python/indoor_localization-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Traceback (most recent call last):\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\", line 18, in swig_import_helper\n    return importlib.import_module(mname)\n  File \"D:\\Tools\\Anaconda3\\lib\\importlib\\__init__.py\", line 126, in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\n  File \"<frozen importlib._bootstrap>\", line 978, in _gcd_import\n  File \"<frozen importlib._bootstrap>\", line 961, in _find_and_load\n  File \"<frozen importlib._bootstrap>\", line 950, in _find_and_load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 648, in _load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 560, in module_from_spec\n  File \"<frozen importlib._bootstrap_external>\", line 922, in create_module\n  File \"<frozen importlib._bootstrap>\", line 205, in _call_with_frames_removed\nImportError: DLL load failed: The specified module could not be found.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 41, in <module>\n    from tensorflow.python.pywrap_tensorflow_internal import *\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\", line 21, in <module>\n    _pywrap_tensorflow_internal = swig_import_helper()\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\", line 20, in swig_import_helper\n    return importlib.import_module('_pywrap_tensorflow_internal')\n  File \"D:\\Tools\\Anaconda3\\lib\\importlib\\__init__.py\", line 126, in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\nModuleNotFoundError: No module named '_pywrap_tensorflow_internal'\n\n\nFailed to load the native TensorFlow runtime.\n\nSee https://www.tensorflow.org/install/install_sources#common_installation_problems\n\nfor some common reasons and solutions.  Include the entire stack trace\nabove this error message when asking for help.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\u001b[0m in \u001b[0;36mswig_import_helper\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m    125\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 126\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36mmodule_from_spec\u001b[1;34m(spec)\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mcreate_module\u001b[1;34m(self, spec)\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_call_with_frames_removed\u001b[1;34m(f, *args, **kwds)\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: DLL load failed: The specified module could not be found.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdlopenflags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_default_dlopen_flags\u001b[0m \u001b[1;33m|\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRTLD_GLOBAL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m   \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpywrap_tensorflow_internal\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m   \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpywrap_tensorflow_internal\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     20\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_pywrap_tensorflow_internal'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m     \u001b[0m_pywrap_tensorflow_internal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mswig_import_helper\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     22\u001b[0m     \u001b[1;32mdel\u001b[0m \u001b[0mswig_import_helper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\u001b[0m in \u001b[0;36mswig_import_helper\u001b[1;34m()\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_pywrap_tensorflow_internal'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m     \u001b[0m_pywrap_tensorflow_internal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mswig_import_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\importlib\\__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m    125\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 126\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named '_pywrap_tensorflow_internal'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-601cc9fb38c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TF_CPP_MIN_LOG_LEVEL'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2'\u001b[0m \u001b[1;31m# supress warning messages\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;31m# pylint: disable=wildcard-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m \u001b[1;31m# pylint: enable=wildcard-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[1;31m# Protocol buffers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msome\u001b[0m \u001b[0mcommon\u001b[0m \u001b[0mreasons\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0msolutions\u001b[0m\u001b[1;33m.\u001b[0m  \u001b[0mInclude\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mentire\u001b[0m \u001b[0mstack\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m above this error message when asking for help.\"\"\" % traceback.format_exc()\n\u001b[1;32m---> 52\u001b[1;33m   \u001b[1;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;31m# pylint: enable=wildcard-import,g-import-not-at-top,unused-import,line-too-long\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: Traceback (most recent call last):\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\", line 18, in swig_import_helper\n    return importlib.import_module(mname)\n  File \"D:\\Tools\\Anaconda3\\lib\\importlib\\__init__.py\", line 126, in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\n  File \"<frozen importlib._bootstrap>\", line 978, in _gcd_import\n  File \"<frozen importlib._bootstrap>\", line 961, in _find_and_load\n  File \"<frozen importlib._bootstrap>\", line 950, in _find_and_load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 648, in _load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 560, in module_from_spec\n  File \"<frozen importlib._bootstrap_external>\", line 922, in create_module\n  File \"<frozen importlib._bootstrap>\", line 205, in _call_with_frames_removed\nImportError: DLL load failed: The specified module could not be found.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 41, in <module>\n    from tensorflow.python.pywrap_tensorflow_internal import *\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\", line 21, in <module>\n    _pywrap_tensorflow_internal = swig_import_helper()\n  File \"D:\\Tools\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\", line 20, in swig_import_helper\n    return importlib.import_module('_pywrap_tensorflow_internal')\n  File \"D:\\Tools\\Anaconda3\\lib\\importlib\\__init__.py\", line 126, in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\nModuleNotFoundError: No module named '_pywrap_tensorflow_internal'\n\n\nFailed to load the native TensorFlow runtime.\n\nSee https://www.tensorflow.org/install/install_sources#common_installation_problems\n\nfor some common reasons and solutions.  Include the entire stack trace\nabove this error message when asking for help."
     ]
    }
   ],
   "source": [
    "# Slighlty updated for Keras 2.0 APIs\n",
    "# - by Kyeong Soo (Joseph) Kim\n",
    "\n",
    "import datetime\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2' # supress warning messages\n",
    "import tensorflow as tf\n",
    "\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.models import Sequential\n",
    "from sklearn.preprocessing import scale\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_train = \"../data/UJIIndoorLoc/trainingData2.csv\"\n",
    "path_validation = \"../data/UJIIndoorLoc/validationData2.csv\"\n",
    "path_results = \"../results/indoor_localization-1\" # TODO: implement auto-detection of the file name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Explicitly pass header=0 to be able to replace existing names \n",
    "train_df = pd.read_csv(path_train,header = 0)\n",
    "train_df = train_df[:19930]\n",
    "train_AP_strengths = train_df.iloc[:,:520] #select first 520 columns\n",
    "\n",
    "#Scale transforms data to center to the mean and component wise scale to unit variance\n",
    "train_AP_features = scale(np.asarray(train_AP_strengths).astype(float), axis=1) # convert integer to float and scale jointly (axis=1)\n",
    "\n",
    "#The following two objects are actually pandas.core.series.Series objects\n",
    "building_ids_str = train_df[\"BUILDINGID\"].map(str) #convert all the building ids to strings\n",
    "building_floors_str = train_df[\"FLOOR\"].map(str) #convert all the building floors to strings\n",
    "\n",
    "res = building_ids_str + building_floors_str #element wise concatenation of BUILDINGID+FLOOR\n",
    "train_labels = np.asarray(building_ids_str + building_floors_str)\n",
    "\n",
    "#convert labels to categorical variables, dummy_labels has type 'pandas.core.frame.DataFrame'\n",
    "dummy_labels = pd.get_dummies(train_labels)\n",
    "\n",
    "\n",
    "\"\"\"one hot encode the dummy_labels.\n",
    "this is done because dummy_labels is a dataframe with the labels (BUILDINGID+FLOOR) \n",
    "as the column names\n",
    "\"\"\"\n",
    "train_labels = np.asarray(dummy_labels) #labels is an array of shape 19937 x 13. (there are 13 types of labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#generate len(train_AP_features) of floats in between 0 and 1\n",
    "train_val_split = np.random.rand(len(train_AP_features))\n",
    "#convert train_val_split to an array of booleans: if elem < 0.7 = true, else: false\n",
    "train_val_split = train_val_split < 0.70 #should contain ~70% percent true"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aren't given a formal testing set, so we will treat the given validation set as the testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We will then split our given training set into training + validation \n",
    "train_X = train_AP_features[train_val_split]\n",
    "train_y = train_labels[train_val_split]\n",
    "val_X = train_AP_features[~train_val_split]\n",
    "val_y = train_labels[~train_val_split]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Turn the given validation set into a testing set\n",
    "test_df = pd.read_csv(path_validation,header = 0)\n",
    "test_AP_features = scale(np.asarray(test_df.iloc[:,0:520]).astype(float))\n",
    "test_labels = np.asarray(test_df[\"BUILDINGID\"].map(str) + test_df[\"FLOOR\"].map(str))\n",
    "test_labels = np.asarray(pd.get_dummies(test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs = 20\n",
    "batch_size = 10\n",
    "input_size = 520\n",
    "num_classes = 13\n",
    "# nums_hidden_nodes = [16] # for test\n",
    "nums_hidden_nodes = [16, 32, 64, 128, 256, 512]\n",
    "# dropout_rates = [0.5] # for test\n",
    "dropout_rates = [0.0, 0.2, 0.5]\n",
    "# dropout_rates = np.arange(11)*0.05 # 0.0, 0.05,...,0.5\n",
    "losses = np.zeros((len(nums_hidden_nodes), len(dropout_rates)))\n",
    "accuracies = np.zeros_like(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classifier(num_hidden_nodes, dropout_rate):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(num_hidden_nodes, input_dim=input_size, activation='tanh', use_bias=True))\n",
    "#     model.add(Dropout(dropout_rate))\n",
    "#     model.add(Dense(256, activation='tanh', use_bias=True))\n",
    "#     model.add(Dropout(dropout_rate))\n",
    "#     model.add(Dense(128, activation='tanh', use_bias=True))\n",
    "#     model.add(Dropout(dropout_rate))\n",
    "#     model.add(Dense(64, activation='tanh', use_bias=True))\n",
    "#     model.add(Dropout(dropout_rate))\n",
    "    model.add(Dense(num_classes, activation='softmax', use_bias=True))\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13820 samples, validate on 6110 samples\n",
      "Epoch 1/20\n",
      "13820/13820 [==============================] - 8s - loss: 0.4107 - acc: 0.9159 - val_loss: 0.0949 - val_acc: 0.9858\n",
      "Epoch 2/20\n",
      "13820/13820 [==============================] - 8s - loss: 0.0605 - acc: 0.9896 - val_loss: 0.0471 - val_acc: 0.9902\n",
      "Epoch 3/20\n",
      "13820/13820 [==============================] - 9s - loss: 0.0340 - acc: 0.9930 - val_loss: 0.0373 - val_acc: 0.9903\n",
      "Epoch 4/20\n",
      "13820/13820 [==============================] - 9s - loss: 0.0218 - acc: 0.9942 - val_loss: 0.0324 - val_acc: 0.9900\n",
      "Epoch 5/20\n",
      "13820/13820 [==============================] - 9s - loss: 0.0164 - acc: 0.9949 - val_loss: 0.0298 - val_acc: 0.9902\n",
      "Epoch 6/20\n",
      "13820/13820 [==============================] - 9s - loss: 0.0138 - acc: 0.9958 - val_loss: 0.0225 - val_acc: 0.9925\n",
      "Epoch 7/20\n",
      "13820/13820 [==============================] - 8s - loss: 0.0110 - acc: 0.9962 - val_loss: 0.0299 - val_acc: 0.9895\n",
      "Epoch 8/20\n",
      "13820/13820 [==============================] - 9s - loss: 0.0094 - acc: 0.9965 - val_loss: 0.0226 - val_acc: 0.9931\n",
      "Epoch 9/20\n",
      "13820/13820 [==============================] - 9s - loss: 0.0122 - acc: 0.9956 - val_loss: 0.0256 - val_acc: 0.9912\n",
      "Epoch 10/20\n",
      "13820/13820 [==============================] - 8s - loss: 0.0092 - acc: 0.9966 - val_loss: 0.0233 - val_acc: 0.9918\n",
      "Epoch 11/20\n",
      "13820/13820 [==============================] - 8s - loss: 0.0085 - acc: 0.9968 - val_loss: 0.0253 - val_acc: 0.9921\n",
      "Epoch 12/20\n",
      "13820/13820 [==============================] - 9s - loss: 0.0083 - acc: 0.9966 - val_loss: 0.0231 - val_acc: 0.9930\n",
      "Epoch 13/20\n",
      "13820/13820 [==============================] - 7s - loss: 0.0065 - acc: 0.9976 - val_loss: 0.0240 - val_acc: 0.9928\n",
      "Epoch 14/20\n",
      "13820/13820 [==============================] - 10s - loss: 0.0060 - acc: 0.9975 - val_loss: 0.0221 - val_acc: 0.9928\n",
      "Epoch 15/20\n",
      "13820/13820 [==============================] - 8s - loss: 0.0077 - acc: 0.9963 - val_loss: 0.0261 - val_acc: 0.9928\n",
      "Epoch 16/20\n",
      "13820/13820 [==============================] - 6s - loss: 0.0068 - acc: 0.9967 - val_loss: 0.0265 - val_acc: 0.9921\n",
      "Epoch 17/20\n",
      "13820/13820 [==============================] - 3s - loss: 0.0056 - acc: 0.9972 - val_loss: 0.0245 - val_acc: 0.9925\n",
      "Epoch 18/20\n",
      "13820/13820 [==============================] - 3s - loss: 0.0056 - acc: 0.9974 - val_loss: 0.0246 - val_acc: 0.9933\n",
      "Epoch 19/20\n",
      "13820/13820 [==============================] - 3s - loss: 0.0072 - acc: 0.9966 - val_loss: 0.0293 - val_acc: 0.9915\n",
      "Epoch 20/20\n",
      "13820/13820 [==============================] - 2s - loss: 0.0067 - acc: 0.9974 - val_loss: 0.0246 - val_acc: 0.9918\n",
      "Model trained with nh=16 & dr=0.50 in 1.587085e+02 s.\n",
      "\n",
      "  32/1111 [..............................] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "for nh in range(len(nums_hidden_nodes)):\n",
    "    for dr in range(len(dropout_rates)):\n",
    "        c = classifier(nums_hidden_nodes[nh], dropout_rates[dr])\n",
    "        startTime = timer()\n",
    "        c.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=batch_size, epochs=epochs)\n",
    "        elapsedTime = timer() - startTime\n",
    "        print(\"Model trained with nh=%d & dr=%.2f in %e s.\\n\" % (nums_hidden_nodes[nh], dropout_rates[dr], elapsedTime))\n",
    "        loss, acc = c.evaluate(test_AP_features, test_labels)\n",
    "        losses[nh, dr] = loss\n",
    "        accuracies[nh, dr] = acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "now = datetime.datetime.now()\n",
    "path_results += \"_\" + now.strftime(\"%Y%m%d-%H%M%S\") + \".csv\"\n",
    "f = open(path_results, 'w')\n",
    "for nh in range(len(nums_hidden_nodes)):\n",
    "    for dr in range(len(dropout_rates)):\n",
    "        f.write(\"nh=%d & dr=%.2f: loss=%e, accuracy=%e\" % (nums_hidden_nodes[nh], dropout_rates[dr], losses[nh, dr], accuracies[nh, dr]))\n",
    "f.close()"
   ]
  }
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