{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predikce počtu obyvatel z velikosti města \n",
    "\n",
    "Načteme si data. Obsahují informace o městech, jejich rozloze a počtu obyvatel. Chtěli bych vytvořil model závislosti počtu obyvatel na rozloze města. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Oblast</th>\n",
       "      <th>Rozloha</th>\n",
       "      <th>Počet obyvatel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Harbin,</td>\n",
       "      <td>200</td>\n",
       "      <td>4609000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Johannesburg-East Rand</td>\n",
       "      <td>1000</td>\n",
       "      <td>7960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Tianjin, TJ</td>\n",
       "      <td>740</td>\n",
       "      <td>9596000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>1020</td>\n",
       "      <td>13913000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Pune, MAH</td>\n",
       "      <td>185</td>\n",
       "      <td>5376000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Bangkok</td>\n",
       "      <td>950</td>\n",
       "      <td>14910000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Oblast  Rozloha  Počet obyvatel\n",
       "19                 Harbin,      200         4609000\n",
       "58  Johannesburg-East Rand     1000         7960000\n",
       "68             Tianjin, TJ      740         9596000\n",
       "79            Buenos Aires     1020        13913000\n",
       "32               Pune, MAH      185         5376000\n",
       "82                 Bangkok      950        14910000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_populace = pd.read_csv(\"population.csv\", index_col=0)\n",
    "df_populace.sample(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data si zobrazíme. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "\n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", ax=ax[0]) \n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", xlim=[0,2000], ax=ax[1]);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vsutpní proměnná (X) bude \"Rozloha\", odezva (y) je \"Počet obyvatel\".  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_populace[[\"Rozloha\"]]\n",
    "y = df_populace[\"Počet obyvatel\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jako *baseline* řešení zkusíme vzít průměrnou hodnotu populace. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_populace[\"baseline\"] = df_populace.mean()[\"Počet obyvatel\"]\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "\n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", ax=ax[0]) \n",
    "df_populace.plot(x=\"Rozloha\", y=\"baseline\", color=\"red\", ax=ax[0]);\n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", xlim=[0,2000], ax=ax[1])\n",
    "df_populace.plot(x=\"Rozloha\", y=\"baseline\", xlim=[0,2000], color=\"red\", ax=ax[1]);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Máme 100 datových vzorků, vezměme si tedy 70 jako trénovací data a zbylých 30 nechme na testování."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = X[:70], y[:70]\n",
    "X_test, y_test = X[70:], y[70:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jako model zvolme lineární regresi, je to nejjednodušší, co můžeme použít."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model natrénujeme a učíme predikci jak na testovacíh, tak na trénovacích datech. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cerna_krabicka = LinearRegression()\n",
    "cerna_krabicka.fit(X_train, y_train)\n",
    "\n",
    "df_populace.loc[df_populace.index[:70],\"predikce\"] = cerna_krabicka.predict(X_train)\n",
    "df_populace.loc[df_populace.index[70:], \"predikce\"] = cerna_krabicka.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zobrazíme si výslednou predikci."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "\n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", ax=ax[0])\n",
    "df_populace.plot(x=\"Rozloha\", y=\"baseline\", color=\"red\", ax=ax[0]);\n",
    "df_populace.plot(x=\"Rozloha\", y=\"predikce\", color=\"blue\", ax=ax[0]);\n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", xlim=[0,2000], ax=ax[1])\n",
    "df_populace.plot(x=\"Rozloha\", y=\"baseline\", color=\"red\", ax=ax[1]);\n",
    "df_populace.plot(x=\"Rozloha\", y=\"predikce\", xlim=[0,2000], color=\"blue\", ax=ax[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Přijdeš na to, proč černá krabička tolik podhodnocuje?\n",
    "\n",
    "- Nápověda: zkus si zobrazit zvlášť trénovací a testovací data a podívat se, jak na nich vypadá predikce."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jak na to lépe?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named '____________'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-356297f6cb50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0m____________\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m________\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m________\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcerna_krabicka\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named '____________'"
     ]
    }
   ],
   "source": [
    "from ____________ import ________ \n",
    "\n",
    "X_train,  X_test, y_train, y_test = ________\n",
    "\n",
    "cerna_krabicka = LinearRegression()\n",
    "cerna_krabicka.fit(X_train, y_train)\n",
    "\n",
    "df_populace.loc[X_train.index,\"predikce2\"] = cerna_krabicka.predict(X_train)\n",
    "df_populace.loc[X_test.index, \"predikce2\"] = cerna_krabicka.predict(X_test)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "\n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", ax=ax[0])\n",
    "df_populace.plot(x=\"Rozloha\", y=\"baseline\", color=\"red\", ax=ax[0]);\n",
    "df_populace.plot(x=\"Rozloha\", y=\"predikce\", color=\"blue\", ax=ax[0]);\n",
    "df_populace.plot(x=\"Rozloha\", y=\"predikce2\", color=\"green\", ax=ax[0]);\n",
    "df_populace.plot.scatter(x=\"Rozloha\", y=\"Počet obyvatel\", xlim=[0,2000], ax=ax[1])\n",
    "df_populace.plot(x=\"Rozloha\", y=\"baseline\", color=\"red\", ax=ax[1]);\n",
    "df_populace.plot(x=\"Rozloha\", y=\"predikce2\", color=\"green\", ax=ax[1]);\n",
    "df_populace.plot(x=\"Rozloha\", y=\"predikce\", xlim=[0,2000], color=\"blue\", ax=ax[1]);\n"
   ]
  }
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