notebook/shelterAnimalOutcomes-AdaBoost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shelter Animal Outcomes 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn import cross_validation\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from time import time\n",
    "from operator import itemgetter\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../Shelter_train.csv')\n",
    "df_test = pd.read_csv('../Shelter_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df_train.ix[:, :-1]\n",
    "y = df_train.ix[:, -1]\n",
    "df_test = df_test.drop('ID', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.56608423, -1.565736  , -1.56166871])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = AdaBoostClassifier()\n",
    "cross_validation.cross_val_score(clf, X, y, scoring=\"log_loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = {\"clf__learning_rate\" : [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
    "         \"clf__algorithm\" : [\"SAMME\", \"SAMME.R\"],\n",
    "         \"clf__n_estimators\" : [10, 30, 100, 300, 1000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def report(grid_scores, n_top=3):\n",
    "    top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]\n",
    "    for i, score in enumerate(top_scores):\n",
    "        print(\"Model with rank: {0}\".format(i + 1))\n",
    "        print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
    "              score.mean_validation_score,\n",
    "              np.std(score.cv_validation_scores)))\n",
    "        print(\"Parameters: {0}\".format(score.parameters))\n",
    "        print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 1276.17 seconds for 60 candidate parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: -1.045 (std: 0.005)\n",
      "Parameters: {'clf__learning_rate': 0.01, 'clf__algorithm': 'SAMME.R', 'clf__n_estimators': 10}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: -1.050 (std: 0.004)\n",
      "Parameters: {'clf__learning_rate': 0.03, 'clf__algorithm': 'SAMME.R', 'clf__n_estimators': 10}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: -1.051 (std: 0.004)\n",
      "Parameters: {'clf__learning_rate': 0.01, 'clf__algorithm': 'SAMME.R', 'clf__n_estimators': 30}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adoption</th>\n",
       "      <th>Died</th>\n",
       "      <th>Euthanasia</th>\n",
       "      <th>Return_to_owner</th>\n",
       "      <th>Transfer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.048811</td>\n",
       "      <td>0.022419</td>\n",
       "      <td>0.126710</td>\n",
       "      <td>0.103214</td>\n",
       "      <td>0.698846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.546901</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.034359</td>\n",
       "      <td>0.218970</td>\n",
       "      <td>0.197280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.546901</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.034359</td>\n",
       "      <td>0.218970</td>\n",
       "      <td>0.197280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.048811</td>\n",
       "      <td>0.022419</td>\n",
       "      <td>0.126710</td>\n",
       "      <td>0.103214</td>\n",
       "      <td>0.698846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.546901</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.034359</td>\n",
       "      <td>0.218970</td>\n",
       "      <td>0.197280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Adoption      Died  Euthanasia  Return_to_owner  Transfer\n",
       "1  0.048811  0.022419    0.126710         0.103214  0.698846\n",
       "2  0.546901  0.002489    0.034359         0.218970  0.197280\n",
       "3  0.546901  0.002489    0.034359         0.218970  0.197280\n",
       "4  0.048811  0.022419    0.126710         0.103214  0.698846\n",
       "5  0.546901  0.002489    0.034359         0.218970  0.197280"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "        ('featureSelection', RFECV(estimator=AdaBoostClassifier(n_estimators=50), scoring='log_loss')),\n",
    "        ('clf', AdaBoostClassifier(n_estimators=1000))\n",
    "        ])\n",
    "grid_search = GridSearchCV(pipeline, params, n_jobs=-1, scoring='log_loss')\n",
    "start = time()\n",
    "grid_search.fit(X, y)\n",
    "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n",
    "      % (time() - start, len(grid_search.grid_scores_)))\n",
    "report(grid_search.grid_scores_)\n",
    "predictions = grid_search.predict_proba(df_test)\n",
    "output = pd.DataFrame(predictions, columns=['Adoption', 'Died', 'Euthanasia', 'Return_to_owner', 'Transfer'])\n",
    "output.index.names = ['ID']\n",
    "output.index += 1\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output.to_csv('../submission-AdaBoostClassifier.3.0.csv', index_label = 'ID')"
   ]
  }
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