{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploratory Analysis #6: Explore Categorical Variables to Classify Company Status" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from modules import *" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "import pandas as pd\n", "import sklearn\n", "import scipy\n", "\n", "plt.style.use('seaborn-dark')\n", "plt.rcParams['figure.figsize'] = (10, 6)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#read in the data from data prep notebook\n", "data = pd.read_hdf('results/classification_data.h5', 'classification_data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's examine the data a bit, first the frequency of our response variable." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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ulStXyrIsbdq0SZmZmUpKStLq1av18ssva8GCBcrJydGqVasUFhamlStXqnPn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\n1a5dW7fffrvpOCUGI2OlwL5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#frequencies of status variables\n", "data['status'].value_counts().plot(kind = 'bar')\n", "plt.title('Distrubtion of Status Response Variable')\n", "plt.xlabel('Category')\n", "plt.ylabel('Frequency')\n", "plt.savefig(\"results/classification_status_variable_dist.png\")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#frequncies of closed varible\n", "data['closed'].value_counts().plot(kind = 'bar')\n", "plt.title('Distrubtion of Two Category Response Variable')\n", "plt.xlabel('Category')\n", "plt.ylabel('Frequency')\n", "plt.savefig(\"results/classification_closed_variable_dist.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the vast majority of the data represents companies that are still operating. I am going to start with the easier two class response closed vs not closed, and then move on to the company status. The issue that we have here, and one that we need to keep in mind for our classification models, is that we have a huge difference in our response variable prior probabilities. The issue may arrive when evaluated performance, here is an example of why this may be an issue. Say we are modeling a rare disease where 99.99 percent of our data does not have the disease and 0.01 percent of our data does have the disease. Then a model that just predicts \"no\" every time has an error rate of 0.0001, which is very low, but our model obviously sucks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest Classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am going to start by using a random forest to classify status of a company. The reason I am going to focus on random forests is that random forests allow us to view the variable importance very easily, allowing us to see what predictors are most impactful in company status. Another reason random forests are nice in this case is that the sklearn RandomForestClassifier allows me to weight the response categories by setting class_weight, reducing the issue I discussed before of unbalanced prior probabilities. I also just want to try out sklearn's random forest classifier and compare it to the r package randomForest." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I did not realize sklearn's random forest method does not except categorical variables yet. So I'm going to have to convert a bunch of variables into dumby variables. I don't really like this, as an example why, say my 3 levels of a category are 'green', 'red', and 'blue' and an encoder make green = 0, red = 1, blue = 2. Then by this classification red is \"in between\" green and blue, when this is not the case. However, the alternative is create dumby columns for each category of each variable, and that would create a huge number of predictors, so for now I am going to go with the more naive approach." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import preprocessing\n", "from sklearn.preprocessing import LabelEncoder" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#function to transform to encoded numerical variables\n", "def transform_dumby(df, preds_index):\n", " \"\"\"\n", " Description: transform a data predictors into dumby variables\n", " \n", " inputs:\n", " df: pandas data frame\n", " preds_index: preds_index: eiter an a two item list consisting of the start and stop column index or tuple of predictors column index or a single integer. If it is a single integer we assume this is the first index and predictors are the rest of the df.\n", " \n", " out: pandas df with encoded variables\n", " \"\"\"\n", " rt_df = df.copy()\n", " \n", " if not isinstance(preds_index, int):\n", " start = preds_index[0]\n", " stop = preds_index[1]\n", " \n", " predictor_list = df.columns.values[start:stop]\n", " else:\n", " predictor_list = df.columns.values[preds_index:]\n", " \n", " for col_name in predictor_list:\n", " col = df[col_name]\n", " if not np.issubdtype(col.dtype, np.number):\n", " label_encoder = preprocessing.LabelEncoder()\n", " label_encoder.fit(col)\n", " new_col = label_encoder.transform(col)\n", " rt_df[col_name] = new_col\n", " return(rt_df)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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closedstatusnamecategory_codehad_fundingnum_investmentnum_relationshipsnum_milestoneslogo_heightlogo_widthregiondegree_typeinstitutionsubjectbirthplacefirst_namelast_name
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11NoacquiredJumptap2210.045.03.0165.0650.057522635298011471748
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" ], "text/plain": [ " closed status name category_code had_funding \\\n", "7 No operating Fundable.com 11 0 \n", "8 No operating Wevod 12 1 \n", "11 No acquired Jumptap 22 1 \n", "18 Yes closed FairSoftware 40 1 \n", "22 No operating WPP 30 0 \n", "\n", " num_investment num_relationships num_milestones logo_height \\\n", "7 3.0 3.0 4.0 120.0 \n", "8 0.0 2.0 0.0 89.0 \n", "11 0.0 45.0 3.0 165.0 \n", "18 0.0 1.0 1.0 67.0 \n", "22 21.0 23.0 3.0 59.0 \n", "\n", " logo_width region degree_type institution subject birthplace \\\n", "7 120.0 110 69 701 465 745 \n", "8 250.0 336 317 273 1116 330 \n", "11 650.0 57 52 26 352 980 \n", "18 250.0 372 317 894 394 790 \n", "22 86.0 309 199 1182 310 492 \n", "\n", " first_name last_name \n", "7 344 366 \n", "8 797 634 \n", "11 1147 1748 \n", "18 29 1508 \n", "22 507 1731 " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#transform the data so random forest can use it\n", "dat = transform_dumby(data, 3)\n", "dat.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I need to split into test and training sets. One common convention is to use 80 percent of the data as the training set." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2348, 17)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#shape of the data\n", "dat.shape" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1878, 17)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "#split into test and training set using 80 percent of the data\n", "training = dat.sample(frac = 0.8, random_state = 1)\n", "training.shape" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(470, 17)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get test data being removing the training rows from data\n", "training_index = list(training.index)\n", "\n", "test = dat.drop(training_index, axis = 0)\n", "test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I can seperate the predictors and response variables in the training and test data." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#function to seperate predictors and response in one line\n", "def seperate_preds_response(df, response_var, preds_index):\n", " \"\"\"\n", " decription: function that makes it so I don't have to split into predictors and response for test and traiing data.\n", " \n", " inputs:\n", " df: training or test data frame\n", " response_var: either a string of the name of the response variable or a list of strings for multiple different response variables\n", " preds_index: eiter an a two item list consisting of the start and stop column index or tuple of predictors column index or a single integer. If it is a single integer we assume this is the first index and predictors are the rest of the df.\n", " output:\n", " List of predictors and response. \n", " First item of the list is the data frame of predictors. The rest are the response series.\n", " \"\"\"\n", " if not isinstance(df, pd.core.frame.DataFrame):\n", " raise ValueError(\"df must be Pandas DFs\")\n", " \n", " dfs = []\n", " \n", " if not isinstance(preds_index, int):\n", " start = preds_index[0]\n", " stop = preds_index[1]\n", " \n", " predictor_list = df.columns.values[start:stop]\n", " else:\n", " predictor_list = df.columns.values[preds_index:]\n", " \n", " predictors = df[predictor_list] \n", " \n", " dfs.append(predictors)\n", " \n", " for var in response_var:\n", " if not isinstance(var, str):\n", " raise TypeError(\"columns of response_var must be strings in the data frame\")\n", " \n", " resp = df[var]\n", " dfs.append(resp)\n", " \n", " return(dfs)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#seperate predictors and response\n", "train_preds, train_closed, train_status = seperate_preds_response(training, ['closed', 'status'], 3)\n", "\n", "test_preds, test_closed, test_status = seperate_preds_response(test, ['closed', 'status'], 3)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1878, 14)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#training shape\n", "train_preds.shape" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(470, 14)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#test shape\n", "test_preds.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am now going to fit a random forest classifier on the status of the data." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import ensemble\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before I fit the random forest model I am going to use GridSearchCV to perform cross validation in order to tune the hyperparameters of the random forest. In the case of this random forest I am going to tune the number of trees in the random forest 'n_estimators', the parameter 'max_features', and the maximum depth (terminal nodes) of the tree 'max_depth'. The 'max_features' parameter is the most important parameter for random forests, and determines the maximum number of predictors to random forest will look at when decided how to best split the data. The reason random forests do not look at all of the predictors in the data when creating splits lies in the bias-variance trade off: the random forest will risk increasing bias while reducing variance in limiting the maximum number of features. This reduces any error causes my highly correlated predictors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cross Validation allows us to tune these hyperparameters. When looking at the predictive power of a learning model, the accuracy of the model in predicting the data used to train it is a dis-honest evaluation of the model. Cross Validation splits the data in k folds, then for each value of the hyperparameters we are looking at it training the model on all but one of the folds at a time, tests the model on each left out fold. The average perforamnce of each of the k folds is used as the estimated performance of the hyperparameters. Using GridSearchCV I can find the best combination of my two hyperparameters. " ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#list of number of predictors examined in the CV\n", "num_preds = np.arange(2, np.ceil(train_preds.shape[1] / 2)).astype(int)\n", "\n", "#list of the number of trees used in the CV\n", "n_ests = [50, 100, 150, 200, 250]\n", "\n", "#max depth\n", "m_depth = np.arange(1, 11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that for scoring I am using an f1 score, f1_weighted. This is to attempt to not overclassify the highly frequent \"operating\" status. For example, I will show an example after these steps as to what happens when I use accuracy." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#dictionary of parameters to be tuned\n", "params = {'max_features': num_preds, 'max_depth': m_depth, 'n_estimators': n_ests}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cell below may take a view minutes to run. As side note, while this next cell does take a but of time to run, if we run to do the same in r it would take much longer. GridSearchCV is one of the most usefull parts of sklearn in my limited experience with it." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 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average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: 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average, warn_for)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise',\n", " estimator=RandomForestClassifier(bootstrap=True, class_weight='balanced',\n", " criterion='gini', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_impurity_split=1e-07,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", " oob_score=False, random_state=100, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'max_features': array([2, 3, 4, 5, 6]), 'max_depth': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), 'n_estimators': [50, 100, 150, 200, 250]},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_weighted', verbose=0)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#intialize a random forest classifier.\n", "#random state sets the random set of the classifier\n", "#class_weight = 'balanced_subsample'\n", "rcf = RandomForestClassifier(random_state = 100, class_weight = 'balanced')\n", "\n", "#do 5-fold cross validation the hyperparameters\n", "cv_rcf = GridSearchCV(rcf, params, cv = 5, scoring = 'f1_weighted', return_train_score = False)\n", "\n", "#fit the training data\n", "cv_rcf.fit(train_preds, train_status)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_cross_validation_result(cv_model, col, hue, x_var, title, savefig):\n", " \"\"\"\n", " description: plot results of parameter tuning cross validation with 3 parameters\n", " inputs:\n", " cv_results: GridSearchCV object with cv_results_ instance\n", " col: String, column name in cv_results_ to facet\n", " hue: String, column in cv_results_ to split by\n", " x_var: String, column in cv_results_ to plot as x\n", " title: String, title\n", " savefig: savefig file name\n", " \"\"\"\n", " cv_results = pd.DataFrame(cv_model.cv_results_)\n", " scores = cv_results[['mean_test_score', col, hue, x_var]]\n", " \n", " p = sns.FacetGrid(data = scores, col = col, hue = hue, col_wrap=3)\n", " p.map(plt.plot, x_var, 'mean_test_score').add_legend()\n", " plt.suptitle(title)\n", " p.fig.subplots_adjust(top=0.9)\n", " plt.savefig(savefig)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": 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uJD0AR48eZdq0aXz33Xf2r3aL3gn+RxV96oMkSVJJ7kdfIxXYs2cPycnJ9uu+\nZ86cicFguO3ypz+q8BvCh/FY4JSUFHuiXHhfkSQ9SHLEXHooJk2axMGDB5k3b16p11tKkiRJ5V/1\n6tVZuXIlK1euxGazUatWLftN4Y+y9evX8+677/Lcc8/95Un5/v37mTNnTonvNW/e/LZHNkr/O+SI\nuSRJkiRJkiSVA/LmT0mSJEmSJEkqB2RiLkmSJEmSJEnlgEzMJUmSJEmSJKkckIm5JEmSJEmSJJUD\nMjGXJEmSJEmSpHJAJuaSJEmSJEmSVA7IxFySJEmSJEmSygGZmEuSJEmSJElSOSATc0mSJEmSJEkq\nB2RiLkmSJEmSJEnlgEzMJUmSJEmSJKkckIm5VKa8vDy++uorACIiItixY8efKm/NmjX3o1p3bdu2\nbXTu3Jnw8HDCw8M5ePAgAEuWLGHAgAEMGTKEkydP/qV1kh5dj3o8ANhsNv7xj3+we/du+7SS4iEl\nJYWnn36aoUOH8sorr5CTk/OX11V6dPyvxsbzzz/PkCFDCA8PZ8yYMYCMDekBE5JUhujoaDFw4MD7\nVl6rVq3uW1l349133xVbtmwpNu306dMiPDxcqKoqYmNjRb9+/f7SOkmPrkc9HiIjI8XgwYNFhw4d\nxM8//yyEKD0eZsyYITZu3CiEEGL58uXik08++UvrKj1a/hdjQwghQkNDhaqqxeaVsSE9SLqH/cHg\nURcREcH27dvJysoiNTWVF198ka5du7JlyxY+//xzrFYriqKwZMkSLl26xMKFC9Hr9QwaNAij0Vji\nPB999BF6vZ6bN28yZMgQ9u/fz/nz5xkxYgRDhw4tsR4HDhxgxYoV6PV6YmJi6N69O88//3yp9d68\neTOffvopGo2GJk2aMGHCBI4cOcK8efPQ6XSYTCbef/99li1bxuXLl1myZAlCCLy9valateod61jS\n9q9bt4709HTefvttpkyZwuTJk4mJicFmszF69Gi6d+9OeHg4np6epKen8+abb/LGG2+g0+lQVZV/\n/etf+Pv727dhzZo1/Pjjj8W2a968eVSsWNH++syZM5w7d47PPvuMBg0a2LezTZs2KIpCxYoVsdls\npKSk4Onp+SdbgyTjoXzHQ3Z2NrNmzWLFihX2aaXFw5EjRxg3bhwA7dq1491332XUqFF/pFlIyNh4\nFGMjKSmJjIwMnnvuOTIyMhg7diwhISEyNqQH6yF/MHjkbdy4UYwaNUrYbDaRmJgoOnToICwWi1i6\ndKnIzs4L3RGXAAAgAElEQVQWQggxbdo08d///lfs379f9OzZ075safN0795d5Ofni2PHjol27dqJ\nvLw8ERUVJXr16lVqPfbv3y9CQ0OFxWIRWVlZIjg4uNR5U1NTRWhoqH3dEyZMEHv27BFz584Vq1at\nEjabTWzbtk3ExsYWGwVZvHix+OKLL+6qjiVtmxC/j4KsXr1azJo1SwghhNlsFl26dBHJycli+PDh\nYuvWrUIIIdasWSNmzZol8vPzxd69e8WFCxfu8egIsWrVKhEVFSVUVRXTpk0Tq1evFh9++KH4/PPP\n7fMMHTpUXL9+/Z7Llm4n46F8x0OhSZMm2UcFS4uHzp07i5ycHCGEEFFRUWLIkCF/eH2SjI1HMTZu\n3LghVq5cKSwWi0hKShJdunQRSUlJMjakB0qOmN8HzZo1Q6PR4O3tjaurKykpKXh5eTFp0iScnJy4\nevUqjRo1AqBKlSr25Uqbp3r16uj1elxcXKhUqRIODg64ubmRl5dXZj1q1KiBTqdDp9NhNBpLnS8q\nKoqUlBTGjh0LQFZWFlFRUTz33HMsW7aMkSNH4ufnR4MGDcjPzy+xjDvVsbRtK3TlyhVatWoFgLOz\nM9WqVSM6OrrYPhowYAArVqxgzJgxuLi48OqrrxYr425GQfr374+rqysAnTp14scff6RWrVpkZWXZ\n58nKysLFxaXU/SXdGxkP5TceSuLs7FxiPBRONxqNZGVl2eNI+uNkbDxaseHt7c2QIUPQ6XR4eXlR\nu3Ztrl27JmNDeqBkYn4fnDlzBij42iszMxOTycTixYvZtWsXAKNHj0YIAYBGU3C/rdlsLnUeRVH+\nUD3udrnAwED8/f1ZtWoVer2eiIgIateuzaZNm+jbty+TJk1i+fLlrF+/nn79+qGq6j2tq6xtK/xd\nrVo1Dh8+TJcuXcjMzOTixYsEBgYWK3vHjh00adKEl156ie+++46PP/6YOXPm2NczfPhwhg8fXmo9\nhBD06tWLL7/8kgoVKrBv3z7q1q1Lw4YNWbBgAc888ww3b95EVVV5Gct9JOOhuPISD6UJDg4uMR6C\ng4P5+eef6devH7t376ZJkyb3XLZUnIyN4sp7bOzdu5c1a9awYsUKsrKyuHTpElWrVpWxIT1QMjG/\nD5KSkhg5ciRms5m33noLZ2dngoODGTx4MDqdDldXVxISEuydCXBX8zwonp6ejBo1ivDwcGw2GwEB\nAYSGhpKfn8/UqVMxmUxoNBqmT5+Ol5cXFouFBQsWlDmyUlRp2wYFneyECROYPXs206ZN46mnniIv\nL4+XXnoJLy+vYuXUq1ePSZMmsXTpUlRVZfLkyfe0nYqiMHPmTF566SWMRiPVqlVj0KBB6PV6mjZt\nyuDBg1FVlTfffPOeypXKJuOhuPISD6WpV69eifHw/PPPM2nSJNavX4+Hhwf/+te/7sv6/s5kbBRX\n3mOjffv27Nmzh0GDBqHRaBg/fjyenp4yNqQHShGFH0ulPyQiIoKrV68yYcKEh10VSXroZDxIUslk\nbEiSdDfkiPkjZsmSJRw4cOC26bNnzyYoKKjYtB07dvDpp5/eNu+IESPo0qXLg6qiJP1lZDxIUslk\nbEjSo0mOmEuSJEmSJElSOSD/86ckSZIkSZIklQMyMZckSZIkSZKkcuCRusY8MdH8sKsgSX8pH5+7\ne766jA3p7+Ru4wJkbEh/L/cSG1L5JEfMJUmSJEmSJKkckIm5JEmSJEmSJJUDMjGXJEmSJEmSpHJA\nJuaSJEmSJEmSVA7IxFySJEmSJEmSyoEH8lQWVVV5++23uXDhAg4ODsycOZPKlSvb39+0aROffPIJ\nGo2G/v37M3To0DsuI0l/xIX4TLIsVur4uWDUax92dSSpXDgfb+brkzfJt6nU9HWmpq8z1X2ccDY8\n/Ad1peal4KJ3Rad5+HWRJEn6qz2Qnm/79u3k5+ezbt06jh8/zty5c1m6dKn9/fnz5/Pdd9/h6OhI\njx496NGjBwcOHChzGUm6W0II9kem8tnBaI5EpwOg0yjU9nOmQUU3GgW40jDAFQ9Hh4dcU0n661hs\nKjsuJrH+2A1OxWXwuD4JX10Oa844kYIrFnQEuRup6etMjd+S9Zq+zng5Pdg4salWzqSd5kDCPg4k\n7uOq+TKDqw5jXK0XH+h6Jakkqdn5HIlOR1EgwM1IgJsJF6P8kCj9dR5Iazty5Aht27YFoFGjRpw+\nfbrY+zVr1sRsNqPT6RBCoCjKHZeRpDuxqYKfLiXx2cFoLiRk4uvswCvtqxLkYeJEbAYnb6Sz/ngs\nnx+JAaCSh6kgSa/oRsMAVyp5mFAU5YHULd+Wz1XzZS6kn+N82jkuZVygS0Aog6sOfSDrk6RCiZl5\nRJyII+JkHFnZWQxzOcFS358JzDgKAjAWzJerdSHV6kZ8tAs3rrmQLFzZgiu5ek9Mbn64efvj6xdI\nUMVA/Lx9UTR//ErIlLwUDiXu50DiPg4lHiDLmolW0VLfoyFja71IaGCP+7PxknQHuRYbx2PTORiZ\nxoHIVC4mZt02j4tBR4CbkYpuxt9/uxup6GrE39WIg05eFSzdPw8kMc/MzMTZ2dn+WqvVYrVa0ekK\nVle9enX69++PyWSiS5cuuLq63nEZSSpNnlXl+7PxrDkUTXRaLpU8TEx7sgbdannjFPcrWnM0XXyN\n4G/AouiJNKtcSrZwLjmBM5fzOX1GwyfoMRpM1KjoRa0Ab+oFeFOrggt67b13uDZhIzozivPpZ7mQ\ndo7z6ee4ar6MRbUA4O7gTk232lR3rXG/d4UkAQXfGh2PzWD9sRvsvJxEVRHNLM99dNL+hIMlnTxT\nJY40GYvVNZBAq4p7XiaanCQ8cpLxykmiTlYSZF/EIT+tIHlP++3nckH5FqElQ+tOnoMHwuSNg6sf\nejd/FLcAcAnE5hKA6uyPMLiDomATNi6mn/9tVHwvF9LPA+Bp8KJdhQ4092lJsHcznPXOpW2SJN0X\nqhBcSMi0J+InYtPR2nII1l5jpFskzf2uUCn3HIpqI1fvSqbiShrOJOY7c/OGIzHXTcSozpwSzqTi\nQppwQePogZO7Lz7urr+Nsv/+4+Xk8KcGfIQQZFjSic+5SWJuIvU9GuLq4Hof94hU3jyQrNfZ2Zms\nrN8/daqqak+wz58/z65du9ixYweOjo5MnDiRzZs3l7mMJJUkM89KxIk4vjgaS3JWPrX9nJnXqw4d\nAjQ4XViP6cvVaDMib1vOCwguOsFQ5O8bv/0cgnyhw6JxQGgNaHRGdAYTitEdm2sQqksgNpcgrM4V\nuWEwcdaWxnnzFS6kn+Ni+gVybNkAOOocqeFai/6PDaKmW21qutfGz1jhgY3MS39vuRYbW84lsP74\nDaITU+hvOMRPbrvxzj3DCeHI0qA6HHV05lxOHIEXEjFYs0kzxZPvmomHuwv+vgFUdGyIv2NFKjoG\nUNFYAR+hQZ+bitWcSFLiDdKSb5CTFo8tMxFddjKeSfG4JV7FlSwc3fJxcLGi1QvSNBp2mlzY6ejK\nYZMGs1agCPC3edGMlvjqm+CobYw+y5GofC2JCRkYdVmY9BqeqOxRLq53l/433EjP5WBkKgci0zgU\nlYJr3g2ClUsMd7rGIpfL+OddRSNsJOZp2OxSmele/uQp4CkUvG0q3tYUvPNj8cnNpLouGy+birfN\nhqMQBSuwAcmQk2wgRTiTJpxJES5cxpmjiisavQGDTmv/Meq1GPRajDotOp2GLL2VNG0eyZo8EkUe\nN9Xs335yiLdlk4vNvi3PPTaMQXXkZV7/yx5IzxccHMzOnTvp3r07x48fp0aN30cGXVxcMBqNGAwG\ntFotnp6eZGRklLmMJBWVkp3Pl0dj+er4DTLzbDxRyZ0Z3WvSwhCF6fQcjDu+QbHlYfF/gqwWr2Hx\nbwa2fBRbPootD6x5KLaCH2x5KNbffv/2fnZ2FglpGSRlZJJqNpOdnYVDrgVDlgWTPgNzxkFiDLs5\nZ9BxxuBAqrbgplK9EFSxaGljc8af6njrquKkrUmeJYD87Ipkqc6cMWu4rEvBoNNQt4Irjg7yhlTp\nz4tJy2HD8Tg2nb5JUP5lRrj+hIfPUU7pBROcXLioq4SKQCMSaWSuztCLo9GnFv/X3TatBbNjEvHG\nGM6ZDpNq+p5UUzw5jun4OvpR0TEAf6eKPKZ3o0qWDr94BcOpNMSNPHLRkYubvaw0Zw3RXoJYL3D1\ngN4ugprGbNorKXiIKOAYsA6AROFKnPDihvDmhvDikvAisl4/Rndu/hfuQel/iTnXyuHoghHx49dv\n4plxjmDNJYY7XOZd7SVcDWkAqDiR4t2QL93rsFWkcyz7OoZ8R9rG9cZR60SCUxRHDZeI0V9DddCA\nsyvw+2i1SeOAp9YJL40BL/R4qgreeVb8o7PwjcyiUmwKDvEqwqIgFFB/+7FpwKZApkbBqimYplXA\nRwEvBeoooEGgUwQ6QIdAj8BBEbgMSIY6D2e/Sn8NRYjCj3z3T+ETVi5evIgQgtmzZ3P27Fmys7MZ\nPHgwa9euZePGjej1eipVqsSMGTPQ6XS3LVOtWrVi5SYmmu93VaVHSGx6DmsOxfDtmXjyrSoda3gz\nMtiPRpk7MZ36DH38MYTORG6NfuTUH4nN+8/3XmpSElG7vyNu9xZ8LkRiyLeR5ArJLgppriZynJxR\nTE64OOipoLfhbUzDX5OEv0jEQbEWKytJuBIjvIlRfYix1CKjaltG9+tW5vp9fFzKfL+QjI2/H1UI\n9l9PZf3xGE7EnKCB8y68XC5ySZ9HrL5gzMWocaC2R33qezSktrEB6hEPoo+lYXTW06hbED5VXDEn\n5ZCRmEtGYg7m337nZFjs6xGKCro0dLlxuCdH45d0E6fsmwhbPBcDLFyr5kRy7QBuWpJwikulYgrU\nznClSpoDbvFmNFk5v1faaEIXUAGdnwd6bxMO7gp6p1z0Dqnoc2+iy7qB1pqFOfgf5LZ8rdRtv9u4\nABkbfxdnbprZfTmJa9cu4pJ8jMbKJZpoL1NHuY7utxFnq9tjWCs0JcO3AbsMCtszznIo6QBWYeUx\nfTU6pgzCeKECwgooCkItSI90Bg3Ovg44+AhUzxzy3NNJd0wgxZJMdlIczpei8b6SRMB1M5VvWHH4\nbYA73h0uBiikOYFGgCJAKxScNI6YFBMGxYBeGNAJBzSqA4pNh2rVYbWoWKyCPMURi8YFq9YV1cEd\noXdFqeVG33GdS90P9xIbUvn0QBLzB0V2sH9PlxOz+PRgFNsvJKIoCj3q+vFMbYXHYzZgPLsWTW4K\nVveq5NYbQW6tgQiD250LLYXIzcVy4hipe3eQfXAPLjEpAGSYILqmJ64+lfA2KzilZCESkxCpKbeV\noXh4ovX1RePljsbVCI4KmVpHUq0eJOf5kJgTiEUYqR4UTeOx/cqsj0zMpVulZGfx2cl9bL9+EDQn\nwBRLjlYFwEuFBo6PUSeoC/V8WlDNtToatFw7ksipbTFY8lSqt/SlbkgAekPJ39ao6WlkHz5G2tEL\npF9NIDNLQ5ZTBbKd/MkxeiOU3++7EE755DpnkOaYgN6oIdAxCH/HQIwaI0KAUAUiJwc1Ix1buhlh\nzkA1m1HNmYicHAQKQlEADTg6oji5gKOJap1r4t+yZqn7QCbmUqGjMWms//UEXW6uoIP2BBWUVABs\nWhNW34bY/JtiqdCELN/6HMy8yE83trMvYQ+5tly8jT6E+DxJvbg2JB2yYcm1EVTfk3odA3B0dyAj\nIYfUuGxSb2STFpdF2s0cbJaCWFOw4ZybgHPKVVwyo3HJicOtojMOdWuRW6sKadUqkOykkpKfjElr\nwtdUAT9TBTwNnmiVgthTVUFOej7m5FzMSbmYk3PJTMrFnJxHdloeRbMzB0cdBncHgrsG4Ve19GvM\nZWL+6JOJuVRuHY9J57ND0ey5moKjXku/+n6MqXgd/8tf4BC5HYD8x7qQU38klsA2oBS/UVOogujT\nKWSn5+Pqa8LV14STmwOKRikyj4rt0kXyDx0g68AexOnTaKw2LFo4F6QQX7sCXq06E/zEILwd/W6r\no8jLQ01MwJYQjxp/EzUhHuvNeDKTsknKciQFX1JdqmJxKOgsTdnxeKRfwkuNJzCsOS79+pa5D2Ri\nLhWKSTczad+bxNkOg1IwJFcl30LjfCv13OtRu+YwfIOeLPa0lOSYTI5+G0nqjWx8qrgQ3KMybn6m\nYuWqZjOWE8ewHD2M5dgRbFcugxBgNKKv3xB9cFP0jYPR1ayNiobM5DwyEgtG2QtH281JufaEBUBR\nKIgzBRRFQVEo9reiFLyHakNRbWC1otisYLWA1ULNKvnUeKF3qftCJubSkeg0Vuy9TpW4b5mmX4OT\nkk9ulVBE4BNYKzTB6lUbm6JwPPkoP93Yxi83fybTasZV70Z7/4508O2E45WKnP85jrwsK/413ajX\nKRAPf0f7OtTsLKxnz2A9fQrL6ZNYzpwmS3XE7BxIpld1snyqk6H3wWIrSLQVBVy8jbj7O+Lu74iH\nvxPu/o6oNkFm0eT7t78zU/JQbb+nYDoHDS7eRpy9jLh4GX/724CLlxEH091deSwT80efTMylcsVi\nU9lzNYXPD8dw4kYG7iY9Ixu4MNz4Kx4XPkeXdhXV5EVu7afIqReO6hJQYjlpN7M5+m0kSVGZxaZr\n9Rpc3LU4i3RMydcwXj6CU9JVTDlJRPkITlZRSKobROUWPWj3WDcqOPrfVb2z0vJIuGom4WoGCdcy\n7JcCmFz1+AaZ8Paw4qVLxWCOR42PR02Ix6FDRwxt25dZrkzMpaSsfD45cI1dSe+Q43ydwRnZtM7J\noq5TFUx1hpNXvQ/CUHwELS/LwqntsVw9kmi/bCWovieKoqCmpmI5dQLrqRNYjh3BeukiqCo4GNDX\nq48+uAn6xk3R1a6DotffVR2FEAjB70n3n1D4CN2yyMT870kIweHoNFbsiyIp9iILDJ/QgpPkVniC\n7I7zsXk8jhCCs2mn+enGNnbF/URqfgomrSNtKrSjo38XGns0JeZkGmd33iA7PR+fx1yo3yUA70ou\nCCGwnjlF3k/bsRw/hu3KpYLYUBS0Vaqir9cAXf0G6Os1QBMQiKIoCCHITs8nLS6b1Ljsgt83sopd\nDlaURqvg7GkoSL69iybgRozOuj8dPzIxf/TJxFx66IQQnI4z88PZeLZdSCQ910oFFwOv1MkmLO8H\nnC5/g2LNweIXTE79keQ9HgZaQ4llWfNtnNl5g4t749EbtTTsGoR/ZQdS950k7fR10mMzyFSdyHL0\nJ8/oYV9OVazgbsGngjv+Fb0LRth9jDh7GtFob+8oczMtJFzLsCfjmSl5ABgcdfhWdcG3qiu+VV1x\n9jT8qY5WJuZ/X6nZ+aw5GMWNk9+j+m3kkIuVySlm+gaEklt3OFbfBrcto6qiyGUrNqq38KNWTQHn\nT2E5eRzrqRPYon57UpFej65OPfTBTXBo3BRdnboohpLjqryRifnfixCCg5FpfLw/klOxqbzsuI0X\nWYdWqyer1RRy6w4jKiuKH2M2szNuOzdz4tBrHGjh04qOFTvTwrc1DooDMWdSOb0jFnNyLp4BTtTr\nHIBvVRfUq5fJ276NvJ+2osbFgYPDb0l4Q/T1G6CrUw+Ny70lvHlZloJE/WY2Wp0GFy8jzt5GHN0c\n0Gge3FO5ZGL+6JOJufTQxKTlsPlcAlvOJRCVmoNBp6FDVXee8TxNo5vrcLh5CKE1kFujDzk1h2Ex\nVUZkZaFmZSIyMxHZWYjMrN9+Z3IzWc/p9MfIFSYCrJeonr4HvTkZW3QU2GyoDnqiqrqwJyCDY1UE\nVHyMDo6h1KUpDmZnMhIKbnzLTsu311GjVXDxNuLqY8LV10h+tpWEq2bSEwpuaNMbtPhUcSlIxqu4\n4uZrKnapzJ8lE/O/n/QcC+sOXcZ6Yh3D+J41PnlsdHXmBedGDHxiFqLIB8qikmMyObrpOqlxOXg5\nZVMrcw/GU78gUgrug1BcXNE3aFgw4le/EbqatR6ZRPxWMjH/eyj8L84r9kZxKi6D1k43eNe4Er+s\nc+Q99iSZ7Wdic/Jn4/X1LD+/BAE09W5GR/8utPJri7PeGSEENy+lc2pbLGk3s3H1NVG/UwB+Lmby\nd2wjb/tWbJHXQKtF36QZhs5P4tC2AxrnR/OZ+jIxf/TJxFz6S2XkWth+MYnNZ+M5HpsBQJMgN3rU\n9iZMsx/3Y4s5uDcNJd6A3qpHn6+gz7Witaqllplr8ODi4wNI8mmEY85NHkvYhImb2BwNWE0ORLvb\n2OoXx6mKFtydfQnx70SIf2dqutUucTTbkmfDnJRLRkIOGYk5pCfkkJGQS1ZaHlqdBu9Kzr+NiLvg\n4e9U4oj6/SIT87+PzDwr3+4/juHUZwxiG+5KJnP8H2etMZ9hVYfzTK0XbltGzc4i6+hpTv+aQrTZ\nCwdLBo9f3ohfwhG0/hXRN2iIvkEjdPUboq382J/6b53liUzM/7cJIdh7PZWP90VyOs5MkLPCogo/\nEnxjDcLgjrndTPKr9SDTmsWCk7P5JX4Xrf3a8mq9SXgaPO3lJFzL4NT2WJKjMnHyMFCnqTO+sfuw\n/LQV6/lzAOgaNsLQ6UkMHTqh8Sj5Q++jRCbmjz6ZmEsPnMWmsvdaKpvPxfPLlWTybYLHPE10r+NH\naE1PKt/cguORxZB8lZ+PBVLxssrZKnoynTRkGxRyDJBlwP4720H97bdCBXM7aiWHoqBwOHAzJ/13\noWqKJ/EeDh60qxBCSMXO1PNogEb5Y8mJ1aKiKKD9C//9skzM//dl59vY+etPeJ39hK7iV3SKSmpA\nZ/5T6TFW3fyRvpUH8FKdVwuuD09Lw3L0EJaTJ8g/dYroLG+uPBaGTWeiUtYJalTKwbFRvYJE3Nvn\nYW/aAyMT8/9NQgh+vZbCin1RnL1ppoKLgTdqJRIaNR99+lVyag0mq/VUhNGDS+kXeOfYVG7m3GRs\nzecZWOUp+0BLSmwWp7bHEH85A6OTluqeCfid/Ab15DEQAl3NWjh0ehJDxy5o/W6/qf9RJhPzR59M\nzKUHQgjB2ZtmfjibwNYLiaTlWPAw6Xmylg/d6/hR29uA6WIEjkc+QJsRSZZLLfbtVAm4nMnhQcE8\n+dK/y0ygk6IyObLpOunxOfjXcKNhj0Ac3R1QhYqKihAqqhCo2HDUOdkfT/WokYn5/67cfAtHdn1F\nwKXPaMYZchQTKdUGYGwxjnXJ+1h6/gO6BnRnYoM30Cga8nbvJHPODERmJhle1blYZzgZWm+8vQSN\n+1bDo7LXw96kv4xMzP+3CCHYfSWFlfsjORefSUVXA8828WRQ2gqczn2BzbUy5g5zsQS1RQjB99H/\n5YOzi3BzcOPNRjOo51lwv0V6Qg6nd8QSezYVB51KlZxjVDj4BVpLLtrKjxWMjHfqgrZS5Ye8xQ+O\nTMwfffJ/Hkv3VVxGLpvPJvDD2XgiU3Nw0Cq0q+ZN9zq+tHzMAx0WjOe/wnHrErTmGCw+DYhv8x7n\nFiyjQlQmJ8Z0pOuIOaXeMJmXbeXUthiuHk7E5Kqn9VOPU7G2u/wX99IjIz8nk0s7P6bKtc/pQxxJ\nGh8u1ZmAR4vROBjc2BT1DUvPf0D7Ch2Z0GAyitVG5rL3yV2/FrVOMNdbjOX6VRtGFz0tijxtRZIe\nNUIIfr6czMf7o7iQkEmAm5FpT9agr+kYbnteQJOdSHajcWQ9MQH0JnKsOSw6s4BtsVto4t2MKQ3f\nxt3ggVAFRzdd5cqRZLTCSpXoHQRFbsPBxwPDoEEYOj+Jtlp1GSfSI0Em5tJ9sedqMqsPxXA0Jh2A\nxoFuDG8aSKcaPrgYdWDNxXj2Pzge/RBtZhwWv8ZktptFokMVYv5vFD6JuVx6pT+d+k0qsXwhBJEn\nkjmxJZr8HCs1WvuV+U9SJKm8saXHcmPnhzweu5EOZHFRV5MTjf5JxWYDcNfoEMCO2K28d3oBzX1a\n8kajtyAhkfS33sB65jRK32EcVNuTc91S0P47BKA3yvYvPZrSsi2M/+Y0p+LMBLobebNrDXpUBvdf\n38Jw5XusXnVI6/6J/elDkZnXefvoFKIyrzOq+hiGPT4SraJFWCwcX7aTKwkeBMb8TNX0vTi3a41h\n4hJ0devLZFx65MjEXPrTDkSmMuGbM/i7GXm+9WN0q+1LRTdjwZvWHEwnPsV0dCna7HgsFZpiDlmI\nJagdiddOkfx8OG5mCzfeeJY2Tz5bYvkZiTkc+TaSxGtmvIKcaNKzJu5F/gmEJJVriedI37mQxxK3\n4yNU9ju0QnniOao3CqHorWa/xu9mzskZNPRszNvBsxEHDpI28y2w2nB8azZ7r1QkLz6Hjs/Wxivw\n0XxihCQBpGTn88JXJ4lJy2XakzXoXscX5wvrcFo3E8WaS2aL18lpNA60Bc/R3xG7lX+dnodRa2D+\nE4to4t0M8f/s3Xdc1dX/wPHXHVwu3MtlCKKIAwQXils0d+XINDV3O6tvZqmpuTVXjsw0M0d7OMrR\nUMzM3HujgCIyBJG94Q7u/Pz+oC/GF0tSNP11nn8Jn3M+9/N5yL33/TnnvN/H4cC8dzeJ3x4gzm8g\ntSxXaD26K6qW45EpRWgjPLjEX69wR5LzjEwLj6FeNVc+H9ECjer3PymrEZfodbhGrEVuysbi157i\nHh9irfUQyGSkXDyCedJbqK0OChdMpnWHwRXObbM6iDmYRuyRDBROclo/UZfA1j5VWo5QEO6mguxU\n/Db3x1mS2K7uh9tDr9K8cdMKo3hnc04zL2IWDXQNmd9yIbbPPse0/isUQcG4zV3I6eM28lLz6Dg8\nSATlwgMtR29m9JYo0opKWDYghPbuBbiFj0CVehSLX3v03Zdg9wgEwGI3s+rSCsJTfqKZZ3NmtpyH\nj9oHy7kzGNesJP96MRdbvYWXp0TYmOEoncQMkvDgE4G5cNuKSqxM+OkiSrmMZQOaolEpkVn0qKO/\nxjXiY+QleVj8O2NsuwarX/uyfvGndqCcOR8UMuxLFxLS/NEK506PK+RceDKGfDN1m1ejee/aqLWV\n22X6bj0AACAASURBVIVQEO4HuQYLKd9Ppq5k4UDHrXRq0fqm0+rReZHMOjuF2pq6LAqcgfWtydjO\nn8O53wC04yZw8WguKdFZNOvhT60mD345N+HfK6vYzGtbIsnWm1nxZFM65X2P5peFSApniru9S0mT\nEfB70n+q4TpzI2YSX3SF4YFP81KDV5GuJlG4dgHWE8ew1QwguuNknJ3VdHwlRATlwv8bIjAXbovN\n7mBqeAzpRSWsHhxKLbUFlzOf4HL+E+TmAix1umFo8ya2mm3K9Yvetx73BR9i0ChwW/YhtYLalj+v\nxc7Z7ckkX8jFzVtNtxcbUj2w/HbjgnC/yzFY+PjbDXxgP0h8w1dp17LNTdtdKYxl2pmJeDv78K7T\nC9hefQ3JZEQ7Yw7q3n24FpnLpf1p1GvpTaPONe7xXQhC1ckoKuG1LZHkG62sHNSMsPxwtEdmY67X\nA323RTg0N/6+D2ccZEnkAmTImN/6XdrTAOPihZh3/YxMo0U9agynjK2xpBnp/lQwLm5i0Eb4/0ME\n5sJtWbo/gdPXCpjduwEtajjjsbkPyvwrmOs9irHNOGy+LSv0ORu+kprvryOvmooaKz7D279RueOG\nfDNHN8ZRkGmiSXc/GnepeU9rhgtCVcjRmxmz+SxrS9Zi1NTCvdtbN22XVHyVyafeRCvXsDSuA9K6\naSjq1MNtxWqUAYHkpug59eNVvOtqaf1EXZHEJjywUgtNjN4cSZHZxkeDm9HSdgHtoRmY6z5M0WOf\ngbx0tNvmsPFp7Gq2XP2Ohu6NeDt4KroffiN/y3SQHLgMewqXZ18g4mA+OZezCRsciFctzT98d4JQ\ntURgLvxtmyPS+P5COs+19advSA1cTyxBmX+Fwsc+xxLY66Z9jm6YR9DHO8jwdyXww/W4efuXO56V\nWMSxTQlIDonOzzagZrD7vbgVQahSOXozozZH0s/wE0HyVAq7fQlOLhXapRlTmXRqHJ5GOe/u80F+\nbiPOPR9D+9ZUZC4uGApKH1Jd3FR0HBEkHlCFB1ZKvonXtkRistpZNTiUZs6Z6La+it0ziOKeq8qC\n8ixTJvMiZnGpIJqBfgN44bIfloWjMRUV4dzzMVxffhVFTT8STmeRcCqbhp1qULf5v6d2v/DvIQJz\n4W85mZTPsv3xdKlfjdGdAlDkXMI1YjUlDQffNCiXJImDa8fTdOMxrgV70GTFJtRunuWOx53I4sKu\na7hVU9Px6WDcqqnv5S0JQpX4b1Au16cyXvUj5to9sQT0qNAuuySbt06OpW6SgcnhSuTFMWgnT8e5\nb39kMhlWs50j6+OwWyW6vhiMs0ZM0wsPpqQ8I6O3RGKxOVg9JJRGOivuW54HhYrCx79CUpVuhnM6\n+wQLzs/FZrewVP8k9RYexpyejlPbMDSj3kDZoCEA2UnFnNtxjRrB7jTr4f9XLy0IDywRmAuVlpRr\nZOqOSwRU0zCvT0MUOHDbPwnJ2R19p9kV2tscNg4sfYUW4RdJDq1B86XfonS5Me1otzo4G55MUkQO\nfo08CBsUKOoyCw+k7N+D8hy9hf3+25BnS+g7z63QLt+cx+TjY+h4MIuh+60o/Gqhe28lyuAGADgc\nEie3JFKUZaLzsw1wr15xtF0QHgSJuQZGb4nC4ZBYO7Q5QV5O6LY/j9yQQcGAzTh0tbFLdr6J+4L1\n8V/xSKYvLx/SIY/fjCwoGN37H6Jqd6NogKHAzLHv4tF6OtN+SCByUZ1L+H9KBOZCpRSaSjeDUCnk\nLBsYgkalxOX8JzhlXaCo5yokdflqEWabmcNzn6HFgWSSO9Sn5YJvkDvdGPkzFlk49m08edcNNOnu\nR0g3P1EGUXgg/bfSRI7ewvpOBfge/w1D2GQcutrl2umtxcw5OJYR3yXTKs6OqtvDaKfORK65Uf4w\navd10mILaNm3DjXEci7hARWfbWD0lkjkchlrh4US6OWKdt9bqNJOUtTjI2w1WmN32JhxdgoZF4+x\n9Hg1al+6jty3Bq4z5+Lcoxcy+Y3lWzaLnaMb43HYJDo+HYTKRYQuwv9f4q9buCWb3cHUHTFkFJtZ\nMySUmjo18sJkNCeXYK73KOagJ8q1N5iLODF9BC1OZZPSowWtZq4t9yGbc62YY9/GY7M4eGhEEP6i\nBJzwgMosNvPa5gvkGa18NCCY0ENPYvMIxNjy1XLtTDYjK38Yxah1cXgbFGjGTUQ9aGi5hM7Es9nE\nHs0gKKw6wWG+9/pWBKFKxGbpeX1LJM5KOauHhFLXyxWXc6txubwJQ9vxmBsMAGBjwjeEbDjCxHMS\ncm0JLq+Pw2XgYGTOzuXOJ0kSp39MoiDDSOdngtH5iFkk4f83EZgLf0mSJN7bl8CZawXM6d2Q5rXc\nQZJwOzAVSaZE33Uh/CG4yC/OJHLS0zS/WETaoK60GLekXPCRcCabiB3JuLqr6PpCI9x9xYes8GDK\nLDYzavMF8o1WPhzUjLDUL1AWJlHwxEZQ3AguzLYSflzxPC9uT8ZRzROPJctwahJS7lxZV4s4uz0Z\n3yAdLR6rc69vRRCqxKWMYsZ8H4Wrk4I1Q0Px93BBlfgLmuOLKAnuj7HtBAAuF1wifttnjDkroe4/\nENdXX0fudvOyuJcPpZMSnUezHv7UbOBxL29HEP4RIjAX/tLmiDR+iEzn+Xa1eTykdBTP+fIWVNcP\nU9x1IQ6tX1nbjLyrJEx4gZAEE9kvDiB05PSyY3abg/O/XCPhVDa+QTo6DK0vpiOFB9b/1mRuri3A\n9exKSur3xVq7S1k7u8POyclD6Xk6g8JWDQiYvwq5rvwSleLcEo59G4/Wy5kOQ+sjV4glXcKDJyqt\niDHfR+GuVrJmaHP83NUos6PQ/TYWm28Lih9eCjIZJpuJ5SdmM2WPA1mjRmjGT0amuHluUVpsAVF7\nU6nTzEvU8Rf+NURkJPyp40l5LDuQQNf61RjdqR4AMmM22qNzsdZsR0nIM2Vtr6ZdIGviaIJTrRSO\nfZHGQ14rO1ait3Lsu3hykvU07FSDZj38ReKO8MDKKCph1OZICkxWPhrcjKY13ND+PAZkCgyd3i7X\n9vA3s2h6OoOkfu1o/daH5ZZ0AVhMNo6sjwOZjM7PBouHVeGBdCG1kHE/ROPp6sSaIaHU0KmR69PR\n/fwCDrUXhY99DsrS2dGPL39E959TcDOB++SZfxqUF2WZOLElAc+arrQZUE/U8Rf+NcS3gHBTV3ON\nTN8RQ31vDfP6NEL++4ei9tAsZFYTxd2XlG2dXGDKJXviaOqkWzFPH0/93iPKzpOXauDoxjgsRhth\ngwNF3VnhgZZRVMKrmyMpKrGyanAzQmrqUCX+inPyXvQPzSo3gxQdu4/A9XtIre9Jq4krKgTlDruD\n45sSMOSb6fpCQ7Reokyo8OA5m1LA+B+j8dE6s2ZIKNXdnMFqRLdzJDKLnoInf0TSVAfgRNZRYo/+\nwDPnHbgMf6asGtH/sphsHNkYh0Ipp+NTQShVolqX8O8hdq0QKij4YwWWASG4/v6hqEr8FXXCDoxt\n38TuGQSUrkE/sGI0gdetWN96g9p/CMqTL+Sy/7MYZDIZD7/SWATlwgMt/Q9B+UeDQwmpqQOrCe2R\n2di8GmIKHVnWNq8kl7xFb6OQZATO/Qj5/4wKSpJExM/XyEwoovUT9fCp53avb0cQ7tip5HzG/RBN\nDTc1Hw9rXhqUSw50e8aizLlIcc/V2L2bAKWlQpdFLOCN3QpkvjVwHfnKTc/pcEic2JyAscDCQyOC\ncHV3vmk7Qfj/SoyYC+VY7Q6mhl8is9jM2qHNqaErHcWTmYvQHpqOrVpjjC1vLFPZfewTwnZdJbtt\nAxr3fQ4Ah10icncKV45l4lPPjQ7D6qPWik1ShAdXWmEJr22+QLG5dPfCJjVKA2nXsx+iKL5OwcCt\noCj9G7dLdn75+HV6JVgwjX4Rt7rBFc4XdyKLhNOluxcGtPK+p/ciCFXh2NU8Jm+/RG0PF1YNaYaX\nqwoAzYl3cU7chb7THCz1HgFKH0Tfj1pM1yMF+GbZcFsyBZnLzRP/o3ZfJyO+iNb96+FTVzywCv8+\ndyUwdzgczJkzh9jYWFQqFe+88w5169YFIDs7mwkTJpS1jYmJYeLEiYwYMYKBAwei1ZbW9PX392fR\nokV34/KEPyFJEkv2xnM2pZC5jzUk1O9Glrzm+ELkxmwKHvu8LAC5WnAF9w+/xKpW0mDmCgDMRhsn\nNieQmVBEUFh1WjxWG7lCTMwID660whJGbb6A3mxn1ZBmNPYtDRYU+Qm4RqylpOEgrH43NkL58fhH\ndN2WSEHTAOoPH1XhfOlXCrjwyzVqNfYgVOxeKDyADifkMiX8EgFerqwaHIqHa+l3gnPMZlzPrcIU\n8gym0JfK2u+8Hk7C5cOMOQqq7o+g6tDxpudNPp9D7NEM6rerTv02PvfkXgThfnNXAvM9e/ZgsVjY\ntGkT58+fZ/HixaxZswYAHx8f1q1bB0BERATLly9n6NChmM1mJEkqOybce99FpPFTVAYvtKtNnyY3\n6ig7pR7H5eJ6jM3/g823BQBmu5lDq97k8TQHshlTUHhVoyDDyNGN8ZiKLLTpX49A8cEqPOBSC028\ntjkSg8XO6iHNaPR7UI4koT00E0npgv6hmWXtz2WdovqqjcgVSurN/qBCwlphponjmxNw93Wl3aBA\nsamW8MA5EJfDtB0xBPtoWDmoGe4upUG5U9oJ3A5MweLfCX3n+WVldFMN11l18QPm7tOgVNnRjJ1w\n0/PmpRo4vS0Jn3putOxT+6ZtBOHf4K4MZZ49e5bOnTsD0KJFC6Kjoyu0kSSJ+fPnM2fOHBQKBZcv\nX8ZkMjFy5Eiee+45zp8/fzcuTfgTR6/m8cGBBLoFVeO13yuwAGAzod0/GbuuDoawt8p+vfHAQnr8\nloMhLBSvXgNIjcln36cx2K0Ouo1sJIJy4YF3vcDEqE2lQfmqwX8IygHn+B2orh/GEDYJybX0bz3P\nnMuxT6fS5JqEdswElDVqljtficHKkfVXUDop6PRMME7OIqFNeLBsjkhjSvglGvlqWTU4tCwolxdc\nRbfzZey6OhT1WntjWZfDxsILc+l00UFAXBGur76Owrvid4Op2MLRjXGotU50GF5fzLIK/2p3ZcRc\nr9eXLUkBUCgU2Gw2lMobL7dv3z6Cg4MJDAwEQK1W89JLLzFkyBCSkpJ45ZVX2LVrV7k+wt2RmGtg\nxo4Ygrw1zH3sRgUWAM3pFSgLr1LwxLfg5ArAsbRDNPj0FyS1itrTFmPIN3NiSyI6HzUdnw7GVaf6\np25FEKrE9QITozZHUmK1s3pwKA19b3yeySx6NEfnYPVuSknT0rwKu2Rn9e7JvLhHj61tS6r1G1Tu\nfHabg2Mb4ynRW+n+UiNc3cV7RHhw2BwSHxxIYFNEGp0CvVjweOOyogCykgLcf34BgMLHv0JS39gE\naEPCN1xLj2bWPjXKJk1R93+ywrntNgfHvo3HYrLzyCuNUWtEPpLw73ZXol6tVovBYCj72eFwVAiw\nt2/fznPPPVf2c0BAAHXr1kUmkxEQEICHhwfZ2dnUrFl+1EmoWgVGKxN+vIizUs77f6jAAqDIvohL\nxBpMjYZhrV06A5JTkk3EZ28zLBVcZkxF5lWNs19fQSaDjk8FiaBceKBJksSuy1ks35+IQ5JYNSSU\nhtW15dq4nl6OwpBJUe9PQF76fvk69lO6b4hC4azGZ/o75ZawSJLEmZ+SyLmmp8PQ+nj5lz+fINzP\nDBYbM3Zc5ujVPJ5qXYuxXQJR/HcJlt2K7tfXUBRdo/CJjTg8Asr6xRRc5Jv4L5l90g+lIQ3tpGkV\nSoZKksS58GRyUwx0GFYfj5qu9/LWBOG+VKn5oitXrvDUU0/Rt29fPvnkE/bv3/+X7Vu1asWhQ4cA\nOH/+PA0aVKxVGh0dTatWrcp+3rp1K4sXLwYgMzMTvV6Pj49YDnE3We0OJodfIltvZmn/kLIKLAA4\nbLjtfwtJ7YWhY+kaWofk4OM90xm434itfRtcez1O8vlcMhOKaNbTX5S1Eh5oyXlGXt8axds7Y6nl\noeaT4c0rBOWK3Mu4XPgMU5OnsNVoDcDp7BMUbfyKhqngMWF6han6y4czSL6QS8jDtajdzOue3Y8g\n3KmMohJe/vYCJ5LymPZoEOO71b8RlEsS2sOzSneB7rYYa60OZf1MNiMLz88lLMONxsdTcBn2FMqg\nitWJ4k9mcfVcDk26+VG7qXhvCAJUMjBfsGABixYtwtPTk8GDB7Ny5cq/bN+jRw9UKhXDhw9n0aJF\nTJs2jfDwcDZt2gRAXl4eWq223KjS4MGDKS4uZsSIEYwfP56FCxeKZSx3UXGJjbd3xhJxvZBZvRrS\n7A8VWABczn+KU3YUxV3mI6k9AdicsIGuGy4gV6mpPmUeZoON879co1odLUFtq/8TtyEId8xic/Dp\nsWRGfHOWmMxipjwSxGfDWxBYTVO+oSShPTgDyVmHocM0ALJNWXy5522GHZZQdOqMc49e5bpkJxUT\nvec6tZt50aSbmP0THhwX04t4fkMEGcUlrHiyGU829yt33CXy89KiAK1GY248rNyxNTErySq6zhu7\nlchr1sT1hZcrnD8rsYjzv1zDr5EHId39KhwXhH+rSke+/11m4uXlhUaj+cu2crmcefPmlftd/fr1\ny/7t5eXFtm3byh1XqVS8//77lb0c4TZJksTuy9ksO5BAgcnK653q0btx+aBaXnAVzamlmAN6Yan/\nOACxBTGkbljNo9dBO30Kcm9vIjYnYLM4aNO/nqguITyQTl/LZ/GeeK7lm+jVyIc3u9XHW3Pz5VjO\nsd+jSj9JcfclSGpPbA4bC86+zQs/FqF01eI+aXq5wQaLycaJLQloPJ1L3yNiS3HhAbEnNps5u2Kp\nplGxZmhohYdUVdIeNEfmYg7ohaH91HLHjmUeYUfKNmZfaYrT9fNo3/ugQs1yQ4GZ45sScKumJkxU\nJxKEcioVmLu7u/Pdd99hMpn4+eef0el0t+4k3HdS8k28uzeOk8kFNPbV8sGTTctqMpeRJNwOTEVS\nqNB3eQdkMkw2I2v3TmfSQQeysDCce/chLbaAlKg8Qh72w736zTeKEIT7VZ7RwgcHEvklJgt/DzUr\nBzWlfb0/n0qXmQvRHnsHq28rShoPB+CLKx9T/5fzBKY7cJs3DbnXjZ1t/7uu3Gyw8fArjUUFFuGB\nIEkSX51KYfWRJEL9dCzt3wRP1/IPqoqcS7jtfh2bdwhFPVaC7MbEe545j6VRC2lnrkvIzkuoHu6B\nqv1D5frbLHaObozH4ZDo+FQwTmrx3hCEP6pUYL5w4ULWrl2Lp6cn0dHRLFiw4G5fl1CFLDYH35xO\n4cuT13BSyJn0cH0GNfe7sVbwD9Qx36FKPUpxt8U4tKVT7yujlzHw+zSUKjUeU2Zhszg4uz0JXXUX\nGnUW0/PCg8MhSWyLyuCjw1cxWuyMbF+HF9vVRu3018GB5uQSZCV56PutB5mc45lHOX58PYuPSqge\n7oFz90fLtb96Nofrl/IJ7eWPV62/nmEUhPuBxeZg4Z44fr6YSa9GPszq1RBnZfnVrjJTLu4/v4ik\n0lL0+JdllbqgNKhfGrUIg9XAm3t9kTmr0I4ZX66/JEmc2ZZEQYaRTk8H4+atRhCE8ioVmM+ePVss\nM3lAnU0pYNFvcSTnm3i0gQ8Tugfio715kqbckInm6HwsfmGUNHkKgP1pe5C276BJioR26lsofKpz\nYUcypmIrHYYFoVCKerPCgyE+28CiPXFEphXRyt+daY8GU6/aratAKLOjUEevo6Tpc9h8mpJhSue9\niLnM+UWJUqdBO2FSufZF2SYidl7Dt76Ohg/VuFu3IwhVpsBkZfK2i0SkFvGfh+rycvs6FZde2a3o\ndr2K3JRDwZM/lA3c/NfPKds4kXWUuXk9UF74BdeJpUse/+jKsUyuRebR9NFa+DX0QBCEiioVmFss\nFi5fvkxAQEDZm1WlEmXx7mf5RgsrDiby86Us/NzVrHiyKQ8F/HXWu/bwLGR2M/ru74FMToYxnXWH\nF/HOQVC2a49zn77kJBcTfyqL4LDqeNcRZd+E+5/Jauez48lsOJuKVqVgdu8GPN7Et3JrviUH2oPT\nkdTVMIRNwuqwMj/ibR4/ZMIv3Yx20QLk7jcCDLvNwYnNiSid5LQbFCDWzgr3vaQ8IxN+jCaz2Mw7\nfRrRq/HNE/k1R+ehSjtB0aMfYKvevNyxFP01Vsd8SEd1C5p8dxxFSFPUTwws1yYjvpDIX1PwD/Gk\ncRcx0yoIf6ZSgXlSUhKjR48u+1kmk7F37967dlHC7XNIEuHRGaw8dBWDxc6LYbUZGVbnllP1qsRf\ncE7Yib79VOwegdgdNhZEzObFcCNOSmfcJs/AYS+dhnTVqWj6qP89uiNBuH2HE3J5b1886UVmnmjq\ny5gugXi4VH4DE/Wlb3HKjKDo0RVIzu58cmkF5phonjgq4dy7D86dupRrH/Xb9dJp+meCcXETgxfC\n/e3MtQImb7+EUi5j9ZBQmtdyv2k755hNuEZ9ibH5K5gbDi53zOawsejCPJzkTow97omkL0Y7aXq5\nmuX6vBJObE5A5+NC24EBIhFaEP5CpQLz8PBwAHJzc/Hw8EChEMka96OEHAOL98RxPrWIlrV0TO0R\nXLHk203IzIVoD87E6h2CqcWrAKyL/wq//ZE0TnagnTweha8v0ftSKcouofOzYjtx4f6WWWzm/f0J\n7I/LIaCaK58Ma05L/5sHHX9GZspDc3wRFr/2mBs8yeGMg2yP/47Vv2pQVFOjGTuxXPv0uEKuHMsk\nKKy6mKYX7nvbotJZtCeeOp4uLB8YQi33myfxKzPO4XZgGhb/ThgemlHh+Pr4r7hceInFziOR7foE\nl6efR1k/qOz4f5M9JQk6Ph0kvjsE4RYqFZifPHmS6dOn4+bmRlFREfPnz6djx453+9qESiqx2vns\nxDXWn7mOVqVgVq8G9Aup5FQ9oDm2ALkpuzSZR+FEVN4Ffj3zJe8fkOHUNgznvv0pzDRx+VA6dUK9\nqNlABB3C/cnmkNhyPo21R5KwSxKjO9XjmTb+OCn+fi6E5sQiZFY9+i4LSDOlsSRyAaNOeeKenoN2\n6XzkbjcqGpXorZz6PhH36i4071W7Km9JEKqUQ5JYdfgq35y+Tvu6nizq1xit881DAbkhE92uV3Bo\nfCnqtQbk5dtdzI9ifcLX9Kreg+DlvyLV9MP1hZfKjkuSxKkfrlKUZaLzsw3QeolkT0G4lUoF5h98\n8AEbN27E19eXzMxM3njjDRGY3yeOJuaxZG8caUVm+ob4Mq5LIB6ulZ+qd0o9hsuljRhbjsJWPZRi\naxELImYzZrcClVyJdsoMJAlO/3QVpbOCFn3q3MW7EYTbF5NZzMLdcVzO0tOhnieTHwnC3+P2Snkq\nM87iculbjC1exeRRj7nHRxF03U6nw0U49xuAKuzGLoeSozT4sJnttH+xEQonkRAt3J9MVjtv77zM\ngfhcBjWvyVsPB6H8szwIuxndrv8gNxeRP2hb2UZzZeeyGVl0YR4+ah9eveCL7dov6JauQKa+EXxf\nPpzB9Yv5hPb0p0bw35uxEoR/q0oF5gqFAl9fXwB8fX1xdhZbr//TsorNLDuQwN4rOQR4ubJ2aCit\na//NkWybCe3+ydh1dTG0nYgkSSyPfo/mJ7NomGBD89ZUFL41iDuRSd51A2GDAlBrKh/0C8K9Epup\n5z/fXUDrrGRR38Y80sD7ttexyix63PZPxq6pgaHtBNbErCQ59zJf7PJEUV2L5o1x5dpfOZFJRlwh\nrfrVxd1X1PQX7k/ZejMTfrxIbJaeCd3rM7yl35+/RyQJ7aGZOGWcpbDXWuzeTSo0WRWzgnRjGh/W\nnoVt/nxUj/Qs98CaHldI1J7r1G7qRcNOojqRIFRWpQJzrVbLunXraNu2LadPn8bdXTz5/lPs/52q\nP5qEzSHxWsd6PNv2dqfq30NZmERB/03g5MKulB1EXd7Dyv1ynFq1Qf3EQAwFZqJ+u06NIB11mle7\n9UkF4R7LMViYuO0iOrWSr59p9ac7d1bK7yXhFPnxFPb9hv05x9l27QcWRDRAlR6D9oNVyF1v5G3k\npxmI2n0dv0Ye1G/rUwV3IwhVLzZTz4Sfoik223h/QAid6//1Z7n64vrSGaNWb2AJ6lvh+NHMQ+xM\nCWd4wNP4r92GzVmNdsybZceLc0uTPT18XWg7UOx6Kwh/R6Wiuffee4+0tDSWL19Oeno6CxcuvNvX\nJdxEjsHCfzZd4P39CTTz0/Hd860Z2b7O3w/KJQnXk0txvfAJpqbPY/XvyDV9Misvvs+kPRqUstIl\nLADnwpMBaC22FBfuQ2abg8nbLlJosrJsQNM7C8olCbf9k1ClHKS4+xISvQJYGrWYPnn1CN4bg/rJ\nIahaty1rbrPYObElEWdXJW0HiPeHcH+KySzm1c0XAPhseItbBuVOaSfRHp6FuU53DGGTKhzPM+ey\nNGoxQbpgnr7qj/XcWTSvvYG8WmnNcqvZztENccjkMjo+FYxSJZI9BeHvqFREl5+fT0hICB9//DFy\nuZzi4uK7fV3C/4jL1vPihgiuZOmZ16chHz7Z9PbWz0oSmqPz0Jz5AFPj4eg7z8PqsLLg/By6R0Lg\nlSI0r41B4VeLa5F5pF8ppOmjtdB4iOVLwv1FkiQW/XaFqPRi5jzWkIa+d1ZX3/XkEtSxWzGETaKo\nwQDmRsxEa1Py/LZi5LX80Yx6o1z7iJ3XKM4tIWxwIM5iiZdwH0rKNTL2+2h0aiVfPNWSBtX/+j0i\nL05Dt+tV7G61Ke75EcjLB9UOycGSyIWYbEam15tAyeqPUDYNxblvf+D3fIvvEynOKaHD0PpoPMX3\nhiD8XZUKzCdPnoy/f2nd6q5duzJjRsWSScLdczghl5e/vYBdkvh0eHMea1z5iivlSA60B6bieuFT\njKEj0XdfAnIFn8d+TO71yzy3x45Ty9ao+z+J2WDl/M5rePlrCArzrfqbEoQ7tP7MdX6+lMV/qEZ4\nUwAAIABJREFUHqrLIw3ubBmJOvobNGdXYmryNMbWY1l5aRmJxfEsPNcYWWYWbtPfRuZy40E4JTqP\nq2dzaNS5JtUDdXd6K4JQ5TKKSnh9ayRyGawaHIqv2y2CZJsJ3S8vg62Eoj5fIDlXXLL6TdwXnMo+\nzqhGY6j2zTYkvR7tpGllNctjDqWTGlNAaO/a+NYX7wtBuB2VXgPRokULANq2bYvD4bhrFyTcIEkS\nG89eZ+JPF6nr5cJXT7Wkka/brTvejMOG2543cbm0AUPrMRg6zQWZnDPZp9icuIFZB3xQSKCdMgOZ\nXM75X1KwlNhp078ecrF7oXCfOZyQy8pDV3m0gQ8vt7+zSkGqxF1oD83EXK8H+q4L2JW6k50p4Ywr\neQT33UdRDx2BU2iLsvaGAjNntiXh5a+h6cN+d3orglDl8owWXt8ahdFqZ+WgZtT2vMXsqiThdmAq\nTtmRFD+6ArtXcIUmh9L38038F/T2f5w+uXUw/7IDlxHPogysD0Da5QKi96VSt3k1GnQQgzmCcLsq\nlfyp0+nYtGkTLVq0IDIyEo3m1pvWCHfGZnewZF88P0Zm0D3Ym3mPNbzl7p1/ym5Gt/t1nBN3YQib\ngrHNGAAKzPksjpzPk3He1LqYgWbcRBS1/MmIKyT5Qi5NuvnhUcO1Cu9KEO5cfI6BmT9fpmF1LbN7\nN7ijtd3K9DPodr+OrXpzinquJkF/lQ+i3yPMNZROn0Ugr1sPzSujyto7HBIntyYiOSTaDwlEfhtJ\n14JwN+nNNsZ+H01msZlVg5vdcvkKgEvk56hjv8fQbiKWwF4VjicUxbM4cj5NPEIYFzQWw8svIver\nhesLIwEoyjZxcmsinjVdRT6SINyhSgXmixcvZs2aNfz2228EBQWJ5M+7rKjEypTwGM5cK+CFdrV5\nrVM95Lf7QWc14b7rFVTXDqDvNBdT89LNH6wOKwsvzEWZW8iwXXKUzVugfnIIVrOdM9uTcPNW07hr\nzSq8K0G4cwVGKxN/uoiLSsHSASG3/7AKKPITcP/5BexaPwof/wo9duacm4Gbk46JR6oh5Z5HO/8z\nZM436jLHHEwnJ1lPu0EBYrMU4b5TYrUz4cdoEnIMvD8ghOa1bl1BzSnlCJqj8zEH9MLYZlyF44WW\nAmadnYJGqWVuq0XYNn6LPeUauvc/ROasxlJi4+jGeORKGQ+NCEIp6vgLwh2pVGDu5eXF2LFjkclk\n7NmzB4VCZFnfLdfyTYz/MZq0whLm9G7I4yG3PyUos+jR/fwCTmknKe6+lJImwwGwO2y8EzGbM9kn\n+fhIIDLbNdymzEQmlxO99xrGAgvdX26EQik+YIX7h9XuYEr4JXL0Zj4e1vzWa2b/gtyQiXv4MyBX\nUthvPQ61F++em0a6KZ2PcwfBrxtxeX4kTiFNy/rkXCvm0oFU6oR6Ua+Fd1XckiBUGZvdwbQdMZxP\nLeKdxxvxUIDXLfvIi66h2/0ado/6FD+6AmTlP/NtDhtzI2aSa87lg/ar0V1JpfCbL3Hu0QtVu/al\nyZ5br6LPK6HrCw1FkQBBqAKVCszHjx9Pt27diIiIwOFw8Ntvv7Fq1aq7fW3/OmdTCpi8/RIyYPWQ\nUFr63369eFlJAe7hz6DMjqK450eYg0uz5u2SnUUX5nM48wBz8nvgGbELzRtvoqhdh9wUPXEnMqnf\nzgefure5ll0Q7gJJkliyN55z1wuZ16chTWvefmKZzKJHt+N55KZcCgZuxeFel82JGzmaeYjp9v64\nf7YJVcfOuL74SlkfS4mNE1sScXV3pnW/elVwR4JQdRySxJxdsRxJzGPao0H0bFT91p2sRtx3vgSS\ng8I+nyOpKi55WR3zIedzzzE1dBYNbdXJn/Us8pp+aMZPBuDi/jTSYgto+XgdqgeIZE9BqAqVGhLN\nysqif//+JCQkMG/ePAwGw92+rn+d7VEZvL41imquKr56uuWdBeXGHDx+Gooy5xJFvT8pC8odkoOl\nkYvYl/4bb8n70+TrAyibhqIePAy7zcGZbUm4uDkR2qN2Vd2WIFSJzRFp/BSVwQvtavNY4ztILLNb\n0O16FWVuDIW9P8ZWPZQLeRF8GruGfvJ2tFy9G0Xdemjfnofs95lBSZI4uz0ZU5GF9kMCcVKLGUPh\n/iFJEkv3JfDr5Wxe71SPJ5tXIiFZknDbNxFFXixFPT7C4RFQocnPKdv5KXkrQwKG08P3UYrenoZk\nNKBbsAS5mxvXL+Vz6UAa9Vp6ExRWiQcBQRAqpVIj5larld27dxMUFEReXp4IzKuQ3SGx6vBV1p25\nTlhdDxb1bYKbulL/LTcl16fhvm0ECn0qhX2/wlq7C1D64b0ieim/pu5kjKIvYct2Ifepjm7Bu8gU\nCmIPpFGYaaLjU0Ei8BDuKyeS8lh2IIGu9avxWqd6t3+iP2wgVPTwMqx1u5NnzmV+xNsEymrw3Dcp\noFCgW7S03O6eyedzSYnKo+mjtahW+85qpQtCVfv4WDJbzqfxbBt/nm9XuUEVl4jVqOPD0XeYjrVu\n9wrHo/OjWBG9lDbe7fhPw9EYVizHFnUBtzkLUAbWpzDLxKnvE/GqpaF1v7oi2VMQqlClIsCXX36Z\nnTt3MnXqVNatW8fo0aPv9nX9Kxgtdt7eeZmDCbkMbl6TiQ8HobyD0oTywmQ8tg1HVpJPYb8NWP3C\ngNKgfFXMCsJTfuJVp8fpsnwfMnd3dB+sQu5VjaJsE5cOpFG7qSe1GntW1e0Jwh1LyjMybUcMgdU0\nzO3T8PaToAHNiXdLK0+ETcLceCh2h435EW9jMhcz71dfpPRLuH+wCoVfrbI+xbklnNuRjE+AG406\ni2Ro4f6y8ex1Pj9xjf5NazCmS0ClAmRV8j40xxdTEtwfU8vXKhzPMmUy++w0fF1qMKvlPKy7f6Xk\nhy24DH8a50d6YDHZOLohDqVKzkMjglCIZE9BqFKVCsx79uxJz549ARg37kbW9uzZs5k7d+7dubL/\n5zKLzUz4MZr4HANvda/PsFa1bt3pLyjy43HfNhyZrYTCAZuwVW8OlAbln8au5oekzbzg3JtHPziE\nzEWN+4rVKKr7IjkkzmxLQqmS0/LxulVxa4JQJYpKSiuwKOVy3h8QgkZ1+zNJ6qivcT33EaaQZzC2\nHgvA51c+4UJeBB9FtEZ57iTaKTNwat6yrI/d5uDE5gTkChlhgwJFPX/hvhIencHyA4k80sCbaT2C\nKxWUKwoScdv9BjbvJhR3Xwr/08dsN/P22WmYHSW833olLlfTKXhvMU4tW+P66utIv5cLNRRY6Day\nIa7uqrt1e4Lwr3X733TA1atXq+o6/lUuZhTz1k8XMVntLBvYlI6VyJ7/K4rsi3iEPwXIKRi4BXu1\nxmXHvo77nO8SN/CUugePf3gC5HLcP1iNombpOsSEM9nkJOtpOzAAtVZsKy7cH2wOiek7YkgrLGH1\nkFD83G+/NGG5DYS6vAMyGUczD/Fd4nomJIdSfddJ1ENHoP59W/H/it6bSn6akYdGBIkARLivHIjL\n4Z3dVwir68G8xxqhqMRDo8yiR7fzJZArKHrsM3Aqv+mQJEm8H7WIuKJY5rd+lzoOTwpmPo/cwx23\nuQuQKZVcPpJO+pVCWvatIwoECMJdckeBufD37YnNZs6uWKq5OrFycAuCvO9ssyZlxjncdzyL5KSh\nsP932D0Cy45tiP+ab+K/YJBrd578KALJZsV95VoUtUt3SjQWWYjcnUL1QB31Wla7o+sQhKr0wYEE\nTiYXMLNn8B0lQpdtIOTbgqKeq0GuJNVwncUX3qFXjj/tN13AKawDmtFjy/XLTCgk9kgGgW198G8i\nlncJ949TyflM/zmGkBpuLHkiBFVlytpKDtz2jENRkEjhExtx6CquRd989Vv2pO1mZIP/0MG7A0WT\n3sSRk4P7R58g9/Qi97qeqN9S8W/iSVA7kewpCHeLCMzvEUmS+PJkCmuOJhHqp+O9/k3wcr2zUTin\n1GPofn4RycWbgv6bcOj8y45tSfyWz698TD9NF0asuYRk0OO+Yg3KgNLtkyWHxNltSUgOaNNfJO8I\n948fItPZFJHGiFa16N/s9td1K/Lj/7CB0Nfg5ILZbmbOuRn45jsY+V0uCv86uM1ZUFaBBUrXlZ/Y\nkojOR02L3qJCkXD/uJhexFvbLlLH04XlA5viqqpcor7r6eU4X/0Vfae5WP07Vjh+KvsEn15eTdca\nD/N0/ecxfroG6+mTaCdPx6lJCNYSOyc2J+Li5kSbAWJnT0G4m0Rgfg9YbA7e2X2FX2Ky6N24OjN7\nNsD5DjfvcUrej/svL2PX1aWw/0Ycmhplx35K+p41l1fSS/MQz3+aiJSXj275SpQNGpa1id6bWjYl\nKXYwFO4XZ1MKWLI3nvb1PBnbNfDWHf7E/24gJLmULhf78OL7pOVc4dOfvJHLTOgWL0WuvVFpxVhk\n4eBXsSBRuothJQMfQbjbEnMNjPshGi9XFR8Naoa7SyWWHkoSrqeWojmzgpJGQzGFjqzQJEV/jfkR\nbxPgVp/JoTOwHD6Aad1XOPcbgLrfgN/LhSZhLDTTfWQjVC4ibBCEu+mO3mGSJN309w6Hgzlz5hAb\nG4tKpeKdd96hbt3SxMLs7GwmTJhQ1jYmJoaJEycybNiwP+3zoDJZ7fwak8V3Eakk5BgZ1bEuI8Pq\n3PFogyphZ+n0vFdDCp/YWBZ0AOxMCefDS+/TTRPGK1+l4chIx/29FTiFNCtrk3whl5hD6QS28RFT\nksJ9I7XQxJTtl/B3V7Pw8ca3XaFIZilGt+M55Ka8sg2EAH5J2cGua+F8+JsfqvQ03JatROF/Y0Tc\nbLRx6OsrWIw2uo1shM7H5c9eQhDuqbTCEt7YGoWTQs5Hg5vhra3EDpsOG9qD03G5tBFT42Hou71b\nIdlTb9Uz6+wUlHIl81svxik1k8J35qJsHIL2zbcASIrI4VpUHk0fqYW3WFcuCHfdXw7b2u12LBYL\nb7zxBlarFYvFgtls5rnnngPgiy++uGm/PXv2YLFY2LRpExMnTmTx4sVlx3x8fFi3bh3r1q1jwoQJ\nNGnShKFDh/5lnwdNfI6BJXvjeWztCRb8FockwZInmvBS+ztfMuIc+wO6X1/DVj2UwgGbygXlv6Xu\n4v2oxXRya8Mb6/NwJCejW/geTi1blbXJva7n9E9X8annRsvH7/whQRCqgt5sY8KPF5GAZQOb3n4t\n/7INhC6XbSAEEF90hRUXlzLxRHV8o1LQjJ+EqlWbsm42i50j66+gzy2h09PBeNW6s9wPQagqOQYL\nb2yNxGxzsHJwM/w9KvHAaDOh2/UqLpc2Ymg9Fn33pSAv/56yS3YWnp9DqvE6c1ouoLrkRvGMyeDs\njNv8xchUKoqyTJzbcY3qAW406iLKhQrCvfCX337ff/89a9euJScnh969eyNJEnK5nDZtSr/QnJxu\nPpV29uxZOnfuDECLFi2Ijo6u0EaSJObPn8/SpUtRKBSV6nM/s9gc7I3L5ocL6ZxPLcJJIeORBj4M\nbl6TUD9dlQTATtcO4rZ3PFa/MAr7fAmqG8HDgfR9vHvhHdpoQxn/rRF73BXcFixB1a59WRtjkYWj\nG+Nx0Trx0PD6KO5wOY0gVAW7Q2LWzssk5xlZMagZdTxvc6S6bAOhQ2UbCAHorcXMPjednhedaHcw\nHfWTQ3Dp/+SN17c5OPptPHnXDXQYHkT1QLG1uHB/KC6xMfb7KLL1FlYPCa1UsQBZSQHuO0eiTD9N\ncef5lIS+eNN2X175lBPZxxgX8hahXi0onjkF+/UUdMtWovD1xW51cHxzAkonOWGDRblQQbhX/jIw\nHzp0KEOHDmXr1q0MHjy40ifV6/Vo/7BuU6FQYLPZUCpvvNy+ffsIDg4mMDCw0n3uRyn5Jn6MTCf8\nYiYFJiv+HmrGdgmgX0gNPFyrrvygIvcyul2vYvdqQFGfL8oF5UczD7Hg/GxCtSFM+R7sF6Nxm/MO\nzh07l7WxWR0c3RiHzWyn638a46wRpRGF+8PqI0kcScxj0sNBhNW9zQookgPNsQW/byA0GXPjoaW/\nliTejXwHzyvpPLvDgVObdmjGjC/r5nBInPr+KpnxRbQZUE9UYBHuGyarnTd/jOZqrpEPBjalmd+t\nHxjl+jTcw59FUXCV4p6rMQf3u2m7/Wl72JjwDX1r9+eJOgMxbfgGy6EDaF4fVzaTdOHXFAozTXR6\nJhgXnSgXKgj3SqWi3qZNmxIREYFcLmfZsmWMGjWKDh06/Gl7rVaLwWAo+9nhcFQIsLdv3162JKay\nfe4XNofE4YRcfriQzonkfBQy6BLkzaDQmrSt63FHuxPejMyQhfuO50tLIj7+NZLqxgPMyazjzD03\nk0auwcwMd8YRcRLt9Nk4d3+0rI0kSZz+8Sr5aUY6jgjC3de1Sq9PEG6HJElsi8rgm9MpDGpekyEt\nbm+qXG7IxG3Pm6iuH8bU9HmMrceUHduUuIErsYdY/pMKRQ1v3OYtRPb754okSUTsSCYlOo/QXv4E\ntvapkvsShMqSJAm92U62wUy23kKO3kK23kyOwUJkWhGxWXoW9m1MWL1bPzAq8uJwD38ambmIwn7r\nblp9BSCuMJYlkQto5tmcMSETsJ4+ifHTNage7oF62FMApMbkE38yi+AOvvg19KjSexYE4a9VKvKd\nM2cOs2bNYuXKlYwfP5733nvvLwPzVq1asX//fvr06cP58+dp0KBBhTbR0dG0atXqb/X5p2UWm9kW\nlc5PURlk6y1U16p49aG69G9WA5/KJOPcDqsJ950vIi/Jo2Dg9zjc/MoOncs5w+xz0wh0DWDurx44\nThxBO2ka6t59yp0i5mA6KVF5NOvhT63GYkRQ+GcVGK3suJTJtqh0kvJMtKntzlvd69/Wci9V8j7c\n9o5HZjVQ3O1dSpo8VZbgdj73HOsj1/D+NhecJRm6d5chd7sx6hi9N5WE09k06lyDRp3E+lmhahks\nNrL1FnINFrL/EHCXBuBmsn//t9nmqNBX66zAR+vM7N4NeaTBrR8YlRlncd/xPMidKBy4FZtP05u2\nyzfnMevsVNxVHsxutQB5ZjYFc2eiqBeA29SZyGQyjIUWTv94FY+aroT29L/peQRBuHsqFZirVCqC\ng4OxWq20aNECufyv1yb36NGDo0ePMnz4cCRJYuHChYSHh2M0Ghk2bBh5eXlotdpyX8Q363M/cEgS\nJ5Pz+f58OocTc5Ek6BDgyZRHgukY6HXblSMqRXKg2zMGZVYkRX0+L0tkA4jMO8/Ms5Op7eLPgv01\ncBzej2bsBNRPDCx3iuuX8onem0rd5tVo1LnG/76CINwTDknizLUCforKYH9cDjaHRLOaOmb1qk2v\nRtVRKv5mvoPdgub4YlwvfIKtWiOKem7B7nXjYT63JId3zs3irZ1OVMsw4rZ0Bco6N6o8XTmWQczB\ndAJae9Oshwg+hDuXVWxm+YFErmTrydFbMFrtFdqolXKquznjrVERUsMNb40zPloVPloV3loVPhpn\nvLUqXJwqX6ZTlbQH3a+jsGtqUNhvQ1kVov9ldViZc24GBZZ8PuzwMZ64UjBjHDgc6BYsQebigsMh\ncXJrIg67RIehIg9JEP4JlQrMZTIZkydPpkuXLuzcufNPkz7/Sy6XM2/evHK/q1+/ftm/vby82LZt\n2y37/JPyjRa2R2fyQ2Q6aYUleLo48Wzb2gxoVqNyWfFVQHNsAc6Ju9B3moMloGfZ7y/lRzPt9Fv4\nqqqz6Ggg0t5duL76Oi5DhpfrX5Bu5OTWRLz8NbTpLzaFEO69HL2Z8IuZbIvKILWwBJ1ayeAWfvRv\nVuO2d72VF1xFt/t1nLIjMTV9Hn3HmaC88Z60OWzMi5jF43sKaHrZimbcRFRtw8qOJ0XkcP6XFGo1\n8aT1E+J9Idy5Qwm5zNsVi9nmoGOgFw8FeOGj+T3Y/kPArVEpqvTvzTlmE277J2PzaVq6zNHV+0/b\nfnTpA6LyLzCjxRyCdQ3QL5yLPS4W3bvLysqGxhxIIzupmHZPBuDmLfa3EIR/QqUC8+XLlxMVFUXX\nrl05ceIEy5Ytu9vX9Y/aFZPFu3vj0JvttPJ35/VO9ege7I3T3x3VuwPq6PW4nv8YU7PnMYW+VPb7\nK4WXmXJ6Ap4qD947FwK/hOPy/Eu4PvN8uf4leitHNsShclHQ8akgFE5i5EO4N+wOiRNJ+fwUlc7h\nhFzsErTyd2dUx9L30Z1sruUc+z3ag9NLNw567DMsgb0rtPksdi0ehyLod8yBuv9A1IOGlh1Lu1zA\n6Z+uUj1QR/shotKEcGfMNgcrDyWyKSKNBj4aFvRtTD2ve5DDI0m4nFuF9sRiLLW7UNT7k3K5R/9r\ne/KPhF/7kRGBz/KIX09MP27FvGsnriNfQfVQJwCyk4q5dCCNus2rUa/lnwf4giDcXZVeynLu3Dl2\n7dpF9+7dKSwsxMPj/19CSFGJlXf3xLM7NptmNXVM7xl826N6d8Lp2gG0h2ZgrtMdfae5ZWtmrxRe\nZtKpcWiVGpZFt4GffsBl+NO4vvSfcv3tNgfHvo3HbLTR/aVGuLiJjHrh7ssoKmF7dAbbozPJLDbj\n6eLEU6396d+sBnXvMFiRWfRoD81EHbsVS80winusLJdv8V+HMw4ScWQD83aBU8vWaN6cVDZCmZ1U\nzPFN8XjW1JQ+rIppeuEOJOUamf5zDHHZBoa3qsWYzgGo7sXflORAc3g2rlFfUhI8gOJHloHi5p/x\nNoeNL658wneJ6wnz6cDIhv/BGhWJYcX7OD3UCZfnSwd9zEYbJ7YkoPF0plW/B3tjP0F40FUqMJ8+\nfTpdunTh9OnTeHt7M2PGDNavX3+3r+2eOpWcz9xdseQarbzWsR7Ptat9d9eP/wlFbgy6XaOwezWg\nuNeask0hYgouMfnUm7g5ubE8riPyzd+hHjAI19Fjy02NSpLE2fBkcq7p6TC0vtgoRbirbHYHRxLz\n+Ckqg2NX8wAIq+vJ+G6BdKlfrUpmmZRZkbjtfh1FUTKGthMwthlbYbMUgOuGFD47PI85P8hQVvfF\nbf6isgos+WkGjqyPw9XDmc7PBuPkXPk1vILwR5IkER6dyXv74lE7KVg2IITO9avdmxe3m3Hb8ybq\n+HCMzf+DoeNMkN38PZZhSuediNlcKoimb+3+jG4yDlluPsWzpiKvURO3mXORyeVlVbvMBhsPv9JY\nvDcE4R9WqcC8oKCAwYMHs337dlq1aoXDUTGL/EFltjlYdfgq355Lpa6nC1+MCKFJjX9m22G5IRP3\nHS+UlkXse6Ms4sX8KKaenoDOSceyxC4o1m3AuU8/NOMnVViveOV4JknncmjSzY/azbxu9jKCcMeu\nF5jYFpVB+MVMcg0WfLQqXmxfhyea+lLLvYpyMCQJlwufoTm+EIerN4UDNmP1a3/TppmmDOYdn8S4\nzUa0DmfcFy9D7l46q1ecW8Khb67gpFbQ9YUGooa/cNv0ZhsLf4vjt9hs2tTxYN5jDe9eRa7/IbMU\no9v5MqrUo+gfmomp5ag/bXso4wBLIxch4WBWi3l093sUyWql8O1pOAx6PN7/ELlb6fdc/Kks0i4X\n0Pyx2mIgRxDuA5UuFJ6QkABARkYGCsX/jyfq2Cw9b++8TGKukSEt/BjbJQD138iGr1JWI7qdI0vL\nIj75Aw5t6TR9VN4Fpp6eiJ/kwcLd/siObMC5Ry+0k6cj+5/qOOlX/o+9+w5vsmofOP5NmqRNN52M\nDtpC2cgQUWQKiKCggAiyh/gTQURFUWTKEMTXASjIRkAUXlDxRUHBgUwZZY8WSgeF7pa2aZv5/P6o\nVpG2FOiivT/XxUWbPONOmpPcOc8590nn5I5Y/BpWo1Gnmy/zC3G3rueYmbUznN8upaBWQZsgD55q\nUqPEKxSpspNx+flV7KN/xhjUjcxH3kdxKLjU57n0M8z9/XWGb03HP9GG2/y5aILyFi7LzjDx25oL\noED7YaE4upVNEiUqn1NXM5iy/RwJmUZebFuboa38sSujq6p5a1kMQZN6gYwuH2GsV/CCfyarkSXn\nFvFtzFbquTVgavN3qOlYCwDDJx9jOXUCl+mz0YTUAfIKBJzYEUuNUDdCH/Itk8cihChasRLzKVOm\nMHnyZC5dusT48eOZMWNGKYdVuqw2hQ1HrrBkXxRuei0f92lMm6By7F22WXHdNf7PsoirsHg3AeBE\nShhvHZlI0zRXXvvagirhEI4vjkc/YNBNPeUZiTkc3BSJm68jD/QNQiWT2kQJi07N5tVvznAtI5fR\nDwXwZJMa+LqUfKKrjd2Ly67xqI3XyWw/h9zGQ/PnWfzbr9d289Om6czYYcE1R4XzhNfRPdgGyBs3\nu2dtOKZsCx1H1sfVu2yqKYl7i/vmx1G0ThgemIil5gM33W9TFD7/I5al+6LwcbFn2YBmNC3GKpwl\nRZ1+GffvBqHOTuJ6j9WYAzsVuF1MVjSzwqZxKTOCfkHP8ly9F9Cq864O5e74ntwtm3DoPxD7LnkV\nviwmKwc2XUKn19CqT5BUJxKigihWYh4XF8dXX32V//v3339Pw4YNSy2o0nT1ei4zdlwg7Mp1OtX1\nYnKXurg7lu+lbacDc/9RFrErkLd40NuHJ9LrrBP9tieidq+Gy8KlaJved9P+xmwLezdEoNaoeHhQ\nHTS6ynFFQ1Qcf0Sn8eZ357BTq/j06aY083Mr+ZNYzTj98R/0xz7BWq0OaT3XY/Uq+H1GURQ2hS3B\nYelaXjmnQEgI7m/PRFM3r5a52Wjl93XhZKXm0n5IqFyiF4XKaTIC5/1zqPZ1H0z+HTA88BqW6nmL\n3yVnGZn2wwUOx6TTJdSbyV3r4uJQditSaxJP4Pa/oaAopD+1CYtv8wK323nlez4+8x90ah1z71/A\ngz55q35ar10le8VnGH/akTcZ+oVx+fuEbY8hMyWXDsPr4SDDu4SoMIp8h/nll184duwY27dvJyws\nDACbzcbu3bvp0aNHUbtWOIqi8P3ZRBb8fBGA6Y+F8nhD33LvJXA4ve7PsojD88siHkmJtAh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jQ6nIDKZsO+c1f0A4eiqXN7ixmZciwc2HSJhIsZ1H3Il/u6+aO2k3kXVY0k5ve+YiXmvXr1IuvP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot the results of the cross validation grouped by max_features and faceted by number of trees\n", "plot_cross_validation_result(cv_rcf, 'param_n_estimators', 'param_max_features', 'param_max_depth',\n", " \"Random Forest Hyperparamter Cross Validation Results For Status Response Variable grouped by max_features\",\n", " \"results/rf_cv_results_status_max_features.png\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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57bXXvGGxO+64gxNOOOFHbWeWuNuBQqFQpOJgtDUKl2XLlrFr1y5v/u+0adMI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot the results of the cross validation grouped by max_depth and faceted by number of trees\n", "plot_cross_validation_result(cv_rcf, 'param_n_estimators', 'param_max_depth', 'param_max_features',\n", " \"Random Forest Hyperparamter Cross Validation Results For Status Response Variable grouped by max_depth\",\n", " \"results/rf_cv_results_status_max_depth.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I can now fit a random forest using the best combination of hyperparameters." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fit the best cross validation random forest\n", "best_rcf = cv_rcf.best_estimator_" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def var_imp_plot(model, train_preds, title, savefig, score = 'gini'):\n", " \"\"\"\n", " description: plot variable importance for randomforest and extratrees model\n", " inputs:\n", " model: sklearn randomforest or extratrees model\n", " train_preds: list of training predictor column names\n", " title: title of plot\n", " savefig: savefig name\n", " score: (option) String, evaluation parameter, deafult set to gini, if not default supply own vector of variable importance\n", " \n", " output:\n", " variable importance plot\n", " \"\"\"\n", " if score == 'gini':\n", " imps = model.feature_importances_\n", " else:\n", " imps = score\n", " x = range(train_preds.shape[1])\n", " feature_names = train_preds.columns\n", " \n", " f, ax = plt.subplots()\n", " plt.bar(x, imps, color = 'b', align = 'center')\n", " plt.xticks(x, feature_names, rotation='vertical')\n", " plt.xlim(-1, train_preds.shape[1])\n", " plt.ylabel(\"Importance\")\n", " plt.xlabel(\"Variable\")\n", " plt.title(title)\n", " plt.savefig(savefig) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I can look at the feature importance of each variable. This is the variable that best splits the data into the status categories." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ginivariable
00.059958category_code
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30.167173num_relationships
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60.075504logo_width
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\n", "
" ], "text/plain": [ " gini variable\n", "0 0.059958 category_code\n", "1 0.047761 had_funding\n", "2 0.061400 num_investment\n", "3 0.167173 num_relationships\n", "4 0.080897 num_milestones\n", "5 0.079395 logo_height\n", "6 0.075504 logo_width\n", "7 0.057165 region\n", "8 0.045197 degree_type\n", "9 0.069924 institution\n", "10 0.060716 subject\n", "11 0.059804 birthplace\n", "12 0.070913 first_name\n", "13 0.064193 last_name" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#create data frame of variable importance\n", "d = {'variable': train_preds.columns.values, 'gini': best_rcf.feature_importances_}\n", "feature_imp = pd.DataFrame(d)\n", "feature_imp" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "image/png": 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RH7DRo0fr22+/lSTt3LlT48eP/20qAwAA+B2qUQDbv3+/4uLiJEnDhg1Tbm6u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\n/PFHuueQqC0oKAgxMTG4cOECTExM8O2337KORAhpgAaxqqG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "title = \"Variable Importance of Tuned Random Forest for Status Response\"\n", "savefig = \"results/variable_importance_status_random_forest.png\"\n", "var_imp_plot(best_rcf, train_preds, title, savefig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems num_relationships best splits our data into the status categories." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I am going to move on to testing. Recall before I mentioned how the issue of class imbalance may effect our learning model. While we were able to find out number of relationships split the data well the status category, we still don't know how well that model predicts unseen data? First let's look at the accuracy of our model." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.82340425531914896" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#look at accuracy of the data\n", "test_status_score = best_rcf.score(test_preds, test_status)\n", "test_status_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's not horrible. However, I mentioned before the issue when attempting to classify on a data set with highly unbalanced accuracy is that a model can return \"accurate\" results by simply classifying every observation as the class with the overwhelmingly high frequency. Below I will show the confusion matrix of the predictions our model made. In a confusion matrix, the number in the ith row and jth column is the number of individuals we predicted to be in the \"ith\" class, but is really in the \"jth\" class, based on some ordering. I provide the ordering in a list before the confusion matrix is printed." ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['acquired', 'closed', 'operating', 'ipo']\n", "[[ 4 0 31 0]\n", " [ 3 2 9 0]\n", " [ 19 6 377 5]\n", " [ 2 0 8 4]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "#class predictions\n", "preds_status_forest = best_rcf.predict(test_preds)\n", "#confusion matrix\n", "labs = list(set(test_status))\n", "print(labs)\n", "cf = confusion_matrix(y_true = test_status, y_pred = preds_status_forest, labels = labs)\n", "print(cf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before I said I would show what happened with the best results based on accuracy. Accuracy and tree depth are highly correlated, and deep trees can provide very accurate results, but overfit. You can see that tree depth is almost maximum for our best random forest model here based on f1_score, because f1 score is still reliant on us getting many accurate \"operating\" classifications. However, if I had kept the score on accuracy in the cross validation process, I would have gotten a model with maximum tree depth. Let's look at what happens in that case, by keeping all other parameters constant." ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.86808510638297876" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rcf_max_depth = RandomForestClassifier(bootstrap=True, class_weight='balanced',\n", " criterion='gini', max_depth=None, max_features=2,\n", " max_leaf_nodes=None, min_impurity_split=1e-07,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", " oob_score=10, random_state=100, verbose=0, warm_start=False)\n", "rcf_max_depth.fit(train_preds, train_status)\n", "rcf_max_depth.score(test_preds, test_status)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['acquired', 'closed', 'operating', 'ipo']\n", "[[ 0 0 35 0]\n", " [ 0 0 14 0]\n", " [ 0 0 407 0]\n", " [ 0 0 13 1]]\n" ] } ], "source": [ "#class predictions\n", "preds_max = rcf_max_depth.predict(test_preds)\n", "#confusion matrix\n", "labs = list(set(test_status))\n", "print(labs)\n", "cf = confusion_matrix(y_true = test_status, y_pred = preds_max, labels = labs)\n", "print(cf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar results would have occured if we had set max_depth to none. As you can see, the model did not do very well. Very few cases in the test sample were predicted to be not 'operating', showing the model really is not predicting anything, this shows the issue with model accuracy. You can see that the model based on the weighted f1 scores really does not perform much better, and is still overpredicting operating. I could have based the scoring criteria only on precision, but then model accuracy would suffer.\n", "\n", "As a side note, I struggled with what you see above for a long time. In r, dealing with these kind of datasets tends to be a little easier, and when searching for ways to solve the issue of overpredicting frequent classes I read some pretty heated comments on stack overflow by people critizing sklearn for people behind.\n", "\n", "One way to make this problem a little easier to to switch to a two variable case, and base the cross validation scoring statistic to AUC. Using sklearn, I can do is switch to binary variables and fit my models hyper-parameters using the sklearn's roc_auc_score to measure test performance via auc. A receiver operating characteristic curve (ROC curve) is a good way to measure predictive performance of a two class response variable. AUC, short for area under the curve, provides a performance metric for a predictive model by calculating the area under the ROC curve." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Closed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, I realized that sklearn prefers binary 1 or 0 encoding for categorical responses in order to find the ROC curve and AUC so I am going to switch the encoding. Then I will run the sam process as before, but this time on the binary variable indicating whether a business was closed or not." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#change encoding\n", "train_closed_binary = [1 if v == 'Yes' else 0 for v in train_closed]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: the below cell may take a while" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise',\n", " estimator=RandomForestClassifier(bootstrap=True, class_weight='balanced',\n", " criterion='gini', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_impurity_split=1e-07,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", " oob_score=False, random_state=100, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'max_features': array([2, 3, 4, 5, 6]), 'max_depth': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), 'n_estimators': [50, 100, 150, 200, 250]},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='roc_auc', verbose=0)" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#make closed model\n", "#params = {'max_features': num_preds, 'n_estimators': n_ests}\n", "params = {'max_features': num_preds, 'max_depth': m_depth, 'n_estimators': n_ests}\n", "\n", "rcf_closed = RandomForestClassifier(random_state = 100, class_weight = 'balanced')\n", "\n", "cv_rcf_closed = GridSearchCV(rcf_closed, params, cv = 5, scoring = 'roc_auc',\n", " return_train_score = False)\n", "\n", "#cv_rcf.fit(train_preds, train_closed)\n", "cv_rcf_closed.fit(train_preds, train_closed_binary)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "image/png": 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5c6ZI6ySVXCU9HgBMJhP/+9//2L9/v+W93OIhISGBkSNHMmjQICZMmEBGRkaR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\n9/LftLd4JXEAia0aQUK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot grouped by max_features\n", "plot_cross_validation_result(cv_rcf_closed, 'param_n_estimators', 'param_max_features',\n", " 'param_max_depth',\n", " \"Random Forest Closed Response Hyperparamter Cross Validation Results grouped by max_features\",\n", " 'results/rf_closed_cv_results_max_features.png')" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "image/png": 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NixYtYtiwYXeyW0jI3HgQcyMpKYn09HReeukl0tPTGTFiBO3bt5e5Id2e+1zs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\nNVXV9xMIfIfT8RPS02/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QnIKhyInm7P2emY6Odt59959UV1dy9NGjOPnkU+Oa4zYSaWHj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\nmu0//elP3HvvvSxYsIATTjiB5cuXJ7pdA5o7HOGeGjc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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot grouped by max_depth\n", "plot_cross_validation_result(cv_rcf_closed, 'param_n_estimators','param_max_depth',\n", " 'param_max_features', \n", " \"Random Forest Hyperparamter Closed Response Cross Validation Results grouped by max_depth\",\n", " 'results/rf_closed_cv_results_max_depth.png')" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fit the best model based on the best hyperparameters\n", "best_rcf_closed = cv_rcf_closed.best_estimator_" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "image/png": 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7008/KSgoSMYYLV68WGXLlpVUMHi88AOjkNPpvKhep9PpqvVaFNZWKDc3t8j6Sz2Owv2+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\nLsCsra2xadMmbNu2DYGBgcy3Zd6FZb9Vc9rCG25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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "title = \"Variable Importance of Tuned Random Forest for Two Category Response\"\n", "savefig = \"results/random_forest_two_class_variable_importance.png\"\n", "var_imp_plot(best_rcf_closed, train_preds, title, savefig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it seems the feature that is the most important is had_funding, followed by category_code. The number of investments is suprisingly low, however last name of founder is also very low in importance for determining the status of a company, which is expected. One other thing I notice, school subject of a founder is more important than the institution of a founder, which is more important than the degree type." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can look at the test performance of our new random forest model. Let's start with accuracy." ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7191489361702128" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#encode test response variibale\n", "test_closed_binary = [1 if v == 'Yes' else 0 for v in test_closed]\n", "#look at test score\n", "best_rcf_closed.score(test_preds, test_closed_binary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is low, much lower than the test score we got for our previous random forest. However, the issue there was that the model had no true predictive power." ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1]\n", "[[327 129]\n", " [ 3 11]]\n" ] } ], "source": [ "#look at test confusion matrix\n", "preds_closed_forest = best_rcf_closed.predict(test_preds)\n", "#confusion matrix\n", "labs = list(set(test_closed_binary))\n", "print(labs)\n", "cf_closed = confusion_matrix(y_true = test_closed_binary, y_pred = preds_closed_forest, labels = labs)\n", "print(cf_closed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can see that the model is actually predicting negative cases. In the model on status we were constantly predicting a business would be operating, and were rarely able to predict when a business would have any other status. In this case, we were able to predict almost all of the closed businesses, even though we predicted many operating business would be closed.\n", "\n", "The issue now with the model is the high false positve rates, but in the data sets like the one we are working with, this example becomes a lot like attempting to predict a rare disease. When predicting a rare disease, obviously having too high of a false positive rate is much prefferable to a high false negative rate. While originally we set out to attempt find out how to predict succes, I'm actually now kind of trying to find how to predict failure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember I picked the hyperparameters of this model based on the AUC score, so let's look at our model performance bu looking at the ROC curve and AUC statistic." ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import roc_curve\n", "from sklearn.metrics import roc_auc_score\n", "def roc_auc_plot(model, title, savefig):\n", " \"\"\"\n", " description: plot the roc and report the auc of a curve\n", " inputs:\n", " model: learning model with two category response\n", " title: title\n", " savefig: savefig file\n", " \n", " outputs:\n", " ROC curve\n", " auc\n", " \"\"\"\n", " pred_probs_closed_pos = model.predict_proba(test_preds)[:,1]\n", " fpr, tpr, thresh = roc_curve(test_closed_binary, pred_probs_closed_pos)\n", " auc = roc_auc_score(test_closed_binary, pred_probs_closed_pos)\n", " auc_str = \"AUC: \" + str(auc)\n", " print(auc_str)\n", " \n", " plt.figure()\n", " plt.plot([0, 1], [0, 1], 'k--')\n", " plt.plot(fpr, tpr)\n", " plt.title(title)\n", " plt.xlabel(\"True Positve Rate\")\n", " plt.ylabel(\"False Negative Rate\")\n", " plt.savefig(savefig)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AUC: 0.794564536341\n" ] }, { "data": { "image/png": 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58mWmTZtGbGwshmFw69YttmzZ4ohsIiLylGJjY1myZAFTpkwkU6ZMfPnlMdKl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\niIikKCpaf/nx8m3Wf3eJrBnccHNJmqV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4duwYGhsb4XK50NzcjI6ODhQUFMBms+HSpUvY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\nIiIiIpX8D5hXUgb1zpNoAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "roc_auc_plot(best_rcf_closed, title = \"Random Forest ROC curve\", savefig = \"results/random_forest_roc_curve\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again our performance isn't great. If we wanted great performance we could have turned every feature into a dumby indicator variable and run a KNN model, however, then we would not have any good inference on important features, and would have run the risk of overfitting the data. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Extremeley Randomized Trees Classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I spent a lot of time reading sklearn documentation for this project on models I was familiar with, and several time I just theme suggest reading the ExtraTreesClassifier. I had not heard of an Extra Trees model before, so I did some research and read some of [this](http://www.montefiore.ulg.ac.be/~ernst/uploads/news/id63/extremely-randomized-trees.pdf) paper from 2006 introducing Extremeley Randomized Trees, or in sklearn speak ExtraTrees. \n", "\n", "Extremeley Randomized Trees are very similar to Random Forests, and sklearn sets up the user input up in a very similar way. Extremeley Randomized Trees are similar to Random Forests in that they take a random rubsample of features, but drops the idea of bootstraping many trees samples in order to find optimal cut off points for feature node splits, and instead randomizes the picks a decision boundary at random for these node splits. This is why they are \"extremeley\" random, and as far as the bias-variance tradeoff is concerned, the model's increase in randomness seeks to further lower the variance of a model. Based on what I read, the performance of Extremley Randomized Trees can be similar, if not usually better, than that of a Random Forest." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am going to use GridSearchCV to tune the hyperparameters of the model again, however, this time I am not tuning the number of trees, or n_estimators, of the model. There are several reasons for this, for one, in general as the number of trees increases, generally model accurately increases at the sake of increases runtime, and I this notebook already has several cells that can take a few minutes to run. In addition, as the number of estimators increases, so does generally the chance of overfitting, and I am purposely using ExtraTrees for its ability to reduce model variance. " ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", "/Users/jackmoorer/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise',\n", " estimator=ExtraTreesClassifier(bootstrap=False, class_weight='balanced',\n", " criterion='gini', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_impurity_split=1e-07,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", " oob_score=False, random_state=5, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'max_features': array([2, 3, 4, 5, 6]), 'max_depth': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_weighted', verbose=0)" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "\n", "params_two = {'max_features' : num_preds, 'max_depth' : m_depth}\n", "\n", "ext = ExtraTreesClassifier(n_estimators = 100, criterion = 'gini',\n", " class_weight = 'balanced', random_state = 5)\n", "\n", "cv_ext = GridSearchCV(ext, params_two, cv = 5, scoring = 'f1_weighted',\n", " return_train_score = False)\n", "\n", "cv_ext.fit(train_preds, train_status)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_cv_two_param(cv_model, group, x_var, ylab, title, savefig):\n", " \"\"\"\n", " description: similar to plot before, but now with two hyperparameters\n", " inputs:\n", " cv_model: GridSearchCV model\n", " group: column name to group by\n", " xvar: param to put on x variable\n", " y_lab: metric used for cv\n", " title: title\n", " savefig: savefig file\n", " \"\"\"\n", " ext_cv_results = pd.DataFrame(cv_model.cv_results_)\n", " ax = plt.subplot()\n", " ext_cv_results.groupby(group).plot(y = 'mean_test_score',\n", " x = x_var,\n", " kind = 'line', ax = ax)\n", " leg = plt.legend()\n", " ax.set_xlim(0, 11)\n", " for item, text in zip(leg.texts, num_preds):\n", " plt.setp(item, 'text', text)\n", " ax.set_ylabel(ylab)\n", " ax.set_title(title)\n", " plt.savefig(savefig)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "image/png": 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SkpgJIYQQQmQS/4kBZoUQQggh3jSDwcDs2VMJCQkhKUlP374DqVu3/kvFlMRM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807TcQiGqPPy2Xc7DyMZulJJoQ4NUgwE+IIKKXY3R1gVV0vq+t6qW7uJxzTMBt0TClOY2ZZOjPK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\n3ulC50pFG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot cv results\n", "plot_cv_two_param(cv_ext, 'param_max_features', 'param_max_depth', 'Mean F1',\n", " \"Extremeley Randomized Trees Status Response Cross Validation Results Groups by Max Features\",\n", " 'results/extra_trees_status_cv_results.png')" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fit extremeley randomized tree\n", "best_ext = cv_ext.best_estimator_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the variable importance plot." ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "image/png": 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Vr0QFrEaNGvz4NgAAwB+kRAWsTJkyeuSRR9SwYUM5HA5JUmxsrFeDAQAAXKlK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\nxrKOxC0eCwseh57a2NhgxIgRkEqlEASBizszCSGENB0qwHQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot var importance\n", "title = \"Variable Importance of Tuned ExtraTrees for Status Response\"\n", "savefig = \"results/variable_importance_status_extra_trees.png\"\n", "var_imp_plot(best_ext, train_preds, title, savefig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like the Random Forest model on status number of relationships is the most important, but now number of milestones is also important. Let's look at the perforamnce of this model." ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.78936170212765955" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#report test score\n", "acc = best_ext.score(test_preds, test_status)\n", "acc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is worse than for our Random Forest model on the status variable, but let's look at the confusion matrix." ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['acquired', 'closed', 'operating', 'ipo']\n" ] }, { "data": { "text/plain": [ "array([[ 6, 4, 25, 0],\n", " [ 5, 3, 6, 0],\n", " [ 19, 24, 358, 6],\n", " [ 3, 2, 5, 4]])" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#report confusion matrix\n", "pr = best_ext.predict(test_preds)\n", "labs = list(set(test_status))\n", "print(labs)\n", "confusion_matrix(y_true = test_status, y_pred = pr, labels = labs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike the random forest, the extremeley randomized tree is able to predict other classes, however, not very accurately. I do think that if I had tuned the number of estimators along side it I could have gotten better results, but I want to conserve runtime on this notebook. Notice once again I used accuracy as the perfomance score. This ended up yeilding that the greatest maximum depth of the tree yeilded the best results. This is what happened for the random forest model as well. Accuracy will increase an depth increases, so accuracy is just not a good metric to try to tune tree depth. I can quickly see what would happen if the tree had a lower max depth keeping the maximum feature parameter constant." ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def quick_examine_extra(depth):\n", " \"\"\"\n", " descrition: function to quickly examine how results change for depth change ExtraTrees\n", " input:\n", " max_depth: int, max depth of the tree\n", " output:\n", " print:\n", " model accuracy\n", " confusion matrix\n", " return:\n", " model with depth param\n", " \"\"\"\n", " \n", " ext_depth = ExtraTreesClassifier(n_estimators = 100, criterion = 'gini',\n", " class_weight = 'balanced', random_state = 5,\n", " max_features = 2, max_depth = depth)\n", " ext_depth.fit(train_preds, train_status)\n", " \n", " acc = ext_depth.score(test_preds, test_status)\n", " \n", " \n", " pr = ext_depth.predict(test_preds)\n", " labs = list(set(test_status))\n", " cf = confusion_matrix(y_true = test_status, y_pred = pr, labels = labs)\n", " \n", " print(\"Accuracy is:\", acc)\n", " print(labs)\n", " print(cf)\n", " \n", " return(ext_depth)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is: 0.542553191489\n", "['acquired', 'closed', 'operating', 'ipo']\n", "[[ 0 18 13 4]\n", " [ 0 11 3 0]\n", " [ 3 147 241 16]\n", " [ 0 5 6 3]]\n" ] } ], "source": [ "#depth 2 tree\n", "ext_depth_2 = quick_examine_extra(2)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is: 0.578723404255\n", "['acquired', 'closed', 'operating', 'ipo']\n", "[[ 0 17 12 6]\n", " [ 1 10 3 0]\n", " [ 22 115 253 17]\n", " [ 1 4 0 9]]\n" ] } ], "source": [ "#depth 5 tree\n", "ext_depth_5 = quick_examine_extra(5)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is: 0.697872340426\n", "['acquired', 'closed', 'operating', 'ipo']\n", "[[ 6 11 18 0]\n", " [ 4 4 6 0]\n", " [ 39 49 311 8]\n", " [ 5 1 1 7]]\n" ] } ], "source": [ "#depth 8 tree\n", "ext_depth_8 = quick_examine_extra(8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that smaller values of depth allow us to better predict closed, but not small tree depths make it difficult for the tree to predict acquired and ipo. What would happen if we picked a depth outside of the corss validation range I picked?" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is: 0.840425531915\n", "['acquired', 'closed', 'operating', 'ipo']\n", "[[ 4 0 31 0]\n", " [ 3 1 10 0]\n", " [ 10 7 388 2]\n", " [ 1 0 11 2]]\n" ] } ], "source": [ "#depth 12 tree\n", "ext_depth_12 = quick_examine_extra(12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the perforamance begins to act more like it did for the best random forest model. Accuracy is high, but that is just because the model overpredicts operating." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is difficult to say which of ExtraTrees and RandomForest performed better on the status dataset, however, the fact that most gave similar variable importance statistics gives us a good idea about which features are important in creating this decision trees." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![var_imp_rf_status](results/variable_importance_status_random_forest.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![var_imp_status_ext](results/variable_importance_status_extra_trees.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like for both tree based methods number of relationships is the most important variable. Number of milestones is also important for both types of methods. Interestingly, first name is as important or more important than features many features, including academic features." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Closed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am going to repeat the process of using the two category feature on closed." ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise',\n", " estimator=ExtraTreesClassifier(bootstrap=False, class_weight='balanced',\n", " criterion='gini', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_impurity_split=1e-07,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", " oob_score=False, random_state=5, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'max_features': array([2, 3, 4, 5, 6]), 'max_depth': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='roc_auc', verbose=0)" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params_two = {'max_features' : num_preds, 'max_depth' : m_depth}\n", "\n", "ext_closed = ExtraTreesClassifier(n_estimators = 100, criterion = 'gini',\n", " class_weight = 'balanced', random_state = 5)\n", "\n", "cv_ext_closed = GridSearchCV(ext_closed, params_two, cv = 5, scoring = 'roc_auc',\n", " return_train_score = False)\n", "\n", "cv_ext_closed.fit(train_preds, train_closed_binary)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "image/png": 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oKHq9jt69P6J+/YZvVKZMwCRJkiRJkjKwe/cO7O0dmTJlJrGxMfTp010mYJIk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\no6PH8UH+DNZXrvXbOLr7HwSZDMtbr/ttjF/yasw0TGqpkm9c8QCqwq/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MZkFOOQXVFiak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7lWCrahxMcjx8ajxMUxIx8yCOb5Oy/EEh3ZajNQql/jsQU7+GnPIa4bWcB3h94mwhTJM31foFNw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\nOwoB2L17N2JjY7FgwQJ89NFHsuMQ7vwOS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_cv_two_param(cv_ext_closed, 'param_max_features', 'param_max_depth', 'Mean AUC',\n", " \"Extremeley Randomized Trees Closed Response Cross Validation Results Groups by Max Features\",\n", " 'results/extra_trees_closed_cv_results.png')" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "best_ext_closed = cv_ext_closed.best_estimator_" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "image/png": 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S09NVrVo1T5QIAABgG490QTZu3FiJiYmSpOTkZIWGhhZZP2nSJOXk5Oi1115z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\npJX9P3GdK6LDMvIvAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot var importance\n", "title = \"Variable Importance of Tuned ExtraTrees for Closed Response\"\n", "savefig = \"results/variable_importance_closed_extra_trees.png\"\n", "var_imp_plot(best_ext_closed, train_preds, title, savefig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar to the random forest closed variable importance plot, whether the company had funding, what category the company was, and the school subject of the founder were relatively important variables. One interesting note here is taht the number of relationships is now note important." ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.63404255319148939" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#look at accuracy\n", "acc = best_ext_closed.score(test_preds, test_closed_binary)\n", "acc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a very low accuracy, let's look at the confusion matrix." ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1]\n" ] }, { "data": { "text/plain": [ "array([[287, 169],\n", " [ 3, 11]])" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#report confusion\n", "pr_closed = best_ext_closed.predict(test_preds)\n", "labs = list(set(test_closed_binary))\n", "print(labs)\n", "confusion_matrix(y_true = test_closed_binary, y_pred = pr_closed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is very similar to the confusion matrix, in fact, it looks like this model just overpredicts \"No\" without actually improving the ability to predict \"No\" seemingly to imply, in this case, Random Forest is ideal. Let's look at the ROC and AUC." ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AUC: 0.715068922306\n" ] }, { "data": { "image/png": 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Kl9d/bgTs6upKQkICADdu3ODYsWN07dqV999/n4MHD3LgwIEn/DJERDKHH388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\nCQ4OpUmTpmbHEhERkf+iopXO3F2HlTO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Ei8XC6dOnKSgo4MiRI6SlpVFQUEB4ePgvX0ch\nxN9N/utQCCGEEMJN5ImWEEIIIYSbSKMlhBBCCOEm0mgJIYQQQriJNFpCCCGEEG4ijZYQQgghhJtI\noyWEEEII4SbSaAkhhBBCuIk0WkIIIYQQbvIf4MnWZeXezi8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "roc_auc_plot(best_ext_closed, title = \"ExtraTrees ROC curve\", savefig = 'results/extra_trees_roc_curve.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model really did not perform well. The black dotted line on the ROC curve donates the ROC of a random classify that classifies \"Yes\" or \"No\" by random. This mean this extratrees classifier, at times, is worse than a random guess classifier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can still examine the varible importances of the random forest vs the extremeley randomized tree to see if we can gather any insights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![var_imp_rf_closed](results/random_forest_two_class_variable_importance.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![var_imp_closed_ext](results/variable_importance_closed_extra_trees.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Had funding clearly looks like an important feature to predicting whether a business is closed or not based on our tree based bethods. Category code also seems to be an important variable for both methods. Because the methods are very similar in nature, it is not a suprise both ranomd forests and extremely randomized trees gave similar variable importance for the two response variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the two category case it seemed like random forest performed better than extremely randomized trees." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Looking at Data using Dumby" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to run the tree based methods in this section I had to encode the features becasue sklearn does not accept categorical variables. In this section instead of encoding the variables, I am going to look at only the categorical features by creating a longer dumby matrix." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "start_preds, all_closed, all_status = seperate_preds_response(data, ['closed', 'status'], 3)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2348, 7996)" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "long_preds = pd.get_dummies(start_preds)\n", "long_preds.shape" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_investmentnum_relationshipsnum_milestoneslogo_heightlogo_widthcategory_code_advertisingcategory_code_analyticscategory_code_automotivecategory_code_biotechcategory_code_cleantech...last_name_paunikarlast_name_rajlast_name_rokadelast_name_seolast_name_sklast_name_terminilast_name_van Apeldoornlast_name_van Loolast_name_van der Chijslast_name_von Wallenstein
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5 rows × 7996 columns

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" ], "text/plain": [ " num_investment num_relationships num_milestones logo_height \\\n", "7 3.0 3.0 4.0 120.0 \n", "8 0.0 2.0 0.0 89.0 \n", "11 0.0 45.0 3.0 165.0 \n", "18 0.0 1.0 1.0 67.0 \n", "22 21.0 23.0 3.0 59.0 \n", "\n", " logo_width category_code_advertising category_code_analytics \\\n", "7 120.0 0 0 \n", "8 250.0 0 0 \n", "11 650.0 0 0 \n", "18 250.0 0 0 \n", "22 86.0 0 0 \n", "\n", " category_code_automotive category_code_biotech category_code_cleantech \\\n", "7 0 0 0 \n", "8 0 0 0 \n", "11 0 0 0 \n", "18 0 0 0 \n", "22 0 0 0 \n", "\n", " ... last_name_paunikar last_name_raj \\\n", "7 ... 0 0 \n", "8 ... 0 0 \n", "11 ... 0 0 \n", "18 ... 0 0 \n", "22 ... 0 0 \n", "\n", " last_name_rokade last_name_seo last_name_sk last_name_termini \\\n", "7 0 0 0 0 \n", "8 0 0 0 0 \n", "11 0 0 0 0 \n", "18 0 0 0 0 \n", "22 0 0 0 0 \n", "\n", " last_name_van Apeldoorn last_name_van Loo last_name_van der Chijs \\\n", "7 0 0 0 \n", "8 0 0 0 \n", "11 0 0 0 \n", "18 0 0 0 \n", "22 0 0 0 \n", "\n", " last_name_von Wallenstein \n", "7 0 \n", "8 0 \n", "11 0 \n", "18 0 \n", "22 0 \n", "\n", "[5 rows x 7996 columns]" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "long_preds.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With get_dummies, pandas turns my data frame into a completely numeric data drame by splitting any categorical features into dumby variables. I am going to only keep the categorical dumby variables in this section" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": true }, "outputs": [], "source": [ "long_preds_cat = long_preds.drop(['num_investment', 'num_relationships', 'num_milestones', 'logo_height', 'logo_width'], axis = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am now going to examine the data in two dimensions using MDS like last project. I considered running a PCA in order to do a dimension reduction, but I decided against it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am going to focus on Eucliean Distances rather than Jensen-Shannon divergence. The reason I am doing this is that orgianlly I had planned on fitting a KNN on the data using euclidean distances. However, due to the curse of dimensionality it was have been important to first do feature selection to reduce the dimensions of the data. I first wanted to look at a 2D few of these distances using MDS, and you will see the result. " ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#take transpose so that features are rows\n", "pres_mat = long_preds_cat.transpose()" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#normalize on probability scale\n", "column_sums = pres_mat.sum(axis = 0).values\n", "pmm = pres_mat / column_sums" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7991, 2348)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#look at shape of data\n", "pmm = np.array(pmm)\n", "pmm.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next code chunk takes a very long time." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#calculate euclidean distances\n", "from scipy.spatial import distance\n", "\n", "#Intialize empty matrix of Euclidean distances\n", "euc_dists = np.zeros(shape = (pmm.shape[1], pmm.shape[1]))\n", "\n", "#loop through columns\n", "for i in range(pmm.shape[1]):\n", " #catch first column to compare\n", " cur_col = pmm[:, i]\n", " #loop through remaining columns\n", " for j in range(i, pmm.shape[1], 1):\n", " #catch second column to compare\n", " comp_col = pmm[:, j]\n", " \n", " #compute Euclidean Distance\n", " euc_dist = distance.euclidean(cur_col, comp_col)\n", " euc_dists[i, j] = euc_dist\n", " \n", " if (i != j):\n", " euc_dists[j, i] = euc_dist" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fit MDS in 2 dimensions to get 2 dimensional view\n", "from sklearn.manifold import MDS\n", "mds = MDS(n_components = 2, dissimilarity='precomputed', random_state=123)\n", "mds_fit = mds.fit(euc_dists)\n", "points = mds_fit.embedding_" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "image/png": 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V0KVlDLkDk1iTf5b/d08rhi3LJy5KzdxHmqNY8ySc2Q0t0rBmLGLxfjPZ97TimWX54r5v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gCmDrkc/xyTDofXh7XfHdOkeJ54NyiUyAMTV+3SLDqTNhVj+biMIFRfH+Ivk8XKTOWiT9r9hiL0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\n7Rux40gFX+4uJtlq4NE/X874Xq3p0jyVmVGBs9slafRq25BASGTTwXLVP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Ewt3MmGA2WM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\na53+NTSbC6879BqazeXVptMA3tx3isPlobtcKHYbJ+ct8BdqeL0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#intialize data frame for plots\n", "dists = {'status': all_status,\n", " 'closed': all_closed,\n", " 'x_cords': points[:, 0],\n", " 'y_cords': points[:, 1]}\n", "mds = pd.DataFrame(dists)\n", "\n", "#plot via status\n", "sns.pairplot(x_vars = 'x_cords', y_vars = 'y_cords', data = mds,\n", " hue = 'status', size = 5)\n", "plt.title(\"2D MDS plot of Euclidean Distances\")\n", "plt.savefig(\"results/mds_euc_status.png\")" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "image/png": 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eit1ioNblVxSIzhyWxZvbDnJ959akpdhw+YKUfLpPlc0WHTZKcZjxBUI4veps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W1yIoqwUT1+7GvHcbZFyeH9YNTpaRslQTK+Z6E5h4ICypihOZoa3TbkdmEidrpJHMhDxfD726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\nL89PpSzT9NcD5pOehOK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot via closed\n", "sns.pairplot(x_vars = 'x_cords', y_vars = 'y_cords', data = mds,\n", " hue = 'closed', size = 5)\n", "plt.title(\"2D MDS plot of Euclidean Distances\")\n", "plt.savefig(\"results/mds_euc_closed.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see there is no clear pattern in seperation based on Euclidean distances, do a method like a KNN would possibly not be affective in a situation like this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This may also partly explaine why the tree based methods in the previous section had trouble classifying the data. It may be that the features we are looking are aren't really indicators of success or failure in business." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Final Thoughts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we tried to classify business status via categorical variables about business location and founders. In doing so, we were not only looking to build an accurate model, we also wanted to understand feature importance in order to identify categorical variables that predicted business success or failure.\n", "\n", "We used two random tree based methods, Random Forest and Extremely Randomized Trees as our predictive models. These methods were choosen primarly for their ability to report feature importance, and this notebook acted an a comparison of the two similar models. I also wanted to compare sklearn's RandomForestClassifier with the r package randomForest.\n", "\n", "Prediction was difficult, primarly due to highly imbalanced reponse categories. We were still able to extract feature importances for the status and closed reponse variables and measure the test performance of each model. \n", "\n", "Finally, we decided to convert the purely string based categorical variables in a dumby data frame and and examine a two dimensional view of the categorial used Euclidean Distances and MDS. This showed that, while we were able to extract the importance of the features used, these features may not actually seperate the data enough to predict company status. Further analysis certainly could have been done, but the runetime on this notebook is already significant so we kind of leave with an ambigous answer to our question of what categorical features predict company status." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }