iPython Notebook file - Boston

boston.ipynb

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   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "import pandas as pn\n",
    "import sklearn.preprocessing\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "boston = sklearn.datasets.load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = boston.data\n",
    "data = sklearn.preprocessing.scale(data)\n",
    "target = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "kfold = KFold(len(data), n_folds=5, shuffle=True, random_state=42)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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    {
     "data": {
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       "1.135678391959799"
      ]
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     "execution_count": 5,
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   "source": [
    "scores = []\n",
    "p_values = []\n",
    "\n",
    "p_arr = np.linspace(1, 10, 200)\n",
    "for p in p_arr:\n",
    "    kn_regressor = KNeighborsRegressor(metric='minkowski', p=p, weights='distance')\n",
    "    score = cross_val_score(kn_regressor, data, y=target, scoring='mean_squared_error', cv=kfold)\n",
    "    \n",
    "    scores.append(score.max())\n",
    "    p_values.append(p)\n",
    "    \n",
    "opt_p = p_values[np.array(scores).argmax()]\n",
    "opt_p"
   ]
  }
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