{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting start with the classification problem.\n",
    "\n",
    "Let's start with the baby step example for classification problem.\n",
    "Below is the basic example of the configuration for binary classification problem using example data contained in the package. For the required data type or more detailed configuration, please check the detailed information about each option in the [documantation](https://young-won.github.io/deepbiome/prerequisites.html#configuration) and the detailed [examples](https://young-won.github.io/deepbiome/example_with_the_list_of_inputs.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pkg_resources import resource_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "network_info = {\n",
    "    'architecture_info': {\n",
    "        'batch_normalization': 'False',\n",
    "        'drop_out': '0',\n",
    "        'weight_initial': 'glorot_uniform',\n",
    "        'weight_l1_penalty':'0.01',\n",
    "        'weight_decay': 'phylogenetic_tree',\n",
    "    },\n",
    "    'model_info': {\n",
    "        'decay': '0.001',\n",
    "        'loss': 'binary_crossentropy',\n",
    "        'lr': '0.01',\n",
    "        'metrics': 'binary_accuracy, sensitivity, specificity, gmeasure, auc',\n",
    "        'network_class': 'DeepBiomeNetwork',\n",
    "        'normalizer': 'normalize_minmax',\n",
    "        'optimizer': 'adam',\n",
    "        'reader_class': 'MicroBiomeClassificationReader',\n",
    "        'taxa_selection_metrics': 'sensitivity, specificity, gmeasure, accuracy',\n",
    "    },\n",
    "    'training_info': {\n",
    "        'batch_size': '200', \n",
    "        'epochs': '10',\n",
    "        'callbacks': 'ModelCheckpoint',\n",
    "        'monitor': 'val_loss',\n",
    "        'mode' : 'min',\n",
    "        'min_delta': '1e-7',\n",
    "    },\n",
    "    'validation_info': {\n",
    "        'batch_size': 'None', \n",
    "        'validation_size': '0.2'\n",
    "    },\n",
    "    'test_info': {\n",
    "        'batch_size': 'None',\n",
    "    },\n",
    "}\n",
    "\n",
    "path_info = {\n",
    "    'data_info': {\n",
    "        'data_path': resource_filename('deepbiome', 'tests/data'),\n",
    "        'idx_path': resource_filename('deepbiome', 'tests/data/onefile_idx.csv'),\n",
    "        'tree_info_path': resource_filename('deepbiome', 'tests/data/genus48_dic.csv'),\n",
    "        'x_path': 'onefile_x.csv',\n",
    "        'y_path': 'classification_y.csv'\n",
    "    },\n",
    "    'model_info': {\n",
    "        'evaluation': 'eval.npy',\n",
    "        'history': 'hist.json',\n",
    "        'model_dir': './',\n",
    "        'weight': 'weight.h5'\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For logging, we used the python logging library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "logging.basicConfig(format = '[%(name)-8s|%(levelname)s|%(filename)s:%(lineno)s] %(message)s',\n",
    "                    level=logging.DEBUG)\n",
    "log = logging.getLogger()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the `deepbiome.deepbiome_train` function for training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "[root    |INFO|deepbiome.py:115] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:153] -------1 simulation start!----------------------------------\n",
      "[root    |INFO|readers.py:58] -----------------------------------------------------------------------\n",
      "[root    |INFO|readers.py:59] Construct Dataset\n",
      "[root    |INFO|readers.py:60] -----------------------------------------------------------------------\n",
      "[root    |INFO|readers.py:61] Load data\n",
      "[root    |INFO|deepbiome.py:164] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:165] Build network for 1 simulation\n",
      "[root    |INFO|build_network.py:521] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:522] Read phylogenetic tree information from /DATA/home/muha/github_repos/deepbiome/deepbiome/tests/data/genus48_dic.csv\n",
      "[root    |INFO|build_network.py:528] Phylogenetic tree level list: ['Genus', 'Family', 'Order', 'Class', 'Phylum']\n",
      "[root    |INFO|build_network.py:529] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:537]      Genus: 48\n",
      "[root    |INFO|build_network.py:537]     Family: 40\n",
      "[root    |INFO|build_network.py:537]      Order: 23\n",
      "[root    |INFO|build_network.py:537]      Class: 17\n",
      "[root    |INFO|build_network.py:537]     Phylum: 9\n",
      "[root    |INFO|build_network.py:546] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:547] Phylogenetic_tree_dict info: ['Class', 'Number', 'Order', 'Family', 'Phylum', 'Genus']\n",
      "[root    |INFO|build_network.py:548] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [ Genus, Family]\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [Family,  Order]\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [ Order,  Class]\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [ Class, Phylum]\n",
      "[root    |INFO|build_network.py:571] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:586] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:587] Build network based on phylogenetic tree information\n",
      "[root    |INFO|build_network.py:588] ------------------------------------------------------------------------------------------\n",
      "[tensorflow|WARNING|deprecation.py:328] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/resource_variable_ops.py:432: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "[root    |INFO|build_network.py:670] ------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 48)                0         \n",
      "_________________________________________________________________\n",
      "l1_dense (Dense_with_tree)   (None, 40)                1960      \n",
      "_________________________________________________________________\n",
      "l1_activation (Activation)   (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "l2_dense (Dense_with_tree)   (None, 23)                943       \n",
      "_________________________________________________________________\n",
      "l2_activation (Activation)   (None, 23)                0         \n",
      "_________________________________________________________________\n",
      "l3_dense (Dense_with_tree)   (None, 17)                408       \n",
      "_________________________________________________________________\n",
      "l3_activation (Activation)   (None, 17)                0         \n",
      "_________________________________________________________________\n",
      "l4_dense (Dense_with_tree)   (None, 9)                 162       \n",
      "_________________________________________________________________\n",
      "l4_activation (Activation)   (None, 9)                 0         \n",
      "_________________________________________________________________\n",
      "last_dense_h (Dense)         (None, 1)                 10        \n",
      "_________________________________________________________________\n",
      "p_hat (Activation)           (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 3,483\n",
      "Trainable params: 3,483\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:61] Build Network\n",
      "[root    |INFO|build_network.py:62] Optimizer = adam\n",
      "[root    |INFO|build_network.py:63] Loss = binary_crossentropy\n",
      "[root    |INFO|build_network.py:64] Metrics = binary_accuracy, sensitivity, specificity, gmeasure, auc\n",
      "[root    |INFO|deepbiome.py:176] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:177] 1 fold computing start!----------------------------------\n",
      "[root    |INFO|build_network.py:137] Training start!\n",
      "[tensorflow|WARNING|deprecation.py:328] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:2862: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 640 samples, validate on 160 samples\n",
      "Epoch 1/10\n",
      "640/640 [==============================] - 1s 1ms/step - loss: 0.6898 - binary_accuracy: 0.6047 - sensitivity: 0.8222 - specificity: 0.1731 - gmeasure: 0.1118 - auc: 0.4496 - val_loss: 0.6786 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.4840\n",
      "Epoch 2/10\n",
      "640/640 [==============================] - 0s 205us/step - loss: 0.6752 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5014 - val_loss: 0.6618 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5005\n",
      "Epoch 3/10\n",
      "640/640 [==============================] - 0s 90us/step - loss: 0.6596 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5386 - val_loss: 0.6450 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5134\n",
      "Epoch 4/10\n",
      "640/640 [==============================] - 0s 63us/step - loss: 0.6434 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.4943 - val_loss: 0.6301 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5203\n",
      "Epoch 5/10\n",
      "640/640 [==============================] - 0s 78us/step - loss: 0.6323 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5018 - val_loss: 0.6195 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5243\n",
      "Epoch 6/10\n",
      "640/640 [==============================] - 0s 68us/step - loss: 0.6243 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5288 - val_loss: 0.6160 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5334\n",
      "Epoch 7/10\n",
      "640/640 [==============================] - 0s 62us/step - loss: 0.6238 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5577 - val_loss: 0.6169 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5526\n",
      "Epoch 8/10\n",
      "640/640 [==============================] - 0s 81us/step - loss: 0.6257 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5954 - val_loss: 0.6169 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5736\n",
      "Epoch 9/10\n",
      "640/640 [==============================] - ETA: 0s - loss: 0.6430 - binary_accuracy: 0.6650 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.53 - 0s 78us/step - loss: 0.6254 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.6563 - val_loss: 0.6161 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.6002\n",
      "Epoch 10/10\n",
      "640/640 [==============================] - 0s 91us/step - loss: 0.6238 - binary_accuracy: 0.6844 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.6318 - val_loss: 0.6161 - val_binary_accuracy: 0.6938 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.6208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:87] Load trained model weight at ./weight_0.h5 \n",
      "[root    |INFO|build_network.py:147] Training end with time 3.954432725906372!\n",
      "[root    |INFO|build_network.py:83] Saved trained model weight at ./weight_0.h5 \n",
      "[root    |DEBUG|deepbiome.py:185] Save weight at ./weight_0.h5\n",
      "[root    |DEBUG|deepbiome.py:188] Save history at ./hist_0.json\n",
      "[root    |INFO|build_network.py:173] Evaluation start!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "800/800 [==============================] - 0s 7us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:178] Evaluation end with time 0.015036821365356445!\n",
      "[root    |INFO|build_network.py:179] Evaluation: [0.6221581101417542, 0.6862499713897705, 1.0, 0.0, 0.0, 0.5438174605369568]\n",
      "[root    |INFO|build_network.py:173] Evaluation start!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "200/200 [==============================] - 0s 22us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:178] Evaluation end with time 0.013112068176269531!\n",
      "[root    |INFO|build_network.py:179] Evaluation: [0.6190831661224365, 0.6899999976158142, 1.0, 0.0, 0.0, 0.6127863526344299]\n",
      "[root    |INFO|deepbiome.py:199] Compute time : 5.095764636993408\n",
      "[root    |INFO|deepbiome.py:200] 1 fold computing end!---------------------------------------------\n",
      "[root    |INFO|deepbiome.py:153] -------2 simulation start!----------------------------------\n",
      "[root    |INFO|readers.py:58] -----------------------------------------------------------------------\n",
      "[root    |INFO|readers.py:59] Construct Dataset\n",
      "[root    |INFO|readers.py:60] -----------------------------------------------------------------------\n",
      "[root    |INFO|readers.py:61] Load data\n",
      "[root    |INFO|deepbiome.py:164] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:165] Build network for 2 simulation\n",
      "[root    |INFO|build_network.py:521] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:522] Read phylogenetic tree information from /DATA/home/muha/github_repos/deepbiome/deepbiome/tests/data/genus48_dic.csv\n",
      "[root    |INFO|build_network.py:528] Phylogenetic tree level list: ['Genus', 'Family', 'Order', 'Class', 'Phylum']\n",
      "[root    |INFO|build_network.py:529] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:537]      Genus: 48\n",
      "[root    |INFO|build_network.py:537]     Family: 40\n",
      "[root    |INFO|build_network.py:537]      Order: 23\n",
      "[root    |INFO|build_network.py:537]      Class: 17\n",
      "[root    |INFO|build_network.py:537]     Phylum: 9\n",
      "[root    |INFO|build_network.py:546] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:547] Phylogenetic_tree_dict info: ['Class', 'Number', 'Order', 'Family', 'Phylum', 'Genus']\n",
      "[root    |INFO|build_network.py:548] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [ Genus, Family]\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [Family,  Order]\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [ Order,  Class]\n",
      "[root    |INFO|build_network.py:558] Build edge weights between [ Class, Phylum]\n",
      "[root    |INFO|build_network.py:571] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:586] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:587] Build network based on phylogenetic tree information\n",
      "[root    |INFO|build_network.py:588] ------------------------------------------------------------------------------------------\n",
      "[root    |INFO|build_network.py:670] ------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 48)                0         \n",
      "_________________________________________________________________\n",
      "l1_dense (Dense_with_tree)   (None, 40)                1960      \n",
      "_________________________________________________________________\n",
      "l1_activation (Activation)   (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "l2_dense (Dense_with_tree)   (None, 23)                943       \n",
      "_________________________________________________________________\n",
      "l2_activation (Activation)   (None, 23)                0         \n",
      "_________________________________________________________________\n",
      "l3_dense (Dense_with_tree)   (None, 17)                408       \n",
      "_________________________________________________________________\n",
      "l3_activation (Activation)   (None, 17)                0         \n",
      "_________________________________________________________________\n",
      "l4_dense (Dense_with_tree)   (None, 9)                 162       \n",
      "_________________________________________________________________\n",
      "l4_activation (Activation)   (None, 9)                 0         \n",
      "_________________________________________________________________\n",
      "last_dense_h (Dense)         (None, 1)                 10        \n",
      "_________________________________________________________________\n",
      "p_hat (Activation)           (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 3,483\n",
      "Trainable params: 3,483\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:61] Build Network\n",
      "[root    |INFO|build_network.py:62] Optimizer = adam\n",
      "[root    |INFO|build_network.py:63] Loss = binary_crossentropy\n",
      "[root    |INFO|build_network.py:64] Metrics = binary_accuracy, sensitivity, specificity, gmeasure, auc\n",
      "[root    |INFO|deepbiome.py:176] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:177] 2 fold computing start!----------------------------------\n",
      "[root    |INFO|build_network.py:137] Training start!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 640 samples, validate on 160 samples\n",
      "Epoch 1/10\n",
      "640/640 [==============================] - 1s 1ms/step - loss: 0.6910 - binary_accuracy: 0.6062 - sensitivity: 0.7500 - specificity: 0.2500 - gmeasure: 0.0000e+00 - auc: 0.5193 - val_loss: 0.6810 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5068\n",
      "Epoch 2/10\n",
      "640/640 [==============================] - 0s 62us/step - loss: 0.6791 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.4727 - val_loss: 0.6677 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.4705\n",
      "Epoch 3/10\n",
      "640/640 [==============================] - 0s 61us/step - loss: 0.6666 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5033 - val_loss: 0.6550 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.4520\n",
      "Epoch 4/10\n",
      "640/640 [==============================] - 0s 74us/step - loss: 0.6557 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5027 - val_loss: 0.6428 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.4691\n",
      "Epoch 5/10\n",
      "640/640 [==============================] - 0s 74us/step - loss: 0.6456 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5123 - val_loss: 0.6316 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.4974\n",
      "Epoch 6/10\n",
      "640/640 [==============================] - 0s 61us/step - loss: 0.6358 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5090 - val_loss: 0.6218 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5056\n",
      "Epoch 7/10\n",
      "640/640 [==============================] - 0s 91us/step - loss: 0.6285 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5027 - val_loss: 0.6129 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5174\n",
      "Epoch 8/10\n",
      "640/640 [==============================] - 0s 60us/step - loss: 0.6203 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.4762 - val_loss: 0.6051 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5225\n",
      "Epoch 9/10\n",
      "640/640 [==============================] - 0s 59us/step - loss: 0.6145 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.4875 - val_loss: 0.5979 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5271\n",
      "Epoch 10/10\n",
      "640/640 [==============================] - 0s 77us/step - loss: 0.6093 - binary_accuracy: 0.7156 - sensitivity: 1.0000 - specificity: 0.0000e+00 - gmeasure: 0.0000e+00 - auc: 0.5059 - val_loss: 0.5916 - val_binary_accuracy: 0.7375 - val_sensitivity: 1.0000 - val_specificity: 0.0000e+00 - val_gmeasure: 0.0000e+00 - val_auc: 0.5252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:87] Load trained model weight at ./weight_1.h5 \n",
      "[root    |INFO|build_network.py:147] Training end with time 2.9402072429656982!\n",
      "[root    |INFO|build_network.py:83] Saved trained model weight at ./weight_1.h5 \n",
      "[root    |DEBUG|deepbiome.py:185] Save weight at ./weight_1.h5\n",
      "[root    |DEBUG|deepbiome.py:188] Save history at ./hist_1.json\n",
      "[root    |INFO|build_network.py:173] Evaluation start!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "800/800 [==============================] - 0s 8us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:178] Evaluation end with time 0.013414382934570312!\n",
      "[root    |INFO|build_network.py:179] Evaluation: [0.6027529239654541, 0.7200000286102295, 1.0, 0.0, 0.0, 0.4951443076133728]\n",
      "[root    |INFO|build_network.py:173] Evaluation start!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "200/200 [==============================] - 0s 28us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[root    |INFO|build_network.py:178] Evaluation end with time 0.011273860931396484!\n",
      "[root    |INFO|build_network.py:179] Evaluation: [0.6027520895004272, 0.7200000286102295, 1.0, 0.0, 0.0, 0.4978918731212616]\n",
      "[root    |INFO|deepbiome.py:199] Compute time : 3.5541372299194336\n",
      "[root    |INFO|deepbiome.py:200] 2 fold computing end!---------------------------------------------\n",
      "[root    |INFO|deepbiome.py:211] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:212] Train Evaluation : ['loss' 'binary_accuracy' 'sensitivity' 'specificity' 'gmeasure' 'auc']\n",
      "[root    |INFO|deepbiome.py:213]       mean : [0.612 0.703 1.000 0.000 0.000 0.519]\n",
      "[root    |INFO|deepbiome.py:214]        std : [0.010 0.017 0.000 0.000 0.000 0.024]\n",
      "[root    |INFO|deepbiome.py:215] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:216] Test Evaluation : ['loss' 'binary_accuracy' 'sensitivity' 'specificity' 'gmeasure' 'auc']\n",
      "[root    |INFO|deepbiome.py:217]       mean : [0.611 0.705 1.000 0.000 0.000 0.555]\n",
      "[root    |INFO|deepbiome.py:218]        std : [0.008 0.015 0.000 0.000 0.000 0.057]\n",
      "[root    |INFO|deepbiome.py:219] -----------------------------------------------------------------\n",
      "[root    |INFO|deepbiome.py:230] Total Computing Ended\n",
      "[root    |INFO|deepbiome.py:231] -----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from deepbiome import deepbiome\n",
    "\n",
    "test_evaluation, train_evaluation, network = deepbiome.deepbiome_train(log, network_info, path_info, number_of_fold=2)"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}