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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Kaggle Titanic Competition\n",
      "\n",
      "This notebook shows how to do a logistic regression using pandas and statsmodels to predict whether titanic passengers will survive!\n",
      "\n",
      "I used this [nice tutorial](http://blog.yhathq.com/posts/logistic-regression-and-python.html) as a reference.\n",
      "\n",
      "## Part 1: Data cleaning\n",
      "\n",
      "Let's start by firing up the ipython notebook and loading the data.  When you're doing data analysis you'll often find that you spend a lot more time than you would have guessed inspecting and cleaning the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set some ipython defaults\n",
      "%pylab inline\n",
      "rcParams['figure.figsize'] = 8, 6  # that's default image size for this interactive session\n",
      "font = {'family' : 'normal',\n",
      "        'weight' : 'bold',\n",
      "        'size'   : 16}\n",
      "\n",
      "rc('font', **font)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# first let's load up the data and look at it\n",
      "\n",
      "import pandas as pd  # used to keep data looking pretty\n",
      "import numpy as np\n",
      "\n",
      "# load the raw_data first\n",
      "raw_data = pd.read_csv('titanic.csv')\n",
      "raw_columns = raw_data.columns\n",
      "# We'll also re-index\n",
      "raw_data.index = raw_data['PassengerId']\n",
      "# and we'll take all of our training data and put it into the \"real_train\" index\n",
      "real_train = raw_data.index\n",
      "\n",
      "# now let's load the test data set\n",
      "test_data = pd.read_csv('titanic_test.csv')\n",
      "# We'll make the survival NA for the test data set\n",
      "test_data['Survived'] = np.NaN\n",
      "test_data.index = test_data['PassengerId']\n",
      "real_test = test_data.index\n",
      "\n",
      "# Now let's merge the dataframes\n",
      "# I'm going to merge them together for convenience so that any transformations I make are applied to both data sets\n",
      "data = pd.concat([raw_data, test_data])\n",
      "\n",
      "# remove raw training and testing data\n",
      "del raw_data\n",
      "del test_data\n",
      "\n",
      "# Show the first 5 rows\n",
      "print(data.head())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "             Age Cabin Embarked     Fare  \\\n",
        "PassengerId                                \n",
        "1             22   NaN        S   7.2500   \n",
        "2             38   C85        C  71.2833   \n",
        "3             26   NaN        S   7.9250   \n",
        "4             35  C123        S  53.1000   \n",
        "5             35   NaN        S   8.0500   \n",
        "\n",
        "                                                          Name  Parch  \\\n",
        "PassengerId                                                             \n",
        "1                                      Braund, Mr. Owen Harris      0   \n",
        "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0   \n",
        "3                                       Heikkinen, Miss. Laina      0   \n",
        "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0   \n",
        "5                                     Allen, Mr. William Henry      0   \n",
        "\n",
        "             PassengerId  Pclass     Sex  SibSp  Survived            Ticket  \n",
        "PassengerId                                                                  \n",
        "1                      1       3    male      1         0         A/5 21171  \n",
        "2                      2       1  female      1         1          PC 17599  \n",
        "3                      3       3  female      0         1  STON/O2. 3101282  \n",
        "4                      4       1  female      1         1            113803  \n",
        "5                      5       3    male      0         0            373450  \n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# If I want to look at just the training data set, I can do that too\n",
      "print(data.ix[real_train].describe())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "              Age        Fare       Parch  PassengerId      Pclass       SibSp    Survived\n",
        "count  714.000000  891.000000  891.000000   891.000000  891.000000  891.000000  891.000000\n",
        "mean    29.699118   32.204208    0.381594   446.000000    2.308642    0.523008    0.383838\n",
        "std     14.526497   49.693429    0.806057   257.353842    0.836071    1.102743    0.486592\n",
        "min      0.420000    0.000000    0.000000     1.000000    1.000000    0.000000    0.000000\n",
        "25%     20.125000    7.910400    0.000000   223.500000    2.000000    0.000000    0.000000\n",
        "50%     28.000000   14.454200    0.000000   446.000000    3.000000    0.000000    0.000000\n",
        "75%     38.000000   31.000000    0.000000   668.500000    3.000000    1.000000    1.000000\n",
        "max     80.000000  512.329200    6.000000   891.000000    3.000000    8.000000    1.000000\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Check that we've gotten rid of all the NaNs or other bad values\n",
      "def is_bad(series):\n",
      "    \"\"\"Returns a binary series indicating whether there are any bad values (null, +/- inf, or '')\n",
      "    input: series\n",
      "    return: Boolean series\n",
      "    \"\"\"\n",
      "    return pd.isnull(series) | series.apply(lambda x: (x == np.inf or x == -np.inf or x == ''))\n",
      "print(data.apply(is_bad).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Age             263\n",
        "Cabin          1014\n",
        "Embarked          2\n",
        "Fare              1\n",
        "Name              0\n",
        "Parch             0\n",
        "PassengerId       0\n",
        "Pclass            0\n",
        "Sex               0\n",
        "SibSp             0\n",
        "Survived        418\n",
        "Ticket            0\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Crap, there's a lot of missing values!  Let's fix Age, Embarked, and Fare."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's fix the missing embarked data\n",
      "\n",
      "# first we'll see what the most common embarkment types are:\n",
      "print(data.groupby('Embarked').count()['PassengerId'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Embarked\n",
        "C           270\n",
        "Q           123\n",
        "S           914\n",
        "Name: PassengerId, dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# So let's fill the missing values with S, which is by far the most commong embarkation point\n",
      "data['Embarked'] = data['Embarked'].fillna('S')\n",
      "print(data.groupby('Embarked').count()['PassengerId'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Embarked\n",
        "C           270\n",
        "Q           123\n",
        "S           916\n",
        "Name: PassengerId, dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data[is_bad(data['Fare'])])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "              Age Cabin Embarked  Fare                Name  Parch  \\\n",
        "PassengerId                                                         \n",
        "1044         60.5   NaN        S   NaN  Storey, Mr. Thomas      0   \n",
        "\n",
        "             PassengerId  Pclass   Sex  SibSp  Survived Ticket  \n",
        "PassengerId                                                     \n",
        "1044                1044       3  male      0       NaN   3701  \n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# and let's fix the missing fare value.  While I'm at it, I'll also nudge any zero values so that I can later take the logarithm\n",
      "# Other than being old, the guy doesn't seem unusual, so I'll just use the median fare of his class and gender\n",
      "data['fFare'] = data['Fare'].fillna(data[(data['Sex'] == 'male') & (data['Pclass'] == 3)]['Fare'].dropna().median())\n",
      "data['fFare'] = data['Fare'].apply(lambda x: .01 if (x == 0 or pd.isnull(x))  else x)\n",
      "data['zFare'] = data['Fare'].apply(lambda x: x == 0).astype(bool) # I'm guessing these people were comp'd, may be important!\n",
      "print(data['fFare'].describe())\n",
      "print(data[data['zFare']]['fFare'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "count    1309.000000\n",
        "mean       33.270181\n",
        "std        51.746974\n",
        "min         0.010000\n",
        "25%         7.895800\n",
        "50%        14.454200\n",
        "75%        31.275000\n",
        "max       512.329200\n",
        "dtype: float64\n",
        "PassengerId\n",
        "180            0.01\n",
        "264            0.01\n",
        "272            0.01\n",
        "278            0.01\n",
        "303            0.01\n",
        "414            0.01\n",
        "467            0.01\n",
        "482            0.01\n",
        "598            0.01\n",
        "634            0.01\n",
        "675            0.01\n",
        "733            0.01\n",
        "807            0.01\n",
        "816            0.01\n",
        "823            0.01\n",
        "1158           0.01\n",
        "1264           0.01\n",
        "Name: fFare, dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fill in empty cells\n",
      "data['fSibSp'] = data['SibSp'].fillna(data['SibSp'].dropna().median())\n",
      "data['fParch'] = data['Parch'].fillna(data['Parch'].dropna().median())\n",
      "print(data[['fSibSp', 'fParch']].describe())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "            fSibSp       fParch\n",
        "count  1309.000000  1309.000000\n",
        "mean      0.498854     0.385027\n",
        "std       1.041658     0.865560\n",
        "min       0.000000     0.000000\n",
        "25%       0.000000     0.000000\n",
        "50%       0.000000     0.000000\n",
        "75%       1.000000     0.000000\n",
        "max       8.000000     9.000000\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# For now let's just use a simple class model to fill in missing Ages.\n",
      "avg_age_dict = data.groupby(['Pclass', 'Sex']).mean()['Age'].to_dict()\n",
      "data['avg_Age'] = [avg_age_dict[(c, s)] for c, s in\n",
      "                  zip(data['Pclass'], data['Sex'])]\n",
      "\n",
      "data['fAge'] = data['Age'].fillna(data['avg_Age'])\n",
      "\n",
      "#Also fix any zeros:\n",
      "data['fAge'] = data['fAge'].apply(lambda x: 1 if x == 0 else x)\n",
      "\n",
      "print(data.apply(is_bad).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Age             263\n",
        "Cabin          1014\n",
        "Embarked          0\n",
        "Fare              1\n",
        "Name              0\n",
        "Parch             0\n",
        "PassengerId       0\n",
        "Pclass            0\n",
        "Sex               0\n",
        "SibSp             0\n",
        "Survived        418\n",
        "Ticket            0\n",
        "fFare             0\n",
        "zFare             0\n",
        "fSibSp            0\n",
        "fParch            0\n",
        "avg_Age           0\n",
        "fAge              0\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Random forest regression to predict age\n",
      "\n",
      "Since we think age is pretty important, let's use a random forest to predict it based on as many of the other variables as possible.  \n",
      "\n",
      "First I'm going to go about extracting even more data from some of the variables as well as get them in the right form to use."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# First, we need to convert categorical variables to integer form\n",
      "cat_map = {}\n",
      "cat_cols = [col for col, t in data.dtypes.to_dict().items() if t == np.dtype('O')]\n",
      "def factorize(data, cat_cols, cat_map=cat_map):\n",
      "    \"\"\"Convenience function for making categorical Series from string Series\n",
      "    inputs: dataframe, list of string categorical columns\n",
      "    outputs: dataframe, cat_map\n",
      "    \"\"\"\n",
      "    for col in cat_cols:\n",
      "        temp, rev = pd.factorize(data[col])\n",
      "        # temp = pd.Categorical.from_array(data[col])\n",
      "        # print(temp)\n",
      "        # print(rev)\n",
      "        cat_map['_' + col] = pd.Factor(temp, rev)\n",
      "        data['_' + col] = temp\n",
      "    return data, cat_map\n",
      "data, cat_map = factorize(data, cat_cols)\n",
      "print(data.apply(is_bad).sum())\n",
      "print([c == np.dtype('O') for c in data.dtypes])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Age             263\n",
        "Cabin          1014\n",
        "Embarked          0\n",
        "Fare              1\n",
        "Name              0\n",
        "Parch             0\n",
        "PassengerId       0\n",
        "Pclass            0\n",
        "Sex               0\n",
        "SibSp             0\n",
        "Survived        418\n",
        "Ticket            0\n",
        "fFare             0\n",
        "zFare             0\n",
        "fSibSp            0\n",
        "fParch            0\n",
        "avg_Age           0\n",
        "fAge              0\n",
        "_Name             0\n",
        "_Embarked         0\n",
        "_Sex              0\n",
        "_Ticket           0\n",
        "_Cabin            0\n",
        "dtype: int64\n",
        "[False, True, True, False, True, False, False, False, True, False, False, True, False, False, False, False, False, False, False, False, False, False, False]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/core/categorical.py:197: FutureWarning: Factor is deprecated. Use Categorical instead\n",
        "  warn(\"Factor is deprecated. Use Categorical instead\", FutureWarning)\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's pick the columns to use\n",
      "def is_too_many(df, col):\n",
      "    \"Checks if there are too many categorical variables\"\n",
      "    return len(df[col].drop_duplicates()) > 120\n",
      "\n",
      "def find_clean_cols(data):\n",
      "    clean_features =  set([col for col, t in data.dtypes.to_dict().items()\n",
      "                          if (t != np.dtype('O') and\n",
      "                              not any(is_bad(data[col])) and not\n",
      "                              (is_too_many(data, col) and\n",
      "                              t == np.dtype('int64') ))])\n",
      "\n",
      "    clean_features = clean_features - set(['avg_Age', 'Parch', 'SibSp', 'Age_guess', '_Age', 'fAge', 'sqrt_fare', 'log_fare', 'emp prediction', 'Prediction', 'sqrt_age', 'log_age', 'Prediction %', 'survival chance', 's_Age', 'Best LLRP', 'Best AIC', 'Best BIC'])\n",
      "    clean_features = clean_features - set(['sttl_Miss', 'sttl_Mr', 'Tick_pref', 'sttl_Lady', 'sttl_Dr', 'sttl_Rev', 'sttl_Mst', 'sttl_Mrs', 'sttl_Mlle'])\n",
      "    clean_features = list(clean_features)\n",
      "    return clean_features\n",
      "\n",
      "clean_features = find_clean_cols(data)\n",
      "print(clean_features)\n",
      "# print(data.apply(is_bad).sum()[clean_features])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['_Embarked', 'fSibSp', 'zFare', 'fParch', 'Pclass', 'fFare', '_Sex']\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Verify that our data is now clean\n",
      "print(data.apply(is_bad).sum()[clean_features + ['_Age']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "_Embarked     0\n",
        "fSibSp        0\n",
        "zFare         0\n",
        "fParch        0\n",
        "Pclass        0\n",
        "fFare         0\n",
        "_Sex          0\n",
        "_Age        NaN\n",
        "dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So now we have clean data to use!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Simple models (logistic regression)\n",
      "\n",
      "In this section we'll look at the performance of two simple models: just using the most likely outcome of the gender, class tuple; and doing a very simple logistic regression."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see that survival rates of females are a lot higher, and higher passenger classes also matter.  Let's visualize the data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Okay, so now we have a sense of the data and we know what we should expect.  To start, let's first do a really simple logistic regression using just those variables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's clean up the data for doing the regression\n",
      "# We'll start simple with just a few columns\n",
      "\n",
      "# Define some easy columns to use\n",
      "simple_columns = []\n",
      "\n",
      "# Create dummy variables\n",
      "# I use .ix[:, :-1] to avoid multicollinearity / dummy variable trap\n",
      "sex_dummies = pd.get_dummies(data['Sex'], prefix='sex').ix[:, :-1]\n",
      "class_dummies = pd.get_dummies(data['Pclass'], prefix='class').ix[:, :-1]\n",
      "embark_dummies = pd.get_dummies(data['Embarked'], prefix='embarked').ix[:, :-1]\n",
      "\n",
      "bin_cols = list(sex_dummies.columns.values) + list(class_dummies.columns.values) + list(embark_dummies.columns.values)\n",
      "\n",
      "# merge them together.  Do it conditionally so that this cell is idempotent\n",
      "for col in bin_cols:\n",
      "    if col in data.columns.values:\n",
      "        data = data.drop(col, 1)\n",
      "data = data.join(sex_dummies, how='inner').join(class_dummies, how='inner').join(embark_dummies, how='inner')\n",
      "\n",
      "# now we'll add an intercept\n",
      "data['const'] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Check for bad data in the training set\n",
      "print(data.ix[real_train, ['const', 'Survived'] + bin_cols].apply(is_bad).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "const         0\n",
        "Survived      0\n",
        "sex_female    0\n",
        "class_1       0\n",
        "class_2       0\n",
        "embarked_C    0\n",
        "embarked_Q    0\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's pick the columns we'll use for regression and quickly look at their values\n",
      "simple_reg_columns = bin_cols + simple_columns\n",
      "simple_reg_columns = ['const'] + simple_reg_columns \n",
      "\n",
      "print(data.ix[real_train, simple_reg_columns].describe())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "       const  sex_female     class_1     class_2  embarked_C  embarked_Q\n",
        "count    891  891.000000  891.000000  891.000000  891.000000  891.000000\n",
        "mean       1    0.352413    0.242424    0.206510    0.188552    0.086420\n",
        "std        0    0.477990    0.428790    0.405028    0.391372    0.281141\n",
        "min        1    0.000000    0.000000    0.000000    0.000000    0.000000\n",
        "25%        1    0.000000    0.000000    0.000000    0.000000    0.000000\n",
        "50%        1    0.000000    0.000000    0.000000    0.000000    0.000000\n",
        "75%        1    1.000000    0.000000    0.000000    0.000000    0.000000\n",
        "max        1    1.000000    1.000000    1.000000    1.000000    1.000000\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Okay, so things look good so far.  Now let's do an actual regression!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We'll use statsmodels for the logistic regression\n",
      "import statsmodels.api as sm\n",
      "\n",
      "# Let's pick the columns we'll use for regression\n",
      "simple_reg_columns = bin_cols + simple_columns\n",
      "simple_reg_columns = ['const'] + simple_reg_columns \n",
      "\n",
      "log_reg_model = sm.Logit(data.ix[real_train,'Survived'], data.ix[real_train, simple_reg_columns])\n",
      "\n",
      "log_reg_result = log_reg_model.fit()\n",
      "print(log_reg_result.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization terminated successfully.\n",
        "         Current function value: 0.459610\n",
        "         Iterations 6\n",
        "                           Logit Regression Results                           \n",
        "==============================================================================\n",
        "Dep. Variable:               Survived   No. Observations:                  891\n",
        "Model:                          Logit   Df Residuals:                      885\n",
        "Method:                           MLE   Df Model:                            5\n",
        "Date:                Sun, 02 Mar 2014   Pseudo R-squ.:                  0.3098\n",
        "Time:                        13:36:27   Log-Likelihood:                -409.51\n",
        "converged:                       True   LL-Null:                       -593.33\n",
        "                                        LLR p-value:                 2.797e-77\n",
        "==============================================================================\n",
        "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "const         -2.4049      0.174    -13.821      0.000        -2.746    -2.064\n",
        "sex_female     2.6142      0.185     14.099      0.000         2.251     2.978\n",
        "class_1        1.8472      0.224      8.236      0.000         1.408     2.287\n",
        "class_2        1.1698      0.227      5.143      0.000         0.724     1.616\n",
        "embarked_C     0.5914      0.228      2.595      0.009         0.145     1.038\n",
        "embarked_Q     0.4483      0.316      1.420      0.156        -0.171     1.067\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's do it with regularization!\n",
      "import statsmodels.api as sm\n",
      "\n",
      "# Set regularization penalty\n",
      "alpha = 0.01 * len(data.ix[real_train]) * np.ones(data.ix[real_train, simple_reg_columns].shape[1])\n",
      "alpha[0] = 0  # don't regularize the constant\n",
      "# print(alpha)\n",
      "\n",
      "log_reg_result_reg = log_reg_model.fit_regularized(method='l1', alpha=alpha)\n",
      "print(log_reg_result_reg.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization terminated successfully.    (Exit mode 0)\n",
        "            Current function value: 0.511942131596\n",
        "            Iterations: 32\n",
        "            Function evaluations: 32\n",
        "            Gradient evaluations: 32\n",
        "                           Logit Regression Results                           \n",
        "==============================================================================\n",
        "Dep. Variable:               Survived   No. Observations:                  891\n",
        "Model:                          Logit   Df Residuals:                      886\n",
        "Method:                           MLE   Df Model:                            4\n",
        "Date:                Sun, 02 Mar 2014   Pseudo R-squ.:                  0.2952\n",
        "Time:                        13:36:27   Log-Likelihood:                -418.16\n",
        "converged:                       True   LL-Null:                       -593.33\n",
        "                                        LLR p-value:                 1.480e-74\n",
        "==============================================================================\n",
        "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "const         -1.8412      0.141    -13.084      0.000        -2.117    -1.565\n",
        "sex_female     2.2744      0.169     13.485      0.000         1.944     2.605\n",
        "class_1        1.2895      0.203      6.338      0.000         0.891     1.688\n",
        "class_2        0.5178      0.208      2.486      0.013         0.110     0.926\n",
        "embarked_C     0.1813      0.215      0.843      0.399        -0.240     0.603\n",
        "embarked_Q          0        nan        nan        nan           nan       nan\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def viz_sm_coefs(result_obj):\n",
      "    params = result_obj.params.copy()\n",
      "    params.sort()\n",
      "    plt.figure(figsize(15, 6))\n",
      "    fig, axes = plt.subplots(nrows=1, ncols=2)\n",
      "    params.plot(kind='bar', ax=axes[0])\n",
      "    axes[0].set_title('Regression Coefficients')\n",
      "    \n",
      "    params_abs = params.apply(abs)\n",
      "    params_abs.sort()\n",
      "    params_abs.plot(kind='bar', ax=axes[1])\n",
      "    axes[1].set_title('Abs(Regression Coefficients)')\n",
      "    return None\n",
      "\n",
      "viz_sm_coefs(log_reg_result)\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x1082ad550>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/python.app/Contents/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['normal'] not found. Falling back to Bitstream Vera Sans\n",
        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zZszQ5MmTJUnt2rXTmWeembpAAIBQSfiJSKZMmaI//elP9d7WvHlz3XXXXYl+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\nhBBCSAtGgzZCCCGEEEIIacH+H2zL4CVa6w3uAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1082ad950>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So we've not completed the logistic regression, and as expected, it looks like pretty much all the variables are significant and in the expected direction (females more likely to survive; lower classes are more likely to die).\n",
      "\n",
      "But this is hard to interpret.  So let's look at the survival prediction rates."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Predict results\n",
      "\n",
      "# Now let's see if they would survive!\n",
      "data['survival chance'] = 0  # can help to initialize for all values\n",
      "data['survival chance'] = log_reg_result.predict(data[simple_reg_columns])\n",
      "# print(data.ix[real_train, 'survival chance'])\n",
      "print(pd.crosstab(data.ix[real_train, 'Sex'], data.ix[real_train, 'Pclass'], values=data.ix[real_train, 'survival chance'], aggfunc=np.mean))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Pclass         1         2         3\n",
        "Sex                                 \n",
        "female  0.908633  0.807757  0.598604\n",
        "male    0.414660  0.237134  0.094528\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So as we expected, we see that females in first class have the highest survival chance at just over 90% whereas males in third class have less than a 10% survival rate.  Let's compare this to the means again:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's compare to the realized values\n",
      "empirical_survival_rates = pd.crosstab(data.ix[real_train, 'Sex'], data.ix[real_train, 'Pclass'], values=data.ix[real_train, 'Survived'], aggfunc=np.mean)\n",
      "print(empirical_survival_rates)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Pclass         1         2         3\n",
        "Sex                                 \n",
        "female  0.968085  0.921053  0.500000\n",
        "male    0.368852  0.157407  0.135447\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# And let's make sure we have enough data points for this to be meaningful\n",
      "print(pd.crosstab(data.ix[real_train, 'Sex'], data.ix[real_train, 'Pclass']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Pclass    1    2    3\n",
        "Sex                  \n",
        "female   94   76  144\n",
        "male    122  108  347\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## More complicated regression with more feature engineering\n",
      "\n",
      "Okay, so that was cool, but as we showed above... we haven't really done anything particularly awesome yet.  In fact, you could argue that we'd actually be better of just going with the empircal survival rates instead of the results of the logistic regression (maybe or maybe not given the low sample size).\n",
      "So let's add some more possibly informative covariates, like age, siblings, and parents and then inspect the results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add more data!\n",
      "more_cols = ['fAge', 'fFare', 'fSibSp', 'fParch']\n",
      "print(data[simple_reg_columns + more_cols].describe())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "       const   sex_female      class_1      class_2   embarked_C   embarked_Q  \\\n",
        "count   1309  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000   \n",
        "mean       1     0.355997     0.246753     0.211612     0.206264     0.093965   \n",
        "std        0     0.478997     0.431287     0.408607     0.404777     0.291891   \n",
        "min        1     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
        "25%        1     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
        "50%        1     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
        "75%        1     1.000000     0.000000     0.000000     0.000000     0.000000   \n",
        "max        1     1.000000     1.000000     1.000000     1.000000     1.000000   \n",
        "\n",
        "              fAge        fFare       fSibSp       fParch  \n",
        "count  1309.000000  1309.000000  1309.000000  1309.000000  \n",
        "mean     29.376186    33.270181     0.498854     0.385027  \n",
        "std      13.169015    51.746974     1.041658     0.865560  \n",
        "min       0.170000     0.010000     0.000000     0.000000  \n",
        "25%      22.000000     7.895800     0.000000     0.000000  \n",
        "50%      26.000000    14.454200     0.000000     0.000000  \n",
        "75%      37.000000    31.275000     1.000000     0.000000  \n",
        "max      80.000000   512.329200     8.000000     9.000000  \n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's even use some transformations of some covariates!\n",
      "data['log_fare'] = data['fFare'].apply(np.log)\n",
      "data['sqrt_fare'] = data['fFare'].apply(np.sqrt)\n",
      "data['log_age'] = data['fAge'].apply(np.log)\n",
      "data['child'] = data['fAge'].apply(lambda x: 1 if x < 18 else 0)\n",
      "data['sqrt_age'] = data['fAge'].apply(np.sqrt)\n",
      "data['fam_size'] = data['fSibSp'] + data['fParch']\n",
      "\n",
      "more_cols = ['fAge', 'fFare', 'fSibSp', 'fParch'] + ['log_fare', 'log_age', 'sqrt_fare', 'sqrt_age', 'fam_size', 'child']\n",
      "\n",
      "for col in more_cols:\n",
      "    if col not in data.columns:\n",
      "        data = data.join(data.ix[real_train, col])    \n",
      "\n",
      "print(data[more_cols].describe())\n",
      "# print(data.head())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "              fAge        fFare       fSibSp       fParch     log_fare  \\\n",
        "count  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000   \n",
        "mean     29.376186    33.270181     0.498854     0.385027     2.845042   \n",
        "std      13.169015    51.746974     1.041658     0.865560     1.292548   \n",
        "min       0.170000     0.010000     0.000000     0.000000    -4.605170   \n",
        "25%      22.000000     7.895800     0.000000     0.000000     2.066331   \n",
        "50%      26.000000    14.454200     0.000000     0.000000     2.670985   \n",
        "75%      37.000000    31.275000     1.000000     0.000000     3.442819   \n",
        "max      80.000000   512.329200     8.000000     9.000000     6.238967   \n",
        "\n",
        "           log_age    sqrt_fare     sqrt_age     fam_size        child  \n",
        "count  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000  \n",
        "mean      3.224335     4.907291     5.255172     0.883881     0.117647  \n",
        "std       0.704259     3.032441     1.326913     1.583639     0.322313  \n",
        "min      -1.771957     0.100000     0.412311     0.000000     0.000000  \n",
        "25%       3.091042     2.809947     4.690416     0.000000     0.000000  \n",
        "50%       3.258097     3.801868     5.099020     0.000000     0.000000  \n",
        "75%       3.610918     5.592406     6.082763     1.000000     0.000000  \n",
        "max       4.382027    22.634690     8.944272    10.000000     1.000000  \n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data[['log_fare', 'log_age', 'sqrt_fare', 'sqrt_age', 'fam_size', 'child']].dtypes)\n",
      "print(data[['log_fare', 'log_age', 'sqrt_fare', 'sqrt_age', 'fam_size', 'child']].apply(is_bad).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "log_fare     float64\n",
        "log_age      float64\n",
        "sqrt_fare    float64\n",
        "sqrt_age     float64\n",
        "fam_size       int64\n",
        "child          int64\n",
        "dtype: object\n",
        "log_fare     0\n",
        "log_age      0\n",
        "sqrt_fare    0\n",
        "sqrt_age     0\n",
        "fam_size     0\n",
        "child        0\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Rescaling the data\n",
      "\n",
      "That's great!  Unfortunately, a lot of ML algorithms are sensitive to the scale of the variables.  We have to **normalize** the variables in order to get around this."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Rescale (demean, unit variance) quantitative columns\n",
      "\n",
      "from sklearn.preprocessing import StandardScaler\n",
      "scale_transforms = {}\n",
      "def make_standard_scale(series, name):\n",
      "    \"\"\"Helper function to demean and transform a series to unit variance\n",
      "    inputs: series, name for outputing series\n",
      "    outputs: transformed series\n",
      "    \"\"\"\n",
      "    temp_scaler = StandardScaler().fit(series)\n",
      "    scale_transforms[name] = temp_scaler\n",
      "    result_series = temp_scaler.transform(series.copy())\n",
      "    return result_series\n",
      "\n",
      "scale_cols = []\n",
      "for col in [col for col, t in data[more_cols].dtypes.to_dict().items()\n",
      "            if t == np.dtype('float64')]:\n",
      "    print(col)\n",
      "    data['s' + col] = make_standard_scale(data[col], col)\n",
      "    scale_cols.append('s' + col)\n",
      "\n",
      "print(data[scale_cols].head())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sqrt_fare\n",
        "sqrt_age\n",
        "fFare\n",
        "log_fare\n",
        "fAge\n",
        "log_age\n",
        "             ssqrt_fare  ssqrt_age    sfFare  slog_fare     sfAge  slog_age\n",
        "PassengerId                                                                \n",
        "1             -0.730618  -0.425779 -0.503027  -0.668734 -0.560331 -0.189338\n",
        "2              1.166388   0.685493  0.734877   1.100280  0.655107  0.587014\n",
        "3             -0.690188  -0.117726 -0.489978  -0.599835 -0.256471  0.047958\n",
        "4              0.785042   0.498269  0.383354   0.872359  0.427212  0.470196\n",
        "5             -0.682892   0.498269 -0.487561  -0.587723  0.427212  0.470196\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Run the logistic regression.  First set the columns\n",
      "\n",
      "alpha_cols = simple_reg_columns + scale_cols\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now actually run the regression\n",
      "more_model = sm.Logit(data.ix[real_train, 'Survived'], data.ix[real_train, alpha_cols])\n",
      "alpha_more = 0.01 * len(data.ix[real_train]) * np.ones(data.ix[real_train, alpha_cols].shape[1])\n",
      "alpha_more[0] = 0\n",
      "\n",
      "more_results = more_model.fit_regularized(method='l1', alpha=alpha_more, n_jobs=4)\n",
      "print(more_results.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization terminated successfully.    (Exit mode 0)\n",
        "            Current function value: 0.501243434109\n",
        "            Iterations: 54\n",
        "            Function evaluations: 54\n",
        "            Gradient evaluations: 54\n",
        "                           Logit Regression Results                           \n",
        "==============================================================================\n",
        "Dep. Variable:               Survived   No. Observations:                  891\n",
        "Model:                          Logit   Df Residuals:                      883\n",
        "Method:                           MLE   Df Model:                            7\n",
        "Date:                Sun, 02 Mar 2014   Pseudo R-squ.:                  0.3173\n",
        "Time:                        13:36:29   Log-Likelihood:                -405.06\n",
        "converged:                       True   LL-Null:                       -593.33\n",
        "                                        LLR p-value:                 2.559e-77\n",
        "==============================================================================\n",
        "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "const         -1.8294      0.156    -11.721      0.000        -2.135    -1.523\n",
        "sex_female     2.1890      0.173     12.651      0.000         1.850     2.528\n",
        "class_1        1.3665      0.269      5.072      0.000         0.838     1.894\n",
        "class_2        0.5445      0.213      2.552      0.011         0.126     0.963\n",
        "embarked_C     0.0841      0.222      0.378      0.705        -0.352     0.520\n",
        "embarked_Q          0        nan        nan        nan           nan       nan\n",
        "ssqrt_fare          0        nan        nan        nan           nan       nan\n",
        "ssqrt_age           0        nan        nan        nan           nan       nan\n",
        "sfFare         0.0176      0.121      0.145      0.885        -0.220     0.255\n",
        "slog_fare      0.1485      0.127      1.172      0.241        -0.100     0.397\n",
        "sfAge               0        nan        nan        nan           nan       nan\n",
        "slog_age      -0.3128      0.084     -3.739      0.000        -0.477    -0.149\n",
        "=============================================================================="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Visualize coefficients\n",
      "viz_sm_coefs(more_results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x106a0f150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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N1Zo1a5Sdna0hQ4bIsiyNGzdOCxYskCR99tlnWrRokdauXau9e/fqu+++U1JS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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108407550>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's do a likelihood ratio test:\n",
      "llr = -2 * (log_reg_result.llf - more_results.llf)\n",
      "df_diff = more_results.df_model - log_reg_result.df_model\n",
      "\n",
      "from scipy.stats import chi2\n",
      "print(1-chi2.cdf(llr, df_diff))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0116586389642\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So it looks like adding more data is helpful!\n",
      "\n",
      "A more convincing analysis would be to repeat the above work, but this time use cross-validation.  Let's do that."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# First let's pick a training set at random and repeat the crosstab approach\n",
      "import random\n",
      "random.seed(0)\n",
      "train = random.sample(data.ix[real_train].index, len(data.ix[real_train].index)/2)\n",
      "test = data.index.drop(list(train) + list(real_test))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's calculate performance!  \n",
      "# I'll make a helper function that we can re-use later to compare performance of\n",
      "# multiple models\n",
      "from sklearn.metrics import confusion_matrix, classification_report\n",
      "\n",
      "def perf_report(df, true_class, pred_class, string_title):\n",
      "    \"\"\"\n",
      "    Prints the confusion matrix and precision, recall of the classifer\n",
      "    inputs: df, true_class, pred_class, string_title\n",
      "    outputs: confustion matrix, class_report\n",
      "    \"\"\"\n",
      "    print(string_title + ' Confusion Matrix')\n",
      "    conf_matrix = confusion_matrix(df[true_class], df[pred_class])\n",
      "    print(conf_matrix)\n",
      "    print(string_title + ' Performance Summary')\n",
      "    class_report = classification_report(np.array(df[true_class]),\n",
      "                                         np.array(df[pred_class]),\n",
      "                                         target_names=['Died', 'Survived'])\n",
      "    print(class_report)\n",
      "    print(string_title + ' accuracy: ', \n",
      "          float(np.diag(conf_matrix).sum())/float(conf_matrix.sum().sum()))\n",
      "    return conf_matrix, class_report\n",
      "\n",
      "data['emp prediction'] = 0\n",
      "female = data['Sex'] == 'female'\n",
      "high_class = data['Pclass'] != 3\n",
      "data.ix[female & high_class, 'emp prediction'] = 1\n",
      "\n",
      "perf_report(data.ix[real_train], 'Survived', 'emp prediction', 'emp prediction')\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "emp prediction Confusion Matrix\n",
        "[[540   9]\n",
        " [181 161]]\n",
        "emp prediction Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.75      0.98      0.85       549\n",
        "   Survived       0.95      0.47      0.63       342\n",
        "\n",
        "avg / total       0.82      0.79      0.77       891\n",
        "\n",
        "('emp prediction accuracy: ', 0.7867564534231201)\n",
        "()\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# let's look at the perf report for the best performing regressions:\n",
      "def make_pred(result_model, input_df, output_df, ix, title, cols=None):\n",
      "    \"\"\"\n",
      "    Makes a series prediction\n",
      "    inputs: result_model, input_df, output_df, ix, title\n",
      "    outputs: outputdf[title]\n",
      "    \"\"\"\n",
      "    if title not in list(output_df.columns):\n",
      "        output_df[title] = 0\n",
      "    if cols == None:\n",
      "        cols = list(sig_cols[result_model.ix])\n",
      "    input_data = input_df.ix[ix, cols]\n",
      "    output_df.ix[ix, title] = result_model.predict(input_data)\n",
      "    output_df.ix[ix, title] = output_df.ix[ix, title].apply(lambda x: 1 if x > 0.5 else 0)\n",
      "    # print(output_df[title])\n",
      "    return output_df[title]\n",
      "\n",
      "data['log simple'] = log_reg_result.predict(data[simple_reg_columns])\n",
      "data['log simple'] = data['log simple'].apply(lambda x: 1 if x > 0.5 else 0)\n",
      "\n",
      "data['log more'] = more_results.predict(data[alpha_cols])\n",
      "data['log more'] = data['log more'].apply(lambda x: 1 if x > 0.5 else 0)\n",
      "\n",
      "perf_report(data.ix[test], 'Survived', 'log simple', 'log simple')\n",
      "perf_report(data.ix[test], 'Survived', 'log more', 'log more')\n",
      "perf_report(data.ix[test], 'Survived', 'emp prediction', 'emp rediction')\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "log simple Confusion Matrix\n",
        "[[221  51]\n",
        " [ 39 135]]\n",
        "log simple Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.85      0.81      0.83       272\n",
        "   Survived       0.73      0.78      0.75       174\n",
        "\n",
        "avg / total       0.80      0.80      0.80       446\n",
        "\n",
        "('log simple accuracy: ', 0.7982062780269058)\n",
        "log more Confusion Matrix\n",
        "[[229  43]\n",
        " [ 44 130]]\n",
        "log more Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.84      0.84      0.84       272\n",
        "   Survived       0.75      0.75      0.75       174\n",
        "\n",
        "avg / total       0.80      0.80      0.80       446\n",
        "\n",
        "('log more accuracy: ', 0.804932735426009)\n",
        "emp rediction Confusion Matrix\n",
        "[[265   7]\n",
        " [ 83  91]]\n",
        "emp rediction Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.76      0.97      0.85       272\n",
        "   Survived       0.93      0.52      0.67       174\n",
        "\n",
        "avg / total       0.83      0.80      0.78       446\n",
        "\n",
        "('emp rediction accuracy: ', 0.7982062780269058)\n",
        "()\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So we see that using the logistic regression does in fact out perform simply using the class frequencies.  That being said, we might think that we could do even better."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cool, so that did alright.  Now let's use the best performing classifier to predict the future outcomes."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Tuning parameters for a number of supervised learning algorithms\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a grid of 1 to max_features, 1 to max_depth; fix all possible variants\n",
      "from sklearn.metrics import accuracy_score\n",
      "\n",
      "scaled_cols = ['sfAge', 'fSibSp', 'fParch', 'slog_fare', 'slog_age', 'ssqrt_age', 'ssqrt_fare', 'fam_size']\n",
      "quant_cols = ['sfAge', 'fSibSp', 'fParch', 'fam_size', 'sfFare']\n",
      "bin_cols = ['_Sex', 'Pclass', '_Embarked']\n",
      "dummy_cols = (\n",
      "#               list(fm_id_dummies.columns.values) + \n",
      "              list(sex_dummies.columns.values) + \n",
      "              list(class_dummies.columns.values) + \n",
      "              list(embark_dummies.columns.values))\n",
      "print(data.ix[real_train, scaled_cols + dummy_cols].apply(is_bad).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sfAge         0\n",
        "fSibSp        0\n",
        "fParch        0\n",
        "slog_fare     0\n",
        "slog_age      0\n",
        "ssqrt_age     0\n",
        "ssqrt_fare    0\n",
        "fam_size      0\n",
        "sex_female    0\n",
        "class_1       0\n",
        "class_2       0\n",
        "embarked_C    0\n",
        "embarked_Q    0\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Random Forests"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# WARNING!  THIS TAKES ABOUT 15 MINUTES TO RUN ON MY COMPUTER!!!\n",
      "from sklearn.grid_search import GridSearchCV\n",
      "from sklearn.ensemble import RandomForestClassifier\n",
      "\n",
      "n_features = list(range(1, len(bin_cols + quant_cols)+1))\n",
      "depths = list(range(1, len(bin_cols + quant_cols)+1))\n",
      "\n",
      "param_grid = {'n_estimators': [50], 'criterion': ['gini', 'entropy'],\n",
      "              'max_features': n_features, 'max_depth': depths,\n",
      "              'oob_score': [True]}\n",
      "\n",
      "rf_cv_clf = GridSearchCV(RandomForestClassifier(),\n",
      "                         param_grid, scoring='accuracy',\n",
      "                         refit=True,\n",
      "                         verbose=1, cv=3, n_jobs=4)\n",
      "\n",
      "rf_cv_clf.fit(\n",
      "              data.ix[real_train, bin_cols + quant_cols],\n",
      "              data.ix[real_train, 'Survived'])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fitting 3 folds for each of 128 candidates, totalling 384 fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=4)]: Done   1 jobs       | elapsed:    0.2s\n",
        "[Parallel(n_jobs=4)]: Done  50 jobs       | elapsed:    2.5s\n",
        "[Parallel(n_jobs=4)]: Done 200 jobs       | elapsed:   10.9s\n",
        "[Parallel(n_jobs=4)]: Done 378 out of 384 | elapsed:   23.5s remaining:    0.4s\n",
        "[Parallel(n_jobs=4)]: Done 384 out of 384 | elapsed:   23.8s finished\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "GridSearchCV(cv=3,\n",
        "       estimator=RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
        "            criterion='gini', max_depth=None, max_features='auto',\n",
        "            min_density=None, min_samples_leaf=1, min_samples_split=2,\n",
        "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
        "            verbose=0),\n",
        "       fit_params={}, iid=True, loss_func=None, n_jobs=4,\n",
        "       param_grid={'n_estimators': [50], 'max_features': [1, 2, 3, 4, 5, 6, 7, 8], 'oob_score': [True], 'criterion': ['gini', 'entropy'], 'max_depth': [1, 2, 3, 4, 5, 6, 7, 8]},\n",
        "       pre_dispatch='2*n_jobs', refit=True, score_func=None,\n",
        "       scoring='accuracy', verbose=1)"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rf_grid_val_scores = [tup.mean_validation_score for tup in rf_cv_clf.grid_scores_]\n",
      "\n",
      "print(rf_cv_clf.best_params_)\n",
      "print(rf_cv_clf.best_score_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'max_features': 4, 'n_estimators': 50, 'oob_score': True, 'criterion': 'gini', 'max_depth': 7}\n",
        "0.833894500561\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(rf_cv_clf.best_estimator_.oob_score_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.821548821549\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid_std_scores = [np.std(tup.cv_validation_scores) for tup in rf_cv_clf.grid_scores_]\n",
      "print('grid std scores: \\n%s' % grid_std_scores[0:10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "grid std scores: \n",
        "[0.0027491467371303781, 0.021820675752215003, 0.0027491467371304236, 0.0041993910064803139, 0.010408101566212915, 0.010408101566212915, 0.010408101566212915, 0.010408101566212915, 0.012697764869792095, 0.011445610580455219]\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Visualize performance across two dimensions\n",
      "# Use grid_scores_.parameters, loop through results - may want to abstract the loop into a helper function\n",
      "\n",
      "def cv_grid_viz(cv_clf, default_params_dict,\n",
      "                x_param, y_param, title, x_exp=False, y_exp=False, markersize=500):\n",
      "    \"\"\"\n",
      "    Helper function to visualize a grid of CV results\n",
      "    inputs: cv_clf, default_params_dict, x_param, y_param, title\n",
      "    keyword inputs: x_exp=False, y_exp=False, markersize=500\n",
      "    outputs: x_list, y_list, acc_graph_list\n",
      "    side-effects: make matplotlib graph\n",
      "    \"\"\"\n",
      "    cv_params_list = [tup.parameters for tup in cv_clf.grid_scores_]\n",
      "    cv_scores = cv_grid_val_scores = [tup.mean_validation_score for tup in cv_clf.grid_scores_]\n",
      "    x_list = []\n",
      "    y_list = []\n",
      "    acc_graph_list = []\n",
      "    for params, score in zip(cv_params_list, cv_scores):\n",
      "        x_temp = None\n",
      "        y_temp = None\n",
      "        fail = False\n",
      "        for param_key in params:\n",
      "            if param_key in default_params_dict:\n",
      "                if default_params_dict[param_key] == params[param_key]:    \n",
      "                    continue\n",
      "                else:\n",
      "                    fail = True\n",
      "            elif param_key == x_param:\n",
      "                x_temp = params[param_key]\n",
      "            elif param_key == y_param:\n",
      "                y_temp = params[param_key]\n",
      "            else:\n",
      "                continue\n",
      "        if x_temp and y_temp and not fail:\n",
      "            x_list.append(x_temp)\n",
      "            y_list.append(y_temp)\n",
      "            acc_graph_list.append(score)\n",
      "    if x_exp:\n",
      "        x_list = [np.log(x) for x in x_list]\n",
      "        x_param = 'log(' + x_param + ')'\n",
      "    if y_exp:\n",
      "        y_list = [np.log(y) for y in y_list]\n",
      "        y_param = 'log(' + y_param + ')'\n",
      "    figsize(10, 8)\n",
      "    scatter(x_list, y_list, c=acc_graph_list, marker='o', s=markersize)\n",
      "    legend(loc='best')\n",
      "    xlabel(x_param)\n",
      "    ylabel(y_param)\n",
      "    suptitle(title)\n",
      "    colorbar()\n",
      "    show()\n",
      "    return x_list, y_list, acc_graph_list\n",
      "\n",
      "cv_grid_viz(rf_cv_clf,\n",
      "            {'criterion':'entropy','oob_score':True,'n_estimators':50},\n",
      "            'max_features', 'max_depth', 'RF Parameter Tuning (CV Accuracy)')\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/python.app/Contents/lib/python2.7/site-packages/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n",
        "  warnings.warn(\"No labeled objects found. \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EEEIIQOZcaUV6roQQQgghNCQ9V0IIIYQApCjQiryPQgghhABkWFArUly50MWL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lTP+IpG3G0cONh0G/qe1dJ3yGd8MW68U4tekiTGf/klS2bO5Gu9nozXun1WCjBbreavQoX4e+dO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\nf5/fSLFzSh4sAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108ca8fd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "()\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['SK RF'] = rf_cv_clf.best_estimator_.predict(data[bin_cols + quant_cols])\n",
      "\n",
      "# Save the file\n",
      "import csv\n",
      "\n",
      "def save_to_csv(fp, col):\n",
      "    \"Helper function to save a column of the dataframe into the preferred kaggle format\"\n",
      "    with open(fp, 'w') as f:\n",
      "        writer = csv.writer(f)\n",
      "        writer.writerow(['PassengerId', 'Survived'])\n",
      "        for pid, s in zip(data.ix[real_test, col].index.values, data.ix[real_test, col].values):\n",
      "            writer.writerow([pid, int(s)])\n",
      "\n",
      "save_to_csv('sk_rf.csv', 'SK RF')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Support Vector Machines (SVM)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dummy_cols = (\n",
      "#               list(fm_id_dummies.columns.values) +\n",
      "              list(sex_dummies.columns.values) +\n",
      "              list(class_dummies.columns.values)\n",
      "#              + list(aCab.columns.values)\n",
      "             )\n",
      "print(dummy_cols)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['sex_female', 'class_1', 'class_2']\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Train an SVM classifier\n",
      "from sklearn.svm import SVC\n",
      "\n",
      "rbf_param_grid = {'C': list(np.exp(range(-10,10))), 'kernel': ['rbf'], 'gamma': list(np.exp(range(-10,10)))}\n",
      "\n",
      "svm_clf = GridSearchCV(SVC(), param_grid=rbf_param_grid,\n",
      "                       scoring='accuracy',\n",
      "                       refit=True,\n",
      "                       verbose=1, cv=3, n_jobs=4)\n",
      "lscale_cols = ['slog_age', 'slog_fare']                                                             "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svm_clf.fit(data.ix[real_train, lscale_cols + dummy_cols], data.ix[real_train, 'Survived'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fitting 3 folds for each of 400 candidates, totalling 1200 fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=4)]: Done   1 jobs       | elapsed:    0.1s\n",
        "[Parallel(n_jobs=4)]: Done  50 jobs       | elapsed:    0.6s\n",
        "[Parallel(n_jobs=4)]: Done 200 jobs       | elapsed:    2.4s\n",
        "[Parallel(n_jobs=4)]: Done 450 jobs       | elapsed:    4.8s\n",
        "[Parallel(n_jobs=4)]: Done 800 jobs       | elapsed:    8.8s\n",
        "[Parallel(n_jobs=4)]: Done 1200 out of 1200 | elapsed:  1.0min finished\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "GridSearchCV(cv=3,\n",
        "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
        "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False),\n",
        "       fit_params={}, iid=True, loss_func=None, n_jobs=4,\n",
        "       param_grid={'kernel': ['rbf'], 'C': [4.5399929762484854e-05, 0.00012340980408667956, 0.00033546262790251185, 0.00091188196555451624, 0.0024787521766663585, 0.006737946999085467, 0.018315638888734179, 0.049787068367863944, 0.1353352832366127, 0.36787944117144233, 1.0, 2.7182818284590451, 7.3890560989... 148.4131591025766, 403.42879349273511, 1096.6331584284585, 2980.9579870417283, 8103.0839275753842]},\n",
        "       pre_dispatch='2*n_jobs', refit=True, score_func=None,\n",
        "       scoring='accuracy', verbose=1)"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Visualize SVM performance across two dimensions\n",
      "\n",
      "cv_grid_viz(svm_clf,\n",
      "            {'kernel':'rbf'},\n",
      "            'C', 'gamma', 'SVM Parameter Tuning (CV Accuracy)',\n",
      "            x_exp=True, y_exp=True, markersize=175)\n",
      "\n",
      "print(svm_clf.best_score_)\n",
      "print(svm_clf.best_estimator_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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D8ddRLW2K+8Lly5cZMeJ9ypSpTFhYJMWLl6Nv3wHOxQ19JTo6mkGDXqVkyQqE\nhUVSqlQl3njjTc6ePZstnX379tGzZ2+KFy9HWFgk5cpVYeTIT5xr/PmCiLBp0yZat+5IZGRpChcu\nTpUqtZkyZQpxcXE+69jtdpYvX07jxs2JiChJePhD1KnTmIULF5KcnOyzTmJiInPmzKFmzQaEhz9E\nREQpHn+8JStXrvRp4+F0bt26xZdffkmlSjUICytGZGQZOnbsxvbt27M1wPfSpUtuNu/X7zkOHz7s\nswbAyZMneemlVyhRogJhYcUoVaoSgwe/9ads3qNHX4oVq0RYWEnKlavJJ5+Mcm5N4wt/2LwrkZGV\nKFy4LFWqNGTq1KnEx8f7rJOamsqyZcto3LgVERHlCQ8vS926zVi0aFG2bT579mxq1HiU8PCyRERU\noFmz9qxatSrbNh83bhyVKtUlLKw0kZEV6dSp55+y+fDh71OmTDXCwkrw0EMP07//Cxw5csRnDYAT\nJ07w4ouvUKJEZcLCSlCqVBXefPMdzp07ly2dvXv30qNHH4oVq0hYWAnKl6/JqFGjs23zjRs30qpV\nJyIjy1O4cCmqVKnPtGnTsm3zpUuX0qjRExQpUoYiRcpQr97jfPvtt5kuJeGJhIQEZs2aRY0ajQkP\nL03RouVo1qwtq1evzpbNFf8SfNpj4V+MqoJ7S2pqqjz//EtiMFjEYKgu0FPg/wT6iL9/QzEag6VB\ng0fl2rVrWerExcVJq1YdxGAIkoCAugK9BZ4XeFYCA2uLwWCVzp2floSEhCx1Ll++LDVq1BOjMY/4\n+TUW6HunPD3EaKwqer1ZXn/9TbHb7VnqHD16VIoXLy1mc6hoWnOB/nd0uorZXEFMJqtMmDDRa/1s\n2bJFQkJCxWotJtBaYIDAcwLtxWotKbly5ZMffvjBq86CBQvEag0Wi6WMQKc7GgMEWorFUkRCQyNk\n165dWWo4HA755JNRYjRaxGx+WOCpO9fUT3S6x8VsLiBlyz4sp06dylInJSVFnntuoOj1FjEYamRi\n86Zy/fr1LHXSbN7+js3rC/QTeFGgj+j1dUWvt0q3bj0kMTExS52LFy9KtWoNxGQKFT+/QQL/E/hJ\nYIYYjW1Frw+SwYPf8WrzI0eOSLFiFcRiKS2aNkZgm8BugW/FbH5STKY8MmnSlCw1REQ2bdok+fKF\ni9VaVWCswHqBDQKTxGKpLblzF5Qff/zRq878+fPFYsknFktjga/uaKwTGC0WS3kJC3tIoqKistRw\nOBzy0Udelpw0AAAgAElEQVSjxGDIJSZTS4HZdzR+Ep3ubTGbi0q5ctXl9OnTWeqkpKTIgAEvil6f\nSwyG9gJTBb4VmCX+/n3EaMwnjRq1kBs3bmSpExsbK08+2VEMhjwSENBTYMYdna9Er+8ien0u6dbt\nWZ9sXrVqfTGZComf3/8JzLmjM1GMxidFrw+St94aKg6HI0udw4cPS7Fi5cRiKS6a9obANwKLBD4T\ni6WRmEzBMnny1Cw1REQ2btwoefOGidX6sMBwgQV3jg/Faq0quXMXkJ9++smrzty588RiySsWS12B\n0QILBeYLDBGLpZSEhT0ke/bs8arjib/72QfItPtw/Nee4WrJD7Xkxz3D4XDQoUMXVq3aj83WBjB5\nSJVKYOBGwsJi2LNnO7lz53ZLkZCQQJ06jThyJJXExGZ4Hr6ahNH4E488Esz69asIDAx0S3HlyhUe\neaQGly8XJTW1HuDnQScOk2kJ7ds3YNasrz2uYn3kyBFq1qxHbGxtRB4BPG1DchWTaTHvvDOIt94a\n7CEeNmzYwJNPtsNmawGU9JgGzmI0fsfs2VPp0KGDxxTTp0/nhRfeICGhPRDqIYUAhzGbV7Nu3Spq\n1KjhUWfw4CGMHz8Hm609EOwhhQOdbie5c+8hKmoHRYsWdU/hcNCuXSfWrDmIzdaazG2+gcKFrxEV\ntS1Tm9eu3ZCjRyExsTmZ23w5VavmZd26lc6taTJy+fJlKleuTUxMC1JT/w/PHTIxmEwD6dixKjNm\nTPZo88OHD1OrVmNiY4ch0hPPNj+GydSBd98dwODBr3mIT9sOp1WrLthsY4EmHtPADkymfsyZM5F2\n7dp5TPHVV1/z0kvDsNlmAhU8pBDge8zmIWzY8FOm2/W9/voQJk5chs32NVDYQwoHfn5fkzv310RF\n/UJERIR7CoeDNm26sG7d79hsHwK5POikEBg4jiJFfmX37i3kyuWexmazUatWY44fL0hi4huAwYNO\nPEbjcKpX92fNmu+zsHktYmIeJzX1GTzb/Cpm8xt07lyLr76a6NHmhw4donbtRsTGPodIKzzb/BQm\n0yu8995LvP76Kx7iYe3atbRu3QWb7T2gtsc0sBej8S2++WYqbdq08Zhi6tRpDBo0jISET4HSHlII\nsAaz+VM2blxJ1apVMzmXZx7Ekh8z7oPuM/y3lvxQTpty2u4Z48dPYPDgMdhs3ch6npAQGLiGxx8v\nyPLl/3OLfe65gcyatYWEhNZk3YNvx2j8Hy++2IaRIz90i23Y8DG2bk0mJaWRl5InYTbPZeLED+jR\no4dLjMPhIDKyJOfOVUDkYS86tzCZZrN69TLq1KnjGnPrFuHhkcTGtgIivehcxGRawNGjvzoXgUzn\n6NGjPPJITRISngZCvOgcIzh4LefPn8FoNLrErFy5kg4dehIf3xMwZ6mi0+2gdOkLHDy41+1hN3bs\nON5+eyzx8V3xZnO9fjXNmhVm6dJFbrH9+z/PnDnbSEhoi3ebL+Lll9vy4Yfvu8XWr9+M7duLk5Iy\nKMtrgjjM5qeYPHkITz/9tOsZ7HaKFi3L+fOvIfJ0JvnT+R2TqSFr1y6mVq1aLjE3b96kSJESxMZO\nJfOHdzoHMJm6cOzYfgoXdnWmjhw5QpUq9UlIWAo85EVnFcHBb3LhQjQGg6sT9NNPP9Gp0/PExy8D\n8mSp4uc3jdKlV/Lrr9vdbP7ZZ18wdOgcbLbxgD4LFUGv/4QWLQJYsmSeW2zfvs8zd+5pEhNH4NlB\nSicVo/FVXn31Ud5/f5hbbN26Tdmxoxipqf2zvKY0m/dl2rT36Nq1q0uM3W4nIqIUFy70ROQJLzoX\nMZl6s379MrcXohs3blCkSAni4kYCj3jROYzJ9CK//XaQ0FDXl69Dhw5RrVoDEhKmAe6OsysbyJNn\nDBcunEKvz8oeriinLWeixrQp7gkiwscff4rN1hDvE7s1kpMbsGbNas6fP+8SExsby6xZs0hIaIz3\nn6cfCQmNmThxMklJSS4xv/32Gzt27CQlpa4PpdcTH1+fDz4Y7Xbzr169muvXUxGp5INOLhISqjNy\npPsSkrNmzcJuj8C7wwZQCLu9LBMmTHKL+fzzcaSkPIx3hw2gFCkpIXz77bduMR98MJr4+Np4c9gA\nHI5qnDlzme3bt98V7uCTT8YQH98QX2yelNSAlStXcOHCBZeY27dvM2fOHBISmuKbzR9l/PhJbjY/\nduwYu3ZFkZLyf16vCSzExw/iww+/cItZtWoVt25ZfXDYAAqTkDCIkSPHucXMmDETu70+3h02gIrY\n7a2ZNGmqW8yYMeNJSemOd4cN4HFSUkqxePFit5gPPvic+PhBeHPYAOz23pw+fYWdO3e6hDscDkaP\nHofN9iJZO2yQZvPn+emnH7l06ZJLzK1bt5g7dx6JiS+StcMG4E9CwiC+/HKi2/i/I0eOsGfPPlJT\ns95GKA0L8fHP8cEHn7nFrFixgtu3zT44bACFSEjoxiefjHWLmT59Bg5HTbw7bABlsdsfzcTmX5Kc\n3B7vDhtAI5KTI/jf/9xfgP9p/F07IvybUU6b4p6wYcMGbt+2A0V8zKFHpAKTJ7v+Yc2bNw+dLhLP\nXS6eyIdIAbc/rHHjJmK3V8L327o4589fYffu3S6ho0ePJS6uIt4fLGmIVGTNmjVcvnzZJfzTT8dh\ns3lrqfuDpKTKTJo01WXAss1mY+7cuaSm+r4dW1xcRT75xNUxiY6OJipqN1DORxUdNltFxoxxdUzW\nr19PbCxAuMdc7hiAckyZMs0ldO7cueh0DwFBPuqE4HDk57vvvnMJHTduEqmpHQD3rnLP1OfsWXeb\njxo1kdjYPj5qgMhTrF69kpiYGJfwMWMmYbP1yCSXO0lJPZg4cRqpqanOsPj4eL75Zj6pqd191omL\n68moURNdwk6ePMnevfsAX5wSAB0JCU8xZoyrztq1a4mPNwLlfdSxommPMmXKVy6hc+bMQaeriW8v\nHwCR2O2RLF261CV03LhJpKS0xvf7vA6nT19gz549LqGjRo0nNjbzPSHvRqQlK1b85DKRSUQYM2Yi\nNpvnLm5PJCW1Z/z4KdjtdmdYXFwcCxYswG73vTxxce0ZNWq8z+kVORfltCnuCXv37iU5uQi+OjcA\nSUnh/PKL65v8tm27iI/3NNYmc+Liwti1K+ounZ2kpPjylpqODijK3r17XUL3798HFMuGjhGDIcxl\nxmRSUhLnz5/Gt7fmdPKTmupwcf5OnTqFn18Q4D4mLHOKcfy464zdAwcOEBhYlOy8p4oUY/du1wfd\n3r17SUrKrs2LuNl869ZdxMf76vilERdXmN2777b5XlJTa2ZDxQ9Nq8G+fftcQg8c2A9461LPSDB6\nfVkXmyckJHDp0mnA83hCz5QhKcnBlStXnCHR0dH4+xfA89jFzKjP0aP7XUL2799PYGBVPI8b84zD\nUZddu1zvh71795KQUJXs2DwxsSpbt7r+drZu3YPNlr0xWHFxVdx+g9u27SE11fP4Pc/4oWlVPdh8\nH1A9Gzq50OuLu8ySTUhI4MqV3wFfWuXTKUFiYoqLw3/y5EkCAgoA+bOhU42jRw9kI/2Dwf8+HP81\nlNOmuCckJSWRmprdn5M/CQkJLiEJCYlk/1b0Jz7e5hKSmJiUbR273Y/ExESXsLTumOyWx1UnKSkJ\nP78AsvOgA9DpAt10NC37dWO3p7osDZCUlISIp0kZWevc3R35523uWsd/1uZxca42Tyuf72N6AOx2\nvZvNU1ISs60DBg8215N9m+vddHS67JYlkNTURJeu/jSbZ1dHT3Kyu83t9ux2SukzsbmvLaLpBBIf\n7/p/8Wds7nAEerB5crZ1wFUnMTHxT9ncz0/vpqNp2bdVSkqS92SKHI9y2hT3hJCQEAwG39cwSuM2\nhQoVcAkJCyuITnc7Wyr+/rGEhhZ0CStYMD9wK1s6gYFxhIS4dtcEB+fJpo7gcNxy0bFarYg4AFvm\n2dxIISnpNnnz5nWG5MuXj+Tkm0B21ma6hcWSC53uj1s9X758aFr26hhukTdvPpeQkJAQjMbs2vwW\noaGebJ49W/n7xxIW5mrz/PlDgIvZ0gkIuORm89y5Q4DsrA8m2O2/u+gEBQXhcCQDN7Ohk0hKyg0P\nNr8I2DPP5sYFrNZ8LhMI0mx+Pos8njjv0eYGw5VM0mfGJUJDXVuNwsLyo9NdyiS9Z/z9r7jppNk8\n+zruNs+XTR3Bbnf97eTKlQuHIwmIzYZOIsnJN11sHhISQnLyJbJ3n1/Gas3rPdkDRo1p++sop01x\nT2jdujUOx3EgwWvadKzWwzz7rOtYnaee6orReBjf/7BSCQg4TJcunV1Ce/fujtWanUVdY0lJOUWL\nFi1cQnv2fAq9/mA2dM5jMul45JE/BiJrmkarVm3RtP1Z5Lubw1StWoPg4D+W4ihSpAjFikUCv/ms\n4ud3gE6dOrmE1atXD027BcR4zuQBk+kQvXu7Dsxv3bo1dvsxINFzJg9YrYd55hlXnaef7orReJDs\n2fyg23X16dMFi+W7TPJ44jKpqVE0b97cJbRnz47o9XOzobMTq9XBww//MWZRp9PxxBPt0DT3mbKZ\ns5xq1eq4LI9RtGhRIiKKAOt9VvH3X0CXLq51U79+feAscMJnHZPpW/r0cb2v0u7zzYCvi0kLFssP\n9OrVxSX06ae7YDT+hO82TyYgYLUHm3fFYvnRRw2AK6SmHqBZs2YuoT16dEav/yEbOvsJCvKnYsWK\nzhA/Pz+aN2+FpmVHZzW1atXDarU6QyIjIwkPLwxs81nF3/97unbt7D3hA0Y5bX8d5bQp7gn58+fn\n8cebo2l7vCcG4Bx6faLbA7Nq1aqEhxcEjvmoc5Dy5ctSurTrOkbt2rVD02KAy56z3YWf3246duzo\ntp7UgAH9gIOAby1KRuNuXnlloEvLFsBrr72EybQPSPWc0QUHFss+Bg92X7Zi8OCXMZv3kLZGkzeS\nCQzcx8svD3QJDQwMZMCAvgQG7vJBA+AWDsdxnnmml0towYIFadq0aTZsfhajMZXHH3/cJbRatWqE\nheXHd5sfoGLFipQqVcoltEOHDmjaUeC4Typ+ft/QpUsXgoJcJ0A891w/NO1bwLdV9I3GL3nllQFu\nNn/99ecxmWYBvnRb2bFYvmbw4OfdYgYPfh6zeRq+2TyegIBvGDTIdQatXq+nf//eBAZO90ED4DwO\nxyZ69uzpEhoaGkqTJo+iaUszyXc3+zCbbTz22GMuoTVq1KBQoVzAzz7qrODhhytRokQJl9COHTsC\nR4Fon1T8/BbRrVtXFycJ4Pnn+wMr8LVV3WhcwKuv/p8Hmw/EZPof4MuOB6lYLN/yxhuu96emaQwe\n/AJm8wJ8t/kyN5sr/p0op01xzxg5cgRm8268v81fx2hcyvjxn+Hn5z62auLELzAaV+G9u+J3TKaN\njBv3qVuMXq/ns88+wWRagvc/4iNYLAcZPnyoW0xoaCgvvPDcnT/irFuUdLpthITE0r9/P7e4mjVr\n0qBBTQyG78m6q8tBYOBqypYN44kn3Gf6de7cmaJFTQQErCfrP/QUjMaltG3binLl3GeJvvrqy+TO\nfR5Ni/KQNyM2TKbFvP32Wx4XxR016kPM5p34ZvNlHm2uaRoTJ35+p+XFm83PYTKtZ+zYUW4xer2e\nTz/9CJPpBR90VmGxfMe777ovhBwWFsaAAX0xmToBWXcj+/l9QUjIEfr16+sWV7t2berXr4TB8BJZ\nP8QdBAa+Rfnyed1aegG6dOlCRISNgICPyNrmCZhM/ejQoSVlypRxi33ttUHkyrUJTVuQ5TXBdUym\n3gwdOsTjorijRg3HZJoN7PCicw6jcSgTJnzq5tyk2fxTjMaP8P7bOYDJNJ6xYz92izEYDIwe/SEm\n0+t4bzleg9X6E++++6ZbTOHChenfvw8m02t4e0Hz85tF/vzn6NOnt1tc3bp1qVu3IgbDcLzZXK8f\nRcWKBd1a/QC6du1KeHgC/v6TyNrmiZhMb9GpU1u3l5h/Imoiwl9HOW2Ke0bp0qVZufJ7rNYf8fdf\nj7uzlAjsxGSaw6hRw+nc2XNzfqNGjZg5cwom03x0uq24jwWLQ6fbgsn0LQsXzqFmTc8zBnv37s07\n77xy5wETBdy9x+MN/P3XkivXOtatW0VkpOc11EaNGknnzk0wm+eQ1up2t9N1Cb3+e0JDj7N58zqP\nDzpN01i8eD61ahXAZJpP2oMqY9eQAGcwGr+lXDlh1arv8fd3/0vS6/Vs2LCK4sVvYTAsAX7H9U/d\nQdpK/fNo0qQUM2dOc9OAtHEzW7asJyRkD4GBK3B/4KWS9rCcRb9+nXn77bc86pQpU4YVK9JtvoHM\nbG40zuHTT9+/0zLiTpMmTZg+fSIm01w07Rc823wTJtMCvv12Xqa7PPTr14chQwZgMnUBFnnQOUtA\nwEhy5/6Q9et/9LjLA8CYMR/TsWMFTKbGwBLcH8AH0Ov7EBo6iy1bVrq11kGazf/3v7nUrJl0xwHc\nhLvNt2E0Pk358tGsXLnE40uMwWBgw4YfKVbsZwyGPsDdLa12YDUmU1uaNs3H119PdNOAtNbwLVtW\nky/flwQGDsHdWUoElmAytWbAgCd5663XPeqUK1eOn35agsUyFD+/Sbi3ZsehaQsxmfry2Wfv0b59\ne486TZs25auvxmI0/h+aNhf33841/Py+xmR6lcWL52a6y8OAAf14881+mEzPAktxH6LxOwEBn5E7\n9xesX7+CIkU8L0v0+eej6NDhEczmPsBa3G1+DL3+XcLCVrJly+pMbb5kyTdUr27HZBoIbMfd5lGY\nTIOoUOEKK1Z859HmRqORjRtXEBm5C4PhTeDuPZvtwCbM5n489lhhvvrKs80V/0IexN5Z/yRUFdx7\noqOjpV+/58RotEpQUGmxWKpIUFAFMRgs8vjjLeXnn3/2SWfv3r3Svn0X0evNEhRU7o5OWTEYLNK1\na3c5ePCgTzrr16+Xxo2bicFglaCgimK1PiJBQaXEbM4lL7zwkpw9e9arhsPhkMWLF0vlyjXEaAyW\noKBKYrU+IlZrpAQH55ehQ4d53VtTJG3PxsmTJ0uxYqXFbC4oVmtlsVori8USJqGhReWzzz73up+q\nSNo+nSNHfiL58xcWq7XInWt6WEymEClduqLMnDnT696aIml7s77xxpsSFJRXgoKK39GpJEZjLqlZ\ns758//33XjVERE6ePCl9+gzwaPPmzVvJL7/84pPOnj17pF27zndsXl4slqoSFFRODAaLdOvWQw4d\nOuSTzrp166RRoyfEYAiWoKBHxWptKUFBNcRsziMvvPCynDt3zquGw+GQRYsWSeXK9cVoDJWgoFZi\ntXaQoKCqEhwcJu++O9xnm0+aNEkiIyuI2VxMrNaWYrW2EoulpISFlZQvvvjCJ5vHxsbKhx9+LCEh\nEWK1lhertbUEBT0pJlNhKVOmmsyaNctnm7/++ltitYZIUFBVsVrbSFBQczEY8knNmk182vtWROTE\niRPSu/dzYjTmlqCgmmKxtJCgoIai1+eSFi3ay9atW33SiYqKkrZtu4peHyRBQfXu6NQRgyGXPPXU\ns3L48GGfdNauXSsNGzYXgyG3BAU1Equ1uQQFVRWzOY8MHOi7zRcuXCgPP1xbjMYCd347zcRqLS95\n8oTKsGEjvO6nKiKSnJwskyZNkqJFy4nFUlSs1sfEan1cLJbiUrhwms297acqktHmRcRqLSNW6+Ni\ntT4qJlOolC1bVWbPnu2TzT3xdz/7AFlzH47/2jNcbWOltrG6b8TFxbF582Zu3LiB2Wy+M3YpLNs6\nMTExbNu2jdu3b5MrVy7q1q3rMkDfV86ePUtUVBQ2m428efNSv359TCZPe2VmzZEjRzh8+DBJSUkU\nLFiQevXqedwTMStEhKioKKKjo3E4HBQpUoRatWp53BMxKxwOB7/88gvnz5/Hz8+PEiVKuAyI95Xk\n5GQ2bdpETEwMBoOBChUquI0f8oXY2Fi2bNnitHn16tXdtujxhZiYGLZu3UpsbOxfsvmZM2fYs2fP\nX7b54cOHOXLkiNPm9evX99gSmhV32zwiIoKaNWtm2+Z2u91pc39/f0qWLEmlStlZGyyNpKQkNm/e\n7LR5xYoVeeghX3ZdcCU2NpbNmzdz8+bNv2TzK1eusG3bNqfN69Wr57FL3hunT59m7969Tps3aNDA\nbRs3X8ho80KFClGvXr0/ZfPdu3cTHR2NiNwTmwcEBFCyZEmXSRB/hgexjdWG+6DbiP/WNlbKaVNO\nm0KhUCj+YyinLWfyXxzHp1AoFAqF4m/mv7hEx71GTURQKBQKhUKhyAEop01xX9i5cyedOz91ZzV+\nPwwGM3XqNGLZsmUuG2JnhcPhYM2aNTz22BOYTFZ0Oj9MpiCeeKItGzdu9LlJPCUlhcWLF1O9ej0M\nBjM6nR9WazDduz/jtgdhVthsNr7++mtKl65IYKABPz9/8uQpwEsvvcLJkyd91rl+/TqjR39KkSIP\nERCgx98/gIIFizB8+AguXfJ9Vfbff/+dIUPeIX/+MPz9AwgI0BMZWZpx48Zx65bvOwwcPXqUAQOe\nJ3fuEPz8/AkMNFKhQhXmzJnjtt1PZoiIR5vXrduY5cuXu2yInRUOh4PVq1fz6KNPYjSm2zwXTz7Z\ngU2bNmXL5t9++y3VqjVEr7fcsXk+evToky2bx8fH89VXX1G6dDUCA034+QWQJ09hBg16g+ho39YG\nA7h27RqjRo0mPLwsAQEG/P0DKVjwIUaM+MBlf1lvnDt3jjfffIeQkKL4+wcSEGCkWLFKfPnll9my\n+ZEjR+jffyC5cxe6Y3MzFSvWybbNd+zYQceOPbFY8t6xeRD16jXj+++/z5bNV61aRZMmrTAac92x\neTAtW3Zm8+bNPts8OTmZRYsWUbVqA6fNg4JC6NmzL/v3+76wdXx8PNOmTaNUqUcy2DyMQYNe/5M2\nL4W/vx5/fz2FChXn/fc/zJbNz549y+DBbxMSUiSDzSswfvx4bt/O7s4mDxa15Mc94G+e+PCPQ1XB\nvSU2NlYefbS5mEwhotM1FXhFYKjAYIG2YrUWlyJFisuJEyey1Llw4YKUK1dZLJZwgScFXr+j85po\nWguxWELlkUdqSkxMTJY6hw4dkoIFw8VqLSHQXuDNOzqDxM+viZhMeaVly3Zis9my1NmwYYMEBeUR\ni6W8wFMCQwTeEXhBAgLqicEQJC+88JLXmVxz5swVo9EiJlMVgWcF3r5z9BODoYYYDBb5/PMvstRw\nOBwyYsQHYjBYRa+vJTDgTlmGCPQUs/lhMZmCZOnSpVnqpKSkSK9efcRozC3+/g0EBt7ReUugi1gs\nZSQ4OL/s2LEjS53bt29Lo0aPi9mcX3S6x+6yeRuxWotJRMRDcvLkySx1zp8/L2XKVBaL5SGB/xOY\nK/CdwCzRtH5isRSVKlXqyNWrV7PUOXjwoBQsGClWaw2BzwWiBA4JbBA/v0FiMoVKq1advM7YXL9+\nvVitIWKxtBRYIRAnkCRwRAICXhODIZ+8+OLrXm0+a9ZsMRpzi8n0lMAmgVsCtwW2icHwrBgMuWXs\n2C+z1HA4HDJs2PtiMASLXj9AYIfADYGrAivEbG4nJlMeWbZsWZY6ycnJ0r17XzEaC4i//5sCRwTi\nBa4JLBaLpakEB4fKzp07s9S5ffu2NGzYQszmSNHpPhI4cee6zgtME6u1mhQtWlaio6Oz1Dl//ryU\nLv3Infvq0zt2Oifwq2jaB2I2l5Bq1RrItWvXstT59ddfpUCBondsPuZO/ewXWCN+fgPFZCoobdp0\n8WrzdevWidWaTyyWJgJTBXYL7BP4XgIDnxGDIVgGDXrNq81nzJgpBkMuMRrbCHwjsEdgr8AiMRg6\niMGQS778ckKWGg6HQ4YOHS4GQ27R658WWHqnLFEC08VsbiZmc7DPM7zv5u9+9gGy/T4c/7VnuJqI\noCYi3DOSkpKoU6cRhw6lkJjYHPC8KblOF0Xu3DvYs2cnERERbvFXr16lcuXqXLpUjNTUunjegNlB\nYOBGChe+wp492z2ujXb8+HGqVatNbGx9RDKbXZeKwfAD1arlYd26lR5ngW7evJnmzVtjs7UCimWi\nY8NkWkLnzo34+uspHmeHzZ49h+eeexmbrRNQwF0CgBuYTIt4771Xef31Vz2mePvtoXzxxcw7Ou5r\nRaVxHqNxMd98M502bdq4xYoIHTp0YeXKA9hsbcl8s+xjmM0/sXHjWqpWreoWm5iYSO3aDTlyxEFi\nYjMyt/lugoN3sWfPTo/rZF29epVKlapz5UpdUlM7kJnNAwLmUKTIUfbs2epxnaxjx45RvXp9YmNf\nR6R1JteUhNH4JtWrp7J27Q8eZwRu2rSJFi06YrMtJG2osyeuYzK1okuXSnz11XiPNp85cxbPPz8U\nm2054L7IcRqnMJmeZPjw53jttZc9pnjzzaGMH/898fFLgEKZ6OzGaOzEggVTaNWqlVusw+Ggffun\nWb36OjbbfMCSic73mM392bx5lct2bOkkJCRQu3ZTjhwpQVLSODJr79DpJhMc/Bl7924lPDzcLf7K\nlSs8/HAtYmK6kJo6kMzv8/eJiPiFqKif3XYygLSW4urV6xMX9zoiT2ZyTUkYjW9Rs6bG6tXLPdp8\nw4YNPPlkR2y2MUD1THRuYjK9wFNP1WLKlC892nz69BkMHDgUm20SkNls3HOYTP354INBvPzySx5T\nvPHG20yYsAybbSIQ4jENHMBoHMiiRdN58snMrt0zD2IigrelvP8MVfhvTURQTpty2u4Zw4YNZ/To\nxSQktMNbz7uf31aqV09i69aNbnEdOnRl+fJzpKQ09XJGQa9fwVNPPczXX09xi61QoQqHDhVCxPOi\nnH9gx2RaxIgR/Xj11VdcYpKTkylYMJwbNx4HinvRScRsns2CBVPc/kAvXbpEsWIlSUjoDuT3nN3J\nTYzGGURFbXNb2X7nzp00atQcm+1ZMn/opnMek2khFy6cdXNq582bR//+Q4iP74734cGHKVRoO7//\nfoqGaoUAACAASURBVMptZft33nmXzz77zkeb/0LNmqn8/LP7Pprt2nXlhx+SSEl5xktZBL1+It27\nF2PaNPcFRcuVq8aRI60Q6eIhb0ZSMJn68uGHnRg0yPWhmbasRyQ3b84BmnjRuYXZXINFiz5z283g\nwoULFC9ensTEjYD7DgWunMForM2ePZvdtmTbvn07TZp0xGb7Ge+/nd2YTG24ePGUm1M7Z84cnntu\nLPHx6wFvS2AsJixsGGfPHnWz+VtvDWXs2F9JSJiHd5uPonbt7WzevMItrnXrrqxYkZeUFPedSFwR\n9PqX6dkzhClTxrnGiFC2bFWOHWuNiOeFm/8gBZOpHyNHPsXAga5bRyUlJVGgQAS3bn0MeF6s+w9i\nMZu7snjxBLfdDM6fP0+JEhVISJhD5i94ztQYjZ3Zt28bJUuWdInZunUrTZt2xGZbBOTxorMfs/n/\nuHjxjEenNjMehNOWnd2XfaUS/y2nTY1pU9wTUlJS+PLLiSQk1MOXn5XdXp19+/Zz7JjrfpMxMTH8\n+OMPpKTU9eGsGklJdZk/f77b2I609bDOIVLFBx0/bLa6jBkzFofDdQPr7777jpSUYLw7bAAG4uOr\nM3Lk524xkydPRaQs3h+6ALlJSanMF1986RYzevQXJCZWxbvDBhAGFGfWrFluMSNHfkZ8fG18m89V\nhthYjdWrV7uEpqSkMGHCJBIS6uObzWsQFbWH335z3fD+ypUrrFjxEykp3h66kGbzLsyb9w2xsbEu\nMbt27eLMmSs+PLwBArDZBjJ69Hi3P/wlS5aQmloW7w4bQC7i49/kk08muMVMnjztTlm8OWwAEaSk\nPMvYsZPcYkaPnkBCwgv49tupiqY1ZPbs2W4xI0dOID7+bbw7bADtuXXLxNq1a11Ck5OTmTTpKxIS\nhuGbzV9k164otzGfly5dYtWqlaSkeG5lckUjKekN5s6dR1yc60b1u3bt4ty5q4h43nXBlQBstucZ\nNcrd5osXL8ZuL4F3hw3ASnz8M3zyifv9OWnSVByO5nh32ADCSE1tz7hx7i8fo0Z9SUJCD7w7bJDm\ntlRnzpw5PqRV5HSU06a4J6xYsYLU1Fxk3u13N/6kpFRi/PjJLqEzZ85C08rg24MFIAhNK8b8+fNd\nQseNm0hiYiV8/4kXJi5OY+PGjS6hn376JXFx2VnEshxRUbs5c+aMS+j48ZNITPR90dvU1MrMnTuX\npKQ/Nhu/desWP/ywHIfDdx2brRKffebqUBw8eJDo6DOAr4vnasTF/T971x0eRdW939nNtpkt6SSB\nNCCUJCSEKkjvTZQiXUrovQpYUD8bUgSRLh0p0pEOKoqAdJLQCT20JBBI2002W87vjxQyzO5mFuOn\nv899n2eeB86d+2Zm3t29Z+6955wozJmzgGfdu3cvLBZPiHMmAMANFksUFi7ka75q1WoA9SHOEQUA\nL0gk1fDDD/wamt9+uxQ5Od1gb4lWiBrIypLhyJEjPOvs2d8hO3u4SA4A6I7Tp0/h/v37RRYiwoIF\ny2A0CuvQ2oPZPBhr1/I1T09Px969u0HURzSPXj8Yc+bwy5dduHABd+8+BtBWJAuD7OyhmDPnO551\nz549sForAahiu5sASlgsvbFoEf96Vq5cDYbpAPvL+y8jAAzzGjZt2sSzzpu3BDk5b0P897wm0tMZ\nHD16lGedPXsJsrO7ieQAgHY4efIEHjx4UGQhIixcuAxGo+3yfLZgMnXDqlVrkZf3osTes2fPcODA\nPgfL+0Lo9d0wZ45wteGfBtlfcPzb4HLaXCgVXL9+HTk5Yh22fJjN/rhwgV9TLyHhstM8BoMvLl26\nyrNduHAFVqszWdkZmM3+SExM5Flv3boJoJwTPDIoFAG4efNFXcecnBykp6cB8HOCRwdAzosyS0pK\nglzuAcCZjP5l8eDBHd7MQmJiItzcykG8cwMA5XDtGn9WNF9zsQ5bPkymACQk8DWPj7+C3FwxM5kv\noNdXwOXL13i2Cxeuw2p1pjIAA4ulmkDz27cTIW7GpRAqKBSRAs2zsp4BcMbhDwKgxJMnL+rA3rt3\nDwpFIMTNuBSiDu7f599TYmIipNIacE7zOrh6lc9z/fp1GAwlbTfgw2Sqg4QEPk9CwnXk5gr3yzmC\nXh+DK1f4n8ELF645rbnVak9zZ3hUUCjCeDOIer0een0GxL8MAUBZEMnw9OnTIssLzYX7dO0jCklJ\nL9eSdeF/Ef/KiFkXSh8WiwVEzpVmASSC9B/5/3eex2TiF3fOTzfg3DsJESO4Hovl1a6nOI/FYgHD\nSJzmkUikAh7n37MkIOKnXigtrSwWC6xWZ3lsPeNXuS8pTKY/z0PkZkdzZ5wbAODzmM1mMIyzHADD\nuNnQ3FkeKaxWM4ioaKP8q/KYzcLvldXq/Gfw5e+n2fxqmufl8bWyWl9Fc6lA81fhyX8+f17zl7/n\n+TzOXosbrFZxqZT+Trgcjj8P10ybC6WCgIAAqFTi80QBAMOkISSEH0kYGhoEmSzdKR6FIgOhoXye\noKByANKc4pHJ0gW1UcuU8XeSxwqz+Smv9iLHcQXRas48n1zk5WXBx+dF1Jifnx+MxmcATPa7CZAG\nd3cfXpRb/rWlIT9aXjzPy/UkAwICwLLO5YlimGcCrUJDy8HN7bFTPApFMkJC+DOgQUEBAO46xSOT\n3RVo7utbFsB12x1swgqT6QaPR6PRQCJhADx0gicDJtMznub+/v7Iy7sPwGi/mwA34OERYEPzG/a7\n2OF5+dnka+7cjA7D3ERoKJ8nNDQAbm7ic54BgEJxG6GhfM0DA8vCec3vCe7Lx8cfwB0nWCwwmfg8\nWq0WDEMAntjvJkAmTKZMeHt7F1kCAgJgND4AkGe/mwB34enpzEy+C/9f4XLaXCgVvPXWW7BY7gAQ\nO4gTOO4ihg0byLP2798Xbm4XAYh9azSCYS6jd+/ePOuIEYOg0VwUyQEAaSBKRtu2/D0/w4cPBMs6\nw3Mbfn7eiIyMLLIwDIPevftAKo0TzcIwF9CiRSteNJifnx9q1KgF4IpoHrk8HgMH9ufZ6tWrh/ya\n6Q+EHexArb6IUaMG82ydOnWC2XwLQJbtTgJYwbIXMHQoX/MBA/pBJvsN4p1RAxjmGHr16sWzjhjR\nHxrNFpEcAHAHRDcEEYDDh78Dll1mp48tHETZst68SF+GYdCrV2+4ua0UzcIw36NVq/bgOK7I5u/v\nj+joGAA7RPMoFCswaNA7PFv9+vWhUmUBOCOaR61egVGj+vJsnTt3hsXyKwCxyWGtYNlVGDq0H88a\nG9sXMtlmiHdMssAwe9CjBz8qOF/zbSI5gHzH7DZatWrFsw4f3g8qlTM8xxAY6MeL9JVIJOjevSek\n0q2iWRhmJ9q06QCWfbHloWzZsqhWLRrAT6J5FIotGDy4X8kn/s1w7Wn783A5bS6UCrRaLXr06AE3\nt9Mie1yHt7caDRrwo0QrV66MqKgoAOKy1jPMOTRo0BDlyvHfwFu3bg2VygxA3Nu8XH4KsbH9oVQq\nefYBA/rDak2EuNk2K1j2DN59d4wgf9P48aMhlycAMIjgyQPLxmHSJGFk3ZQp48Bx5yDOqc2CRHIR\nI0fyN9VLJBJMmDAaKtUpiJttSwbRA3Tvzt9grdPp0K1bN7i5nRLBAQDX4eurQ/369XnWKlWqIDIy\nHIAwFYgtMMwBNG7cRDBb0rZtWyiVzwCIux65fAUGDx4AhYKfoy42tj+s1r0AxFS5sIBlZ2Hy5OE2\nNB8BmWw5gOciePRg2UWYNEkYADFlykhw3HyIc3Aeg2E2YeTIoTyrVCrF+PFDoVLNgjjN4wGcQ7du\n/M357u7u6NKlC6TSBba7CbALfn5qvPYaf49geHg4wsMrAxDn4Egkq9G0aXPBbG/79u2hUDwFcFYU\nj1y+EkOGxNrQfACIfoO4FxkLOG41Jk8eKWiZMGEk5PItEPfyagDLbsSkSUKeKVNGgePWQtyLTCoY\nZh+GDxcf9PJ3weW0/Xm4nDYXSg1ffPEfeHjcBsOU9AN6HyrVfqxZs8xmcsplyxZArT6GkpdzrkCj\nOYtFi74RtEilUqxZswwq1S4AjpfepNKT8PFJxUcffSBoc3d3x+zZM8CyWwA4Wra1Qi4/hKpVPdG/\nf39Ba0REBGJj3wHLbgOQ44DHBJVqJ9q0aYwmTZoIWjt06ID69SOhVO6GY8dND5bdgkmTxttMYDxy\n5AgEB0sgk/0Kx4N4Glh2K5YsWQiVShjRO336Z3B3vwWGKSltZhJY9gDWrl1uU/Ply+eD4zYCOF8C\nz3FoNHuwYMHXgpZ8zZdApZoI4KqwK+/c5fD1jcP7708WtHl4eGDmzC/Asu0A3Bd2LoIFcvkohIdb\n8c477whaq1Wrhv79u4Flu8Dx0rgBKlUvtGtXH40aNRK0duzYEfXqlYNSORiOHbdUsGwXTJ483mYy\n21GjRiIoKAlubtPgWPNEqFRdsHTpPMFLDADMmPEfuLtvBsOscsABACfAsmOxZs1CO5p/A477EsBv\nJfDsgkazHPPnzxC0SKVSrF69GCrVJDhe0iZIpctRpswFm5p7eXnhq68+A8sOB+ColJwFcvlnCA9X\noE8fYURvdHQ03nnnbbDsGDiegc6BSjUBb7zRWPDiCuSvXNStGwil8gM4dtzSwLLDMXXqJMGLqwv/\no/hvl2D4p8H1CEoXiYmJ5O8fRCwbTUB/Aj4m4JOCYxTJ5a8Tx7nT3r17HfIcP36ctFovUirrFpRp\n+qTYMZhUqtrk4eFD586dc8izZcsWYlkdyWSNCBhTjONjAvoSy1aj4OCKdPfuXYc8M2fOJpb1IImk\nOQGTivFMI6AbcVxFql27Pj1//twuh8VioSFDhhPHlSGgXUFJrUKe9wnoSBxXjjp1epuMRqNdHoPB\nQC1btiOOCyagc0HpqUKeKcQwrYllvWnSpClktVrt8qSmplJERAxxXGUCehLwUTGeCSSVNiWWdacl\nS5Y6fDbXr18nP79AYtnqBAywq/n+/fsd8hw7doy0Wm9SKtsTMI+AXcWOr0mlakkeHn50/vx5hzyb\nNm0ilvUimWwIAb8QkFhwXCNgFXFcCwoOrkr37t1zyPPVV7NJpfIrKNOUQgAVHHkEbCWOe53q1GlK\n6enpdjnMZjMNHDiCOK4iAd8WlJ3KKzjSCfiOOC6CunTp41BzvV5PLVq8QRxXg4CVBSWsDAXHA2KY\nr4hlA2ny5A8cap6SkkLh4bWJ45oXlEXKKXY9t0kq/YBY1pe++26Zw2dz7do1KlMmlFSqrgT8TPml\nsAqvJ57k8pHEcT504MABhzxHjx4lrdaXFIp+BPxKQHKxYz+pVN3J0zOA4uLiHPL88MMPBZoPJuAn\nAq4UHJcIWE4c14xCQqpSUlKSQ57p02cSy5YhiWQ8AUeL8SQQMJc4ribVrdukRM1jY4cTx4UUfKfO\nFOM5R8BnxHFh9PbbfSgvL88uj16vp2bN2hHHRREwi/JLWBXy/EEMM5lYNoCmTp3mUHN7+G+PfQAo\n+S84/m1j+L/rbm3g3yb4fwMZGRn0zTffUNmyocSy3qTTlSeNJoA0Gk+aOPHdEh2kQjx+/Jg++ugT\n8vDwJbXaj3S68sRxvuTtHUCff/5FiXVHC3Hz5k0aNWoccZyONJqypNOFEst6UUhIJVq0aBFlZWWJ\n4jl//jz17t2PlEo1abWBpNOFkFKppejo2rRhwwaHP8CFsFqtdPjwYWrbtiMpFBxptcGk1QaTQqGm\nxo1b0t69e0X9AJvNZtqxYwe99lojUio1pNWGkFYbRAoFR2+99TYdP35c1D3l5OTQ6tWrqWrVaFKp\n3EmnCyWtthypVBqKjR1Cly5dEsWTnp5Oc+fa1nzSpMklOkiFePz4MU2b9jF5ePiRWh1IOl1V4riy\n5OMTSF98MV205jdu3CjQ3JM0mjDS6aKIZf0oJCSCFi9eTNnZ2aJ4zp8/T716DSSl0p202kjS6WqQ\nUulN1as3pI0bNzqleZs2XUmhcCetNoq02mhSKNypSZMOtG/fPtGab9u2jerWbU4KhSdptdVJq61G\nCoWOOnXqRX/88Yeoe8rJyaFVq1ZRlSq1SaUqQzpdDdJqI0il8qCBA0fQ5cuXRfGkp6fTnDlzKSCg\nEnFcEOl0NUmjqURabRl69933RGv+6NEj+vDDj8nd3Z/U6vKk08UQxwWTr28IffnlVyXWmi1EYmIi\njRgxtpjm1Yhl/Sg0NNIpzc+dO0c9e/YnpVJHWm1l0ukiSan0pJiYBvTDDz+I1vyXX36h1q07kUKh\nI602nLTaCFIodNS0aXunNa9TpykpFB6k1UaSVluFFAotde7ci06cOCHqnmzB5bT9/4SrjJWrjNVf\nBiLCzZs38fz5c3Ach4oVKwr2koiB2WzGjRs3kJmZCZ1Oh7CwMEilzofW5+Tk4NatWzAYDPDy8kL5\n8uVtLtuUhMzMTNy9e7eg3JGfzaUoMXj69Cnu378Pq9WKcuXKoUwZ5/LTFeLx48d49OgRpFIpgoOD\n4eHh8Uo8d+/exZMnT6BUKhEaGgq1Wmyy2xcoLc1NJhNu3LiBrKwsl+Y28OjRIzx69Ahubm7/CM1v\n3LiB9PT0/ynNMzIycO/evVLRPCkpCUSEwMBA+Po6l9uwEIWay2QyBAUFvbLmhfg7ylil/QU5P7zM\n/64yVi6nzeW0ueCCCy648C+Dy2n7/wlXrjsXXHDBBRdccOEvh9tf4XH883MKlypcTpsLfymePXuG\n9PR0sCwLX19fSCTOBywTEZ4+fYqsrCxotVp4eXm90nKH1WpFamoqDAYDPD094e7u7jQHkL+Mk5qa\nCpPJBC8vL14uNWdgNBqRmpoKq9UKX19fm9GZYqDX6/H06VNIpVL4+vpCLpe/Ek9mZibS0tKgUCjg\n6+tbkBDYeZSW5k+ePEF2dvaf1jwlJQU5OTmlprm3t/crLSMCQG5uLp48efI/qznHcfD19X0lrUpL\nc4vFgtTU1H+U5qmpqQAAHx+fv13zvxMy51e7XXgZ//VddP8wuB5B6SM3N5fWr19PUVG1SC5nieN8\nSKnUkp9fIM2YMVP0xuLMzExauHAhhYRUIoWCI7Xal+RyjipWjKBly5aJ3lickpJCn332OXl7B5BS\nqSOO8yGZTEU1a9ajzZs3i9pYTER0+/ZtmjBhEmk0HsSyngU8SmrevC0dPHiQLBaLKJ6EhATq338Q\nqVQaYlkv4jgvUihY6tatF506dUoUh9VqpaNHj1LHjl1JLlcRx3kTy3oSy2pp6NARdPXqVVE8ZrOZ\ndu/eTQ0bNieZTEkc50MqlTu5u3vT1Knv0/3790Xx5Obm0rp166hatZovaR5EM2fOorS0NFE8GRkZ\ntGDBAgoOrkwKhZbU6gCSyzUUFhbllObJycn06aefk7d3ICmV3sRxgSSTqalWrSa0ZcsWpzQfN2Ey\naXQ+xGr9ifMIIpmCoxat36JDhw6J1jw+Pp76xQ4lpVpHrFdZ4rzKkYLVULde/ZzS/MiRI/RG5x4k\nV6mJ8w4k1jOAWK0nDR0xxinNd+3aRQ2btSWZkiPOO5hU7mXI3duf3v/gI6c0//777ymyRj2SqzTE\neQeTUuNF/kEVadbsr53SfP78BRRUIZwUag/ivINJzuqoUmRNWr58Oen1elE8ycnJ9OlnX5C3XxAp\ntT7EeQeTTKmm2q83c0rzW7du0dgJ75LG3YdYd3/ivIJIpuSoZbtO9NNPP4mO1IyPj6d3+g0hJasj\nVluWOG05Uqi01L1Hfzp9+rQojkLNO7zRneQKNXGaQGLVAcRynjR8xDi6du2aKB5b+G+PfQDIwJX+\nYe8+0tLSaOzYsRQUFERyuZz8/f0pNjZW9Oc7MTGR+vTpQ+XKlSOZTEYsy1L16tVpxowZZDKZBOev\nWrWKateuTSzLkkajocaNG9OePXtscu/evZsaNWpEGo2GWJalOnXq0OrVq8U9R1Fn/Q/D5bSVLh48\neEAVK4aTWl2ZgO4FKTEKU2wMJJWqJmm1XnTs2DGHPBcuXCBvb/+CcPe+xdJIfERAH1KrI8nPL5Cu\nX7/ukOfnn38mtdqDlMraBAwploriQwK6klpdgSIiYiglJcUhz6pVq0ml0pJc/joBo4vxvEfAG6RW\nl6OWLds5HGCsVit9+OFHpFJ5kFTajIAJxXjeJYmkFbGsNw0ZMpzMZrNdnry8POrR4x3iuDLEMG0I\nmFKMZxy5uTUhlUpHM2bMcnhPGRkZ9PrrTUitDiLgTQI+KMYzghSK+sSyOtq8ebNDnvv371OFClXt\nas6y+ZqXFNGakJBA3t4BxHENCficgB8L0n3sJOBj4rjXyN8/hBITEx3yHDp0iDjOi5TKngTsJeB+\nwXGLgIWkVtemyMg6lJqa6pBnxYpVpFJ7kTxsAqFRIqEt5R8tswiRS0ntE0Gt275Voubvf/gxqTz8\nSNr0U8K4R4RplH9MfEKSFjOI9QqkYSPHlqh5t179iCtTkZjW8wjvPn/BM/ouuTV6n1Q6H5o1e67D\ne0pPT6f6jVqQOrAGoc1qwlgDYRLlH/0vkaL2SGK1XrRt2zaHPElJSVS+UiSpw5oT3tpJmGDK55ho\nJfQ8TmxUb9J5+ZUY0RofH09eZcoRF9GV0PVXwjgrYTwRxlkInfaTukp7CgiqSDdu3HDIc/DgQeJ0\n3qSKGUTodS6fYzwRxhgJ7TaSOqQeRdWsV2L08bJlK0il8SJZrYmEd24QRlP+MTSL0HQJqf3DqW2H\nzmQwGOxyWK1WmjJ1Gqk0/iQN/JQQ/ZhQi/KP6FSSBM0gVhtIw0eOc+j0G41GertbX+K0FYnx+JZQ\nNp0QSPmH/x2Seb5HKtab5syZ5/Ce7OHvcNrydKV/2LqP9PR0qlKlCjEMQwzDkEQiKfp3QEBAiZHN\nDx8+JA8PD17/4hz9+/fnnf/ee+/ZPfe7777jnbtkyRK7577//vslPkdXIIIrEKHU8OzZM8TE1MGj\nR6Ewm1+H/QLpN8Bxe/D774dRo0YNQeutW7dQs+ZryMhoBCDK7t9jmPPw9DyFuLgzNiO7jh8/jlat\n2sNg6AQgxA4LQSb7FSEhaTh37oTNpc6NGzdi4MDRyMnpAcBHSAEAMEOp3It69Xxw6NBem8tMH3/8\nH8yevQIGQzcA9pZUc8Cy29CnT0ssXbpIeLVE6NGjD3bvPo+cnM4A7C2RpINlf8AXX0zFuHHCygpG\noxENGzbDhQtmGI2tYb+Y+GOoVJuxefNadOjQQdCalpaG6tVr4/HjCrBYStJ8L44ePYyYmBhB682b\nN1GrVn1kZPQDIEwwWwiGOQhPzx2Ijz9lM5nosWPH0Lp1JxgMSwDUtcNCkMm+QmjoHzh79nebmq9b\ntwFDR02FIeoQoK5igwOAxQjl9X54vbIBB/Zut6n5tI8/xZxVO2DoegBQ24kUzU0Hu/0N9G1TE4sX\nCBNFExG69eqHvfFPkfPmFkDO2SABkH4P7OaWmP7BWIwZLcyybzQa8XqTVriUVwXGJosAiR3NU86D\n3d0eW9avQLt27QTNaWlpiK5ZD8mhA2GpORmwt4R5ex+4w/1x/LefEB0dLWhOTExE7XqNkFnvW6BS\nNxsE+ZBcXALPy9MRf+YPQRUMAPj999/RtmNXGFpvB8oKE9UCAMgK2YmpKK//BWdPHLG51Ll27ToM\nH/8BDO0OAR6VbfNYjFD++g4aBBtxYM92m9GtH3z4Cb5ZvAuGwAOAzE6kqPk52Acd0L97XSxcMEd4\nuUTo+vY72P/Lc+RwWwAJa4MEgPku2OyWmPHlBIwaJaym4Qh/RyBCnq70eeUZwkCEiRMnYu7cuQCA\nKVOmYMqUKVi3bh3GjBkDAOjSpQu2bLFf8m7u3LmYOHEiAKBdu3bYsGEDrl+/jsaNGyM3NxdSqRQZ\nGRlgWRYJCQlFv2mRkZHYs2cPMjMz0bp1azx+/Bgsy+L27dvw9fVFSkoKypcvj5ycHAQEBODgwYPQ\naDRo3749Ll++DIlEgri4OFSrVs3utbkqIrhQavjoo/8gOdkLZnMD2B+8ASAMen0z9O49wGbroEEj\nkJVVA44cNgAgqoH09KoYNWqCjTZCz559YTC0hX2HDQAYmExNcf++HNOnCzOuZ2dnY9CgYcjJeRv2\nHTYAcENubgecPn0TGzduFLTevHkTM2fOKcFhAwAVDIYuWLduK06ePCloPXToEPbu/a0Ehw0A3GEw\ndMd7732Ix4+FFSG+++47XL78DEZjG9h32ADAHzk5ndCnT3/k5Qmz8X/44cdITfWFxSJG8ybo0yfW\nZuvAgSORldURjhw2ACBqjfT0hhgz5l1Bm9VqRY8esTAYZsK+wwbkaz4VSUnBmDlTWFkhKysLQ4eP\ngqHaXvsOGwBIFcitshYnLzzBpk2bBM2JiYn4et4CGLrut++wAYDSHYbOu7F20w6cPi0sA3fgwAHs\n//0cct7aat9hAwD3YBjePoAp73+I5GRhVv8lS5bi6jMVjE0X23fYAKBMDRjabEHvfoNsav7+tP8g\n1bslLLWm2HfYAKB8O+jrzUTvAcNsNg8cNhZZUVMdOmwAYK02DM+D+2DsxPeEbVYrevYdBEOTVfYd\nNgBgJDDVm4F7VBGzvp4raM7MzMSwUWNgaLPPvsMG5GvedB1OXE7G5s2bBc3Xr1/H3HmLYQjcb99h\nAwA3DxjK7cHq77fg7FlhBZl9+/bh4M/xjh02AHALgUF9AO9Ofr9o39w/GTK30j9eBhFhzZo1AACO\n4/DZZ5/B3d0do0aNQvny5QEAP/74I9LT7Ve4ycl5UbWmY8eO0Gq1qF27NsLCwgDkf+4Kvxtr164t\nOnfq1KkICgpCZGQkhg/Pd6INBkPRZ2Xz5s1F3CNGjEBERASCgoIwZcqUIt7Ca7cHl9PmQqnAYDBg\n9eo1yMurJ7JHNdy/n4wzZ/gFrO/evYuTJ0/Caq0lisViqYtDhw4gJYVfwPrXX3/F8+d5ABz8ABeB\nQW5uPSxevBQmE79kzLp168AwIQD8RPBIodfXwYwZwkHh228XwmKJhmOHrRAq5ObGYPbseYKWO48T\n3QAAIABJREFUmTO/gV5fE44dtkJ4AIjAkiXf8axEhFmz5sFgqAtxPwFBsFi8sX37dp5Vr9dj7drv\nndA8CnfvPhQMUrdv38bp02dgtbax048Pi+VN7N+/TzBIHT58GBkZMgAtRLAwyM0diQULvhNo/v33\n68B4NQU09t92iyCRQ+8/GTPnLBY0fbtgCcxRsYBaxGdH6Y7c6iPx9Tzh7OrMbxZBX2MCIHMweBfC\nozxQtSu+W7aCZyYizJq3CIaY9wFGhOblGsCsq4SdO3fyzNnZ2fh+3TqYak4tmQMAqr6DO0kPcf48\nvzzZrVu3cPbsWVA12w7dy7BUn4C9e3fj6dOnPPvPP/+MTAsHhApnBAVgGOTGvI/5i5bCbOaHHK5d\n+z0kgc0Br4iSeaRy6CMnY+ZcoebffLsYJveBgExE/j03D+TqRuLruTY0n7UIeslExw5bEU8FgO2M\n775bUfK5/wLcuXMHz549AwBUrFiRNwMeEZGvr9lsRlxcnF2Otm3bFgXB/Pjjj8jIyMDp06dx/Xp+\nqbS6desWBbgUjmEMwxTxA/n1dQtR+JtXfLyzd+7LY+LLcDltLpQKfvzxR0gk5ZDvKIiBBDk5UVi8\neBnPumrValitkRDnlACACkBVrF+/nmddsGApsrOrwfHsT3H4wmr1wMGDB3nW+fO/g14vYvAuQhju\n3EnCtWvXiixEhJUrV8FkEi4L2oPVWh179uyCXq8vsj158gTHjx8FIP56cnOrY8kS/jM+c+YMnj/P\ngeMZSD6ys6th3rwlPNvOnTshlQYBEBudJ0FubhSWLFnOs65cuRpWa2MAYhOyqsEwdQUzmgsWrEB2\ndi+I17wqrNZA/PTTTzzr/MWrofcZaqePDfi+gRs3byMxMbHIRERYtWY1TNHieazRsfhx53YYDIYi\nW2pqKk78cQyI7CmaJzd6GBYtW8WznTp1ChlGCVCuoWie7KrDMG8xn2fHjh2QBjYAtCITzUqkMFYd\njKXLV/PMK1augaVKX8BNWNvUJlReYCp0FGg+f8kqZFce6njGrzh8omHhgvDzzz/zzN8uWQV9mBOa\nh3bE9Rs3cPPmzSJT4QyP2UN84Xar50Ds2L4Vubm5Rbbk5GScOn0CYLuL5smVDsOil7T6R0L6Fxwv\nofgLvE7HX4/VarVF/37y5Indy4yJicHWrVtRoUIF7N+/Hx4eHnjttdeQl5eHjh07YseOHSX+veL/\nLnzBFHOuo+sCXE6bC6WEpKQk5OQ4l6HbavVBYuItni0x8Tby8ryc4snN9cDNm3d4tlu37gJwLvO4\nyeSFe/fu8WwPH953kkcKmcwXSUlJRRaDwQCjMQeApxM8LNzcON5s0qNHj6BQeEG8QwsAPnj6NJm3\n5yMpKQkSiS/EOzcAwL+nQh6D4c9rfuPGXeTlOVfsOienHG7ceFnzJIibWX0BkylMoPmjB/cATaR4\nEokb5LoqvOeTnZ2NPKMR8AgVz8N6Q6rS8X60Hz58CIVnsLhZtkL4ROLp4ySB5ox3hHjnBgC8InEv\nif9skpKSYNA68WwAWLwikXiLz5N4OwkmD+d4cjQRuHmbz3P7bhLg5RyP2T1CoPnjh07ySGWQe1fm\naZ6ZmZk/g6cIEc8j84FUpuFp/uDBAyhUIeJm2Yp4IvAkNank8/5uuP0FhxMQu4fPYrEgLi4OaWlp\nAPJn0Qpn3m7fvo2rV6+W2t9y9lyX0+ZCqeBV8ikBBOalpZr8nF7Ob459+e+/2vWUFg/x+r3qtdjm\n+fMbh1+Vp/SeDV/zV+WRSGxdz5+/L6ccm2LX84/RnKyCe2AYJr/WvZPXIrGplbPXQ2BeytUneeXr\nKZ37KpXPMv2VmjvP8Wqf2/9/+M0IfJL54ngZfn4vtiO8vG8tM/NFB0flxObNm4fPP/8c6enpGDFi\nBDIzM5GYmIhKlSrh0qVL6NChQ9FeYXt/z9bfKl62rqRz7cHltLlQKihfvjxUqqcln1gMUmkqwsPD\neLaqVcOgUDjHo1KloUoVPk+VKmFgGOFmbEdwc0st2qhaiKCgEADO8JiRl5eC0NAXMywqlQosqwHg\neNqbj2xYLLm8L3m5cuVgND4DkGu/mwDJ8PML5A0EoaGhsFiSAVid4HmMChX4z6Z8+fJg2T+veXh4\nGBSKu07xsOw9VK5ckWerUqUCGOayUzxubleEmgeXBzLt73cRwJqHvPQrPM05joOK5YC0RAcdX0LW\nY1iN2bwf7XLlysGYdg8w2hid7CElHv6B5QWaU2pCvkMnFqlxNjXnnjvxbAC4PY1DeCU+T3jl8pA/\nc46HzYhHpTA+T+Ww8mCeOMcjTYsXaB4YXB5whsdihPHJVZ7mGo0GCoUSyL0hnifvEawWA3x8XgQ5\nBQUFwWi4A1izxPOY4uEfUL7k8/5ulMLMWhMO+MTzxfEyQkJC4OWVv1pz8+ZN3p7Vy5fzfx9kMpnN\nKPZC/PLLLwDyHej+/fuD4zhUqFABLVu2BJC/enLixAkAQO3atQHkz5YV8hf/W8XPqVOnjs12W+fa\ng8tpc6FU8MYbbwBIBSB2ELdAoYjHiBH8fSQDBvQHw1wCYBTJowfRdfTq1YtnHT16GFg2AeIdk0dQ\nKHKKvpSFGD9+BNTqCyI5AOAaqlatgooVXzgUDMNg6NBBkMvFDwpSaRy6dOnKy57u6emJ5s1bgGHE\nX49KFY/Ro/mbvWNiYuDn5wXgtmgejeYixo0bwbN17NgRRMkA0kSyWCCXJwg0j43tD+B3ADm2OtlA\nOqzWczY0HwSWXQ/xM0EJUCjS0Lx5c551/OiBUD9ZYqePDSRvR2RkJM8RYBgGQwYNhDxBPI80YTm6\nvd0dSuWLfV7e3t5o2qwFcHGdaB5VwmKMHjqQZ6tZsyZ83Vng3i+ieTTXlmDcCD7Pm2++CUo5B6Tf\nstPrJVhMkF1dgeFD+FHDsQP6QXJ9A2DS2+n4EvQpsN45gJ49+Xv7xgwfCO76EvGzbclnoLI8Q9Om\nTXnm8SMHQn3DCc1vbkN0dDRCQkKKTAzDYNDAWMjTl4qmcXu+DD2694RC8WI/p4+PDxo1bgoY1jvo\nyQdrWYwxowaWfOK/AAzDoF+/fgDynatp06bh+fPnmD9/Pu7cyd9S8eabb0Kn0+G3336DRCKBRCLB\ngAEvshl4eORv+yAirFq1CtnZ2bh16xYOHTokOKdv375FL0hfffUV7t27h4sXL2Lx4vxAFY7j0K1b\nfoR0t27dwLL5y96LFi3CpUuXcPfuXcyYkZ+5QCqVFl27PbicNhdKBQqFAkOGDIRCcRxiBk2GiUNY\nWAVB/qayZcuiadNmkEqF6S5swc3tBDp2fLPozaoQ9evXh5+fB4BLIlisUKn+wOjRwwV5l7p37w7g\nIYD7InhM4LjTmDJlnKBl1KgRkEguAngugicbCkUcJk4cI2iZPHk8WPYsxDk4T0B0DYMG8X/MGYbB\n1KnjwbInAFhE8NyCXJ5V4Ji/gFKpxKBBsU5ofh5VqoQJchCVK1cOTZo0gVS6S8S1ADLZdnTq1Kno\nR7MQDRo0gK+vEsCPIlisUKnmYdy4YQLNe/ToAaSfBNJPlUxjyQH3eAYmTxDmyBo1YigkF9YA6XdL\n5slOgSJ+McaPEfJMHj8S3Pk5QG5GyTxProASd2PgQH46HYZhMHn8SLDnPwcsJjudi+HOQShyHqB9\n+/Y8s0qlQmz//lCc/lSUo8RcXIrwypV4kXJA/mzS6w0aQhovjLS2Bdn56ejSpaugJFWjRo3grZYA\nicL0GwKQFapz/8H4UULNe/bsCXp8HEhxHLkHADAZwF2agcnjhFqNGT0MkuerAeM9YT8BTzJkz5dg\nvA2eKe+OBGf+GrCK0Nx0GWTYW/Dy8w/Hf2lP20cffYQqVfLT9cycORNeXl4YOzY/X6W/vz++/lqY\n6qf4zPTIkSMhk8kAAIsXL4ZWq0VYWBhu3MifRY2JiUGjRvnpiaKiojB1an409eXLlxEaGoro6Ggk\nJyeDYRjMmTOnaCbV19cXc+bk5+V7/PgxoqKiUL58eVy5cgUMw2DKlCmIjHS8t9LltLlQavj442kI\nDjZBJvsFjme4LkGt/gMbN9rOR7Ns2SK4u1+FRCLMX/QCBKn0BHx8kjB/vjA5JcMw2Lp1AzjuMIDr\nDnisUCgOoVIlFd59d5KgVaVSYePG76FSbUO+82YPeVCpdqBFi9ro2rWroDUoKAjTp38Glt0Ex45b\nNlh2M0aNGmpz+r5Ro0bo3bsLWHYrAIOwexGegGU3YdGi+fD29ha09u/fH6+9VhFK5S44rrh8Dyy7\nC1u3/mAzeeynn36MoCAjZLLDKElzjeaEXc2XL18Id/fDkEgOOOAgSKXb4eOTgHnzZgla8zVfA477\nD4DDDnjMUCg+QOXKekycOF7QyrIsNqxbCdXlt4CMcw5o9FBd7oJWjcLRuXNnQXNISAg+/+QjsFta\nA+kOBvHsZLBb22LMiMGoXr26oLlJkybo+VYbsNs6ADkOPjtProLd2hZLFnwreIkBgIEDY1GnvBrK\nn94BzA5msu//Dvbnd7Bt0zqbmn/2n2kINCVA9sd7jh23axuhOf85Nqz5zmbzyqXfQndjKSSXbLcD\nAIjgdnY6fJ/swzezpwuaGYbB1o1rwB0fBdzZb5/HaobiyDBU9cjE+PHCZNMcx2H9mhVQHeoIpJ63\nQVCAvGyofu6CNg2i0KlTJ0FzaGgoPv3kA7D3Wzt23EzJYO+3wYRxwxAVJcxH2axZM3Tv1gqsviNg\ntZ9PDKYrYLPbYunSBfD0dCbQ6X8bWq0Wx48fx5gxYxAUFAS5XA5/f38MGDAAp0+fLkrGXuiovbyP\nsF69ejh69Cg6d+6MgIAAyGQyqFQqVK1aFZMnT8bhw4d5jv8XX3yBVatWoVatWmBZFhqNBo0bN8bu\n3bsxePBgHveQIUOwe/duNGrUCBqNBizLonbt2li1ahU+//zzkm+uxJoJ/+NwPYLSxdOnTykmpg6p\n1cEEdCTg/WLlp3oTx0WSt7cfxcfHO+S5efMmBQaWJ42mMgFvFyuN9CEBXUijqUjly1ehpKQkhzyn\nTp0id3efgnJY7xRcR2H5qfakVgdSvXqNKT093SHPzp07iWW1pFLVImDwS+WnWhLL+lC3br3IaDQ6\n5Jk79xtSKjUkl9cnYFQxnrEkkzUmlcqd3nvvA4f1DS0WC40cOaagHFZTAsYX4xlOCkU9Uio1tHz5\ncofXYjAYqF27N4nj/IhhWhcrh/UxAbHEsjVIrXanQ4cOOeR58uQJVa9e247mvUitjiBvbz9KSEhw\nyHPjxg0qV64CqdU1CJhKwPaCMlbbCJhIGk01qlAhosTagSdOnCCdrgxxXDsCNhCQVFDG6ioBX5Ba\nXZXq129BGRkZDnm2b99OrNqTVOUHEOqfeVHGqlkKSapMJ9Y9mHr0HlBiTcvZX39DSo0nyeuPI4y4\nXqz81B2SNZxKKp0PfTDtE4eam81mGjF6PKk8/Ena5GPC2AcveIZcIEXd4aTUeNDKlY7rFxoMBmr7\nRhfiyoQR02wuYdSzF+WnehwlNqonqd296aeffnLIk5qaSlE165E6qCah9UrCWH0+zwQzodMeUldp\nRz7+QXThwgWHPImJiVQ2OCy/HFaHbYSxpvzyU6NzCW3Xkya0PlWsGk0PHjxwyPPHH3+QzsuPuIgu\nhC4/vyiHNTKT0HwRqQMiqEHT1iVqvnXrNmI1nqSMjiV0P/uijNXAFJLU/5JYryDq9U5siZrPmj2X\nlJwXycuOJ0Ref1HGqtptkpWbQirOh6Z99GmJmg8bNpZUXABJPT8hBDx8UcaqTAIpvYaRivWgVavW\nOLwWe/hvj30AiMJK//i3jeH/rru1gX+b4P8NWCwW2rdvHzVp0ookEinJ5RxJJFKqVCmSVqxYIboA\ntNFopI0bN1J0dO0iHoaRUO3aDWjr1q2iC0BnZWXR4sWLKTS0CkkkbiSXsySVulHLlu3p559/Fl0A\nOjU1lb78cjr5+pYjqVRGcjlLMpmCunfvI7oANBHRnTt3aNKkyaTReJCbm4JkMiWpVBoaPnyUUwWg\nL168SLGxQ0ihYEkmU5Gbm4Lc3X3ogw+mlTjIFcJqtdLx48epU6du5OYmL3g2MgoICKGvv55Dz549\nE8VTqHnjxkLNV65cKVrz3Nxc2rBhA0VF1S3QSkMSiRvVqdOYtm3bZrNQsy1kZmbSokWLKCQkkiQS\nGcnlGpJKZdSqVSenNf/iy6/Ixy+EpG4Kkis1JFew1LN3LJ05c0YUB1GB5pPfI42HD7kpWJIpOWK1\nHjRi9PgS6+cWx4ULF2jA4OGkYDUkU6nJTaEiD58A+nDaJ/Tw4UNRHFarlY4dO0Zvdu1FbjIFyVkt\nSd3kFBBSiebM/Ua05mazmfbu3UuNW7QnidSN5KyOJFI3qlytFq1atcphfc7iyM3NpfXr11O1mvV5\nPHUbtqDt27c7pfnChYsoJCySJG6ygvuSUesOXeiXX34RrXlKSgp9/sV08vYPJqlMQXKVhuRKlnr2\niaWzZ8+K4iAiun37Nk2cNJU0Oh9yk7Mkk3PEqj1o1KgJJdbPLY6EhATq338YKRRqksnV5OamJA/P\nsjRt2n9Ea24Lf4vTVrX0j3/bGO6qPeqqPfqXwmw2IzMzExzH8TbbOou8vDxkZ2dDo9EU7TV4FeTm\n5iInJwcajcbm0o8YEBFycnKQl5cHrVZbkKbk1Xj0ej2sVis0Gs0rpwywWq3IysqCm5sbWJZ9ZR6L\nxYKsrCwoFApeAISzcGnumOd/VfOsrCywLPuP0lyr1dqsDyoGpal5dnY2iOgfoXkh/o7ao1T1L+C9\n6lyes//vcDltLqfNBRdccMGFfxn+FqfNmeIyYnkv/rucNlcgggsuuOCCCy644ML/A7zaWoELLoiA\nyWRCXFwcnj9/DpZlER0dzav9JhYGgwHx8fHIysqCVqtFTEwML5eVWDx//hyXLl2CwWCAp6cnatSo\n8UpLJ8nJybh27RpMJhPKlCmDatWqvdJSxd27d3H79m1YrVYEBQWhUqVKTnMAwNWrV/Hw4UNIpVJU\nrFixKDLKGVitViQkJODJkydQKBQIDw/nJfwUC5PJhPPnzyM9Pf1Pax4XF4fs7Ow/rfnFixeRk5MD\nLy8vxMTEvJLmjx8/xvXr12EymeDn54fIyMhX0vzOnTu4c+cOrFYrgoODERYWVnKnl0BEuHbt2j9S\nc47jEB0dDY1G4zSPXq9HfHx8keY1atR4paXWf6Lmt2/n50QMCgp6Zc2vXr2KR48eQSqVIiwsDOXK\nOVf67R+BV1updqE4/st76P5xcD2C0kdKSgp9+OFH5O7uQ1ptEOl04aTTVSCVSkP9+g2kS5cuieK5\ndesWjRw5hjhOR1ptedLpwkmrDSW12oPGjZtYYuRoIeLj46lnz3dIqVSTTleRdLpw0mjKkbe3P332\n2eeUlpYmiue3336j1q3fKOCpTDpdVeK4MhQcHEYLFiwQtdnearXS9u3bqU6dBqRSuZNOV4V0uirE\nsp5UrVpNWrdunaiN10ajkVauXEmVK0cRy3qTTleVdLrKpFRqqWHD5rRnzx5RG68zMzPp66/nUEBA\nCKnV/gU8lUipVFPHjl3pjz/+EPVsimuu0fA1799/EF2+fFkUz61bt2j48NHEce6k1YaRThdNWm0F\n0mg8acKESaI1j4uLox49+pNS6U46XR3S6RqTRlOFvL2D6LPPvhCt+a+//kqtOnUmpbsH6eo1JF2j\npsSFlKfgiEhauHChqM32VquVtm3bRnWaNSWVrw95NGpAHo0bEuvvR1H169P69evJbDaXyGM0GmnF\nihVUKaYmsQGBpKvfjHSvNSalpxc1atOO9u7dK1rz2V/PIf/yYaQOrkS6Oi1IV70BKbXu1LFbTzpx\n4oSoZ5OcnEwfTPuY3P0CSFspOp+n2muk0nnQgKHD6cqVK6J4bt68ScNGjSXW3ZO0VWqTrkYL0laq\nQRovX5o4eWqJ0cKFOH/+PHXr05+UGnfShdcnXfUWpAkOJ++ywfT5l9NFB1gcPnyYWr7RiZRaD9JF\nNSJdTHPiAkIppGo1WrRosSjNLRYLbd26lWo3bEYqD1/SRTUhXXRTUnn5U3TdBrRhwwbRmi9fvpzC\nomoQ6xdEuphmpItuTEqdFzVu3Z72798vOsDiZfy3xz4ARNVL//i3jeGuPW2uPW2ligsXLqBp01Yw\nGEKQm1sDQJlirVmQSuOgUJzHihWL85OY2sGhQ4fQuXN35OVFwWSKAVA8kWoa5PI4KBRXsHfvTjRs\n2NAuz4oVKzB69CQYjbVgtcYA4Iq1PoJSeR5a7WMcOfJzUTLGl0FEeO+9DzF//jIYDHUARAEonAEg\nAHfBsmcQFOSG3347xCs9VRwmkwm9evXD/v3HoNfXAVAVLya7LQASwXFnUKdORezZs6Moc/bLyMzM\nRKtW7XHpUgr0+toAKuLFTgcTgMvguNPo1q09li9fYncD9f3799GoUQukpCiRk1MLQCBeFJHPAcNc\ngEp1GtOmTcbUqZNtcgBAfHw8mjdvDb0+FEbjy5pnQiqNh0JxHitXLilIVmwbBw8eRJcuPZGXVxcm\n0+sAiucaS4VcfhxK5Xns3bsTDRo0sMuzbNlyjBv3AXJzB8Nq7QmgeJ66BCiVK6HTncKRIwdQubLt\nAvNEhCkffoiF6zbAMHwi0KUnwKkLG4E/joBd+g2C05/it3177dYLNJlM6DGgP366eAF5E8bA7c32\nYOTyfBqzGZZ9ByGbswB1yvhh9+bNdoMBMjIy0LLjW7iSB+hjJwMNWwOFuubmAPs2gVv+FXq2boGl\n87+1q3lSUhIatWqLVP9w5HQeD4TXe1GzMjsdzKE1UG2ZiY/enYApkybafcZxcXFo3q4DDHU6wvjm\nKCCkWPLcp4/gtn8Z5LsWYvWSRXj7bWHewkLs378fb/fpB2PjQTA3Gwr4BL9ofJQI+eHFUJ7ciH07\nt+H111+3y7Nk6TJMfH8acltOgLVhLKAppvmds1Ae/hbud4/jyE/77c5oExHenfoBFn+/EYb2U4EG\nvQFlMc0v/wp2/2yEWJ/it4N77c5K5uXlocc7A3Do3BXoW08FanYC3PI1h8UMxO0Gd2gG6lf0w49b\nNtrVPD09HS07vIWrWRLo200Golq90NxoAE5sArf3K/R+ozUWz//G6UCJv2VPW62/gPfsv2tPm8tp\nczltpYZ79+6hevXaSE9vBMDRjtMUqFQ/YPv2DWjTpo2g9fTp02jatDUMhk4AgoXdi3ALHLcbJ078\nLsiyDwBbt25F375DkZPTC/yBmw+GOQ8vrzO4cOEc/P39Be1ffjkDX3yxAAZDDwBqOywEN7ffULHi\nc5w7d8Kmw9W37wBs23YSBkNnAHI7PBYolbvRsGEADhzYLfghNpvNaNiwOeLicmE0toH9bam5YNkt\niI3tgPnzvxG0ZmRkIDq6Fh48CIXFYn8wBDLAshsxa9ZHGDFCmLn97t27iImpjfT0JgAcZfJOhkr1\nA3bs+AGtW7cWtJ46dQrNmrWBwTAQQAUHPFfAcetx8uTvNjOHb9myBf36jUNOzlYA9msxMsx6eHvP\nw4ULp3gFnwvx+Vdf4at1G6HfuBfwsrNkSATZ9GmoePI3nP39iE3N+wwciF3378G6YSUYO0445eWB\nGTwKDcyEvdu2CTQ3mUxo2KoN4stUhPHjRYC9pb6sDLDD2mNQ4/qYN3umoDk9PR3RderhYdMBsHS3\n74Qj9T7Yyc3w9QeTMWzoYEHz7du3UaPe68gY9i3Q+G37PDfjofqgDX7c8L2gPBwAnDhxAs3bd0TO\n+F1ApXr2eeIPQP1dX5z8/VdBZQUA2LRpM2LHTIJh0mGgTEUbBPlgfvsOPj9Nx4WzJ22+WH36xXTM\nWLUZhik/AVo7vxdEkP0wBWFJv+Hs8SM2Ha6efWPx4+UU5AzfCsjtROSa86Bc1gdNy1ixd8cWwbKr\nyWTC681aIYGrgrx+C184ay/DkAF2djsMadcQc2d9ZffebeFvcdpe+wt4T7qctn8VXE5b6aFXr77Y\ntOk+rNYmIs6+hYCA33H//m3BIBUT8xri4wMARNvuysMZNGxoxO+//8Szms1m+PqWxfPnbyB/Bskx\n3Nx+wsCB0ViyZCHP/vTpUwQGhiI3dzAAXQksBJbdiq++GorRo0fzWs6fP4+GDVvBYBiCF7N09mAG\nx63Gtm3LBQ7O5s2bERv7HvT6Pig5jsgApXIJLl48x6uFCgCffvoZpk/fgdzcN0vgAIA0qFSrkZr6\nCGo132nt3r03tm59DKu1sQiemyhb9jju378lGKSio+vgwoUIAHVsd+XhCJo0Scevv/KrJ+TvMQzG\n8+crANQokUUmm4bBgzksXMgvpZSamorgSpWR+8s5wL+sYxIisP06YWanDhg5ciSv6ezZs2jSuTNw\n7igYjrNDUEBjNELSoCW2fTNP4OD88MMPGDR7PvTf/27fYStE+jMo21bGpdMnUaEC3/n9+NPPMON4\nIoxTvnfMAQD3r4MdVx+pD5LAvXTtXXv3xQ55RVjf+ahkntMHELhqAu5duyzQvFqd+rj02mjg9Z52\nOhfDgflolvozftnLL1FmMpngWy4Y6SN2AaElT+PINozB0KpyzJ87m2dPSUlBSFgV5M64DHgGOCYh\nAju7HWYPfBPDh/Pr+p45cwZN3ngbhs8uAwrHmsNkBPdpDH5cOV9Q/3bDhg0Y8sUi6D88AkhK0Dwr\nDcpJlXEl7gyvgH1JcDlt/z/hih51oVTw/Plz7NixA1ar2Pnv8sjMtOLwYX65oYsXL+L69RtwPGtT\nHNVx5szpoo2+hdi9ezfMZi3EOGwAYDbXxvffr4Nezy9gvWzZcjBMFZTssAEAA4OhFmbNmif4EZkz\n51sYjdVRssMGAG7Q62Mwc6ZwhmzGjLnQ62tA3FeXhcUSjXnz+I6o2WzGt98uQm5ubREcAOAFiSQU\n69bxi5anpaVh165dTmheARkZJvz66688a0JCAm7evAugpkieejh58iTu3r3Ls+7atQtK3PT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x8Jn54Wz4ZDROMTID0+7JduXt+jR49KYLacYixWTRi4Sph5V5j1h/DpEdHW7i4ao698+sVX6Wr+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+t5wq5sXQAPW9Xj/Nkw8cwH/VXekYgWiTyUdzp9NJRmZmUIpXoGAEsPiPdkpKCpVNJlXjqoVc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6VLlvDL4YNkpKdTqGg4XXr0+Lc0/3LhQi6dOY3dbqNY6bK81KuX15qfOXOGpV8u5vqli2g0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\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Mt2/fBgD07dvX7B7pDz/8EBEREfDw8IBcLseQIUMwYsSIMv+FCxcsXhfgCNocqCA4OTlB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\nhjYiIiIiE/gDjAaayAJj8jcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1082d70d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.824915824916\n",
        "SVC(C=2.71828182846, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n",
        "  gamma=2.71828182846, kernel=rbf, max_iter=-1, probability=False,\n",
        "  random_state=None, shrinking=True, tol=0.001, verbose=False)\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make predictions and save\n",
      "data['SVM Pred'] = svm_clf.best_estimator_.predict(data[lscale_cols + dummy_cols])\n",
      "print(data['SVM Pred'].head())\n",
      "\n",
      "save_to_csv('svm.csv', 'SVM Pred')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "PassengerId\n",
        "1              0\n",
        "2              1\n",
        "3              1\n",
        "4              1\n",
        "5              0\n",
        "Name: SVM Pred, dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Logistic Regression revisited"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LogisticRegression\n",
      "\n",
      "alpha_params = np.sqrt(np.exp(range(-25, 10, 1)))\n",
      "C_list = [1/a for a in alpha_params]\n",
      "\n",
      "parameter_grid = {'C' : C_list,\n",
      "                  'penalty': ['l1', 'l2'],\n",
      "                  'intercept_scaling': [1000.],\n",
      "                  #'class_weight':['auto']\n",
      "                  }\n",
      "log_reg = LogisticRegression()\n",
      "log_clf = GridSearchCV(log_reg, parameter_grid,\n",
      "                   scoring='accuracy', refit=True,\n",
      "                   verbose=1, cv=10, n_jobs=4)\n",
      "log_clf.fit(data.ix[real_train, lscale_cols + dummy_cols], data.ix[real_train, 'Survived'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fitting 10 folds for each of 70 candidates, totalling 700 fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=4)]: Done   1 jobs       | elapsed:    0.0s\n",
        "[Parallel(n_jobs=4)]: Done  50 jobs       | elapsed:    0.1s\n",
        "[Parallel(n_jobs=4)]: Done 200 jobs       | elapsed:    0.4s\n",
        "[Parallel(n_jobs=4)]: Done 450 jobs       | elapsed:    0.9s\n",
        "[Parallel(n_jobs=4)]: Done 700 out of 700 | elapsed:    1.4s finished\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "GridSearchCV(cv=10,\n",
        "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
        "          intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001),\n",
        "       fit_params={}, iid=True, loss_func=None, n_jobs=4,\n",
        "       param_grid={'penalty': ['l1', 'l2'], 'C': [268337.28652087448, 162754.79141900392, 98715.771010760494, 59874.141715197817, 36315.502674246636, 22026.465794806714, 13359.726829661871, 8103.0839275753833, 4914.7688402991344, 2980.9579870417283, 1808.0424144560632, 1096.6331584284585, 665.1416330443619...4, 0.030197383422318504, 0.018315638888734182, 0.011108996538242306], 'intercept_scaling': [1000.0]},\n",
        "       pre_dispatch='2*n_jobs', refit=True, score_func=None,\n",
        "       scoring='accuracy', verbose=1)"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cv_grid_viz(log_clf,\n",
      "            {'penalty':'l1','class_weight':'auto'},\n",
      "            'C', 'intercept_scaling', 'Logistic Regression Parameter Tuning (CV Accuracy)',\n",
      "            x_exp=True, markersize=175)\n",
      "\n",
      "print(log_clf.best_score_)\n",
      "print(log_clf.best_params_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XXpDtm5SUVKFyevfuLcvZu3evEEKIlStXynXavcnan0lr1qwxWN6FCxdkTOPGjY0etzI9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\nERERyTBIJCIiIiKZ/wfxznocZarIxgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1092e5450>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.806958473625\n",
        "{'penalty': 'l1', 'C': 2.7182818284590451, 'intercept_scaling': 1000.0}\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['SK SM'] = log_clf.predict(data.ix[:, lscale_cols + dummy_cols])\n",
      "print(data['SK SM'].head())\n",
      "\n",
      "perf_report(data.ix[real_train], 'Survived', 'SK SM', 'SK SM train')\n",
      "save_to_csv('sk_logit.csv', 'SK SM')\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "PassengerId\n",
        "1              0\n",
        "2              1\n",
        "3              1\n",
        "4              1\n",
        "5              0\n",
        "Name: SK SM, dtype: float64\n",
        "SK SM train Confusion Matrix\n",
        "[[474  75]\n",
        " [ 97 245]]\n",
        "SK SM train Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.83      0.86      0.85       549\n",
        "   Survived       0.77      0.72      0.74       342\n",
        "\n",
        "avg / total       0.81      0.81      0.81       891\n",
        "\n",
        "('SK SM train accuracy: ', 0.8069584736251403)\n",
        "()\n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Adaboost\n",
      "\n",
      "We might as well train an Adaboost classifier while we're at it too."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Train an adaboost classifier\n",
      "from sklearn.ensemble import AdaBoostClassifier\n",
      "\n",
      "ada_param_grid = {'n_estimators':[x*50 + 100 for x in range(1,11)],\n",
      "                  'learning_rate':[x/100. for x in range(1,11)]}\n",
      "\n",
      "ada_clf = GridSearchCV(AdaBoostClassifier(), param_grid=ada_param_grid,\n",
      "                       scoring='accuracy',\n",
      "                       refit=True,\n",
      "                       verbose=1, cv=3, n_jobs=4)\n",
      "ada_clf.fit(data.ix[real_train, bin_cols + quant_cols], data.ix[real_train, 'Survived'])\n",
      "print('Done')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=4)]: Done   1 jobs       | elapsed:    0.7s\n",
        "[Parallel(n_jobs=4)]: Done  50 jobs       | elapsed:   16.7s\n",
        "[Parallel(n_jobs=4)]: Done 200 jobs       | elapsed:  1.1min\n",
        "[Parallel(n_jobs=4)]: Done 294 out of 300 | elapsed:  1.6min remaining:    1.9s\n",
        "[Parallel(n_jobs=4)]: Done 300 out of 300 | elapsed:  1.6min finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Done\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(ada_clf.best_score_)\n",
      "\n",
      "cv_grid_viz(ada_clf,\n",
      "            {},\n",
      "            'n_estimators', 'learning_rate', 'AdaBoost Parameter Tuning (CV Accuracy)')\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.810325476992\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AKZUprwoVKvCv846LU33Gu5OTU6HtXLlyJebNm4fk5GQEBQUhNTUVT548QfXq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MzBUI77zzTnYgN3r0aK5evcrHH38MwIULF3jttdfu2f7777/PDuSefvppLly4QHR0NGazGYfD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\njjHGGGOsCeNkjjHGGGOsCfs/Fr5bj4C1WtkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1092d9710>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "()\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try doing a long, staged fit\n",
      "ada_clf_staged = AdaBoostClassifier(n_estimators=1000, learning_rate=0.05)\n",
      "ada_clf_staged.fit(data.ix[train, bin_cols + quant_cols], data.ix[train, 'Survived'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "AdaBoostClassifier(algorithm='SAMME.R',\n",
        "          base_estimator=DecisionTreeClassifier(compute_importances=None, criterion='gini',\n",
        "            max_depth=1, max_features=None, min_density=None,\n",
        "            min_samples_leaf=1, min_samples_split=2, random_state=None,\n",
        "            splitter='best'),\n",
        "          learning_rate=0.05, n_estimators=1000, random_state=None)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Visualize training set performance to ensure convergence\n",
      "staged_scores_train = ada_clf_staged.staged_score(data.ix[train, bin_cols + quant_cols], data.ix[train, 'Survived'])\n",
      "staged_scores_test = ada_clf_staged.staged_score(data.ix[test, bin_cols + quant_cols], data.ix[test, 'Survived'])\n",
      "plt.plot(list(staged_scores_train), c='blue')\n",
      "plt.plot(list(staged_scores_test), c='red')\n",
      "plt.ylabel('Accuracy')\n",
      "plt.xlabel('n_estimators')\n",
      "plt.title('AdaBoost Convergence Test')\n",
      "plt.show()\n",
      "print(ada_clf_staged.score(data.ix[test, bin_cols + quant_cols], data.ix[test, 'Survived']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FR+Vs9sJC15bJFsMjERER6WJ4dD1H4dHec1djeCQiIiJdDI+ux/BIREREdRbD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\nAQAA4DHCIwAAADz2/2U0JiF3uLXPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109343e90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.820627802691\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Save the results\n",
      "data['ada'] = ada_clf.predict(data[bin_cols + quant_cols])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "perf_report(data.ix[real_train], 'Survived', 'SK SM', 'SK SM train')\n",
      "perf_report(data.ix[real_train], 'Survived', 'SVM Pred', 'SVM Pred train')\n",
      "perf_report(data.ix[real_train], 'Survived', 'SK RF', 'SK RF train')\n",
      "perf_report(data.ix[real_train], 'Survived', 'ada', 'AdaBoost train')\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SK SM train Confusion Matrix\n",
        "[[474  75]\n",
        " [ 97 245]]\n",
        "SK SM train Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.83      0.86      0.85       549\n",
        "   Survived       0.77      0.72      0.74       342\n",
        "\n",
        "avg / total       0.81      0.81      0.81       891\n",
        "\n",
        "('SK SM train accuracy: ', 0.8069584736251403)\n",
        "SVM Pred train Confusion Matrix\n",
        "[[507  42]\n",
        " [ 80 262]]\n",
        "SVM Pred train Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.86      0.92      0.89       549\n",
        "   Survived       0.86      0.77      0.81       342\n",
        "\n",
        "avg / total       0.86      0.86      0.86       891\n",
        "\n",
        "('SVM Pred train accuracy: ', 0.8630751964085297)\n",
        "SK RF train Confusion Matrix\n",
        "[[533  16]\n",
        " [ 74 268]]\n",
        "SK RF train Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.88      0.97      0.92       549\n",
        "   Survived       0.94      0.78      0.86       342\n",
        "\n",
        "avg / total       0.90      0.90      0.90       891\n",
        "\n",
        "('SK RF train accuracy: ', 0.898989898989899)\n",
        "AdaBoost train Confusion Matrix\n",
        "[[488  61]\n",
        " [ 96 246]]\n",
        "AdaBoost train Performance Summary\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "       Died       0.84      0.89      0.86       549\n",
        "   Survived       0.80      0.72      0.76       342\n",
        "\n",
        "avg / total       0.82      0.82      0.82       891\n",
        "\n",
        "('AdaBoost train accuracy: ', 0.8237934904601572)\n",
        "()\n"
       ]
      }
     ],
     "prompt_number": 55
    }
   ],
   "metadata": {}
  }
 ]
}