Raksel_to_send.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В приложенном файле 3 датасета:\n",
    "\n",
    "1. Данные треков - Richtracks (описание приложено)\n",
    "2. Данные точек этих треков - IncomingTrackPoints (описание также приложено)\n",
    "3. Наборы токен-платформа (iOS или Android)\n",
    "\n",
    "Задание:\n",
    "\n",
    "1. Некоторые параметры набора точек для трека имеют различную размерность в зависимости от платформы (iOS или Android) - какие это параметры?\n",
    "2. Переведите все параметры в одинаковую размерность\n",
    "3. В richtracks для каждого трека есть поле TrackOrigin, которое описывает кем была произведена поездка: владельцем смартфона или нет. \n",
    "\n",
    "Задание: постройте модель, предсказывающую на основании данных из входящих точек, была ли поездка пассажирской или нет."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Некоторые параметры набора точек для трека имеют различную размерность в зависимости от платформы (iOS или Android) - какие это параметры?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Подключим библиотеки.\n",
    "2. Загрузим все файлы и соединим их, добавив бинарную переменную, показывающую была ли совершена поездка пассажиром.\n",
    "3. Посмотрим на данные и поймём, где проблемы с размерностью"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling #pandas profiling library\n",
    "import pandasql as ps #library to use sql with pandas df\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns #ploting library\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn import ensemble\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import fetch_20newsgroups, load_files\n",
    "from sklearn.model_selection import train_test_split #used to split dataset into train and test randomly\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold, GridSearchCV\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inc_points = pd.read_csv('inc_points_2.csv')\n",
    "rich_tracks = pd.read_csv('rich_tracks_2.csv')\n",
    "os_data = pd.read_csv('os.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1 = \"\"\"SELECT case when TrackOrigin == 'Passanger' then 1 else 0 end ispassanger, inc_points.*, rich_tracks.*, os_data.*\n",
    "        FROM inc_points join rich_tracks on inc_points.incomingtrackid=rich_tracks.incomingtrackid\n",
    "                        join os_data on rich_tracks.DeviceToken=os_data.DeviceToken\n",
    "     \"\"\"\n",
    "my_t = ps.sqldf(q1, locals())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#duplicate columns remover:\n",
    "my_t = my_t.loc[:,~my_t.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_in_inc_points = list(inc_points.columns.values)\n",
    "cols_in_rich_tracks = list(rich_tracks.columns.values)\n",
    "cols_in_os_data = list(os_data.columns.values)\n",
    "cols_to_analize = cols_in_inc_points + [\"TrackOrigin\"] + [\"ispassanger\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ispassanger</th>\n",
       "      <th>IncomingTrackId</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>PointDate</th>\n",
       "      <th>Height</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Deceleration</th>\n",
       "      <th>TickTimestamp</th>\n",
       "      <th>...</th>\n",
       "      <th>AccelerationICount</th>\n",
       "      <th>DecelerationICount</th>\n",
       "      <th>AccelerationIDuration</th>\n",
       "      <th>DecelerationIDuration</th>\n",
       "      <th>RatingCornering</th>\n",
       "      <th>VehicleId</th>\n",
       "      <th>BeaconId</th>\n",
       "      <th>DeviationRatio</th>\n",
       "      <th>DeviationMeters</th>\n",
       "      <th>os_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2287060</th>\n",
       "      <td>0</td>\n",
       "      <td>20658432</td>\n",
       "      <td>2.160000</td>\n",
       "      <td>60.775667</td>\n",
       "      <td>28.807924</td>\n",
       "      <td>2019-10-17 11:23:16.0000000 +03:00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1571300675</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.444747</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>20681388</td>\n",
       "      <td>61.608958</td>\n",
       "      <td>57.985000</td>\n",
       "      <td>56.184080</td>\n",
       "      <td>2019-10-17 15:56:52.0000000 +05:00</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1571309814</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.002551</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198014</th>\n",
       "      <td>0</td>\n",
       "      <td>20704584</td>\n",
       "      <td>0.020737</td>\n",
       "      <td>53.711947</td>\n",
       "      <td>-6.361111</td>\n",
       "      <td>2019-10-17 10:37:42.0000000 +02:00</td>\n",
       "      <td>33590.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1571301521</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.455781</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944093</th>\n",
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       "      <td>20671479</td>\n",
       "      <td>18.560119</td>\n",
       "      <td>-31.898344</td>\n",
       "      <td>115.825460</td>\n",
       "      <td>2019-10-17 17:26:26.0000000 +08:00</td>\n",
       "      <td>34063.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>6.146293</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ispassanger  IncomingTrackId      Speed   Latitude   Longitude  \\\n",
       "2287060            0         20658432   2.160000  60.775667   28.807924   \n",
       "880595             0         20681388  61.608958  57.985000   56.184080   \n",
       "2198014            0         20704584   0.020737  53.711947   -6.361111   \n",
       "1944093            0         20671479  18.560119 -31.898344  115.825460   \n",
       "\n",
       "                                  PointDate    Height  Acceleration  \\\n",
       "2287060  2019-10-17 11:23:16.0000000 +03:00   17538.0           0.0   \n",
       "880595   2019-10-17 15:56:52.0000000 +05:00  130000.0           0.0   \n",
       "2198014  2019-10-17 10:37:42.0000000 +02:00   33590.0           0.0   \n",
       "1944093  2019-10-17 17:26:26.0000000 +08:00   34063.0           0.0   \n",
       "\n",
       "         Deceleration  TickTimestamp  ...  AccelerationICount  \\\n",
       "2287060           0.0     1571300675  ...                 0.0   \n",
       "880595            0.0     1571309814  ...                69.0   \n",
       "2198014           0.0     1571301521  ...                 2.0   \n",
       "1944093           0.0     1571304387  ...                 0.0   \n",
       "\n",
       "         DecelerationICount  AccelerationIDuration  DecelerationIDuration  \\\n",
       "2287060                 0.0                    0.0                    0.0   \n",
       "880595                144.0                  100.0                  153.0   \n",
       "2198014                91.0                    7.0                  108.0   \n",
       "1944093               338.0                    0.0                  366.0   \n",
       "\n",
       "        RatingCornering VehicleId BeaconId DeviationRatio  DeviationMeters  \\\n",
       "2287060             NaN      None     None            0.0        27.444747   \n",
       "880595              NaN      None     None            0.0         8.002551   \n",
       "2198014             NaN      None     None            0.0         5.455781   \n",
       "1944093             5.0      None     None            0.0         6.146293   \n",
       "\n",
       "         os_version  \n",
       "2287060         iOS  \n",
       "880595          iOS  \n",
       "2198014         iOS  \n",
       "1944093         iOS  \n",
       "\n",
       "[4 rows x 77 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_t.sample(round(my_t.shape[0]*0.000001))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a8adonlC023k1cE97/fjMfKKor3si4qPArTQfFb8mM6+EvszPdP324m/b+s6P57729lNAV7VXzdV0fc3aucrMGyLiYJrXh01t/VdFxCMd1d7zXPkn9yVJ5ebCYTFJUp8ZLpKkcoaLJKmc4SJJKme4SJLKGS6SpHKGiySp3P8BT2nUC85gG/MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_t['os_version'].value_counts().plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c16bcdbe0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"Speed\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c3b9ee908>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"Latitude\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c3c3460f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"Longitude\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c3f135518>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"Acceleration\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14dc7e22cc0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"Deceleration\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c92f460b8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IOBz4OLAgIgZT8mVMMzPbeZUNMt8gWyTzB5L+RPaI68JkrTIzs26h7H4yv2DrDcreKWlTmiaZmVl3UXY/mf7AD4ADgSOB2RHxr5KeTdk4MzPbsZV9XHYV2Vv064AXgAfJlnIxMzNrUdkgM1DSDGCzpA2SzqPE+mBmZrZzKxtkNkfEa2UjYo82nGtmZjupsoHiF8B1QJ+ImADcBvw0WavMzKxbKBVkJF1Ctu3y/cAxwH+RrXZsZmbWotJvyufL7M9O2BYzM+tmyk5hPpms57JnMV3SXikaZWZm3UPZnsxU4N+AJxO2xczMupmyQWaFpF8lbYmZmXU7ZYPMNRFxGXAzhY3FJN2ZpFVmZtYtlA0yHyHbKvnYQtoW4H0d3iIzM+s2ygaZQ4F9y2wiZmZm1qTsy5gr8cZgZmbWRmUDx1+BhyLiVuDVpkRJX0jSKjMz6xbKBpknaef05YjoDdwDHC9pRUXeZGAM0JgnzQDuBmYVivUDGiUNLpx3MbCpaRvmiOhLtuzNAcBq4CRJK9vTXjMz6zhlNy27qD2VR8QwssBxUAtF6oGRkhZXpA/Jz98dWAKcnh/3AaYDp5C9u9Pk/wF3Sfp4RPwLcAVwcnvabGZmHafVIBMRiyR9KCL+QTabbCuSelepfxxwFnBtC/n1wPkRsT9wJ3BOxeSCicBCSYvy4xOAx4FpFfV8HPhw/vnHwHciYldJG2hB3vvpW5E8oMr9mNkObt68eYwYMWKrY0unWk/mM/n3wa2WaoGksQARsU1eRPQClgLnAk+QPSKbBFyQ5/cBxgMHF+qbnedNqahuH+DZvMzGiPg72WO2Z1pp3tnA5DbflJmZlVYtyPwI+CdJT3X0hSWtJXv3BoCImAbMJA8ywGhgrqRVJaqra+Z4c5VzLmfrsR/IejJ3lbieme2gir2YpmP3ZtKpFmTenOrCEbEfcLSkmXlSHYXVBIATgUtKVvdXoD/wl4jYBdgDeL61EyStAdZUtKnk5czMrIxqQaZHROzJtj0FACS9sB3XXgdMjYjbgRVkYzc3AkREHTAUqJwQ0JL5wKlkQelkskkALY7HmJlZ56gWZA4GnqP5ILMF6NnWC0bEfOBrkn6X77I5D9gNWMTrA/r9gPVtWGFgEjArIv5A1jsZ1dZ2mZlZx6sWZB6SdMj2XkTSwMLnhsLnOcCcZsqvInv81VJ9UyqOXwA+sb3tNDOzjlVtWZltpi2bmZmVVS3IeJMyMzNrt1aDjKTPAETEgZV5EXFuqkaZmVn3UHYV5vkRsQ9kU48jYiFwUrpmmZlZd1A2yEwEbo6IM4EHgN8CRyRrlZmZdQtlF8j8ef6S42zgo5LuSNoqMzPrFqotkDmP12eY1QEvAFdHxOMAkjxt2MzMWlStJ/PzKsdmZmYtqja77BpJ15At97JP/vkO4BAccMzMrIqyA/8/BN6Sf15D9ghtRpIWmZlZt1F2++UDJX0KQNKLwJci4qF0zTIzs+6gbE9m14h4bRfMfMOxZldmNjMza1K2JzMbuC8ifkb2qOyTZI/QzMzMWlSqJyPpUuA8oA/QC/iKpOkpG2ZmZju+VoNM0yOyiHgz2X4vFwHfIOvVJNs108zMuodqj8vuA95NtnFZcdn/Otq5aZmZme08qgWZVwAklZ0gYAbAvHnzGDFixFbHZrbzqRZktmvTsvxx2z3A8ZJWVORNBsYAjXnSDOBuYFahWD+gUdLgiNgP+BGwFyBglKS1EbEncB2wL/AqMF7Sg9vTbjMz6xjVgswbI+IQWpiuLOn3LZ0YEcPIAsdBLRSpB0ZKWlyRPiQ/f3dgCXB6nn4VcJWkGyJiEjCJbDLCl4FHJDVExAjgSuBDVe7LzMw6QbUgcwAwh+aDzJY8vyXjgLOAa1vIrwfOj4j9gTuBcyS9UsifCCyUtCgidgU+DJyY580CFpIFmZ7AHnn6m4B1Ve4JgIjoC/StSB5Q5lyrrviorOnYj8zMdj7Vgsyjkg5pT8WSxgJExDZ5+cucS4FzgSfIgsYk4II8vw8wHjg4P+WtwN8lbcyPn+X1gPAt4N6IeAboDRxTsolnA5Pbck9mZtY2NRnQl7RWUoOkx/LAMQ1oKBQZDcyVtCo/7sG240Ob8+9XAldK2ocswPwkD2LVXA4Mqvg6sl03ZGZmzarWk7kzxUXzQfyjJc3Mk+qADYUiJwKXFI5XAX0ioqekTcDewDN53glkvR4kLY6Iv5FNu76/tTZIWkO22GexXe27ITMza1a1pf6/mOi664CpETEoIurIxm5uBMiPhwKvTQiQtAG4Czg5TzoVuCn//BD5WE1EHAjsA/wxUbvNzKwNOvVxWUTMj4h6SauBCcA8sunIdWSPzCCbtry+YhIAwJnA+Ih4lOyx1oV5+mnAmIhYBtwAnJavFG1mZjVWdoHMdpM0sPC5ofB5DtnMtcryq4D+zaQ/BRzVTPrjwPCOaa2ZmXUkv8lvZmbJOMiY2U6l8n0tv7+VloOMJeH/yGYGDjJmZpaQg4yZmSXjIGNJNLd2mVlX4J/NzuUgY2ZmyTjImJlZMg4yZmaWjIOMmZkl4yBjZjsVv8PVuRxkzMwsmeQLZJqZdTXuvXQe92TMzCwZBxkzM0vGQcbMzJJxkDEzs2SSD/xHRG/gHuB4SSsq8iYDY4DGPGkGcDcwq1CsH9AoaXBE7Af8CNiLbNvmUZLW5te4GnhPfs7nJP0+zR2ZmVlZSYNMRAwjCxwHtVCkHhgpaXFF+pD8/N2BJcDpefpVwFWSboiIScAk4DxgOvC0pFERcSxZwBnWoTdjZmZtlronMw44C7i2hfx64PyI2B+4EzhH0iuF/InAQkmLImJX4MPAiXneLGBhRHwV+BQwCEDSzRHxdLWGRURfoG9F8oBSd2VmO7TiysuezpxW0iAjaSxARGyTFxG9gKXAucATZEFjEnBBnt8HGA8cnJ/yVuDvkjbmx8+SBYW9gFeBMyNiBLAO+FKJ5p0NTG7HbZmZWUk1G/iXtFZSg6TH8sAxDWgoFBkNzJW0Kj/uAWypqGYzWaB8G/CipA8AlwI3lmjC5WS9n+LXke29HzPbMXg/mc5Vszf+80H8oyXNzJPqgA2FIicClxSOVwF9IqKnpE3A3sAzwHPARuB6AEkLIqJXROxVCFDbkLQGWFPRpu28KzMzK6rlFOZ1wNSIGBQRdWRjNzcC5MdDgdcmBEjaANwFnJwnnQrcJOlVYAEwMj/3COAlsuBjZmY11OlBJiLmR0S9pNXABGAe2XTkOrJHZpBNW15fMQkA4ExgfEQ8SvZo68I8/XPAcRGxjGxm2UhJmxPfipmZVdEpj8skDSx8bih8ngPMaab8KqB/M+lPAUc1k/4s8ImOaa2ZmXUUv/FvSXjPDuuq/LPZuRxkLAnP4DEz8H4yZrYTcu+l87gnY2ZmyTjImJlZMg4yZmaWjIOMJeEZPNaVjRgx4rUvS8tBxszMknGQsSQ8hdm6Kv9sdi4HGTMzS8ZBxszMknGQMTOzZBxkzMwsGQcZMzNLxkHGkvB7MtZV+WezcznImJlZMl6F2ZJo7l0E/8VoXYV/FjtP0iATEb2Be4DjJa2oyJsMjAEa86QZwN3ArEKxfkCjpMERsR/wI2Avsu2aR0laW6hvAPAwcGjltczMrDaSBZmIGEYWOA5qoUg9MFLS4or0Ifn5uwNLgNPz9KuAqyTdEBGTgEnAeXnZHsD3gd069CbMzGy7pOzJjAPOAq5tIb8eOD8i9gfuBM6R9EohfyKwUNKiiNgV+DBwYp43C1hIHmSArwC3Au8q27iI6Av0rUgeUPZ8MzOrLtnAv6Sxku5qLi8iegFLgXOBQ8l+2U8q5PcBxgMX5UlvBf4uaWN+/Cx5QIiIocBwYHobm3g28KeKr2bba2Zm7VOT2WWS1kpqkPRYHjimAQ2FIqOBuZJW5cc9gC0V1WzOH6ldBYyVtLmNzbgcGFTxdWQb67AWeJqomUGNZpflg/hHS5qZJ9UBGwpFTgQuKRyvAvpERE9Jm4C9gWfIgsLbgF9FBMA+wPyI+GdJaq0NktYAayra1f6bMjOzbdRqCvM6YGpE3A6sIBu7uREgIuqAocBrEwIkbYiIu4CTgeuBU4GbJN0CDGwqFxErgAbPLusa3Hsxs04NMhExH/iapN9FxARgHtmMsEVkj8wgm7a8vmISAMCZwDURcSHwZ+CUTmq2tVPxXRkHHLOdU/IgI2lg4XND4fMcYE4z5VcB/ZtJfwo4quy1zMys9rysjCXh3QfNDBxkzMwsIQcZMzNLxkHGzMyScZAxM7NkHGQsCb/xb2bgIGNmZgl50zJLxr0XM3NPxszMknGQMTOzZBxkzMwsGY/JbK0nwMqVK2vdDjOzHUbhd2bPyjwHma3tDTBq1Khat8PMbEe0N/BkMcFBZmv3k22E9iywqcZt6Q4GkG1pfSTwlxq3xazIP5sdqydZgLm/MqNuy5bKXY3NOkZEDAT+BAzyRnLWlfhns/N44N/MzJJxkDEzs2QcZMzMLBkHGUtpDXBR/t2sK/HPZifxwL+ZmSXjnoyZmSXjIGNmZsk4yJiZWTIOMmZmloyDjJmZJeMgY2ZmyXiBTGu3iKgHTpc0NiI+Bnwd6A1sBn4DXCDp5bzsp4GJZD9zPYDZki6rTcutO4iIwcAjwKclzWnDeQOBOyQNbMM53we+K+l3Femz8rpmla1rZ+OejLWbpN/lAeafgO8BZ0h6N3AIsCvwy4ioi4h9gWnARyW9H/gAMDIiPlGzxlt3MAb4GTAh9YUkja0MMFaOezLWbhFxFDAlP5wi6fcAktZHxJeAFcCHgL+TBZ3dgeclrY2I04BXOrvN1j1ExK7AKLKl+u+JiHdIejIiVgDXAh8D3gScKumBiDgE+EF++kOFemYBbwHeCXwFWA1cAfwv4DlggqQnIuIOsp/1hWR/MB0PPEO2xP0d6e50x+eejHWEw4AlxQRJG4DFwGGSHgJ+CSyPiCUR8R9AT0lPdH5TrZv4OPCUpD8Cc4HxhbznJR0OfBc4P0+bDZwn6VBgeUVdz+c98FuAG4DP5z3u7wI/rij7KbKe+nuBz5AFJ2uFg4x1hC003yveLc9D0hnAQOBqYH/g3oj4ZGc10Lqdz/J6APgJ8NmI2C0/vjn/vgx4c0S8FdhH0oI8fVZFXffl3w8CGiXdDyDpZ8A7I6JPoexRwC8kbZC0GpjfQffTbflxmXWE+8jGWR5uSsj/wx8KXBYRHwd6SfoJ8EPghxExDvgc8IsatNd2YBGxF3AcMDQivgjUAXsCTX+0ND2G3ZLnNX1vsrGiynX59+b+6K5j633rq9VlFdyTsY4wGbgwIg6F156Xfxt4DFgEvAxcms/qISLqgCHA0pq01nZ0/wL8VtIASQMl7Q98Azi9ucKSngeeyv/YAfi/LdQr4C0RcRhARJxE9kjuhUKZW4GTIuINEbEncGwH3E+35iBj203SIuBU4IqI+APwB7K/Jk+QtEXS7WTLqv86IkQWfDYBF9eqzbZD+1fgqoq07wCHkw3YN2c0MDkilgLvaK6ApFeBk4ErI2IZ8Pn8uFjml2QD/cuAXwGPtusOdiJe6t/MzJJxT8bMzJJxkDEzs2QcZMzMLBkHGTMzS8ZBxszMknGQMTOzZBxkzLqxiKiPiJ/Xuh228/J7MmZmlozXLjPbDhExHvgC2QoGfyN7S3wvYDrZmldbgEtb21Qrr2OEpBH58buA3wL7kS3aeAXZcvQ9gW9Lmplvs3AF8BLQi2zJ++8DB5JtGvcA2T4rHwaulDQ4X+jxO2RL+mwBbgLOl7QxIl4Bvgl8FNgbmCrp6o74N7Kdmx+XmbVTRAwn24PkI/nS8NeTLTt/ETBd0lCyjbWGV6nqx8CHIqJ/fvxZsoVE64CfA1/N6/o/wDkRcURebjBwiqT3AZ8A9pA0hGzrBYADKq7zbeB54GCgHng/cE6e9wbgOUkfBD4N/GdEtLREi1lpDjJm7Xcs8JN8yXfyLXj3BX4KfCcirgOG8vqeJs2S9A+y1ahHR0RPss24fkDWi3kHMDMiHiTbMOuNZPuZADwt6an88yLgvfnmWl8FLm9mv57jyHo1W/J1ur6bpzX5Zf7992RB500l/x3MWuQgY9Z+TY/DiuqA28l6CwvIdmh8uESvYAbZIqPHAv8t6U95/aDl+QkAAAEcSURBVC9KGtL0BRxB1ssBWNt0cl7+ncClQG/g1ogYUXGNHhXt7UG2Y2mTdXldTWWKS9qbtYuDjFn73QyMjIh+ABHxWbLHUbOAQ/KezXigL9C/hToAkHQv2S/1r5EFHMiWnl8XEaPz+t9Otvrv0MrzI+IMsuDzG0nnke3yeGhFsVuAz0dEXUS8IW/bAswScpAxa6d8p8X/BG7Ltzg4jWzv968AX8+Xlb8DuEjSihJVziAbR5mb178eOAEYGxEPA78BJkm6u5lzZ5P1fB6NiAeAPmRjMEVfIJuU8Ej+JbJ9WMyS8RRmMzNLxlOYzTpBRJxLNqDfnMskXdeZ7THrLO7JmJlZMh6TMTOzZBxkzMwsGQcZMzNLxkHGzMyS+R+u4T902e3xJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"TickTimestamp\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14e14637c88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"AccelerationXOriginal\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14e18d592b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"AccelerationYOriginal\", data=my_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14e35323438>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"os_version\", y=\"AccelerationZOriginal\", data=my_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All values that represent acceleration look like they have different dimentions in different OS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Переведите все параметры в одинаковую размерность"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Посмотрим на коррелирующие признаки и отбросим те, которые могут помешать модели\n",
    "2. Нормализуем численные признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IncomingTrackId', 'Speed', 'Latitude', 'Longitude', 'PointDate', 'Height', 'Acceleration', 'Deceleration', 'TickTimestamp', 'AccelerationXOriginal', 'AccelerationYOriginal', 'AccelerationZOriginal', 'TrackOrigin', 'ispassanger']\n"
     ]
    }
   ],
   "source": [
    "print(cols_to_analize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_t = my_t[cols_to_analize]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-80c5314b4116>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mProfileReport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_t\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_t\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m0.01\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprofile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_file\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"output.html\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas_profiling\\__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, df, minimal, config_file, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m         \u001b[1;31m# Get dataset statistics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m         \u001b[0mdescription_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdescribe_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\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     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m         \u001b[1;31m# Build report structure\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas_profiling\\model\\describe.py\u001b[0m in \u001b[0;36mdescribe\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m    524\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mThreadPool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpool_size\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m             \u001b[0mseries_description\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m             \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstarmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmultiprocess_1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    527\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescription\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m                 \u001b[0mseries_description\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdescription\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mstarmap\u001b[1;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[0;32m    274\u001b[0m         \u001b[0;31m`\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[0mbecomes\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m         '''\n\u001b[1;32m--> 276\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstarmapstar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m     def starmap_async(self, func, iterable, chunksize=None, callback=None,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    649\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    650\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 651\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\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    652\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mready\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    653\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 648\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\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    649\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    650\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    550\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 552\u001b[1;33m                 \u001b[0msignaled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\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    553\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "pandas_profiling.ProfileReport(my_t.sample(round(my_t.shape[0]*0.01)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets drop TickTimestamp and Longitude due to correllation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_t = my_t.drop(columns=['TickTimestamp', 'Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3822689, 12)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3822689 entries, 0 to 3822688\n",
      "Data columns (total 12 columns):\n",
      "IncomingTrackId          int64\n",
      "Speed                    float64\n",
      "Latitude                 float64\n",
      "PointDate                object\n",
      "Height                   float64\n",
      "Acceleration             float64\n",
      "Deceleration             float64\n",
      "AccelerationXOriginal    float64\n",
      "AccelerationYOriginal    float64\n",
      "AccelerationZOriginal    float64\n",
      "TrackOrigin              object\n",
      "ispassanger              int64\n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 350.0+ MB\n"
     ]
    }
   ],
   "source": [
    "my_t.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2899056\n",
       "1     923633\n",
       "Name: ispassanger, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_t['ispassanger'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All unique TrackOrigin values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['OriginalDriver', 'Passanger', 'Taxi'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_t['TrackOrigin'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_t['AccelerationZOriginal_zscore'] = (my_t.AccelerationZOriginal - my_t.AccelerationZOriginal.mean())/my_t.AccelerationZOriginal.std(ddof=0)\n",
    "# my_t['AccelerationZOriginal_zscore']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PointDate', 'TrackOrigin']\n",
      "['IncomingTrackId', 'Speed', 'Latitude', 'Height', 'Acceleration', 'Deceleration', 'AccelerationXOriginal', 'AccelerationYOriginal', 'AccelerationZOriginal', 'AccelerationZOriginal_zscore']\n"
     ]
    }
   ],
   "source": [
    "categorical_columns = [c for c in my_t.columns if my_t[c].dtype.name == 'object']\n",
    "numerical_columns   = [c for c in my_t.columns if my_t[c].dtype.name != 'object']\n",
    "numerical_columns.remove('ispassanger')\n",
    "print(categorical_columns)\n",
    "print(numerical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2019-10-17 15:35:23.0000000 +03:00' '2019-10-17 15:35:34.0000000 +03:00'\n",
      " '2019-10-17 15:37:28.0000000 +03:00' ...\n",
      " '2019-10-23 12:34:44.0000000 +04:00' '2019-10-23 12:34:45.0000000 +04:00'\n",
      " '2019-10-23 12:34:46.0000000 +04:00']\n",
      "['OriginalDriver' 'Passanger' 'Taxi']\n"
     ]
    }
   ],
   "source": [
    "for c in categorical_columns:\n",
    "    print(my_t[c].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>IncomingTrackId</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Height</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Deceleration</th>\n",
       "      <th>AccelerationXOriginal</th>\n",
       "      <th>AccelerationYOriginal</th>\n",
       "      <th>AccelerationZOriginal</th>\n",
       "      <th>AccelerationZOriginal_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "      <td>3.822689e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.359237e-13</td>\n",
       "      <td>-5.343200e-13</td>\n",
       "      <td>-3.101774e-13</td>\n",
       "      <td>-1.227142e-14</td>\n",
       "      <td>-2.693514e-15</td>\n",
       "      <td>2.428010e-13</td>\n",
       "      <td>-1.568428e-14</td>\n",
       "      <td>-2.918194e-14</td>\n",
       "      <td>7.152220e-14</td>\n",
       "      <td>9.298588e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.453707e+00</td>\n",
       "      <td>-8.625992e-01</td>\n",
       "      <td>-2.095684e+00</td>\n",
       "      <td>-3.686349e+00</td>\n",
       "      <td>-3.040488e+01</td>\n",
       "      <td>-3.253421e+01</td>\n",
       "      <td>-2.687885e+01</td>\n",
       "      <td>-1.781270e+01</td>\n",
       "      <td>-1.344837e+01</td>\n",
       "      <td>-1.344837e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.440812e-01</td>\n",
       "      <td>-8.600586e-01</td>\n",
       "      <td>-8.554802e-01</td>\n",
       "      <td>-5.885655e-01</td>\n",
       "      <td>-8.113991e-02</td>\n",
       "      <td>-1.030642e-01</td>\n",
       "      <td>-1.795231e-01</td>\n",
       "      <td>-3.718626e-01</td>\n",
       "      <td>-4.035180e-01</td>\n",
       "      <td>-4.035180e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-5.999127e-02</td>\n",
       "      <td>-4.613654e-01</td>\n",
       "      <td>5.744294e-01</td>\n",
       "      <td>-1.024264e-01</td>\n",
       "      <td>-8.113991e-02</td>\n",
       "      <td>-1.030642e-01</td>\n",
       "      <td>-5.537089e-02</td>\n",
       "      <td>-1.431376e-01</td>\n",
       "      <td>-2.198127e-01</td>\n",
       "      <td>-2.198127e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.360261e-02</td>\n",
       "      <td>7.087255e-01</td>\n",
       "      <td>6.606778e-01</td>\n",
       "      <td>2.441756e-01</td>\n",
       "      <td>-8.113991e-02</td>\n",
       "      <td>-1.030642e-01</td>\n",
       "      <td>5.569406e-02</td>\n",
       "      <td>2.589308e-02</td>\n",
       "      <td>-1.301652e-02</td>\n",
       "      <td>-1.301652e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.495149e+00</td>\n",
       "      <td>6.060875e+00</td>\n",
       "      <td>1.043311e+00</td>\n",
       "      <td>1.896468e+01</td>\n",
       "      <td>3.521833e+01</td>\n",
       "      <td>3.228169e+01</td>\n",
       "      <td>2.343636e+01</td>\n",
       "      <td>1.251143e+01</td>\n",
       "      <td>1.410654e+01</td>\n",
       "      <td>1.410654e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IncomingTrackId         Speed      Latitude        Height  \\\n",
       "count     3.822689e+06  3.822689e+06  3.822689e+06  3.822689e+06   \n",
       "mean     -1.359237e-13 -5.343200e-13 -3.101774e-13 -1.227142e-14   \n",
       "std       1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min      -3.453707e+00 -8.625992e-01 -2.095684e+00 -3.686349e+00   \n",
       "25%      -1.440812e-01 -8.600586e-01 -8.554802e-01 -5.885655e-01   \n",
       "50%      -5.999127e-02 -4.613654e-01  5.744294e-01 -1.024264e-01   \n",
       "75%       9.360261e-02  7.087255e-01  6.606778e-01  2.441756e-01   \n",
       "max       3.495149e+00  6.060875e+00  1.043311e+00  1.896468e+01   \n",
       "\n",
       "       Acceleration  Deceleration  AccelerationXOriginal  \\\n",
       "count  3.822689e+06  3.822689e+06           3.822689e+06   \n",
       "mean  -2.693514e-15  2.428010e-13          -1.568428e-14   \n",
       "std    1.000000e+00  1.000000e+00           1.000000e+00   \n",
       "min   -3.040488e+01 -3.253421e+01          -2.687885e+01   \n",
       "25%   -8.113991e-02 -1.030642e-01          -1.795231e-01   \n",
       "50%   -8.113991e-02 -1.030642e-01          -5.537089e-02   \n",
       "75%   -8.113991e-02 -1.030642e-01           5.569406e-02   \n",
       "max    3.521833e+01  3.228169e+01           2.343636e+01   \n",
       "\n",
       "       AccelerationYOriginal  AccelerationZOriginal  \\\n",
       "count           3.822689e+06           3.822689e+06   \n",
       "mean           -2.918194e-14           7.152220e-14   \n",
       "std             1.000000e+00           1.000000e+00   \n",
       "min            -1.781270e+01          -1.344837e+01   \n",
       "25%            -3.718626e-01          -4.035180e-01   \n",
       "50%            -1.431376e-01          -2.198127e-01   \n",
       "75%             2.589308e-02          -1.301652e-02   \n",
       "max             1.251143e+01           1.410654e+01   \n",
       "\n",
       "       AccelerationZOriginal_zscore  \n",
       "count                  3.822689e+06  \n",
       "mean                   9.298588e-14  \n",
       "std                    1.000000e+00  \n",
       "min                   -1.344837e+01  \n",
       "25%                   -4.035180e-01  \n",
       "50%                   -2.198127e-01  \n",
       "75%                   -1.301652e-02  \n",
       "max                    1.410654e+01  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = my_t[numerical_columns]\n",
    "data_numerical = (data_numerical - data_numerical.mean()) / data_numerical.std()\n",
    "data_numerical.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3822689, 11)\n",
      "Index(['IncomingTrackId', 'Speed', 'Latitude', 'Height', 'Acceleration',\n",
      "       'Deceleration', 'AccelerationXOriginal', 'AccelerationYOriginal',\n",
      "       'AccelerationZOriginal', 'AccelerationZOriginal_zscore', 'ispassanger'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "my_t = pd.concat((data_numerical, my_t['ispassanger']), axis=1)\n",
    "my_t = pd.DataFrame(my_t, dtype=float)\n",
    "print (my_t.shape)\n",
    "print (my_t.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # 3. В richtracks для каждого трека есть поле TrackOrigin, которое описывает кем была произведена поездка: владельцем смартфона или нет. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Поделим данные на обучающую и тестовые выборки\n",
    "2. Обучим модель логистической регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_cols = set(my_t.columns)\n",
    "my_cols.remove('ispassanger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['AccelerationYOriginal', 'Speed', 'AccelerationZOriginal', 'Height',\n",
      "       'AccelerationZOriginal_zscore', 'Latitude', 'AccelerationXOriginal',\n",
      "       'IncomingTrackId', 'Deceleration', 'Acceleration'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "y = my_t['ispassanger']\n",
    "X = my_t[my_cols]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2)\n",
    "feature_names = X.columns\n",
    "print(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3058151 764538\n"
     ]
    }
   ],
   "source": [
    "N_train, _ = X_train.shape \n",
    "N_test,  _ = X_test.shape \n",
    "print(N_train, N_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.77      0.98      0.87    579811\n",
      "         1.0       0.66      0.09      0.17    184727\n",
      "\n",
      "    accuracy                           0.77    764538\n",
      "   macro avg       0.72      0.54      0.52    764538\n",
      "weighted avg       0.75      0.77      0.70    764538\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression(n_jobs=-1)\n",
    "\n",
    "# fit the model with data\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "y_pred=logreg.predict(X_test)\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(y_test, y_pred))\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 257.44, 'Predicted label')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7692881190993777\n",
      "Precision: 0.65689557855127\n",
      "Recall: 0.09450161589805496\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:\",metrics.recall_score(y_test, y_pred))"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}