Linear_Regression_test.ipynb

{
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   "cell_type": "code",
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   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSet Size : (8124, 23)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>o</td>\n",
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       "      <th>2</th>\n",
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       "      <td>o</td>\n",
       "      <td>p</td>\n",
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       "      <td>n</td>\n",
       "      <td>m</td>\n",
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       "      <td>p</td>\n",
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       "      <td>s</td>\n",
       "      <td>u</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
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       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color   ...   stalk-surface-below-ring  \\\n",
       "0            c         n          k   ...                          s   \n",
       "1            c         b          k   ...                          s   \n",
       "2            c         b          n   ...                          s   \n",
       "3            c         n          n   ...                          s   \n",
       "4            w         b          k   ...                          s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import scipy.stats\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# simple linear regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "dataset = \"./data/mushrooms.csv\"\n",
    "df = pd.read_csv(dataset)  # read DataSet\n",
    "\n",
    "print(\"DataSet Size : {}\".format(df.shape))\n",
    "df.head(5)  # top 5 contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       object\n",
       "cap-shape                   object\n",
       "cap-surface                 object\n",
       "cap-color                   object\n",
       "bruises                     object\n",
       "odor                        object\n",
       "gill-attachment             object\n",
       "gill-spacing                object\n",
       "gill-size                   object\n",
       "gill-color                  object\n",
       "stalk-shape                 object\n",
       "stalk-root                  object\n",
       "stalk-surface-above-ring    object\n",
       "stalk-surface-below-ring    object\n",
       "stalk-color-above-ring      object\n",
       "stalk-color-below-ring      object\n",
       "veil-type                   object\n",
       "veil-color                  object\n",
       "ring-number                 object\n",
       "ring-type                   object\n",
       "spore-print-color           object\n",
       "population                  object\n",
       "habitat                     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the type of colums\n",
    "df.dtypes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "<p>0 rows × 23 columns</p>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [class, cap-shape, cap-surface, cap-color, bruises, odor, gill-attachment, gill-spacing, gill-size, gill-color, stalk-shape, stalk-root, stalk-surface-above-ring, stalk-surface-below-ring, stalk-color-above-ring, stalk-color-below-ring, veil-type, veil-color, ring-number, ring-type, spore-print-color, population, habitat]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124, 22)\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
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       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "      <td>o</td>\n",
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       "      <td>g</td>\n",
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       "      <td>m</td>\n",
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       "      <th>4</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
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       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  cap-shape cap-surface cap-color bruises odor gill-attachment gill-spacing  \\\n",
       "0         x           s         n       t    p               f            c   \n",
       "1         x           s         y       t    a               f            c   \n",
       "2         b           s         w       t    l               f            c   \n",
       "3         x           y         w       t    p               f            c   \n",
       "4         x           s         g       f    n               f            w   \n",
       "\n",
       "  gill-size gill-color stalk-shape   ...   stalk-surface-below-ring  \\\n",
       "0         n          k           e   ...                          s   \n",
       "1         b          k           e   ...                          s   \n",
       "2         b          n           e   ...                          s   \n",
       "3         n          n           e   ...                          s   \n",
       "4         b          k           t   ...                          s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop \"class\" column\n",
    "df = df.drop('class', axis=1)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap-shape_b</th>\n",
       "      <th>cap-shape_c</th>\n",
       "      <th>cap-shape_f</th>\n",
       "      <th>cap-shape_k</th>\n",
       "      <th>cap-shape_s</th>\n",
       "      <th>cap-shape_x</th>\n",
       "      <th>cap-surface_f</th>\n",
       "      <th>cap-surface_g</th>\n",
       "      <th>cap-surface_s</th>\n",
       "      <th>cap-surface_y</th>\n",
       "      <th>...</th>\n",
       "      <th>population_s</th>\n",
       "      <th>population_v</th>\n",
       "      <th>population_y</th>\n",
       "      <th>habitat_d</th>\n",
       "      <th>habitat_g</th>\n",
       "      <th>habitat_l</th>\n",
       "      <th>habitat_m</th>\n",
       "      <th>habitat_p</th>\n",
       "      <th>habitat_u</th>\n",
       "      <th>habitat_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            1            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
       "0            1              0              0              1              0   \n",
       "1            1              0              0              1              0   \n",
       "2            0              0              0              1              0   \n",
       "3            1              0              0              0              1   \n",
       "4            1              0              0              1              0   \n",
       "\n",
       "     ...      population_s  population_v  population_y  habitat_d  habitat_g  \\\n",
       "0    ...                 1             0             0          0          0   \n",
       "1    ...                 0             0             0          0          1   \n",
       "2    ...                 0             0             0          0          0   \n",
       "3    ...                 1             0             0          0          0   \n",
       "4    ...                 0             0             0          0          1   \n",
       "\n",
       "   habitat_l  habitat_m  habitat_p  habitat_u  habitat_w  \n",
       "0          0          0          0          1          0  \n",
       "1          0          0          0          0          0  \n",
       "2          0          1          0          0          0  \n",
       "3          0          0          0          1          0  \n",
       "4          0          0          0          0          0  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one-hot encoding for input data\n",
    "for col in df.columns:\n",
    "    df = pd.get_dummies(df, columns=[ col ])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8124, 117)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 cap-shape_b\n",
      "1 cap-shape_c\n",
      "2 cap-shape_f\n",
      "3 cap-shape_k\n",
      "4 cap-shape_s\n",
      "5 cap-shape_x\n",
      "6 cap-surface_f\n",
      "7 cap-surface_g\n",
      "8 cap-surface_s\n",
      "9 cap-surface_y\n",
      "10 cap-color_b\n",
      "11 cap-color_c\n",
      "12 cap-color_e\n",
      "13 cap-color_g\n",
      "14 cap-color_n\n",
      "15 cap-color_p\n",
      "16 cap-color_r\n",
      "17 cap-color_u\n",
      "18 cap-color_w\n",
      "19 cap-color_y\n",
      "20 bruises_f\n",
      "21 bruises_t\n",
      "22 odor_a\n",
      "23 odor_c\n",
      "24 odor_f\n",
      "25 odor_l\n",
      "26 odor_m\n",
      "27 odor_n\n",
      "28 odor_p\n",
      "29 odor_s\n",
      "30 odor_y\n",
      "31 gill-attachment_a\n",
      "32 gill-attachment_f\n",
      "33 gill-spacing_c\n",
      "34 gill-spacing_w\n",
      "35 gill-size_b\n",
      "36 gill-size_n\n",
      "37 gill-color_b\n",
      "38 gill-color_e\n",
      "39 gill-color_g\n",
      "40 gill-color_h\n",
      "41 gill-color_k\n",
      "42 gill-color_n\n",
      "43 gill-color_o\n",
      "44 gill-color_p\n",
      "45 gill-color_r\n",
      "46 gill-color_u\n",
      "47 gill-color_w\n",
      "48 gill-color_y\n",
      "49 stalk-shape_e\n",
      "50 stalk-shape_t\n",
      "51 stalk-root_?\n",
      "52 stalk-root_b\n",
      "53 stalk-root_c\n",
      "54 stalk-root_e\n",
      "55 stalk-root_r\n",
      "56 stalk-surface-above-ring_f\n",
      "57 stalk-surface-above-ring_k\n",
      "58 stalk-surface-above-ring_s\n",
      "59 stalk-surface-above-ring_y\n",
      "60 stalk-surface-below-ring_f\n",
      "61 stalk-surface-below-ring_k\n",
      "62 stalk-surface-below-ring_s\n",
      "63 stalk-surface-below-ring_y\n",
      "64 stalk-color-above-ring_b\n",
      "65 stalk-color-above-ring_c\n",
      "66 stalk-color-above-ring_e\n",
      "67 stalk-color-above-ring_g\n",
      "68 stalk-color-above-ring_n\n",
      "69 stalk-color-above-ring_o\n",
      "70 stalk-color-above-ring_p\n",
      "71 stalk-color-above-ring_w\n",
      "72 stalk-color-above-ring_y\n",
      "73 stalk-color-below-ring_b\n",
      "74 stalk-color-below-ring_c\n",
      "75 stalk-color-below-ring_e\n",
      "76 stalk-color-below-ring_g\n",
      "77 stalk-color-below-ring_n\n",
      "78 stalk-color-below-ring_o\n",
      "79 stalk-color-below-ring_p\n",
      "80 stalk-color-below-ring_w\n",
      "81 stalk-color-below-ring_y\n",
      "82 veil-type_p\n",
      "83 veil-color_n\n",
      "84 veil-color_o\n",
      "85 veil-color_w\n",
      "86 veil-color_y\n",
      "87 ring-number_n\n",
      "88 ring-number_o\n",
      "89 ring-number_t\n",
      "90 ring-type_e\n",
      "91 ring-type_f\n",
      "92 ring-type_l\n",
      "93 ring-type_n\n",
      "94 ring-type_p\n",
      "95 spore-print-color_b\n",
      "96 spore-print-color_h\n",
      "97 spore-print-color_k\n",
      "98 spore-print-color_n\n",
      "99 spore-print-color_o\n",
      "100 spore-print-color_r\n",
      "101 spore-print-color_u\n",
      "102 spore-print-color_w\n",
      "103 spore-print-color_y\n",
      "104 population_a\n",
      "105 population_c\n",
      "106 population_n\n",
      "107 population_s\n",
      "108 population_v\n",
      "109 population_y\n",
      "110 habitat_d\n",
      "111 habitat_g\n",
      "112 habitat_l\n",
      "113 habitat_m\n",
      "114 habitat_p\n",
      "115 habitat_u\n",
      "116 habitat_w\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df.columns)):\n",
    "    print(i , df.columns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv again in order to make label data\n",
    "df_class = pd.read_csv(dataset, usecols=['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "\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>e</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   e  p\n",
       "0  0  1\n",
       "1  1  0\n",
       "2  1  0\n",
       "3  0  1\n",
       "4  1  0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one-hot encoding for label data\n",
    "df_label = pd.get_dummies(df_class['class'])\n",
    "\n",
    "df_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   e\n",
       "0  0\n",
       "1  1\n",
       "2  1\n",
       "3  0\n",
       "4  1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop \"p\" category column \n",
    "df_label = df_label.drop('p', axis=1)\n",
    "df_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = df.iloc[:]\n",
    "y_data = df_label.iloc[:]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# train data : test data = 80 : 20\n",
    "x_train, x_test, y_train, y_test = \\\n",
    "        train_test_split(x_data, y_data, random_state=42, test_size=.2)\n",
    "\n",
    "x_train = np.asarray(x_train)\n",
    "x_test = np.asarray(x_test)\n",
    "y_train = np.asarray(y_train)\n",
    "y_test = np.asarray(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df.iloc[:,22:31]\n",
    "y = df_label.iloc[:]\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,y)\n",
    "lm.coef_  # coefficients\n",
    "lm.intercept_ # intercepter\n",
    "yhat = lm.predict(X) # prediction\n",
    "r2 = lm.score(X,y) # R2\n",
    "rmse = np.sqrt(((y-yhat)**2).mean())\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (4, 4)\n",
    "# plt.plot(X, y, 'bo')\n",
    "# plt.plot(X, yhat, 'r-', linewidth=.5)\n",
    "plt.scatter(y, yhat, color=['red', 'green'])\n",
    "plt.xlabel(\"label\")\n",
    "plt.ylabel(\"predict\")\n",
    "plt.title('%s vs. edibile: %.2f' % (X.columns[0], r2))\n",
    "plt.savefig('mlr-plot-1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = x_data\n",
    "y = y_data\n",
    "lm = LinearRegression()\n",
    "lm.fit(X, y)\n",
    "lm.coef_  # coefficients\n",
    "lm.intercept_ # intercepter\n",
    "yhat = lm.predict(X) # prediction\n",
    "r2 = lm.score(X,y) # R2\n",
    "rmse = np.sqrt(((y-yhat)**2).mean())\n",
    "\n",
    "plt.scatter(yhat, y, color=['red', 'green'])\n",
    "plt.xlabel(\"label\")\n",
    "plt.ylabel(\"predict\")\n",
    "plt.title('All features vs. edibile : %.2f' % r2)\n",
    "plt.savefig('mlr-plot-2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  0  cost:  3.6487134\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  1000  cost:  0.025972364\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  2000  cost:  0.015582061\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  3000  cost:  0.01040024\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  4000  cost:  0.007998717\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  5000  cost:  0.006907519\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  6000  cost:  0.0064182547\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  7000  cost:  0.006203941\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  8000  cost:  0.0061137704\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  9000  cost:  0.006076669\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  10000  cost:  0.006061022\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = tf.placeholder(tf.float32, shape=[None, 117]) # X\n",
    "Y = tf.placeholder(tf.float32, shape=[None, 1])   # Y\n",
    "\n",
    "w_init = tf.contrib.layers.variance_scaling_initializer(factor=1., mode='FAN_AVG', uniform=True)\n",
    "b_init = tf.zeros_initializer()\n",
    "\n",
    "W = tf.get_variable(initializer=tf.random_normal(shape=[117, 1]), name='weight_lireg')\n",
    "b = tf.get_variable(shape=[1], initializer=b_init, name='bias_lireg')\n",
    "Y_ = tf.add(tf.matmul(X, W), b)\n",
    "\n",
    "cost = tf.sqrt(tf.reduce_mean(tf.square(Y_ - Y)))  # RMSE\n",
    "# train = tf.train.GradientDescentOptimizer(learning_rate=1e-3).minimize(cost)\n",
    "train = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(cost)\n",
    "\n",
    "# preds = tf.cast(1. if Y_ < Y else 0., dtype=tf.float32)\n",
    "# acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(preds, 1), tf.argmax(Y, 1)), tf.float32))\n",
    "\n",
    "# summary\n",
    "tf.summary.histogram(\"Weights\", W)\n",
    "tf.summary.histogram(\"Biases\", b)\n",
    "tf.summary.scalar(\"loss\", cost)\n",
    "\n",
    "merged = tf.summary.merge_all()\n",
    "writer = tf.summary.FileWriter('./model/')\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    writer.add_graph(sess.graph)\n",
    "    \n",
    "    # Training\n",
    "    for step in range(10001):\n",
    "        cost_val, _ = sess.run([cost, train],\n",
    "                               feed_dict={\n",
    "                                   X: x_train,\n",
    "                                   Y: y_train,\n",
    "                               })\n",
    "\n",
    "        if step % 1000 == 0:\n",
    "            summary = sess.run(merged, feed_dict={X: x_train, Y: y_train})\n",
    "            writer.add_summary(summary, global_step=step)\n",
    "            print('step: ', step, ' cost: ', cost_val)\n",
    "            \n",
    "            # predicts, accs = sess.run([preds, acc], feed_dict={X: x_test, Y: y_test})\n",
    "            # print('acc', accs)\n",
    "            # print('Y/pred: ', y_test[:5], predicts[:5])\n",
    "\n",
    "            fig, ax = plt.subplots()\n",
    "            ax.scatter(y_test, sess.run(Y_, feed_dict={X: x_test}), color=['red','green'])\n",
    "            # ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=3)\n",
    "            ax.set_xlabel('Measured')\n",
    "            ax.set_ylabel('Predicted')\n",
    "            plt.savefig('./predict-%d' % step)\n",
    "            plt.show()\n",
    "        \n",
    "    # tf.equal(tf.argmax(y_test, 1), tf.argmax(sess.run(Y_, feed_dict={X: x_test}), 1))\n",
    "\n",
    "            "
   ]
  }
 ],
 "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
}