breast_cancer_clean.ipynb

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     "text": [
      "//anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "//anaconda/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#First import all the libraries needed\n",
    "\n",
    "import numpy as np #for linear algebra\n",
    "import pandas as pd #for chopping, processing\n",
    "import csv #for opening csv files\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt #for plotting the graphs\n",
    "from sklearn.linear_model import LogisticRegression #for logistic regression\n",
    "from sklearn.pipeline import Pipeline #to assemble steps for cross validation\n",
    "from sklearn.preprocessing import PolynomialFeatures #for all the polynomial features\n",
    "from sklearn import svm #for Support Vector Machines\n",
    "from sklearn.neighbors import NearestNeighbors #for nearest neighbor classifier\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier #for decision tree classifier\n",
    "from sklearn.naive_bayes import GaussianNB  #for naive bayes classifier\n",
    "from scipy import stats #for statistical info\n",
    "from sklearn.model_selection import train_test_split # to split the data in train and test\n",
    "from sklearn.model_selection import KFold # for cross validation\n",
    "from sklearn.grid_search import GridSearchCV  # for tuning parameters\n",
    "from sklearn.neighbors import KNeighborsClassifier  #for k-neighbor classifier\n",
    "from sklearn import metrics  # for checking the accuracy \n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load data\n",
    "data = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to know the number of cases and the number of variables\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 569 cases in this dataset\n",
      "There are 31 features in this dataset\n",
      "There are 212 cases diagnosed as malignant tumor\n",
      "There are 357 cases diagnosed as benign tumor\n",
      "The percentage of malignant cases is: 37.2583%\n"
     ]
    }
   ],
   "source": [
    "#Description of the dataset\n",
    "\n",
    "#how many cases are included in the dataset\n",
    "length = len(data)\n",
    "#how many features are in the dataset\n",
    "features = data.shape[1]-1\n",
    "\n",
    "# Number of malignant cases\n",
    "malignant = len(data[data['diagnosis']=='M'])\n",
    "\n",
    "#Number of benign cases\n",
    "benign = len(data[data['diagnosis']=='B'])\n",
    "\n",
    "#Rate of malignant tumors over all cases\n",
    "rate = (float(malignant)/(length))*100\n",
    "\n",
    "print \"There are \"+ str(len(data))+\" cases in this dataset\"\n",
    "print \"There are {}\".format(features)+\" features in this dataset\"\n",
    "print \"There are {}\".format(malignant)+\" cases diagnosed as malignant tumor\"\n",
    "print \"There are {}\".format(benign)+\" cases diagnosed as benign tumor\"\n",
    "print \"The percentage of malignant cases is: {:.4f}%\".format(rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.diagnosis.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.8</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.1184</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.6</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.1189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38           122.8     1001.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0           0.1184            0.2776          0.3001               0.1471   \n",
       "\n",
       "   symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "\n",
       "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0            184.6      2019.0            0.1622             0.6656   \n",
       "\n",
       "   concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                   0.1189  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop ID because we do not need the ID number as shown above\n",
    "\n",
    "data.drop('id',axis=1,inplace=True)\n",
    "#check that dropped\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst']\n"
     ]
    }
   ],
   "source": [
    "# Extract feature columns where everything but the diagnosis is included.\n",
    "# I am separating all the features that are helpful in determining the diagnosis\n",
    "features = list(data.columns[1:30])\n",
    "print features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'diagnosis'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Our target is predicting the diagnosis in benign or malignant, so we need\n",
    "#to extract this one as the dependent variable - the variable we will predict\n",
    "target = data.columns[0:1]\n",
    "print target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Now we need to separate the data into feature data and target data\n",
    "X = data[features] #our features that we will use to predict Y\n",
    "Y = data[target] #our dependent variable, the one we are trying to predict from X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X should have 29 variables and 569 cases\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y should have 1 variable - just the diagnosis and 569 cases\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature values:\n",
      "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
      "0        17.99         10.38           122.8     1001.0           0.1184   \n",
      "\n",
      "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
      "0            0.2776          0.3001               0.1471         0.2419   \n",
      "\n",
      "   fractal_dimension_mean       ...        fractal_dimension_se  radius_worst  \\\n",
      "0                 0.07871       ...                    0.006193         25.38   \n",
      "\n",
      "   texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
      "0          17.33            184.6      2019.0            0.1622   \n",
      "\n",
      "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \n",
      "0             0.6656           0.7119                0.2654          0.4601  \n",
      "\n",
      "[1 rows x 29 columns]\n",
      "\n",
      "Target values:\n",
      "  diagnosis\n",
      "0         M\n"
     ]
    }
   ],
   "source": [
    "# Show the feature information by printing the first row\n",
    "# Show the traget information by also printing the first row\n",
    "print \"\\nFeature values:\"\n",
    "print X.head(1)\n",
    "print \"\\nTarget values:\"\n",
    "print Y.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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lqfDv+/Dnuhqf0/hCQtmk8/5EL/b/eriJRm3C/zwYt4nMc9XbesVNxH+QnYf/CSvbNv4v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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a1aa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Research shows that any variables that are highly correlated\n",
    "#should be removed from further analysis. But, PCA takes care of multicollinearity, so maybe \n",
    "#I identify them which ones there are and let PCA to do its job.  \n",
    "#Just in case let's see how two highly correlated variables look like\n",
    "#using prettyplots\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1)\n",
    "for i in range(1):\n",
    "    x=df['perimeter_mean']\n",
    "    y=df['area_worst']\n",
    "    ax.scatter(x,y, label=str(i))\n",
    "#ax.legend()\n",
    "ax.set_title('Correlation of perimeter_mean and area_worst with correlation .99 or r-square= .81')\n",
    "fig.savefig('scatter.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAqYOzRRCNAFZxGTs2bPH+295aE4vALt3725YW4RoRxqRHfMp4DxjzLnGmKOB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\nzKB8aI0aCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a91850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's visualize another set of variables that are not correlated as highly as the first ones\n",
    "#These have a correlation coefficient of .75 which means an r-squared score of approximately .49\n",
    "fig, ax = plt.subplots(1)\n",
    "for i in range(1):\n",
    "    x=df['concavity_mean']\n",
    "    y=df['compactness_worst']\n",
    "    ax.scatter(x,y, label=str(i))\n",
    "#ax.legend()\n",
    "ax.set_title('Correlation of the mean of concavity and worst compactness')\n",
    "fig.savefig('scatter.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed feature columns (29 total features):\n",
      "['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst']\n",
      "Target columns (1 total features):\n",
      "['diagnosis']\n"
     ]
    }
   ],
   "source": [
    "def preprocess_features(X):\n",
    "    \n",
    "    # Initialize new output DataFrame\n",
    "    output = pd.DataFrame(index = X.index)\n",
    "\n",
    "    # Investigate each feature column for the data\n",
    "    for col, col_data in X.iteritems():\n",
    "        \n",
    "        # If data type is non-numeric, replace all M/B malignant/benign values with 1/0\n",
    "        if col_data.dtype == object:\n",
    "            col_data = col_data.replace(['M', 'B'], [1, 0])\n",
    " \n",
    "        # Collect the revised columns\n",
    "        output = output.join(col_data)\n",
    "    \n",
    "    return output\n",
    "\n",
    "X = preprocess_features(X)\n",
    "Y = preprocess_features(Y)\n",
    "print \"Processed feature columns ({} total features):\\n{}\".format(len(X.columns), list(X.columns))\n",
    "print \"Target columns ({} total features):\\n{}\".format(len(Y.columns), list(Y.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 400 samples.\n",
      "Testing set has 169 samples.\n"
     ]
    }
   ],
   "source": [
    "# import cross_validation to split the train and testing\n",
    "from sklearn.cross_validation import train_test_split\n",
    "# Set the number of training points\n",
    "nr_train = 300\n",
    "# Set the number of testing points\n",
    "nr_test = X.shape[0] - nr_train\n",
    "\n",
    "# Shuffle and split the dataset into the number of training and testing points above\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=nr_test, random_state=40)\n",
    "\n",
    "# Show the results of the split\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "def train_classifier(clf, X_train, Y_train):\n",
    "    ''' Fits a classifier to the training data. '''\n",
    "    \n",
    "    # Start the clock, train the classifier, then stop the clock\n",
    "    start = time()\n",
    "    clf.fit(X_train, Y_train)\n",
    "    end = time()\n",
    "    \n",
    "    # Print the results\n",
    "    print \"Trained model in {:.4f} seconds\".format(end - start)\n",
    "\n",
    "    \n",
    "def predict_labels(clf, features, target):\n",
    "    ''' Makes predictions using a fit classifier based on F1 score. '''\n",
    "    \n",
    "    # Start the clock, make predictions, then stop the clock\n",
    "    start = time()\n",
    "    Y_pred = clf.predict(features)\n",
    "    end = time()\n",
    "    \n",
    "    # Print and return results\n",
    "    print \"Made predictions in {:.4f} seconds.\".format(end - start)\n",
    "    return f1_score(target.values, Y_pred, pos_label=1)\n",
    "\n",
    "\n",
    "def train_predict(clf, X_train, Y_train, X_test, Y_test):\n",
    "    ''' Train and predict using a classifer based on F1 score. '''\n",
    "    \n",
    "    # Indicate the classifier and the training set size\n",
    "    print \"Training a {} using a training set size of {}. . .\".format(clf.__class__.__name__, len(X_train))\n",
    "    \n",
    "    # Train the classifier\n",
    "    train_classifier(clf, X_train, Y_train)\n",
    "    \n",
    "    # Print the results of prediction for both training and testing\n",
    "    print \"F1 score for training set: {:.4f}.\".format(predict_labels(clf, X_train, Y_train))\n",
    "    print \"F1 score for test set: {:.4f}.\".format(predict_labels(clf, X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.neural_network import MLPClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_A = KNeighborsClassifier()\n",
    "clf_B = DecisionTreeClassifier(random_state=0)\n",
    "clf_C = SVC()\n",
    "clf_D = GaussianNB()\n",
    "clf_E = RandomForestClassifier(n_estimators=10)\n",
    "clf_G = AdaBoostClassifier()\n",
    "clf_H = QuadraticDiscriminantAnalysis()\n",
    "clf_I = MLPClassifier(alpha=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_100 = X_train[:100]\n",
    "Y_train_100 = Y_train[:100]\n",
    "\n",
    "X_train_200 = X_train[:200]\n",
    "Y_train_200 = Y_train[:200]\n",
    "\n",
    "X_train_300 = X_train[:300]\n",
    "Y_train_300 = Y_train[:300]\n",
    "\n",
    "X_train_300 = X_train[:400]\n",
    "Y_train_300 = Y_train[:400]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training a KNeighborsClassifier using a training set size of 300. . .\n",
      "Trained model in 0.0012 seconds\n",
      "Made predictions in 0.0029 seconds.\n",
      "F1 score for training set: 0.9156.\n",
      "Made predictions in 0.0017 seconds.\n",
      "F1 score for test set: 0.8738.\n",
      "/n\n",
      "Training a KNeighborsClassifier using a training set size of 400. . .\n",
      "Trained model in 0.0006 seconds\n",
      "Made predictions in 0.0029 seconds.\n",
      "F1 score for training set: 0.9290.\n",
      "Made predictions in 0.0019 seconds.\n",
      "F1 score for test set: 0.9038.\n",
      "/n\n",
      "Training a DecisionTreeClassifier using a training set size of 300. . .\n",
      "Trained model in 0.0030 seconds\n",
      "Made predictions in 0.0002 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0002 seconds.\n",
      "F1 score for test set: 0.8654.\n",
      "/n\n",
      "Training a DecisionTreeClassifier using a training set size of 400. . .\n",
      "Trained model in 0.0049 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0001 seconds.\n",
      "F1 score for test set: 0.9074.\n",
      "/n\n",
      "Training a SVC using a training set size of 300. . .\n",
      "Trained model in 0.0049 seconds\n",
      "Made predictions in 0.0032 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0022 seconds.\n",
      "F1 score for test set: 0.0000.\n",
      "/n\n",
      "Training a SVC using a training set size of 400. . .\n",
      "Trained model in 0.0110 seconds\n",
      "Made predictions in 0.0068 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0025 seconds.\n",
      "F1 score for test set: 0.0000.\n",
      "/n\n",
      "Training a GaussianNB using a training set size of 300. . .\n",
      "Trained model in 0.0007 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 0.9182.\n",
      "Made predictions in 0.0002 seconds.\n",
      "F1 score for test set: 0.9444.\n",
      "/n\n",
      "Training a GaussianNB using a training set size of 400. . .\n",
      "Trained model in 0.0007 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 0.9164.\n",
      "Made predictions in 0.0002 seconds.\n",
      "F1 score for test set: 0.9541.\n",
      "/n\n",
      "Training a RandomForestClassifier using a training set size of 300. . .\n",
      "Trained model in 0.0388 seconds\n",
      "Made predictions in 0.0059 seconds.\n",
      "F1 score for training set: 0.9957.\n",
      "Made predictions in 0.0056 seconds.\n",
      "F1 score for test set: 0.9630.\n",
      "/n\n",
      "Training a RandomForestClassifier using a training set size of 400. . .\n",
      "Trained model in 0.0403 seconds\n",
      "Made predictions in 0.0058 seconds.\n",
      "F1 score for training set: 0.9936.\n",
      "Made predictions in 0.0053 seconds.\n",
      "F1 score for test set: 0.9358.\n",
      "/n\n",
      "Training a AdaBoostClassifier using a training set size of 300. . .\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained model in 0.1778 seconds\n",
      "Made predictions in 0.0075 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0055 seconds.\n",
      "F1 score for test set: 0.9533.\n",
      "/n\n",
      "Training a AdaBoostClassifier using a training set size of 400. . .\n",
      "Trained model in 0.1767 seconds\n",
      "Made predictions in 0.0057 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0044 seconds.\n",
      "F1 score for test set: 0.9541.\n",
      "/n\n",
      "Training a QuadraticDiscriminantAnalysis using a training set size of 300. . .\n",
      "Trained model in 0.0021 seconds\n",
      "Made predictions in 0.0006 seconds.\n",
      "F1 score for training set: 0.9694.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.9369.\n",
      "/n\n",
      "Training a QuadraticDiscriminantAnalysis using a training set size of 400. . .\n",
      "Trained model in 0.0013 seconds\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for training set: 0.9585.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.9381.\n",
      "/n\n",
      "Training a MLPClassifier using a training set size of 300. . .\n",
      "Trained model in 0.0145 seconds\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for training set: 0.1587.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.1379.\n",
      "/n\n",
      "Training a MLPClassifier using a training set size of 400. . .\n",
      "Trained model in 0.0102 seconds\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for training set: 0.5663.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.4843.\n",
      "/n\n"
     ]
    }
   ],
   "source": [
    "for clf in [clf_A, clf_B, clf_C, clf_D, clf_E, clf_G, clf_H, clf_I]:\n",
    "    for size in [300, 400]:\n",
    "        train_predict(clf, X_train[:size], Y_train[:size], X_test, Y_test)\n",
    "        print '/n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf_D = GaussianNB()\n",
    "clf_G = AdaBoostClassifier(algorithm = 'SAMME')\n",
    "clf_H = QuadraticDiscriminantAnalysis(reg_param = 0.001, store_covariances=True, tol = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_300 = X_train[:300]\n",
    "Y_train_300 = Y_train[:300]\n",
    "\n",
    "X_train_300 = X_train[:400]\n",
    "Y_train_300 = Y_train[:400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training a GaussianNB using a training set size of 300. . .\n",
      "Trained model in 0.0015 seconds\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for training set: 0.9182.\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for test set: 0.9444.\n",
      "/n\n",
      "Training a GaussianNB using a training set size of 400. . .\n",
      "Trained model in 0.0010 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 0.9164.\n",
      "Made predictions in 0.0002 seconds.\n",
      "F1 score for test set: 0.9541.\n",
      "/n\n",
      "Training a AdaBoostClassifier using a training set size of 300. . .\n",
      "Trained model in 0.1797 seconds\n",
      "Made predictions in 0.0031 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0027 seconds.\n",
      "F1 score for test set: 0.9720.\n",
      "/n\n",
      "Training a AdaBoostClassifier using a training set size of 400. . .\n",
      "Trained model in 0.1851 seconds\n",
      "Made predictions in 0.0035 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0026 seconds.\n",
      "F1 score for test set: 0.9369.\n",
      "/n\n",
      "Training a QuadraticDiscriminantAnalysis using a training set size of 300. . .\n",
      "Trained model in 0.0012 seconds\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for training set: 0.9422.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.9550.\n",
      "/n\n",
      "Training a QuadraticDiscriminantAnalysis using a training set size of 400. . .\n",
      "Trained model in 0.0013 seconds\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for training set: 0.9408.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.9550.\n",
      "/n\n"
     ]
    }
   ],
   "source": [
    "for clf in [clf_D, clf_G, clf_H]:\n",
    "    for size in [300, 400]:\n",
    "        train_predict(clf, X_train[:size], Y_train[:size], X_test, Y_test)\n",
    "        print '/n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169, 1)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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JQ3loaKhq1Kihr776qtCY/vzzT/n4+DiV+/r6yhijL774wl5PUoF1//e//+m3336TJH37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\nOyIiQps2bVLz5s0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AAKgBCgAAAACoAc4A2EOPPPJIHnrooQwODuao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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124fcb690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (307219405)>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for Naive Bayes\n",
    "preds = clf_D.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fpr=fpr, tpr=tpr))\n",
    "ggplot(df, aes(x='fpr', y='tpr')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for Naive Bayes has an area under the curve of \" + str(metrics.auc(fpr,tpr)))\n",
    "#auc = metrics.auc(fpr,tpr)\n",
    "#ggtitle (\"Area under the curve is \"+ str(auc))\n",
    "#ggtitle(\"ROC Curve w/ AUC=%s\" % str(auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fee8bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (301165321)>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc = metrics.auc(fpr,tpr)\n",
    "ggplot(df, aes(x='fpr', y='tpr')) + geom_area(alpha=0.2) + geom_line(aes(y='tpr', color = 'blue'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VKo0bN85RNnLkSL344ovq2rWrevbsqQMHDujtt9/WnXfeqV9//VXXXXddjueTl/vH/fff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\nCORFaGioNm7cmGu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PPxzDw8Mxf/78WLFiRZSXlx/0+pGRkfj+978f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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1256c9b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (303455085)>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for AdaBoost\n",
    "preds = clf_G.predict_proba(X_test)[:,1]\n",
    "fprA, tprA, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fprA=fprA, tprA=tprA))\n",
    "ggplot(df, aes(x='fprA', y='tprA')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for AdaBoost has an area under the curve of \" + str(metrics.auc(fprA,tprA)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120728e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (301939085)>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc = metrics.auc(fprA,tprA)\n",
    "ggplot(df, aes(x='fprA', y='tprA')) + geom_area(alpha=0.2) + geom_line(aes(y='tprA', colour = 'orange'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x125aa9750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (307940881)>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for QDA\n",
    "preds = clf_H.predict_proba(X_test)[:,1]\n",
    "fprH, tprH, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fprH=fprH, tprH=tprH))\n",
    "ggplot(df, aes(x='fprH', y='tprH')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for QDA has an area under the curve of \" + str(metrics.auc(fprH,tprH)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122462990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (304400941)>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc = metrics.auc(fprH,tprH)\n",
    "ggplot(df, aes(x='fprH', y='tprH')) + geom_area(alpha=0.2) + geom_line(aes(y='tprH', color = 'red'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EAEAmk2Hjxo3o0qULbG1tsWnTJsTHx6Nbt25691dRUYHTp0/rTSwHBwfj77//1vvdqKZO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x123f90bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (305764329)>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for K-Neighbors\n",
    "preds = clf_A.predict_proba(X_test)[:,1]\n",
    "fprN, tprN, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fprN=fprN, tprN=tprN))\n",
    "ggplot(df, aes(x='fprN', y='tprN')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for K-Neighbors has an area under the curve of \" + str(metrics.auc(fprN,tprN)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R0YiOjgYAvPHGG/jss88wa9Ysg9tq2rQphBC4cuUKevToofPalStXqu38VIbm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126e00590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (309199065)>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for Decision trees\n",
    "preds = clf_B.predict_proba(X_test)[:,1]\n",
    "fprD, tprD, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fprD=fprD, tprD=tprD))\n",
    "ggplot(df, aes(x='fprD', y='tprD')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for Decision Trees has an area under the curve of \" + str(metrics.auc(fprD,tprD)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 50 samples.\n",
      "Testing set has 519 samples.\n"
     ]
    }
   ],
   "source": [
    "# Set the number of training points much smaller than before\n",
    "nr_train = 50\n",
    "# Set the number of testing points\n",
    "nr_test = X.shape[0] - nr_train\n",
    "\n",
    "# Shuffle and split the dataset into the number of training and testing points above\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=nr_test, random_state=40)\n",
    "\n",
    "# Show the results of the split\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training a GaussianNB using a training set size of 50. . .\n",
      "Trained model in 0.0014 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 0.9302.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.9162.\n",
      "/n\n",
      "Training a AdaBoostClassifier using a training set size of 50. . .\n",
      "Trained model in 0.1402 seconds\n",
      "Made predictions in 0.0026 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0036 seconds.\n",
      "F1 score for test set: 0.9171.\n",
      "/n\n",
      "Training a QuadraticDiscriminantAnalysis using a training set size of 50. . .\n",
      "Trained model in 0.0011 seconds\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for training set: 0.9000.\n",
      "Made predictions in 0.0011 seconds.\n",
      "F1 score for test set: 0.9076.\n",
      "/n\n"
     ]
    }
   ],
   "source": [
    "for clf in [clf_D, clf_G, clf_H]:\n",
    "    for size in [50]:\n",
    "        train_predict(clf, X_train[:size], Y_train[:size], X_test, Y_test)\n",
    "        print '/n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pel9/8uRJjscCgPnz5xvUCVZ3wrMv1ZaSkqKzTOWrCAwMRGxsrNbvy8OHD7Fh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1277a7cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (309831529)>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for QDA\n",
    "preds = clf_H.predict_proba(X_test)[:,1]\n",
    "fprH, tprH, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fprH=fprH, tprH=tprH))\n",
    "ggplot(df, aes(x='fprH', y='tprH')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for QDA has an area under the curve of \" + str(metrics.auc(fprH,tprH)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A9+/fh1qtNphYycnJQV5eXoEJLw1NTzBj3r9XrlyBpaXlMyVVbt68iY0bNyIy\nMlLvsVatWsHT07PIoT3p6ekQQiA1NfW5EgCRkZFQKBQYPHhwofWys7Oxbds2vPLKK3oTnAK6r3P+\nFX+SkpJw69YtjBo1Sm8be3t7nQT8vn37ULNmTTRu3PiZz4eIShfnACCq4Dp27Ahvb28sXbpU20VT\npVJh6tSpiI+Px8yZM/W22b17NyIiItCtWzftLL0qlQrTp0/H+fPntYmEp0VGRhZ7LDcABAQE4Pbt\n2/jxxx+1Zbm5ufjyyy9ha2uLjh07FnufwP+NeQwJCcHrr7+u89O/f3907NhR++HM1dUVnp6eiIiI\n0BnmsG/fPr2lwpRKJSRJ0jbigScf9AtaWtCQnTt3AoD2g3H+42vmWwCAs2fPIjY2FoGBgQCeJHW6\ndOmCHTt26Cw59t9//2HTpk3aJQQB/W+4rKysUL9+fZ3um9bW1gCgc8ySoFQq9b4J3rJlCxITE3XK\nno7RzMxM20OlsLGvhr71PXr0aJHdf4vL2PMozIABA3D48GHExsbqPZaSkqLtSfC815WHhweOHDmi\ns/2uXbtw8+ZNo2PNz8vLC87Ozvj222919rl27Vq9RMOAAQNw69YtfPfdd3r7yczMfK4lv6ytrQ0m\nNozVtWtX2NnZYd68eTrnoXHv3r1Ct+/Xrx/++eefQl8HTaM8/3wOeXl5eokjYwwcOBD//vsvVq1a\nhX/++Ufvm9jXX38dCoUCYWFhBrcv7Jvt4tw/iuPVV1+Fk5MTli9frlO+fPlyWFtba+9fwJN7zcWL\nF4u85xw6dAjR0dF4++23tUPRXFxc4ODggOjoaJ3XMi0tDT/99BOaNGmibSgnJyfr9dJRq9UIDw+H\nhYWFTrLK0DXwzz//4KeffkLXrl2NfBZ0bd++HdHR0di+fbv2Z+DAgdreKEuWLNHWvXv3rt72Dx8+\nRFRUFGrXrm1wuJGx1Gq1dh4SQyva5Ld79248fPiwwGUCmzZtisaNG2PlypU698VvvvkGCoVCO79O\nQX788UccO3YMkyZNKv6JEFGZYQ8AogqkoK67ISEhCAoKwrp167QTZs2YMQOnTp3CggULcPjwYfTr\n1w8qlQp//fUXIiMj0axZM6xbt05vP+fPn8fixYvx22+/oX///nB1dcXt27exfft2xMXF4dChQ8WO\n+91338WKFSswbNgwHDt2TLsM4OHDh/HFF19oG6nFFRkZCU9PT72uihq9evXC+PHjcerUKXh6euKz\nzz5Djx7jB60vAAAgAElEQVQ98PLLL2PEiBG4f/8+vvrqKzRv3lxnYqzAwEAsXrwYXbt2xZAhQ/Df\nf/9p514wtM59YmKiNtGQnZ2NU6dOYeXKlXBxccG4ceO09RYuXIiAgAD4+Phg5MiRSE9Px1dffQVH\nR0ed7vBz5szB/v378fLLL2PMmDFQKpVYuXIlsrOzsWDBAm29pk2bwt/fH61bt4aTkxPi4uKwdetW\nnSWwNONtx48fj65du0KpVGLgwIHP9Hzn16NHD8yePRsjRoxA+/btcebMGURGRup9E92lSxe4urri\n5ZdfRvXq1XH+/Hl8/fXX6NGjR6Gve48ePbBt2zb06dMHgYGBuHr1KlasWIFmzZo90yRmz3sehQkJ\nCcHOnTvRo0cPDBs2DK1bt8bjx49x+vRpbNu2DdevX4eTk1Oxr6unvf3229i6dSu6du2KAQMG4MqV\nK9iwYUOxvyXWMDMzw5w5czBq1Ci88sorGDhwIK5du4a1a9fqnf+bb76JzZs3Y/To0fjtt9/w8ssv\nIzc3FxcuXMCWLVsQGxurHZpUXK1bt8a3336LuXPnon79+nBxcdE23gq65+Uvt7W1xfLly/HWW2+h\nVatWGDRoEJydnXHjxg3s3r0bHTp00Blj/7SQkBBs3boVQUFBGD58OFq3bo379+/jp59+wooVK9Ci\nRQs0bdoUPj4+mDFjBu7fvw8nJyf88MMPBoeJFEWz5N7UqVNhZmaG119/XefxevXqYc6cOZg5cyau\nXbuGPn36wNbWFlevXsX27dvx3nvvYfLkyQXu39j7R3FYWlpi9uzZGDduHAYMGICuXbvizz//xMaN\nGzFv3jydteCjo6MxfPhwrFu3Dm+99RaAJ13iBwwYgF69esHV1RVnz57FihUr4Onpiblz52q3VSgU\nmDp1KkJDQ9G2bVu89dZbUKvVWL16NRITE3Xi37lzJ+bMmYP+/fujbt26ePDgATZu3Ihz587hs88+\ng4uLi7buwIEDoVKp0L59e7i4uODcuXP47rvvYGNjg88++0znXG/cuIH169cDgDbhrYmxTp06GDp0\nKAAYHCqnmUumW7duOhPZdu/eHTVr1kTbtm3h4uKChIQErFu3DklJSXrj/8+cOaNNIF++fBkpKSna\n47/44ovaXnsae/fuxf379wts1OcXGRkJS0tLvWsuv4ULF6J3797o3LkzBg0ahDNnzuDrr7/GO++8\nozNp4F9//YVPP/0UXbp0QdWqVXH48GGsW7cOAQEBBS7DSETlRGkuMUBEJUezDNXx48f1HsvLyxP1\n69cXDRo00FmSRwghIiIihK+vr3BwcBBWVlaiRYsWYs6cOSI9Pb3AY23btk1069ZNVKtWTZibm4sX\nXnhBBAUF6Sx1VZC6deuKXr166ZXfvXtXjBw5Uri4uAhLS0vx4osviu+//16nzvXr1/WWhCrIiRMn\nhEKhEJ988kmBdRISEoRCoRBTpkzRlkVHR4tmzZoJlUolmjdvLrZv3y6GDRsm6tWrp7Pt2rVrRaNG\njYRKpRJNmzYVERER2iWe8tMsm6f5MTMzE66urmLo0KHi6tWrejH9+uuvwtfXV1hbWwsHBwfRp08f\ng8ulnTp1SnTv3l3Y2dkJGxsb0alTJ3H06FGdOvPmzRM+Pj7CyclJWFtbi6ZNm4rw8HCdpd9yc3PF\nxIkTRfXq1YVSqSxyScCCXj9/f3/x6quvav/OysoSISEhokaNGsLa2lr4+fmJo0ePildeeUWn3nff\nfSf8/f2Fs7OzUKlUokGDBmLGjBk6SzEWJDw8XNStW1eoVCrRunVrERMTo/daFXbNKBQK8emnnxZ6\nDGPPQ7PcW1RUlMH9PH78WHz44YeiYcOGwtLSUri4uIgOHTqIJUuW6Lwexl5XBVmyZImoVauWUKlU\nws/PT5w4cULvtSkoVs1zFRERoVP+7bffCg8PD6FSqYS3t7c4cOCA3vkL8WTJwoULF4oWLVoIlUol\nqlatKtq0aSPmzJmj83oqFAoxYcIEo85HCCH+++8/0bNnT2Fvby8UCoV2ScCC7nmGlt4TQog//vhD\ndO/eXTg6OgorKyvRoEEDMWLECHHixIkiY0hOThYTJkwQtWrVEpaWlqJ27dpixIgROkt5Xrt2TXTp\n0kWoVCrh5uYmQkNDxS+//GJwGcCWLVsWeryhQ4cKhUKht8RhftHR0cLPz0/Y2toKW1tb0bRpUzFh\nwgRx6dKlIs/HmPtHce63GqtWrRJNmjQRlpaWokGDBmLZsmV6dTSvW/7rLDk5WfTt21e88MILwtLS\nUnh4eIiZM2fqLQuosWnTJp17W7t27UR0dLROnePHj4vevXtrXzM7Ozvh5+dn8D365ZdfCh8fH+3/\naTVq1BDBwcF6S3cK8X/LaOa/r2t+DC1XmV9BywB+8803ws/PT7i4uAhzc3NRvXp10adPH53lVp9+\n/gz9GFoGdPDgwcLS0lIkJycXGtujR4+ElZWVCAoKKrSeEELs2LFDtGrVSqhUKlG7dm3x8ccf69zH\nhBDiypUrolu3bsLFxUV7P1uwYIHe8q1EVP5IQjzDbEBEREREREREVKFwDgAiIiIiIiIiE8AEABER\nEREREZEJYAKAiIiIiIiIyAQwAUBERERERERkApgAICIiIiIiIjIBTAAQERERERERmQAmAIiIiIiI\niIhMABMARERERERERCaACQAiIiIiIiIiE8AEABEREREREZEJYAKAiIiIiIiIyAQwAUBERERERERk\nApgAICIiIiIiIjIBTAAQERERERERmQAmAIiIiIiIiIhMABMARERERERERCaACQAiIiIiIiIiE8AE\nABEREREREZEJYAKAiIiIiIiIyAQwAUBERERERERkApgAICIiIiIiIjIBTAAQERERERERmQAmAIiI\niIiIiIhMABMARERERERERCaACQAiIiIiIiIiE8AEABEREREREZEJYAKAiIiIiIiIyAQwAUBERERE\nRERkApgAICIiIiIiIjIBTAAQERERERERmQAmAIiIiIiIiIhMABMARERERERERCaACQAiIiIiIiIi\nE2AmdwAVQUJCAg4ePIikpCSkpqZi0KBBaNy4caHbXLt2DbGxsbhz5w7s7e3h5+cHT0/PMoqYiIiI\niIiISBd7ABghOzsbrq6uCAwMhCRJRdZPTk7Gxo0bUbduXYwePRo+Pj7YuXMnrly5UgbREhERERER\nEeljDwAjNGjQAA0aNAAACCGKrH/s2DE4OjqiS5cuAIBq1arhxo0bOHz4MDw8PEo1ViIiIiIiIiJD\n2AOgFNy6dQv16tXTKfPw8MCtW7dkioiIiIiIiIhMHRMApSAtLQ02NjY6ZTY2NsjKyoJarZYpKiIi\nIiIiIjJlHAIgo9mzZxf4WGhoaBlGQkRERERERJUdEwClwMbGBmlpaTplaWlpsLCwgJmZcU+5MXMN\nEBERERGRcYQQyM3NRXZ2NrKzs5GVlaX9vaif4tQt7o+hfZdEW8Dc3BwWFhYwNzc36kepVBo14Xl5\nlpGRgRMnTuDBgwdsTxWACYBSULNmTVy+fFmn7MqVK6hZs6ZOWWHf8gshkJSUVCrxmSpDiRl6dm5u\nbgDA67SE8TotebxWSwev1ZLl5uaGtLQ0pKamyh1KpcLrtOQZuqcKIaBWq5GTk4Ps7Gydf5/+PSsr\nS6+8JOpqGs5F1SmJRqGFhQWqVKmCKlWqwNzcXPt7QeXm5uZQqVSws7PTlmsa3Zrfra2tIYTQ2644\nx8j/r5mZWYVvzBfX/v37MXHiRFhZWeHPP/+UO5xyiwkAI2RnZ+tkkZKTk3H79m2oVCrY29tj//79\nSE1NRd++fQEAXl5eiIuLw759+/DSSy/h6tWrOH/+PN544w05T4OIqNwSQmg/7GVmZhr8V/OTv9yY\neVXs7OwAAI8ePSrt0zApFhYWyMrKkjuMSsPOzk57jVPJ4XX6RP4GekENZGMb03l5ecjOzkZmZqZO\n3eclSVKRjdunG84WFhawsbEpsjFcnIazocZ5/n9L61tyJquen5mZGdq3b48FCxagadOmcodTbjEB\nYIR///0X69atgyRJkCQJsbGxAIAXX3wRffr0QVpaGlJSUrT1HR0dMWTIEPz88884evQo7Ozs0Lt3\nby4BSETlWv5GuKaBnb/xbej3whrpBW379Haa34v7rYyxH8Q0j7MrYMmSJInPaQnidVo6eJ3+HzMz\nswIbt4Yay5qG9dMNZ0dHR5ibmyMrK6vQxrQxjez8dZVKpdxPEVVw/v7+8Pf3lzuMck8SvCuWSxwC\nUPKYWS1Z7FZdOqytrZGcnFyshnRhj2dkZBjVCNeU5eXlFSteMzMzWFpawsLCQvtjaWmpU2bo96f/\nNfR4YY9ZWFgY/WGR12rp4D21ZHEIQOngdVryeE8tHbxWS5abm5vJDYEwFnsAEBEA4NatW3j8+LHR\n9R88eAAAuHv3bmmFVO6o1eoCvw03thFuTCP+WRrhxjSwnZycdBrUKpXKYAPbmEa8paUlzM3NjZ7Y\nlIiIiIjkx09uRCbu/v37CAsLQ1RUlNyhVGhKpVLvG+v8DWzNYw4ODoU21O3s7LTjIAv7ljx/GRvh\nREREZApSUlJgb28vdxgVGj81EpkoIQS2bNmCTz/9FEIIfPbZZ8WaMKVatWoAgHv37pVWiOXO0438\n/A38kmqEswsgERERkS61Wo2lS5di7dq1iI2NRY0aNeQOqcJiAoDIBF29ehUzZszAwYMH0bdvX3z8\n8cdwdnYu1j44BpCIiIiISltiYiLGjRuHY8eOYfLkyXB1dZU7pAqNCQAyGUII7NixAytXrsTXX38N\nd3d3ncfPnTuHiIgI/PLLL8jNzZUnyDLy8OFDuLm5ITIykrOlEhEREVG5FBMTg5CQEFhZWSEqKgre\n3t5yh1ThMQFAJuHGjRsIDQ3F/v37AQCXL1+Gu7s7srOzsWfPHqxbtw5///03XF1d0adPH9ja2soc\ncemyt7fHoEGDoFKp5A6FiIiIiEhHRkYGwsLCsH79egQEBGDBggVwdHSUO6xKgQkAqtTUajVWrVqF\nhQsXolq1aliwYAGmTZuG//77D59//jkiIyNx584dtGvXDitWrEDXrl1RpUoVucMmIiIiIjJJaWlp\n6NWrFxISEhAeHo6hQ4dySb8SxAQAVVqPHj1C//79ceHCBQwfPhxhYWHaJeumTZsGKysr9O/fH8HB\nwWjcuLHM0RIRERERkY2NDXr37o1u3bqhUaNGcodT6TABQJXWiRMncO7cOWzatAl+fn6wsbFBbm4u\nBg0ahGbNmqF///6ws7OTO0wiIiIiIspn4sSJcodQaTEBQJWeh4eH9nczMzMsWrRIxmiIiIiIiIjk\nwQQAlTupqan4+eefn3sm/gsXLpRQRERERERERBUfEwBU7uzcuRPTpk0rkX05ODiwmz8RERERUTmS\nmJgIc3NzODs7yx2KyWECgMqdnJwcVKlSBVeuXHnufUmSBIVCUQJRERERERHR84qJiUFISAg6d+6M\npUuXyh2OyWECgMolSZKgVCrlDoOIiIiIiEpARkYGwsLCsH79egQEBODjjz+WOySTxAQAERERERER\nlZr4+HiMGTMGCQkJCA8Px9ChQyFJktxhmST2jSYiIiIiIqISJ4RAREQEAgMDIUkSYmJi8Oabb7Lx\nLyMmAIiIiIiIiKjErVy5EjNnzsTAgQOxa9cuNGrUSO6QTB6HABAREREREVGJGzhwIOrWrYsuXbrI\nHQr9f+wBQERERERERCXOwcGBjf9yhgkAIiIiIiIiIhPABAARERERERGRCWACgIiIiIiIiIotIyMD\nGzduhBBC7lDISJwEkIiIiIiIiIolPj4eY8aMQUJCAry9vVG/fn25QyIjsAcAERERERERGUUIge+/\n/x6BgYGQJAkxMTFs/Fcg7AFARERERERERUpOTkZISAj27NmD4OBghIaGQqVSyR0WFQMTAERERERE\nRFSoI0eOYNy4ccjIyMCqVavQvXt3uUOiZ8AEABERERERERUoMzMTY8aMgbu7O7788kvUqFFD7pDo\nGTEBQERERERERAWytLTEli1b4O7uDqVSKXc49ByYACAiIiIiIqJCeXh4yB0ClQCuAkBERERERERk\nAtgDgMrM0qVLsXfv3iLr3bt3rwyiISIiIiIiMi1MAFCZ2b9/PzIyMtCuXbsi67KLERERERFR2RBC\nYP369ahatSoCAwPlDodKERMAVKbatGmD8PBwucMgIiIiIiIAycnJCAkJwZ49ezB27FgmACo5zgFA\npWLbtm24ffu29u+8vDxkZmbKGBEREREREeV35MgRdO7cGYcPH8aqVaswc+ZMuUOiUsYEAJW4y5cv\nY/z48Vi6dCkAID4+Hn379sWFCxfQunVrmaMjIiIiIjJtarUaixYtQlBQEGrXro3Y2Fh0795d7rCo\nDHAIAJW4yMhIAICrqyvCw8OxfPly1KlTB1FRUfDx8ZE5OiIiIiIi05WYmIjx48cjLi4OkydPxoQJ\nE6BUKuUOi8oIEwBUorKysrBlyxYAwJIlS6BQKDBx4kSMHTsWFhYWMkdHRERERGTajh8/jps3byIq\nKgre3t5yh0NljAkAKlF79+5FcnIyAMDLywvz589H/fr1ZY6KiIiIiIgAoFevXujcuTNUKpXcoZAM\nmACgErVhwwb4+Phg8+bNUCgUkCRJ7pCIiIiIiCgfNv5NFycBpBJz9epVHDp0CEOGDIFSqWTjn4iI\niIiIqBxhAoBKzI4dO2BnZ4eAgAC5QyEiIiIiMllCCLlDoHKKCQAqMSkpKahevTq7FBERERERyeTo\n0aPo0aMH7t69K3coVA4xAUBERERERFTBqdVqLF68GP3794eFhQXUarXcIVE5xEkA6blkZGRg3Lhx\nSElJwbVr12Brayt3SEREREREJiUxMRHjx49HXFwcJk+ejAkTJkCpVModFpVDTADQc0lKSsLevXvh\n6+uL9u3bo23btnKHRERERERkMmJiYhASEgIrKytERUXB29tb7pCoHGMCgErExIkT0a5dO7nDICIi\nIiIyCTk5OQgNDcX69esREBCABQsWwNHRUe6wqJxjAoCIiIiIiKiCMTMzw+PHjxEeHo6hQ4dyCW4y\nChMAREREREREFYwkSfjyyy/lDoMqGK4CQERERERERGQCmAAgIiIiIiIiMgFMABAREREREZVDarUa\n6enpcodBlQgTAEREREREROVMYmIiBgwYgJCQELlDoUqECQAiIiIiIqJyJCYmBl26dMHNmzcRHBws\ndzhUiXAVACrSgAEDcOLECYOPCSEAAEqlsixDIiIiIiKqdDIyMhAWFob169cjICAACxcuhIODg9xh\nUSXCBAAV6cKFC2jfvj06duxo8HGVSgVPT88yjoqIiIiIqPKIj4/HmDFjkJCQgPnz5+ONN96AJEly\nh0WVDBMAZJQ2bdpg5MiRcodBRERERFTp7N27F8HBwXB3d0dMTAwaNWokd0hUSTEBQNixYweio6ML\nfDw1NbUMoyEiIiIiMi2enp4YPnw4pkyZApVKJXc4VIlJQjOIm8qV1NRUlNVLM3jwYMTFxcHLy8vg\n4wqFAiEhIXjppZfKJB6qGDRd0ngLofKO1ypVBLxOqaLgtUoVgSRJsLW1lTuMcok9AMopGxsbJCUl\nlcmx1Go1XnzxRaxatarQemlpaWUST2mxsbGp8OdQnri5uQFAmV2npoLXacnjtVo6eK2WLDc3N6Sl\npfE5LWG8Tkse76mlg9dqydJcp6SPCQATlZmZia1btyIrKws3btxAzZo15Q6JiIiIiIiIShETACbq\n77//xvTp02FpaQlJktCpUye5QyIiIiIiqrTOnj2L5s2byx0GmTgmAExUbm4uAODAgQPsIkNERERE\nVEoyMjIQFhaG9evXY/v27WjTpo3cIZEJYwKAiIiIiIioFMTHx2PMmDFISEhAeHh4gZNuE5UVhdwB\nEBERERERVSZCCERERCAwMBCSJCEmJgZvvvmmdhUFIrmwBwAREREREVEJSU5OxtSpU7F3714EBwcj\nNDQUKpVK7rCIADABQEREREREVCJyc3PRr18//Pfff1i1ahW6d+8ud0hEOpgAICIiIiIiKgFKpRKf\nfPIJPDw8UKNGDbnDIdLDBAAREREREVEJ8fPzkzsEogJxEkAiIiIiIiIiE8AeACYkNTUVy5cvx82b\nN3H79m25wyEiIiIiIqIyxB4AJmLv3r3w9/fHypUrkZSUBCEEAgMDUa1aNblDIyIiIiKqMOLj43H0\n6FG5wyB6JuwBUMn9+++/CA0Nxd69e/Haa69h3rx5qFmzptxhERERERFVKEIIfP/99/j000/h7e2N\nTZs2yR0SUbExAVCJbdy4EWFhYbCyssK3336LHj16QJIkucMiIiIiIqpQkpOTERISgj179iA4OBih\noaFyh0T0TJgAqKTS09Mxffp0BAYGYv78+bC3t5c7JCIiIiKiCufIkSMYN24cMjIysHr1anTr1k3u\nkIieGecAqKRyc3ORl5eHwMBANv6JiIiIiIpJrVZj0aJFCAoKQp06dRAbG8vGP1V47AFQSdy/fx+Z\nmZnav9PS0mSMhoiIiIioYtu7dy+WLl2KyZMnY8KECVAqlXKHRPTcmACoBC5evIhXX33V4GOWlpZl\nHA0RERERUcUXGBiIffv2oXHjxnKHQlRimACoBB4+fAgAWLRoEdzc3LTl5ubmaNOmjVxhERERERFV\nWJIksfFPlQ4TAJVImzZt4OHhIXcYREREREREVA5xEkAiIiIiIiIiE8AEABERERERmZzk5GR8+OGH\nePTokdyhEJUZJgCIiIiIiMikHDlyBJ07d8b27dtx6dIlucMhKjNMABARERERkUlQq9VYtGgRgoKC\nULt2bcTGxqJ169Zyh0VUZjgJIBERERERVXqJiYkYP3484uLiMHnyZEyYMAFKpVLusIjKFBMARERE\nRERUqe3evRshISGwtrZGVFQUvL295Q6JSBYcAlBBXLhwAVlZWXKHQURERERUoQghEBERgZdffhn7\n9u1j459MGhMAFUBWVhYCAgKwd+9eg4//888/kCQJtra2ZRwZEREREVH5JkkS1q5di5UrV8LBwUHu\ncIhkxSEAFUBKSgqys7ORmZmp99idO3ewePFivPnmm3BxcZEhOiIiIiKi8s3a2lruEIjKBfYAqABS\nUlIKfGzevHkwMzPDtGnTyjAiIiIiIiIiqmjYA6ACePjwocHyuLg4bNmyBQsWLICjo2MZR0VERERE\nVH4IISBJktxhEJVr7AFQARjqAZCbm4tZs2bhxRdfxKBBg2SIioiIiIhIfmq1GosWLUJoaKjcoRCV\ne0wAVACGEgAbNmzA2bNnMXfuXK5fSkREREQmKTExEQMGDMDSpUtRtWpVCCHkDomoXOMQgArg0aNH\nOn8/ePAACxYswODBg/HSSy/JFBURERERkXxiYmIQEhICKysrREVFcXk/IiOwB0AF8PQcAOHh4RBC\n4IMPPpApIiIiIiIieWRkZGDGjBl455130L59e8TGxrLxT2Qk9gCoAPIPATh16hQ2btyIOXPmoGrV\nqjJGRURERERUti5fvox3330XCQkJCA8Px9ChQznxH1ExMAFQAWgSAEIIfPjhh2jSpAmGDh0qc1RE\nRERERGXLzMwMtra2iImJQaNGjeQOh6jCYQKgAtAkAH744QecOnUK27dvh5kZXzoiIiIiMi3u7u7Y\nvn07v/UnekacA6AC0CQA4uLi0K9fP7Rp00bmiIiIiIiI5MHGP9GzYwKgAsg/B8CsWbNkjISIiIiI\niIgqKiYAKoDk5GQAQJ06deDi4iJzNEREREREpSctLU3uEIgqLSYAKgDNMoAvvPCCzJEQEREREZWe\nmJgY+Pj44ODBg3KHQlQpMQFQzuXk5CAzM1PuMIiIiIiISk1GRgZmzJiBd955B+3atUOzZs3kDomo\nUuJU8uVc/vH/RERERESVTXx8PMaMGYOEhATMnz8fb7zxBif6Iyol7AFQzmm6/xMRERERVSZCCHz/\n/fcIDAyEJEmIiYnB0KFD2fgnKkXsAVDOsQcAEREREVU2QgiMHz8e0dHRCA4ORmhoKFQqldxhEVV6\nTACUc48ePZI7BCIiIiKiEiVJEjp06IAePXqgW7ducodDZDKYACjn2AOAiIiIiCqjQYMGyR0Ckcnh\nHADlHOcAICIiIiIiopLABEA5l5KSAjs7O7nDICIiIiIiogqOCYByLiUlBfb29nKHQURERERULBkZ\nGbh69arcYRBRPkwAlHNMABARERFRRRMfH4/AwEC8/fbbyMvLkzscIvr/mAAoxzIyMnDjxg0mAIiI\niIioQhBCICIiAoGBgZAkCcuXL4dCwSYHUXnBd2M51qlTJxw6dAhOTk4AgPr168scERERERGRYcnJ\nyXjnnXcwc+ZMDBw4ELt27UKjRo3kDouI8uEygEb6+++/cejQIaSlpaF69eoICAhAjRo1Cqx/+vRp\nHDx4EA8ePICFhQUaNGiAzp07w8rKyuhjXr9+HQDg5OSEY8eOoXr16s97GkREREREJe7IkSMYN24c\nMjIysGrVKnTv3l3ukIjIAPYAMMLZs2fx888/w9/fH++99x5cXV2xfv16PH782GD9GzduIDo6Gq1b\nt8bYsWMxYMAAJCYm4qeffnqm49vb28PNzY3dp4iIiIio3Dly5AiCgoJQu3ZtxMbGsvFPVI6xRWmE\nw4cPw8vLC56ennB2dkaPHj1QpUoVnDx50mD9W7duwdHREd7e3nBwcEDt2rXRunVrJCYmPtPxOQcA\nEREREZVXbdq0wcKFC7F58+ZCe8gSkfyYAChCbm4ukpKSULduXW2ZJEmoV68ebt26ZXCbmjVrIiUl\nBZcuXQIApKWl4fz582jYsOEzxeDg4PBM2xERERERlTalUolBgwbBzIyji4nKO75Li5Ceno68vDzY\n2NjolNvY2OD+/fsGt6lduzZef/11bNmyBWq1Gnl5eWjUqBECAgKeKQb2ACAiIiIiIqLnxQRAKbhz\n53coVpAAACAASURBVA727t0Lf39/eHh4IC0tDbGxsfjpp5/Qu3dvbb3Zs2cXuI9Zs2Zpf69Xrx7c\n3NxKNWZTYWtrK3cIlQ6vzZLH67R08FotebxWS5aNjY3eFw70/Hidlg7eU0ser1UqCxwCUAQrKyso\nFAqkpaXplKelpRX4n/SBAwdQq1YttG/fHtWrV4eHhwcCAwNx8uRJvf0Yg0MAiIiIiEguQghERkYi\nIyND7lCI6DmxB0ARlEol3NzccO3aNTRu3BjAk5vgtWvX0LZtW4Pb5OTkQKlU6pRJkgRJkiCE0JaF\nhoYWeNz89XJycpCUlPQ8p0F48s3KsyRgyDBN5p/XZsnidVryeK2WDl6rJcvNzQ1paWlITU2VO5RK\nhdfp80tOTkZISAj27NmD1NRUjBo1CgDvqSWN12rJYg+VgrEHgBHatWuH48eP49SpU7h79y527dqF\nnJwceHp6AgD279+P6Ohobf1GjRrhwoULiIuLQ3JyMm7cuIE9e/agRo0az9S1h3MAEBEREVFZO3r0\nKLp06YLDhw9j9erVOkNZiahiYg8AIzRv3hzp6en47bff8PjxY7i6umLo0KGwtrYG8GQ4QEpKira+\np6cnsrOzERcXh9jYWFhaWqJevXro1KnTMx2f4wGJiIiIqKyo1WosW7YMS5Ysgbe3N5YtW8bl/Ygq\nCSYAjOTt7Q1vb2+Dj/Xp06dY9YtLkqQS2Q8RERERUWESExMxfvx4xMXFYfLkyZgwYYLe0FYiqriY\nACAiIiIiIgBASEgIbt68iaioqBL7MouIyg8mAIiIiIiICACwcOFCWFlZwdHRUe5QiKgUMAFARERE\nREQAwLH+RJUcVwEgIiIiIiIiMgFMABARERERERGZACYAiIiIiIhMxJEjR7BixQq5wyAimTABQERE\nRERUyanVanz++ecICgrCvn37oFar5Q6J/h97dxtdZ1nni/93N6WatEVAeQiF8KBMmAEUxUkPAQR1\nERviE2PToqQnngMZMZNmMBIMnBVGdNYxOKeA4HIYDYyh6qglgw91Z0g5w6gHGgwzoIKT8WFKrLGM\ngBFJuwXS7v8Lx/4HKdCmO7nv7P35rMULkr2bb9Nr7eT67t9135ACFwEEAIASNjExER0dHXHfffdF\nV1dXdHZ2RkVFRdqxgBQoAAAAoETlcrno7u6OqqqqGBwcjLq6urQjASlyBAAAAEpMPp+Pnp6eaGtr\ni/r6+hgeHrb5B0wAAABAqRkfH4+vfe1r0dfXFy0tLZEkSdqRgAxQAAAAQIk58cQT4957742lS5em\nHQXIEEcAAACgBNn8A79PAQAAAABlQAEAAAAAZUABAAAA88zExESsXr06HnjggbSjAPOIAiCjdu3a\nlXYEAAAyaGhoKBoaGuLf//3fY+fOnWnHAeYRBUBGTU9Ppx0BAIAMyefzccUVV8TFF18c9fX1sWnT\npjjttNPSjgXMI24DmFEKAAAAfmdsbCza29tjfHw8rrnmmrjwwgsjSZK0YwHzjAmAjDLOBQBARMTn\nPve5aGpqiiRJIpfLRUtLi80/MCMKgIyamppKOwIAABnw2GOPxerVq2Pjxo1RW1ubdhxgHnMEIKMG\nBgbSjgAAQAb8+Z//uXf8gaIwAZBRzzzzTEQoAgAAyp3NP1AsCoCM+t01ABYuNKQBAADA/lMAZNTv\n7gKwYIF/IgCAUrdjx460IwBlwO4yo35XAJgAAAAoXfl8Pj70oQ/F29/+9njqqafSjgOUOAVARjkC\nAABQ2sbGxqKpqSluu+22aG1tjUWLFqUdCShxCoCMcgQAAKA0FQqFGBgYiKampkiSJHK5XKxZs8bF\n/oBZ5+3ljHIEAACg9ExOTkZ3d3cMDQ1Fa2tr9Pb2RmVlZdqxgDJhd5lRjgAAAJSWn/zkJ7F69erI\n5/PR398fjY2NaUcCyozdZUaNj49HRERFRUXKSQAAKIajjz46zj333Ojo6Ihly5alHQcoQwqAjBoa\nGooIBQAAQKlYtGhRfOxjH0s7BlDGXGEu4xQAAAAAFIMCIONcAwAAAIBiUABknAkAAID54yc/+cnu\nuzkBZI0CIOMUAAAA2VcoFOLWW2+NhoaG+Nu//du04wDskfnyjHMEAAAg2yYnJ6O7uzuGhobiv//3\n/x4tLS1pRwLYI7vLjDMBAACQXSMjI9HR0RH5fD76+/ujsbEx7UgAz8sRgIxTAAAAZM/09HSsW7cu\nmpub45hjjonh4WGbfyDzTABknCMAAADZc/nll8eGDRuiq6srOjs7vWkDzAt2lxnnhwkAQPZcdNFF\nccEFF0RdXV3aUQD2mgIg4xQAAADZc9JJJ6UdAWCfuQZAxikAAAAAKAYFQIYtWLAgFizwTwQAAMD+\ns7vMMO/+AwCkY3JyMoaHh9OOAVBUrgGQYQoAAIC5NzIyEh0dHTE9PR1nnnlmVFVVpR0JoChMAGSY\nWwACAMyd6enpWLduXTQ3N0dNTU184xvfsPkHSoodZoaZAAAAmBsTExOxdu3aGB0dja6urujs7PS7\nGFByFAAZ5ocOAMDsy+Vy0d3dHVVVVTE4OBh1dXVpRwKYFY4AZJgjAAAAs+vhhx+OSy65JOrr62N4\neNjmHyhpdpgZ5haAAACz69hjj42NGzfGKaecEkmSpB0HYFYpADLMBAAAwOx79atfnXYEgDnhLeYM\nUwAAAABQLAqADHMRQAAAAIpFAZBhCgAAgP0zPT0d1113XfzoRz9KOwpA6syYZ5gCAABg5rZu3RoX\nXXRRjI6OxuGHHx4nnHBC2pEAUmUCIMNcAwAAYGZyuVyceeaZsXXr1rjtttviPe95T9qRAFKXFAqF\nQtoheK4kSeJ1r3td3HXXXWlHgT363a2SvISQddYq84F1Wjz5fD6uvPLKuOWWW+Jtb3tb3HDDDXHI\nIYekHatkWKvMB0mSxNKlS9OOkUneYs6wJEliamoq7RglY8mSJb6fRVRdXR0REdu2bUs5SWmxTovP\nWp0d1mpxVVdXx9TUlO/pfhobG4v29vYYHx+Pvr6+uOSSS2L79u2+r0XkNXV2eE0trt+tU55LAZBh\njgAAAOy9DRs2RJIkkcvlora2dve71QD8lmsAZNiCBf55AAD21uWXXx4bN26M2tratKMAZJK3mDPM\nBAAAwN57yUteknYEgEzzFnOGKQAAAAAoFgVAhjkCAAAAQLHYYWaYCQAAgP/f0NBQvPe9743p6em0\nowDMSwqADKuoqEg7AgBA6vL5fFxxxRVx8cUXxwEHHBC/+c1v0o4EMC95iznDTAAAAOVubGws2tvb\nY3x8PPr6+qKlpcXt/QBmyARAhpkAAADKVaFQiFtvvTWampoiSZLI5XKxZs0am3+A/eAt5gxTAAAA\n5eiJJ56ID37wgzE0NBStra3R29sblZWVaccCmPcUABnmCAAAUI4WLlwY//Ef/xH9/f3R2NiYdhyA\nkmGHmWEmAACAcrR48eL42te+ZtwfoMhcAyDDFAAAQLmy+QcoPgVAhjkCAAAAQLEoADJswQL/PABA\nacrn87Fr1660YwCUFTvMDDMBAACUorGxsWhqaor+/v60owCUFQVAhikAAIBSUigUYmBgIJqamiJJ\nkjj77LPTjgRQVuwwM8wRAACgVExOTkZ3d3cMDQ1Fa2tr9Pb2RmVlZdqxAMqKAiDDTAAAAKVgZGQk\nOjo6Ip/Px8033xwrVqxIOxJAWfIWc4a5DSAAMN/dcsst0dzcHMccc0wMDw/b/AOkSAGQYSYAAID5\n7qSTToqurq748pe/HMuWLUs7DkBZs8PMMBMAAMB8t3z58li+fHnaMQAIEwCZpgAAAACgWBQAGeYI\nAAAAAMWiAMgwtwEEALKuUCjEj3/847RjALAX7DAzzAQAAJBlk5OT8ad/+qfR2NgYjz32WNpxAHgR\ndpgZpgAAALJqZGQkOjo6Ip/Pxw033BCveMUr0o4EwIswAZBhjgAAAFkzPT0d69ati+bm5qipqYnh\n4eFobGxMOxYAe8FbzBlmAgAAyJKJiYlYu3ZtjI6ORldXV3R2drprEcA8YoeZYX6gAgBZ8fjjj0dD\nQ0NUVVXF4OBg1NXVpR0JgH2kAMgwEwAAQFa8/OUvj7/4i7+IhoaGOOigg9KOA8AM2GFmmAkAACBL\nVq1alXYEAPaDq8xlmAIAAACAYlEAZJgjAAAAABSLAiDDTAAAAHPp3nvvjYmJibRjADBLFAAZpgAA\nAObC9PR0rFu3LlauXBmf+cxn0o4DwCwxY55hjgAAALNtYmIi1q5dG6Ojo9HV1RWdnZ1pRwJglthh\nZtiCBQY0AIDZk8vloru7O6qqqmJwcDDq6urSjgTALLLDzDATAADAbMjn89HT0xNtbW1RX18fmzZt\nsvkHKAN2mBmmAAAAZsMNN9wQGzZsiGuuuSYuvPDCSJIk7UgAzAE7zAxzBAAAmA0dHR1x/vnnxx/8\nwR+kHQWAOWSHmWEmAACA2bB48WKbf4AypADIMLcBBAAAoFgUABlmAgAAAIBiUQBkmAkAAGAmJiYm\n4pprrolCoZB2FAAyRAGQYQoAAGBf5XK5aGhoiNtuuy22bduWdhwAMkQBkGGOAAAAeyufz0dPT0+0\ntbVFfX19DA8Px5FHHpl2LAAyxA4zw0wAAAB7Y2xsLNrb22N8fDz6+vqipaUlkiRJOxYAGWMCIMMU\nAADACykUCjEwMBBNTU2RJEnkcrlYs2aNzT8Ae6QAyDBHAACAFzI1NRU33nhjrF69OjZu3Bi1tbVp\nRwIgw+wwM8wEAADwQpYuXRp33nlnHHTQQWlHAWAeMAGQYQoAAODF2PwDsLcUABnmCAAAAADFogDI\nsAUL/PMAAABQHN5i3kvf+c534p577ompqak4/PDD47zzzotly5Y97+Onp6fjm9/8Znzve9+Lqamp\nWLp0aZx99tnx2te+dg5TAwDzWT6fj6uvvjpe+9rXxurVq9OOA8A8pwDYCw8++GDccccd8ba3vS2W\nLVsWIyMjsX79+li7dm0sXrx4j8/ZsGFDbN++Pd75znfGwQcfHFNTU1EoFOY4OQAwX42NjUV7e3uM\nj4/HqaeemnYcAEqAGfO9sHnz5nj9618fp556ahx66KHx1re+NQ444IC4//779/j4H/3oRzE+Ph4X\nXnhhHHfccXHQQQfFUUcdFUcfffQcJwcA5ptCoRC33nprNDU1RZIkkcvl4oILLkg7FgAlwATAi9i5\nc2ds27YtzjrrrN0fS5Ikjj/++PjZz362x+f88Ic/jCOPPDLuvvvu+O53vxuLFi2K2traeOMb3xgH\nHHDAXEUHAOaZycnJuPzyyyOXy0Vra2v09vZGZWVl2rEAKBEKgBexY8eO2LVrVyxZsuRZH1+yZEk8\n/vjje3zO5ORk/PSnP42FCxfGBRdcEDt27IhvfOMbkc/n4x3veMdcxAYA5pmRkZF473vfGzt27Ij+\n/v5obGxMOxIAJUYBMAsKhUIkSRLvete74iUveUlERLzlLW+JL3/5y9HU1LT79n4f/ehHX/DPqa6u\nnvWs5Wbp0qVpRyg51mnxWaezw1otPmu1uPL5fLzyla+M9evXR01NTdpxSoZ1Oju8phaftcpcUAC8\niKqqqliwYEFMTU096+NTU1PPmQr4nSVLlsTSpUt3b/4jIl7xildERMSvf/3rOOSQQ/bqa//+1wQA\nStcb3/jGOOeccyJJEr8DAOwnhcqeKQBeREVFRVRXV8eWLVvixBNPjIjfvsO/ZcuWWL58+R6fU1NT\nEz/4wQ/i6aefjkWLFkVExOOPPx5JksSBBx64+3G9vb3P+3WvuuqqePLJJ4v4N2HJkiV+oSqi3zX/\n27ZtSzlJabFOi89anR3WanFVV1fH1NSU72mRWafF5zV1dlirxWVC5fm5C8BeOP300+Of//mf44EH\nHohHH300Nm7cGM8888zuW/Lceeedcfvtt+9+/CmnnBJVVVXx1a9+NR599NF4+OGHY9OmTfHa1752\n9/g/AAAAzCW70b1w8sknx44dO+Kuu+6K7du3xxFHHBEtLS2xePHiiPjtqP4TTzyx+/GLFi2KNWvW\nxNDQUHz605+OqqqqOOmkk+JNb3pTWn8FACADfvOb38RLX/rStGMAUKYUAHuprq4u6urq9vi5d77z\nnc/52Cte8YpYs2bNbMcCAOaBQqEQ69evj0984hPx9a9/PY488si0IwFQhhQAAACzaHJyMi6//PLI\n5XLR2toaBx98cNqRAChTCgAAgFly7733RkdHR+zYsSNuvvnmWLFiRdqRAChjLgIIAFBk09PTce21\n18bKlSujpqYmhoeHbf4BSJ0JAACAIsrn83HhhRfG6OhodHV1RWdnZ1RUVKQdCwAUAAAAxVRZWRmn\nnXZa9PT0PO8FhAEgDQoAAIAi+1//63+lHQEAnsM1AAAAAKAMKAAAAACgDCgAAAD20eTkZPzyl79M\nOwYA7BMFAADAPhgZGYlzzz03rrrqqrSjAMA+UQAAAOyF6enpWLduXTQ3N0dNTU1cccUVaUcCgH3i\nLgAAAC9iYmIi1q5dG6Ojo9HV1RWdnZ1RUVGRdiwA2CcKAACAF5DL5aK7uzuqqqpicHAw6urq0o4E\nADPiCAAAwPP43Oc+F21tbVFfXx/Dw8M2/wDMayYAAACex3nnnRcLFy6M1atXR5IkaccBgP2iAAAA\neB6HHHJIXHDBBWnHAICicAQAAAAAyoACAAAAAMqAAgAAKFvT09PxjW98I+0YADAnFAAAQFmamJiI\nVatWxSWXXBL/+q//mnYcAJh1CgAAoOwMDQ1FQ0NDbN26NQYHB+MP//AP044EALNOAQAAlI18Ph9X\nXHFFXHzxxVFfXx+bNm2Kurq6tGMBwJxwG0AAoCyMjY1Fe3t7jI+PR19fX7S0tESSJGnHAoA5owAA\nAEre008/HS0tLfGyl70scrlc1NbWph0JAOacAgAAKHmLFi2Kz372s/HKV74yKisr044DAKlQAAAA\nZeHkk09OOwIApMpFAAEAAKAMKAAAAACgDCgAAICSkMvl4s4770w7BgBklgIAAJjX8vl8fOhDH4q2\ntrb4h3/4h7TjAEBmuQggADBvjY2NRXt7e4yPj0dfX1+0tLSkHQkAMssEAAAw7xQKhRgYGIimpqZI\nkiRyuVysWbMmkiRJOxoAZJYJAABgXpmcnIzu7u4YGhqK1tbW6O3tjcrKyrRjAUDmKQAAgHnlm9/8\nZmzevDn6+/ujsbEx7TgAMG8oADLqhBNOSDsCAGTSO97xjnjDG94QhxxySNpRAGBecQ2AjFIAAMCe\nJUli8w8AM6AAyKiFCw1nAAAAUDwKgIxSAAAAAFBMCoCMqqioSDsCAKRibGws3vOe98Qvf/nLtKMA\nQElRAGSUCQAAyk2hUIhbb701mpqa4j/+4z/iiSeeSDsSAJQUBUBGKQAAKCeTk5PR1tYWV1xxRaxe\nvTo2btwYxx13XNqxAKCk2GVmlCMAAJSLkZGR6OjoiHw+HzfffHOsWLEi7UgAUJJMAGSUAgCAUrdz\n585Yt25dNDc3R01NTQwPD9v8A8AsUgBklAIAgFK3YMGCeOihh6Krqys2bNgQy5YtSzsSAJQ0RwAy\nasEC3QwApS1Jkujv7/czDwDmiJ+4AEBqbP4BYO74qQsAAABlQAEAAMyaQqEQTz31VNoxAIBQAAAA\ns2RycjLa2tqiu7s77SgAQCgAAIBZMDIyEueee25s3rw5Ghsb044DAIQCAAAoounp6Vi3bl00NzdH\nTU1NDA8PKwAAICPcBhAAKIqJiYlYu3ZtjI6ORldXV3R2dkZFRUXasQCA/6QAAAD22z333BNtbW1R\nVVUVg4ODUVdXl3YkAOD3OAKQUQ899FDaEQBgrx177LHR0NAQw8PDNv8AkFGzOgGwa9euWLBAxzAT\nIyMjaUcAgL125JFHxnXXXZd2DADgBczK7nx0dDQuvfTSWLZs2Wz88QAAAMA+KtoEwI9//OP4/Oc/\nH1/4whfixz/+cVRUVMSZZ55ZrD8eAAAA2A9JoVAozPTJv/jFL+KLX/xifP7zn4/77rsvIiLe/OY3\nx//4H/8jzjvvvHjZy15WtKDlZtGiRfHYY4+lHQOeV5IkERGxHy8hMCes1eLZsmVLHHfccWnHKEnW\nKfOFtcp8kCRJLF26NO0YmbTPBcD27dvj7//+7+Pzn/98/OM//mMsXLgwmpqaor6+Pj74wQ/Gbbfd\nFn/yJ38yW3nLxqJFi+Lhhx9OO0ZJWbJkSUxNTaUdo2RUV1dHRMS2bdtSTlJarNPis1b33/T0dNxw\nww1x/fXXx5e+9KU4/fTTrdUiq66ujqmpqXjyySfTjlJSrNPi85o6O6zV4qqurt5dVvFs+3QNgHe/\n+91x+OGHx8UXXxwVFRVxyy23xC9+8YvYsGFDvP3tb5+tjABASiYmJmLVqlVx3XXXxQc+8AFX+AeA\neWyfrgHwpS99KY477ri45ZZb4uyzz56tTABABuRyueju7o6qqqoYHBy0+QeAeW6fJgAuu+yyeOaZ\nZ+JNb3pTnHLKKfGxj30s/v3f/322sgEAKcjn89HT0xNtbW1RX18fmzZtsvkHgBKwTwXAxz/+8fjp\nT38ad955Zyxfvjz+6q/+Kk444YRYvnx5/M3f/I1zFgAwz+3atSuam5tjw4YNcc0118SnP/3pOOig\ng9KOBQAUwYxuA/jGN74x3vjGN8anPvWp+PrXvx5f+MIX4sYbb4xCoRBXX311jI2Nxdve9rY45ZRT\nip0XAJhFCxYsiLVr18axxx4btbW1accBAIponyYAft+iRYviXe96VwwODsYjjzwSf/M3fxOHHHJI\n9Pb2xqmnnhrHH398sXICAHPkLW95i80/AJSg/SoA/quXvexl0dbWFnfddVeMj4/H//7f/9u9FwEA\nACAjZlQA7NixI0477bS46aab9vj5o446Kj70oQ/Fd7/73f0KBwAAABTHjAqAqqqq2LJli4v+AcA8\nNDExEQ888EDaMQCAOTbjIwArVqyIO+64o5hZAIBZlsvloqGhIa666qooFAppxwEA5tCMC4De3t74\n4Q9/GGvWrIn/9//+X0xMTMQvf/nL5/wHAKQvn89HT09PtLW1RX19fQwMDJjkA4AyM6PbAEZEnHTS\nSRER8YMf/CC+8IUvPO/jdu7cOdMvAQAUwdjYWLS3t8f4+Hj09fVFS0uLzT8AlKEZFwBXXXWVXx4A\nIMMKhULceuut8ZGPfCSOPfbYyOVybu8HAGVsxgXAhz/84SLGAACKbdOmTXHllVdGa2tr9Pb2RmVl\nZdqRAIAUzbgA+K8KhUI8+uijERFx6KGHmgwAgAw499xz4+///u9j+fLlaUcBADJgxhcBjPjt+f+V\nK1fGgQceGNXV1VFdXR0HHnhgrFy5Mh588MFiZQQAZiBJEpt/AGC3GU8AfPvb347GxsbYtWtXvOMd\n74g/+IM/iIiIf/u3f4uvfe1rMTQ0FP/wD/8QZ511VtHCAgAAADMz4wLgAx/4QBx22GHxzW9+M44+\n+uhnfW7r1q3xhje8Ibq6umJ0dHS/QwIAAAD7Z8ZHAB566KFob29/zuY/IuLoo4+O97///fHQQw/t\nVzgA4Pnl8/n4q7/6q9i+fXvaUQCAeWDGBcAxxxwTTz311PN+/umnn95jOQAA7L+xsbFoamqKm266\nKR544IG04wAA88CMC4Crrroqbrjhhj3+0nH//ffHjTfe6FaBAFBkhUIhBgYGoqmpKZIkiVwuF2ec\ncUbasQCAeWDG1wAYGRmJww8/PE477bSor6+PV73qVRER8aMf/Sg2b94cJ598cmzevDk2b968+zlJ\nksQnPvGJ/U8NAGVocnIyuru7Y2hoKFpbW6O3tzcqKyvTjgUAzBNJoVAozOSJCxbs+/BAkiSxc+fO\nmXy5srNo0aJ4+OGH045RUpYsWRJTU1NpxygZ1dXVERGxbdu2lJOUFuu0+EplrY6MjERHR0fk8/lY\nt25drFixItU81mpxVVdXx9TUVDz55JNpRykp1mnxlcpratZYq8VVXV0dSZKkHSOTZnwEYMuWLTE1\nNRW7du3a439TU1OxZcuWZ33M5h8A9l2hUIj/83/+TxxzzDExPDyc+uYfAJifZlwAHH/88fHVr371\neT//9a9/PV75ylfO9I8HAP5TkiTxmc98Jr785S/HsmXL0o4DAMxTM74GQKFQiBc6PfDMM88YuwCA\nIjn44IPTjgAAzHP7VAD8+te/jl/96le7///xxx+Pn/70p8953K9+9av44he/uPuMEAAAAJCufSoA\nrrvuuvjIRz4SEb8dR7z00kvj0ksv3eNjC4VC/OVf/uX+JwQAAAD22z4VAA0NDbFkyZIoFApx+eWX\nx7vf/e543ete96zHJEkSixcvjtNOOy1e//rXFzUsAJSiQqEQ69evj5/97Gdx5ZVXph0HAChR+1QA\nnH766XH66adHRMT27dvjXe96V5x88smzEgwAysHk5GR0d3fH0NBQtLa2RqFQcA0dAGBWzPgigH/x\nF39RzBwAUHZGRkaio6Mj8vl89Pf3R2NjY9qRAIASNuPbAAIAMzM9PR3r1q2L5ubmqKmpieHhYZt/\nAGDWzXgCAADYdxMTE9HR0RH33XdfdHV1RWdnZ1RUVKQdCwAoAwoAAJhD+Xw+JicnY3BwMOrq6tKO\nAwCUEQUAAMyhV73qVfGP//iPsWCBU3gAwNzy2wcAzDGbfwAgDX4DAQAAgDKgAACAInv66afTjgAA\n8BwKAAAoopGRkXjDG94QIyMjaUcBAHgWBQAAFMH09HSsW7cumpub48gjj4yjjz467UgAAM/iLgAA\nsJ8mJiZi7dq1MTo6Gl1dXdHZ2RkVFRVpxwIAeBYFAADsh6GhobjsssuiqqoqBgcHo66uLu1IAAB7\n5AgAAMzQVVddFRdffHHU19fHpk2bbP4BgEwzAQAAM3T88cdHX19ftLS0RJIkaccBAHhBCgAAmKH3\nvve9aUcAANhrjgAAAABAGVAAAAAAQBlQAGTUBRdckHYEgLI3PT0dP//5z9OOAQBQFAqAjFq8+vW9\njwAAIABJREFUeHHaEQDK2sTERKxatSre8573xM6dO9OOAwCw3xQAAPB7crlcNDQ0xNatW+PjH/94\nVFRUpB0JAGC/KQAA4D/l8/no6emJtra2qK+vj+Hh4airq0s7FgBAUbgNIABExNjYWLS3t8f4+Hj0\n9fVFS0tLJEmSdiwAgKJRAABQ9u6///5YuXJlHHvssZHL5aK2tjbtSAAARacAAKDsnXLKKdHd3R2t\nra1RWVmZdhwAgFmhANhL3/nOd+Kee+6JqampOPzww+O8886LZcuWvejzfvrTn8ZnP/vZOOyww+KS\nSy6Zg6QA7KuFCxd6jQYASp6LAO6FBx98MO64444455xz4n3ve18cccQRsX79+ti+ffsLPu83v/lN\n3H777XH88cfPUVIAAADYMwXAXti8eXO8/vWvj1NPPTUOPfTQeOtb3xoHHHBA3H///S/4vI0bN8ar\nX/3qOOqoo/b5az700EMzjQsAAADPoQB4ETt37oxt27bFcccdt/tjSZLE8ccfHz/72c+e93n3339/\nTE5Oxtlnnz2jrzsyMjKj5wGwZ//0T/8UTz31VNoxAABSowB4ETt27Ihdu3bFkiVLnvXxJUuWxNTU\n1B6f8/jjj8f//b//N971rnfFggW+xQBpyufz8f73vz8uvPDC+MpXvpJ2HACA1LgIYJHt2rUrBgcH\n45xzzolDDjkkIiIKhcIeH/vRj370Bf+s6urqoucrd0uXLk07QsmxTovPOi2eBx98MBoaGuInP/lJ\n/PVf/3X86Z/+aSRJknaskmGtFteSJUue84YD+886nR1+/heftcpcUAC8iKqqqliwYMFz3u2fmpra\n4w/pp59+On7+85/HI488ErlcLiJ+WwAUCoX4yEc+EmvWrHnWcQIAiq9QKMRNN90UH/zgB+NVr3pV\njI6OxkknnZR2LACAVCWF53t7mt0+85nPxFFHHRWNjY0R8dtfLK+77rpYvnx5nHHGGc96bKFQiEcf\nffRZHxsdHY0tW7bE6tWr46CDDooDDjjgRb/mokWL4uGHHy7a34EXPrbBvvtd879t27aUk5QW63T/\nTU5ORnd3dwwNDUVra2t86lOfisrKSmu1yKzV4qquro6pqal48skn045SUqzT4vPzf3ZYq8VVXV1t\n4u95mADYC6effnp85Stfierq6li2bFmMjIzEM888E6eeempERNx5553x5JNPxvnnnx9JksRhhx32\nrOcvXrw4Fi5cGIceemga8QHKyqWXXhr33Xdf3HzzzbFixYqorKxMOxIAQCYoAPbCySefHDt27Ii7\n7rortm/fHkcccUS0tLTE4sWLI+K3xwGeeOKJlFMCEBHx4Q9/OBYtWhTLli1LOwoAQKYoAPZSXV1d\n1NXV7fFz73znO1/wueecc06cc845s5AKgN/nOisAAHvmHnUAAABQBhQAAAAAUAYUAADMK2NjY/H5\nz38+7RgAAPOOAgCAeaFQKMTAwEA0NTXFwMBAPP3002lHAgCYVxQAAGTe5ORktLW1xZVXXhmrV6+O\nr371q7Fo0aK0YwEAzCvuAgBApo2MjERHR0fk8/m4+eabY8WKFWlHAgCYl0wAAJBJ09PTsW7dumhu\nbo5jjjkmhoeHbf4BAPaDAgCATPrxj38cn/rUp6Krqyu+/OUvx7Jly9KOBAAwrzkCAEAmnXjiibF5\n8+Y47LDD0o4CAFASTAAAkFk2/wAAxaMAAAAAgDKgAAAAAIAyoAAAIBWTk5Px/ve/Px588MG0owAA\nlAUFAABzbmRkJM4999z41re+FY899ljacQAAyoICAIA5Mz09HevWrYvm5uaoqamJ4eHhOOecc9KO\nBQBQFtwGEIA5MTExEWvXro3R0dH4wAc+EH/+538eFRUVaccCACgbCgAAZt3Q0FBcdtllUVVVFbfd\ndlssX7487UgAAGXHEQAAZt33v//9OP3002N4eNjmHwAgJSYAAJh1H/zgB2PBggWRJEnaUQAAypYC\nAIBZ56w/AED6HAEAAACAMqAAAKAonnnmmbQjAADwAhQAAOyX6enpuPbaa+P888+Pp556Ku04AAA8\nDwUAADM2MTERq1atiuuuuy7e/OY3x8KFLi0DAJBVflMDYEaGhobisssui6qqqhgcHIy6urq0IwEA\n8AJMAACwT/L5fFxxxRVx8cUXR319fWzatMnmHwBgHjABAMBem5iYiDVr1sT4+Hj09fVFS0tLJEmS\ndiwAAPaCAgCAvfbyl788TjzxxPjrv/7rqK2tTTsOAAD7QAEAwF576UtfGp/61KfSjgEAwAy4BgAA\nAACUAQUAAAAAlAEFAADP8sgjj8TOnTvTjgEAQJEpAADYLZfLxZve9Kbo7+9POwoAAEWmAAAg8vl8\n9PT0RFtbW5xxxhmxatWqtCMBAFBk7gIAUObGxsaivb09xsfHo6+vL1paWiJJkrRjAQBQZCYAAMpU\noVCIgYGBaGpqiiRJIpfLxZo1a2z+AQBKlAIAoEx9+MMfjiuvvDJWr14dGzdujNra2rQjAQAwixwB\nAChT559/fvy3//bforGxMe0oAADMAQUAQJk69dRT49RTT007BgAAc8QRAAAAACgDCgAAAAAoAwoA\ngBKVz+fjnnvuSTsGAAAZoQAAKEFjY2PR1NQUF198cTz55JNpxwEAIAMUAAAlpFAoxK233hpNTU2R\nJEncfvvtsXTp0rRjAQCQAe4CAFAiJicn4/LLL49cLhetra3R29sblZWVaccCACAjFAAAJeDee++N\njo6O2LFjR/T390djY2PakQAAyBhHAADmuYmJiVi9enUcffTRMTw8bPMPAMAemQAAmOeWLVsWn/vc\n5+L000+PioqKtOMAAJBRCgCAEnDmmWemHQEAgIxzBAAAAADKgAIAAAAAyoACACDjCoVCrF+/PrZs\n2ZJ2FAAA5jEFAECGTU5ORltbW/T09MTw8HDacQAAmMdcBBAgo0ZGRqKjoyPy+Xz09/e7vR8AAPvF\nBABAxkxPT8e6deuiubk5ampqYnh42OYfAID9ZgIAIEO2bt0aF110UYyOjkZXV1d0dnZGRUVF2rEA\nACgBCgCADLnpppti69atMTg4GHV1dWnHAQCghCSFQqGQdgiea9GiRfHYY4+lHQOeV5IkEfHbK9RT\nPPl8PvL5fBxyyCFpRykZ1irzgXXKfGGtMh8kSRJLly5NO0YmmQDIsKmpqbQjlJQlS5b4nhZRdXV1\nRERs27Yt5SSlZcmSJbFz505rtYis1dnhNbW4qqurY2pqyve0yKzT4vOaOjus1eL63TrluVwEEAAA\nAMqAAgAAAADKgAIAYA7de++9sXbt2ti5c2faUQAAKDMKAIA5MD09Hddee22sXLkyfv7zn8eTTz6Z\ndiQAAMqMiwACzLKJiYlYu3ZtjI6ORldXV3R2dkZFRUXasQAAKDMKAIBZlMvloru7O6qqqmJwcDDq\n6urSjgQAQJlyBABgFuTz+ejp6Ym2traor6+PTZs22fwDAJAqEwAAs2B6ejq+853vxDXXXBMXXnhh\nJEmSdiQAAMqcAgBgFixdujSGh4dj4UIvswAAZIMjAACzxOYfAIAsUQAAAABAGVAAAMzQzp07o1Ao\npB0DAAD2igIAYAYmJiaiubk5BgYG0o4CAAB7RQEAsI9yuVw0NDTE1q1b44/+6I/SjgMAAHtFAQCw\nl/L5fPT09ERbW1vU19fH8PBw1NXVpR0LAAD2iktUA+yFsbGxaG9vj/Hx8ejr64uWlpZIkiTtWAAA\nsNdMAAC8iMHBwWhqaookSSKXy8WaNWts/gEAmHcUAAAv4rDDDotVq1bFxo0bo7a2Nu04AAAwI44A\nALyIs846K84666y0YwAAwH4xAQAAAABlQAEAAAAAZUABABARjzzySNoRAABgVikAgLKWz+ejp6cn\nzj77bCUAAAAlzUUAgbI1NjYW7e3tMT4+HldffXUcfvjhaUcCAIBZYwIAKDuFQiEGBgaiqakpkiSJ\nXC4XLS0tkSRJ2tEAAGDWmAAAysrk5GR0d3fH0NBQtLa2Rm9vb1RWVqYdCwAAZp0CACgbv/rVr6Kh\noSF27NgRN998c6xYsSLtSAAAMGcUAEDZOOigg+KSSy6JFStWxLJly9KOAwAAc0oBAJSViy66KO0I\nAACQChcBBAAAgDKgAAAAAIAyoAAASsrY2Fg88sgjaccAAIDMUQAAJaFQKMTAwEA0NTXFddddl3Yc\nAADIHBcBBOa9ycnJ6O7ujqGhoWhtbY3e3t60IwEAQOYoAIB5bWRkJDo6OiKfz0d/f380NjamHQkA\nADLJEQBgXpqeno5169ZFc3Nz1NTUxPDwsM0/AAC8AAUAMC998pOfjOuvvz66urpiw4YNsWzZsrQj\nAQBApjkCAMxL//N//s8444wz4o//+I/TjgIAAPOCCQBgXjrwwANt/gEAYB8oAAAAAKAMKAAAAACg\nDCgAgEyanJyMm266KQqFQtpRAACgJCgAgMwZGRmJc889N2688cbYunVr2nEAAKAkKACAzJieno51\n69ZFc3Nz1NTUxPDwcNTU1KQdCwAASoLbAAKZMDExEWvXro3R0dHo6uqKzs7OqKioSDsWAACUDAUA\nkLqhoaG47LLLoqqqKgYHB6Ouri7tSAAAUHIcAQBStX379rjyyiujvr4+Nm3aZPMPAACzxAQAkKrF\nixfHN77xjaiuro4kSdKOAwAAJUsBAKTuyCOPTDsCAACUPEcAAAAAoAwoAAAAAKAMKACAWTU9PR3X\nXnttfOUrX0k7CgAAlDUFADBrJiYmYtWqVXHdddfFtm3b0o4DAABlzUUAgVmRy+Wiu7s7qqqqYnBw\n0O39AAAgZQqAvfSd73wn7rnnnpiamorDDz88zjvvvFi2bNkeH/uv//qvMTo6Go888kjs3LkzDj30\n0DjnnHPiVa961RynhrmXz+fj6quvjvXr18d5550XH//4x+Pggw9OOxYAAJQ9RwD2woMPPhh33HFH\nnHPOOfG+970vjjjiiFi/fn1s3759j48fHx+PV77yldHS0hLve9/74rjjjou/+7u/i0ceeWSOk8Pc\nGhsbi6amptiwYUP09fXFpz/9aZt/AADICBMAe2Hz5s3x+te/Pk499dSIiHjrW98aP/zhD+P++++P\nM8888zmPX7FixbP+/81vfnOMjY3Fv/3bv8URRxwxJ5khDT/5yU8iSZLI5XJRW1ubdhwAAOC/UAC8\niJ07d8a2bdvirLPO2v2xJEni+OOPj5/97Gd79WcUCoV4+umno7KycrZiQiY0NTVFQ0NDHHDAAWlH\nAQAAfo8jAC9ix44dsWvXrliyZMmzPr5kyZKYmpraqz/j7rvvjqeffjpOOumk2YgImWLzDwAA2WQC\nYJZ973vfi29961vx7ne/OxYvXvysz330ox99wedWV1fPZrSytHTp0rQjlBzrtPis09lhrRaftVpc\nS5Ysec4bDuw/63R2eE0tPmuVuWAC4EVUVVXFggULnvNu/9TU1Iv+kP7+978fX//616O5uTmOO+64\n2YwJc2bnzp1pRwAAAGbABMCLqKioiOrq6tiyZUuceOKJEfHbM/1btmyJ5cuXP+/zvv/978fXvva1\nWLlyZZxwwgl7fExvb+/zPv+jH/1obNu2bf/C8yz7cmyDPcvlcvGxj30sbrvttt0XxbROi8s6Lb7f\nvUtlrRaXtVpc1dXVMTU1FU8++WTaUUqKdVp8XlNnh7VaXCZUnp8JgL1w+umnxz//8z/HAw88EI8+\n+mhs3Lgxnnnmmd0boDvvvDNuv/323Y//3ve+F7fffns0NDTEsmXLYmpqKqampuI3v/lNWn8F2C/5\nfD56enqira0tTjzxxFi0aFHakQAAgH1kAmAvnHzyybFjx4646667Yvv27XHEEUdES0vL7jP9U1NT\n8cQTT+x+/L/8y79EoVCIXC4XuVxu98df85rXxDvf+c45zw/7Y2xsLNrb22N8fDz6+vqipaUlkiRJ\nOxYAALCPFAB7qa6uLurq6vb4ud/f1L/3ve+dg0QwuwqFQqxfvz6uvvrqOPbYYyOXy0VtbW3asQAA\ngBlSAADP8dRTT0VHR0fkcrlobW2N3t7eqKysTDsWAACwHxQAwHMsWrQoXv7yl0d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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127b73e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (310090285)>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for AdaBoost\n",
    "preds = clf_G.predict_proba(X_test)[:,1]\n",
    "fprA, tprA, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fprA=fprA, tprA=tprA))\n",
    "ggplot(df, aes(x='fprA', y='tprA')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for AdaBoost has an area under the curve of \" + str(metrics.auc(fprA,tprA)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Nzc2L9Jl2dnbGsWPHkJ2dnWeywcrKCkIIrevQzZs3X3l/JX2Neuutt7R+f8Dr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128004590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (310379585)>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ROC curve for Naive Bayes\n",
    "preds = clf_D.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(Y_test, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fpr=fpr, tpr=tpr))\n",
    "ggplot(df, aes(x='fpr', y='tpr')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')+\\\n",
    "    ggtitle (\"ROC for Naive Bayes has an area under the curve of \" + str(metrics.auc(fpr,tpr)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 20 samples.\n",
      "Testing set has 549 samples.\n"
     ]
    }
   ],
   "source": [
    "# Set the number of training points much smaller than before\n",
    "nr_train = 20\n",
    "# Set the number of testing points\n",
    "nr_test = X.shape[0] - nr_train\n",
    "\n",
    "# Shuffle and split the dataset into the number of training and testing points above\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=nr_test, random_state=40)\n",
    "\n",
    "# Show the results of the split\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training a GaussianNB using a training set size of 20. . .\n",
      "Trained model in 0.0008 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 0.8889.\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for test set: 0.9041.\n",
      "/n\n",
      "Training a AdaBoostClassifier using a training set size of 20. . .\n",
      "Trained model in 0.1402 seconds\n",
      "Made predictions in 0.0043 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0037 seconds.\n",
      "F1 score for test set: 0.9138.\n",
      "/n\n",
      "Training a QuadraticDiscriminantAnalysis using a training set size of 20. . .\n",
      "Trained model in 0.0006 seconds\n",
      "Made predictions in 0.0002 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0006 seconds.\n",
      "F1 score for test set: 0.8516.\n",
      "/n\n"
     ]
    }
   ],
   "source": [
    "for clf in [clf_D, clf_G, clf_H]:\n",
    "    for size in [20]:\n",
    "        train_predict(clf, X_train[:size], Y_train[:size], X_test, Y_test)\n",
    "        print '/n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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