sinking_ship.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style()\n",
    "pd.options.display.notebook_repr_html = False\n",
    "pd.options.display.max_rows = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv', header=0)\n",
    "test = pd.read_csv('test.csv', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predictions_export(predicted_labels, file_name):\n",
    "    \"\"\"Prediction's export to .csv. Kaggle compliant format.\"\"\"\n",
    "    result_df = pd.read_csv('test.csv', usecols=['PassengerId'], header=0)\n",
    "    result_df['Survived'] = predicted_labels.astype(int)\n",
    "    result_df.to_csv(file_name, index=False)\n",
    "    print 'Result Set Shape: ', shape(result_df), '\\n'\n",
    "    print 'Sample: ', result_df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings to numbers conversions. Filling up empty records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Gender: 'Sex' to numbers mapping\n",
    "train['Gender'] = train['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "test['Gender'] = test['Sex'].map( {'female': 0, 'male': 1} ).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Port_no: 'Embarked' to number mapping. Two 'Embarked' values in train set are missing. No visible rule to conclude 'Port_no'\n",
    "train['Port_no'] = train['Embarked'].dropna().map({'S':0, 'C':1, 'Q':2}).astype(int)\n",
    "test['Port_no'] = test['Embarked'].dropna().map({'S':0, 'C':1, 'Q':2}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  29.6991176471\n",
      "Median:  28.0\n",
      "         Age            \n",
      "Pclass     1     2     3\n",
      "Gender                  \n",
      "0       35.0  28.0  21.5\n",
      "1       40.0  30.0  25.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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U0dFh9AXUeGzTAJjANiJ2ouhHMRzo0Y6GXqVmzjA288qFDzX9nnnG5gGmsI2InSj6MTC9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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa64dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age_fill: Age and missing age data into new column\n",
    "print 'Mean: ', train['Age'].mean()\n",
    "print 'Median: ', train['Age'].median()\n",
    "train.Age.hist(figsize=(6,4), bins=range(0,81,5));\n",
    "\n",
    "train['AgeFill'] = train['Age']\n",
    "test['AgeFill'] = test['Age']\n",
    "\n",
    "age_map_df = train[['Gender', 'Pclass', 'Age']].groupby(['Gender', 'Pclass']).median().unstack()\n",
    "print age_map_df\n",
    "\n",
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        train.loc[(train.Age.isnull()) & (train.Gender == i) & (train.Pclass == j+1), 'AgeFill'] = age_map_df.ix[i,j]\n",
    "        test.loc[(test.Age.isnull()) & (test.Gender == i) & (test.Pclass == j+1), 'AgeFill'] = age_map_df.ix[i,j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# AgeIsNull: Whether age was missing or not (1=missing)\n",
    "train['AgeIsNull'] = pd.isnull(train.Age).astype(int)\n",
    "test['AgeIsNull'] = pd.isnull(test.Age).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DPDw8OXMm2/m8qKio2I1cqkphLiIiUkO6du3Gli2fA7Br13f4+19bLevVCXAiInJZyv7t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\nmIuIiJjc/wfE5qh2eq7pngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf85908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Title column\n",
    "train.loc[train['Name'].str.contains('Mr. ')==True, 'Title'] = 'MR' \n",
    "train.loc[train['Name'].str.contains('Ms. ')==True, 'Title'] = 'MS' \n",
    "train.loc[train['Name'].str.contains('Mrs. ')==True, 'Title'] = 'MRS' \n",
    "train.loc[train['Name'].str.contains('Miss. ')==True, 'Title'] = 'MISS' \n",
    "train.loc[train['Name'].str.contains('Master. ')==True, 'Title'] = 'MASTER' \n",
    "train.loc[train['Name'].str.contains('Rev. ')==True, 'Title'] = 'REV' \n",
    "train.loc[train['Name'].str.contains('Dr. ')==True, 'Title'] = 'DR' \n",
    "train.loc[train['Name'].str.contains('Mme. ')==True, 'Title'] = 'MME' \n",
    "train.loc[train['Name'].str.contains('Major. ')==True, 'Title'] = 'Major' \n",
    "train.loc[train['Name'].str.contains('Sir. ')==True, 'Title'] = 'SIR' \n",
    "train.loc[train['Name'].str.contains('Col. ')==True, 'Title'] = 'COL' \n",
    "train.loc[train['Name'].str.contains('Capt. ')==True, 'Title'] = 'CAPT' \n",
    "\n",
    "test.loc[test['Name'].str.contains('Mr. ')==True, 'Title'] = 'MR' \n",
    "test.loc[test['Name'].str.contains('Ms. ')==True, 'Title'] = 'MS' \n",
    "test.loc[test['Name'].str.contains('Mrs. ')==True, 'Title'] = 'MRS' \n",
    "test.loc[test['Name'].str.contains('Miss. ')==True, 'Title'] = 'MISS' \n",
    "test.loc[test['Name'].str.contains('Master. ')==True, 'Title'] = 'MASTER' \n",
    "test.loc[test['Name'].str.contains('Rev. ')==True, 'Title'] = 'REV' \n",
    "test.loc[test['Name'].str.contains('Dr. ')==True, 'Title'] = 'DR' \n",
    "test.loc[test['Name'].str.contains('Mme. ')==True, 'Title'] = 'MME' \n",
    "test.loc[test['Name'].str.contains('Major. ')==True, 'Title'] = 'Major' \n",
    "test.loc[test['Name'].str.contains('Sir. ')==True, 'Title'] = 'SIR' \n",
    "test.loc[test['Name'].str.contains('Col. ')==True, 'Title'] = 'COL' \n",
    "test.loc[test['Name'].str.contains('Capt. ')==True, 'Title'] = 'CAPT' \n",
    "\n",
    "train.Title.fillna('xxx', inplace=True) \n",
    "test.Title.fillna('xxx', inplace=True) \n",
    "\n",
    "# Title to number mapping\n",
    "train['Title_num'] = train['Title'].map( {'xxx': -1, 'MR': 0, 'MS': 1, 'MRS': 2, 'MISS': 3, 'MASTER': 4, 'REV': 5, 'DR': 6, 'MME': 7, 'Major': 8, 'SIR': 9, 'COL': 10, 'CAPT': 11} ).astype(int)\n",
    "test['Title_num'] = test['Title'].map( {'xxx': -1, 'MR': 0, 'MS': 1, 'MRS': 2, 'MISS': 3, 'MASTER': 4, 'REV': 5, 'DR': 6, 'MME': 7, 'Major': 8, 'SIR': 9, 'COL': 10, 'CAPT': 11} ).astype(int)\n",
    "\n",
    "sns.countplot(x='Title', data=train[['Title', 'Survived']], hue='Survived')\n",
    "#sns.countplot(x='Title', data=test[['Title']], color='g')\n",
    "plt.title('Survival distribution by title');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Filling up missing 'Fare' value\n",
    "\n",
    "#test.ix[test.isnull().any(axis=1)]\n",
    "for i in range(1, 4):\n",
    "    test.loc[test.Fare.isnull() & test.Pclass==i,'Fare'] = test.groupby('Pclass').mean()['Fare'][i].round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VmpqqOXPmaODAgZVGoIaHh6ugoECFhYWVfvoTFhamkydP+ro0AAACgiWj2Z966ikdPXpU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\ngYEwB4B6oLxXfm4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QGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac029e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1)\n",
    "sns.boxplot(x='Survived', y='Fare', data=train[['Fare', 'Survived', 'Gender']],ax=ax1)\n",
    "plt.title('Fare price dist. for survived and not survived.')\n",
    "ax1.set_ylim([0,300]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# FamilySize\n",
    "train['FamilySize'] = train['SibSp'] + train['Parch']\n",
    "test['FamilySize'] = test['SibSp'] + test['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf857b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age*Class\n",
    "train['Age*Class'] = train.AgeFill * train.Pclass\n",
    "test['Age*Class'] = test.AgeFill * test.Pclass\n",
    "\n",
    "train['Age*Class'].hist(bins=25, figsize=(6,4))\n",
    "test['Age*Class'].hist(bins=25, figsize=(6,4));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-numeric columns removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train.dtypes[train.dtypes.map(lambda x: x=='object')]\n",
    "test.dtypes[test.dtypes.map(lambda x: x=='object')]\n",
    "\n",
    "train.dtypes[train.dtypes.map(lambda x: x=='object')].index\n",
    "test.dtypes[test.dtypes.map(lambda x: x=='object')].index\n",
    "\n",
    "# count NaN values\n",
    "train.isnull().sum()\n",
    "\n",
    "train.drop([u'PassengerId', u'Age', u'Name', u'Sex', u'Ticket', u'Cabin', u'Embarked', u'Title'], axis=1, inplace=True)\n",
    "test.drop([u'PassengerId', u'Age', u'Name', u'Sex', u'Ticket', u'Cabin', u'Embarked', u'Title'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train - number of NaNs:  2\n"
     ]
    }
   ],
   "source": [
    "# Remove remaining recoords with NaN values \n",
    "print 'Train - number of NaNs: ',  len(train) - len(train.dropna())\n",
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set - number of NaNs:  0\n"
     ]
    }
   ],
   "source": [
    "# Are there any NaN values in Test set?\n",
    "print 'Test set - number of NaNs: ',  len(test) - len(test.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Scikit-learn needs arrays\n",
    "train_data = train.values\n",
    "test_data = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oiFuXiYiIiKwZExJEREREREREJDrWkLASer0e5eXlRt6LBmVlKqPuwcXFBVIp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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfec438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization\n",
    "\n",
    "# specifies the parameters of our graphs\n",
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "alpha=alpha_scatterplot = 0.1 \n",
    "alpha_bar_chart = 0.55\n",
    "\n",
    "# lets us plot many diffrent shaped graphs together \n",
    "ax1 = plt.subplot2grid((2,3),(0,0))\n",
    "# plots a bar graph of those who surived vs those who did not. \n",
    "train.Survived.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "# this nicely sets the margins in matplotlib to deal with a recent bug 1.3.1\n",
    "ax1.set_xlim(-1, 2)\n",
    "plt.xticks(rotation=0)\n",
    "# puts a title on our graph\n",
    "plt.title(\"Distribution of Survival, (1 = Survived)\") \n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "sns.boxplot(x='Survived', y='AgeFill', data=train[['Survived', 'AgeFill']])\n",
    "#plt.scatter(train.Survived, train.AgeFill, alpha=alpha_scatterplot)\n",
    "# sets the y axis lablea\n",
    "plt.ylabel(\"Age\")\n",
    "# formats the grid line style of our graphs \n",
    "plt.grid(b=True, which='major', axis='y') \n",
    "plt.title(\"Survial by Age, (1 = Survived)\")\n",
    "\n",
    "ax3 = plt.subplot2grid((2,3),(0,2))\n",
    "train.Pclass.value_counts(sort=False).plot(kind=\"bar\", alpha=alpha_bar_chart)\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Class Distribution\")\n",
    "\n",
    "ax4 = plt.subplot2grid((2,3),(1,0), colspan=2)\n",
    "# plots a kernel desnsity estimate of the subset of the 1st class passanges's age\n",
    "train.AgeFill[train.Pclass == 1].plot(kind='kde', ax=ax4) \n",
    "train.AgeFill[train.Pclass == 2].plot(kind='kde', ax=ax4)\n",
    "train.AgeFill[train.Pclass == 3].plot(kind='kde', ax=ax4)\n",
    "# plots an axis lable\n",
    "ax4.set_xlabel(\"Age\") \n",
    "plt.title(\"Age Distribution within classes\")\n",
    "# sets our legend for our graph.\n",
    "plt.legend(('1st Class', '2nd Class','3rd Class'),loc='best') \n",
    "\n",
    "ax5 = plt.subplot2grid((2,3),(1,2))\n",
    "train.Port_no.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "ax5.set_xlim(-1, len(train.Port_no.value_counts()))\n",
    "plt.xticks(rotation=0)\n",
    "# specifies the parameters of our graphs\n",
    "plt.title(\"Passengers per boarding location\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import cross_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Change the column's order\n",
    "train_nona = train[['Pclass',\n",
    " 'SibSp',\n",
    " 'Parch',\n",
    " 'Fare',\n",
    " 'Gender',\n",
    " 'Port_no',\n",
    " 'AgeFill',\n",
    " 'AgeIsNull',\n",
    " 'Title_num',\n",
    " 'FamilySize',\n",
    " 'Age*Class', \n",
    " 'Survived']].dropna().copy()\n",
    "train_data = train_nona.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMV (Support Vector Machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Using Grid Search to find best classificator's parameters\n",
    "\n",
    "from sklearn import grid_search\n",
    "svr = svm.SVC(cache_size=2000) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 41min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "########## not normalized data ########\n",
    "\n",
    "param_grid = [\n",
    "  {'kernel': ['linear'], 'C': [0.01, 0.1, 1, 10, 100, 1000, 10000]},\n",
    "  {'kernel': ['rbf'], 'C': [0.1, 0.01, 1, 10, 100, 1000, 10000], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1, 10]}\n",
    " ]\n",
    "\n",
    "\n",
    "## uwaga, cv zmienic na 15. zajmuje to ok 4,5 h.\n",
    "clf_svm = grid_search.GridSearchCV(svr, param_grid, cv=3)\n",
    "\n",
    "clf_svm.fit(train_data[:,:-1], np.reshape(train_data[:,-1:], (1, len(train_data)))[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:  0.814398200225 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}\n",
      "Best estimator:  SVC(C=1000, cache_size=2000, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.0001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "# Results:\n",
    "print 'Best score: ', clf_svm.best_score_, ', using metric: ', clf_svm.scorer_.func_name\n",
    "print 'Best parameters: ', clf_svm.best_params_\n",
    "print 'Best estimator: ', clf_svm.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf_svm_df = pd.concat([pd.DataFrame(pd.DataFrame(clf_svm.grid_scores_).parameters.tolist())\n",
    "           ,pd.DataFrame(clf_svm.grid_scores_).drop(['parameters', 'cv_validation_scores'], axis=1)],axis=1)\n",
    "\n",
    "clf_svm_df['NORM']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:  0.823397075366 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'kernel': 'rbf', 'C': 10000, 'gamma': 0.0001}\n",
      "Best estimator:  SVC(C=10000, cache_size=2000, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.0001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "           C    gamma  kernel  mean_validation_score   NORM\n",
      "0       0.01      NaN  linear               0.791901  False\n",
      "1       0.10      NaN  linear               0.788526  False\n",
      "2       1.00      NaN  linear               0.785152  False\n",
      "3      10.00      NaN  linear               0.784027  False\n",
      "4     100.00      NaN  linear               0.797525  False\n",
      "5    1000.00      NaN  linear               0.799775  False\n",
      "6   10000.00      NaN  linear               0.797525  False\n",
      "7       0.10   0.0001     rbf               0.670416  False\n",
      "8       0.10   0.0010     rbf               0.676040  False\n",
      "9       0.10   0.0100     rbf               0.619798  False\n",
      "..       ...      ...     ...                    ...    ...\n",
      "88   1000.00   0.0100     rbf               0.732283   True\n",
      "89   1000.00   0.1000     rbf               0.719910   True\n",
      "90   1000.00   1.0000     rbf               0.647919   True\n",
      "91   1000.00  10.0000     rbf               0.637795   True\n",
      "92  10000.00   0.0001     rbf               0.823397   True\n",
      "93  10000.00   0.0010     rbf               0.782902   True\n",
      "94  10000.00   0.0100     rbf               0.723285   True\n",
      "95  10000.00   0.1000     rbf               0.698538   True\n",
      "96  10000.00   1.0000     rbf               0.642295   True\n",
      "97  10000.00  10.0000     rbf               0.636670   True\n",
      "\n",
      "[98 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "########## normalized data ############\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# L2 normalization. Normalize each feature so remember to normalize along axis=0. SVM based on normalized data is many times faster!\n",
    "train_data_norm = preprocessing.normalize(train_data, norm='l2', axis=0).astype(float).round(2)\n",
    "#X_test_norm = preprocessing.normalize(X_test, norm='l2', axis=0).astype(float).round(2)\n",
    "\n",
    "param_grid = [\n",
    "  {'kernel': ['linear'], 'C': [0.01, 0.1, 1, 10, 100, 1000, 10000]},\n",
    "  {'kernel': ['rbf'], 'C': [0.01, 0.1, 1, 10, 100, 1000, 10000], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1, 10]}\n",
    " ]\n",
    "\n",
    "clf_svm_norm = grid_search.GridSearchCV(svr, param_grid, cv=15)\n",
    "\n",
    "clf_svm_norm.fit(train_data[:,:-1], np.reshape(train_data[:,-1:], (1, len(train_data)))[0])\n",
    "\n",
    "# Results:\n",
    "print 'Best score: ', clf_svm_norm.best_score_, ', using metric: ', clf_svm_norm.scorer_.func_name\n",
    "print 'Best parameters: ', clf_svm_norm.best_params_\n",
    "print 'Best estimator: ', clf_svm_norm.best_estimator_\n",
    "\n",
    "\n",
    "clf_svm_df_norm = pd.concat([pd.DataFrame(pd.DataFrame(clf_svm_norm.grid_scores_).parameters.tolist())\n",
    "           ,pd.DataFrame(clf_svm_norm.grid_scores_).drop(['parameters', 'cv_validation_scores'],axis=1)\n",
    "          ],axis=1)\n",
    "\n",
    "clf_svm_df_norm['NORM']=True\n",
    "\n",
    "clf_svm_df = clf_svm_df.append(clf_svm_df_norm, ignore_index=True)\n",
    "\n",
    "print clf_svm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2efNm6+DBnxQff0bp6emqWbOWKlcO1rPPsswDAACAp3O3UFDeRUQMKlbgjowc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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc422390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi,ax1=plt.subplots(figsize=(15,6))\n",
    "sns.boxplot(x='C', y='mean_validation_score', hue=('NORM'), data=clf_svm_df)\n",
    "plt.title('NORMED vs NOT NORMED input data',size=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAmRugDAAAAABNj9k4AAAAA5VpCwgrt2PGd0+unp6dLkjw9PZ3epkWLVoqI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\nmj4AAAAASGI0fQAAAACQxP4HdUeiM9ot2u4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1b4208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi,ax1=plt.subplots(figsize=(15,6))\n",
    "sns.boxplot(x='C', y='mean_validation_score', hue=('kernel'), data=clf_svm_df.ix[((clf_svm_df['NORM']==True))])\n",
    "#sns.boxplot(x='C', y='mean_validation_score', hue=('kernel'), data=clf_svm_df.ix[clf_svm_df.NORM==False])\n",
    "plt.title('Kernel comparison for normalized input data',size=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wQh8AAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0AQAAAICB\nEfoAAAAAwMAIfQAAAABgYIQ+AAAAADAwQh8AAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0\nAQAAAICBmV25c5vNphkzZujAgQOyWCyaPXu2/Pz87Ov37NmjefPmSZLq1Kmj+fPnq0qVKg7bAAAA\nAACc59LQl5CQoNzcXK1Zs0bJycmKiYlRbGysff20adO0aNEi+fn56cMPP9Tx48f1+++/O2wDAAAA\nAHCeS4d37t69WyEhIZKkgIAApaSk2NcdOnRIPj4+WrZsmSIjI5Wenq4mTZo4bAMAAAAAKB6Xhr7M\nzEx5e3vbX5vNZlmtVknS2bNnlZSUpMjISC1btkzff/+9fvjhB4dtAAAAAADF49LhnV5eXsrKyrK/\ntlqtcnO7nDN9fHzUqFEjNW3aVJIUEhKilJQUeXt7F9kGAAAAAFA8Lg19wcHB2rZtm3r06KGkpCS1\nbNnSvs7Pz0/Z2dk6cuSI/Pz8tHv3bj3++ONq1KhRkW2K4uvrfd1tAAAAAKAyMtlsNpurdn7l7J2S\nFBMTo7179yonJ0dhYWH68ccf9eqrr0qSgoKC9MILLxTaJr83EAAAAABQPC4NfQAAAACA8sXNcgAA\nAABgYIQ+AAAAADAwQh8AAAAAGBihDwAAAAAMjNBXwdlsNk2fPl0RERGKiorSkSNHCqxPTEzU448/\nroiICH3wwQcO2/z5558aNGiQhgwZopdffrnAftLS0tS9e3fl5uaWzYnBpUrzuskXExOjtWvXltk5\noOzdyHWTLzk5WZGRkWVZLiqI6103kpSTk6OBAwfq0KFD5VAhKqKiPjMcfc6g8rjy+nD0/VVy7jMI\nhL4KLyEhQbm5uVqzZo0mTJigmJgY+7pLly5p7ty5Wr58uVasWKG1a9cqLS2tyDYxMTF69tlntXLl\nSlmtViUkJEiSvvvuO0VHR+vMmTPlco4ofaV53aSlpWnEiBHatm1beZ0OysiNXDeS9M9//lMvvvii\nLl68WF6loxw5um4kKSUlRUOGDOGLGOyK+sxw9DmDyuPq66Oo76/5rvcZhMsIfRXc7t27FRISIkkK\nCAhQSkqKfd3BgwfVuHFjeXl5qUqVKmrXrp127tx5TZu9e/dKkvbu3at27dpJkh544AHt2LFDkuTu\n7q7ly5erZs2aZXlqcKHSvG6ys7M1duxY9e3bt+xPBGWqONdN27Zt9dNPP0mSGjdurCVLlpRLzSh/\njq4bSbp48aJiY2PVrFmz8igPFVBRnxmOPmdQeVx9fRT1/TXf9T6DcBmhr4LLzMyUt7e3/bXZbJbV\nai10naenpzIyMpSVlVVgubu7u/Ly8nTlIxmrV6+ujIwMSdJ9992nmjVrikc2GkdpXTdWq1UNGzbU\nXXfdVXbFo9wU57q58jMkNDRU7u7uZVssKgxH140kBQUFqV69evwfA7uiPjMcfc6g8rj6+ijq+2u+\n630G4TJCXwXn5eWlrKws+2ur1So3Nzf7uszMTPu6rKws1axZs9A27u7u9nb529aoUaPAsUwmk6tO\nA2WstK6bK68ZGF9xr5urP0NQOfHZgdLC5wwKc73vr3wGOYd3pIILDg7W119/LUlKSkpSy5Yt7eua\nN2+uf//73zp//rxyc3O1a9cuBQYGKigoqNA2d955p32YxDfffKO2bdsWOBa/whpHaV43qDyKc938\n9NNPCgwMLNCez5DKydF1Azhy9WeGM58zqHyu9/2VzyDnmMu7ADgWGhqq7du3KyIiQtLlm1k3bdqk\nnJwchYWFacqUKRo2bJhsNpsef/xx1a1bt9A2kjR58mS99NJLunjxopo3b64ePXoUOBY9fcZRmtcN\nKo/iXDdhYWGqW7dugfZ8hlRO17tu8nF94Gr510RxPmdQ+RT1/XXy5MkaP34831+cZLLx0ywAAAAA\nGBbDOwEAAADAwAh9AAAAAGBghD4AAAAAMDBCHwAAAAAYGKEPAAAAAAyM0AcAAAAABkboAwAAAAAD\nI/QBAAAAgIGZy7sAAADK02uvvaYtW7aoVq1a8vX1VZcuXXT48GH98MMPSk9PV61atbR48WLdcsst\n6tixozp37qxdu3bJ19dXgwYN0ooVK3Ty5EnNnTtX7dq1U2RkpO688059//33ys3N1dSpU7VixQod\nPHhQUVFRGjp0qE6ePKmpU6cqMzNTp06dUq9evTRhwoTyfisAAAZFTx8AoNLatm2bfv75Z3322Wd6\n5513tG/fPuXl5enQoUNau3atPv/8czVq1EgbN26UJJ0+fVpdunTR5s2bJUkJCQlatWqVxowZo/ff\nf9++X5PJpI0bN6pPnz6aPXu2lixZopUrVyo2NlaS9Omnn6p3795as2aNNmzYoNWrV+vcuXNl/wYA\nACoFevoAAJXW9u3b1bNnT7m7u6tGjRp66KGHZDabNXnyZMXHx+vQoUNKSkpSo0aN7G1CQkIkSbfd\ndpvatm0rSWrQoIHS09Pt2zzwwAP2bQICAmSxWNSgQQNlZGRIkoYNG6Yff/xRS5cu1e+//65Lly4p\nJydHPj4+ZXXqAIBKhJ4+AECl5e7uLqvVWmDZ2bNnNWzYMNlsNvXo0UNdu3aVzWazrzebzYX+/UpV\nqlQpcIyrzZ07VytXrlTDhg31j3/8Qz4+PgWOAQBAaSL0AQAqrfvvv19btmzRxYsXlZmZqa+++krZ\n2dlq3769BgwYoGbNmmn79u3XBMOS+v777xUdHa1u3brp+PHjOnXqlPLy8kr1GAAA5GN4JwCg0urU\nqZN+/vln9e/fXzVr1lTdunV122236ZtvvlHfvn1VpUoV+fv76+jRo5Iu36t3Pc5s89RTT+m5555T\njRo1VKdOHbVu3VpHjx6Vn59fic8JAICrmWyMJwEAVFJJSUk6fPiw+vXrp0uXLmnAgAGKiYlRy5Yt\ny7s0AABKDaEPAFBppaena8KECUpNTZXNZlP//v01dOjQ8i4LAIBSRegDAAAAAANjIhcAAAAAMDBC\nHwAAAAAYGKEPAAAAAAyM0AcAAAAABkboAwAAAAADI/QBAAAAgIH9P2Lb62xlkBZ0AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0fb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi,ax1=plt.subplots(figsize=(15,6))\n",
    "sns.boxplot(x='gamma', y='mean_validation_score', data=clf_svm_df.ix[((clf_svm_df['NORM']==True)\n",
    "                                                                      & (clf_svm_df['kernel']=='rbf')\n",
    "                                                                      & (clf_svm_df['C']==100)\n",
    "                                                                     )])\n",
    "#sns.boxplot(x='C', y='mean_validation_score', hue=('kernel'), data=clf_svm_df.ix[clf_svm_df.NORM==False])\n",
    "plt.title('kernel=rbf, norm=True, C=100',size=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          C   gamma  kernel  mean_validation_score   NORM\n",
       "92  10000.0  0.0001     rbf               0.823397   True\n",
       "86   1000.0  0.0001     rbf               0.822272   True\n",
       "37   1000.0  0.0001     rbf               0.814398  False\n",
       "53    100.0     NaN  linear               0.805399   True\n",
       "43  10000.0  0.0001     rbf               0.804274  False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best results\n",
    "\n",
    "clf_svm_df.sort_values('mean_validation_score', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        C    gamma kernel  mean_validation_score  NORM\n",
       "80  100.0   0.0001    rbf               0.803150  True\n",
       "81  100.0   0.0010    rbf               0.790776  True\n",
       "82  100.0   0.0100    rbf               0.750281  True\n",
       "83  100.0   0.1000    rbf               0.733408  True\n",
       "84  100.0   1.0000    rbf               0.654668  True\n",
       "85  100.0  10.0000    rbf               0.633296  True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# params for best score C=10, kernel=rbf, NORM=True\n",
    "\n",
    "clf_svm_df.ix[((clf_svm_df['C']==100) & (clf_svm_df['NORM']==True) & (clf_svm_df['kernel']=='rbf'))].sort_values('mean_validation_score', ascending=False)#.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             C    gamma  kernel mean_validation_score                \n",
      "NORM                                            False      True  diff\n",
      "43    10000.00   0.0001     rbf              0.804274  0.823397 -0.02\n",
      "36     1000.00   0.0001     rbf              0.814398  0.822272 -0.01\n",
      "28      100.00      NaN  linear              0.797525  0.805399 -0.01\n",
      "29      100.00   0.0001     rbf              0.795276  0.803150 -0.01\n",
      "35     1000.00      NaN  linear              0.799775  0.799775  0.00\n",
      "0         0.01      NaN  linear              0.791901  0.798650 -0.01\n",
      "30      100.00   0.0010     rbf              0.787402  0.790776  0.00\n",
      "7         0.10      NaN  linear              0.788526  0.788526  0.00\n",
      "23       10.00   0.0010     rbf              0.787402  0.788526  0.00\n",
      "14        1.00      NaN  linear              0.785152  0.785152  0.00\n",
      "..         ...      ...     ...                   ...       ...   ...\n",
      "10        0.10   0.0100     rbf              0.619798  0.632171 -0.01\n",
      "13        0.10  10.0000     rbf              0.617548  0.626547 -0.01\n",
      "12        0.10   1.0000     rbf              0.617548  0.626547 -0.01\n",
      "11        0.10   0.1000     rbf              0.617548  0.624297 -0.01\n",
      "6         0.01  10.0000     rbf              0.617548  0.617548  0.00\n",
      "4         0.01   0.1000     rbf              0.617548  0.617548  0.00\n",
      "3         0.01   0.0100     rbf              0.617548  0.617548  0.00\n",
      "2         0.01   0.0010     rbf              0.617548  0.617548  0.00\n",
      "1         0.01   0.0001     rbf              0.617548  0.617548  0.00\n",
      "5         0.01   1.0000     rbf              0.617548  0.617548  0.00\n",
      "\n",
      "[49 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "norm_comp = clf_svm_df.set_index(['C', 'gamma', 'kernel', 'NORM']).unstack().reset_index()\n",
    "norm_comp[('mean_validation_score', 'diff')] =  ((norm_comp[('mean_validation_score', False)] - norm_comp[('mean_validation_score', True)])*1.0).round(2)\n",
    "print norm_comp.sort_values(('mean_validation_score', True), ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cv_folds = [3,5,10,15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  3\n",
      "Best score:  0.817772778403 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 32, 'criterion': 'gini', 'max_depth': 11, 'min_samples_leaf': 11}\n",
      "Best estimator:  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=11,\n",
      "            max_features=None, max_leaf_nodes=None, min_samples_leaf=11,\n",
      "            min_samples_split=32, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best')\n",
      "CV:  5\n",
      "Best score:  0.820022497188 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 42, 'criterion': 'entropy', 'max_depth': 11, 'min_samples_leaf': 1}\n",
      "Best estimator:  DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=11,\n",
      "            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
      "            min_samples_split=42, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best')\n",
      "CV:  10\n",
      "Best score:  0.820022497188 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 32, 'criterion': 'gini', 'max_depth': 51, 'min_samples_leaf': 1}\n",
      "Best estimator:  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=51,\n",
      "            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
      "            min_samples_split=32, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best')\n",
      "CV:  15\n",
      "Best score:  0.82114735658 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 62, 'criterion': 'gini', 'max_depth': 11, 'min_samples_leaf': 11}\n",
      "Best estimator:  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=11,\n",
      "            max_features=None, max_leaf_nodes=None, min_samples_leaf=11,\n",
      "            min_samples_split=62, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best')\n",
      "Wall time: 7min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import sklearn.tree as tree\n",
    "\n",
    "min_samples_split = range(2,110,10)\n",
    "min_samples_leaf = range(1,110,10)\n",
    "max_depth = range(1,110,10) + [None]\n",
    "\n",
    "parameters = {'criterion':('gini', 'entropy'), \n",
    "              'min_samples_split':min_samples_split, \n",
    "              'min_samples_leaf':min_samples_leaf,\n",
    "              'max_depth': max_depth \n",
    "             }\n",
    "\n",
    "for i in range(len(cv_folds)):\n",
    "    clf_dt = tree.DecisionTreeClassifier()\n",
    "    clf_dt = grid_search.GridSearchCV(clf_dt, parameters, cv=cv_folds[i])\n",
    "    clf_dt.fit(train_data[:,:-1], np.reshape(train_data[:,-1:], (1, len(train_data)))[0])\n",
    "\n",
    "    # Results:\n",
    "    print 'CV: ', cv_folds[i]\n",
    "    print 'Best score: ', clf_dt.best_score_, ', using metric: ', clf_dt.scorer_.func_name\n",
    "    print 'Best parameters: ', clf_dt.best_params_\n",
    "    print 'Best estimator: ', clf_dt.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          mean criterion  max_depth  min_samples_leaf  min_samples_split\n",
      "1227  0.821147      gini      101.0                11                 62\n",
      "1348  0.821147      gini        NaN                11                 62\n",
      "138   0.821147      gini       11.0                11                 62\n",
      "380   0.820022      gini       31.0                11                 62\n",
      "2679  0.820022   entropy      101.0                11                 62\n"
     ]
    },
    {
     "data": {
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96yf8MdX84UlXGIZx9iVWDEMJCQlduj0gFnSq6YuPj9enn36qwYMHa9++fbr88st18uTJ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\nRERERERBjE0fERERERFREPt/71utFaoXDnUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xacb4e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_dt_df = pd.concat([pd.DataFrame(clf_dt.grid_scores_, columns=['parameters', 'mean', 'cv_val_score']).drop(['parameters', 'cv_val_score'], axis=1),\n",
    "           pd.DataFrame(pd.DataFrame(clf_dt.grid_scores_, columns=['parameters', 'mean', 'cv_val_score']).parameters.tolist())],\n",
    "           axis=1\n",
    "         )\n",
    "\n",
    "print clf_dt_df.sort_values('mean', ascending=False)[:5]\n",
    "\n",
    "fig, ((axis1,axis2),(axis3,axis4)) = plt.subplots(2,2,figsize=(15,8))\n",
    "sns.boxplot(x='criterion', y='mean', data=clf_dt_df, ax=axis1)\n",
    "sns.boxplot(x='min_samples_leaf', y='mean', data=clf_dt_df, ax=axis2)\n",
    "sns.boxplot(x='min_samples_split', y='mean', data=clf_dt_df, ax=axis3)\n",
    "sns.boxplot(x='max_depth', y='mean', data=clf_dt_df, ax=axis4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NGyer1aqjR49Kkr788kulp6fLZrPpww8/tO93jpx7jClJ27ZtdfjwYf3444+SpC1btsjH\nx0dXX321Pv/8c8XFxem9996rEAWfRE8fLkF4eLief/55Pfjgg7JYLKpVq5YmTZqkffv2FbtMs2bN\nNHXqVA0ePFjTpk3TCy+8oGeffVZSwZfwzTffLHLW8nx+fn6aPn264uLi9MYbb8hms2nw4MFq0aJF\nkctLymL48OEaPXq07rnnHlWvXl116tSxDyQuTVI/d57Ro0frpptuUu/evcsUw+X68TBo0CANGzZM\n3bp1U5UqVdS4cWN16dJFYWFhGjt2rJYsWSIfHx81adJEP/30k8O2S4olOztbvXr10tmzZ9WvXz/d\ndtttOnz4sH2Z6Oho/f777/bLe/72t78pPj5evr6+GjVqlIYMGaIqVarIx8dH8fHxF5xNLI6j9b74\n4ou64oor9Mgjj6h79+6qUaOGatSooebNm+vgwYNq2bJlubZrRf0hB3gi8oQumIc8UXnyxO+//65H\nHnlEb7/9tmrVqnXJ6yzL5yCp2G165ZVXFrtNCi/fLU0MAQEB+vTTT/Xyyy+ratWqmjlz5gXLvvLK\nK4qNjVW3bt2Ul5enbt26qWvXrrLZbNq4caO6du2qoKAgVa9eXbGxsSW+nyeffFKjRo3Shx9+KB8f\nH91111269dZbtXnzZtWsWVOPPPKITp48qVtvvVWPPvposdvo/GNMcWrWrKkZM2Zo/Pjx9stop0+f\nLn9/f02ZMkVSwffZGCOLxaKIiAiNGTOmxPfgThbjKacrADd5//331aRJEzVt2lQ5OTnq16+fnn76\nafu19WXxzTff6Oeff77o3bQqotLcfQ4AvBF5onTIExcaNmyYRo4cqWrVqrk7lMvq8OHD6tq1q77/\n/nt3h6Lp06fr1KlTJV6iDSf39BljNH78eO3Zs0f+/v6Ki4tTWFiYffqyZcvst3Hu2bOn+vbtK5vN\nptGjR2vfvn3y8fHRhAkTigwABi6366+/Xi+88IJsNpvy8vLUqVOnciVySTp16pR69ep1mSMsql+/\nfkUGoEuyn2VasGBBqcbJlIczesDi4+O1efPmIusufC8jRoxQixYtLnubgCcgP1Ys5InSIU8UlZWV\npdatWzu14Nu8ebPi4+Mdbp/bbrtNw4cPd1rbl/vzzsjIUL9+/S5YrzFGVqtV8+fPv6ztVTZO7elb\ns2aNEhISFB8fr+3bt2vWrFmaOXOmfXrr1q21atUqBQYGqkuXLlq8eLE2b96sdevWKS4uTklJSZoz\nZ06RZQAAqOjIjwAAV3JqT9/WrVvtZ8KaNm2q5OTkItMbNWqktLQ0e0VvsVjUoUMH++DLw4cPe113\nOAAA5EcAgCs5tehLT09XSEjIn435+clms9lv59qgQQNFRUUpKChIkZGRslqtkiQfHx8NHz5ca9eu\n5TbrAACvQ34EALiSUx/ZYLValZGRYX99bkLbs2eP1q9fr4SEBCUkJCglJUWrV6+2z/viiy9q9erV\nGj16tLKyskpsJy+v/LfdBQDA1ciPAABXcmpPX0REhNatW6dOnTpp27ZtCg8Pt08LCQlR1apV5e/v\nL4vFotDQUJ0+fVqffvqpjh07pkceeUQBAQHy8fEp8qBHR06ePFvidACAd6hVK+TiM1UA5EcAwOVW\nUo506o1czr07mVRwB6adO3cqMzNT0dHRWrRokRYvXix/f3/VrVtXsbGxys3N1YgRI3TixAnl5eXp\n0UcfVfv27Uts5/jxM856CwAAD+ItRR/5EQBwubmt6HMVkhoAVA7eUvS5CvkRACqPknKkU8f0AQAA\nAADci6IPAAAAALwYRR8AAAAAeDGKPgAAAADwYhR9AAAAAODFKPoAAAAAwItR9AEAAACAF6PoAwAA\nAAAvRtEHAAAAAF6Mog8AAAAAvBhFHwAAAAB4MYo+AAAAAPBiFH0AAAAA4MUo+gAAAADAi1H0AQAA\nAIAXo+gDAAAAAC9G0QcAAAAAXoyiDwAAAAC8GEUfAAAAAHgxij4AAAAA8GIUfQAAAADgxSj6AAAA\nAMCLUfQBAAAAgBej6AMAAAAAL0bRBwAAAABejKIPAAAAALwYRR8AAAAAeDE/dwcAwPMtXDhXSUmb\nHE7LyEiXJAUHWx1Ob9Gipfr2Hei02ABULAsXztXnn3/mcJrNZrukdfv4OD6X3alTF45DAJzOWce3\ny3Fso6cPwCXJzs5Wdna2u8MAAABAMSzGGOOslRtjNH78eO3Zs0f+/v6Ki4tTWFiYffqyZcs0Z84c\n+fr6qmfPnurbt6/y8vI0cuRIHT58WLm5uXrsscd0xx13lNjO8eNnnPUWAFxETMwTkqQpU2a6ORJU\nBrVqhbg7hMuC/AgAuNxKypFOvbxz7dq1ysnJ0aJFi7R9+3bFx8dr5sw/fxi+9NJLWrVqlQIDA9Wl\nSxd17dpVa9asUY0aNfTSSy8pLS1N3bt3v2hSAwCgIiE/AgBcyalF39atW9WmTRtJUtOmTZWcnFxk\neqNGjZSWliaLxSJJslgs6ty5szp16iSp4NpXPz+GHQKuEBs7WqmpqWVeLjU1RdKfPX6lFRoaqjFj\nJpa5PcAbkB8BAK7k1IyRnp6ukJA/uxn9/Pxks9nsgxEbNGigqKgoBQUFKTIyUlartciyzzzzjGJi\nYpwZIoA//PbbQWVmZkp//MgstT+uED+RcqJMyxTeAAaojMiPnsGTbirjyTeAqOwqw35yOWPhhkye\nyalFn9VqVUZGhv31uQltz549Wr9+vRISEhQUFKShQ4dq9erV6tixo44cOaKnnnpK/fv31913333R\ndmrUCJKfn6/T3gdQGVgsloKCr5i7cF5WGemyWCxeMz4LKCvyo2eoWtXfLW06Ova5Opbi4sCFKvN+\n4kmxsM9eGqcWfREREVq3bp06deqkbdu2KTw83D4tJCREVatWlb+/vywWi0JDQ3X69GmlpKTowQcf\n1NixY9WyZctStXPy5FlnvQWg0ggKCtZZH1/59+nv9LZyFs1XUGAgN5lAmXlLwic/eobu3fuoe/c+\nLm/X0bHPHbFwDC6dyr6feFIs7LMlc9uNXCIjI5WYmKg+fQp2iPj4eK1YsUKZmZmKjo7Wvffeq/vu\nu0/+/v6qW7euevToocmTJ+v06dOaOXOmZsyYIYvFonfeeUf+/q4/swEAgDOQHwEAruTURza4ClU/\ncOliYp7Qiawsl/X01QwM5DEPKDNv6elzFfIjysqTxowBKBu39fQBqGAy0pWzaH7ZlsnOKvh/QGCZ\n2lFgGeYHAABAuVH0AZBU8AiF8kg9W3AzitCyFHGBgeVuDwDgPH37DqTXDfBCXN4J4JIUPp+PSzXh\nClzeWTbkRwCoPErKkRR9AC5q4cK5Skra5HBa4cPZQ0OvdDi9RYuWnDXGZUPRVzbkR1Rkrh5fyNjC\niolxqH9iTB8ApwkICHB3CAAAACgBPX0AgAqDnr6yIT8CQOVRUo503GcJAAAAAPAKXN4JeKiSxtFl\nZKRLkoKDrQ6nM44OAAAAhejpAyqg7OxsZWdnuzsMAAAAVACM6QMqIB6TgMqKMX1lQ34EgMqDMX0A\nAAAAUEnR0we40dNPP6K0tLQyL1f43Jninh9TkmrVqumNN2aXeTnAE9DTVzbkRwCoPHhOH+ChsrKy\nCgo4i6Vcy9vKes7GGGVlZZWrLQAAAFRMFH2AGwUHW5Xpa5Fv344uaS9/4WoFBwa7pC0AAAB4Bsb0\nAQAAAIAXo+gDAAAAAC/G5Z2Au2VkKn/h6rItk51T8P8A/zK3JS7vBAAAqFQo+gA3Cg0NLddyqWcL\nbsYSWtYCLjC43G0CAACgYuKRDUAFxMPZUVnxyIayIT8CQOXBw9kBAAAAoJKipw/wUAsXzlVS0iaH\n01JTUyRJoaFXOpzeokVL9e070GmxAe5CT1/ZkB8BoPLg4eyAlwkICHB3CAAAAKgg6OkDAFQY9PSV\nDfkRACoPxvQBAAAAQCVF0QcAAAAAXoyiDwAAAAC8GEUfAAAAAHgxij4AAAAA8GI8sgGAR4mNHa3U\n1FSH0zIy0pWdnV2u9QYEBCg42OpwWmhoqMaMmViu9QIAAHg6p/b0GWM0btw49enTRwMHDtRvv/1W\nZPqyZcvUs2dPRUdHa+HChUWmbd++XQMGDHBmeAA8UGpqqk6kpCglK++C/zLzbLIZleu/zDybw3We\nSEkptsgEnIX8CABwJaf29K1du1Y5OTlatGiRtm/frvj4eM2cOdM+/aWXXtKqVasUGBioLl26qGvX\nrgoJCdE777yjTz/9VMHBwc4MD4CHsgRXk7XvKJe0lb4wziXtAOciPwIAXMmpPX1bt25VmzZtJElN\nmzZVcnJykemNGjVSWlqa/XIti8UiSapXr55mzJjhzNAAAHAb8iMAwJWc2tOXnp6ukJA/nwzv5+cn\nm80mH5+CWrNBgwaKiopSUFCQIiMjZbUWjLeJjIzU4cOHnRkaPMzChXOVlLTJ4bSMjHRJKnY8VosW\nLdW370CnxQbXyshIl8nKdlkPnMlIU0Z+gEvaAgqRHwEAruTUos9qtSojI8P++tyEtmfPHq1fv14J\nCQkKCgrS0KFDtXr1anXs2LHM7dSoESQ/P9/LFjdcr2pVf/n4WBxOKzzTfe4PpPOXrVXL8TRUPIU9\nGq5uk30IrkR+BAC4klOLvoiICK1bt06dOnXStm3bFB4ebp8WEhKiqlWryt/fXxaLRaGhoTp9+nSR\n5Y0xpWrn5MmzlzVuuF737n3UvXsfh9NiYp6QJL36avGXNB0/fsYpccH1goKCdTYzq8zLmeyC44Al\nIKhcbbIPVQzeUpyTHwEAl1tJOdKpRV9kZKQSExPVp0/Bj/n4+HitWLFCmZmZio6O1r333qv77rtP\n/v7+qlu3rnr06FFkeXec8QfgXqGhoeVaLvVsbsHygWU8rAVeWe42gfIiPwIAXMliSnu60INxht67\nFfb0TZky8yJzojJjP6kcvKWnz1XIjwBQebitpw84V0kP3S5JamqKpD9/1JcFD90GAABAZUfRB5dJ\nTU1VSsrvKuuQK8sf9yBIz/y9TMtlM5QFAAAAoOiD62RkpKs81xJX8S9fe0Z/Pu4BAAAAqKyc+nB2\nAAAAAIB70dMHlwkOtiory/E1l7k5Un5e+dbr6+e4N9Ci4h/ojopp4cK5Skra5HDaxcZ+tmjRUn37\nDnRabAAAAJ6Kog8uU9Jt8TNs6crOzy7Xev39AhRc9cLizlq1/Lf/R8UTEBDg7hAAAAA8Eo9sAABU\nGDyyoWzIjwBQeZSUIxnTBwAAAABejMs7gfMUN26s8E6gxY0TZMwYAAAAPBE9fUApZWdnKzu7fOMO\nAQAAAHdhTB9QSoV3hZwyZaabIwEqL8b0lQ35EQAqj5JyJEUfKqXY2NFKTU0t0zKFjwQIDb2yzO2F\nhoZqzJiJZV4OQFEUfWVDfgSAyqOkHMmYPjco6VljjBtzjdTUVJ1IOS5ZfUu/kG/B+ZET2WUrFpWe\nX7b5AQAAgMuIos/DFI4Z46HiLmD1laV/mNObMfN/c3obAAAAQHEo+tygb9+BxfbWMW7MNTIy0qWs\nfNcUZOn5yshLd347AAAAgAPcvRMAAAAAvFil6eljHN2FKvM2CQ62KjMr0/HELJuUV477G/lZpEDH\n51G4XBfO4knfY0+KBQAA/KnSFH0lYRzdhbx9m4SGhhY7LSMvXdn5ZX8eX4BfgIIDHGyvgJLbA5zF\nk77HnhQLAACVDY9skGeNo/OUWDwlDgDl50nf48sVC49sKBse2QAAlQePbACACu5Sni1ZWHCVBc+W\nBADAe3hVT195fhRJznnotqfE4ilxALg0MTFPKDUlRdUCS3+p8NncDElSUJXgMrWVlpWq0CuvdNgr\n5+5jCj19ZUNPHwBUHpWmpy81NVWpKScUGhhUpuUCfP54QHfG2bK1l1X8/AWxHFf1wDKtUv5/3AfE\nlnG8TMudyio+jpSU47JWLVscvn/EkX22bHGkF3NvFACXrlpgqMZFvur0diasGVLstIJjW4pCA64o\n0zoDLFUK/pGeW6blUrNPl2l+AABwIa8q+iQpNDBIU+7q4ZK2Yr5YWuL06oFS7B0BLollTELxNx6x\nVpUGdbW4JI7/W1HhO44BXERowBV6td2zLmlryPrXXNIOAADerFRF38aNGzVlyhSdPn1axhgZY2Sx\nWPTll1/HicioAAAgAElEQVQ6Oz4AADwW+REAUBGUquibOHGihg8frgYNGshicU2PEQAAno78CACo\nCEpV9NWoUUPt27d3diyXLCMjXdlZWRe97PJySc06qwDZSoil5MsuL6dTWVKA0h3GkZXlussu0zOl\nPHNhHAC8Q+Fx1lWXXaZmpSnAUsbB0S5UUfIjAKByK1XR17x5c8XHx6tNmzYKCPhzjNqtt97qtMDK\ny2ZMiTdYcaTwBqZlPUtrq/g3PgWAMrPJKDUrrUzLGP1xnFUZj7Py7ONsRcqPAIDKq1RF3w8//CBJ\n+vHHH+1/s1gsmjt3rnOiKqewsLpuuZW4I8HBVlVVpktv5OITbHUYh58l06U3cgkIujAOAJemoIct\nu8Q7a14uaVmpCpDjY5cnHWc9QUXJjwCAyq1URd+8efOcHcdlUd5nwxU+uNjRM6kAAH/iOFtURcmP\nAIDKrVRF33fffad3331XZ8+elTFGNptN//vf/5SQkFDicsYYjR8/Xnv27JG/v7/i4uIUFhZmn75s\n2TLNmTNHvr6+6tmzp/r27XvRZcpr4cK5Skra5HBa4Rnowh8l52vRoqX69h14yTG4S3qm4zF9WTlS\nXn751unnKwX6O24roGyPSQRQCsHBVgUq2GXP6fMNLvvVAZXxOOsN+REA4P1KVfSNHj1aDz/8sJYu\nXaoBAwboq6++0g033HDR5dauXaucnBwtWrRI27dvV3x8vGbO/PMs70svvaRVq1YpMDBQXbp0Udeu\nXbVp06YSl3GGc8dhXE6nynEjl7N/PLc4qErZ2woNvvDvJV0WlWfSlW8r341m/KoEOLyMMyDIsy/F\nAuAezjrOupu350cAgHcoVdEXGBioqKgoHT58WFdccYUmTpyonj17XnS5rVu3qk2bNpKkpk2bKjk5\nucj0Ro0aKS0tzX4DFYvFctFlyqtv34EuPYtc3sIn54+z4dbgso17CQ123GZ5L8UCgLJy9XHWE3hD\nfgQAeL9SFX0BAQE6deqU6tevr+3bt6tVq1Y6e/bid8hMT09XSEjIn435+clms8nHx0eS1KBBA0VF\nRSkoKEiRkZGyWq0XXaaiYNwLAHg/8iMAoCIoVdF3//33KyYmRtOmTVOvXr20fPly3XjjjRddzmq1\nKiMjw/763OS0Z88erV+/XgkJCQoKCtLQoUP1+eefKyQkpNhlilOjRpD8/HxL81Y8no9PwVndWrVC\nLjIngMrEx8ei1KyUC+7eeTY3Q7n55btMu4pvgIKqXHhdeFpWqmqG1OQ4VArkRwBARVCqoq9z587q\n1KmTLBaLlixZov3796tRo0YXXS4iIkLr1q1Tp06dtG3bNoWHh9unhYSEqGrVqvL395fFYlFoaKjO\nnDmjiIgIJSQkOFymOCdPlu25fJ7MZiu44crx42fcHAkAT1K9eg378eFcPhmSbOVbp08VObxhS2jw\nlapevYZHHoc8rRAlPwIAPEVJObJURV9aWppefvllHTx4UK+//rrmzZun4cOHq1q1aiUuFxkZqcTE\nRPXp00eSFB8frxUrVigzM1PR0dG69957dd9998nf319169ZVjx495Ovrq6+//rrIMgBQ2TE+1zOR\nHwEAFYHFGHPhqePzPP3007r99tu1YMECffzxx5oxY4Z27dql2bNnuyLGi/LEs9HlxZg+ACiep/X0\nkR8BAJ7iknv6Dh06pN69e2vhwoXy9/dXTEyM7rnnnssWYGVTGZ9lBQDeiPwIAKgISlX0+fr66syZ\nM/ZbR+/fv5+7hTmJtz7LCgC8EfkRAFARlOryzq+++kqvvfaajhw5oubNm2vbtm2aNGmS2rVr54IQ\nL47LVwCgcvC0yzvJjwAAT1FSjizV6cgbb7xRHTp00NVXX60jR44oMjKSh8ICACo98iMAoCIo1eWd\nDz/8sBo2bKj27ds7Ox4AqBBKGpubkZEuSQoOtjqczthc70F+BABUBKUq+iRp0qRJzowDALxGdnbB\nw9KLK/rgXciPAABPV6oxfW+++aZq1qypli1bytfX1/732rVrOzW40mLMAgBPwqNXnMfTxvSRHwEA\nnuKSH9lw5swZzZ49WzVq1LD/zWKx6Msvv7z06ADAQ8XGjlZqamqZl7vYo1dKEhoayoPYKxDyIwCg\nIihV0ffFF1/o22+/VWBgoLPjAQCPkZqaqpSUFAUHh5ZpOV/fgkevZGVd9EKKIjIyyl5gwr3IjwCA\niqBURV9YWJjS0tJIagAqneDgUN3bd6pL2vpw4b9d0g4uH/IjAKAiKFXRZ7FY1KVLFzVo0EBVqlSx\n/33u3LlOCwwAAE9HfgQAVASlKvoee+wxZ8cBAECFQ34EAFQEpSr6WrRo4ew4AACocMiPAICKoNTP\n6QOAyiYjI11ZWdkuG2uXkZGq/PwAl7QFAAAqDx93BwAAAAAAcB56+gCgGMHBVvn6Brv07p2BgRaX\ntAUAACoPevoAAAAAwIvR0wcAJcjISHU4pi87O0N5ednlWqefX4ACAoIdthUYeGW51gkAAFAcij4A\nKEZoaGix0/LzC/4rDz8/ObyMMzDwyhLbBAAAKA+LMca4O4hLdfz4GXeHAABwgVq1QtwdQoVCfgSA\nyqOkHMmYPgAAAADwYhR9AAAAAODFKPoAAAAAwItR9AEAAACAF6PoAwAAAAAvRtEHAAAAAF6Mog8A\nAAAAvBhFHwAAAAB4MYo+AAAAAPBiFH0AAAAA4MX8nLlyY4zGjx+vPXv2yN/fX3FxcQoLC5MknThx\nQjExMbJYLDLGaPfu3Ro6dKh69OihESNG6NChQ7JarRo3bpzq1q3rzDABAHAp8iMAwJWcWvStXbtW\nOTk5WrRokbZv3674+HjNnDlTklSzZk3NmzdPkrRt2zZNnTpV9957r95//30FBwfrgw8+0L59+zRh\nwgS9++67zgwTAACXIj8CAFzJqUXf1q1b1aZNG0lS06ZNlZyc7HC+2NhYvfbaa7JYLPrll1/Utm1b\nSVL9+vW1d+9eZ4YIAIDLkR8BAK7k1KIvPT1dISEhfzbm5yebzSYfnz+HEiYkJCg8PFz16tWTJDVu\n3Fjr169Xhw4dtG3bNv3+++8yxshisRTbTo0aQfLz83XeGwEA4DIiPwIAXMmpRZ/ValVGRob99fkJ\nTZKWLVumQYMG2V9HRUXp119/Vb9+/RQREaEmTZqUmNAk6eTJs5c3cACAR6pVK+TiM1UA5EcAwOVW\nUo506t07IyIitGHDBkkF4xLCw8MvmCc5OVnNmjWzv96xY4datWqlBQsWqGPHjvaB7QAAeAvyIwDA\nlZza0xcZGanExET16dNHkhQfH68VK1YoMzNT0dHRSk1NLXJ5iyTVq1dPr7/+ut566y1dccUViouL\nc2aIAAC4HPkRAOBKFmOMcXcQl+r48TPuDgEA4ALecnmnq5AfAaDycNvlnQAAAAAA96LoAwAAAAAv\nRtEHAAAAAF6Mog8AAAAAvBhFHwAAAAB4MYo+AAAAAPBiFH0AAAAA4MUo+gAAAADAi1H0AQAAAIAX\no+gDAAAAAC9G0QcAAAAAXoyiDwAAAAC8GEUfAAAAAHgxij4AAAAA8GIUfQAAAADgxSj6AAAAAMCL\nUfQBAAAAgBej6AMAAAAAL0bRBwAAAABejKIPAAAAALwYRR8AAAAAeDGKPgAAAADwYhR9AAAAAODF\nKPoAAAAAwItR9AEAAACAF6PoAwAAAAAvRtEHAAAAAF6Mog8AAAAAvBhFHwAAAAB4MT9nrtwYo/Hj\nx2vPnj3y9/dXXFycwsLCJEknTpxQTEyMLBaLjDHavXu3hg4dqqioKA0bNkyHDx+Wn5+fYmNjVb9+\nfWeGCQCAS5EfAQCu5NSevrVr1yonJ0eLFi3SkCFDFB8fb59Ws2ZNzZs3T3PnztWQIUPUpEkT3Xvv\nvdqwYYNsNpsWLVqkJ554QlOmTHFmiAAAuBz5EQDgSk7t6du6davatGkjSWratKmSk5MdzhcbG6vX\nXntNFotF11xzjfLz82WM0ZkzZ1SlShVnhggAgMuRHwEAruTUoi89PV0hISF/NubnJ5vNJh+fPzsY\nExISFB4ernr16kmSgoODdejQIXXq1EmnTp3SrFmznBkiAAAuR34EALiSU4s+q9WqjIwM++vzE5ok\nLVu2TIMGDbK/njNnjtq0aaOYmBgdO3ZMAwcO1PLly+Xv719sOzVqBMnPz/fyvwEAAJyA/AgAcCWn\nFn0RERFat26dOnXqpG3btik8PPyCeZKTk9WsWTP762rVqsnPryCskJAQ5eXlyWazldjOyZNnL2/g\nAACPVKtWyMVnqgDIjwCAy62kHOnUoi8yMlKJiYnq06ePJCk+Pl4rVqxQZmamoqOjlZqaWuTyFkka\nNGiQRo4cqX79+ikvL09DhgxRYGCgM8MEAMClyI8AAFeyGGOMu4O4VMePn3F3CAAAF/CWnj5XIT8C\nQOVRUo7k4ewAAAAA4MUo+gAAAADAi1H0AQAAAIAXo+gDAAAAAC9G0QcAAAAAXoyiDwAAAAC8GEUf\nAAAAAHgxij4AAAAA8GIUfQAAAADgxSj6AAAAAMCLUfQBAAAAgBej6AMAAAAAL0bRBwAAAABejKIP\nAAAAALwYRR8AAAAAeDGKPgAAAADwYhR9AAAAAODFKPoAAAAAwItR9EnatWundu3a6e4wJHlOLJ4S\nh+Q5sXhKHBKxeHIckufE4ilxSJ4VCwAAlQ1Fn6QlSz7UkiUfujsMSZ4Ti6fEIXlOLJ4Sh0QsnhyH\n5DmxeEockmfFAgBAZVPpi75du3Zq9+4ftXv3j24/C+0psXhKHJ4Ui6fEQSyeHYcnxeIpcXhaLAAA\nVEZ+7g7A3c4987xkyYcaNWpCpY/FU+LwpFg8JQ5i8ew4PCkWT4nD02KpzBYunKvPP//M4TSbzXZJ\n6/bxufAccqdOXdS378BLWi8A4PKo9D19AAAAAODNKn1PX8+e92rSpPH2fxOL58ThSbF4ShzE4tlx\neFIsnhKHp8VSmfXtO5CeNwCopCp90de4cRM1anSD/d/E4jlxeFIsnhIHsXh2HJ4Ui6fE4WmxAABQ\nGVX6ok/yrDPPnhKLp8QheU4snhKHRCyOeEockufE4ilxSJ4VCwAAlY3FGGPcHcSlOn78jLtDAAC4\nQK1aIe4OoUIhPwJA5VFSjuRGLgAAAADgxSj6AAAAAMCLOXVMnzFG48eP1549e+Tv76+4uDiFhYVJ\nkk6cOKGYmBhZLBYZY7R7924NHTpU/v7+WrJkiSwWi7Kzs7V7924lJibKarU6M1QAAFyG/AgAcCWn\njulbs2aNEhISFB8fr+3bt2vWrFmaOXPmBfNt27ZNU6dO1X/+8x9ZLBb731944QU1btxY0dHRJbbD\nmAUAqBy8ZUwf+REAcLm5bUzf1q1b1aZNG0lS06ZNlZyc7HC+2NhYTZgwoUhC27Fjh3755ZeLJjQA\nACoa8iMAwJWcWvSlp6crJOTPitPPz082m63IPAkJCQoPD1e9evWK/H327Nl66qmnnBkeAABuQX4E\nALiSU8f0Wa1WZWRk2F/bbDb5+BStM5ctW6ZBgwYV+duZM2e0f/9+tWjRolTteMvlPgCAyoH8CABw\nJaf29EVERGjDhg2SCsYlhIeHXzBPcnKymjVrVuRvW7ZsUcuWLZ0ZGgAAbkN+BAC4klN7+iIjI5WY\nmKg+ffpIkuLj47VixQplZmYqOjpaqampRS5vKbRv3z77XcwAAPA25EcAgCs59e6dAAAAAAD34uHs\nAAAAAODFKPoAAAAAwItR9AEAAACAF3PqjVwqgry8PI0cOVKHDx9Wbm6uHnvsMd1xxx0ujWH79u16\n5ZVXNG/ePO3atUsTJ06Ur6+v/P399dJLLyk0NNTpMTjaDrVr11ZsbKzLY7HZbBo9erT27dsnHx8f\nTZgwQbm5uW7ZLpKUkpKiqKgo/ec//1FWVpbb4ujZs6esVqsk6eqrr9bAgQPd8vnMnj1bCQkJys3N\n1X333acmTZq4JY6lS5dqyZIlslgsys7O1u7du/XBBx+4JZa8vDwNGzZMhw8flp+fn2JjY92yr+Tk\n5GjEiBE6dOiQrFarxo4dq7Nnz7o8jnOPaQcPHtTw4cPl4+OjBg0aaNy4cZKkOXPmaOXKlbJYLGrb\ntq2efPJJp8aE0jv38ztXQkKCZs6cKT8/P0VFRTnt4fCO2o+Pj9e1116r3r17F5nXGKPx48drz549\n8vf3V1xc3CXf6Kak3wWujENynA+vv/56t8QiFc2H9evXd1sc5+fDSZMmuS2W83NiVFSUy2NxlA8T\nExNltVpdvk0c5UN37SuOcmLhc09dFUtp8qEkpaamqm/fvlq+fLn8/f2LrKOk5crEVHKLFy82kyZN\nMsYYc+rUKdOuXTuXtv/222+brl27mt69extjjOnfv7/ZvXu3McaYRYsWmfj4eJfEce52SEtLM+3a\ntXNbLGvWrDEjR440xhizefNm8/jjj7stltzcXPPkk0+ajh07ml9//dVtcWRnZ5sePXoU+Zs7Ytm8\nebN57LHHjDHGZGRkmGnTprltm5xrwoQJ5sMPP3RbLGvXrjX//ve/jTHGfPPNN2bw4MFuiWX+/Plm\nzJgxxhhj9u3bZx544AGXx3H+Me2xxx4zW7ZsMcYYM3bsWLNmzRpz8OBBExUVZV+mT58+Zs+ePU6N\nC6Vz/udXKDc310RGRpozZ86YnJwcExUVZVJSUpzefkpKinnooYdMZGSkWbRo0QXzf/HFF2b48OHG\nGGO2bdtmHn/88UuOwdHvgtTUVJfHYYzjfOiObWJM0Xy4d+9et8XhKB+6KxZHOdFdsRQqzIfuiuPc\nfJiYmGgGDx7stljOzYl79+41DzzwgEtjKU0+NMaYjRs3mu7du5vmzZub7OzsC9ZT3HJlVekv7+zc\nubOeeeYZSQVn1Pz8XNv5Wa9ePc2YMcP+esqUKWrYsKGkgrMlAQEBLonj3O2Qn58vPz8/TZ061S2x\ndOjQQbGxsZKkw4cPq1q1am7bLpMnT1bfvn111VVXyWKxuC2O3bt36+zZs3rwwQd1//33a/v27W6J\n5euvv1Z4eLieeOIJPf7442rfvr3btkmhHTt26JdfflF0dLTbYrnmmmuUn58vY4xOnz6tKlWquCWW\nX375RW3btrXHtHfvXpd/j88/pu3cuVO33HKLJKlt27b69ttvVbt2bb3zzjv2edyx38Cx8z+/Qr/+\n+qvq1asnq9WqKlWqqHnz5tqyZYvT2z979qwGDx6se+65x+H8W7duVZs2bSRJTZs2VXJy8iXH4Oh3\ngTvikBznw8zMTLfEcm4+lOS2OBzlQ3fF4ignuisWqWg+dNc+e24+PHPmjKpUqeK2bXJuTqxfv772\n7t3r0lhKkw8lydfXV3PmzFG1atUcrqe45cqq0hd9VatWVVBQkNLT0/XMM88oJibGpe1HRkbK19fX\n/rpmzZqSpP/+9796//33df/997skDkfb4corr3RLLJLk4+Oj4cOHKy4uTt26dXPLdlmyZImuvPJK\n3X777TJ/PNnEXZ9PYGCgHnzwQb377rsaP368hg4dar9Ez5WxnDx5UsnJyXrjjTc0fvx4DRkyxG3b\npNDs2bP11FNPSXLf5xMcHKxDhw6pU6dOGjt2rAYMGOCWWBo3bqz169dLKnjg9++//+7y/eT8Y5o5\n56lAwcHBOnPmjHx9fVW9enVJBT8kb7jhBvslN3Cv8z+/Qunp6UWeG1j4WTq7/auvvlo333xzsfOf\nH5efn59sNtslxeAoH9apU8flcRQ6Px+6IxZH+dBd28RRPvzb3/7mllgc5UR37ivn5kN3fHckx/nQ\nXdvEUU6sXbu2y2IpTT6UpFatWqlatWpFphfnUo69lX5MnyQdOXJETz31lPr376+7777b3eFo5cqV\nmjVrlmbPnq0aNWq4rF1H28FdsUjSiy++qJSUFEVHR2vlypVKSEhwaSyF18cnJiZq9+7dGjZsmN58\n801t3rzZ5dvkmmuusf8ovuaaa1S9enUdP35cW7dudWks1atX13XXXSc/Pz/Vr19fAQEBSk1N1aZN\nm9yyn5w5c0b79+9XixYt7H9zxz47Z84ctWnTRjExMTp27JgGDhyo5cuXa+3atS6NJSoqSr/++qv6\n9euniIgINWnSRBaLxa3fYx+fP88tZmRk6IorrpD051iLkJAQjR8/3qUxoeysVqvS09Ptr8/9LN3J\narUqIyPD/tpmsxXZ58qrrL8LnBVHofPzYWBgoEtjKS4fFp4cdlUcUvH58C9/+YvLYykuJ5Y0btpZ\nsTjKhyVxVhzF5cPzx6m5IpbicmJJnPldLi4fFioutostV+r2y7WUFzlx4oQefPBBPffcc+rRo4e7\nw9Gnn36qBQsWaN68eapTp47L2nW0HdwVy6effqrZs2dLkgICAuTj46PVq1e7PJb58+dr3rx5mjdv\nnho1aqTJkyfr66+/dss2Wbx4sV588UVJ0rFjx5SRkaHNmze7PJbmzZtr48aN9jiysrL01VdfuWWb\nSNKWLVvUsmVL+2t37bPVqlWz31QgJCREeXl5Wrlypctj2bFjh1q1aqUFCxaoY8eOCgsL07Jly9z2\n+UjSDTfcYL8M8KuvvlLz5s0lSY8//rgaN26s8ePHXzQJw/XOP+N83XXX6cCBAzp9+rRycnK0ZcsW\n/b//9/9c1n5xIiIitGHDBkkFZ/LDw8Mvue3y/C5wRhyS43x4sR+gzojFUT4sqeBzVhyS43xYq1Yt\nt8TiKCde7MSas2I5Px9ejLPicJQPL9ZT5qxYHOXEi3FWLFLx+bBQcce9xo0bl7hcaVX6nr5Zs2bp\n9OnTmjlzpmbMmCGLxaJ33nmnxDMSzmKz2TRp0iTVrl1bTz75pCwWi1q0aGHvqnem87eDzWbTL7/8\n4pZY7rrrLo0YMUL9+/e330VtxIgRbomlkMViUX5+vts+n169emnEiBG677775OPjo7i4OPsdVl0Z\nS7t27fTdd9+pV69eMsZo7NixevbZZ9322ezbt89+EHfn92fQoEEaOXKk+vXrp7y8PMXExCg2Ntbl\nsdSrV0+vv/663nrrLV1xxRWaOHGiunXr5tbvzrBhwzRmzBjl5ubquuuuU6dOnbR27Vp99913ys3N\n1YYNG2SxWDRkyBA1bdrUZXGhZIWF+IoVK5SZmano6GiNGDFCDzzwgIwxio6Oto/tcmb7xRk2bJhi\nYmIUGRmpxMRE9enTR1LBHfkuVVl+FzgzDunCfDhq1Khif584O5ZC7vxspAvz4aRJk4othJ0dy/k5\ncdy4ccVuH2fHcm4+LImz4zg/Hw4ZMqTYnmlnx3J+ToyLiyt2Xld8fxzlw3Odu+/8+uuvWrBggcaO\nHXvR5UrLYkp7Og0AAAAAUOFU+ss7AQAAAMCbUfQBAAAAgBej6AMAAAAAL0bRBwAAAABejKIPAAAA\nALwYRR8AAAAAeDGKPqCUSvuQXndaunSpRowY4bb2BwwYoC1btig5OVljxoyRJH344YdauXKl22IC\nAFRuI0aM0CeffFKuZadNm6atW7dK+jPHARURRR9QSkuXLnV3CBXGjTfeqNjYWEnS999/r5ycHDdH\nBABA2SUlJclms7k7DOCS+bk7AMATJCUl6a233pIxRr/99pvuuusuhYSEaO3atZKk2bNn6/bbb9fu\n3bs1ffp0HTt2TPv379eRI0fUq1cvPfbYY8Wu+9ixYxo6dKgyMzPl4+Oj0aNH6+abb9aqVas0Z84c\nZWdnKysrSxMnTtQtt9yiAQMG6IYbbtA333yjnJwcjRo1SvPmzdOvv/6qQYMGadCgQZo+fbr27dun\n3377TWlpaerdu7ceeOCBIu3+8MMPevHFF5WVlaUaNWrohRdeUJ06dfSf//xHn3zyiXx9fXXTTTdp\nwoQJZY79jjvu0J133qnvvvtOFotFkyZNUqNGjYpsz2nTpumJJ55QQkKCNm/erFq1aun222+/xE8K\nAFBRXSzXvv3221q5cqWWLVtmzztTpkxRUFCQevbsqfnz5yssLExRUVEaMmSI/vGPfxTbVnx8vNav\nX6+rrrpKNptNt912myTpk08+0dy5c2WMUZMmTTR27Fj5+/urVatWateunXbu3Cmr1aqXX37ZfuXK\n6NGjNX36dEkFV6/Ex8frzJkzGjVqlNq1a+f07QZcDvT0AX8oLJJWrFihhQsXqmbNmlq8eLEaNmyo\nzz77TBaLxT7vTz/9pDlz5ujDDz/U7NmzlZ6eXux6P/roI7Vv314ff/yxhg4dqq1bt8oYow8//FCz\nZs3SJ598oocffljvvvuufRmLxaLly5erW7duiouL04wZMzR//nzNmDHDPs8vv/yiuXPnavHixVq0\naJF27dpln5abm6sxY8botdde05IlS/Svf/1Lo0ePVn5+vmbPnq0lS5Zo8eLF8vHx0e+//16m2AvV\nqFFDS5cu1eDBg/X8889fsKzFYlGrVq10xx136Omnn6bgAwCUmGtXrFihhIQEzZ8/X8uXL9edd96p\n999/X3/961/13HPPady4cZo+fboiIiJKLPhWr16t3bt3a9WqVXr99dd14MABSQV586OPPtKiRYu0\ndOlShYaG6r333pMknTx5Ui1bttSyZct09913a+LEierevbtuvPFGxcXFqUGDBpKkatWqacmSJRo1\napS9EAQqAnr6gD80aNBAf/nLXyQVFDQtW7aUJNWuXVunT58uMu9tt90mX19fhYaGqnr16jpz5oys\nVqvD9f7973/X008/rZ07d6pdu3bq16+fLBaLpk2bpnXr1mnfvn1KSkqSr6+vfZm2bdtKkurUqaOm\nTZvK399ftWvX1pkzZ+zzdOnSRYGBgZKkO++8U5s2bVL16tUlSfv379fBgwf1+OOPyxgjSTp79qx8\nfX0VERGhqKgo3XnnnerXr5+uuuqqYreJo9gL3XvvvZKk9u3ba/jw4Tp16lQptjIAoDK7WK595ZVX\ntGLFCu3fv18bN25U48aNJRWMq1+5cqU+++wzrVixosQ2kpKSdNddd8nHx0ehoaH23rjNmzfrwIED\n6n6yDIoAAAP2SURBVN27t4wxysvLU5MmTSRJAQEB+uc//ylJ6t69u1577TX7+grzqFSQbyXp+uuv\nJ++hQqHoA/5QpUqVIq/PLcLO5+/vX+T1uQnhfBEREfrss8+0bt06rVy5UkuXLtX06dPVq1cvde/e\nXbfeeqsaNmyoBQsWOIyluDj8/P78+ubn5xeZLz8/X3Xr1rWPQzTG6Pjx45KkGTNmaPv27frqq6/0\n4IMP6tVXX9Utt9xS6tgLz4qe254xpsTtBQCAVHKuPXLkiHr37q3+/furbdu2qlmzpv0qlpycHB09\nelT5+fk6evSorrnmmmLbsFgsRcbh+fgUXNiWn5+vzp07a9SoUZKkzMxM5efnF5lHkmw2W5Ece67C\nv1sslhJzP+BpuLwTcLKXX35Zn3zyibp3766xY8fqxx9/1IEDB+Tr66vHHntMLVu21FdffVWqgeLn\nJpg1a9YoNzdXaWlpWr9+fZHLJ6+99lqlpaXpu+++k1RwmebQoUOVmpqqzp07Kzw8XIMHD9btt9+u\nPXv2lCr2MWPG6Mcff7RPK7wj55o1a3TttdcqJCTE4Tp8fX2Vm5t70fcGAKjcduzYoXr16mnQoEG6\n+eabi+TGKVOmqGXLlhoxYoSGDx9e4npatWqlzz//XDk5OUpLS9PXX38tSWrRooXWrl2r1NRUGWM0\nbtw4zZkzR1JBAbh+/XpJ0pIlS+xX3Pj5+SkvL89hOxR9qEjo6QMcOHf8Xkl/K+nvhQYMGKAhQ4Zo\n6dKl8vX11YQJE9SwYUM1atRIHTt2VFBQkG699Vb973//u+j6zp0WGBio++67TxkZGXr00Ud13XXX\n6YcffpBU0BM5depUxcXFKScnR1arVZMnT1ZoaKj69OmjqKgoVa1aVbVr1y7xURSOYi/03//fzt2r\nKAxEYRj+UgVtUoWAtUVscgESmwg24jVYCDZ6ESlsYqF9wM4uYGUhXoOIkCbixcxWu7B/Liwu2Z19\nn34yhzSH7wwz57OKolCz2dRyufy09m63q/V6Lc/zNBgM7v4rAMD/8bZnxHGs6/Wq4XAo13UVRZFu\nt5sul4uOx6P2+70ajYZ2u502m40mk8mH3+33+yrLUqPRSL7vq91uS5LCMNRsNtN4PJYxRp1OR9Pp\n9GXd4XDQarVSEATKskyS1Ov1lKapsix7V+9X/R/4TRzDmAL4c54vj8/n81r2T5JE2+1WrVarlv0B\nAHikMAxVVVXdZQA/hpM+4AFOp5MWi8WrqZ8xRo7jKM9z+b5fY3X3fad2ppsAgDo9uu/S12A7TvoA\nAAAAwGI85AIAAAAAFiP0AQAAAIDFCH0AAAAAYDFCHwAAAABYjNAHAAAAABYj9AEAAACAxZ4ATZeh\nU+RZCTQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0e57b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "\n",
    "sns.boxplot(x='min_samples_split', y='mean', data=clf_dt_df.ix[((clf_dt_df['criterion']=='gini') \n",
    "                                                               & (clf_dt_df['min_samples_leaf']==11))\n",
    "                                                               ], ax = axis1)\n",
    "axis1.set_title('criterion=gini, min_samples_leaf=11')\n",
    "\n",
    "sns.boxplot(x='max_depth', y='mean', data=clf_dt_df.ix[((clf_dt_df['criterion']=='gini') \n",
    "                                                               & (clf_dt_df['min_samples_leaf']==11))\n",
    "                                                              & (clf_dt_df['min_samples_split']==62)\n",
    "                                                              ], ax = axis2)\n",
    "axis2.set_title('criterion=gini, min_samples_leaf=11, min_samples_split=62');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# tree visualization\n",
    "#from sklearn.externals.six import StringIO  \n",
    "#from IPython.display import Image\n",
    "#import pydot \n",
    "#dot_data = StringIO()\n",
    "#tree.export_graphviz(clf_dt.best_estimator_, out_file=dot_data, \n",
    "#                     feature_names=train.columns.tolist(), \n",
    "#                     class_names=['Not Survived','Survived'], \n",
    "#                     filled=True, \n",
    "#                     rounded=True,                           \n",
    "#                     special_characters=True)\n",
    "#graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "#Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  3\n",
      "Best score:  0.836895388076 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 22, 'n_estimators': 71, 'criterion': 'gini', 'max_depth': 91, 'min_samples_leaf': 1}\n",
      "Best estimator:  ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
      "           max_depth=91, max_features='auto', max_leaf_nodes=None,\n",
      "           min_samples_leaf=1, min_samples_split=22,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=71, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "CV:  5\n",
      "Best score:  0.839145106862 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 12, 'n_estimators': 21, 'criterion': 'gini', 'max_depth': 101, 'min_samples_leaf': 1}\n",
      "Best estimator:  ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
      "           max_depth=101, max_features='auto', max_leaf_nodes=None,\n",
      "           min_samples_leaf=1, min_samples_split=12,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=21, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "CV:  10\n",
      "Best score:  0.842519685039 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 12, 'n_estimators': 101, 'criterion': 'entropy', 'max_depth': 71, 'min_samples_leaf': 1}\n",
      "Best estimator:  ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='entropy',\n",
      "           max_depth=71, max_features='auto', max_leaf_nodes=None,\n",
      "           min_samples_leaf=1, min_samples_split=12,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=101, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "CV:  15\n",
      "Best score:  0.843644544432 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 12, 'n_estimators': 91, 'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1}\n",
      "Best estimator:  ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='entropy',\n",
      "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "           min_samples_leaf=1, min_samples_split=12,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=91, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "Wall time: 3h 45min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "clf_et = ExtraTreesClassifier()\n",
    "\n",
    "clf_et.get_params().keys()\n",
    "max_depth = range(1, 110, 10) + [None]\n",
    "n_estimators = range(1, 110, 10)\n",
    "min_samples_split = range(2, 31, 10)\n",
    "min_samples_leaf = range(1, 31, 10)\n",
    "\n",
    "\n",
    "parameters = {'criterion': ('gini', 'entropy'), \n",
    "              'n_estimators': n_estimators, \n",
    "              'max_depth': max_depth,\n",
    "              'min_samples_split': min_samples_split,\n",
    "              'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "for i in range(len(cv_folds)):\n",
    "    clf_et = ExtraTreesClassifier()\n",
    "    clf_et = grid_search.GridSearchCV(clf_et, parameters, cv=cv_folds[i])\n",
    "    clf_et.fit(train_data[:,:-1], np.reshape(train_data[:,-1:], (1, len(train_data)))[0])\n",
    "\n",
    "    # Results:\n",
    "    print 'CV: ', cv_folds[i]\n",
    "    print 'Best score: ', clf_et.best_score_, ', using metric: ', clf_et.scorer_.func_name\n",
    "    print 'Best parameters: ', clf_et.best_params_\n",
    "    print 'Best estimator: ', clf_et.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          mean criterion  max_depth  min_samples_leaf  min_samples_split  \\\n",
      "2297  0.843645   entropy        NaN                 1                 12   \n",
      "1901  0.841395   entropy       71.0                 1                 12   \n",
      "1503  0.840270   entropy       31.0                 1                 12   \n",
      "1992  0.840270   entropy       81.0                 1                 12   \n",
      "612   0.839145      gini       61.0                 1                 12   \n",
      "\n",
      "      n_estimators  \n",
      "2297            91  \n",
      "1901            91  \n",
      "1503            71  \n",
      "1992            11  \n",
      "612             71  \n"
     ]
    }
   ],
   "source": [
    "clf_et_df = pd.concat([pd.DataFrame(clf_et.grid_scores_, columns=['parameters', 'mean', 'cv_val_score']).drop(['parameters', 'cv_val_score'], axis=1),\n",
    "           pd.DataFrame(pd.DataFrame(clf_et.grid_scores_, columns=['parameters', 'mean', 'cv_val_score']).parameters.tolist())],\n",
    "           axis=1\n",
    "         )\n",
    "\n",
    "print clf_et_df.sort_values('mean', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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uXZfHU1JS9NBDD5mZFgAAlqJ9BADEUnzd/Q0AAAAAiApFH/okIyMjbAwAgNVcLlfYGImv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dfe668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((axis1,axis2),(axis3,axis4)) = plt.subplots(2,2,figsize=(15,8))\n",
    "sns.boxplot(x='criterion', y='mean', data=clf_et_df, ax=axis1)\n",
    "sns.boxplot(x='min_samples_leaf', y='mean', data=clf_et_df, ax=axis2)\n",
    "sns.boxplot(x='min_samples_split', y='mean', data=clf_et_df, ax=axis3)\n",
    "sns.boxplot(x='max_depth', y='mean', data=clf_et_df, ax=axis4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J5/hqO8Fr6preXlOOxHGdoj19u3fvRmFhIXJzc1FSUoKXXnoJeXl58vbrrrsO77zzDgID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\nEuCS4O+PrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde523c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "\n",
    "sns.boxplot(x='min_samples_split', y='mean', data=clf_et_df.ix[((clf_et_df['criterion']=='gini') \n",
    "                                                               & (clf_et_df['min_samples_leaf']==1))\n",
    "                                                               ], ax = axis1)\n",
    "axis1.set_title('criterion=gini, min_samples_leaf=1')\n",
    "\n",
    "sns.boxplot(x='max_depth', y='mean', data=clf_et_df.ix[((clf_et_df['criterion']=='gini') \n",
    "                                                               & (clf_et_df['min_samples_leaf']==1))\n",
    "                                                              & (clf_et_df['min_samples_split']==12)\n",
    "                                                              ], ax = axis2)\n",
    "axis2.set_title('criterion=gini, min_samples_leaf=1, min_samples_split=12');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm afraid, that min_samples_leaf=1 may cause the **overfitting**. If this is true, how to choose the best params, if that settings give the best scores?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t4G99vRPPfrJFmCtxJEAreYqiWSvbOkOoa+pAIBhGSxIVLT68mFjpahs7km4QuH+GOJz4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ZR0V3/x0Ghjr845odnVPlc016UnhvKbH8ofF526EA/++hWsXVzvvDHyPsiTt+S5afbt6Bk2/U1a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\nt6o9PT2qqubv+dvZ2alu2bJF7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yMrKws8Hg92dnZYvXo1qqurERYWhtbWVtTU1GDRokVY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\nIYQQQgghhAwjtMgjhBBCCCGEkGHk/wAweM3Eh+VPsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe8fd320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1, figsize=(15,5)) \n",
    "clf_et_df.set_index(['criterion', u'max_depth', u'min_samples_leaf',\n",
    "       u'min_samples_split', u'n_estimators']).plot(ax=ax1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  3\n",
      "Best score:  0.842519685039 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 12, 'n_estimators': 21, 'criterion': 'gini', 'min_samples_leaf': 1}\n",
      "Best estimator:  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_samples_leaf=1, min_samples_split=12,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=21, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "CV:  5\n",
      "Best score:  0.835770528684 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 72, 'n_estimators': 71, 'criterion': 'gini', 'min_samples_leaf': 21}\n",
      "Best estimator:  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_samples_leaf=21, min_samples_split=72,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=71, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "CV:  10\n",
      "Best score:  0.841394825647 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 22, 'n_estimators': 101, 'criterion': 'entropy', 'min_samples_leaf': 1}\n",
      "Best estimator:  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_samples_leaf=1, min_samples_split=22,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=101, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "CV:  15\n",
      "Best score:  0.842519685039 , using metric:  _passthrough_scorer\n",
      "Best parameters:  {'min_samples_split': 22, 'n_estimators': 41, 'criterion': 'gini', 'min_samples_leaf': 1}\n",
      "Best estimator:  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_samples_leaf=1, min_samples_split=22,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=41, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "Wall time: 4h 16min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "n_estimators = range(1,110,10)\n",
    "min_samples_split = range(2,110,10)\n",
    "min_samples_leaf = range(1,110,10)\n",
    "\n",
    "parameters = {'criterion':('gini', 'entropy'), \n",
    "              'n_estimators':n_estimators, \n",
    "              'min_samples_split':min_samples_split, \n",
    "              'min_samples_leaf':min_samples_leaf}\n",
    "\n",
    "\n",
    "for i in range(len(cv_folds)):\n",
    "    clf_rf = RandomForestClassifier()\n",
    "    # clf.get_params()\n",
    "    clf_rf = grid_search.GridSearchCV(clf_rf, parameters, cv = cv_folds[i]) \n",
    "    clf_rf.fit(train_data[:,:-1], np.reshape(train_data[:,-1:], (1, len(train_data)))[0])\n",
    "\n",
    "    # Results:\n",
    "    print 'CV: ', cv_folds[i]\n",
    "    print 'Best score: ', clf_rf.best_score_, ', using metric: ', clf_rf.scorer_.func_name\n",
    "    print 'Best parameters: ', clf_rf.best_params_\n",
    "    print 'Best estimator: ', clf_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          mean criterion  min_samples_leaf  min_samples_split  n_estimators\n",
      "26    0.842520      gini                 1                 22            41\n",
      "1358  0.838020   entropy                 1                 22            51\n",
      "28    0.838020      gini                 1                 22            61\n",
      "20    0.838020      gini                 1                 12            91\n",
      "1362  0.836895   entropy                 1                 22            91\n"
     ]
    }
   ],
   "source": [
    "clf_rf_df = pd.concat([pd.DataFrame(clf_rf.grid_scores_, columns=['parameters', 'mean', 'cv_val_score']).drop(['parameters', 'cv_val_score'], axis=1),\n",
    "           pd.DataFrame(pd.DataFrame(clf_rf.grid_scores_, columns=['parameters', 'mean', 'cv_val_score']).parameters.tolist())],\n",
    "           axis=1\n",
    "         )\n",
    "\n",
    "print clf_rf_df.sort_values('mean', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EauLrXizRH1gu5+3t45dfHusUR0Tcek3311P33JMgFxX33JMg9P33wx+OlOc5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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf4c4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((axis1,axis2),(axis3,axis4)) = plt.subplots(2,2,figsize=(15,8))\n",
    "sns.boxplot(x='criterion', y='mean', data=clf_rf_df, ax=axis1)\n",
    "sns.boxplot(x='min_samples_leaf', y='mean', data=clf_rf_df, ax=axis2)\n",
    "sns.boxplot(x='min_samples_split', y='mean', data=clf_rf_df, ax=axis3)\n",
    "sns.boxplot(x='n_estimators', y='mean', data=clf_rf_df, ax=axis4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+/fu3eJuArzQ3P0q1lzUlJibq6aefVrdu3XwRfptGnmiatponOKbuae4x5Uoc93m0p2/b\ntm3KyspSenq69u/fr3Xr1mnt2rXO5VdffbXeeOMNhYWFacSIEXr55Zd18OBBPf3003r00UdVWlqq\np556SnfccYenQgQAwOuakx8jIyNVXV2tu+++W5988okeffRRij4AQJN4dCCXvXv3Os9+9e7dW7m5\nufWWx8XFqaioyDmUq8Vi0VtvvaXY2Fjdfvvtuu2223TNNdd4MkQAALyuOflRklasWKGkpCTnVAAA\nADSFRy/vLCkpUWRk5HeNhYTI4XA4h1Tt3r27EhMTFR4ervj4eNlsNp04cUJfffWV1q1bpy+++EK3\n3Xabtm7d6skwAQDwqubkx8zMTHXq1EkDBw7UY4895qvQAQCtkEd7+mw2m+x2u/P56QktPz9fO3fu\nVFZWlrKyslRQUKCtW7eqY8eOzokou3XrptDQUBUWFjbaTnV184faBQDA25qTHzMzM5Wdna2pU6cq\nLy9Pc+bMUUFBQaPtkB8BAJKHe/r69u2rHTt2KCEhQfv27VNsbKxzWWRkpNq3by+r1SqLxaLo6GgV\nFxerX79+ysjI0IwZM3T06FGVl5crKiqq0XZOnChtdDkAIDB07hx57he1As3Jjxs3bnS+ZurUqbr3\n3nvVqVOnRtshPwJA29FYjvRo0RcfH6/s7GxNnDhRUu0IPVu2bFFZWZnGjRun8ePHa9KkSbJarera\ntatGjx6tkJAQvf/++xo7dqyMMVqyZAkj9AAAAkpz8uPpyIsAAHcExDx9x44V+zoEAIAXBEpPn7eQ\nHwGg7WgsR3r0nj4AAAAAgG9R9AEAAABAAKPoAwAAAIAA5tGBXAAAAADAU1544Vlt3fpXl8scDsd5\nbbtuKp0zJSSMUFLStPPatrfR0wcAAAAAAYzROwEArQajd7qH/AgAbQejdwIAAABAG0XRBwAAAAAB\njKIPAAAAAAIYRR8AAAAABDCKPgAAAAAIYBR9AAAAABDAKPoAAAAAIICF+DoAAACAtu6FF57V1q1/\ndbnM4XA0e7tBQa7P7yckjFBS0rRmbxdA60JPHwAAAAAEMIsxxvg6iPN17Fixr0MAAHhB586Rvg6h\nVSE/AkDb0ViOpKcPAAAAAAIYRR8AAAAABDAGcgEAAIDf8dTgNpLrAW4Y3MY9DR0fTxwbieNzvujp\nAwAAAIAAxkAuAIBWg4Fc3EN+BIC2g4FcAAAAAKCNougDAAAAgADGQC4AAMBrvD04h8QAEABATx8A\nAAAABDAGcgEAtBoM5OIe8iMAtB0M5AIAAAAAbRT39LVhL7zwrHJy3nW5zG4vkSRFRNhcLu/ffwD3\nRwAAAACtAEUfXKqoqJDUcNEHAAAA4DueGqiqJQap8mjRZ4xRSkqK8vPzZbValZaWppiYGOfyzZs3\na8OGDQoODtaYMWOUlJTkXFZQUKDExEQ9/fTT6tatmyfDbLOSkqY1+EFJTr5dkvTQQ2u9GRIAAACA\nFubRom/79u2qrKzUpk2btH//fqWnp2vt2u+KiJUrV+qNN95QWFiYRowYoZEjRyoyMlLV1dVasmSJ\nwsLCPBkeAAAAALSIxjpUfM2jA7ns3btXgwYNkiT17t1bubm59ZbHxcWpqKjIeSmhxWKRJK1YsUJJ\nSUm68MILPRkeAAAAAAQ8jxZ9JSUlioz8bujQkJCQetezdu/eXYmJibrhhhs0ZMgQ2Ww2ZWZmqlOn\nTho4cKACYDYJAAAAAPApj17eabPZZLfbnc8dDofzRsT8/Hzt3LlTWVlZCg8P1+zZs7V161ZlZmbK\nYrEoOztbeXl5mjNnjh599FF16tSpwXaiosIVEhLsybfS5gQF1fa6MicWAAAA0Lp5tOjr27evduzY\noYSEBO3bt0+xsbHOZZGRkWrfvr2sVqssFouio6NVXFysjRs3Ol8zdepU3XvvvY0WfJJ04kSpx95D\nW+Vw1PayMrEvAH/CiSjAszw1+qDUMiMQ+oI/7RN/igWti0eLvvj4eGVnZ2vixImSpPT0dG3ZskVl\nZWUaN26cxo8fr0mTJslqtapr164aPXp0vfXr7vELNMyPBwAAAMBbLCYAbpxrbb1RjRV9hYUFkqTo\naNe9m94q+piyAYA/CpSevuZMaeRwOLRw4UIdOnRIQUFBWrp0qS677LJG22lt+REA0HyN5UgmZ/cB\n5scDgLatOVMa7dmzRxaLRS+88IJycnL04IMP1lsHAICGUPQBAOBlTZ3SqO42B4vFomHDhmno0KGS\npCNHjqhDhw7eDRoA0GpR9AEA4GUNTWlUN5BC3ZRG4eHhio+Pl81We593UFCQ5s6dq+3bt2vVqlU+\niR0A0PpQ9AEA4GXuTmn05ptvavjw4ZKk++67TwUFBRo3bpxef/11hYWFNdgOUxoBACSKvoCXmrpQ\nhYWFbq9XN6BM3T2G7oiOjtaiRcvcXs/fHTx4QJLUs2cvH0cCoLVzd0qjU6dO6dVXX9XRo0f161//\nWqGhoQoKCmpwiPU6TGkEAG0HA7n4gL8UW4WFhSooOCZbe/e2Ffzf3xEVpcfcWq+kzL126rSGaSwy\nM1+SJC1YsNTjbQEIbM2Z0qiqqkrz5s3TlClTVF1drQULFshqtfr4nQAAWgOKPg8pLCxUYcExdWz4\nqhuXrP8tthx294qtk+UNL7O1l6aP9M6ch89safkZQCoqKiQ1XPR5w8GDB5SX95HzMb19AM6HxWLR\n0qX1TyB169bN+XjixInOgrBOSEiIHn74Ya/EBwAILBR9HtQxTEodGuqVthZlVXilHU/x92ks6nr5\n6h57o7evNfR+ehv7BAAAwH0UffAaf7nkNRD4Q++nv2GfAAAAuEbR5yF2e4kqyr3XA3eyXApVics4\nyss9c9mlKyVlUrU5Ow6p7v7CbxUa7t42LcF12/7WrfUqWnD8gjFjxmv58hTnY2/w995PX/CnfUKv\nIwAAaC0o+uBVoeHS/yV6p61dL7fctnr27KW4uMudj4HG0OsIAAD8CUWfh0RE2FRR7nooy9IqqbKm\nedu1Bkvh7Rpu09XfQixlXh3IJTS8df/QbagH59SpIkkNX2YayL03bbVXq7mXJJ9LTs67De7P1nxJ\nclv9nAAA4O8o+jwkOjq6wWUWe4nkaN5ln5Z2oQpy8aMpOqLxNnH+qqurfR2CXwrkXq3aS5ILFBHh\n3ncrOLh2AKfycvcuq7bbW77A9BeB/DkBAMDfUfR5SGs9U4+G7xtrq/fRSd69l87fBvyJiIjW+CTv\nDJP/0gt3u72OP/Wu+dM9lwAA4DsUfW1ASZn7A7mUV9b+G+bmvL8lZXJ7oBbgdIWFhTpeUCBLRAe3\n1jPBtdc9F5S71yNr7EVuvb41oXcNAABIFH0Br7mXfNrLa3tNQsM7ubVeaDiXmeL8WSI6yJa0wCtt\nlbyQ1uCy2tFvK5rVA9ccdnuhampcz+3pifsLA/XeQgAAUB9FX4Br7o82LsXyDn+7lBH+q7CwUIUF\nBeoQ1vSTKu2CagvIGrt7Pf1F5YF7byEAAG0RRR/gQ7WXMh6XItq7t2JwkCTpeLndvfXsrkeUlbxf\ngLaG4jMiwqbg4Aiv3tMXFtbwSLsdwqK1JP4Bj8exdNssj7cBAAC8h6IPbVJzChyP9a5FtFdw0nC3\nt9kcNS+82eCy7wpQN+//Cg6WJB0vL2/6Ov8dYAQAAACeR9EHr6m9P6plJ01vTHmpZHG4Li5qC5xj\nki246RsMrr1E7niFm71hJc2clNHL7M0txELDvNteG2W3l6iivMIrvXBF5YUKVcveW8glyQAA+A5F\nH9ouW7AsU2I83ozZ+IXH2wC8pe7ewujQC9xaL9RSO7qqSqrca6/ilFuvBwAAZ6Poa8Mam9/rXGfl\nmzO/V0SETSaoVP+X6F6czbXrZSmivetLFe32Eqm8xjsFWUmN7NWue7Vq4yhv9LLLFmUvk73G9aAe\nERE2lQWHyDpxisfDqNy0URFhrnsI7fYSmfKKRkfVbEnGXiR7AyNm+pOICJvCFOG1e/qCI1zfW2i3\nl0jGvYFhJCminZv3rdYxhl5hAADOE0UfXAoN9f8fwQAAAADOjaKvDUtKmuZ2b12giIiwqSyk0muX\nd0aEuu5xrO1ds3h1IJeIsAivtNVcERE2lQeHeXWevogw/itsqogImyJMqB4Yco9X2pu180Epop1X\n2gIAIFDxSwcAGmG3F7o9OXtFRe1UGqGh7hXYdnuhwsI6ubUOAADAuVD0wasqSt0fvbOqsvbfdlb3\n27I18zYiQKodNbI5SksrJElhYe5NfxEW1qnRNovKC90avbO0qrb4DG/nXvFZVF6o6AiKTwAAAgVF\nH7ymsR+zdnuJKioqXC5zOBySJFMT5HJ5aGioIlzMLWdrf44f7SVuDuRSXhuHwlzH0Vg7DYx+X8te\n5v5ALhX/rYRD3ayE7WVSY5d32ktUuWmjm7H8d34+d6ZusJdIDQzk4k+aO01A3QBIDz20tsViaeiz\n3JTvTpXD9fKGvjvREY0XnwAAoHWh6IPXNPYDurGRROtG7nP141Rq3kiizflBW2ivHdE0OtTNdUMb\nbq+5P6wLS2sLrWh3788Li/BALPb/xuJGERcW1mh7xl7k9uidpqJUkmQJDXdvPXuR1AouqWzo++Pt\n7w4AAGh9LMY0Y+xtP3PsWLGvQ0Ab4Inem+bydixNmd4jOtp14eRuYXG+k383FEdjmjP5tzf3iT9J\nTr5dKqny7kAutnbOz3rnzpFeaTdQkB8BoO1oLEd6tKfPGKOUlBTl5+fLarUqLS1NMTHfjZa4efNm\nbdiwQcHBwRozZoySkpJUXV2t+fPn68iRI6qqqtKtt96qoUOHejJMAOehpaf38KdLKpuLKU8AAIA/\n8WjRt337dlVWVmrTpk3av3+/0tPTtXbtdz/IVq5cqTfeeENhYWEaMWKERo4cqW3btikqKkorV65U\nUVGRRo0aRdEH+Fhbnt6jIewTAADQWni06Nu7d68GDRokSerdu7dyc3PrLY+Li1NRUZEsFoskyWKx\n6LrrrlNCQoKk2kEIQkK47RAA/Elhxanayy7dYK8qkyRFtHNvSN3CilOKtvn/PZcAAPgzj1ZUJSUl\nioz87trSkJAQORwOBQXVjn7YvXt3JSYmKjw8XPHx8bLZbPXWveuuu5ScnOzJEAG/1ZR7xuouaTxT\na75nrDHsE99r7oA/FYWnJEkRtgvca8/GSKIAAJwvjxZ9NptNdrvd+fz0gi8/P187d+5UVlaWwsPD\nNXv2bL355psaPny4vv76a91xxx2aMmWKrr/++nO2ExUVrpCQYI+9D0CSgoJqe6S9NZBE+/ZWZ5tn\nCvvvSJkNLW/f3hqQA16wT3xv1ao/Nmu9adNqC+5nn322JcMBAABN4NGir2/fvtqxY4cSEhK0b98+\nxcbGOpdFRkaqffv2slqtslgsio6O1qlTp1RQUKCbb75Zixcv1oABA5rUzokTpZ56C2iDGupNqutJ\nmjJlqsv1WronadSoiRo1amKz1w/EUfvYJ62Xw1E7UPT5HgMKdwAA3OfRoi8+Pl7Z2dmaOLH2R1p6\nerq2bNmisrIyjRs3TuPHj9ekSZNktVrVtWtXjR49WitWrNCpU6e0du1arVmzRhaLRU888YSsVjcn\noW6lDh48IEnq2bOXjyPBmRiREQAAAK0R8/T5mbS0JZKkBQuW+jgSAGg5LTWlBj197gmk/AgAaFxj\nOTLIi3HgHA4ePKC8vI+Ul/eRs8cPAAAAAM4HRZ8fycx8yeVjAAAAAGguij4AAAAACGBtZubzxub3\nsttLJEkRETaXy701v9eYMeO1fHmK8zEAtCbMowgAgH9qM0VfYyoqKiQ1XPR5S8+evRQXd7nzMQAE\nCka/BQDAdxi9Uy03qlxLYMoGAGgYo3e6h9E7AaDtaCxH0tPnZyj2AAAAALQkij4AALzMGKOUlBTl\n5+fLarUqLS1NMTExzuWbN2/Whg0bFBwcrDFjxigpKUnV1dWaP3++jhw5oqqqKt16660aOnSoD98F\nAKC1oOgDAMDLtm/frsrKSm3atEn79+9Xenq61q797haDlStX6o033lBYWJhGjBihkSNHatu2bYqK\nitLKlStVVFSkUaNGUfQBAJokoIq+1NSFKiwsdHu9c40q15jo6GgtWrTM7fUAAG3X3r17NWjQIElS\n7969lZs3v8LiAAAgAElEQVSbW295XFycioqKZLFYJEkWi0XXXXedEhISJEkOh0MhIQGVwgEAHhRQ\nGaOwsFCFBccVHRbu1nqhQcG1D+yl7rVX7t7rAQCQpJKSEkVGfnfDfUhIiBwOh4KCaqfP7d69uxIT\nExUeHq74+HjZbLZ66951111KTk72etwAgNYpoIo+SYoOC9dD1472SlvJf3vFK+0AAAKLzWaT3W53\nPj+94MvPz9fOnTuVlZWl8PBwzZ49W2+++aaGDx+ur7/+WnfccYemTJmi66+//pztREWFKyQk2GPv\nAwDQOgRc0QcAgL/r27evduzYoYSEBO3bt0+xsbHOZZGRkWrfvr2sVqssFouio6N16tQpFRQU6Oab\nb9bixYs1YMCAJrVz4gRXpABAW8GUDQAA+JH4+HhlZ2dr4sSJkqT09HRt2bJFZWVlGjdunMaPH69J\nkybJarWqa9euGj16tFasWKFTp05p7dq1WrNmjSwWi5544glZrVYfvxsAgL8LqMnZk5Nvl+yl3r28\nMyLcLyZ1B4C2gMnZ3cPk7ADQdjSWI4O8GAcAAAAAwMso+gAAAAAggFH0AQAAAEAAC6iBXOz2ElWU\nl3ttKoXC8lKFyuGVtgAAAACgOejpAwAAAIAAFlA9fRERNkUoyOujdwIAAACAv6KnDwAAAAACGEUf\nAAAAAASwJl3euXv3bj300EM6deqUjDEyxshisejvf/+7p+NzW2F5qdsDudirKiVJEe2sbrcVzeWd\nANBmtab8CABou5pU9C1btkxz585V9+7dZbFYPB1Ts0VHRze4zG4vUUVFhctlDkftCJwVjhqXy0ND\nQxURYTu7vYjwRtsEAAS21pIfAQBtW5OKvqioKF1zzTWejuW8LVq0rMFlL7zwrHJy3nW5zG4vkSSX\nhZ0k9e8/QElJ084/QABAQGkt+RGuvfDCs9q69a8ul9WdEG6uoCDXd9AkJIzgNwUAr2tS0devXz+l\np6dr0KBBCg0Ndf79Zz/7mccCa2lJSdP4TxYA0KICIT8CAAKfxRhjzvWiqVOnnr2ixaJnn33WI0G5\n69ixYl+HAADwgs6dI30dQj3kRwCAv2gsRzap6PN3JDUAaBv8rejzd+RHAGg7GsuRTbq88/3339eT\nTz6p0tJSGWPkcDj01VdfKSsrq9H1jDFKSUlRfn6+rFar0tLSFBMT41y+efNmbdiwQcHBwRozZoyS\nkpLOuQ4AAP6iufkRAABvatI8fQsXLtSwYcNUU1OjyZMn65JLLtGwYcPOud727dtVWVmpTZs2adas\nWUpPT6+3fOXKlXrmmWf0/PPP6+mnn1ZxcfE51wEAwF80Nz8CAOBNTerpCwsLU2Jioo4cOaILLrhA\ny5Yt05gxY8653t69ezVo0CBJUu/evZWbm1tveVxcnIqKipzDXFsslnOuAwCAv2hufgQAwJua1NMX\nGhqqkydPqlu3btq/f78sFotKS0vPuV5JSYkiI7+7tjQkJKTeEMjdu3dXYmKibrjhBg0ZMkQ2m+2c\n6wAA4C+amx8BAPCmJvX0zZgxQ8nJyVq9erXGjh2r1157TVdcccU517PZbLLb7c7nDofDOW9Nfn6+\ndu7cqaysLIWHh2v27NnaunWrIiMjG1ynIVFR4QoJCW7KWwEAoMU0Nz8CAOBNTSr6rrvuOiUkJMhi\nsSgzM1OHDx9WXFzcOdfr27evduzYoYSEBO3bt0+xsbHOZZGRkWrfvr2sVqssFouio6NVXFysvn37\nKisry+U6DTlxgrOqANAW+Nvonc3NjwAAeFOTLu8sKirSokWLNG3aNFVUVCgjI0PFxeceBjo+Pl5W\nq1UTJ07Ufffdp3nz5mnLli3605/+pC5dumj8+PGaNGmSJk+erJKSEo0ePVrDhg07ax0AAPxRc/Mj\nAADe1KR5+u68804NHDhQzz33nP785z9rzZo1OnjwoNavX++NGM+JeYgAoG3wt54+8iMAwF80liOb\n1NP35ZdfasKECQoKCpLValVycrK++eabFgsQAIDWiPwIAGgNmlT0BQcHq7i42Dm1wuHDh885uAoA\nAIGO/AgAaA2aNJDLzJkzNXXqVH399de6/fbbtW/fPi1fvtzTsQEA4NfIjwCA1qBJpyOvuOIKDRs2\nTBdffLG+/vprxcfHM2k6AKDNIz8CAFqDJvX03XLLLerRo4euueYaT8cDAECrQX4EALQGTSr6JHG5\nCgAALpAfAQD+LjglJSXlXC8qLCzU4cOHdcEFF8hut6u4uFjFxcWKjPSPobNLSyt9HQIAwAsiIkJ9\nHUI95EcAgL9oLEc2qaevuLhY69evV1RUlPNvFotFf//7388/OtRz8OABSVLPnr18HAnQuvDd8W+B\nenzIjwCA1qBJRd/f/vY3vfPOOwoLC/N0PG1eZuZLkqQFC5b6OBKgdeG7498C9fiQHwEArUGTRu+M\niYlRUVGRp2Np8w4ePKC8vI+Ul/eR86w44O8OHjzg888r3x3/FsjHh/wIAGgNmtTTZ7FYNGLECHXv\n3l3t2rVz/v3ZZ5/1WGBtUd2Z8LrHgXZGHIHJH3pw+O74t0A+PuRHAEBr0KSi79Zbb/V0HABaoboe\nnLrHgXa/FnAu5EcAQGvQpKKvf//+no4DksaMGa/ly1OcjwF/5y89OHx3/FsgHx/yIwCgNWjyPH3w\nvJ49eyku7nLnYwBNw3fHv3F8AADwLYo+PxNoZ8ER2PypB8fX7aNxHB8AAHyHos/PcBYcrYk/9eD4\nun00juNTnzFGKSkpys/Pl9VqVVpammJiYpzLN2/erA0bNig4OFhjxoxRUlKSc9n+/ft1//33KyMj\nwxehAwBaIYo+AOeFHhzAfdu3b1dlZaU2bdqk/fv3Kz09XWvXrnUuX7lypd544w2FhYVpxIgRGjly\npCIjI/XEE0/o1VdfVUREhA+jBwC0Nk2apw8AGtKzZy96cQA37d27V4MGDZIk9e7dW7m5ufWWx8XF\nqaioSBUVFZJqp4aQpEsuuURr1qzxbrAAgFaPnj4AALyspKREkZGRzuchISFyOBwKCqo9F9u9e3cl\nJiYqPDxc8fHxstlskqT4+HgdOXKkye1ERYUrJCS4ZYMHALQ6FH0AAHiZzWaT3W53Pj+94MvPz9fO\nnTuVlZWl8PBwzZ49W2+++aaGDx/udjsnTpS2WMwAAP/WuXNkg8u4vBMAAC/r27evdu3aJUnat2+f\nYmNjncsiIyPVvn17Wa1WWSwWRUdH69SpU/XWN8Z4NV4AQOtGTx8AAF4WHx+v7OxsTZw4UZKUnp6u\nLVu2qKysTOPGjdP48eM1adIkWa1Wde3aVaNHj663ft09fgAANIXFBMDpwmPHin0dAgD4nYMHD0gK\nrOkSGrt0BWcjPwJA28HlnQDQBmVmvqTMzJd8HQYAAPAxij64dPDgAWcvga/5UyxAa3Hw4AHl5X2k\nvLyP+P4AANDGUfTBJX/qIfCnWIDW4vTvDN8fAADaNoo+nMWfegj8KRYAAACgNaLow1n8qYfAn2IB\nWpMxY8a7fAwAANoepmwAmigQR0JE4OrZs5fi4i53PgYAAG0XPX04iz/1EPhTLNxbiNZmzJjxPv/e\nAAAA3/NoT58xRikpKcrPz5fValVaWppiYmIkScePH1dycrIsFouMMcrLy9Ps2bOVmJioOXPm6MiR\nIwoJCVFqaqq6devmyTBxBn/qIfCXWOruLax77Ov9AjQFn1MAACB5uOjbvn27KisrtWnTJu3fv1/p\n6elau3atJOl73/ueMjIyJEn79u3Tww8/rPHjxysrK0sOh0ObNm3S22+/rYceekirVq3yZJhwwZ96\nB/whljPvLVywYKkPowEAAACazqNF3969ezVo0CBJUu/evZWbm+vydampqXrwwQdlsVh06aWXqqam\nRsYYFRcXq127dp4MEQ3wpx4Cf4oFAAAAaG08WvSVlJQoMjLyu8ZCQuRwOBQU9N2thFlZWYqNjdUl\nl1wiSYqIiNCXX36phIQEnTx5UuvWrTtnO1FR4QoJCW75NwD81//7f9M1Z84c5+POnSPPsQYAAADg\nHzxa9NlsNtntdufzMws+Sdq8ebOmT5/ufL5hwwYNGjRIycnJOnr0qKZNm6bXXntNVqu1wXZOnCht\n+eCB03Tp8kPnvYVduvxQx44V+zQeRhJFW8UJFwAA3OfR0Tv79u2rXbt2Saq9by82Nvas1+Tm5qpP\nnz7O5x06dJDNZpMkRUZGqrq6Wg6Hw5NhAk3iTyMhMpIoAAAAmsqjPX3x8fHKzs7WxIkTJUnp6ena\nsmWLysrKNG7cOBUWFta7/FOSpk+frvnz52vy5Mmqrq7WrFmzFBYW5skwgSbxl141RhIFAACAOyzG\nGOPrIM6Xry+1A7wpLW2Js+iLi7uckUTRpnB5p3vIjwDQdjSWI5mcHQAAAAACGEUf0Mqcfl+hv9xj\nCAAAAP/l0Xv6ALS8nj17OUcS5X4+AAAAnAtFnxj+Hq0PPXwAAABoKoo+yTn0PQNioLXgBAUAAACa\nqs3f01c3/H1e3kfOHj8AAAAACBRtvug7fYJrJrsGAAAAEGjafNEHAAAAAIGszRd9DH8PAAAAIJC1\n+YFcGP4eAAAAQCBr80WfRA8fAAAAgMBF0Sd6+AAAAAAErjZ/Tx8AAAAABDKKPgAAAAAIYBR9AAAA\nABDAKPoAAAAAIIBR9AEAAABAAKPoAwAAAIAARtEHAAAAAAGMog8AAAAAAhhFHwAAAAAEsBBfBwAA\nQFtjjFFKSory8/NltVqVlpammJgY5/LNmzdrw4YNCg4O1pgxY5SUlHTOdQAAaAg9fQAAeNn27dtV\nWVmpTZs2adasWUpPT6+3fOXKlXrmmWf0/PPP6+mnn1ZxcfE51wEAoCH09AEA4GV79+7VoEGDJEm9\ne/dWbm5uveVxcXEqKiqSxWKRJFkslnOuAwBAQyj6AADwspKSEkVGRjqfh4SEyOFwKCio9gKc7t27\nKzExUeHh4YqPj5fNZjvnOgAANISiDwAAL7PZbLLb7c7npxdv+fn52rlzp7KyshQeHq7Zs2dr69at\nioyMbHCdhkRFhSskJNgzbwIA0GpQ9AEA4GV9+/bVjh07lJCQoH379ik2Nta5LDIyUu3bt5fVapXF\nYlF0dLSKi4vVt29fZWVluVynISdOlHrybQAA/EjnzpENLqPoAwDAy+Lj45Wdna2JEydKktLT07Vl\nyxaVlZVp3LhxGj9+vCZNmiSr1aquXbtq9OjRCg4O1ltvvVVvHQAAmsJijDG+DuJ8HTtW7OsQAABe\n0NhZTJyN/AgAbYfPevoam1Po+PHjSk5OlsVikTFGeXl5mj17tiZMmKD169crKytLVVVVmjRpkhIT\nEz0ZJgAAAAAELI8WfafPKbR//36lp6dr7dq1kqTvfe97ysjIkCTt27dPDz/8sMaPH6+cnBx98MEH\n2rRpk0pLS/XUU095MkQAAAAACGgeLfqaOqdQamqqHnzwQVksFr311luKjY3V7bffLrvdrt///vee\nDBEAAAAAAppHi76mzCmUlZWl2NhYXXLJJZKkEydO6KuvvtK6dev0xRdf6LbbbtPWrVs9GSYAAAAA\nBCyPFn2NzUNUZ/PmzZo+fbrzeceOHfWjH/1IISEh6tatm0JDQ1VYWKjo6OgG22EeIgAAAABwzaNF\nX2PzENXJzc1Vnz59nM/79eunjIwMzZgxQ0ePHlV5ebmioqIabYd5iACgbWD0TgAA3OfRou9c8xAV\nFhbWu/xTkoYMGaL3339fY8eOlTFGS5YskcVi8WSYAAAAABCwmKcPANBq0NPnHvIjALQdjeXIoAaX\nAAAAAABaPYo+AAAAAAhgFH0AAAAAEMAo+gAAAAAggFH0AQAAAEAAo+gDAAAAgABG0QcAAAAAAYyi\nDwAAAAACGEUfALSwgwcP6ODBA74OAwAAQBJFHwC0uMzMl5SZ+ZKvwwAAAJBE0QcALergwQPKy/tI\neXkf0dsHAAD8AkUfALSg03v46O0DAAD+gKIPAAAAAAIYRR8AtKAxY8a7fAwAAOArIb4OAAACSc+e\nvRQXd7nzMQAAgK9R9AFAC6OHDwAA+BOKPgBoYfTwAQAAf8I9fQAAAAAQwCj6AAAAACCAUfQBAAAA\nQACj6AMAAACAAEbRBwAAAAABjKIPAAAAAAIYUzYAANAGvPDCs9q69a8ulzkcjvPadlDQ2eeQExJG\nKClp2nltFwDQMujpAwAAAIAAZjHGGF8Hcb6OHSv2dQgAAC/o3DnS1yG0KuRHAGg7GsuR9PQBAAAA\nQACj6AMAAACAAEbRBwAAAAABzKOjdxpjlJKSovz8fFmtVqWlpSkmJkaSdPz4cSUnJ8tiscgYo7y8\nPM2ePVsTJkyQJBUUFCgxMVFPP/20unXr5skwAQDwqubkx9GjR2vevHn68ssvZbPZtGTJEnXt2tXH\n7wQA0Bp4tOjbvn27KisrtWnTJu3fv1/p6elau3atJOl73/ueMjIyJEn79u3Tww8/rPHjx0uSqqur\ntWTJEoWFhXkyPAAAfKI5+fH5559XRESEXnzxRR06dEhLly7Vk08+6cu3AQBoJTx6eefevXs1aNAg\nSVLv3r2Vm5vr8nWpqalaunSpLBaLJGnFihVKSkrShRde6MnwAADwiebkx08++USDBw+WJHXr1k2f\nffaZ1+IFALRuHi36SkpKFBn53dChISEhZ00Am5WVpdjYWF1yySWSpMzMTHXq1EkDBw5UAMwmAQDA\nWZqTH3v27KmdO3dKqu0B/Pbbb8mTAIAm8ejlnTabTXa73fnc4XAoKKh+nbl582ZNnz7d+TwzM1MW\ni0XZ2dnKy8vTnDlz9Oijj6pTp04NtsO8TQCA1qQ5+TExMVGffvqpJk+erL59+6pXr17OK2QaQn4E\nAEge7unr27evdu3aJan2rGRsbOxZr8nNzVWfPn2czzdu3KiMjAxlZGQoLi5OK1asaLTgAwCgtWlO\nfvzwww911VVX6bnnntPw4cOdA78AAHAuHu3pi4+PV3Z2tiZOnChJSk9P15YtW1RWVqZx48apsLCw\n3uUtZzrXGUwAAFqj5uTHSy65RH/84x/12GOP6YILLlBaWpovQgcAtEIWww0BAAAAABCwmJwdAAAA\nAAIYRR8AAAAABDCKPgAAAAAIYB4dyKU1qK6u1vz583XkyBFVVVXp1ltv1dChQ70aw/79+3X//fcr\nIyNDBw8e1LJlyxQcHCyr1aqVK1cqOjra4zG42g9dunRRamqq12NxOBxauHChDh06pKCgIC1dulRV\nVVU+2S+SVFBQoMTERD399NMqLy/3WRxjxoyRzWaTJF188cWaNm2aT47P+vXrlZWVpaqqKk2aNEm9\nevXySRyvvPKKc4qXiooK5eXl6cUXX/RJLNXV1ZozZ46OHDmikJAQpaam+uSzUllZqXnz5unLL7+U\nzWbT4sWLVVpa6vU4Tv8/7fPPP9fcuXMVFBSk7t27a8mSJZKkDRs26PXXX5fFYtHgwYP129/+1qMx\noflOP57+0n56erp++MMfasKECV6JobHfCt6MxVV+vOyyy7wex+lOz5HdunXzSSxn5sfly5f7JA7p\n7ByZmJjok1hc5cjs7GzZbDavxuIqP/rqc+IqR9bNg+qtWJqSHyWpsLBQSUlJeu2112S1WlumcdPG\nvfzyy2b58uXGGGNOnjxphgwZ4tX2H3/8cTNy5EgzYcIEY4wxU6ZMMXl5ecYYYzZt2mTS09O9Esfp\n+6GoqMgMGTLEZ7Fs27bNzJ8/3xhjzJ49e8xtt93ms1iqqqrMb3/7WzN8+HDz6aef+iyOiooKM3r0\n6Hp/80Use/bsMbfeeqsxxhi73W5Wr17ts31yuqVLl5qXXnrJZ7Fs377d3H333cYYY95++20zc+ZM\nn8SyceNGs2jRImOMMYcOHTK//OUvvR7Hmf+n3Xrrrea9994zxhizePFis23bNvP555+bxMRE5zoT\nJ040+fn5Ho0LzXPm8fR1+wUFBeZXv/qViY+PN5s2bfJaHK5+KxQWFno9Flf50Vf7xJj6OfKzzz7z\nSSyu8qOv9omrHOnL41OnLkf6IpbT82N2draZOXOmz/bJ6Tnys88+M7/85S+9GktT8qMxxuzevduM\nGjXK9OvXz1RUVLRY+23+8s7rrrtOd911l6TaM2ghId7t/Lzkkku0Zs0a5/OHHnpIPXr0kFR7diQ0\nNNQrcZy+H2pqahQSEqKHH37YJ7EMGzZMqampkqQjR46oQ4cOPtsvK1asUFJSki688EJZLBafxZGX\nl6fS0lLdfPPNmjFjhvbv3++TWN566y3Fxsbq9ttv12233aZrrrnGZ/ukzocffqhPPvlE48aN81ks\nl156qWpqamSM0alTp9SuXTufxPLJJ59o8ODBzpg+++wzr3+Pz/w/7cCBA/rpT38qSRo8eLDeeecd\ndenSRU888YTzNb743KBpzjyevm6/tLRUM2fO1I033ujVOFz9VvBFLK7yY1lZmU/2iVQ/R0rySSyu\n8qOv9omrHOnL4yPVz5G++Myenh+Li4vVrl07n+2T03Nkt27d9Nlnn3k1lqbkR0kKDg7Whg0b1KFD\nhxZtv80Xfe3bt1d4eLhKSkp01113KTk52avtx8fHKzg42Pn8e9/7niTpn//8p55//nnNmDHDK3G4\n2g+dOnXySSySFBQUpLlz5yotLU033HCDT/ZLZmamOnXqpIEDB8r8d2YTXx2fsLAw3XzzzXryySeV\nkpKi2bNnOy/R82YsJ06cUG5urlatWqWUlBTNmjXLZ/ukzvr163XHHXdI8t3xiYiI0JdffqmEhAQt\nXrxYU6dO9UksPXv21M6dOyXVTvj97bffev1zcub/aea0WYEiIiJUXFys4OBgdezYUVLtj8bLL7/c\neYkN/MuZx9PX7V988cX6yU9+4vU4XOXIiy66yCexnJkffRWHqxzpi1hc5cf/+Z//8ck+cZUjfXV8\n6pyeI33x/XGVH321T1zlyC5dungtlqbkR0m66qqr1KFDh3rLW0Kbv6dPkr7++mvdcccdmjJliq6/\n/npfh6PXX39d69at0/r16xUVFeW1dl3tB1/FIkn33XefCgoKNG7cOL3++uvKysryaix118JnZ2cr\nLy9Pc+bM0aOPPqo9e/Z4fZ9ceumlzh/Fl156qTp27Khjx45p7969Xo2lY8eO+tGPfqSQkBB169ZN\noaGhKiws1LvvvuuTz0lxcbEOHz6s/v37O//mi8/shg0bNGjQICUnJ+vo0aOaNm2aXnvtNW3fvt2r\nsSQmJurTTz/V5MmT1bdvX/Xq1UsWi8Wn3+OgoO/OLdrtdl1wwQWSvru3IjIyUikpKV6NCWgOf/qt\ncGZ+DAsL83oMDeXIuhPG3tJQfvz+97/v1TikhnOkt+79P5OrHOltDeXHFrtPzQ0N5UhfaSg/1mnp\n2Np8T9/x48d1880363e/+51Gjx7t63D06quv6rnnnlNGRoYuuugir7Xraj/4KpZXX31V69evlySF\nhoYqKChIb775ptdj2bhxozIyMpSRkaG4uDitWLFCb731lk/2ycsvv6z77rtPknT06FHZ7Xbt2bPH\n67H069dPu3fvdsZRXl6uf/zjHz7ZJ5L03nvvacCAAc7nvvrMdujQwTmIQGRkpKqrq/X66697PZYP\nP/xQV111lZ577jkNHz5cMTEx2rx5s8+OjyRdfvnleu+99yRJ//jHP9SvXz9J0m233aaePXsqJSXF\np0kXTdPSZ5xbW/v+8lvBVX48/YejN7nKkd4u+CTX+bFz585ej0NynSO9faLtdGfmSF9wlR8dDodP\nYnGVI32pofxYh56+FrZu3TqdOnVKa9eu1Zo1a2SxWPTEE0/45AyEw+HQ8uXL1aVLF/32t7+VxWJR\n//79nd3ynnTmfnA4HPrkk098Esu1116refPmacqUKc4R0+bNm+eTWOpYLBbV1NT47PiMHTtW8+bN\n06RJkxQUFKS0tDTnCKvejGXIkCF6//33NXbsWBljtHjxYt1zzz0+OzaHDh1y/qfty+/P9OnTNX/+\nfE2ePFnV1dVKTk5Wamqq12O55JJL9Mc//lGPPfaYLrjgAi1btkw33HCDT787c+bM0aJFi1RVVaUf\n/ehHSkhI0Pbt2/X++++rqqpKu3btksVi0axZs9S7d2+vxQX3+Low93X7/vJb4cz8uGDBAp/8XjmT\nL4/Pmflx+fLlPiuEz8yRS5Ys8em+OT1H+sqZ+XHWrFk+6ZmWzs6RaWlpPomjjqv8eLqW/uxYjK9P\nnwEAAAAAPKbNX94JAAAAAIGMog8AAAAAAhhFHwAAAAAEMIo+AAAAAAhgFH0AAAAAEMAo+gAAAAAg\ngFH0AU3kywl5m+qVV17RvHnzfNb+1KlT9d577yk3N1eLFi2SJL300kt6/fXXfRYTACDw/Otf/9L9\n998vScrKytLq1atbdJtAoGnzk7MDTfXKK6/4OoRW44orrtAVV1whSfrggw905ZVX+jgiAEAg+fTT\nT1VQUCBJGjp0qIYOHdqi2wQCDUUfICknJ0ePPfaYjDH64osvdO211yoyMlLbt2+XJK1fv14DBw5U\nXl6eHnnkER09elSHDx/W119/rbFjx+rWW29tcNtHjx7V7NmzVVZWpqCgIC1cuFA/+clP9MYbb2jD\nhg2qqKhQeXm5li1bpp/+9KeaOnWqLr/8cr399tuqrKzUggULlJGRoU8//VTTp0/X9OnT9cgjj+jQ\noUP64osvVFRUpAkTJuiXv/xlvXb/9a9/6b777lN5ebmioqJ077336qKLLtLTTz+tv/zlLwoODtaP\nf/xjLV261O3Yhw4dql/84hd6//33ZbFYtHz5csXFxdXbn6tXr9btt9+urKws7dmzR507d9bAgQPP\n80gBAFqLnJwcrVu3TmFhYfr000/Vo0cPPfDAAwoJcf3zc/fu3Vq1apVqamp08cUXKzU1VR06dNCK\nFSv0zjvvKCgoSL/4xS80bdo0rVq1SqWlpVq3bp0uvPBC5eTkKD09XUOHDtX111+vnTt3KiQkRMnJ\nyV2HtX0AAAdrSURBVHrqqaf0+eefa86cOUpISNDHH3+s1NRUlZWVqaCgQL/85S9100031dvmr3/9\na6Wlpendd9+VxWLRjTfeqFtuuUU5OTn6wx/+IIfDodjYWN144436wx/+oKCgIHXo0EEPPPCAOnbs\n6OU9DTSBAWD27Nlj+vXrZ7755htTVlZm/vd//9e89NJLxhhj5s2bZ5555hkTFxdnjDFm9erVZvz4\n8aa6utoUFBSYPn36mOLi4ga3vXr1avPkk08623nqqaeMw+EwM2bMMCdOnDDGGPPnP//Z3HrrrcYY\nY6ZMmWLS09Od61577bWmoqLCHDlyxPzsZz9z/v3GG280ZWVlpri42MTHx5uPPvrIZGZmmrlz55rK\nykpz4403mq+//toYY8zu3bvNjBkzTHV1tRkwYICprq42DofDpKSkmKNHj7oVuzHGXHPNNWbNmjXG\nGGOysrLMDTfc4Iw9JyfH7Nmzx0ydOtUYY8zcuXPNK6+84tbxAAC0fnv27DF9+vRx5pmxY8eaHTt2\nuHxtQUGBuemmm8ypU6eMMcZs2rTJLFiwwBw5csSMGDHCGGNMRUWF+d3vfmcqKiqc+c4YU+/xNddc\nYzIyMowxtfln8uTJpqamxuTk5JjRo0cbY4xJS0sz77zzjjHGmM8//9z06dPnrO1s3LjR3HHHHcYY\nY8rKyszYsWPNzp07zZ49e8zPfvYzU1JSYowxZurUqebDDz80xhiTkZFhsrOzW2jvAS2Lnj7gv7p3\n767vf//7kqSoqCgNGDBAktSlSxedOnWq3muvvPJKBQcHKzo6Wh07dlRxcbFsNpvL7f785z/XnXfe\nqQMHDmjIkCGaPHmyLBaLVq9erR07dujQoUPKyclRcHCwc53BgwdLki666CL17t1bVqtVXbp0UXFx\nsfM1I0aM+P/t3V1Ik30Yx/HvvKk2K43QGAuKqWl0UKgMxDCYkyIiDIyVaXrQQUapqyR2UhBUkINe\nIA/yeEcZbBB1YgehECoSIqh45Au9nIWDUkq3uwMf98yeOX3kCR/m73O27eb6X/ufXFzX7v1vrFYr\nAB6Ph76+vvh0cXJykunpaa5cuYJpmgDMzs5iGAYlJSXU1NTg8Xioq6tjz549K+5JstyXeL1eANxu\nN36/n5mZmTXssoiIbCaFhYXxOpOfn79irRgeHubLly80NDRgmiaxWIxdu3Zht9uxWq3U1tbidrvx\n+Xxs3bo15ZoVFRXAYg212+1kZGTgcDiIRCIA+P1+ent76ezsZHx8nLm5uX/E6O/vj/+X32q1cvr0\nafr6+nC73TidTrZv3w4s1t+rV69SVVWFx+OhvLx8fRsl8ofpIBeRv2zZsmXZ68Qm7He/F5ylxiqZ\nkpISXr9+TUVFBW/evKGpqYnZ2VnOnj3Lp0+fcLlcXLx4cVmMxFxWyiPx9phoNLrsumg0yr59+wiF\nQoTDYUKhEMFgEICOjo74LZ2XLl1icHDwX+WeLC/TNFPul4iIbE6J9dJisax4XTQapbS0NF63Xr58\nydOnT8nIyODFixf4fD5mZmbwer1MTU2lXHO1Gtra2srbt28pKCjg+vXrSWP8XtdN02RhYQGAbdu2\nxd9vbGwkGAyyf/9+AoEAz58/T5mbyEZR0yfyhwUCAcLhMGfOnOHOnTuMjo4yNTWFYRg0NTVRVlZG\nT08PsVhs1ViJRai7u5v5+XkikQjv3r1b9n+5vLw8IpFIvKHr6uqira2Nr1+/cvLkSQoLC2lububo\n0aOMj4+vKffbt28zOjoa/2zpRM7u7m7y8vLYuXNn0hiGYTA/P7/qdxMRkc3ryJEjDA0NMTk5CSwO\nKNvb2xkbG6O+vh6Xy8WtW7c4cOAAExMTGIZBNBpd11rv37+npaWFyspKBgYGgL+Hl0sxy8rKCIfD\nxGIx5ubmePXqVdJDybxeL9++faOhoYHGxkZGRkbWtwEif5hu7xRJItk0cqUJZarJJSw+xuDmzZuE\nQiEMw+Du3bsUFRVx8OBBTpw4QWZmJi6Xi8+fP68aL/Ezq9XKhQsX+P79O5cvXyY/P5/h4WFgcbL6\n5MkT7t+/z8+fP9mxYwcPHz5k9+7dnD9/npqaGmw2Gw6HI+WjKJLlvuTDhw90dXWRmZlJe3v7irmX\nl5fz+PFjsrOzOX78eMq9EhGRzSknJ4cHDx7g8/mIxWLY7XYCgQDZ2dkUFxdz6tQpbDYbhw4d4tix\nY0xPT9PR0cGjR49wOp3xOKvVZIDm5mZqa2vJysrC6XSyd+9ePn78yOHDh+MxW1tbmZiYoLq6moWF\nBaqrq6mqqoo3iUtu3LiB3+/HMAxsNlvKw9FENpLFTHVfmoj8Lz179gyAa9eubcj6lZWVBINBHA7H\nhqwvIiIiImunX/pE/gODg4Pcu3dv2YTRNE0sFgudnZ3k5uZuYHaprSf3tUxSRUREEv348YNz584l\nrTctLS243e4NzE4kvemXPhERERERkTSmg1xERERERETSmJo+ERERERGRNKamT0REREREJI2p6RMR\nEREREUljavpERERERETSmJo+ERERERGRNPYLUX0c10FNpOoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe66d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "\n",
    "sns.boxplot(x='min_samples_split', y='mean', data=clf_rf_df.ix[((clf_rf_df['criterion']=='gini') \n",
    "                                                               & (clf_rf_df['min_samples_leaf']==1))\n",
    "                                                               ], ax = axis1)\n",
    "axis1.set_title('criterion=gini, min_samples_leaf=1')\n",
    "\n",
    "sns.boxplot(x='n_estimators', y='mean', data=clf_rf_df.ix[((clf_rf_df['criterion']=='gini') \n",
    "                                                               & (clf_rf_df['min_samples_leaf']==1))\n",
    "                                                              & (clf_rf_df['min_samples_split']==12)\n",
    "                                                              ], ax = axis2)\n",
    "axis2.set_title('criterion=gini, min_samples_leaf=1, min_samples_split=12');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O23H3Nvqwo87+OIIgiC8rMgXFW5C2vDkl6tDlcPqi3dI2u425JRsSsWqvfLKZkxFOPTJi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\n8ma2HCmOtXnPt8ldUKTn2Qh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kfIQiMdz51Hzc+vg80zFWcrm20Yc7Js/HtLl8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\nBXe9xwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe92eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1, figsize=(15,5)) \n",
    "clf_rf_df.set_index(['criterion', u'min_samples_leaf', u'min_samples_split', u'n_estimators']).plot(ax=ax1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sumarry and export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULTS: \n",
      "   SVC:              0.8144  Kaggle result:   0.76077\n",
      "   SVC (norm data):  0.8234  Kaggle result:   0.75120\n",
      "   Decision Tree:    0.8211  Kaggle result:   0.77512\n",
      "   Extra Tree:       0.8436  Kaggle result:   0.76555\n",
      "   Random Forest     0.8425  Kaggle result:   0.79426\n"
     ]
    }
   ],
   "source": [
    "print 'RESULTS: '\n",
    "print '   SVC:             ', clf_svm.best_score_.round(4), ' Kaggle result:   0.76077'\n",
    "print '   SVC (norm data): ', clf_svm_norm.best_score_.round(4), ' Kaggle result:   0.75120'\n",
    "print '   Decision Tree:   ', clf_dt.best_score_.round(4), ' Kaggle result:   0.77512'\n",
    "print '   Extra Tree:      ', clf_et.best_score_.round(4), ' Kaggle result:   0.76555'\n",
    "print '   Random Forest    ', clf_rf.best_score_.round(4), ' Kaggle result:   0.79426'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BEST RESULT ON KAGGLE: Random Forest  (0.8425),  Kaggle result:   0.79426"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Test set predictions and saving results to file\n",
    "# predictions_export(clf_rf.predict(test_data), 'Titanic_Results_v4_rf.csv')"
   ]
  }
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