notebooks/03 Regression logistique Iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression logistique \n",
    "\n",
    "## sur le dataset  Iris\n",
    "\n",
    "## avec scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "df = pd.read_csv('../data/classification/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "clf = LogisticRegression()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(df['class'])\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yhat = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8.79681649e-01,   1.20307538e-01,   1.08131372e-05],\n",
       "       [  7.99706325e-01,   2.00263292e-01,   3.03825365e-05],\n",
       "       [  8.53796795e-01,   1.46177302e-01,   2.59031285e-05],\n",
       "       [  8.25383127e-01,   1.74558937e-01,   5.79356669e-05],\n",
       "       [  8.97323628e-01,   1.02665167e-01,   1.12050036e-05],\n",
       "       [  9.26986574e-01,   7.30004562e-02,   1.29693872e-05],\n",
       "       [  8.95064974e-01,   1.04895775e-01,   3.92506205e-05],\n",
       "       [  8.61839691e-01,   1.38141399e-01,   1.89095833e-05],\n",
       "       [  8.03156719e-01,   1.96758495e-01,   8.47861140e-05],\n",
       "       [  7.95421554e-01,   2.04552763e-01,   2.56832240e-05],\n",
       "       [  8.92083069e-01,   1.07910759e-01,   6.17176870e-06],\n",
       "       [  8.63364991e-01,   1.36600589e-01,   3.44201798e-05],\n",
       "       [  7.88177618e-01,   2.11794929e-01,   2.74526810e-05],\n",
       "       [  8.35079702e-01,   1.64888155e-01,   3.21426418e-05],\n",
       "       [  9.28349898e-01,   7.16491356e-02,   9.66254924e-07],\n",
       "       [  9.64535656e-01,   3.54620850e-02,   2.25877936e-06],\n",
       "       [  9.40906153e-01,   5.90890027e-02,   4.84421830e-06],\n",
       "       [  8.91740161e-01,   1.08245661e-01,   1.41772124e-05],\n",
       "       [  8.96525617e-01,   1.03467608e-01,   6.77567332e-06],\n",
       "       [  9.23615524e-01,   7.63726510e-02,   1.18248373e-05],\n",
       "       [  8.30668332e-01,   1.69316458e-01,   1.52093733e-05],\n",
       "       [  9.21914602e-01,   7.80675598e-02,   1.78384021e-05],\n",
       "       [  9.26584671e-01,   7.34068679e-02,   8.46162713e-06],\n",
       "       [  8.67785629e-01,   1.32146178e-01,   6.81931916e-05],\n",
       "       [  8.41271506e-01,   1.58655904e-01,   7.25903122e-05],\n",
       "       [  7.77263282e-01,   2.22695181e-01,   4.15365716e-05],\n",
       "       [  8.81389224e-01,   1.18568969e-01,   4.18075826e-05],\n",
       "       [  8.69974782e-01,   1.30013638e-01,   1.15794893e-05],\n",
       "       [  8.60034106e-01,   1.39955486e-01,   1.04082979e-05],\n",
       "       [  8.32052869e-01,   1.67892968e-01,   5.41625519e-05],\n",
       "       [  8.07811588e-01,   1.92136477e-01,   5.19350231e-05],\n",
       "       [  8.72544939e-01,   1.27438925e-01,   1.61360155e-05],\n",
       "       [  9.33948477e-01,   6.60477336e-02,   3.78900866e-06],\n",
       "       [  9.46250501e-01,   5.37475145e-02,   1.98493064e-06],\n",
       "       [  7.95421554e-01,   2.04552763e-01,   2.56832240e-05],\n",
       "       [  8.47610513e-01,   1.52377535e-01,   1.19520539e-05],\n",
       "       [  8.70019435e-01,   1.29976367e-01,   4.19728170e-06],\n",
       "       [  7.95421554e-01,   2.04552763e-01,   2.56832240e-05],\n",
       "       [  8.31024910e-01,   1.68917216e-01,   5.78737851e-05],\n",
       "       [  8.57737250e-01,   1.42246900e-01,   1.58501104e-05],\n",
       "       [  9.00222082e-01,   9.97646975e-02,   1.32206853e-05],\n",
       "       [  6.90741687e-01,   3.09094698e-01,   1.63615590e-04],\n",
       "       [  8.66068303e-01,   1.33887708e-01,   4.39884356e-05],\n",
       "       [  9.16308833e-01,   8.36288777e-02,   6.22895883e-05],\n",
       "       [  9.15519114e-01,   8.44392129e-02,   4.16734713e-05],\n",
       "       [  8.20309627e-01,   1.79642381e-01,   4.79919885e-05],\n",
       "       [  9.09855663e-01,   9.01327650e-02,   1.15724381e-05],\n",
       "       [  8.51214451e-01,   1.48746052e-01,   3.94971199e-05],\n",
       "       [  8.95519736e-01,   1.04472911e-01,   7.35323849e-06],\n",
       "       [  8.51563342e-01,   1.48419676e-01,   1.69821772e-05],\n",
       "       [  2.98900777e-02,   8.60393138e-01,   1.09716785e-01],\n",
       "       [  3.74487166e-02,   7.05572459e-01,   2.56978825e-01],\n",
       "       [  1.17957675e-02,   7.48252356e-01,   2.39951876e-01],\n",
       "       [  1.32920493e-02,   6.51770445e-01,   3.34937506e-01],\n",
       "       [  1.09868088e-02,   6.98832091e-01,   2.90181101e-01],\n",
       "       [  1.07669519e-02,   5.83013186e-01,   4.06219862e-01],\n",
       "       [  2.15200540e-02,   5.37732882e-01,   4.40747064e-01],\n",
       "       [  1.08418544e-01,   7.68766189e-01,   1.22815267e-01],\n",
       "       [  1.77270021e-02,   8.27562690e-01,   1.54710308e-01],\n",
       "       [  3.30493839e-02,   5.28708770e-01,   4.38241846e-01],\n",
       "       [  2.93117962e-02,   7.72717609e-01,   1.97970595e-01],\n",
       "       [  4.09569813e-02,   6.19765980e-01,   3.39277039e-01],\n",
       "       [  1.95378252e-02,   8.79697992e-01,   1.00764183e-01],\n",
       "       [  8.73285529e-03,   5.96503817e-01,   3.94763328e-01],\n",
       "       [  1.67434866e-01,   7.12756209e-01,   1.19808925e-01],\n",
       "       [  4.75535678e-02,   8.43626581e-01,   1.08819852e-01],\n",
       "       [  1.22530319e-02,   4.23869480e-01,   5.63877488e-01],\n",
       "       [  3.84753639e-02,   8.50175432e-01,   1.11349204e-01],\n",
       "       [  3.09968794e-03,   5.96264678e-01,   4.00635634e-01],\n",
       "       [  3.59781700e-02,   8.08752206e-01,   1.55269624e-01],\n",
       "       [  6.20745751e-03,   2.73106189e-01,   7.20686354e-01],\n",
       "       [  5.81151228e-02,   8.19701311e-01,   1.22183566e-01],\n",
       "       [  1.95840574e-03,   5.33800891e-01,   4.64240703e-01],\n",
       "       [  8.77628703e-03,   7.04654010e-01,   2.86569703e-01],\n",
       "       [  3.69274341e-02,   8.38990091e-01,   1.24082475e-01],\n",
       "       [  3.61807169e-02,   8.28744840e-01,   1.35074443e-01],\n",
       "       [  8.14489700e-03,   7.77156946e-01,   2.14698157e-01],\n",
       "       [  4.64006697e-03,   5.23164549e-01,   4.72195384e-01],\n",
       "       [  1.33500103e-02,   5.63205976e-01,   4.23444014e-01],\n",
       "       [  1.28473017e-01,   8.31361691e-01,   4.01652917e-02],\n",
       "       [  3.60902230e-02,   8.03217466e-01,   1.60692311e-01],\n",
       "       [  5.05096042e-02,   8.46149445e-01,   1.03340951e-01],\n",
       "       [  5.69724571e-02,   8.11250984e-01,   1.31776559e-01],\n",
       "       [  1.22453086e-03,   3.99201919e-01,   5.99573550e-01],\n",
       "       [  1.03123407e-02,   3.65034695e-01,   6.24652965e-01],\n",
       "       [  4.17476538e-02,   4.77844283e-01,   4.80408063e-01],\n",
       "       [  1.90525287e-02,   7.45629538e-01,   2.35317933e-01],\n",
       "       [  7.05352060e-03,   7.56932682e-01,   2.36013798e-01],\n",
       "       [  5.57541864e-02,   6.67410837e-01,   2.76834977e-01],\n",
       "       [  2.10790319e-02,   6.62362244e-01,   3.16558724e-01],\n",
       "       [  8.98003281e-03,   5.99716389e-01,   3.91303578e-01],\n",
       "       [  1.52196906e-02,   6.32329159e-01,   3.52451150e-01],\n",
       "       [  3.47695685e-02,   7.98625645e-01,   1.66604786e-01],\n",
       "       [  9.15416570e-02,   7.95877151e-01,   1.12581192e-01],\n",
       "       [  1.98418694e-02,   6.40871800e-01,   3.39286330e-01],\n",
       "       [  4.81040905e-02,   7.31039981e-01,   2.20855929e-01],\n",
       "       [  3.44565240e-02,   6.77463657e-01,   2.88079819e-01],\n",
       "       [  3.38822929e-02,   7.96899915e-01,   1.69217792e-01],\n",
       "       [  2.54574647e-01,   6.90791330e-01,   5.46340233e-02],\n",
       "       [  3.63488963e-02,   7.04234211e-01,   2.59416893e-01],\n",
       "       [  1.86036022e-04,   1.48760823e-01,   8.51053141e-01],\n",
       "       [  8.09069371e-04,   2.94422745e-01,   7.04768186e-01],\n",
       "       [  2.78126551e-04,   3.30535386e-01,   6.69186488e-01],\n",
       "       [  4.56288643e-04,   3.38732197e-01,   6.60811514e-01],\n",
       "       [  2.51393977e-04,   2.57092194e-01,   7.42656412e-01],\n",
       "       [  6.03186905e-05,   3.82744333e-01,   6.17195349e-01],\n",
       "       [  2.04838186e-03,   2.81103453e-01,   7.16848165e-01],\n",
       "       [  1.23247784e-04,   4.24393655e-01,   5.75483097e-01],\n",
       "       [  1.59929758e-04,   4.23195996e-01,   5.76644074e-01],\n",
       "       [  3.56390886e-04,   1.52542892e-01,   8.47100717e-01],\n",
       "       [  2.99635433e-03,   2.78024684e-01,   7.18978962e-01],\n",
       "       [  6.45242833e-04,   3.55681241e-01,   6.43673516e-01],\n",
       "       [  6.81029987e-04,   2.98859721e-01,   7.00459249e-01],\n",
       "       [  6.28418142e-04,   2.96807692e-01,   7.02563890e-01],\n",
       "       [  6.10997845e-04,   1.74593604e-01,   8.24795398e-01],\n",
       "       [  1.09757190e-03,   1.73257823e-01,   8.25644605e-01],\n",
       "       [  7.99254871e-04,   3.48929847e-01,   6.50270898e-01],\n",
       "       [  1.93443479e-04,   2.38473708e-01,   7.61332849e-01],\n",
       "       [  1.30064976e-05,   4.20137191e-01,   5.79849802e-01],\n",
       "       [  6.81548718e-04,   4.69975854e-01,   5.29342597e-01],\n",
       "       [  5.04477452e-04,   2.25292722e-01,   7.74202801e-01],\n",
       "       [  1.33913767e-03,   2.30143290e-01,   7.68517573e-01],\n",
       "       [  3.82097113e-05,   4.28006955e-01,   5.71954836e-01],\n",
       "       [  2.05299242e-03,   4.00421888e-01,   5.97525119e-01],\n",
       "       [  6.77847072e-04,   2.37204010e-01,   7.62118143e-01],\n",
       "       [  4.56383243e-04,   3.97527741e-01,   6.02015876e-01],\n",
       "       [  3.19858866e-03,   3.83866887e-01,   6.12934525e-01],\n",
       "       [  3.42364119e-03,   3.27541103e-01,   6.69035256e-01],\n",
       "       [  3.00544917e-04,   2.98288662e-01,   7.01410793e-01],\n",
       "       [  6.78376797e-04,   5.10705151e-01,   4.88616472e-01],\n",
       "       [  1.61719140e-04,   4.27941843e-01,   5.71896438e-01],\n",
       "       [  6.44775841e-04,   3.44845359e-01,   6.54509865e-01],\n",
       "       [  2.75279882e-04,   2.78027400e-01,   7.21697320e-01],\n",
       "       [  2.07731418e-03,   4.90652652e-01,   5.07270034e-01],\n",
       "       [  3.54683506e-04,   4.42580814e-01,   5.57064503e-01],\n",
       "       [  1.82017584e-04,   3.42008155e-01,   6.57809828e-01],\n",
       "       [  6.30908753e-04,   1.28602511e-01,   8.70766580e-01],\n",
       "       [  9.21940559e-04,   3.20888055e-01,   6.78190005e-01],\n",
       "       [  4.29311663e-03,   3.18426266e-01,   6.77280618e-01],\n",
       "       [  1.16680587e-03,   3.00989509e-01,   6.97843685e-01],\n",
       "       [  4.46290865e-04,   2.02461924e-01,   7.97091785e-01],\n",
       "       [  2.15227432e-03,   2.48822456e-01,   7.49025270e-01],\n",
       "       [  8.09069371e-04,   2.94422745e-01,   7.04768186e-01],\n",
       "       [  2.91162367e-04,   2.24919706e-01,   7.74789132e-01],\n",
       "       [  4.50477099e-04,   1.53984748e-01,   8.45564775e-01],\n",
       "       [  1.15724730e-03,   2.33616548e-01,   7.65226205e-01],\n",
       "       [  9.19025197e-04,   3.79220387e-01,   6.19860588e-01],\n",
       "       [  1.45811816e-03,   2.98379693e-01,   7.00162189e-01],\n",
       "       [  1.09779827e-03,   1.31785617e-01,   8.67116585e-01],\n",
       "       [  1.68397530e-03,   2.81057800e-01,   7.17258224e-01]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_proba = clf.predict_proba(X)\n",
    "yhat_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.41498833,  1.46129739, -2.26214118, -1.0290951 ],\n",
       "       [ 0.41663969, -1.60083319,  0.57765763, -1.38553843],\n",
       "       [-1.70752515, -1.53426834,  2.47097168,  2.55538211]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "       1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0,  0],\n",
       "       [ 0, 45,  5],\n",
       "       [ 0,  1, 49]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y,yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95999999999999996"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "multiclass format is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-fb211fc48cfc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mroc_auc_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myhat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mroc_auc_score\u001b[0;34m(y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[1;32m    275\u001b[0m     return _average_binary_score(\n\u001b[1;32m    276\u001b[0m         \u001b[0m_binary_roc_auc_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m         sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/base.py\u001b[0m in \u001b[0;36m_average_binary_score\u001b[0;34m(binary_metric, y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0my_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multilabel-indicator\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0} format is not supported\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: multiclass format is not supported"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}