MHC-Gait-Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recordId</th>\n",
       "      <th>healthCode</th>\n",
       "      <th>weight</th>\n",
       "      <th>sex</th>\n",
       "      <th>height</th>\n",
       "      <th>currentAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b141e6da-ba2a-4267-82a7-8484ec264afc</td>\n",
       "      <td>660c450e-456c-4835-8cad-3d8842492913</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>75.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aa326e81-349d-4c95-9c15-4cf78fbda5eb</td>\n",
       "      <td>b04ae29d-7c3e-49fb-951b-69a6bead8ad9</td>\n",
       "      <td>209.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>76.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6056f89a-9827-4a28-b67c-fad59b97f793</td>\n",
       "      <td>bfdf60c0-c322-47f0-8a47-a52c7dc50ab9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>73.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66aed6d3-f6e4-4ec0-be76-eed94d6db501</td>\n",
       "      <td>32ee6b1e-e3c9-48f2-b4e4-1d29ca134ce6</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>67.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1da48794-aefb-44a4-84da-e0cbafcff92a</td>\n",
       "      <td>552417f4-a4aa-4004-b1a7-0e97ac3b316a</td>\n",
       "      <td>204.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               recordId                            healthCode  \\\n",
       "0  b141e6da-ba2a-4267-82a7-8484ec264afc  660c450e-456c-4835-8cad-3d8842492913   \n",
       "1  aa326e81-349d-4c95-9c15-4cf78fbda5eb  b04ae29d-7c3e-49fb-951b-69a6bead8ad9   \n",
       "2  6056f89a-9827-4a28-b67c-fad59b97f793  bfdf60c0-c322-47f0-8a47-a52c7dc50ab9   \n",
       "3  66aed6d3-f6e4-4ec0-be76-eed94d6db501  32ee6b1e-e3c9-48f2-b4e4-1d29ca134ce6   \n",
       "4  1da48794-aefb-44a4-84da-e0cbafcff92a  552417f4-a4aa-4004-b1a7-0e97ac3b316a   \n",
       "\n",
       "   weight     sex  height  currentAge  \n",
       "0   200.0    Male    75.0        67.0  \n",
       "1   209.0    Male    76.0        66.0  \n",
       "2    92.0    Male    73.0        34.0  \n",
       "3   123.0  Female    67.0        30.0  \n",
       "4   204.0  Female    63.0        62.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()\n",
    "\n",
    "demographics_csv = \"data/demographics.csv\"\n",
    "demographics_df = pd.read_csv(demographics_csv, low_memory=False)\n",
    "\n",
    "# Version numbers are meaningless\n",
    "sixminutev4 = pd.read_csv('data/6minutewalkV4-V2.csv') # table \n",
    "sixminutev6 = pd.read_csv('data/6minutewalkV4-V6.csv')\n",
    "sixminutev1 = pd.read_csv('data/6minutewalkV4-V1.csv') # table\n",
    "sixminutev2 = pd.read_csv('data/6minutewalkV2.csv')\n",
    "cvhealth = pd.read_csv('data/CVHealth.csv')\n",
    "\n",
    "demographics_df.drop(columns=['ROW_ID', 'ROW_VERSION', 'appVersion', 'phoneInfo', 'dataGroups', 'uploadDate', 'createdOn', \n",
    "                  'validationErrors', 'userSharingScope', 'NonIdentifiableDemographics.json.patientGoSleepTime', \n",
    "                  'NonIdentifiableDemographics.patientGoSleepTime', 'createdOnTimeZone',\n",
    "                 'NonIdentifiableDemographics.json.patientWakeUpTime', 'NonIdentifiableDemographics.patientWakeUpTime'\n",
    "                 , 'externalId'], \n",
    "         inplace=True)\n",
    "demographics_df.rename(columns={'NonIdentifiableDemographics.json.patientWeightPounds': 'weight', 'NonIdentifiableDemographics.patientWeightPounds': 'weight2', \n",
    "                    'NonIdentifiableDemographics.json.patientBiologicalSex': 'sex', \n",
    "                    'NonIdentifiableDemographics.patientBiologicalSex': 'sex2',\n",
    "                   'NonIdentifiableDemographics.json.patientHeightInches': 'height',\n",
    "                   'NonIdentifiableDemographics.patientHeightInches': 'height2',\n",
    "                   'NonIdentifiableDemographics.json.patientCurrentAge': 'currentAge',\n",
    "                   'NonIdentifiableDemographics.patientCurrentAge': 'currentAge2'}, inplace=True)\n",
    "\n",
    "# drop the test version\n",
    "demographics_df = demographics_df.iloc[7:]\n",
    "demographics_df.index = range(44804)\n",
    "demographics_df.dropna(how='all', subset=['currentAge','currentAge2'], inplace=True)\n",
    "demographics_df.dropna(how='all', subset=['weight', 'weight2'], inplace=True)\n",
    "demographics_df.dropna(how='all', subset=['sex', 'sex2'], inplace=True)\n",
    "demographics_df.dropna(how='all', subset=['height', 'height2'], inplace=True)\n",
    "\n",
    "demographics_df['currentAge'] = demographics_df['currentAge'].fillna(demographics_df['currentAge2'])\n",
    "demographics_df['weight'] = demographics_df['weight'].fillna(demographics_df['weight2'])\n",
    "demographics_df['height'] = demographics_df['height'].fillna(demographics_df['height2'])\n",
    "demographics_df['sex'] = demographics_df['sex'].fillna(demographics_df['sex2'])\n",
    "demographics_df = demographics_df.drop('currentAge2',1)\n",
    "demographics_df = demographics_df.drop('weight2',1)\n",
    "demographics_df = demographics_df.drop('height2',1)\n",
    "demographics_df = demographics_df.drop('sex2',1)\n",
    "\n",
    "# Dropping the duplicate healthCode records... Don't know if this is the right thing to do but can easily be reversed\n",
    "demographics_df = demographics_df.drop_duplicates(subset='healthCode', keep='last', inplace=False)\n",
    "\n",
    "# Dropping the rows with 0 for any metric. This was not caught with the NaN cleaning/merging \n",
    "demographics_df = demographics_df[demographics_df.currentAge != 0]\n",
    "demographics_df = demographics_df[demographics_df.weight != 0]\n",
    "demographics_df = demographics_df[demographics_df.height != 0]\n",
    "\n",
    "sixminutev4.drop(columns=['phoneInfo', 'appVersion', 'dataGroups', 'externalId', 'phoneInfo', 'ROW_VERSION', \n",
    "                             'ROW_ID', 'deviceMotion_fitness.rest.items', \n",
    "                             'heartRate_fitness.rest.items', 'userSharingScope',\n",
    "                            'pedometer_fitness.walk.items', 'accel_fitness_walk.json.items',\n",
    "                            'deviceMotion_fitness.walk.items', 'heartRate_fitness.walk.items',\n",
    "                            'accel_fitness_rest.json.items', 'createdOnTimeZone', 'recordId',\n",
    "                            'uploadDate', 'validationErrors', 'createdOn', 'measurementSystem.measurementSystem',\n",
    "                            'measurementSystem.deviceRegion'], inplace=True)\n",
    "sixminutev6.drop(columns=['recordId', 'appVersion', 'externalId', 'dataGroups', 'createdOn', 'phoneInfo', \n",
    "                             'restingEnergyBurned_fitness.rest', 'restingEnergyBurned_fitness.walk', 'activeEnergyBurned_fitness.rest', \n",
    "                             'activeEnergyBurned_fitness.walk', 'measurementSystem.deviceRegion', 'measurementSystem.measurementSystem', \n",
    "                             'heartRate_fitness.rest', 'ROW_ID', 'ROW_VERSION', 'uploadDate',\n",
    "                            'validationErrors', 'userSharingScope', 'accel_fitness_walk.json',\n",
    "                            'deviceMotion_fitness.walk', 'userSharingScope','createdOnTimeZone',\n",
    "                            'heartRate_fitness.walk', 'pedometer_fitness.walk', 'deviceMotion_fitness.rest',\n",
    "                            'accel_fitness_rest.json'], inplace=True)\n",
    "\n",
    "sixminutev1.drop(columns=['ROW_ID', 'ROW_VERSION', 'recordId', 'appVersion', 'uploadDate', 'phoneInfo',\n",
    "                         'externalId', 'dataGroups', 'createdOn', 'createdOnTimeZone', 'userSharingScope',\n",
    "                         'validationErrors', 'pedometer_fitness.walk.items', 'accel_fitness_walk.json.items',\n",
    "                         'deviceMotion_fitness.walk.items', 'HKQuantityTypeIdentifierHeartRate_fitness.walk.items',\n",
    "                         'accel_fitness_rest.json.items', 'deviceMotion_fitness.rest.items',\n",
    "                         'HKQuantityTypeIdentifierHeartRate_fitness.rest.items', 'measurementSystem.measurementSystem',\n",
    "                         'measurementSystem.deviceRegion'], inplace=True)\n",
    "\n",
    "sixminutev2.drop(columns=['ROW_ID', 'ROW_VERSION', 'recordId', 'appVersion', 'uploadDate', 'phoneInfo', 'createdOn', \n",
    "                          'pedometer_fitness.walk.items', 'accel_fitness_walk.json.items', 'deviceMotion_fitness.walk.items', \n",
    "                         'HKQuantityTypeIdentifierHeartRate_fitness.walk.items', 'accel_fitness_rest.json.items',\n",
    "                         'deviceMotion_fitness.rest.items', 'HKQuantityTypeIdentifierHeartRate_fitness.rest.items',\n",
    "                         'externalId', 'dataGroups'], inplace=True)\n",
    "\n",
    "sixminutewalktotal = pd.concat([sixminutev4, sixminutev6])\n",
    "# Re-index to represent the actual length of the series\n",
    "sixminutewalktotal.index = range(3373)\n",
    "#csv\n",
    "demographics_df = demographics_df[demographics_df['healthCode'].isin(sixminutewalktotal['healthCode'])]\n",
    "# Re-index \n",
    "demographics_df.index = range(994)\n",
    "cvhealth.drop(columns=['ROW_ID', 'ROW_VERSION', 'recordId', 'appVersion', 'phoneInfo', 'uploadDate', \n",
    "                             'externalId', 'dataGroups', 'createdOn', \n",
    "                             'createdOnTimeZone', 'userSharingScope', 'validationErrors', \n",
    "                             'family_history', 'medications_to_treat', 'vascular', 'ethnicity',\n",
    "                            'race', 'education'], inplace=True)\n",
    "\n",
    "demographics_df = demographics_df[demographics_df['healthCode'].isin(cvhealth['healthCode'])]\n",
    "# Re-index after dropping those without CVD data\n",
    "demographics_df.index = range(851)\n",
    "healthCodeTotal = demographics_df[\"healthCode\"].values\n",
    "\n",
    "v4 = demographics_df[demographics_df['healthCode'].isin(sixminutev4['healthCode'])]\n",
    "v6 = demographics_df[demographics_df['healthCode'].isin(sixminutev6['healthCode'])]\n",
    "v2 = demographics_df[demographics_df['healthCode'].isin(sixminutev2['healthCode'])]\n",
    "v1 = demographics_df[demographics_df['healthCode'].isin(sixminutev1['healthCode'])]\n",
    "\n",
    "v4 = np.asarray(v4['healthCode'].values)\n",
    "v2 = np.asarray(v2['healthCode'].values)\n",
    "v1 = np.asarray(v1['healthCode'].values)\n",
    "v6 = np.asarray(v6['healthCode'].values)\n",
    "sixminutetotalhc = np.concatenate((v4, v2, v1, v6))\n",
    "demographics_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "present = pd.read_json('data/test_accel_old4.json').set_index('timestamp')\n",
    "present_part = present.iloc[2000:2500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd8d42eada0>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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3afHby4b7lFy1uPPybABodxdsBAnxsThnhPdJxh9TRroeg95m23d7zOBurQls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\nRbH/B/DPW7HTeWuDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(present.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd8d0df3198>,\n",
       " <matplotlib.lines.Line2D at 0x7fd8d0df3320>,\n",
       " <matplotlib.lines.Line2D at 0x7fd8d0df34a8>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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evYrHlz0XUjidNzVttfxl/v2mA3bevLPhYz4JoATu/PYf/H7W7WHLEA9q2+r415JnqGyp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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Sample Gait (5 seconds)')\n",
    "plt.plot(present)\n",
    "#plt.savefig('5sec.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sparsity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "# change this depending on data directory\n",
    "directory_in_str = '/scratch/PI/euan/projects/mhc/data/6mwt/accel_walk_dir'\n",
    "directory = os.fsencode(directory_in_str)\n",
    "\n",
    "# number of gaps in a given 6MWT that surpasses the threshold \n",
    "def find_gaps(directory, table):\n",
    "    '''\n",
    "    Finds the number of gaps over 0.01 seconds and adds a gap if the difference between last and first timestamp is \n",
    "    less than 6 minutes\n",
    "    Checking the length of the dict returned will show whether the amount of files matches the intersection of \n",
    "    6mwt table and demographics\n",
    "    '''\n",
    "    hc_gaps = dict()\n",
    "    for subdir, dirs, files in os.walk(directory):\n",
    "        # make in [list] a parameter of the function -- remember to change \"in [list]\" to the correct list\n",
    "        if subdir.decode()[subdir.decode().rfind('/')+1:] in table:\n",
    "            # Makes sure we only get one file per healthCode\n",
    "            i = 0\n",
    "            for file in files:\n",
    "                while (i < 1):\n",
    "                    a_df = pd.read_json(os.path.join(subdir.decode(), file.decode())).set_index('timestamp')\n",
    "                    inst = list(a_df.index[1:]-a_df.index[:-1] > .02).count(True)\n",
    "                    # This line checks for incomplete 6MWT\n",
    "                    if (a_df.index[-1] - a_df.index[0]) < 358:\n",
    "                        inst = inst + 1\n",
    "                    hc_gaps.update({subdir.decode()[subdir.decode().rfind('/')+1:]: inst})\n",
    "                    i += 1\n",
    "                \n",
    "    return hc_gaps\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v2_only = find_gaps(directory, v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/bhargavy/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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6wKfyCJY92zoNeB/QAXxe0o15+t7AN4E1gVeAoyXdHBG7Az8C5pAG//oWcGVV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\nDwfJUdION00KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "\n",
    "plt.title('Sparsity 6MWT Plot (V2 Only)')\n",
    "# Set x-axis label\n",
    "plt.xlabel('Number of Gaps')\n",
    "# Set y-axis label\n",
    "plt.ylabel('Number of 6MWT Runs')\n",
    "sns.distplot(list(v2_only.values()), bins=250, kde=False, rug=False, color='purple')\n",
    "plt.savefig('sparsity.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6MWT Total Sample Check\n",
    "\n",
    "### This block of code is not necessary anymore as it was just for verifying all of the 6MWT were present in the directory from a data pull.\n",
    "\n",
    "### healthCodes of interest/associated synapse table:\n",
    "\n",
    "1. cab9f4ee-54e0-4b08-8eba-3b48085bd142 (v4-v1)\n",
    "2. 33f22bad-4570-43bb-bf68-263e6865ef76 (v4-v1)\n",
    "3. ff489c8a-f5ff-4f00-9682-33c44df02621 (v4-v2)\n",
    "4. 6fd3148e-f490-417a-9da8-a31d947e7aed (v4-v1)\n",
    "5. bb6613c1-5b48-4744-a5f5-2387149da94d (v4-v2)\n",
    "6. 43dcb6b4-24a9-4b71-bc9f-eef87e8adadb (v4-v2)\n",
    "7. fe1e5f81-ed68-4c45-b1a7-3a7443d5ae76 (v4-v2)\n",
    "8. 047b75f9-2778-4068-bc30-d47e8ea0780e (v4-v1)\n",
    "9. c6a9c011-defc-48a9-bd74-de6d3a72d2bd (v4-v2)\n",
    "10. e30dae44-8b7e-41a5-98e5-3f8abba35352 (v4-v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hc_filenum is the dictionary which stores the healthCode and num of files associated with that healthCode\n",
    "\n",
    "# num_of_files is just a list to count number of instances of certain number of files (Are there 0 files, 1 file...?)\n",
    "\n",
    "directory_in_str = '/scratch/PI/euan/projects/mhc/data/6mwt/accel_walk_dir'\n",
    "directory = os.fsencode(directory_in_str)\n",
    "hc_filenum = dict()\n",
    "for subdir, dirs, files in os.walk(directory):\n",
    "    i = 0\n",
    "    for file in files:\n",
    "        i += 1\n",
    "    hc_filenum.update({subdir.decode()[subdir.decode().rfind('/') + 1:]: i})\n",
    "\n",
    "\n",
    "num_of_files = []\n",
    "for k, v in test.items():\n",
    "        num_of_files.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2291\n",
      "8129\n"
     ]
    }
   ],
   "source": [
    "print(num_of_files.count(0))\n",
    "print(len(hc_filenum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/bhargavy/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fccb3533b38>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0NuvpbSerp5OTwmyf9nmyTOdQXg+W9gPA6in7rSpt7dolSQtgtuG/A9hUljcB905pvzIi\nBiJiHfB0mR66H1gfEcPlRu/60iZJWgCdPOr5FeAC4BURsZ/mqZ1PAXdHxDXAY8DlZfdv0DzmuYfm\nUc+rATLzcER8Aniw7PfxzDz+JvK8+e6PW//RccG5096GkKRFYdrwz8x3t9l0UYt9J4Dr2vSzFdg6\no9FJkv5f+AlfSaqQ4S9JFTL8JalChr8kVcjwl6QKGf6SVCHDX5IqZPhLUoUMf0mqkOEvSRUy/CWp\nQoa/JFXI8JekCnX9L3ktJn7Vs6RaeOUvSRUy/CWpQoa/JFXI8JekChn+klQhw1+SKmT4S1KFDH9J\nqpDhL0kVmvdP+EbEBmALsAS4PTM/Nd9jmCk/+StpsZnX8I+IJcDngLcA+4EHI2JHZv58PscxV9qd\nFGbKk4ik+TbfV/7nAXsy81GAiLgT2Aj0ZfjPlU5PIkPLTmX8maNtt5/sJOJfL5Kmmu/wXwnsm7K+\nHzi/3c4jI0MD3fyyDSND3bx9UXnnW1610ENoaWSRHSPr6W3W82ve8JWkCs13+B8AVk9ZX1XaJEnz\naL6nfR4E1kbEmTShfwXwR/M8Bkmq3rxe+WfmMeDPgfuBR4C7M/Nn8zkGSRIMTExMLPQYJEnzzBu+\nklQhw1+SKrQo/wH3fvwKiakiYjWwHVgBTABfyMwtEXEacBewBtgLXJ6ZYws1zpkqn/D+IXAgMy8t\nN/7vBE4HHgLem5nPLeQYOxURy4HbgbNpjtH7gKRPj09E/AXwJzS1/BS4GjiDPjk+EbEVuBQ4mJln\nl7aW/79ExABNPlwCHAGuysyHF2Lc7bSp56+BPwSeA34BXJ2ZT5VtNwHXAM8D12fm/dP9jkV35T/l\nKyQuBs4C3h0RZy3sqGbsGPChzDwLWAdcV2q4EdiVmWuBXWW9n3yA5kb/pE8Dt2bmK4Exmv94+8UW\n4L7MfBVwDk1dfXl8ImIlcD3w+hI0S2iexOun4/MlYMNxbe2Ox8XA2vJzLXDbPI1xJr7EifXsBM7O\nzN8D/gO4CaBkwxXAq8t7Pl9y8KQWXfgz5SskylXK5FdI9I3MfHzySiQzx2mCZSVNHdvKbtuAyxZm\nhDMXEauAt9FcLVOuvi4E7im79E09EfFy4E3AHQCZ+Vy5Auvb40MzC/DSiBgElgKP00fHJzO/Bxw+\nrrnd8dgIbM/MiczcDSyPiDPmZ6SdaVVPZn6rPDEJsJvmc1LQ1HNnZj6bmb8E9tDk4EktxvBv9RUS\nffsFNhGxBngN8ACwIjMfL5ueoJkW6hefBT4M/G9ZPx14asp/zP10nM4ERoEvRsSPIuL2iHgZfXp8\nMvMA8DfAf9GE/tM00zz9enwmtTseiyEj3gd8syzPqp7FGP6LRkQsA74KfDAzfzV1W2ZO0MzP9ryI\nmJy7fGihxzJHBoHXArdl5muA/+a4KZ4+Oz7DNFePZwK/DbyME6cc+lo/HY/pRMTHaKaGv9xNP4sx\n/BfFV0hExItpgv/Lmfm10vzk5J+n5fXgQo1vht4AvD0i9tJMw11IM2e+vEwzQH8dp/3A/sx8oKzf\nQ3My6Nfj82bgl5k5mpn/A3yN5pj16/GZ1O549G1GRMRVNDeC31NOaDDLehZj+L/wFRIR8RKaGyE7\nFnhMM1Lmw+8AHsnMz0zZtAPYVJY3AffO99hmIzNvysxVmbmG5nh8OzPfA3wHeEfZrZ/qeQLYFxFR\nmi6i+Vryvjw+NNM96yJiaflvb7Kevjw+U7Q7HjuAKyNiICLWAU9PmR7qWeUpxg8Db8/MI1M27QCu\niIhTyhN0a4EfTNffovyEb0RcQjPHvATYmpm3LPCQZiQi3gj8M80jd5Nz5B+lmfe/G/gd4DGaR9eO\nv8nV0yLiAuAvy6Oev0vzl8BpwI+AP87MZxdyfJ2KiHNpbl6/BHiU5tHIF9Gnxyci/gp4F810wo9o\nHvtcSZ8cn4j4CnAB8ArgSWAz8A+0OB7lBPe3NFNbR2gemfzhQoy7nTb13AScAhwqu+3OzD8t+3+M\n5j7AMZpp4m8e3+fxFmX4S5JObjFO+0iSpmH4S1KFDH9JqpDhL0kVMvwlqUKGvyRVyPCXpAr9HyuC\nNoEBG1sCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.distplot(num_of_files, kde=False, rug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Generation - 30 features\n",
    "### Fix for the new dataframe format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import peakutils\n",
    "from numpy import linalg as LA\n",
    "from scipy.fftpack import fft, ifft\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "\n",
    "# Mean - 6 features\n",
    "def get_time_mean(x_, y_, z_):\n",
    "    mux_t = np.mean(x_, axis=1)\n",
    "    muy_t = np.mean(y_, axis=1)\n",
    "    muz_t = np.mean(z_, axis=1)\n",
    "    return (mux_t, muy_t, muz_t)\n",
    "\n",
    "def get_freq_mean(x_, y_, z_):\n",
    "    mux_f = np.mean(np.absolute(fft(x_)), axis=1)\n",
    "    muy_f = np.mean(np.absolute(fft(y_)), axis=1)\n",
    "    muz_f = np.mean(np.absolute(fft(z_)), axis=1)\n",
    "    return (mux_f, muy_f, muz_f)\n",
    "\n",
    "# Median - 6 features\n",
    "def get_time_median(x_, y_, z_):\n",
    "    medx_t = np.median(x_, axis=1)\n",
    "    medy_t = np.median(y_, axis=1)\n",
    "    medz_t = np.median(z_, axis=1)\n",
    "    return (medx_t, medy_t, medz_t)\n",
    "\n",
    "def get_freq_median(x_, y_, z_):\n",
    "    medx_f = np.median(np.absolute(fft(x_)), axis=1)\n",
    "    medy_f = np.median(np.absolute(fft(y_)), axis=1)\n",
    "    medz_f = np.median(np.absolute(fft(z_)), axis=1)\n",
    "    return (medx_f, medy_f, medz_f)\n",
    "\n",
    "# Magnitude - 6 features\n",
    "\n",
    "# Don't really need magnitude since we already have 5 features from magnitude alone\n",
    "#magx_t = (LA.norm(accelx_, axis=1) / 200)\n",
    "#magy_t = (LA.norm(accely_, axis=1) / 200)\n",
    "#magz_t = (LA.norm(accelz_, axis=1) / 200)\n",
    "\n",
    "#magx_f = (LA.norm(fft(accelx_), axis=1) / 200)\n",
    "#magy_f = (LA.norm(fft(accely_), axis=1) / 200)\n",
    "#magz_f = (LA.norm(fft(accelz_), axis=1) / 200)\n",
    "\n",
    "# Cross-correlation - 2 features\n",
    "\n",
    "def get_cross_corr(x_, y_, z_):\n",
    "    meanx = np.mean(x_, axis=1)\n",
    "    meany = np.mean(y_, axis=1)\n",
    "    meanz = np.mean(z_, axis=1)\n",
    "    corr_xz = meanx/meanz\n",
    "    corr_yz = meany/meanz\n",
    "    return (corr_xz, corr_yz)\n",
    "\n",
    "# Peak Count - 1 feature\n",
    "\n",
    "# Don't really need this since we get peak count from magnitude\n",
    "def peak_counter(accel_windows):\n",
    "    '''\n",
    "    This function counts the number of peaks in by window in each acceleration dimension using peakutils library\n",
    "    Below we are averaging peaks over each dimension, combining all axes, and re-averaging for a final feature\n",
    "    '''\n",
    "    peaks = []\n",
    "    for i in range(len(accel_windows)):\n",
    "        peaks.append(len(peakutils.indexes(accel_windows[i])))\n",
    "    return peaks\n",
    "\n",
    "#peaks_x = np.mean(peak_counter(accelx_))\n",
    "#peaks_y = np.mean(peak_counter(accely_))\n",
    "#peaks_z = np.mean(peak_counter(accelz_))\n",
    "#total = [peaks_x, peaks_y, peaks_z]\n",
    "#avg_peaks = np.mean(total)\n",
    "\n",
    "# Distance Between Peaks - 3 features\n",
    "\n",
    "# Don't really want this...\n",
    "def dist_peak(accel_windows):\n",
    "    # This is in seconds because timestamps are in seconds not ms\n",
    "    sec_per_index = 0.01\n",
    "    time_diffs = []\n",
    "    for i in range(len(accel_windows)):\n",
    "        peak_indices = peakutils.indexes(accel_windows[i])\n",
    "        # Finds difference between consecutive index values so that we can multiply by 0.01 seconds\n",
    "        dummy = [t - s for s, t in zip(peak_indices, peak_indices[1:])]\n",
    "        for j in range(len(dummy)):\n",
    "            time_diffs.append(dummy[j] * sec_per_index)\n",
    "    return time_diffs\n",
    "\n",
    "# Spectral Centroid - 3 features\n",
    "\n",
    "def spectral_centroid(x_, y_, z_):\n",
    "    accel_freqx = fft(x_)\n",
    "    accel_freqy = fft(y_)\n",
    "    accel_freqz = fft(z_)\n",
    "    centroids_x = []\n",
    "    centroids_y = []\n",
    "    centroids_z = []\n",
    "    for i in range(len(x_)):\n",
    "        sumx = 0\n",
    "        sumy = 0\n",
    "        sumz = 0\n",
    "        for j in range(len(x_[i])):\n",
    "            sumx += (x_[i][j] * accel_freqx[i][j] / 200)\n",
    "            sumy += (y_[i][j] * accel_freqy[i][j] / 200)\n",
    "            sumz += (z_[i][j] * accel_freqz[i][j] / 200)\n",
    "        centroids_x.append(sumx)\n",
    "        centroids_y.append(sumy)\n",
    "        centroids_z.append(sumz)\n",
    "    return (np.absolute(centroids_x), np.absolute(centroids_y), np.absolute(centroids_z))\n",
    "\n",
    "# Average Difference from the Mean - 3 features\n",
    "\n",
    "def average_dist_mean(x_, y_, z_, mux_t, muy_t, muz_t):\n",
    "    realx = []\n",
    "    realy = []\n",
    "    realz = []\n",
    "    dummyx = []\n",
    "    dummyy = []\n",
    "    dummyz = []\n",
    "    for i in range(len(x_)):\n",
    "        for j in range(len(x_[i])):\n",
    "            dummyx.append(abs(x_[i][j] - mux_t[i]))\n",
    "            dummyy.append(abs(y_[i][j] - muy_t[i]))\n",
    "            dummyz.append(abs(z_[i][j] - muz_t[i]))\n",
    "        realx.append(np.mean(dummyx))\n",
    "        realy.append(np.mean(dummyy))\n",
    "        realz.append(np.mean(dummyz))\n",
    "    return (realx, realy, realz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import json, os\n",
    "from scipy.fftpack import fft, ifft\n",
    "import itertools as it\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "#directory_in_str = '/Users/bhargavyadavalli/Desktop/files'\n",
    "#directory = os.fsencode(directory_in_str)\n",
    "\n",
    "'''\n",
    "for file in os.listdir(directory):\n",
    "    filename = os.fsdecode(file)\n",
    "    print(filename[0:36])\n",
    "'''\n",
    "# The block above will print the file name without the '.json'\n",
    "\n",
    "\n",
    "def moving_window(accelx, length, step=1):\n",
    "    streams = it.tee(accelx, length)\n",
    "    return zip(*[it.islice(stream, i, None, step + 99) for stream, i in zip(streams, it.count(step=step))])\n",
    "\n",
    "'''\n",
    "The following functions are for json files that aren't easily readable my pandas (deviceMotion)\n",
    "'''\n",
    "def getTimestamp(fn):\n",
    "    timestamp = []\n",
    "    with open(fn) as f:\n",
    "        a = json.load(f)\n",
    "        for obj in a:\n",
    "            timestamp.append(obj['timestamp'])\n",
    "    return timestamp\n",
    "\n",
    "def getAccelX(fn):\n",
    "    accelx = []\n",
    "    with open(fn) as f:\n",
    "        a = json.load(f)\n",
    "        accelx = [obj['userAcceleration']['x'] for obj in a]\n",
    "    accelx_norm = [(sample - np.mean(accelx)) for sample in accelx]\n",
    "    for _ in range(200, len(accelx), 100):\n",
    "        accelx_window = list(moving_window(accelx_norm, 200))\n",
    "    accelx_window = np.asarray(accelx_window)\n",
    "    return (accelx, accelx_window, accelx_norm)\n",
    "\n",
    "def getAccelY(fn):\n",
    "    accely = []\n",
    "    with open(fn) as f:\n",
    "        a = json.load(f)\n",
    "        accely = [obj['y'] for obj in a]\n",
    "    accely_norm = [(sample - np.mean(accely)) for sample in accely]\n",
    "    for _ in range(200, len(accely), 100):\n",
    "        accely_window = list(moving_window(accely_norm, 200))\n",
    "    accelx_window = np.asarray(accely_window)\n",
    "    return (accely, accely_window, accely_norm)\n",
    "            \n",
    "def getAccelZ(fn):\n",
    "    accelz = []\n",
    "    with open(fn) as f:\n",
    "        a = json.load(f)\n",
    "        accelz = [obj['z'] for obj in a]\n",
    "    accelz_norm = [(sample - np.mean(accelz)) for sample in accelz]\n",
    "    for _ in range(200, len(accelz), 100):\n",
    "        accelz_window = list(moving_window(accelz_norm, 200))\n",
    "    accelz_window = np.asarray(accelz_window)\n",
    "    return (accelz, accelz_window, accelz_norm)\n",
    "\n",
    "\n",
    "def normalize_dataset(dataframe):\n",
    "    return (dataframe - dataframe.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_demographics = filter_demographics(csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from heapq import nlargest\n",
    "from scipy.fftpack import fft\n",
    "import peakutils\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import linalg as LA\n",
    "import itertools as it\n",
    "import os\n",
    "\n",
    "directory_in_str = '/scratch/PI/euan/projects/mhc/data/6mwt/accel_walk_dir'\n",
    "directory = os.fsencode(directory_in_str)\n",
    "\n",
    "def normalize_dataset(dataframe):\n",
    "    return (dataframe - dataframe.mean())\n",
    "\n",
    "def moving_window(accelx, length, step=1):\n",
    "    streams = it.tee(accelx, length)\n",
    "    return zip(*[it.islice(stream, i, None, step + 99) for stream, i in zip(streams, it.count(step=step))])\n",
    "\n",
    "# prior to calling any of these functions please verify that dataframe is normalized & sliding windows are present\n",
    "def fundamental_frequency(mag_):\n",
    "    fundamental_freqs = []\n",
    "    for mag_seg in mag_:\n",
    "        ft_seg = fft(mag_seg)\n",
    "        fundamental_freqs.append(LA.norm(np.mean(nlargest(3, ft_seg))))\n",
    "    return np.asarray(fundamental_freqs)\n",
    "\n",
    "def average_acceleration(mag_):\n",
    "    return [np.mean(x) for x in mag_]\n",
    "\n",
    "def peak_counter(mag_):\n",
    "    '''\n",
    "    This function counts the number of peaks in by window in each acceleration dimension using peakutils library\n",
    "    Below we are averaging peaks over each dimension, combining all axes, and re-averaging for a final feature\n",
    "    '''\n",
    "    peaks = []\n",
    "    for i in range(len(mag_)):\n",
    "        peaks.append(len(peakutils.indexes(mag_[i])))\n",
    "    return peaks\n",
    "\n",
    "\n",
    "def find_max(mag_):\n",
    "    maxes = [max(x) for x in mag_]\n",
    "    return maxes\n",
    "\n",
    "def find_min(mag_):\n",
    "    mins = [min(x) for x in mag_]\n",
    "    return mins\n",
    "\n",
    "def generate_features(healthcode, fn, merged_demographics):\n",
    "    '''\n",
    "    Applies normalization and sliding windows to the provided data file and \n",
    "    returns a dataframe with calculated features\n",
    "    '''\n",
    "    a_df = normalize_dataset(pd.read_json(fn).set_index('timestamp'))\n",
    "    a_df_norm = np.sqrt(np.square(a_df).sum(axis=1))\n",
    "    mag = np.asarray(a_df_norm)\n",
    "    mag_ = list(moving_window(mag, 200))\n",
    "    x = np.asarray(a_df.x)\n",
    "    y = np.asarray(a_df.y)\n",
    "    z = np.asarray(a_df.z)\n",
    "    x_ = list(moving_window(x, 200))\n",
    "    y_ = list(moving_window(y, 200))\n",
    "    z_ = list(moving_window(z, 200))\n",
    "    index = [healthcode for x in range(len(mag_))]\n",
    "    weight = [merged_demographics.loc[healthcode].weight for y in range(len(mag_))]\n",
    "    height = [merged_demographics.loc[healthcode].height for z in range(len(mag_))]\n",
    "    sex = [merged_demographics.loc[healthcode].sex for q in range(len(mag_))]\n",
    "    currentAge = [merged_demographics.loc[healthcode].currentAge for m in range(len(mag_))]\n",
    "    # Applied log transformation to the max feature\n",
    "    return pd.DataFrame({'healthCode': index, 'weight': weight, 'height': height, 'sex': sex, 'currentAge': currentAge, \n",
    "                         'fundamental_freq': fundamental_frequency(mag_), 'average_accel': average_acceleration(mag_), \n",
    "                         'peakcount': peak_counter(mag_), 'max': find_max(mag_), 'min': np.log(find_min(mag_)),\n",
    "                        'mut_x': get_time_mean(x_, y_, z_)[0], 'mut_y': get_time_mean(x_, y_, z_)[1], \n",
    "                        'mut_z': get_time_mean(x_, y_, z_)[2], 'muf_x': get_freq_mean(x_, y_, z_)[0],\n",
    "                        'muf_y': get_freq_mean(x_, y_, z_)[1], 'muf_z': get_freq_mean(x_, y_, z_)[2],\n",
    "                        'medt_x': get_time_median(x_, y_, z_)[0], 'medt_y': get_time_median(x_, y_, z_)[1],\n",
    "                        'medt_z': get_time_median(x_, y_, z_)[2], 'medf_x': get_freq_median(x_, y_, z_)[0],\n",
    "                        'medf_y': get_time_median(x_, y_, z_)[1], 'medf_z': get_freq_median(x_, y_, z_)[2],\n",
    "                        'cross_xz': get_cross_corr(x_, y_, z_)[0], 'cross_yz': get_cross_corr(x_, y_, z_)[1],\n",
    "                        'spect_cent_x': spectral_centroid(x_, y_, z_)[0], \n",
    "                        'spect_cent_y': spectral_centroid(x_, y_, z_)[1],\n",
    "                        'spect_cent_z': spectral_centroid(x_, y_, z_)[2],\n",
    "                        'average_dist_meanx': average_dist_mean(x_, y_, z_, np.mean(x_, axis=1), np.mean(y_, axis=1), np.mean(z_, axis=1))[0],\n",
    "                        'average_dist_meany': average_dist_mean(x_, y_, z_, np.mean(x_, axis=1), np.mean(y_, axis=1), np.mean(z_, axis=1))[1],\n",
    "                        'average_dist_meanz': average_dist_mean(x_, y_, z_, np.mean(x_, axis=1), np.mean(y_, axis=1), np.mean(z_, axis=1))[2]},\n",
    "                          columns=['healthCode', 'weight', 'height', 'sex', 'currentAge', 'fundamental_freq', \n",
    "                         'average_accel', 'peakcount', 'max', 'min', 'mut_x', 'mut_y', 'mut_z',\n",
    "                         'muf_x', 'muf_y', 'muf_z', 'medt_x', 'medt_y', 'medt_z', 'medf_x',\n",
    "                         'medf_y', 'medf_z', 'cross_xz', 'cross_yz', 'spect_cent_x', 'spect_cent_y',\n",
    "                         'spect_cent_z', 'average_dist_meanx', 'average_dist_meany', 'average_dist_meanz']).set_index('healthCode')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_subjects(directory, table):\n",
    "    # creates dict with healthcodes and number of gaps from find_gaps function\n",
    "    new_dict = find_gaps(directory, table)\n",
    "    # filters dict to remove those entries with more than 2 gaps\n",
    "    filtered_new_dict = { k:v for k, v in new_dict.items() if v <= 10 }\n",
    "    healthcodes = []\n",
    "    for k, v in filtered_new_dict.items():\n",
    "        healthcodes.append(k)\n",
    "    return healthcodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_frame(directory, table):\n",
    "    '''\n",
    "    Creates a final dataframe with all samples from valid healthcodes\n",
    "    This: subdir.decode()[subdir.decode().rfind('/')+1:] just gets the healthcode part of a directory \n",
    "    Function should be slow with large amounts of files due to the concat Pandas function\n",
    "    '''\n",
    "    healthcodes = filter_subjects(directory, table)\n",
    "    frames = []\n",
    "    for subdir, dirs, files in os.walk(directory):\n",
    "        if subdir.decode()[subdir.decode().rfind('/')+1:] in healthcodes:\n",
    "            i = 0\n",
    "            for file in files:\n",
    "                while (i < 1):\n",
    "                    # This line is generating our features dataframe with healthCode as first arg and filename as second arg\n",
    "                    frames.append(generate_features(subdir.decode()[subdir.decode().rfind('/')+1:], \n",
    "                                                    os.path.join(subdir.decode(), file.decode()), \n",
    "                                                    filter_demographics(demographics_df, table)))\n",
    "                    i += 1\n",
    "    return pd.concat(frames)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "test_v4 = create_frame(directory, v4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test_v2 = create_frame(directory, v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_v1 = create_frame(directory, v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         67364370 function calls (67084384 primitive calls) in 367.142 seconds\n",
      "\n",
      "   Ordered by: standard name\n",
      "\n",
      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
      "     3361    0.011    0.000    0.012    0.000 <frozen importlib._bootstrap>:102(release)\n",
      "     3361    0.004    0.000    0.004    0.000 <frozen importlib._bootstrap>:142(__init__)\n",
      "     3361    0.010    0.000    0.052    0.000 <frozen importlib._bootstrap>:146(__enter__)\n",
      "     3361    0.004    0.000    0.015    0.000 <frozen importlib._bootstrap>:153(__exit__)\n",
      "     3361    0.017    0.000    0.028    0.000 <frozen importlib._bootstrap>:159(_get_module_lock)\n",
      "     3361    0.002    0.000    0.002    0.000 <frozen importlib._bootstrap>:173(cb)\n",
      "1848/1214    0.002    0.000    0.638    0.001 <frozen importlib._bootstrap>:197(_call_with_frames_removed)\n",
      "    49425    0.017    0.000    0.017    0.000 <frozen importlib._bootstrap>:208(_verbose_message)\n",
      "     1513    0.003    0.000    0.003    0.000 <frozen importlib._bootstrap>:355(__init__)\n",
      "     5435    0.008    0.000    0.012    0.000 <frozen importlib._bootstrap>:402(parent)\n",
      "     3361    0.007    0.000    0.010    0.000 <frozen importlib._bootstrap>:57(__init__)\n",
      "     1513    0.001    0.000    0.004    0.000 <frozen importlib._bootstrap>:698(find_spec)\n",
      "     3361    0.013    0.000    0.014    0.000 <frozen importlib._bootstrap>:77(acquire)\n",
      "     1513    0.001    0.000    0.003    0.000 <frozen importlib._bootstrap>:771(find_spec)\n",
      "    10591    0.005    0.000    0.007    0.000 <frozen importlib._bootstrap>:834(__enter__)\n",
      "    10591    0.005    0.000    0.007    0.000 <frozen importlib._bootstrap>:838(__exit__)\n",
      "     6052    0.011    0.000    0.018    0.000 <frozen importlib._bootstrap>:852(_find_spec_legacy)\n",
      "     1513    0.046    0.000    0.696    0.000 <frozen importlib._bootstrap>:861(_find_spec)\n",
      "3361/1513    0.016    0.000    0.769    0.001 <frozen importlib._bootstrap>:931(_find_and_load_unlocked)\n",
      "3361/1513    0.024    0.000    0.823    0.001 <frozen importlib._bootstrap>:958(_find_and_load)\n",
      "    68142    0.048    0.000    0.130    0.000 <frozen importlib._bootstrap>:989(_handle_fromlist)\n",
      "    11081    0.010    0.000    0.021    0.000 <frozen importlib._bootstrap_external>:1080(_path_importer_cache)\n",
      "     1513    0.030    0.000    0.607    0.000 <frozen importlib._bootstrap_external>:1117(_get_spec)\n",
      "     1513    0.003    0.000    0.610    0.000 <frozen importlib._bootstrap_external>:1149(find_spec)\n",
      "     9885    0.118    0.000    0.544    0.000 <frozen importlib._bootstrap_external>:1233(find_spec)\n",
      "        5    0.000    0.000    0.029    0.006 <frozen importlib._bootstrap_external>:1281(_fill_cache)\n",
      "     9885    0.002    0.000    0.002    0.000 <frozen importlib._bootstrap_external>:41(_relax_case)\n",
      "    49425    0.049    0.000    0.162    0.000 <frozen importlib._bootstrap_external>:57(_path_join)\n",
      "    49425    0.062    0.000    0.096    0.000 <frozen importlib._bootstrap_external>:59(<listcomp>)\n",
      "     9885    0.006    0.000    0.214    0.000 <frozen importlib._bootstrap_external>:75(_path_stat)\n",
      "       10    4.239    0.424  166.810   16.681 <ipython-input-17-6c8c7d43c293>:8(find_gaps)\n",
      "       27    0.000    0.000    0.471    0.017 <ipython-input-31-6f36754539f5>:11(get_time_mean)\n",
      "       27    5.142    0.190  156.880    5.810 <ipython-input-31-6f36754539f5>:115(average_dist_mean)\n",
      "       27    0.124    0.005    0.638    0.024 <ipython-input-31-6f36754539f5>:17(get_freq_mean)\n",
      "       36    0.000    0.000    0.712    0.020 <ipython-input-31-6f36754539f5>:24(get_time_median)\n",
      "       18    0.082    0.005    0.479    0.027 <ipython-input-31-6f36754539f5>:30(get_freq_median)\n",
      "       18    0.000    0.000    0.312    0.017 <ipython-input-31-6f36754539f5>:49(get_cross_corr)\n",
      "       27   21.004    0.778   21.486    0.796 <ipython-input-31-6f36754539f5>:93(spectral_centroid)\n",
      "        9    0.000    0.000    0.024    0.003 <ipython-input-33-4f75f9e2fec7>:14(normalize_dataset)\n",
      "       36    0.000    0.000    0.003    0.000 <ipython-input-33-4f75f9e2fec7>:17(moving_window)\n",
      "       36    0.002    0.000    0.002    0.000 <ipython-input-33-4f75f9e2fec7>:19(<listcomp>)\n",
      "        9    0.015    0.002    0.342    0.038 <ipython-input-33-4f75f9e2fec7>:22(fundamental_frequency)\n",
      "        9    0.000    0.000    0.112    0.012 <ipython-input-33-4f75f9e2fec7>:29(average_acceleration)\n",
      "        9    0.002    0.000    0.112    0.012 <ipython-input-33-4f75f9e2fec7>:30(<listcomp>)\n",
      "        9    0.008    0.001    0.590    0.066 <ipython-input-33-4f75f9e2fec7>:32(peak_counter)\n",
      "        9    0.000    0.000    0.023    0.003 <ipython-input-33-4f75f9e2fec7>:43(find_max)\n",
      "        9    0.001    0.000    0.023    0.003 <ipython-input-33-4f75f9e2fec7>:44(<listcomp>)\n",
      "        9    0.000    0.000    0.023    0.003 <ipython-input-33-4f75f9e2fec7>:47(find_min)\n",
      "        9    0.000    0.000    0.023    0.003 <ipython-input-33-4f75f9e2fec7>:48(<listcomp>)\n",
      "        9    1.582    0.176  190.551   21.172 <ipython-input-33-4f75f9e2fec7>:51(generate_features)\n",
      "        9    0.000    0.000    0.000    0.000 <ipython-input-33-4f75f9e2fec7>:66(<listcomp>)\n",
      "        9    0.020    0.002    1.268    0.141 <ipython-input-33-4f75f9e2fec7>:67(<listcomp>)\n",
      "        9    0.020    0.002    1.252    0.139 <ipython-input-33-4f75f9e2fec7>:68(<listcomp>)\n",
      "        9    0.020    0.002    1.238    0.138 <ipython-input-33-4f75f9e2fec7>:69(<listcomp>)\n",
      "        9    0.020    0.002    1.237    0.137 <ipython-input-33-4f75f9e2fec7>:70(<listcomp>)\n",
      "       10    0.000    0.000  166.811   16.681 <ipython-input-34-18b8b04b1e5c>:1(filter_subjects)\n",
      "       10    0.000    0.000    0.000    0.000 <ipython-input-34-18b8b04b1e5c>:5(<dictcomp>)\n",
      "        1    0.064    0.064  367.142  367.142 <ipython-input-35-a699e0f32876>:1(create_frame)\n",
      "        9    0.004    0.000  151.799   16.867 <ipython-input-36-cbf955315f45>:1(filter_demographics)\n",
      "        1    0.000    0.000  367.142  367.142 <string>:1(<module>)\n",
      "      598    0.001    0.000    0.001    0.000 <string>:12(__new__)\n",
      "     5770    0.008    0.000    0.013    0.000 __init__.py:205(iteritems)\n",
      "       18    0.000    0.000    0.000    0.000 __init__.py:211(itervalues)\n",
      "     1513    0.003    0.000    0.004    0.000 __init__.py:23(find_module)\n",
      "      299    0.001    0.000    0.002    0.000 _bootlocale.py:23(getpreferredencoding)\n",
      "     1794    0.006    0.000    0.525    0.000 _decorators.py:136(wrapper)\n",
      "        9    0.000    0.000    0.012    0.001 _decorators.py:185(wrapper)\n",
      "     3240    0.002    0.000    0.083    0.000 _methods.py:25(_amax)\n",
      "     6480    0.003    0.000    0.149    0.000 _methods.py:28(_amin)\n",
      "       36    0.000    0.000    0.014    0.000 _methods.py:31(_sum)\n",
      "        9    0.000    0.000    0.000    0.000 _methods.py:34(_prod)\n",
      "     2574    0.002    0.000    0.021    0.000 _methods.py:37(_any)\n",
      "     4924    0.017    0.000    0.074    0.000 _methods.py:40(_all)\n",
      "    36099    0.624    0.000    0.635    0.000 _methods.py:43(_count_reduce_items)\n",
      "    36099    0.523    0.000  151.406    0.004 _methods.py:53(_mean)\n",
      "     2392    0.003    0.000    0.018    0.000 _strptime.py:29(_getlang)\n",
      "     2392    0.022    0.000    0.045    0.000 _strptime.py:321(_strptime)\n",
      "     2392    0.002    0.000    0.047    0.000 _strptime.py:562(_strptime_datetime)\n",
      "     4085    0.003    0.000    0.004    0.000 _validators.py:221(validate_bool_kwarg)\n",
      "        9    0.000    0.000    0.000    0.000 _validators.py:230(validate_axis_style_args)\n",
      "      598    0.002    0.000    0.005    0.000 _validators.py:325(validate_fillna_kwargs)\n",
      "    84973    0.073    0.000    0.073    0.000 _weakrefset.py:70(__contains__)\n",
      "    78386    0.086    0.000    0.159    0.000 abc.py:178(__instancecheck__)\n",
      "      837    0.002    0.000    0.002    0.000 algorithms.py:1421(_get_take_nd_function)\n",
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    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import cProfile\n",
    "import re\n",
    "import os\n",
    "\n",
    "directory_in_str = '/scratch/PI/euan/projects/mhc/data/6mwt/accel_walk_dir'\n",
    "directory = os.fsencode(directory_in_str)\n",
    "table=v6\n",
    "    \n",
    "cProfile.run('create_frame(directory, table)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test_v6 = create_frame(directory, v6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DATA FROM v4 only\n",
    "tengaps_v4 = pd.read_hdf('tengaps_v4.h5', 'df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_v2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-e7c8ad06e1cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewsemitotal_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_v2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_v1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_v6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'test_v2' is not defined"
     ]
    }
   ],
   "source": [
    "newsemitotal_df = pd.concat([test_v2, test_v1, test_v6]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'newsemitotal_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-358cc7aae685>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewsemitotal_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_hdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'newsemitotal_df.h5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'df'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'newsemitotal_df' is not defined"
     ]
    }
   ],
   "source": [
    "newsemitotal_df.to_hdf('newsemitotal_df.h5', key='df', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DATA FROM v6, v2, v1\n",
    "newsemitotal_df = pd.read_hdf('newsemitotal_df.h5', 'df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tengaps_total = pd.concat([tengaps_v4, newsemitotal_df]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 315 total subjects in this data frame. Only 311 are usable due to outlier weight, age, height, etc\n",
    "tengaps_total.to_hdf('tengaps_total.h5', key='df', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "315"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This means there are 315 unique subjects in this dataset\n",
    "len(list(set(tengaps_total.index.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_v4' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-723e42137c61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This is all of the tables together removing any duplicate runs that might appear between tables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# At this point we have not reduced to minimum number of samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnewtotal_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_v4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_v2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_v1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_v6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnewtotal_no_demog_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewtotal_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sex'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'currentAge'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'weight'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'height'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'test_v4' is not defined"
     ]
    }
   ],
   "source": [
    "# This is all of the tables together removing any duplicate runs that might appear between tables\n",
    "# At this point we have not reduced to minimum number of samples \n",
    "\n",
    "# This is old and only has 175 total subjects. When filtered down it has 173 usable subjects.\n",
    "newtotal_df = pd.concat([test_v4, test_v2, test_v1, test_v6]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(list(set(newtotal_df.index)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newtotal_df.to_hdf('newtotal_df.h5', key='df', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newtotal_df  = pd.read_hdf('newtotal_df.h5', 'df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_demographics(demographics, table):\n",
    "    '''\n",
    "    Gives a data frame for the demographic data that is associated with a table\n",
    "    '''\n",
    "    healthcodes = filter_subjects(directory, table)\n",
    "    merged = pd.DataFrame()\n",
    "    indexlist = list(demographics_df.healthCode)\n",
    "    for e in list(set(healthcodes)):\n",
    "        if e in indexlist:\n",
    "            merged = merged.append(demographics_df.loc[demographics_df['healthCode'] == e])\n",
    "    merged = merged.drop(['recordId'], axis=1)\n",
    "    merged = merged.set_index('healthCode')\n",
    "    return merged\n",
    "\n",
    "def find_lowest_num_samples(total_df):\n",
    "    '''\n",
    "    This function finds the minimum number of samples from all of the runs present so that we keep same dimensions\n",
    "    for every run that we have\n",
    "    '''\n",
    "    total = [len(total_df.loc[x].average_accel) for x in list(set(total_df.index))]\n",
    "    return min(total)\n",
    "\n",
    "def min_df(lowest_num_of_samples, total_df):\n",
    "    '''\n",
    "    Returns the dataframe with features for every healthCode present so that there are only the minimum amount of \n",
    "    samples needed\n",
    "    '''\n",
    "    newdf = pd.DataFrame()\n",
    "    unique_healthcodes = list(set(total_df.index))\n",
    "    for elem in unique_healthcodes:\n",
    "        newdf = newdf.append(total_df.loc[elem].iloc[:lowest_num_of_samples])\n",
    "    return newdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest - Authentication Proof\n",
    "1. Start by separating training and testing data... 139 for train and 34 for test\n",
    "2. Create output labels for every training example for i in range(1, 173)... for j in range(0, 351)...\n",
    "3. Build Random Forest\n",
    "4. Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# These labels are for person authentication\n",
    "# Bad use of double for loops here \n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "labels  = []\n",
    "for s in range (0, 139):\n",
    "    for u in range(0, 352):\n",
    "        labels.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lower the number of trees to reduce overfitting\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "#lb = preprocessing.LabelBinarizer()\n",
    "numOfTotalHC = 173\n",
    "numOfTrainHC = 139\n",
    "numOfTestHC = 34\n",
    "\n",
    "# Shouldn't have to one-hot encode the sex because using random forest\n",
    "# One-hot encode ouput\n",
    "training = everything_no_demog.iloc[0:(numOfTrainHC * find_lowest_num_samples(newtotal_df))]\n",
    "\n",
    "training.insert(25, 'class', labels)\n",
    "\n",
    "X = np.array(training.iloc[:, 0:25])\n",
    "X_scaled = preprocessing.scale(X)\n",
    "y = np.array(training.iloc[:, 25])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.33, random_state=42)\n",
    "\n",
    "# Splits the training and testing into 80/20 with no sample scrambling\n",
    "# Must scramble after so that training set has no test samples in it\n",
    "#training = everything.iloc[0:(numOfTrainHC * find_lowest_num_samples(newtotal_df))]\n",
    "testing = everything_no_demog.iloc[(numOfTrainHC * find_lowest_num_samples(newtotal_df)):]\n",
    "\n",
    "#uniques, labeling = pd.factorize(training.sex)\n",
    "#uniques2, labeling2 = pd.factorize(testing.sex)\n",
    "\n",
    "#n = training.columns[2]\n",
    "#training.drop(n, axis = 1, inplace = True)\n",
    "#training.insert(2, 'sex', uniques)\n",
    "# Male is 0 and Female is 1\n",
    "#training['sex'] = training['sex'].astype(np.float64)\n",
    "#training['peakcount'] = training['peakcount'].astype(np.float64)\n",
    "\n",
    "#m = testing.columns[2]\n",
    "#testing.drop(m, axis = 1, inplace = True)\n",
    "#testing.insert(2, 'sex', uniques2)\n",
    "# Male is 0 and Female is 1\n",
    "#testing['sex'] = testing['sex'].astype(np.float64)\n",
    "#testing['peakcount'] = testing['peakcount'].astype(np.float64)\n",
    "\n",
    "# list of all of our features\n",
    "\n",
    "#features = training.columns[:]\n",
    "\n",
    "# labeling our unique outputs\n",
    "#y = pd.factorize(training.index.values)[0]\n",
    "\n",
    "clf = RandomForestClassifier(random_state=1)\n",
    "clf.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for e in clf.predict_proba(testing):\n",
    "    print(max(e))\n",
    "    i += 1\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('fundamental_freq', 0.026474651627758826),\n",
       " ('average_accel', 0.04631644692338057),\n",
       " ('peakcount', 0.021864395065022968),\n",
       " ('max', 0.03461859298376712),\n",
       " ('min', 0.019363659436194596),\n",
       " ('mut_x', 0.03856840176118175),\n",
       " ('mut_y', 0.03464132438446853),\n",
       " ('mut_z', 0.03107869133787603),\n",
       " ('muf_x', 0.03452760007164057),\n",
       " ('muf_y', 0.040845533174714344),\n",
       " ('muf_z', 0.04045499877560984),\n",
       " ('medt_x', 0.026517579175127703),\n",
       " ('medt_y', 0.029857129054181385),\n",
       " ('medt_z', 0.03477163554338808),\n",
       " ('medf_x', 0.016900238505937994),\n",
       " ('medf_y', 0.030616780169017577),\n",
       " ('medf_z', 0.025492989581519965),\n",
       " ('cross_xz', 0.021731244008977586),\n",
       " ('cross_yz', 0.01887216663337867),\n",
       " ('spect_cent_x', 0.006134287371825502),\n",
       " ('spect_cent_y', 0.007940758823966615),\n",
       " ('spect_cent_z', 0.00894115022977494),\n",
       " ('average_dist_meanx', 0.13950772098392378),\n",
       " ('average_dist_meany', 0.12762016267264462),\n",
       " ('average_dist_meanz', 0.13634186170472043)]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(training.columns.values[0:25], clf.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " 66,\n",
       " 99,\n",
       " 70,\n",
       " 19,\n",
       " 68,\n",
       " 10,\n",
       " 42,\n",
       " 28,\n",
       " 47,\n",
       " 10,\n",
       " 96,\n",
       " 44,\n",
       " 102,\n",
       " 45,\n",
       " 13,\n",
       " 122,\n",
       " 138,\n",
       " 130,\n",
       " 91,\n",
       " 111,\n",
       " 41,\n",
       " 14,\n",
       " 9,\n",
       " 71,\n",
       " 82,\n",
       " 55,\n",
       " 54,\n",
       " 123,\n",
       " 2,\n",
       " 117,\n",
       " 59,\n",
       " 100,\n",
       " 114,\n",
       " 37,\n",
       " 22,\n",
       " 90,\n",
       " 38,\n",
       " 86,\n",
       " 73,\n",
       " 76,\n",
       " 102,\n",
       " 65,\n",
       " 79,\n",
       " 130,\n",
       " 45,\n",
       " 7,\n",
       " 19,\n",
       " 88,\n",
       " 16,\n",
       " 136,\n",
       " 113,\n",
       " 95,\n",
       " 96,\n",
       " 69,\n",
       " 19,\n",
       " 26,\n",
       " 113,\n",
       " 65,\n",
       " 134,\n",
       " 12,\n",
       " 123,\n",
       " 113,\n",
       " 30,\n",
       " 28,\n",
       " ...]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bools = []\n",
    "for i in range(0, len(preds)):\n",
    "    if preds[i] != y_test[i]:\n",
    "        bools.append(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "520"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16147"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9677958753948102"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - (520/16147)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Very outdated plots of features\n",
    "### Use df.pairplot() now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/bhargavy/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f52d4904400>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IBwocAAxFgQOAoSIW+KZNm+R0OrVs2bKhZd3d3aqqqlJJSYmqqqp05cqVmIYEJrOpPlUw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\nwAHAUBQ4ABiKAgcAQ/0/kva+8L+15nwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.distplot(everything['min'], bins=10000, kde=False, rug=False, color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/bhargavy/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f71f563b2e8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(total_df['min'], bins=10000, kde=True, rug=False, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/bhargavy/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f71f4b8d668>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j5OXl8cwzz1BeXs5PfvITfv7zn6esaCPa2PnLQaLza6wJV6wJV6gGb8M2vA0V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\nKNBFRBxCgS4i4hAKdBERh1Cgi4g4xP8H5uIpMGXCaXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(total_df['max'], bins=10000, kde=True, rug=False, color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/bhargavy/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f71ef6e8f60>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fc+c0OHGCo3iJgsnrX6BOp8PChQsxe/ZsTJo0CRMnTkRycjKWLVvmOSibk5OD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\nEAUFBfjLX/6CkSNH4uWXXw6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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(total_df['peakcount'], bins=50, kde=True, rug=False, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/bhargavy/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f71e98511d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MLBeKluKZmEHc95/G8NorwY5IiIgmBWCEDGVQ1fDKNnR1B/BkXDOsOYD620ZA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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import linalg as LA\n",
    "normed = [x for x in test_v4['fundamental_freq']]\n",
    "sns.distplot(normed, bins= 10000, kde=True, rug=False, color='red')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}