Galayda2018Problem17/base.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sasha/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(214, 11)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>RI</th>\n",
       "      <th>NA2O</th>\n",
       "      <th>MGO</th>\n",
       "      <th>AL2O3</th>\n",
       "      <th>SIO2</th>\n",
       "      <th>K2O</th>\n",
       "      <th>CAO</th>\n",
       "      <th>BAO</th>\n",
       "      <th>FE2O3</th>\n",
       "      <th>TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   ID       RI   NA2O   MGO  AL2O3   SIO2   K2O   CAO  BAO  FE2O3  TYPE\n",
       "0   1  1.52101  13.64  4.49   1.10  71.78  0.06  8.75  0.0    0.0     1\n",
       "1   2  1.51761  13.89  3.60   1.36  72.73  0.48  7.83  0.0    0.0     1\n",
       "2   3  1.51618  13.53  3.55   1.54  72.99  0.39  7.78  0.0    0.0     1\n",
       "3   4  1.51766  13.21  3.69   1.29  72.61  0.57  8.22  0.0    0.0     1\n",
       "4   5  1.51742  13.27  3.62   1.24  73.08  0.55  8.07  0.0    0.0     1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv ('data_glass.txt', sep=',', names = ['ID','RI','NA2O','MGO','AL2O3','SIO2','K2O','CAO','BAO','FE2O3','TYPE'])\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_col = [col for col in data.columns if col != 'TYPE' and col != 'ID']\n",
    "X = data[X_col]\n",
    "y = data['TYPE']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8684840579814874, 13]\n"
     ]
    }
   ],
   "source": [
    "n_n = [int(n) for n in range(1,51)]\n",
    "Y = []\n",
    "scores = []\n",
    "for n in n_n:\n",
    "    neigh = KNeighborsClassifier(n_neighbors=n)\n",
    "    neigh.fit(X_train, y_train)\n",
    "    pred = neigh.predict_proba(X_test)\n",
    "    t = log_loss(y_test, pred)\n",
    "    Y.append(t)\n",
    "    scores.append([t, n])\n",
    "print (sorted(scores)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.plot(n_n, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.1975418778602676, 1]\n"
     ]
    }
   ],
   "source": [
    "n_n = [int(n) for n in range(1,51)]\n",
    "Y = []\n",
    "scores = []\n",
    "for n in n_n:\n",
    "    clf = DecisionTreeClassifier(max_depth=n)\n",
    "    clf.fit(X_train, y_train)\n",
    "    pred = clf.predict_proba(X_test)\n",
    "    t = log_loss(y_test, pred)\n",
    "    Y.append(t)\n",
    "    scores.append([t, n])\n",
    "print (sorted(scores)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.plot(n_n, Y)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}