruct_data(df, patient_number = 1):\n",
    "    ## Part 1: Reshape the series to the correct dimension i.e 1, 1626\n",
    "    x_test = np.array(df.iloc[patient_number])\n",
    "    \n",
    "    x_test_1 = x_test.reshape((1, 1626))\n",
    "    \n",
    "    with tf.Session() as sess:     \n",
    "        sess.run(init) \n",
    "        saver.restore(sess, \"./model.ckpt\")\n",
    "        latent = sess.run(latent_layer, feed_dict={patient_X: x_test_1})\n",
    "    return latent[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
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      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n"
     ]
    }
   ],
   "source": [
    "latent_df = {}\n",
    "for i in range(len(df)):\n",
    "    latent_df[i]= reconstruct_data(df,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0          1          2          3         4\n",
      "0  -6.687513  17.964842  10.367953  -2.076651 -5.971411\n",
      "1 -22.483128   8.749027  13.003904   6.866226  1.103345\n",
      "2  -0.134073  10.255378  15.712632  -9.783230 -9.374829\n",
      "3 -13.989144  17.080902   1.734007   7.211733 -2.207218\n",
      "4   2.147603  19.993021   6.190531  -4.422985 -8.906800\n",
      "5   1.983543  10.516049  19.775284  -7.650686 -2.023323\n",
      "6  -2.806559  16.168756  20.608555   0.172623  0.876547\n",
      "7   2.272878  14.706274  14.317532 -12.305567  1.449425\n",
      "8   0.882069  19.924334  14.445507  -2.260811 -3.762520\n",
      "9 -17.433243  16.541548  13.681810   0.973529 -8.925259\n"
     ]
    }
   ],
   "source": [
    "l_df = pd.DataFrame(list(latent_df.values()), index=list(latent_df.keys()))\n",
    "l_df.to_csv('latent_var_data.csv')\n",
    "print(l_df.head(10))"
   ]
  }
 ],
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