failure_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages and define constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Plots\n",
    "%matplotlib inline\n",
    "font = {\n",
    "    'family' : 'serif',\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 12\n",
    "}\n",
    "plt.rc('font', **font)\n",
    "PDF= True # A boolean value speficifying whether the figures should be exported to PDF\n",
    "\n",
    "# System information\n",
    "# ieee14: 27 components\n",
    "# ieee57: 100 components\n",
    "SYS = 'ieee14'\n",
    "N_COMP = 27 # Number of components in the system\n",
    "\n",
    "# Input files\n",
    "SEQ_DIR = 'failure_seq'\n",
    "INIT_FILE = os.path.join(SEQ_DIR, SYS + '_smart_initial.txt')\n",
    "RESULT_FILE = os.path.join(SEQ_DIR, SYS + '_smart_result.txt')\n",
    "CMPLX_INIT_FILE = os.path.join(SEQ_DIR, SYS + '_smart_cmplx_initial.txt')\n",
    "CMPLX_RESULT_FILE = os.path.join(SEQ_DIR, SYS + '_smart_cmplx_result.txt')\n",
    "\n",
    "# Neural network\n",
    "N_STEPS = 5000 # Number of training steps\n",
    "REC_INT = 100 # Number of steps between recording measures\n",
    "VERBOSE = True # A boolean value speficifying whether the performance measures should be printed every REC_INT steps\n",
    "BATCH_SIZE = 128 # Size of the batch for training\n",
    "N_HIDDEN_L = 256 # Number of nodes in outer hidden layers\n",
    "N_HIDDEN_M = 128 # Number of nodes in middle hidden layers\n",
    "N_HIDDEN_S = 64 # Number of nodes in centeral hidden layers\n",
    "TEST_SIZE = 0.10 # Portion of data to be used for testing\n",
    "VALID_SIZE = 0.10 # Portion of data to be used for validation\n",
    "LEARNING_RATE = 0.005 # Ratio of knowledge to learn at each new step as compared to previously established knowledge\n",
    "REGULATION_RATE = 2e-6 # Penalizing factor for increased weights (prevents overfitting)\n",
    "FAILURE_THRESHOLD = 0.90 # Threshold for deciding failed/functional state for components in the predictions\n",
    "SEED = 1818 # Seed for random number generators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read initial and resulting failures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:7: VisibleDeprecationWarning: non integer (and non boolean) array-likes will not be accepted as indices in the future\n",
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:17: VisibleDeprecationWarning: non integer (and non boolean) array-likes will not be accepted as indices in the future\n",
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:26: VisibleDeprecationWarning: non integer (and non boolean) array-likes will not be accepted as indices in the future\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simple dataset: 17969, Complex dataset: 1423\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:36: VisibleDeprecationWarning: non integer (and non boolean) array-likes will not be accepted as indices in the future\n"
     ]
    }
   ],
   "source": [
    "init_data = np.zeros((1, N_COMP))\n",
    "with open(INIT_FILE, 'r') as init_file:\n",
    "    for i, line in enumerate(init_file):\n",
    "        indices = line.split()\n",
    "        l = np.zeros((1, N_COMP))\n",
    "        if len(indices) > 1:\n",
    "            l[0, indices[1:]] = 1\n",
    "        init_data = np.append(init_data, l, 0)\n",
    "            \n",
    "if os.path.isfile(CMPLX_INIT_FILE):\n",
    "    cmplx_init_data = np.zeros((0, N_COMP))\n",
    "    with open(CMPLX_INIT_FILE, 'r') as cmplx_init_file:\n",
    "        for i, line in enumerate(cmplx_init_file):\n",
    "            indices = line.split()\n",
    "            l = np.zeros((1, N_COMP))\n",
    "            if len(indices) > 1:\n",
    "                l[0, indices[1:]] = 1\n",
    "            cmplx_init_data = np.append(cmplx_init_data, l, 0)\n",
    "\n",
    "result_data = np.zeros((1, N_COMP))\n",
    "with open(RESULT_FILE, 'r') as result_file:\n",
    "    for i, line in enumerate(result_file):\n",
    "        indices = line.split()\n",
    "        l = np.zeros((1, N_COMP))\n",
    "        if len(indices) > 1:\n",
    "            l[0, indices[1:]] = 1\n",
    "        result_data = np.append(result_data, l, 0)\n",
    "\n",
    "if os.path.isfile(CMPLX_RESULT_FILE):\n",
    "    cmplx_result_data = np.zeros((0, N_COMP))\n",
    "    with open(CMPLX_RESULT_FILE, 'r') as cmplx_result_file:\n",
    "        for i, line in enumerate(cmplx_result_file):\n",
    "            indices = line.split()\n",
    "            l = np.zeros((1, N_COMP))\n",
    "            if len(indices) > 1:\n",
    "                l[0, indices[1:]] = 1\n",
    "            cmplx_result_data = np.append(cmplx_result_data, l, 0)\n",
    "\n",
    "print('Simple dataset: {}, Complex dataset: {}'.format(init_data.shape[0], cmplx_init_data.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data to training, test, and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: 14377, Validation: 1796, Test: 1796, Complex Test: 1423\n"
     ]
    }
   ],
   "source": [
    "x_train_valid, x_test, y_train_valid, y_test = train_test_split(\n",
    "    init_data, result_data, test_size=int(TEST_SIZE*init_data.shape[0]), random_state=SEED)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "    x_train_valid, y_train_valid, test_size=int(VALID_SIZE*init_data.shape[0]), random_state=SEED)\n",
    "x_cmplx_test, y_cmplx_test = cmplx_init_data, cmplx_result_data\n",
    "print('Training: {}, Validation: {}, Test: {}, Complex Test: {}'.format(\n",
    "        x_train.shape[0], x_valid.shape[0], x_test.shape[0], x_cmplx_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification and performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def decision(predictions, threshold=0.5):\n",
    "    if isinstance(predictions, float):\n",
    "        return predictions > threshold\n",
    "    elif predictions.ndim == 1:\n",
    "        return predictions > threshold * 1 #np.amax(predictions)\n",
    "    elif predictions.ndim == 2:\n",
    "        return predictions > threshold * 1 #np.amax(predictions, 1)[np.newaxis].T\n",
    "    return None\n",
    "\n",
    "def performance(actual, predictions_bin, beta=1):\n",
    "    act = np.empty(shape=[0, actual.shape[1]])\n",
    "    pre = np.empty(shape=[0, predictions_bin.shape[1]])\n",
    "    for i in range(actual.shape[0]):\n",
    "        if any(actual[i, :]) and any(predictions_bin[i, :]):\n",
    "            act = np.append(act, actual[i, :][np.newaxis], 0)\n",
    "            pre = np.append(pre, predictions_bin[i, :][np.newaxis], 0)\n",
    "    accuracy = np.sum(predictions_bin == actual) / predictions_bin.size\n",
    "    if act.size > 0 and pre.size > 0:\n",
    "        precision, recall, fscore, _ = precision_recall_fscore_support(\n",
    "            act, pre, average='samples', beta=beta)\n",
    "    else:\n",
    "        precision, recall, fscore = (0, 0, 0)\n",
    "    return {'accuracy': accuracy, 'precision': precision, 'recall': recall, 'fscore': fscore}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # Inputs\n",
    "    tf_train_dataset = tf.placeholder(shape=(BATCH_SIZE, N_COMP), dtype=tf.float64)\n",
    "    tf_train_labels = tf.placeholder(shape=(BATCH_SIZE, N_COMP), dtype=tf.float64)\n",
    "    tf_valid_dataset = tf.constant(x_valid)\n",
    "    tf_valid_labels = tf.constant(y_valid)\n",
    "    tf_test_dataset = tf.constant(x_test)\n",
    "    tf_test_labels = tf.constant(y_test)\n",
    "    tf_cmplx_test_dataset = tf.constant(x_cmplx_test)\n",
    "    tf_cmplx_test_labels = tf.constant(y_cmplx_test)\n",
    "    \n",
    "    # Layer 1\n",
    "    weights_1 = tf.get_variable(\"weights_1\", shape=[N_COMP, N_HIDDEN_L],\n",
    "                                initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float64)\n",
    "    biases_1 = tf.Variable(tf.zeros([N_HIDDEN_L], dtype=tf.float64))\n",
    "    # Layer 2\n",
    "    weights_2 = tf.get_variable(\"weights_2\", shape=[N_HIDDEN_L, N_HIDDEN_M],\n",
    "                                initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float64)\n",
    "    biases_2 = tf.Variable(tf.zeros([N_HIDDEN_M], dtype=tf.float64))\n",
    "    # Layer 3\n",
    "    weights_3 = tf.get_variable(\"weights_3\", shape=[N_HIDDEN_M, N_HIDDEN_S],\n",
    "                                initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float64)\n",
    "    biases_3 = tf.Variable(tf.zeros([N_HIDDEN_S], dtype=tf.float64))\n",
    "    # Layer 4\n",
    "    weights_4 = tf.get_variable(\"weights_4\", shape=[N_HIDDEN_S, N_HIDDEN_M],\n",
    "                                initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float64)\n",
    "    biases_4 = tf.Variable(tf.zeros([N_HIDDEN_M], dtype=tf.float64))\n",
    "    # Layer 5\n",
    "    weights_5 = tf.get_variable(\"weights_5\", shape=[N_HIDDEN_M, N_HIDDEN_L],\n",
    "                                initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float64)\n",
    "    biases_5 = tf.Variable(tf.zeros([N_HIDDEN_L], dtype=tf.float64))\n",
    "    # Output layer\n",
    "    weights_out = tf.get_variable(\"weights_out\", shape=[N_HIDDEN_L, N_COMP],\n",
    "                                initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float64)\n",
    "    biases_out = tf.Variable(tf.zeros([N_COMP], dtype=tf.float64))\n",
    "    \n",
    "    def model(input_data):\n",
    "        activation_1 = tf.nn.relu(tf.matmul(input_data, weights_1) + biases_1)\n",
    "        activation_2 = tf.nn.relu(tf.matmul(activation_1, weights_2) + biases_2)\n",
    "        activation_3 = tf.nn.relu(tf.matmul(activation_2, weights_3) + biases_3)\n",
    "        activation_4 = tf.nn.relu(tf.matmul(activation_3, weights_4) + biases_4)\n",
    "        activation_5 = tf.nn.relu(tf.matmul(activation_4, weights_5) + biases_5)\n",
    "        return activation_5\n",
    "    \n",
    "    # Training\n",
    "    activation_model = model(tf_train_dataset)\n",
    "    logits_train = tf.matmul(activation_model, weights_out) + biases_out\n",
    "    loss_train = (tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits_train, tf_train_labels)) +\n",
    "                  REGULATION_RATE * tf.nn.l2_loss(weights_1))\n",
    "  \n",
    "    # Optimizer\n",
    "    # TODO - Try other optimizers and adjust learning rate. Weight decay is an option\n",
    "    optimizer = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss_train)\n",
    "  \n",
    "    # Predictions\n",
    "    logits_valid = tf.matmul(model(tf_valid_dataset), weights_out) + biases_out\n",
    "    loss_valid = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits_valid, tf_valid_labels))\n",
    "    sigmoid_valid = tf.nn.sigmoid(logits_valid)\n",
    "    logits_test = tf.matmul(model(tf_test_dataset), weights_out) + biases_out\n",
    "    sigmoid_test = tf.nn.sigmoid(logits_test)\n",
    "    logits_cmplx_test = tf.matmul(model(tf_cmplx_test_dataset), weights_out) + biases_out\n",
    "    sigmoid_cmplx_test = tf.nn.sigmoid(logits_cmplx_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the network...\n",
      "Step\tTR-Loss\t\tVL-Loss\t\tAccuracy\tRecall\t\tPrecision\tF-Score\n",
      "-----------------------------------------------------------------------------------------------\n",
      "    0\t0.6919\t\t0.6556\t\t99.40%\t\t0.00%\t\t0.00%\t\t0.00%\n",
      "  100\t0.0120\t\t0.0312\t\t99.40%\t\t100.00%\t\t100.00%\t\t100.00%\n",
      "  200\t0.0009\t\t0.0508\t\t99.43%\t\t71.65%\t\t81.33%\t\t74.46%\n",
      "  300\t0.0002\t\t0.0576\t\t99.45%\t\t70.42%\t\t79.48%\t\t73.26%\n",
      "  400\t0.0002\t\t0.0612\t\t99.45%\t\t71.92%\t\t81.35%\t\t74.81%\n",
      "  500\t0.0002\t\t0.0627\t\t99.45%\t\t71.01%\t\t79.20%\t\t73.44%\n",
      "  600\t0.0002\t\t0.0647\t\t99.46%\t\t72.13%\t\t79.89%\t\t74.43%\n",
      "  700\t0.0001\t\t0.0656\t\t99.47%\t\t73.18%\t\t79.68%\t\t75.08%\n",
      "  800\t0.0001\t\t0.0664\t\t99.47%\t\t74.16%\t\t80.81%\t\t76.12%\n",
      "  900\t0.0001\t\t0.0669\t\t99.47%\t\t74.25%\t\t80.61%\t\t76.07%\n",
      " 1000\t0.0001\t\t0.0673\t\t99.47%\t\t73.79%\t\t80.52%\t\t75.77%\n",
      " 1100\t0.0001\t\t0.0675\t\t99.48%\t\t73.83%\t\t80.36%\t\t75.75%\n",
      " 1200\t0.0001\t\t0.0676\t\t99.48%\t\t74.80%\t\t81.03%\t\t76.59%\n",
      " 1300\t0.0011\t\t0.0666\t\t99.49%\t\t75.08%\t\t80.79%\t\t76.64%\n",
      " 1400\t0.0008\t\t0.0664\t\t99.47%\t\t74.98%\t\t82.19%\t\t77.20%\n",
      " 1500\t0.0001\t\t0.0722\t\t99.44%\t\t66.79%\t\t73.91%\t\t68.79%\n",
      " 1600\t0.0001\t\t0.0654\t\t99.47%\t\t74.57%\t\t80.13%\t\t76.24%\n",
      " 1700\t0.0001\t\t0.0670\t\t99.48%\t\t75.54%\t\t80.43%\t\t77.00%\n",
      " 1800\t0.0001\t\t0.0676\t\t99.49%\t\t76.54%\t\t81.25%\t\t77.91%\n",
      " 1900\t0.0001\t\t0.0677\t\t99.49%\t\t77.88%\t\t82.64%\t\t79.25%\n",
      " 2000\t0.0001\t\t0.0676\t\t99.49%\t\t77.35%\t\t81.99%\t\t78.63%\n",
      " 2100\t0.0001\t\t0.0676\t\t99.49%\t\t78.44%\t\t83.04%\t\t79.73%\n",
      " 2200\t0.0000\t\t0.0676\t\t99.49%\t\t77.66%\t\t82.26%\t\t79.00%\n",
      " 2300\t0.0000\t\t0.0676\t\t99.49%\t\t77.61%\t\t82.12%\t\t78.88%\n",
      " 2400\t0.0000\t\t0.0679\t\t99.50%\t\t77.23%\t\t82.14%\t\t78.63%\n",
      " 2500\t0.0000\t\t0.0678\t\t99.49%\t\t77.42%\t\t82.06%\t\t78.72%\n",
      " 2600\t0.0000\t\t0.0680\t\t99.49%\t\t77.50%\t\t82.10%\t\t78.73%\n",
      " 2700\t0.0000\t\t0.0680\t\t99.49%\t\t76.88%\t\t81.82%\t\t78.25%\n",
      " 2800\t0.0000\t\t0.0683\t\t99.49%\t\t77.18%\t\t82.11%\t\t78.55%\n",
      " 2900\t0.0000\t\t0.0685\t\t99.50%\t\t77.52%\t\t82.34%\t\t78.79%\n",
      " 3000\t0.0000\t\t0.0690\t\t99.50%\t\t77.10%\t\t82.37%\t\t78.55%\n",
      " 3100\t0.0000\t\t0.0692\t\t99.50%\t\t77.51%\t\t82.39%\t\t78.87%\n",
      " 3200\t0.0000\t\t0.0698\t\t99.50%\t\t77.99%\t\t82.55%\t\t79.20%\n",
      " 3300\t0.0673\t\t0.1547\t\t99.35%\t\t51.40%\t\t56.82%\t\t52.68%\n",
      " 3400\t0.0005\t\t0.0612\t\t99.42%\t\t65.62%\t\t71.94%\t\t67.56%\n",
      " 3500\t0.0004\t\t0.0629\t\t99.42%\t\t64.28%\t\t70.77%\t\t66.19%\n",
      " 3600\t0.0003\t\t0.0655\t\t99.42%\t\t65.59%\t\t72.34%\t\t67.58%\n",
      " 3700\t0.0004\t\t0.0676\t\t99.43%\t\t66.89%\t\t73.60%\t\t68.84%\n",
      " 3800\t0.0003\t\t0.0714\t\t99.43%\t\t67.78%\t\t74.27%\t\t69.66%\n",
      " 3900\t0.0002\t\t0.0739\t\t99.44%\t\t68.00%\t\t73.97%\t\t69.72%\n",
      " 4000\t0.0002\t\t0.0758\t\t99.44%\t\t68.74%\t\t74.64%\t\t70.42%\n",
      " 4100\t0.0002\t\t0.0776\t\t99.43%\t\t68.71%\t\t74.53%\t\t70.36%\n",
      " 4200\t0.0002\t\t0.0790\t\t99.44%\t\t69.08%\t\t74.75%\t\t70.68%\n",
      " 4300\t0.0002\t\t0.0803\t\t99.44%\t\t69.52%\t\t75.38%\t\t71.19%\n",
      " 4400\t0.0002\t\t0.0814\t\t99.44%\t\t69.77%\t\t75.59%\t\t71.43%\n",
      " 4500\t0.0001\t\t0.0825\t\t99.44%\t\t70.22%\t\t75.98%\t\t71.87%\n",
      " 4600\t0.0001\t\t0.0835\t\t99.44%\t\t70.93%\t\t76.51%\t\t72.51%\n",
      " 4700\t0.0001\t\t0.0845\t\t99.44%\t\t70.68%\t\t76.34%\t\t72.28%\n",
      " 4800\t0.0001\t\t0.0855\t\t99.44%\t\t69.30%\t\t75.35%\t\t71.00%\n",
      " 4900\t0.0001\t\t0.0861\t\t99.44%\t\t69.64%\t\t75.64%\t\t71.31%\n",
      " 5000\t0.0001\t\t0.0868\t\t99.44%\t\t69.98%\t\t75.84%\t\t71.61%\n",
      "-----------------------------------------------------------------------------------------------\n",
      "\t\t\t\t\tAccuracy\tRecall\t\tPrecision\tF-Score\n",
      "Test Dataset:\t\t\t\t99.45%\t\t70.29%\t\t75.99%\t\t72.01%\n",
      "-----------------------------------------------------------------------------------------------\n",
      "\t\t\t\t\tAccuracy\tRecall\t\tPrecision\tF-Score\n",
      "More Complex Test Dataset:\t\t98.87%\t\t53.56%\t\t63.99%\t\t55.93%\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(graph=graph) as session:\n",
    "    tf.global_variables_initializer().run()\n",
    "    ls_train_log = []\n",
    "    ls_valid_log = []\n",
    "    perf_valid_log = []\n",
    "    print('Training the network...')\n",
    "    for step in range(N_STEPS + 1):\n",
    "        offset = (step * BATCH_SIZE) % (y_train.shape[0] - BATCH_SIZE)\n",
    "        batch_data = x_train[offset:(offset + BATCH_SIZE), :]\n",
    "        batch_labels = y_train[offset:(offset + BATCH_SIZE), :]\n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        _, ls_train, ls_valid, sg_valid = session.run(\n",
    "            [optimizer, loss_train, loss_valid, sigmoid_valid], feed_dict=feed_dict)\n",
    "        ls_train_log.append(ls_train)\n",
    "        ls_valid_log.append(ls_valid)\n",
    "        perf_valid = performance(y_valid, decision(sg_valid, FAILURE_THRESHOLD))\n",
    "        perf_valid_log.append(perf_valid)\n",
    "        if (VERBOSE and step == 0):\n",
    "            print('Step\\tTR-Loss\\t\\tVL-Loss\\t\\tAccuracy\\tRecall\\t\\tPrecision\\tF-Score')\n",
    "            print('-' * 95)\n",
    "        if VERBOSE and step % REC_INT == 0:\n",
    "            print((\n",
    "                    '{step:5d}\\t'\n",
    "                    '{tr_loss:.4f}\\t\\t'\n",
    "                    '{vl_loss:.4f}\\t\\t'\n",
    "                    '{p[accuracy]:.2%}\\t\\t'\n",
    "                    '{p[recall]:.2%}\\t\\t'\n",
    "                    '{p[precision]:.2%}\\t\\t'\n",
    "                    '{p[fscore]:.2%}').format(\n",
    "                    step=step,\n",
    "                    tr_loss=ls_train,\n",
    "                    vl_loss=ls_valid,\n",
    "                    p=perf_valid)\n",
    "                 )\n",
    "    if VERBOSE:\n",
    "        print('-' * 95)\n",
    "    sg_test = sigmoid_test.eval()\n",
    "    print(5 * '\\t' + 'Accuracy\\tRecall\\t\\tPrecision\\tF-Score')\n",
    "    print((\n",
    "            'Test Dataset:\\t\\t\\t\\t'\n",
    "            '{p[accuracy]:.2%}\\t\\t'\n",
    "            '{p[recall]:.2%}\\t\\t'\n",
    "            '{p[precision]:.2%}\\t\\t'\n",
    "            '{p[fscore]:.2%}').format(\n",
    "            p=performance(y_test, decision(sg_test, FAILURE_THRESHOLD)))\n",
    "         )\n",
    "    if VERBOSE:\n",
    "        print('-' * 95)\n",
    "    sg_cmplx_test = sigmoid_cmplx_test.eval()\n",
    "    print(5 * '\\t' + 'Accuracy\\tRecall\\t\\tPrecision\\tF-Score')\n",
    "    print((\n",
    "            'More Complex Test Dataset:\\t\\t'\n",
    "            '{p[accuracy]:.2%}\\t\\t'\n",
    "            '{p[recall]:.2%}\\t\\t'\n",
    "            '{p[precision]:.2%}\\t\\t'\n",
    "            '{p[fscore]:.2%}').format(\n",
    "            p=performance(y_cmplx_test, decision(sg_cmplx_test, FAILURE_THRESHOLD)))\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance over training epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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HO/CGuJ5pZuOA1WY20Tl3oHUhMzsPuA6Y4k/aYGabnHPLzCwLeBJY4Jx70cw+BfzFzIY7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff190333ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aoPumoguvOYGVNTUV/aZNlGzchLBY8Bk8GF1EBNpmgWgDA9EGNlP+9vev1ZUm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QAAAD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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff189e25710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_log = [x['accuracy'] for x in perf_valid_log[0::REC_INT]]\n",
    "rec_log = [x['recall'] for x in perf_valid_log[0::REC_INT]]\n",
    "pre_log = [x['precision'] for x in perf_valid_log[0::REC_INT]]\n",
    "fsc_log = [x['fscore'] for x in perf_valid_log[0::REC_INT]]\n",
    "\n",
    "n = len(ls_train_log) // REC_INT\n",
    "x = np.arange(0, REC_INT * n + 1, REC_INT)\n",
    "fig_loss = plt.figure(figsize=(8, 5))\n",
    "plt.plot(x, ls_train_log[0::REC_INT], '-C0', x, ls_valid_log[0::REC_INT], 'C1--', linewidth=0.8)\n",
    "plt.xlabel('Step')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlim(0, REC_INT * n)\n",
    "plt.ylim(0, 0.1)\n",
    "plt.legend(('Training', 'Validation'))\n",
    "plt.show()\n",
    "if PDF:\n",
    "    fig_loss.savefig(SYS + '_nn_loss.pdf', bbox_inches='tight')\n",
    "\n",
    "fig_perf = plt.figure(figsize=(8, 5))\n",
    "acc_max = (REC_INT * np.argmax(acc_log), np.amax(acc_log))\n",
    "rec_max = (REC_INT * np.argmax(rec_log), np.amax(rec_log))\n",
    "pre_max = (REC_INT * np.argmax(pre_log), np.amax(pre_log))\n",
    "fsc_max = (REC_INT * np.argmax(fsc_log), np.amax(fsc_log))\n",
    "plt.plot(x, acc_log, ':C0',\n",
    "         #acc_max[0], acc_max[1], 'C0s', \n",
    "         x, rec_log, '--C1',\n",
    "         #rec_max[0], rec_max[1], 'C1s',\n",
    "         x, pre_log, '-.C2',\n",
    "         #pre_max[0], pre_max[1], 'C2s',\n",
    "         x, fsc_log, '-C3',\n",
    "         #fsc_max[0], fsc_max[1], 'C3s'\n",
    "         linewidth=0.8\n",
    "        )\n",
    "plt.xlim(0, REC_INT * n)\n",
    "plt.ylim(0.8, 1)\n",
    "plt.xlabel('Step')\n",
    "plt.ylabel('Performance')\n",
    "plt.legend(('Accuracy',\n",
    "            #'Maximum Accuracy: {m[1]:.2%} at step {m[0]}'.format(m=acc_max),\n",
    "            'Recall',\n",
    "            #'Maximum Recall: {m[1]:.2%} at step {m[0]}'.format(m=rec_max),\n",
    "            'Precision',\n",
    "            #'Maximum Precision: {m[1]:.2%} at step {m[0]}'.format(m=pre_max),\n",
    "            'F-Score',\n",
    "            #'Maximum F-score: {m[1]:.2%} at step {m[0]}'.format(m=fsc_max)\n",
    "           ))\n",
    "\n",
    "plt.show()\n",
    "if PDF:\n",
    "    fig_perf.savefig(SYS + '_nn_perf.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction vs. actual failed components for a failure case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Failures: [1, 15, 16, 17, 84, 85, 95, 100]\n"
     ]
    },
    {
     "data": {
      "image/png": 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QGbikFHFKkiRJktq2Up3JhfQ5uSGEqcBfgEExxveAzYFdgQ5pub8A1cD7wAfp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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff189cbd9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predict_test(case, w=0.3, threshold=0.5):\n",
    "    print('Initial Failures: {}'.format((np.nonzero(x_test[case])[0] + 1).tolist()))\n",
    "    fig_pred = plt.figure(figsize=(16, 5))\n",
    "    prd = plt.bar(np.arange(N_COMP) - w/2, sg_test[case], w)\n",
    "    act = plt.bar(np.arange(N_COMP) + w/2, y_test[case], w)\n",
    "    thr = plt.plot(np.arange(-1, N_COMP + 1), np.full(N_COMP + 2, threshold), ':k')\n",
    "    plt.xlabel('Components')\n",
    "    plt.ylabel('Failure Probability')\n",
    "    plt.xticks(np.arange(N_COMP), rotation=90)\n",
    "    plt.legend((prd[0], act[0], thr[0]), ('Predicted', 'Actual', 'Decision Threshold'))\n",
    "    plt.xlim(-1, N_COMP)\n",
    "    plt.show()\n",
    "    if PDF:\n",
    "        fig_pred.savefig(SYS + '_nn_' + str(case) + '_pred.pdf', bbox_inches='tight')\n",
    "\n",
    "plot_predict_test(141, threshold=FAILURE_THRESHOLD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find best decision threshold to maximize F-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bmrHb2/vcU1hrjUpwk7rgVFJPX0TK7NmD7lU6EoNdHRlsbKTjgw/wvP027evX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\nAwh3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1b89afd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fscore_curve(start=0, stop=1, step=0.01):\n",
    "    p = []\n",
    "    threshold = np.arange(start, stop, step)\n",
    "    for t in threshold:\n",
    "        p.append(performance(y_valid, decision(sg_valid, t)))\n",
    "    acc = [x['accuracy'] for x in p]\n",
    "    rec = [x['recall'] for x in p]\n",
    "    pre = [x['precision'] for x in p]\n",
    "    fsc = [x['fscore'] for x in p]\n",
    "    fig_thr = plt.figure(figsize=(10, 5))\n",
    "    plt.plot(\n",
    "        threshold, acc, 'C0',\n",
    "        threshold, rec, 'C1',\n",
    "        threshold, pre, 'C2',\n",
    "        threshold, fsc, 'C3',\n",
    "        threshold[np.argmax(fsc)], np.amax(fsc), 'C3s',\n",
    "        linewidth=0.8)\n",
    "    plt.legend((\n",
    "            'Acuracy',\n",
    "            'Recall',\n",
    "            'Precision',\n",
    "            'F-Score',\n",
    "            'Maximum F-score: {:.2%} at {:.2f}'.format(np.amax(fsc), threshold[np.argmax(fsc)])))\n",
    "    plt.ylim(0.85, 1)\n",
    "    plt.show()\n",
    "    if PDF:\n",
    "        fig_thr.savefig('nn_threshold.pdf', bbox_inches='tight')\n",
    "\n",
    "fscore_curve()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}