13_svm/separating_mushrooms.ipynb

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   "source": [
    "# Separating Mushrooms\n",
    "\n",
    "The [mushrooms dataset](http://archive.ics.uci.edu/ml/datasets/Mushroom) includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like _leaflets three, let it be_ for Poisonous Oak and Ivy.\n",
    "\n",
    "General information about the dataset can be found on the aforementioned website. In particular, we'd be interested in\n",
    "- the [training set](http://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data), and\n",
    "- the [dataset description](http://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.names), or data dictionary.\n",
    "\n",
    "Let's download the dataset into a pandas dataframe."
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data\"\n",
    "data = data = pd.read_csv(url, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8124 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  0  1  2  3  4  5  6  7  8  9  ... 13 14 15 16 17 18 19 20 21 22\n",
       "0  p  x  s  n  t  p  f  c  n  k ...  s  w  w  p  w  o  p  k  s  u\n",
       "1  e  x  s  y  t  a  f  c  b  k ...  s  w  w  p  w  o  p  n  n  g\n",
       "2  e  b  s  w  t  l  f  c  b  n ...  s  w  w  p  w  o  p  n  n  m\n",
       "3  p  x  y  w  t  p  f  c  n  n ...  s  w  w  p  w  o  p  k  s  u\n",
       "4  e  x  s  g  f  n  f  w  b  k ...  s  w  w  p  w  o  e  n  a  g\n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print len(data), \"rows\"\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously you'll need the [data dictionary](http://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.names) to understand what all these columns and letters mean. \n",
    "\n",
    "We'd like to predict the first column (column # 0), which contains an `e` for mushrooms that are definitely edible, and a `p` for definitely poisonous ones, or ones of unknown edibility and not recommended. This will be our **_y_** target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    4208\n",
       "p    3916\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data[0]\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll try to predict the ebibility using SVMs. Note that SVMs require numerical features, not strings, so we need to use such tools as `patsy`'s `dmatrix` and `dmatrices`, or `sklearn`'s `CountVectorizer` or `TfidfVectorizer` again. Please refer the earlier notebooks for details on how to use these. Also note that your features are scaled properly, using `MinMaxScaler` or `StandardScaler`, since SVMs use the notion of distance. However, if you converted your data into a binary feature matrix, that is not necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from patsy import dmatrix, dmatrices\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.cross_validation import cross_val_score, train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "1. Convert your dataset, excluding the first column (!), into a numerical matrix that you could feed into your model\n",
    "1. Make sure you scale your features properly, if necessary, using `MinMaxScaler` or `StandardScaler`\n",
    "1. Fit your model, `LinearSVC` or `SVC`, and compute cross-validated accuracy (and optionally also AUC score)\n",
    "1. Try different options for your numerical representation and see how that impacts your model\n",
    "1. Try different options for model (inverse-regularization parameter `C=`, kernels `kernel=`, etc.)\n",
    "1. Compare your results with another classifier you feel comfortable with (e.g., Logistic Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# your code here"
   ]
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