diff --git a/glm_mgbm_gbm.ipynb b/glm_mgbm_gbm.ipynb new file mode 100644 index 0000000..af710ab --- /dev/null +++ b/glm_mgbm_gbm.ipynb @@ -0,0 +1,4688 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License \n", + "\n", + "Copyright 2017 - 2020 Patrick Hall and the H2O.ai team\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "you may not use this file except in compliance with the License.\n", + "You may obtain a copy of the License at\n", + "\n", + " http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software\n", + "distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "See the License for the specific language governing permissions and\n", + "limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**DISCLAIMER:** This notebook is not legal compliance advice." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO:\n", + "# redo MGBM to use h2o grid search\n", + "# redo PDP function to use h2o\n", + "# only print comparison bar chart for best MGBM\n", + "# ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python imports " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n", + "Attempting to start a local H2O server...\n", + "; Java HotSpot(TM) 64-Bit Server VM (build 25.231-b11, mixed mode)\n", + " Starting server from C:\\Anaconda3\\lib\\site-packages\\h2o\\backend\\bin\\h2o.jar\n", + " Ice root: C:\\Users\\p\\AppData\\Local\\Temp\\tmppd81heaq\n", + " JVM stdout: C:\\Users\\p\\AppData\\Local\\Temp\\tmppd81heaq\\h2o_p_started_from_python.out\n", + " JVM stderr: C:\\Users\\p\\AppData\\Local\\Temp\\tmppd81heaq\\h2o_p_started_from_python.err\n", + " Server is running at http://127.0.0.1:54321\n", + "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n", + "Warning: Your H2O cluster version is too old (4 months and 26 days)! Please download and install the latest version from http://h2o.ai/download/\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
H2O cluster uptime:01 secs
H2O cluster timezone:America/New_York
H2O data parsing timezone:UTC
H2O cluster version:3.26.0.3
H2O cluster version age:4 months and 26 days !!!
H2O cluster name:H2O_from_python_p_6u4e3m
H2O cluster total nodes:1
H2O cluster free memory:3.539 Gb
H2O cluster total cores:8
H2O cluster allowed cores:8
H2O cluster status:accepting new members, healthy
H2O connection url:http://127.0.0.1:54321
H2O connection proxy:None
H2O internal security:False
H2O API Extensions:Amazon S3, Algos, AutoML, Core V3, Core V4
Python version:3.7.4 final
" + ], + "text/plain": [ + "-------------------------- ------------------------------------------\n", + "H2O cluster uptime: 01 secs\n", + "H2O cluster timezone: America/New_York\n", + "H2O data parsing timezone: UTC\n", + "H2O cluster version: 3.26.0.3\n", + "H2O cluster version age: 4 months and 26 days !!!\n", + "H2O cluster name: H2O_from_python_p_6u4e3m\n", + "H2O cluster total nodes: 1\n", + "H2O cluster free memory: 3.539 Gb\n", + "H2O cluster total cores: 8\n", + "H2O cluster allowed cores: 8\n", + "H2O cluster status: accepting new members, healthy\n", + "H2O connection url: http://127.0.0.1:54321\n", + "H2O connection proxy:\n", + "H2O internal security: False\n", + "H2O API Extensions: Amazon S3, Algos, AutoML, Core V3, Core V4\n", + "Python version: 3.7.4 final\n", + "-------------------------- ------------------------------------------" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import h2o # import h2o python bindings to java server\n", + "from h2o.estimators.glm import H2OGeneralizedLinearEstimator # for benchmark model\n", + "from h2o.grid.grid_search import H2OGridSearch # for benchmark model\n", + "from h2o.estimators.gbm import H2OGradientBoostingEstimator # for GBM\n", + "import numpy as np # array, vector, matrix calculations\n", + "import pandas as pd # DataFrame handling\n", + "import shap # for consistent, signed variable importance measurements\n", + "import xgboost as xgb # gradient boosting machines (GBMs)\n", + "\n", + "import matplotlib.pyplot as plt # plotting\n", + "pd.options.display.max_columns = 999 # enable display of all columns in notebook\n", + "\n", + "# enables display of plots in notebook\n", + "%matplotlib inline\n", + "\n", + "SEED = 12345\n", + "np.random.seed(SEED) # set random seed for reproducibility\n", + "\n", + "h2o.init()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Download, explore, and prepare UCI credit card default data\n", + "\n", + "UCI credit card default data: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n", + "\n", + "The UCI credit card default data contains demographic and payment information about credit card customers in Taiwan in the year 2005. The data set contains 23 input variables: \n", + "\n", + "* **`LIMIT_BAL`**: Amount of given credit (NT dollar)\n", + "* **`SEX`**: 1 = male; 2 = female\n", + "* **`EDUCATION`**: 1 = graduate school; 2 = university; 3 = high school; 4 = others \n", + "* **`MARRIAGE`**: 1 = married; 2 = single; 3 = others\n", + "* **`AGE`**: Age in years \n", + "* **`PAY_0`, `PAY_2` - `PAY_6`**: History of past payment; `PAY_0` = the repayment status in September, 2005; `PAY_2` = the repayment status in August, 2005; ...; `PAY_6` = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above. \n", + "* **`BILL_AMT1` - `BILL_AMT6`**: Amount of bill statement (NT dollar). `BILL_AMNT1` = amount of bill statement in September, 2005; `BILL_AMT2` = amount of bill statement in August, 2005; ...; `BILL_AMT6` = amount of bill statement in April, 2005. \n", + "* **`PAY_AMT1` - `PAY_AMT6`**: Amount of previous payment (NT dollar). `PAY_AMT1` = amount paid in September, 2005; `PAY_AMT2` = amount paid in August, 2005; ...; `PAY_AMT6` = amount paid in April, 2005. \n", + "\n", + "These 23 input variables are used to predict the target variable, whether or not a customer defaulted on their credit card bill in late 2005. Because XGBoost accepts only numeric inputs, all variables will be treated as numeric." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import data and clean\n", + "The credit card default data is available as an `.xls` file. Pandas reads `.xls` files automatically, so it's used to load the credit card default data and give the prediction target a shorter name: `DEFAULT_NEXT_MONTH`. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# import XLS file\n", + "path = 'default_of_credit_card_clients.xls'\n", + "data = pd.read_excel(path,\n", + " skiprows=1) # skip the first row of the spreadsheet\n", + "\n", + "# remove spaces from target column name \n", + "data = data.rename(columns={'default payment next month': 'DEFAULT_NEXT_MONTH'}) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assign modeling roles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The shorthand name `y` is assigned to the prediction target. `X` is assigned to all other input variables in the credit card default data except the row indentifier, `ID`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = DEFAULT_NEXT_MONTH\n", + "X = ['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n" + ] + } + ], + "source": [ + "# assign target and inputs for GBM\n", + "y = 'DEFAULT_NEXT_MONTH'\n", + "X = [name for name in data.columns if name not in [y, 'ID', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE']]\n", + "print('y =', y)\n", + "print('X =', X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display descriptive statistics\n", + "The Pandas `describe()` function displays a brief description of the credit card default data. The input variables `SEX`, `EDUCATION`, `MARRIAGE`, `PAY_0`-`PAY_6`, and the prediction target `DEFAULT_NEXT_MONTH`, are really categorical variables, but they have already been encoded into meaningful numeric, integer values, which is great for XGBoost. Also, there are no missing values in this dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LIMIT_BALPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTH
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mean167484.322667-0.016700-0.133767-0.166200-0.220667-0.266200-0.29110051223.33090049179.0751674.701315e+0443262.94896740311.40096738871.7604005663.5805005.921163e+035225.681504826.0768674799.3876335215.5025670.221200
std129747.6615671.1238021.1971861.1968681.1691391.1331871.14998873635.86057671173.7687836.934939e+0464332.85613460797.15577059554.10753716563.2803542.304087e+0417606.9614715666.15974415278.30567917777.4657750.415062
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" + ], + "text/plain": [ + " LIMIT_BAL PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", + "mean 167484.322667 -0.016700 -0.133767 -0.166200 -0.220667 \n", + "std 129747.661567 1.123802 1.197186 1.196868 1.169139 \n", + "min 10000.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% 50000.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 140000.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 240000.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1000000.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 \\\n", + "count 30000.000000 30000.000000 30000.000000 30000.000000 3.000000e+04 \n", + "mean -0.266200 -0.291100 51223.330900 49179.075167 4.701315e+04 \n", + "std 1.133187 1.149988 73635.860576 71173.768783 6.934939e+04 \n", + "min -2.000000 -2.000000 -165580.000000 -69777.000000 -1.572640e+05 \n", + "25% -1.000000 -1.000000 3558.750000 2984.750000 2.666250e+03 \n", + "50% 0.000000 0.000000 22381.500000 21200.000000 2.008850e+04 \n", + "75% 0.000000 0.000000 67091.000000 64006.250000 6.016475e+04 \n", + "max 8.000000 8.000000 964511.000000 983931.000000 1.664089e+06 \n", + "\n", + " BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 \\\n", + "count 30000.000000 30000.000000 30000.000000 30000.000000 \n", + "mean 43262.948967 40311.400967 38871.760400 5663.580500 \n", + "std 64332.856134 60797.155770 59554.107537 16563.280354 \n", + "min -170000.000000 -81334.000000 -339603.000000 0.000000 \n", + "25% 2326.750000 1763.000000 1256.000000 1000.000000 \n", + "50% 19052.000000 18104.500000 17071.000000 2100.000000 \n", + "75% 54506.000000 50190.500000 49198.250000 5006.000000 \n", + "max 891586.000000 927171.000000 961664.000000 873552.000000 \n", + "\n", + " PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", + "count 3.000000e+04 30000.00000 30000.000000 30000.000000 \n", + "mean 5.921163e+03 5225.68150 4826.076867 4799.387633 \n", + "std 2.304087e+04 17606.96147 15666.159744 15278.305679 \n", + "min 0.000000e+00 0.00000 0.000000 0.000000 \n", + "25% 8.330000e+02 390.00000 296.000000 252.500000 \n", + "50% 2.009000e+03 1800.00000 1500.000000 1500.000000 \n", + "75% 5.000000e+03 4505.00000 4013.250000 4031.500000 \n", + "max 1.684259e+06 896040.00000 621000.000000 426529.000000 \n", + "\n", + " PAY_AMT6 DEFAULT_NEXT_MONTH \n", + "count 30000.000000 30000.000000 \n", + "mean 5215.502567 0.221200 \n", + "std 17777.465775 0.415062 \n", + "min 0.000000 0.000000 \n", + "25% 117.750000 0.000000 \n", + "50% 1500.000000 0.000000 \n", + "75% 4000.000000 0.000000 \n", + "max 528666.000000 1.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[X + [y]].describe() # display descriptive statistics for all columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Investigate pair-wise Pearson correlations for DEFAULT_NEXT_MONTH" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate Pearson correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "corr = pd.DataFrame(data[X + [y]].corr()[y]).iloc[:-1]\n", + "corr.columns = ['Pearson Correlation Coefficient']\n", + "\n", + "# display correlation matrix as barchart\n", + "fig, ax_ = plt.subplots(figsize=(8, 6))\n", + "_ = pd.DataFrame(data[X + [y]].corr()[y]).iloc[:-1].plot(kind='barh', ax=ax_)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data rows = 20946, columns = 25\n", + "Test data rows = 9054, columns = 25\n" + ] + } + ], + "source": [ + "np.random.seed(12345) # set random seed for reproducibility\n", + "split_ratio = 0.7 # 70%/30% train/test split\n", + "\n", + "# execute split\n", + "split = np.random.rand(len(data)) < split_ratio\n", + "train = data[split]\n", + "test = data[~split]\n", + "\n", + "# summarize split\n", + "print('Train data rows = %d, columns = %d' % (train.shape[0], train.shape[1]))\n", + "print('Test data rows = %d, columns = %d' % (test.shape[0], test.shape[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "glm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Best penalized GLM AUC: 0.73\n" + ] + } + ], + "source": [ + "def glm_grid(X, y, train, valid):\n", + " \n", + " \"\"\" Wrapper function for penalized GLM with alpha and lambda search.\n", + " \n", + " :param X: List of inputs.\n", + " :param y: Name of target variable.\n", + " :param train: Name of training H2OFrame.\n", + " :param valid: Name of validation H2OFrame.\n", + " :return: Best H2Omodel from H2OGeneralizedLinearEstimator\n", + "\n", + " \"\"\"\n", + " \n", + " alpha_opts = [0.01, 0.25, 0.5, 0.99] # always keep some L2\n", + " \n", + " # define search criteria\n", + " # i.e. over alpha\n", + " # lamda search handled by lambda_search param below\n", + " hyper_parameters = {'alpha': alpha_opts} \n", + "\n", + " # initialize grid search\n", + " grid = H2OGridSearch(\n", + " H2OGeneralizedLinearEstimator(\n", + " family=\"binomial\",\n", + " lambda_search=True,\n", + " seed=SEED),\n", + " hyper_params=hyper_parameters)\n", + " \n", + " # run grid search\n", + " grid.train(y=y,\n", + " x=X, \n", + " training_frame=train,\n", + " validation_frame=valid)\n", + "\n", + " # select best model from grid search\n", + " return grid.get_grid()[0]\n", + "\n", + "best_glm = glm_grid(X, y, h2o.H2OFrame(train), h2o.H2OFrame(test))\n", + "print('Best penalized GLM AUC: %.2f' % \n", + " best_glm.auc(valid=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "glm_coef = pd.DataFrame.from_dict(best_glm.coef(), orient='index', columns=['Penalized GLM Coefficient'])\n", + "corr_glm = pd.concat([corr,glm_coef.iloc[1:]], axis=1)\n", + "\n", + "fig, ax_ = plt.subplots(figsize=(8, 6))\n", + "_ = corr_glm.plot(kind='barh', ax=ax_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train Monotonic XGBoost GBM classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def mgbm_grid(X, mc): \n", + " \n", + " # define random grid search parameters\n", + " hyper_parameters = {'ntrees':list(range(1, 500, 50)),\n", + " 'max_depth':list(range(1, 20, 2)),\n", + " 'sample_rate':[s/float(10) for s in range(1, 11)],\n", + " 'col_sample_rate':[s/float(10) for s in range(1, 11)]}\n", + "\n", + " # define search strategy\n", + " search_criteria = {'strategy':'RandomDiscrete',\n", + " 'max_models':20,\n", + " 'max_runtime_secs':600,\n", + " 'seed': SEED}\n", + "\n", + " # initialize grid search\n", + " gsearch = H2OGridSearch(H2OGradientBoostingEstimator,\n", + " hyper_params=hyper_parameters,\n", + " search_criteria=search_criteria)\n", + "\n", + " # execute training w/ grid search\n", + " gsearch.train(x=X,\n", + " y=y,\n", + " monotone_constraints=mc,\n", + " training_frame=htrain,\n", + " validation_frame=htest,\n", + " stopping_rounds=5,\n", + " seed=SEED)\n", + " \n", + " \n", + " mgbm_model = gsearch.get_grid()[0]\n", + "\n", + " return mgbm_model\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 1/13 with AUC: 0.74 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 2/13 with AUC: 0.76 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 3/13 with AUC: 0.77 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 4/13 with AUC: 0.77 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 5/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 6/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 7/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 8/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 9/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 10/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 11/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 12/13 with AUC: 0.78 ...\n", + "\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n", + "Completed grid search 13/13 with AUC: 0.78 ...\n", + "\n", + "Done.\n" + ] + } + ], + "source": [ + "#### initialize data structures needed to compare correlation coefficients,\n", + "# penalized glm coefficients, and MGBM Shapley values\n", + "# as features are added into the MGBM\n", + "\n", + "# create a list of features to add into MGBM expectation\n", + "# list is ordered by correlation between X_j and y\n", + "abs_corr = corr.copy(deep=True)\n", + "abs_corr['Pearson Correlation Coefficient'] = corr['Pearson Correlation Coefficient'].abs()\n", + "X_selected = ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n", + "next_list = [name for name in list(abs_corr.sort_values(by='Pearson Correlation Coefficient',\n", + " ascending=False).index) if name not in X_selected]\n", + "\n", + "# create a Pandas DataFrame to store new MGBM SHAP values\n", + "# for comparison to correlation and penalized gbm coefficients\n", + "abs_corr_glm_mgbm_shap = corr_glm.copy(deep=True).abs()\n", + "abs_corr_glm_mgbm_shap.columns = ['Absolute ' + name for name in abs_corr_glm_mgbm_shap.columns]\n", + "abs_corr_glm_mgbm_shap['Monotonic GBM Mean SHAP Value'] = 0\n", + "\n", + "# init empty parallel lists to store results \n", + "mgbm_models = []\n", + "corr_glm_mgbm_shap_coefs = []\n", + "\n", + "#### train multiple MGBMs\n", + "# each time adding in the next most y-correlated feature\n", + "for j, name in enumerate(next_list):\n", + "\n", + " # create monotonicity contraints for the current model\n", + " names = list(test[X_selected + [y]].corr()[y].index)[:-1]\n", + " signs = list([int(i) for i in np.sign(test[X_selected + [y]].corr()[y].values[:-1])])\n", + " mc = dict(zip(names, signs))\n", + "\n", + " # convert training and test data to h2o format\n", + " htrain = h2o.H2OFrame(train[X_selected + [y]]) # necessary to ensure ordering of Shapley values matches X_selected\n", + " htrain[y] = htrain[y].asfactor() # ensure y is treated as binomial\n", + " htest = h2o.H2OFrame(test[X_selected + [y]])\n", + " \n", + " # train model and calculate Shapley values\n", + " mgbm_models.append(mgbm_grid(X_selected, mc))\n", + " shap_values = mgbm_models[j].predict_contributions(htest).as_data_frame().values[:,:-1]\n", + " \n", + " # update Pandas dataframe with current model Shapley values\n", + " col = pd.DataFrame({'Monotonic GBM Mean SHAP Value': \n", + " list(np.abs(shap_values).mean(axis=0))}, index=X_selected)\n", + " abs_corr_glm_mgbm_shap.update(col)\n", + " corr_glm_mgbm_shap_coefs.append(abs_corr_glm_mgbm_shap.copy(deep=True)) # deep copy necessary\n", + " \n", + " # retrieve AUC and print progress\n", + " auc_ = mgbm_models[j].auc(valid=True)\n", + " print('Completed grid search %i/%i with AUC: %.2f ...\\n' % (j+1, len(next_list), auc_))\n", + " \n", + " # add the next most y-correlated feature\n", + " # for the next modeling iteration\n", + " X_selected = X_selected + [next_list[j]]\n", + " \n", + "print('Done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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9zZs3j6+//vqcz/dXkjl5p1XVAyAi84FRwItuXgywBhgETFbVTBF5HHhZRDoB1wMPAW2KuUYMsAmIBub5pacAA4G57vEgYAuAqo522xQELMtpo5vWBfgVEKaqP4pI3RLcrzHGXDGCJn7ws9aXNrVXQOXi4uLo0KED8fHxTJ48+WdtQyASExOpWrVqif9S7tixI8uWLePUqVN4PB569+5N69atz7kdqoqqUqbMzzvAlpmZSa9evXjxxRfp06cPAKtWrSI9PZ169eoVe/7Zs2cpV+6nUCErK4uyZcueV5uee+45nnrqKd9xSYPswsyZM4ePPvqIjRs3cvXVV3Ps2DGWLl16TnUtXbqUX/3qV74A/uWXX+bDDz/0zYfr27dvkeefzz3NmzePkJAQrr/++nOuI8e5/jatBm4CEJGqwG3A/TjBFwCquhw4DAwFpuMEf98VVqGINAGqAs/gBHv+vgIqikg9cforewAfBtDOh4Gpqvqj26b/FnLtB0UkSUSS0tPTA6jWGGPM+Tp58iRr165l7ty5xMfH58o7fvw40dHRtGrVilGjRpGdnU1WVhaxsbGEhIQQGhrK9OnTAfB6vdxyyy2EhYURHR3Nd9/l/6vGv4cpKSmJqKgo0tLSmDNnDtOnT8fj8bB69WrS09Pp168fbdu2pW3btqxdu7bIe6hSpQqtW7dmz549ZGVlMW7cONq2bUtYWBivvvqq7z67du1KZGQkoaGh/OMf/wAgLS2Nli1b8sgjjxAZGcmBAwdKdH9RUVFMmDCBdu3a0axZM1avXp2vfe+++y7t27f3BXgAXbp0ISQkhMzMTIYPH05oaCgRERGsWrUKcIKMAQMG0KdPH7p160ZiYiJdunRh8ODBhIY6A3TvvPMO7dq1w+Px8NBDD5GVlZXv2nfffTetW7cmODiY1157DYCJEydy+vRpPB4PQ4YMAaBq1aqAE+iOGzfOd/8LFiwAnEA8KiqK/v3706JFC4YMGYKq5rvec889x8svv8zVV18NQPXq1Rk2bBgAn3zyCREREYSGhjJixAh+/PFHAJKTk+ncuTOtW7eme/fuHD58mH/961/MmDGDv/3tb3Tp0oVRo0axd+9e+vbty/Tp05k3bx6PPvooAEeOHCE6Oprw8HDCw8N9wV3OPQE8//zzvt+JSZMm5frZP/DAAwQHB9OtWzdOnz5NQkICSUlJDBkyBI/Hw+nTpwv5zQtMiYM8ESkH3IXTuwZwN7BcVXcBR0Uk0q/448AUoI6qvl1M1TFAHE4A2byAXrcEYABwK7AZ+DGA5jYDOorI5yLyqYi0LaiQqr6mqm1UtU2dOgVu/2aMMeZntnTpUnr06EGzZs2oWbMmmzdv9uVt3LiRv/zlL6SkpLBnzx4WL16M1+vl0KFDpKamkpKSwvDhwwEYOnQof/7zn9m6dSuhoaEBD58GBQUxatQoxo4di9frpWPHjowZM4axY8eyadMmFi1axMiRI4usIyMjgw0bNhAcHMzcuXOpXr06mzZtYtOmTbz++uvs27ePihUrsmTJEjZv3syqVav4zW9+4wtSvvzyS4YOHcoXX3zBt99+W+L7O3v2LBs3bmTGjBkF3ndqamqhPYyzZ88GICUlhbi4OIYNG+ab2L9+/XrefPNNVq5c6ft5TJkyhe3bt7Njxw4WLFjA2rVr8Xq9lC1blvnz5+er/+9//zvJyckkJSUxc+ZMMjIymDp1KpUqVcLr9eY7J+dnvGXLFj7++GPGjRvH4cOHAfjiiy+YMWMG27dvZ+/evfmC7xMnTnDixAmaNGmSrx2ZmZnExsayYMECUlJSOHv2LK+88gpnzpzhscceIyEhgeTkZEaMGMHTTz9Nz549fb8Xq1atYs6cOVx//fWsWrWKsWPH5qr717/+NZ07d2bLli1s3ryZ4ODgXPkrVqxg9+7dbNy4Ea/XS3JyMp999hkAu3fvZvTo0Wzbto1rrrmGRYsW0b9/f9q0acP8+fPxer1UqlSpwJ9doEoyXFtJRLzu99X8NHQaA8xwv8e7x5sBVPVrEVkJLAug/kFAtKpmi8hinIButl/+QmAB0AInGAykb70cUAO4BWgLLBSRxlrQPwFcKYeO/ezDFhdToEMmxhhzocXFxfH4448DMGjQIOLi4oiMdPoJ2rVrR+PGjQGIiYlhzZo1dO3alb179/LYY4/Rq1cvunXrxrFjx/j+++/p3LkzAMOGDWPAgAHn3KaPP/6Y7du3+46PHz/OiRMnqFatWq5yq1evJiIigjJlyjBx4kSCg4OZNGkSW7du9c0DPHbsGLt376Z+/fo89dRTfPbZZ5QpU4ZDhw755sQ1bNiQW265BYDGjRuX+P7uueceAFq3bk1aWlqJ7nXNmjU89thjALRo0YKGDRuya9cuAO68805q1qzpK9uuXTvfUOUnn3xCcnIybds6/SanT5+mbt38s6FmzpzJkiVLADhw4AC7d++mVq1aRbYnJiaGsmXLUq9ePTp37symTZu4+uqradeuHfXr1wfA4/GQlpZGhw4dfOeqaqEvJnz55Zc0atSIZs2aAc4znD17NnfccQepqanceeedgDMUfd111wXw5H6ycuVK37y/smXLUr169Vz5K1asYMWKFURERABOr+7u3bu58cYbadSoER6PM8PsXH5+gTinOXk5RKQWcDsQIiIKlAVURMb7BVLZ7qdQIhIGNAU+cn9IVwF78QvyVPUbETkD3AmMIbAg7yCw2G3LRhHJBmoDNiZrjDEXUUZGBitXriQ1NRURISsrCxFh2jTn/bi8f2GLCDVq1GDLli38+9//Zvbs2SxcuNA3pFmccuXKkZ3t/FVU1DIU2dnZrF+/vtgelJw5ef5UlVmzZtG9e/dc6fPmzSM9PZ3k5GTKly9PUFCQrw1VqlTxlTuX+6tQoQLgBBhnz57Nlx8cHMynn35a4LlF9HfkalfeY1Vl2LBh/OlPfyr0/MTERD7++GPWr19P5cqViYqKKnb5j6Lak3OfUPC9Xn311VSpUoW9e/f6/nFQXL2qSnBwMOvXry+yXedDVXnyySd56KGHcqWnpaXlu6fzHZotyPnO8OwPvKWqDVU1SFUbAPuADsWcl1cMzpy9IPdzPXCDiDTMU+5ZYIKq5h/8L9hSnCAUEWmGEzwW/dqPMcaYX1xCQgJDhw5l//79pKWlceDAARo1asSaNWsAZ3hw3759ZGdns2DBAjp06MC3335LdnY2/fr14w9/+AObN2+mevXq1KhRwzcf7e233/b1evkLCgoiOTkZgEWLFvnSq1WrxokTJ3zH3bp146WXXvIde71eAtW9e3ffMCA4b7WeOnWKY8eOUbduXcqXL8+qVavYv39/geefz/0VZvDgwaxbt44PPvhphGr58uWkpKTQqVMn35Dprl27+Oqrr2jevHmxdXbt2pWEhAT++19nmvvRo0fz3dOxY8eoUaMGlStXZufOnWzYsMGXV758ed8z8tepUycWLFhAVlYW6enpfPbZZ7Rr1y7ge33yyScZPXo0x48fB5xe2Ndee40WLVqQlpbGf/7zH+CnZ9i8eXPS09N9Qd6ZM2fYtm1bwNfLeRavvPIK4PQE5lw7R/fu3fn73//OyZMnATh06JDvuRUm7+/k+TjfIC8GWJInbREwuIT1DCqgniX4vcgBoKrrVLUkr8r8HWgsIqk4Q8nDihqqNcYYc2HExcURHR2dK61fv368++67ALRv356JEycSEhJCo0aNiI6O5tChQ0RFReHxeIiNjfX1JL355puMGzeOsLAwvF4vzz77bL7rTZo0iTFjxtCxY8dcb4f26dOHJUuW+F68mDlzJklJSYSFhdGqVSvmzCly5a9cRo4cSatWrYiMjCQkJISHHnqIs2fPMmTIEJKSknxzrVq0aFHg+edzf4WpVKkSy5YtY9asWTRt2pRWrVoxb9486tatyyOPPEJWVhahoaEMHDiQefPm5epdKkyrVq344x//SLdu3QgLC+POO+/0zZ3L0aNHD86ePUtYWBi//e1vfUPSAA8++CBhYWG+Fy9yREdHExYWRnh4OLfffjvTpk3j2muvDfheH374Ybp06ULbtm0JCQmhc+fOVK5cmYoVK/LGG28wYMAAQkNDKVOmDKNGjeKqq64iISGBCRMmEB4ejsfjKfFbsX/9619ZtWoVoaGhtG7dOl+Q2K1bNwYPHkz79u0JDQ2lf//+xQZwsbGxjBo16md58UIs5smtTZs2mpSUdLGbYYwxv6gdO3bQsmXLi90MY0wJFPTnVkSSVbXAJepsxwtjjDHGmFKoJC9enDd3B4y8S6n8qKo3X8h2GGOMMcaUdhc0yFPVFMBTbEFjjDHGGHNebLjWGGOMMaYUsiDPGGOMMaYUsiDPGGOMMaYUsiDPGGPMRbNkyRJEhJ07d/rSEhMT6d2793nXHRsb69tirDCJiYklXhstMTGR6tWrExERQcuWLQPeK7ck/Ns+cuTIXFutnYu0tDRCQkIKzNu9eze9e/emSZMmtG7dmi5duvj2V503bx6PPvpovnOCgoLo2LFjrjSPx1PoNXbt2kXPnj256aabaNmyJffee69va7eSGjduHMHBwYwbN4709HRuvvlmIiIiWL16NT179uT7778v9Nw5c+b4tiErqbS0NN86jpeLC/rihTHGmEvU5OrFlylRfccCKhYXF0eHDh2Ij49n8uTJP28bApCYmEjVqlW59dZAdsr8Sc62ZqdOncLj8dC7d29at279i7Txb3/72y9SLzhbvPXq1YsXXniBvn37ApCamkpSUhKdOnUq8twTJ05w4MABGjRowI4dO4q9xosvvkifPn0AWLVqFenp6dSrV6/EbX711VdJT0+nQoUKxMfH06JFC958802AfIFnXqNGjSrx9XLkBHmDB5d0v4eLx3ryjDHGXBQnT55k7dq1zJ07l/j4+Fx5x48fJzo6mlatWjFq1Ciys7PJysoiNjaWkJAQQkNDffu6er1ebrnlFsLCwoiOjua7777Ld62goCC+/dbZ1TIpKYmoqCjS0tKYM2cO06dP9+14kZ6eTr9+/Wjbti1t27Zl7dq1Rd5DlSpVaN26NXv27CErK4tx48bRtm1bwsLCePXVVwEnkIyKiqJ///60aNGCIUOG+PZT/f3vf+/boeHBBx8scJ/VqKgokpKSeP/99/F4PHg8Hpo3b06jRo0ASE5OpnPnzrRu3Zru3bv7dp9ITk4mPDyc9u3bM3v27Hz1AsyfP5/27dv7AjyAkJAQYmNji7xvgHvvvZcFCxYATrAeExNTYLl3332X9u3b+wI8gC5duhASEkJmZibDhw8nNDSUiIgIVq1aBVDos+zbty+nTp3i5ptv5s9//jPjx4/nX//6l293CP+f81tvveXbQeO+++4DYPLkybzwwgsA7Nmzhx49etC6dWs6duzo602OjY3l17/+NbfeeiuNGzf29ahOnDiR1atX4/F4At4z+WKzIM8YY8xFsXTpUnr06EGzZs2oWbMmmzdv9uVt3LiRv/zlL6SkpLBnzx4WL16M1+vl0KFDpKamkpKSwvDhwwEYOnQof/7zn9m6dSuhoaEBD58GBQUxatQoxo4di9frpWPHjowZM4axY8eyadMmFi1axMiRI4usIyMjgw0bNhAcHMzcuXOpXr06mzZtYtOmTbz++uvs27cPgC+++IIZM2awfft29u7d6wseH330UTZt2kRqaiqnT59m2bJlhV6rb9++eL1evF4v4eHhPPHEE5w5c4bHHnuMhIQEkpOTGTFiBE8//TQAw4cPZ+bMmb69WQuybds2IiMjA3peefXv35/FixcD8M9//jNXEOcvNTW10F7OnOAzJSWFuLg4hg0bRmZmZqHP8v3336dSpUp4vV4mTJjA73//ewYOHIjX66VSpUq57mvKlCmsXLmSLVu28Ne//jXftR988EFmzZpFcnIyL7zwAo888ogv7/Dhw6xZs4Zly5YxceJEAKZOnUrHjh3xer2MHTv2nJ7ZhWbDtcYYYy6KuLg4Hn/8cQAGDRpEXFycL+Bo164djRs3BiAmJoY1a9bQtWtX9u7dy2OPPUavXr3o1q0bx44d4/vvv6dz584ADBs2jAEDBpxzmz7++ONc89+OHz/OiRMnqFatWq5yq1evJiIigjJlyjBx4mz8T6cAACAASURBVESCg4OZNGkSW7du9fX8HDt2jN27d3PVVVfRrl076tevDzhz19LS0ujQoQOrVq1i2rRp/PDDDxw9epTg4OBCg6Uc06ZNo1KlSowePZrU1FRSU1O58847AacH7Lrrrsv3XO677z4+/PDDYu8/Ojqa3bt306xZM18AV5iaNWtSo0YN4uPjadmyJZUrVy62/rzWrFnDY489BkCLFi1o2LAhu3btYsWKFQU+y5zey+KsXLmS/v37U7t2bV9b/Z08eZJ169bl+l358ccffd/vvvtuypQpQ6tWrc557uClwII8Y4wxF1xGRgYrV64kNTUVESErKwsRYdq0aQCISK7yIkKNGjXYsmUL//73v5k9ezYLFy4MeNisXLlyZGdnA84cscJkZ2ezfv36XL1CBcmZk+dPVZk1axbdu3fPlZ6YmEiFChV8x2XLluXs2bNkZmbyyCOPkJSURIMGDZg8eXKRbQP45JNPeO+993wvRqgqwcHB+Xrrvv/++3zPsCDBwcG+usB5ESYpKYknnnii2HMBBg4cyOjRo5k3b16R1/j0008LzCtoeDonvaBnGShVLfL+s7Ozueaaa/B6vQXm+/+8Cmvj5cCGa40xxlxwCQkJDB06lP3795OWlsaBAwdo1KgRa9asAZzh2n379pGdnc2CBQvo0KED3377LdnZ2fTr148//OEPbN68merVq1OjRg1Wr14NwNtvv+3rvfIXFBREcnIyAIsWLfKlV6tWjRMnTviOu3XrxksvveQ7LiwIKEj37t155ZVXOHPmDOC8UXrq1KlCy+cEdLVr1+bkyZPFvgm8f/9+HnnkERYuXOgLQps3b056erovyDtz5gzbtm3jmmuuoXr16r7nOX/+/ALrHDx4MGvXruX999/3pf3www8B3rHT8zd+/Pgig7HBgwezbt06PvjgA1/a8uXLSUlJoVOnTr627dq1i6+++ormzZuX+Fnm1bVrVxYuXEhGRgYAR48ezZV/9dVX06hRI9577z3ACeS2bNlSZJ15f1cuBwEHeSKSJSJeEUkVkfdEpLJfXrSIqIi0cI8rishOd6/anDLjRWROMdcYKyKZIlLdLy3Krft+v7QIN+0JEZnttmu7iJx2v3tFpL+ITBaRQ35pPQO9X2OMMb+cuLg4oqOjc6X169fPt0RF+/btmThxIiEhITRq1Ijo6GgOHTpEVFQUHo+H2NhY/vSnPwHw5ptvMm7cOMLCwvB6vTz77LP5rjdp0iTGjBlDx44dKVu2rC+9T58+LFmyxPfixcyZM0lKSiIsLIxWrVoxZ06Rf23lMnLkSFq1akVkZCQhISE89NBDnD17ttDy11xzDQ888AChoaHcfffdtG3btsj6582bR0ZGBtHR0Xg8Hnr27MlVV11FQkICEyZMIDw8HI/H41sS5o033mD06NG0b9++0J7JSpUqsWzZMubMmUPjxo1p3749f/zjH3nmmWdyXbd+/fq+z8GDB3151apVY8KECVx11VWFtjvnGrNmzaJp06a0atWKefPmUbduXR555BGysrIIDQ1l4MCBzJs3jwoVKpT4WeYVHBzM008/TefOnQkPD+f//u//8pWZP38+c+fOJTw8nODgYP7xj38UWWdYWBjlypUjPDz8snnxQgLthhSRk6pa1f0+H0hW1Rfd44XAdcAnqjrZTesBPA10Aq4HPgPaqGr+155+usZG4EdgrqrOc9OigJnAN6razU37M9AdeEdVX3DTgoBlqhriV99k4GROmUC0adNGk5KSAi1ujDGXpR07dtCyZcuL3QxjTAkU9OdWRJJVtU1B5c91uHY1cJNbeVXgNuB+YFBOAVVdDhwGhgLTgcnFBHhNgKrAM0De97C/AiqKSD1xBtl7AMXPIDXGGGOMuUKVOMgTkXLAXUCKm3Q3sFxVdwFHRcT/XezHgSlAHVV9u5iqY4A4nACyuYjUzZOfAAwAbgU24/T4BeJREdkqIn8XkRqF3NODIpIkIknp6ekBVmuMMcYYc+kqSZBXSUS8QBJOz9pcNz0GyFnFMh6/XjhV/RpYCbwSQP2DgHhVzQYW4wR0/ha6aTnBYCBeAZoAHpxexb8UVEhVX1PVNqrapk6dOgFWbYwxxhhz6SrJEiqnVdXjnyAitYDbgRARUaAsoCIyXn+a7JftfgolImFAU+Aj95Xnq4C9gG+JblX9RkTOAHcCY3B69Iqkqr7FbUTkdaDwVSZdKYeOETTxg+KKXXbSpva62E0wxhhjzAV0vkuo9AfeUtWGqhqkqg2AfUCHEtYTgzNnL8j9XA/cICIN85R7FpigqlmBVCoi1/kdRgOpJWyXMcYYY8xl6XwXQ44BpuZJWwQMxplbF6hBOPP8/C1x0z/PSVDVdSVs3zQR8QAKpAEPlfB8Y4wxxpjLUsA9eTnLp+RJi3LfovVPm6mqD/sdx6pqkSs8qmojVd2ZJ+3/VPXPqpqoqr0LOGey/9Ioqprmv3yKm3afqoaqapiq9lXVw8XfqTHGmAtBRHwbxwOcPXuWOnXq0Lt3vv/ln5fvv/+el19++bzqGDlyZK7tzoqzceNGoqKiaNq0KZGRkfTq1YuUFOd9xcmTJ3PDDTfg8Xho0aIFDz/8sG83jtjYWCpXrpxr0d0xY8YgInz77bf5rhMUFETHjh1zpXk8HkJCQvKV/Tl9+eWXvjULW7ZsyYMPPgg4u3vk/fnFxsbmWug5PT2d8uXL8+qrr+YqFxQURGhoKOHh4XTr1o1vvvkmV/7kyZN58sknc6V5vd5ilwKKioriSl0azbY1yyP0huok2fw1Y8wVJvTN0OILlUDKsJRiy1SpUoXU1FROnz5NpUqV+Oijj7jhhht+1nbAT0Ge/wb0JfW3v/0t4LJHjhzh3nvv5d133+XWW53p42vWrGHPnj2EhjrPeezYsTzxxBNkZ2fTqVMnPv30U7p06QLATTfdxD/+8Q/+3//7f2RnZ7Nq1aoin8uJEyc4cOAADRo0YMeOHed8jyXx61//mrFjx/KrX/0KwBfABuK9997jlltuIS4ujoceyj3AtmrVKmrXrs1TTz3Fc889x8yZM315MTEx3HXXXb5FsAHi4+MZPHjwed5N6XVBtzUTkVC/3SdyPp8Xf6YxxpjS6K677vJtdxUXF0dMzE/LpB49epS7776bsLAwbrnlFrZu3Qo4PTojRowgKiqKxo0b5woEXnzxRUJCQggJCWHGjBkATJw4kT179uDxeBg3bhyqyrhx4wgJCSE0NJQFCxYATi9UVFQU/fv3p0WLFgwZMsS3b6l/b9Dy5cuJjIwkPDycrl275runl156iWHDhvkCPIAOHTpw99135yv7v//9j8zMTGrU+GmFr5iYmFxtuu222yhXrvA+mXvvvddXPu8zzMrKYty4cbRt25awsDBf79nJkyfp2rUrkZGRhIaG+nZ7SEtLo2XLljzwwAMEBwfTrVs3Tp8+ne+ahw8fpn79+r7jnOA1EHFxcfzlL3/h4MGDHDp0qMAynTp14j//+U+utObNm3PNNdfw+ec/hQ0LFy5k0CBnid6HH36YNm3aEBwczKRJkwqst2rVnwYlExISiI2NBZzexX79+tG2bVvatm3L2rVrA76fS9kFDfJUNUVVPXk+N1/INhhjjLl0DBo0iPj4eDIzM9m6dSs33/zTXwmTJk0iIiKCrVu38txzzzF06FBf3s6dO/n3v//Nxo0b+d3vfseZM2dITk7mjTfe4PPPP2fDhg28/vrrfPHFF0ydOpUmTZrg9Xp5/vnnWbx4MV6vly1btvDxxx8zbtw4Dh92ZvN88cUXzJgxg+3bt7N37958f9mnp6fzwAMPsGjRIrZs2eLb+9Tftm3biIyMzJfub/r06Xg8Hq677jqaNWuGx/PT4hVNmzYlPT2d7777jri4OF8QU5j+/fuzePFiAP75z3/Sp08fX97cuXOpXr06mzZtYtOmTbz++uvs27ePihUrsmTJEjZv3syqVav4zW9+4wtod+/ezejRo3174Prv9Ztj7Nix3H777dx1111Mnz6d77//3pe3evVqPB6P7+O/L+6BAwf45ptvaNeuXa7gNK9ly5YVGDjGxMQQH++s2rZhwwZq1apF06ZNAZgyZQpJSUls3bqVTz/91PePgkCMGTOGsWPHsmnTJhYtWsTIkSMDPvdSdkGDPGOMMcZfWFgYaWlpxMXF0bNn7u3F16xZ45uzd/vtt5ORkcGxY8cA6NWrFxUqVKB27drUrVuXI0eOsGbNGqKjo6lSpQpVq1blnnvuYfXq/O8ArlmzhpiYGMqWLUu9evXo3LkzmzZtAqBdu3bUr1+fMmXK4PF4SEtLy3Xuhg0b6NSpE40aNQKgZs2axd7jzTffTMuWLRkzZowvbezYsXi9Xv773/9y6tQpX+CS45577iE+Pp7PP/8835y7vGrWrEmNGjWIj4+nZcuWVK7s21qeFStW8NZbb+HxeLj55pvJyMhg9+7dqCpPPfUUYWFh3HHHHRw6dIgjR5xVxxo1auQLOlu3bp3vGQAMHz6cHTt2MGDAABITE7nlllv48Udnj4KOHTvi9Xp9n759+/rOi4+P59577wWcAD8uLveyt126dMHj8XD8+PF88+9yzklISCA7O5v4+PhcvZYLFy4kMjKSiIgItm3bVqI5lB9//DGPPvooHo+Hvn37cvz48VzzIi9XNifPGGPMRdW3b1+eeOIJEhMTycjI8KUXtLe6u5YqFSpU8KWVLVuWs2fPFli+IEWVK6jevOfmtKEwwcHBbN682Tdf7fPPPychIYFly/Iv1Vq+fHl69OjBZ599lqvHbtCgQURGRjJs2DDKlCm+P2bgwIGMHj2aefPm5WvvrFmz6N69e670efPmkZ6eTnJyMuXLlycoKIjMzMwCn0FBw7UA119/PSNGjGDEiBGEhISQmlr8KmVxcXEcOXKE+fPnA/D111+ze/duX29czpy8wjRo0ICgoCA+/fRTFi1axPr16wHYt28fL7zwAps2baJGjRrExsb67sef/8/OPz87O5v169dTqVKlYu/hcmI9ecYYYy6qESNG8Oyzz+YbnuvUqZMvGEhMTKR27dpcffXVhdbTqVMnli5dyg8//MCpU6dYsmQJHTt2pFq1arl6ZTp16sSCBQvIysoiPT2dzz77jHbt2gXU1vbt2/Ppp5+yb98+wJk3mFdOsLVu3U+rfv3www8F1qeqrFu3jiZNmuRKv/HGG5kyZUrAL4tER0czfvz4fMFc9+7deeWVVzhz5gwAu3bt4tSpUxw7doy6detSvnx5Vq1axf79+wO6To7ly5f76vzmm2/IyMgo9qWZL7/8klOnTnHo0CHS0tJIS0vjySefzNeLWZyYmBjGjh1LkyZNfPMCjx8/TpUqVahevTpHjhzhww8L3t6+Xr167Nixg+zsbJYsWeJL79atGy+99JLv2Ov1lqhNlyrryTPGGHNR1a9fP9dQZo7JkyczfPhwwsLCqFy5Mm+++WaR9URGRhIbG+sL2EaOHElERAQAt912GyEhIdx1111MmzaN9evXEx4ejogwbdo0rr32Wnbu3FlU9QDUqVOH1157jXvuuYfs7Gzq1q3LRx99lKvMtddey4IFC5gwYQKHDh2ibt261K5dm2effdZXZvr06bzzzjucOXOGsLCwAoO5vG+eFqVatWpMmDAhX/rIkSNJS0sjMjISVaVOnTosXbqUIUOG0KdPH9q0aeNbyqUkVqxYwZgxY6hYsSIAzz//fLHPMC4ujujo6Fxp/fr1Y9CgQfz2t78N+NoDBgxgzJgxzJo1y5cWHh5OREQEwcHBNG7cmNtuu63Ac6dOnUrv3r1p0KABISEhnDx5EoCZM2cyevRowsLCOHv2LJ06dWLOnDkBt+lSJYF2b18p2rRpo1fqejrGmCvHjh07il1fzBhzaSnoz62IJKtqm4LK23CtMcYYY0wpZEGeMcYYY0wpZEGeMcYYY0wpZEGeMcZcoWxOtjGXj3P582pBnjHGXIEqVqxIRkaGBXrGXAZUlYyMDN/bzIGyJVSMMeYKVL9+fQ4ePEh6evrFbooxJgAVK1bMtV9wIAIO8kQkC0hxz9kBDFPVH9y8aGAx0FJVd4pIRcALDFDVFLfMeKCxqo4q4hpjgT8B9VT1mJsWBawCRqrqXDctAtgMjAMaAbcBV7nfv3Sr+6OqJrjlnwCeB+qo6reB3rMxxpRW5cuX923NZYwpnUoyXHtaVT2qGgL8D/AP1mKANcAgAFXNBB4HXhbHDcBDQP6N6HKLATYB0XnSU4CBfseDgC3utUarqgfoCexx2+jxC/AaAHcCX5XgXo0xxhhjLmvnOidvNXATgIhUxelJux83yANQ1eXAYWAoMB2YrKrfFVahiDQBqgLP4AR7/r4CKopIPXE2nusBFLxnSX7TgfGATTwxxhhjzBWjxEGeiJQD7sLpXQO4G1iuqruAoyIS6Vf8cWAKzjDp28VUHQPE4QSQzUWkbp78BGAAcCvOUO2PAbS1L3BIVbcUU+5BEUkSkSSbn2KMMcaY0qAkQV4lEfECSTg9a3Pd9BggZ3fhePx64VT1a2Al8EoA9Q8C4lU1G2d+34A8+QvdtJxgsEgiUhl4Gni2uLKq+pqqtlHVNnXq1AmgqcYYY4wxl7aSvF172p375iMitYDbgRARUaAsoCIyXn96Lz/b/RRKRMKApsBHzmgsVwF7gdk5ZVT1GxE5gzO/bgxOj15RmuC8iLHFrbM+sFlE2qnqN4WdlHLoGEETPyim6stX2tReF7sJxhhjjLkAzncJlf7AW6r6UE6CiHwKdMAZdg1UDM6cvT/51bNPRBrmKfcsUFdVs9zArVDuW72+IV8RSQPa2Nu1xhhjjLkSnO9iyDHAkjxpi4DBJaxnUAH1LMHvRQ4AVV2nqktLWLcxxhhjzBVHbLXz3Cpc11SvGzbjYjfjF2PDtcYYY0zpISLJqtqmoDzb8SKP0Buqk2SBkDHGGGMucxc0yBORUCDvUio/qurNF7IdxhhjjDGl3QUN8tyXITzFFjTGGGOMMeflfF+8MMYYY4wxlyAL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSiEL8owxxhhjSqGAgzwRyRIRr4ikish7IlLZLy9aRFREWrjHFUVkp4iE+pUZLyJzirnGWBHJFJHqfmlRbt33+6VFuGlPiMhst13bReS0+90rIv1F5A8istU9XiEi1wd6v8YYY4wxl7OS9OSdVlWPqoYA/wNG+eXFAGuAQQCqmgk8DrwsjhuAh4Ani7lGDLAJiM6TngIM9DseBGxxrzVaVT1AT2CP20aPqiYAz6tqmJu/DHi2BPdrjDHGGHPZOtfh2tXATQAiUhW4DbgfN8gDUNXlwGFgKDAdmKyq3xVWoYg0AaoCz+AEe/6+AiqKSD0REaAH8GFxjVTV436HVQAt9s6MMcYYY0qBciU9QUTKAXcBy92ku4HlqrpLRI6KSKSqbnbzHgc2ArtV9e1iqo4B4nACyOYiUldV/+uXnwAMAL4ANgM/BtjeKTiB5jGgSyFlHgQeBLjxxhsDqdYYY4wx5pJWkp68SiLiBZJwetbmuukxQLz7PR6/XjhV/RpYCbwSQP2DgHhVzQYW4wR0/ha6aTnBYEBU9WlVbQDMBx4tpMxrqtpGVdvUqVMn0KqNMcYYYy5ZJenJO+3ObfMRkVrA7UCIiChQFlARGa+qOUOj2e6nUCISBjQFPnJGY7kK2AvMzimjqt+IyBngTmAMcGsJ2g7wLvABMKmoQimHjhE08YMSVn15SJva62I3wRhjjDEXyPkuodIfeEtVG6pqkNtjtg/oUMJ6YnDm7AW5n+uBG0SkYZ5yzwITVDUrkEpFpKnfYV9gZwnbZYwxxhhzWSrxnLw8YoCpedIWAYNx5tYFahDOPD9/S9z0z3MSVHVdCds3VUSa4/Qk7if3G8HGGGOMMaWW/DSqagAqXNdUrxs242I34xdhw7XGGGNM6SIiyarapqC88+3JK3VCb6hOkgVDxhhjjLnMXdAgz90BI+9SKj+q6s0Xsh3GGGOMMaXdBQ3yVDUF8BRb0BhjjDHGnJfzfbvWGGOMMcZcgizIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphQIO8kQkS0S8IpIqIu+JSGW/vGgRURFp4R5XFJGdIhLqV2a8iMwp5hpjRSRTRKr7pUW5dd/vlxbhpj0hIrPddm0XkdPud6+I9BeR5912bBWRJSJyTaD3a4wxxhhzOStJT95pVfWoagjwP2CUX14MsAYYBKCqmcDjwMviuAF4CHiymGvEAJuA6DzpKcBAv+NBwBb3WqNV1QP0BPa4bfSoagLwERCiqmHArgCub4wxxhhTKpzrcO1q4CYAEakK3AbcjxvkAajqcuAwMBSYDkxW1e8Kq1BEmgBVgWdwgj1/XwEVRaSeiAjQA/iwuEaq6gpVPesebgDqB3R3xhhjjDGXuRIHeSJSDrgLp3cN4G5guaruAo6KSKRf8ceBKUAdVX27mKpjgDicALK5iNTNk58ADABuBTYDP5aw6SMoJDAUkQdFJElEktLT00tYrTHGGGPMpackQV4lEfECSTg9a3Pd9Bgg3v0ej18vnKp+DawEXgmg/kFAvKpmA4txAjp/C920nGAwYCLyNHAWmF9Qvqq+pqptVLVNnTp1SlK1McYYY8wlqVwJyp525775iEgt4HYgREQUKAuoiIxXVXWLZbufQolIGNAU+MgZjeUqYC8wO6eMqn4jImeAO4ExOD16xRKRYUBvoKtfmwqVcugYQRM/CKTqy1ra1F4XuwnGGGOM+QWd7xIq/YG3VLWhqgapagNgH9ChhPXE4MzZC3I/1wM3iEjDPOWeBSaoalYglYpID2AC0FdVfyhhm4wxxhhjLlvnG+TFAEvypC0CBpewnkEF1LMEvxc5AFR1naouLUG9LwHVcHoIvcUt4WKMMcYYU1pIACOYV5QK1zXV64bNuNjN+MXZcK0xxhhz+RORZFVtU1BeSebkXRFCb6hOkgVAxhhjjLnMXdAgz90BI+9SKj+q6s0Xsh3GGGOMMaXdBQ3yVDUF8BRb0BhjjDHGnJfzffHCGGOMMcZcgizIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphSzIM8YYY4wphWxbszy2ZWwj9M3Qi90MY85byrCUi90EY4wxF5H15BljjDHGlEIW5BljjDHGlEIBB3kikiUiXhFJFZH3RKSyX160iKiItHCPK4rIThEJ9SszXkTmFHONsSKSKSLV/dKi3Lrv90uLcNOeEJHZbru2i8hp97tXRPqLyAAR2SYi2SLSJtB7NcYYY4y53JVkTt5pVfUAiMh8YBTwopsXA6wBBgGTVTVTRB4HXhaRTsD1wENAcYFWDLAJiAbm+aWnAAOBue7xIGALgKqOdtsUBCzLaaOb1hK4B3g10JsMrhVM0rCkQIsbY4wxxlySznW4djVwE4CIVAVuA+7HCb4AUNXlwGFgKDAdJ/j7rrAKRaQJUBV4BifY8/cVUFFE6omIAD2AD4trpKruUNUvS3BfxhhjjDGlQomDPBEpB9yF07sGcDewXFV3AUdFJNKv+OPAFKCOqr5dTNUxQBxOANlcROrmyU8ABgC3ApuBH0va9sKIyIMikiQiSenp6T9XtcYYY4wxF01JgrxKIuIFknB61nKGTmOAePd7PH69cKr6NbASeCWA+gcB8aqaDSzGCej8LXTTcoLBn42qvqaqbVS1TZ06dX7Oqo0xxhhjLopzmpOXQ0RqAbcDISKiQFlARWS8qqpbLNv9FEpEwoCmwEfOaCxXAXuB2TllVPUbETkD3AmMwenR+9n9//buP9qvur7z/fNF+BkzcL2S6QQEouIs2pKa2u9IRUoRZpEoaskaGHOcGZMuWaEz18XV0VKos+7NtQvF6VzEzp3BScdaYDGk/miUIRVGcahhpNgTevAkVhGbXPl1V1NgokigGt73j7MPfD09yfke8j3n+80+z8daZ529P/vz/ez3Zq8DLz77x3f80b0sv2rrXAzdCruvvWjQJUiSpB4c6itULgFuqqrTqmp5VZ0C7ALOmeU4I0zcs7e8+TkJODnJaVP6/R/Ab1XV/kOsW5IkqdUONeSNAFumtH0eeNcsx1k7zThb6HqQA6Cqvl5VX+h10ObVLo8AbwS2JrlzlnVJkiQdlvLiVVUBHLPstbVs3fWDLmNoeblWkqThkWR7VU37ijq/u3aKFSefwKhBRpIkHebmNeQ134Ax9VUqz1XVWfNZhyRJUtvNa8irqnFg5YwdJUmSdEgO9cELSZIkDSFDniRJUgsZ8iRJklrIkCdJktRChjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklpoxpCXZH+SsSQPJLk/ydlN+/IkO5rl85LcPs1n707S6bWYJJ9I8miSI7ra1iepJBd0ta1p2i5JsqWp76Eke5vlsSRnZ8I1SR5M8pdJrui1FkmSpMPZkT302VdVKwGSrAI+Cvxqvwtpgt0a4GHgXODurs3jwAhwV7O+FngAoKrWNJ8/D/hgVb2ta8xfB04Bzqiq55P8/X7XLUmSNIxme7n2eOCpuSgEeDOwA7iBiUDXbRvwhiRHJVkCnA6M9TDmvwQ+XFXPA1TVX/exXkmSpKHVy0zecUnGgGOBZcD5c1TLCHAr8EXgI0mOqqofN9sK+AqwCjgBuA14VQ9jvgZ4Z5I1wB7giqr67tROSTYAGwBOPfXUQz0OSZKkgetlJm9fVa2sqjOA1cBNSdLPIpIcDbwV+EJV/QC4D7hwyVOiRgAAIABJREFUSrfNTFymXctEGOzFMcCzVdUBfh/4g+k6VdWmqupUVWfp0qUv5RAkSZKGSi8zeS+oqnuTnAj0OwmtZmKGbrzJj4uBZ4CtXfv+RpIzmQidD/aYMx8BPt8sbwE+PdMHxh/dy/Krtv5U2+5rL+plX5IkSUNjViEvyRnAIuAJJoJYv4wAl1XVrc1+XgbsSjJ1H1cDz85i3C8wcXn5D5h4WOTBPtQqSZI09GZzTx5AgHVVtX+ambQLkjzStX5p83trksl76+6tqku7P9QEuVXA5ZNtVfWjJPcAb+/uW1Vf6qHebtcCtyR5P/A0cNksPy9JknRYSlUNuoah0ul0anR0dNBlSJIkzSjJ9ubZg7/Db7yQJElqoVndk3eompcpf2xK867JFxpLkiSpP+Y15FXVncCd87lPSZKkhcjLtZIkSS1kyJMkSWohQ54kSVILGfIkSZJayJAnSZLUQoY8SZKkFjLkSZIktZAhT5IkqYUMeZIkSS1kyJMkSWohQ54kSVILzRjykuxPMpbkgST3Jzm7aV+eZEezfF6S26f57N1JOr0Wk+QTSR5NckRX2/okleSCrrY1TdslSbY09T2UZG+zPJbk7CR/mGRXV9vKXmuRJEk6nPUyk7evqlZW1euAq4GPzkUhTbBbAzwMnDtl8zgw0rW+FngAoKrWVNVK4DJgW1Pryqr6etP3N7vaxuaidkmSpGEz28u1xwNPzUUhwJuBHcAN/HSgA9gGvCHJUUmWAKcDBjZJkqQD6CXkHddc6vw28J+B35mjWkaAW4EtwNuSHNW1rYCvAKuAXwNum8W41yT5ZpKPJzlmug5JNiQZTTK6Z8+el1i+JEnS8Diyhz77msuhJHkjcFOSM/tZRJKjgbcC76+qHya5D7gQ2NrVbTNwBXAC8AHgt3sY+mrg/wOOBjYBvwV8eGqnqtrUbOeYZa+t5VdtndoFgN3XXtTjEUmSJA1WLyHvBVV1b5ITgaV9rmM1E+FtPAnAYuAZukJeVX2jCZf7qurBpt9M9T7eLD6X5NPAB/tctyRJ0lCaVchLcgawCHiCiSDWLyPAZVV1a7OflwG7kkzdx9XAs70OmmRZVT2eiUR4MRP3/EmSJLVeLyHvuCSTDzkEWFdV+6eZSbsgySNd65c2v7cm+XGzfG9VXdr9oSbIrQIun2yrqh8luQd4e3ffqvpSD/V2uyXJ0qbuMeA3Zvl5SZKkw1KqatA1DJVOp1Ojo6ODLkOSJGlGSbZX1bTvJPYbLyRJklpoVvfkHaokq4CPTWneVVVr5rMOSZKktpvXkFdVdwJ3zuc+JUmSFiIv10qSJLWQIU+SJKmFDHmSJEktZMiTJElqIUOeJElSCxnyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFDHmSJEktNGPIS7I/yViSB5Lcn+Tspn15kh3N8nlJbp/ms3cn6fRaTJJPJHk0yRFdbeuTVJILutrWNG2XJNnS1PdQkr3N8thknU3/f5/k6V7rkCRJOtwd2UOffVW1EiDJKuCjwK/2u5Am2K0BHgbOBe7u2jwOjAB3NetrgQcAqmpN8/nzgA9W1dumjNsB/pd+1ytJkjTMZnu59njgqbkoBHgzsAO4gYlA120b8IYkRyVZApwOjM00YJJFwO8CV87Qb0OS0SSje/bseUnFS5IkDZNeZvKOSzIGHAssA86fo1pGgFuBLwIfSXJUVf242VbAV4BVwAnAbcCrehjzvcBtVfV4kgN2qqpNwCaATqdTL/kIJEmShsRsL9e+EbgpyZn9LCLJ0cBbgfdX1Q+T3AdcCGzt6rYZuIKJkPcB4LdnGPMk4FLgvNnUMv7oXpZftXXGfruvvWg2w0qSJM2rXkLeC6rq3iQnAkv7XMdqJsLbeDPjthh4hq6QV1XfaMLlvqp68GAzc41fZOKy7kOTYyZ5qKpO73PtkiRJQ2dWIS/JGcAi4Akmgli/jACXVdWtzX5eBuxKMnUfVwPP9jJgVW0F/sHkepKnDXiSJGmhmM09eQAB1lXV/mlm0i5I8kjX+qXN761JJu+tu7eqLu3+UBPkVgGXT7ZV1Y+S3AO8vbtvVX2ph3olSZIWvFT5nEG3TqdTo6Ojgy5DkiRpRkm2V9W07yT2Gy8kSZJaaFb35B2q5mXKH5vSvGvyhcaSJEnqj3kNeVV1J3DnfO5TkiRpIfJyrSRJUgsZ8iRJklrIkCdJktRChjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklrIkCdJktRCM4a8JPuTjCV5IMn9Sc5u2pcn2dEsn5fk9mk+e3eSTq/FJPlEkkeTHNHVtj5JJbmgq21N03ZJki1NfQ8l2dssjyU5O8mnmrq/meRzSZb0WoskSdLhrJeZvH1VtbKqXgdcDXx0Lgppgt0a4GHg3Cmbx4GRrvW1wAMAVbWmqlYClwHbmlpXVtXXgfdX1euq6heA7wPvnYvaJUmShs1sL9ceDzw1F4UAbwZ2ADfw04EOYBvwhiRHNbNxpwNjMw1YVT8ASBLgOKCm65dkQ5LRJKN79uw5hEOQJEkaDkf20Oe4JGPAscAy4Pw5qmUEuBX4IvCRJEdV1Y+bbQV8BVgFnADcBryql0GTfBp4K/At4APT9amqTcAmgE6nM20QlCRJOpz0EvL2NZdDSfJG4KYkZ/aziCRHMxHE3l9VP0xyH3AhsLWr22bgCiZC3geA3+5l7Kr69SSLgH8PvBP49MH6jz+6l+VXbT1Yl2ntvvaiWX9GkiRprszqcm1V3QucCCztcx2rmQhv40l2A+cw5ZJtVX0DOBM4saoenM3gVbUf+CPgn/SlWkmSpCHXy0zeC5KcASwCngAW97GOEeCyqrq12c/LgF1Jpu7jauDZHmsN8JqqeqhZfjvw7T7WLEmSNLRmc08eQIB1VbV/Ijf9lAuSPNK1fmnze2uSyXvr7q2qS7s/1AS5VcDlk21V9aMk9zARzOhq/1IP9b4wNHBjkuOb5QeAfzmLz0uSJB22UuVzBt06nU6Njo4OugxJkqQZJdleVdO+k9hvvJAkSWqhWd2Td6iSrAI+NqV5V1Wtmc86JEmS2m5eQ15V3QncOZ/7lCRJWoi8XCtJktRChjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklrIkCdJktRChjxJkqQWmjHkJdmfZCzJA0nuT3J20748yY5m+bwkt0/z2buTdHotJsknkjya5IiutvVJKskFXW1rmrZLkmxp6nsoyd5meSzJ2UluSfKdJDuS/EGSo3qtRZIk6XDWy0zevqpaWVWvA64GPjoXhTTBbg3wMHDulM3jwEjX+lrgAYCqWlNVK4HLgG1NrSur6uvALcAZwArguKaPJElS6832cu3xwFNzUQjwZmAHcAM/HegAtgFvSHJUkiXA6cDYTANW1Z9UA/gG8Mo+1yxJkjSUjuyhz3FJxoBjgWXA+XNUywhwK/BF4CNJjqqqHzfbCvgKsAo4AbgNeFWvAzeXaf8F8L8fYPsGYAPAqaee+lLrlyRJGhqzuVx7BrAauClJ+llEkqOBtwJfqKofAPcBF07ptpmJy7RrmQiDs/Efga9V1bbpNlbVpqrqVFVn6dKlsxxakiRp+PQyk/eCqro3yYlAv5PQaiZm6Mab/LgYeAbY2rXvbyQ5k4nQ+WCvOTPJ/9nUe3kv/ccf3cvyq7bO3HGK3ddeNOvPSJIkzZVZhbwkZwCLgCeYCGL9MgJcVlW3Nvt5GbArydR9XA082+ugSS5j4hLvBVX1fL+KlSRJGnazuScPIMC6qto/zUzaBUke6Vq/tPm9NcnkvXX3VtWl3R9qgtwqumbaqupHSe4B3t7dt6q+1EO93T4J/L/AvU29f1xVH57lGJIkSYedTDx4qknHLHttLVt3/aw/5+VaSZI035Jsr6pp30k8q8u1C8GKk09g1MAmSZIOc/Ma8pKsAj42pXlXVa2ZzzokSZLabl5DXlXdCdw5n/uUJElaiGb7jReSJEk6DBjyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFDHmSJEktZMiTJElqIUOeJElSCxnyJEmSWsiQJ0mS1EIzhrwk+5OMJXkgyf1Jzm7alyfZ0Syfl+T2aT57d5JOr8Uk+USSR5Mc0dW2PkkluaCrbU3TdkmSLU19DyXZ2yyPJTk7yXub9kpyYq91SJIkHe6O7KHPvqpaCZBkFfBR4Ff7XUgT7NYADwPnAnd3bR4HRoC7mvW1wAMAVbWm+fx5wAer6m1dY+4Dbp8yliRJUuvN9nLt8cBTc1EI8GZgB3ADE4Gu2zbgDUmOSrIEOB0Ym2nAqvqLqtrd70IlSZKGXS8zecclGQOOBZYB589RLSPArcAXgY8kOaqqftxsK+ArwCrgBOA24FX92nGSDcAGgFNPPbVfw0qSJA1MLzN5+6pqZVWdAawGbkqSfhaR5GjgrcAXquoHwH3AhVO6bWbiMu1aJsJg31TVpqrqVFVn6dKl/RxakiRpIHqZyXtBVd3bPMDQ7yS0mokZuvEmPy4GngG2du37G0nOZCJ0PtjnnPmC8Uf3svyqrTN37MHuay/qyziSJEmzNauQl+QMYBHwBBNBrF9GgMuq6tZmPy8DdiWZuo+rgWf7uF9JkqRWms09eQAB1lXV/mlm0i5I8kjX+qXN761JJu+tu7eqLu3+UBPkVgGXT7ZV1Y+S3AO8vbtvVX2ph3q7x74CuBL4B8A3k/xJVV02mzEkSZIOR6mqQdcwVI5Z9tpatu76vozl5VpJkjSXkmyvqmnfSTyry7ULwYqTT2DUcCZJkg5z8xrympcpf2xK867JFxpLkiSpP+Y15FXVncCd87lPSZKkhWi233ghSZKkw4AhT5IkqYUMeZIkSS1kyJMkSWohQ54kSVILGfIkSZJayJAnSZLUQoY8SZKkFvJrzabY+cROVty4YtBl9MX4uvFBlyBJkgbEmTxJkqQWMuRJkiS10MBDXpL9ScaS7Ejy2SSLu7atSVJJzmjWj03y7SQruvpcmeSTBxn/1CT/LclfJvlWkuVzeTySJEnDIFU12AKSp6tqSbN8C7C9qq5r1j8DLAPuqqqNTdtq4EPAucBJwNeATlU9dYDx7wauqaovJ1kCPF9Vzxyons5Ji2p0w5LeD2Dj3t77SpIk9VGS7VXVmW7bwGfyptgGnA7QBLI3Ae8B1k52qKo7gMeBdwMfBzYeJOD9HHBkVX25+ezTBwt4kiRJbTE0IS/JkcBbgMlHQi8G7qiqB4Enk7y+q/v7gGuApVV180GG/YfA/0zyx0n+IsnvJlk0zb43JBlNMrrnmcHObEqSJPXDMIS845KMAaPA94FPNe0jwOZmeXOzDkBVPQZ8FbhhhrGPBH4F+CDwj4BXA+undqqqTVXVqarO0sV56UciSZI0JIbhPXn7qmpld0OSVwDnA2cmKWARUEmurBdvIny++TmYR4C/qKq/asb9AvDLvBgk/47xejXLn72+9+qv2tp7X2kI7L72okGXIEmaB8MwkzedS4Cbquq0qlpeVacAu4BzZjnOnwMvT7K0WT8f+FYf65QkSRpKwxryRoAtU9o+D7xrNoNU1X4mLtXelWQcCPD7falQkiRpiA38cu3k61OmtJ03TdvvTVlf3+P4XwZ+4SWWJ0mSdFgaeMgbNitOPoFR71mSJEmHuVaEvOYbMKa+SuW5qjprEPVIkiQNWitCXlWNAytn7ChJkrRADOuDF5IkSToEhjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklqoFS9D7qedT+xkxY0rBl3GnBpfNz7oEiRJ0hxzJk+SJKmFDHmSJEktNPCQl2R/krEkO5J8Nsnirm1rklSSM5r1Y5N8O8mKrj5XJvlkD+OPJbltbo9GkiRpOKSqBltA8nRVLWmWbwG2V9V1zfpngGXAXVW1sWlbDXwIOBc4Cfga0Kmqp2YavxedkxbV6Iaeuw+HjXsHXYEkSRqAJNurqjPdtoHP5E2xDTgdIMkS4E3Ae4C1kx2q6g7gceDdwMeBjQcKeJIkSQvV0IS8JEcCbwEmH/28GLijqh4Enkzy+q7u7wOuAZZW1c0zDH1sktEkf5bk4gPse0PTZ3TPM4Od2ZQkSeqHYXiFynFJxprlbcCnmuUR4PpmeXOzfj9AVT2W5KvA7T2Mf2rT/9XAV5OMV9X3ujtU1SZgE0xcrj2ko5EkSRoCwxDy9lXVyu6GJK8AzgfOTFLAIqCSXFkv3kT4fPNzUFX1WPP7r5LcDfwi8L0D9R+vV7P82esPtHk4XbV10BVogdl97UWDLkGSNIOhuVw7xSXATVV1WlUtr6pTgF3AObMZJMnLkxzTLJ/IxD1+3+p7tZIkSUNmWEPeCLBlStvngXfNcpyfBUaTPAD8d+DaqjLkSZKk1hv45drpXm9SVedN0/Z7U9bX9zD214F2f0eZJEnSNAYe8obNipNPYNT7jSRJ0mGuFSGv+QaMqa9Sea6qzhpEPZIkSYPWipBXVePAyhk7SpIkLRDD+uCFJEmSDoEhT5IkqYUMeZIkSS1kyJMkSWohQ54kSVILGfIkSZJayJAnSZLUQoY8SZKkFmrFy5D7aecTO1lx4+H5dbfj68YHXYIkSRoSzuRJkiS1kCFPkiSphQYe8pLsTzKWZEeSzyZZ3LVtTZJKckazfmySbydZ0dXnyiSfnGEfxyd5NMn/M3dHIkmSNDyG4Z68fVW1EiDJLcBvANc120aAe4C1wMaqejbJ+4D/mORc4CTgcqAzwz5+B/jTXor5+ef+ltFd35/9UczGxr1zO74kSVrwBj6TN8U24HSAJEuANwHvYSLkAVBVdwCPA+8GPs5E+HvqQAMm+SXgZ4D/NndlS5IkDZehCXlJjgTeAkw+InoxcEdVPQg8meT1Xd3fB1wDLK2qmw8y5hHA/w385gz73pBkNMnonmfqUA5DkiRpKAxDyDsuyRgwCnwf+FTTPgJsbpY3N+sAVNVjwFeBG2YY+18Bf1JVDx+sU1VtqqpOVXWWLs5LOARJkqThMlT35E1K8grgfODMJAUsAirJlVU1OdX2fPNzMG8EfiXJvwKWAEcnebqqrjrQB8br1Sx/9vqXeiy9uWrr3I4vDZnd11406BIkacEZhpA3nUuAm6rq8smGJH8KnMPEfXs9qap/1vX59UDnYAFPkiSpLYbhcu10RoAtU9o+D7xrALVIkiQddgY+k1dVS6ZpO2+att+bsr5+lvv5Q+APZ1WcJEnSYWrgIW/YrDj5BEa9f0iSJB3mWhHymm/AmPoqleeq6qxB1CNJkjRorQh5VTUOrJyxoyRJ0gIxrA9eSJIk6RAY8iRJklrIkCdJktRChjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaqFWvAy5n3Y+sZMVN64YdBmtML5ufNAlSJK0YDmTJ0mS1EKGPEmSpBYaeMhLsj/JWJIdST6bZHHXtjVJKskZzfqxSb6dZEVXnyuTfPIAY5+WZHsz/s4kvzH3RyRJkjR4qarBFpA8XVVLmuVbgO1VdV2z/hlgGXBXVW1s2lYDHwLOBU4CvgZ0quqpacY+moljfC7JEmAHcHZVPXagejonLarRDUv6eYgLw8a9g65AkqQFJ8n2qupMt23gM3lTbANOB2hC2ZuA9wBrJztU1R3A48C7gY8DG6cLeE3fv62q55rVYxi+45UkSZoTQxN6khwJvAWYfCTzYuCOqnoQeDLJ67u6vw+4BlhaVTfPMO4pSb4JPAx8bLpZvCQbkowmGd3zzGBnNiVJkvphGELecUnGgFHg+8CnmvYRYHOzvLlZB6AJal8Fbphp8Kp6uKp+gYkZwnVJfmaaPpuqqlNVnaWLc0gHI0mSNAyG4T15+6pqZXdDklcA5wNnJilgEVBJrqwXbyJ8vvnpSVU9lmQn8CvA5w7Ub7xezfJnr5/tMeiqrYOuQAvE7msvGnQJknRYGIaZvOlcAtxUVadV1fKqOgXYBZwzm0GSvDLJcc3yy5m4x+87fa9WkiRpyAxryBsBtkxp+zzwrlmO87PAfUkeAP4U+HdV5dcwSJKk1hv4K1SGTafTqdHR0UGXIUmSNKPD6RUqkiRJ6oNhePDikDXfgDH1VSrPVdVZg6hHkiRp0FoR8pr77FbO2FGSJGmB8HKtJElSCxnyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFDHmSJEktZMiTJElqoVa8DLmfdj6xkxU3rhh0GTqI8XXjgy5BkqSh50yeJElSCxnyJEmSWmjgIS/J/iRjSXYk+WySxV3b1iSpJGc068cm+XaSFV19rkzyyQOMvTLJvUl2JvlmknfO/RFJkiQNXqpqsAUkT1fVkmb5FmB7VV3XrH8GWAbcVVUbm7bVwIeAc4GTgK8Bnap6apqx/yFQVfXdJCcB24Gfrar/eaB6OictqtENS/p5iAvbxr2DrkCSpNZKsr2qOtNtG/hM3hTbgNMBkiwB3gS8B1g72aGq7gAeB94NfBzYOF3Aa/o+WFXfbZYfA/4aWDqXByBJkjQMhibkJTkSeAsw+ejkxcAdVfUg8GSS13d1fx9wDbC0qm7ucfw3AEcD35tm24Yko0lG9zwz2JlNSZKkfhiGV6gcl2SsWd4GfKpZHgGub5Y3N+v3w8SsXJKvArf3soMky4CbgXVV9fzU7VW1CdgEcMyy19byZ6+f2kUv1VVbB12BdEh2X3vRoEuQpJdkGELevqpa2d2Q5BXA+cCZSQpYBFSSK+vFmwifb34OKsnxwFbg31TVn/W3dEmSpOE0NJdrp7gEuKmqTquq5VV1CrALOGc2gyQ5GtjSjPXZOahTkiRpKA1ryBthIpx1+zzwrlmO80+ZeAp3ffOalrEkK2f6kCRJ0uFu4K9QGTadTqdGR0cHXYYkSdKMDqdXqEiSJKkPhuHBi0PWfAPG1FepPFdVZw2iHkmSpEFrRcirqnHAe+0kSZIaXq6VJElqIUOeJElSCxnyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFDHmSJEkt1IqXIffTzid2suLGFYMuQ5J0mBhfNz7oEqRpOZMnSZLUQoY8SZKkFhp4yEuyP8lYkh1JPptkcde2NUkqyRnN+rFJvp1kRVefK5N88iDjr0vy3eZn3dwejSRJ0nBIVQ22gOTpqlrSLN8CbK+q65r1zwDLgLuqamPTthr4EHAucBLwNaBTVU9NM/b/CowCHaCA7cAvTdd3UuekRTW6YUn/DlCSJC0sG/fO266SbK+qznTbBj6TN8U24HSAJEuANwHvAdZOdqiqO4DHgXcDHwc2HiS0rQK+XFVPNn2+DKye2inJhiSjSUb3PDPY0CtJktQPQxPykhwJvAWYfEzpYuCOqnoQeDLJ67u6vw+4BlhaVTcfZNiTgYe71h9p2n5KVW2qqk5VdZYuzqEchiRJ0lAYhleoHJdkrFneBnyqWR4Brm+WNzfr9wNU1WNJvgrcPsPY0yW2g07VjderWf7s9QfrIkmttfvaiwZdgqQ+GYaQt6+qVnY3JHkFcD5wZpICFgGV5Mp68SbC55ufg3kEOK9r/ZXA3f0oWpIkaZgNzeXaKS4Bbqqq06pqeVWdAuwCzpnlOHcCFyZ5eZKXAxc2bZIkSa02rCFvBNgype3zwLtmM0hVPQn8DvDnzc+HmzZJkqRWG/grVIZNp9Op0dHRQZchSZI0o8PpFSqSJEnqg2F48OKQNd+AMfVVKs9V1VmDqEeSJGnQWhHyqmocWDljR0mSpAXCy7WSJEktZMiTJElqIUOeJElSCxnyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFWvEy5H7a+cROVty4YtBlLEjj68YHXYIkSa3hTJ4kSVILGfIkSZJaaNaXa5M8XVVLprRtBJ6uqn+X5A+Bfwr8TFX9sNn+CeAKYGlV/U2Sp4E3Ajc3Q5wK7G1+/qaq/vE0+10O/CXwHSDAj4Bfr6rvdPX5BHAJcEpVPd+0rQc6VfXeXo7v51/x84yuG+2lqyRJ0tCaq5m8h4BfA0hyBPBm4NHuDlU1XlUrq2olcBvwm8363wl4Xb7X9HkdcCPw25Mbmv2sAR4Gzu3r0UiSJB1m5irk3Qq8s1k+D/gfwE/6vI/jgae61t8M7ABuAEZmM1CSDUlGk4zu2bOnjyVKkiQNxlyFvO8CS5O8nInAtblP474myViS7wH/Griua9sIE+FyC/C2JEf1OmhVbaqqTlV1li5d2qdSJUmSBmcuX6Hyx8Ba4Czg8j6N+b3m8i5J3glsAlYnORp4K/D+qvphkvuAC4Gts93B+KN7WX7VrD8m9WT3tRcNugRJ0gIxlyFvM3A/cGNVPZ+k3+PfBny6WV4NnACMN/tZDDzDSwh5kiRJbTBnIa+qvp/kQ8BX5mgX5wDfa5ZHgMuq6laAJC8DdiVZPEf7liRJGmovJeQtTvJI1/p1B+pYVf/pJYx/MK9JMsbEK1T+FrisCXKr6LokXFU/SnIP8PamaX2Si7vG+eWq6j4GSZKkVklVDbqGodLpdGp01PfkSZKk4Zdke1V1ptvmN15IkiS10Fw+ePGSJFnBi9+EMem5qjprEPVIkiQdjoYu5FXVOLBy0HVIkiQdzrxcK0mS1EKGPEmSpBby6dopkvwQ+M6g6xAAJwJ/M+giBHguhonnYnh4LobDQj8Pp1XVtN/JOnT35A2B7xzoUWTNrySjnovh4LkYHp6L4eG5GA6ehwPzcq0kSVILGfIkSZJayJD3d20adAF6gedieHguhofnYnh4LoaD5+EAfPBCkiSphZzJkyRJaqEFG/KSrE7ynSQPJblqmu3HJPmjZvt9SZbPf5Xt18N5ODfJ/Ul+kuSSQdS4UPRwLv51km8l+WaSu5KcNog6F4IezsVvJBlPMpbkniQ/N4g6F4KZzkX/32JbAAADG0lEQVRXv0uSVBKf8pwjPfxdrE+yp/m7GEty2SDqHCYLMuQlWQT8B+AtwM8BI9P8S/I9wFNVdTrwceBj81tl+/V4Hr4PrAf+y/xWt7D0eC7+AuhU1S8AnwP+7fxWuTD0eC7+S1WtqKqVTJyH6+a5zAWhx3NBkr8HXAHcN78VLhy9ngvgj6pqZfPzn+e1yCG0IEMe8Abgoar6q6r6W2Az8GtT+vwacGOz/DnggiSZxxoXghnPQ1XtrqpvAs8PosAFpJdz8d+r6plm9c+AV85zjQtFL+fiB12rLwO8uXpu9PLfCoDfYSJsPzufxS0wvZ4LdVmoIe9k4OGu9Ueatmn7VNVPgL3AK+aluoWjl/Og+THbc/Ee4EtzWtHC1dO5SPK/JfkeE+HiinmqbaGZ8Vwk+UXglKq6fT4LW4B6/XfUP2luKflcklPmp7ThtVBD3nQzclP/T7iXPjo0/jMeHj2fiyT/HOgAvzunFS1cPZ2LqvoPVfUa4LeAfzPnVS1MBz0XSY5g4naeD8xbRQtXL38X/xVY3txS8hVevBq3YC3UkPcI0J3wXwk8dqA+SY4ETgCenJfqFo5ezoPmR0/nIsk/Bj4EvKOqnpun2haa2f5dbAYuntOKFq6ZzsXfA84E7k6yG/hl4DYfvpgTM/5dVNUTXf9e+n3gl+aptqG1UEPenwOvTfKqJEcDa4HbpvS5DVjXLF8CfLV8qWC/9XIeND9mPBfNZan/xETA++sB1LhQ9HIuXtu1ehHw3XmsbyE56Lmoqr1VdWJVLa+q5Uzcq/qOqhodTLmt1svfxbKu1XcAfzmP9Q2lIwddwCBU1U+SvBe4E1gE/EFV7UzyYWC0qm4DPgXcnOQhJmbw1g6u4nbq5Twk+UfAFuDlwNuT/F9V9fMDLLuVevyb+F1gCfDZ5hmk71fVOwZWdEv1eC7e28yq/hh4ihf/h1R91OO50Dzo8VxckeQdwE+Y+O/2+oEVPCT8xgtJkqQWWqiXayVJklrNkCdJktRChjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgv9/1USAsuhvQc5AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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BA7/ItZo0aaJZWVlFlpkyZUqe486dO/9s13/22We1ZcuWGh4erlFRUTp37twzqmfLli0aHR2tHo9H//Of/+hf//pXbdWqlQ4dOlTfffddfeqpp4o8/2zu6fXXX9d9+/YVmFfQn1sgVQuJaUoyJ++EqnoARGQeMAp43s2LA1YBQ4DJqnpSRB4GXhKRbkBD4F6gQzHXiAPWuUHjnID0dOAOYLZ7PATYAKCqo902hQFLfW1003oAvwaiVPVHEalfgvs1xphLRtjE937W+jKf7hdSucTERLp06UJSUhKTJ0/+WdsQihUrVlCtWjWuu+66Ep3XtWtXli5dyvHjx/F4PNxyyy20b9/+jNvh+0u5TJmfd4Dt5MmT9OvXj+eff57+/fsDkJKSQlZWFg0aNCj2/NOnT1Ou3E+hQk5ODmXLlj2rNj355JM89thj/uPPPvvsrOrzmTVrFh9++CFr166lRo0aHD58mCVLlpxRXUuWLOHXv/41f/jDHwB46aWXeP/99/3z4QYMGFDk+WdzT3PmzCEiIoKGDRuecR0+Z/rbtBK4CkBEqgHXA3fhBF8AqOoyYD8wHJiGE/x9V1iFItIcqAb8DifYC/Q1UElEGojTX9kHeD+Edt4HPK2qP7pt+m8h175HRFJFJDUrKyuEao0xxpytY8eOsXr1ambPnk1SUlKevCNHjhAbG0ubNm0YNWoUubm55OTkkJCQQEREBJGRkUybNg0Ar9fLtddeS1RUFLGxsXz3Xf6/asLCwvj2228BSE1NJSYmhszMTGbNmsW0adPweDysXLmSrKwsBg4cSMeOHenYsSOrV68u8h6qVq1K+/bt2bFjBzk5OYwbN46OHTsSFRXFK6+84r/Pnj170q5dOyIjI3n33XcByMzMpHXr1tx///20a9eOPXv2lOj+YmJimDBhAp06deLqq69m5cqV+dr39ttv07lzZ3+AB9CjRw8iIiI4efIkI0aMIDIykrZt25KSkgI4QcbgwYPp378/vXr1YsWKFfTo0YOhQ4cSGem8mPjWW2/RqVMnPB4P9957Lzk5Ofmufeutt9K+fXvCw8N59dVXAZg4cSInTpzA4/EwbNgwAKpVqwY4ge64ceP89z9//nzACcRjYmIYNGgQrVq1YtiwYb5RvTyefPJJXnrpJWrUqAFAzZo1iY+PB+Djjz+mbdu2REZGcuedd/Ljjz8CkJaWRvfu3Wnfvj29e/dm//79/Otf/2L69On87W9/o0ePHowaNYqdO3cyYMAApk2bxpw5c3jggQcAOHjwILGxsURHRxMdHe0P7nz3BPDss8/6fycmTZqU52d/9913Ex4eTq9evThx4gTJycmkpqYybNgwPB4PJ06cKOQ3LzQlDvJEpBzQF6d3DeBWYJmqbgMOiUi7gOIPA1OAeqr6ZjFVxwGJOAFkywJ63ZKBwcB1wHrgxxCaezXQVUS+EJFPRKRjQYVU9VVV7aCqHerVK3D7N2OMMT+zJUuW0KdPH66++mpq167N+vXr/Xlr167lL3/5C+np6ezYsYNFixbh9XrZt28fGRkZpKenM2LECACGDx/OM888w8aNG4mMjPT3vhQnLCyMUaNGMXbsWLxeL127dmXMmDGMHTuWdevWsXDhQkaOHFlkHdnZ2Xz++eeEh4cze/Zsatasybp161i3bh2vvfYau3btolKlSixevJj169eTkpLCb3/7W3+QsnXrVoYPH86XX37Jt99+W+L7O336NGvXrmX69OkF3ndGRkahPYwzZ84EID09ncTEROLj4/0T+9esWcPcuXNZvny5/+cxZcoUNm/ezJYtW5g/fz6rV6/G6/VStmxZ5s2bl6/+v//976SlpZGamsqMGTPIzs7m6aefpnLlyni93nzn+H7GGzZs4KOPPmLcuHHs378fgC+//JLp06ezefNmdu7cmS/4Pnr0KEePHqV58+b52nHy5EkSEhKYP38+6enpnD59mpdffplTp07x4IMPkpycTFpaGnfeeSePP/44N998s//3IiUlhVmzZtGwYUNSUlIYO3ZsnrofeughunfvzoYNG1i/fj3h4eF58j/44AO2b9/O2rVr8Xq9pKWl8emnnwKwfft2Ro8ezaZNm7jssstYuHAhgwYNokOHDsybNw+v10vlypUL/NmFqiTDtZVFxOt+X8lPQ6dxwHT3e5J7vB5AVb8RkeXA0hDqHwLEqmquiCzCCehmBuQvAOYDrXCCwVD61ssBtYBrgY7AAhFppgX9E8CVvu/wzz5scT6FOmRijDHnWmJiIg8//DAAQ4YMITExkXbtnH6CTp060axZMwDi4uJYtWoVPXv2ZOfOnTz44IP069ePXr16cfjwYb7//nu6d+8OQHx8PIMHDz7jNn300Uds3rzZf3zkyBGOHj1K9erV85RbuXIlbdu2pUyZMkycOJHw8HAmTZrExo0bSU5OBuDw4cNs376dRo0a8dhjj/Hpp59SpkwZ9u3bx8GDBwFo0qQJ1157LQDNmjUr8f3ddtttALRv357MzMwS3euqVat48MEHAWjVqhVNmjRh27ZtANx0003Url3bX7ZTp07+ocqPP/6YtLQ0OnZ0+k1OnDhB/fr5Z0PNmDGDxYsXA7Bnzx62b99OnTp1imxPXFwcZcuWpUGDBnTv3p1169ZRo0YNOnXqRKNGjQDweDxkZmbSpUsX/7mqWuiLCVu3bqVp06ZcffXVgPMMZ86cyY033khGRgY33XQT4AxFX3755SE8uZ8sX76cN954A4CyZctSs2bNPPkffPABH3zwAW3btgWcXt3t27dz5ZVX0rRpUzweZ4bZmfz8QnFGc/J8RKQOcAMQISIKlAVURMYHBFK57qdQIhIFtAA+dH9IFYCdBAR5qnpARE4BNwFjCC3I2wssctuyVkRygbqAjckaY8x5lJ2dzfLly8nIyEBEyMnJQUSYOnUqkP9NQhGhVq1abNiwgX//+9/MnDmTBQsW+Ic0i1OuXDlyc52/iopahiI3N5c1a9YU24Pim5MXSFV54YUX6N27d570OXPmkJWVRVpaGuXLlycsLMzfhqpVq/rLncn9VaxYEXACjNOnT+fLDw8P55NPPinw3CL6O/K0K/hYVYmPj+epp54q9PwVK1bw0UcfsWbNGqpUqUJMTEyxy38U1R7ffULB91qjRg2qVq3Kzp07/f84KK5eVSU8PJw1a9YU2a6zoao8+uij3HvvvXnSMzMz893T2Q7NFuRsZ3gOAt5Q1SaqGqaqjYFdQJdizgsWhzNnL8z9NASuEJEmQeWeACaoav7B/4ItwQlCEZGrcYLHb0vYNmOMMT+z5ORkhg8fzu7du8nMzGTPnj00bdqUVatWAc7w4K5du8jNzWX+/Pl06dKFb7/9ltzcXAYOHMif/vQn1q9fT82aNalVq5Z/Ptqbb77p7/UKFBYWRlpaGgALFy70p1evXp2jR4/6j3v16sWLL77oP/Z6vYSqd+/e/mFAgG3btnH8+HEOHz5M/fr1KV++PCkpKezevbvA88/m/gozdOhQPvvsM95776cRqmXLlpGenk63bt38Q6bbtm3j66+/pmXLlsXW2bNnT5KTk/nvf51p7ocOHcp3T4cPH6ZWrVpUqVKFr776is8//9yfV758ef8zCtStWzfmz59PTk4OWVlZfPrpp3Tq1Cnke3300UcZPXo0R44cAZxe2FdffZVWrVqRmZnJf/7zH+CnZ9iyZUuysrL8Qd6pU6fYtGlTyNfzPYuXX34ZcHoCfdf26d27N3//+985duwYAPv27fM/t8IE/06ejbMN8uKAxUFpC4GhJaxnSAH1LCbgRQ4AVf1MVUvyqszfgWYikoEzlBxf1FCtMcaYcyMxMZHY2Ng8aQMHDuTtt98GoHPnzkycOJGIiAiaNm1KbGws+/btIyYmBo/HQ0JCgr8nae7cuYwbN46oqCi8Xi9PPPFEvutNmjSJMWPG0LVr1zxvh/bv35/Fixf7X7yYMWMGqampREVF0aZNG2bNmhXyPY0cOZI2bdrQrl07IiIiuPfeezl9+jTDhg0jNTXVP9eqVatWBZ5/NvdXmMqVK7N06VJeeOEFWrRoQZs2bZgzZw7169fn/vvvJycnh8jISO644w7mzJmTp3epMG3atOHPf/4zvXr1Iioqiptuusk/d86nT58+nD59mqioKH7/+9/7h6QB7rnnHqKiovwvXvjExsYSFRVFdHQ0N9xwA1OnTuVXv/pVyPd633330aNHDzp27EhERATdu3enSpUqVKpUiddff53BgwcTGRlJmTJlGDVqFBUqVCA5OZkJEyYQHR2Nx+Mp8Vuxf/3rX0lJSSEyMpL27dvnCxJ79erF0KFD6dy5M5GRkQwaNKjYAC4hIYFRo0b9LC9eiMU8eXXo0EFTU1PPdzOMMeYXtWXLFlq3bn2+m2GMKYGC/tyKSJqqFrhEne14YYwxxhhTCpXkxYuzJiKRQPBSKj+q6jXnsh3GGGOMMaXdOQ3yVDUd8BRb0BhjjDHGnBUbrjXGGGOMKYUsyDPGGGOMKYUsyDPGGGOMKYUsyDPGGHPeLF68GBHhq6++8qetWLGCW2655azrTkhI8G8xVpgVK1aUeG20FStWULNmTdq2bUvr1q1D3iu3JALbPnLkyDxbrZ2JzMxMIiIiCszbvn07t9xyC82bN6d9+/b06NHDv7/qnDlzeOCBB/KdExYWRteuXfOkeTyeQq+xbds2br75Zq666ipat27N7bff7t/araTGjRtHeHg448aNIysri2uuuYa2bduycuVKbr75Zr7//vtCz501a5Z/G7KSyszM9K/jeLE4py9eGGOMuUBNrll8mRLVdzikYomJiXTp0oWkpCQmT57887YhBCtWrKBatWpcd10oO2X+xLet2fHjx/F4PNxyyy20b9/+F2nj3/72t1+kXnC2eOvXrx/PPfccAwYMACAjI4PU1FS6detW5LlHjx5lz549NG7cmC1bthR7jeeff57+/fsDkJKSQlZWFg0aNChxm1955RWysrKoWLEiSUlJtGrVirlz5wLkCzyDjRo1qsTX8/EFeUOHlnS/h/PHevKMMcacF8eOHWP16tXMnj2bpKSkPHlHjhwhNjaWNm3aMGrUKHJzc8nJySEhIYGIiAgiIyP9+7p6vV6uvfZaoqKiiI2N5bvvvst3rbCwML791tnVMjU1lZiYGDIzM5k1axbTpk3z73iRlZXFwIED6dixIx07dmT16tVF3kPVqlVp3749O3bsICcnh3HjxtGxY0eioqJ45ZVXACeQjImJYdCgQbRq1Yphw4b591P94x//6N+h4Z577ilwn9WYmBhSU1P5xz/+gcfjwePx0LJlS5o2bQpAWloa3bt3p3379vTu3du/+0RaWhrR0dF07tyZmTNn5qsXYN68eXTu3Nkf4AFERESQkJBQ5H0D3H777cyfPx9wgvW4uLgCy7399tt07tzZH+AB9OjRg4iICE6ePMmIESOIjIykbdu2pKSkABT6LAcMGMDx48e55ppreOaZZxg/fjz/+te//LtDBP6c33jjDf8OGr/5zW8AmDx5Ms899xwAO3bsoE+fPrRv356uXbv6e5MTEhJ46KGHuO6662jWrJm/R3XixImsXLkSj8cT8p7J55sFecYYY86LJUuW0KdPH66++mpq167N+vXr/Xlr167lL3/5C+np6ezYsYNFixbh9XrZt28fGRkZpKenM2LECACGDx/OM888w8aNG4mMjAx5+DQsLIxRo0YxduxYvF4vXbt2ZcyYMYwdO5Z169axcOFCRo4cWWQd2dnZfP7554SHhzN79mxq1qzJunXrWLduHa+99hq7du0C4Msvv2T69Ols3ryZnTt3+oPHBx54gHXr1pGRkcGJEydYunRpodcaMGAAXq8Xr9dLdHQ0jzzyCKdOneLBBx8kOTmZtLQ07rzzTh5//HEARowYwYwZM/x7sxZk06ZNtGvXLqTnFWzQoEEsWrQIgH/+8595grhAGRkZhfZy+oLP9PR0EhMTiY+P5+TJk4U+y3/84x9UrlwZr9fLhAkT+OMf/8gdd9yB1+ulcuXKee5rypQpLF++nA0bNvDXv/4137XvueceXnjhBdLS0njuuee4//77/Xn79+9n1apVLF26lIkTJwLw9NNP07VrV7xeLwUHC4oAACAASURBVGPHjj2jZ3au2XCtMcaY8yIxMZGHH34YgCFDhpCYmOgPODp16kSzZs0AiIuLY9WqVfTs2ZOdO3fy4IMP0q9fP3r16sXhw4f5/vvv6d69OwDx8fEMHjz4jNv00Ucf5Zn/duTIEY4ePUr16tXzlFu5ciVt27alTJkyTJw4kfDwcCZNmsTGjRv9PT+HDx9m+/btVKhQgU6dOtGoUSPAmbuWmZlJly5dSElJYerUqfzwww8cOnSI8PDwQoMln6lTp1K5cmVGjx5NRkYGGRkZ3HTTTYDTA3b55Zfney6/+c1veP/994u9/9jYWLZv387VV1/tD+AKU7t2bWrVqkVSUhKtW7emSpUqxdYfbNWqVTz44IMAtGrViiZNmrBt2zY++OCDAp+lr/eyOMuXL2fQoEHUrVvX39ZAx44d47PPPsvzu/Ljjz/6v996662UKVOGNm3anPHcwQuBBXlBNmVvInJu5Dm7Xnp8+jm7ljHGXCiys7NZvnw5GRkZiAg5OTmICFOnTgVARPKUFxFq1arFhg0b+Pe//83MmTNZsGBByMNm5cqVIzc3F3DmiBUmNzeXNWvW5OkVKohvTl4gVeWFF16gd+/eedJXrFhBxYoV/cdly5bl9OnTnDx5kvvvv5/U1FQaN27M5MmTi2wbwMcff8w777zjfzFCVQkPD8/XW/f999/ne4YFCQ8P99cFzoswqampPPLII8WeC3DHHXcwevRo5syZU+Q1PvnkkwLzChqe9qUX9CxDpapF3n9ubi6XXXYZXq+3wPzAn1dhbbwY2HCtMcaYcy45OZnhw4eze/duMjMz2bNnD02bNmXVqlWAM1y7a9cucnNzmT9/Pl26dOHbb78lNzeXgQMH8qc//Yn169dTs2ZNatWqxcqVKwF48803/b1XgcLCwkhLSwNg4cKF/vTq1atz9OhR/3GvXr148cUX/ceFBQEF6d27Ny+//DKnTp0CnDdKjx8/Xmh5X0BXt25djh07VuybwLt37+b+++9nwYIF/iC0ZcuWZGVl+YO8U6dOsWnTJi677DJq1qzpf57z5s0rsM6hQ4eyevVq/vGPf/jTfvjhhxDv2On5Gz9+fJHB2NChQ/nss8947733/GnLli0jPT2dbt26+du2bds2vv76a1q2bFniZxmsZ8+eLFiwgOzsbAAOHTqUJ79GjRo0bdqUd955B3ACuQ0bNhRZZ/DvysUg5CBPRHJExCsiGSLyjohUCciLFREVkVbucSUR+crdq9ZXZryIzCrmGmNF5KSI1AxIi3Hrvisgra2b9oiIzHTbtVlETrjfvSIySEQmi8i+gLSbQ71fY4wxv5zExERiY2PzpA0cONC/REXnzp2ZOHEiERERNG3alNjYWPbt20dMTAwej4eEhASeeuopAObOncu4ceOIiorC6/XyxBNP5LvepEmTGDNmDF27dqVs2bL+9P79+7N48WL/ixczZswgNTWVqKgo2rRpw6xZRf61lcfIkSNp06YN7dq1IyIignvvvZfTp08XWv6yyy7j7rvvJjIykltvvZWOHTsWWf+cOXPIzs4mNjYWj8fDzTffTIUKFUhOTmbChAlER0fj8Xj8S8K8/vrrjB49ms6dOxfaM1m5cmWWLl3KrFmzaNasGZ07d+bPf/4zv/vd7/Jct1GjRv7P3r17/XnVq1dnwoQJVKhQodB2+67xwgsv0KJFC9q0acOcOXOoX78+999/Pzk5OURGRnLHHXcwZ84cKlasWOJnGSw8PJzHH3+c7t27Ex0dzf/93//lKzNv3jxmz55NdHQ04eHhvPvuu0XWGRUVRbly5YiOjr5oXryQULshReSYqlZzv88D0lT1efd4AXA58LGqTnbT+gCPA92AhsCnQAdVzf/a00/XWAv8CMxW1TluWgwwAzigqr3ctGeA3sBbqvqcmxYGLFXViID6JgPHfGVC0aFDB01NTQ21uDHGXJS2bNlC69atz3czjDElUNCfWxFJU9UOBZU/0+HalcBVbuXVgOuBu4AhvgKqugzYDwwHpgGTiwnwmgPVgN8Bwe9hfw1UEpEG4gyy9wGKn0FqjDHGGHOJKnGQJyLlgL6A742BW4FlqroNOCQige9iPwxMAeqp6pvFVB0HJOIEkC1FpH5QfjIwGLgOWI/T4xeKB0Rko4j8XURqFXJP94hIqoikZmVlhVitMcYYY8yFqyRBXmUR8QKpOD1rs930OMC3imUSAb1wqvoNsBx4OYT6hwBJqpoLLMIJ6AItcNN8wWAoXgaaAx6cXsW/FFRIVV9V1Q6q2qFevXohVm2MMcYYc+EqyRIqJ1TVE5ggInWAG4AIEVGgLKAiMl5/muyX634KJSJRQAvgQ/eV5wrATsC/RLeqHhCRU8BNwBicHr0iqap/cRsReQ0ofJVJV/q+w4RNfK+4YhedzKf7ne8mGGOMMeYcOtslVAYBb6hqE1UNU9XGwC6gSwnricOZsxfmfhoCV4hIk6ByTwATVDUnlEpF5PKAw1ggo4TtMsYYY4y5KJ3tYshxwNNBaQuBoThz60I1BGeeX6DFbvoXvgRV/ayE7ZsqIh5AgUzg3hKeb4wxxhhzUQq5J8+3fEpQWoz7Fm1g2gxVvS/gOEFVi1zhUVWbqupXQWn/p6rPqOoKVb2lgHMmBy6NoqqZgcunuGm/UdVIVY1S1QGqur/4OzXGGHMuiIh/43iA06dPU69ePW65Jd//8s/K999/z0svvXRWdYwcOTLPdmfFWbt2LTExMbRo0YJ27drRr18/0tOd9xUnT57MFVdcgcfjoVWrVtx3333+3TgSEhKoUqVKnkV3x4wZg4jw7bff5rtOWFgYXbt2zZPm8XiIiIjIV/bntHXrVv+aha1bt+aee+4BnN09gn9+CQkJeRZ6zsrKonz58rzyyit5yoWFhREZGUl0dDS9evXiwIEDefInT57Mo48+mifN6/UWuxRQTEwMl+rSaLatWZDIK2qSavPXjDGXmJ97O8dQtmysWrUqGRkZnDhxgsqVK/Phhx9yxRVX/KztgJ+CvMAN6Evqb3/7W8hlDx48yO23387bb7/Nddc508dXrVrFjh07iIx0nvPYsWN55JFHyM3NpVu3bnzyySf06NEDgKuuuop3332X//f//h+5ubmkpKQU+VyOHj3Knj17aNy4MVu2bDnjeyyJhx56iLFjx/LrX/8awB/AhuKdd97h2muvJTExkXvvzTvAlpKSQt26dXnsscd48sknmTFjhj8vLi6Ovn37+hfBBkhKSmLo0KFneTel1znd1kxEIgN2n/B9vij+TGOMMaVR3759/dtdJSYmEhf30zKphw4d4tZbbyUqKoprr72WjRs3Ak6Pzp133klMTAzNmjXLEwg8//zzREREEBERwfTp0wGYOHEiO3bswOPxMG7cOFSVcePGERERQWRkJPPnzwecXqiYmBgGDRpEq1atGDZsmH/f0sDeoGXLltGuXTuio6Pp2bNnvnt68cUXiY+P9wd4AF26dOHWW2/NV/Z///sfJ0+epFatn1b4iouLy9Om66+/nnLlCu+Tuf322/3lg59hTk4O48aNo2PHjkRFRfl7z44dO0bPnj1p164dkZGR/t0eMjMzad26NXfffTfh4eH06tWLEydO5Lvm/v37adSokf/YF7yGIjExkb/85S/s3buXffv2FVimW7du/Oc//8mT1rJlSy677DK++OKnsGHBggUMGeIs0XvffffRoUMHwsPDmTRpUoH1Vqv206BkcnIyCQkJgNO7OHDgQDp27EjHjh1ZvXp1yPdzITunQZ6qpquqJ+hzzblsgzHGmAvHkCFDSEpK4uTJk2zcuJFrrvnpr4RJkybRtm1bNm7cyJNPPsnw4cP9eV999RX//ve/Wbt2LX/4wx84deoUaWlpvP7663zxxRd8/vnnvPbaa3z55Zc8/fTTNG/eHK/Xy7PPPsuiRYvwer1s2LCBjz76iHHjxrF/vzOb58svv2T69Ols3ryZnTt35vvLPisri7vvvpuFCxeyYcMG/96ngTZt2kS7du3ypQeaNm0aHo+Hyy+/nKuvvhqP56fFK1q0aEFWVhbfffcdiYmJ/iCmMIMGDWLRokUA/POf/6R///7+vNmzZ1OzZk3WrVvHunXreO2119i1axeVKlVi8eLFrF+/npSUFH7729/6A9rt27czevRo/x64gXv9+owdO5YbbriBvn37Mm3aNL7//nt/3sqVK/F4PP5P4L64e/bs4cCBA3Tq1ClPcBps6dKlBQaOcXFxJCU5q7Z9/vnn1KlThxYtWgAwZcoUUlNT2bhxI5988on/HwWhGDNmDGPHjmXdunUsXLiQkSNHhnzuheycBnnGGGNMoKioKDIzM0lMTOTmm/NuL75q1Sr/nL0bbriB7OxsDh8+DEC/fv2oWLEidevWpX79+hw8eJBVq1YRGxtL1apVqVatGrfddhsrV+Z/B3DVqlXExcVRtmxZGjRoQPfu3Vm3bh0AnTp1olGjRpQpUwaPx0NmZmaecz///HO6detG06ZNAahdu3ax93jNNdfQunVrxowZ408bO3YsXq+X//73vxw/ftwfuPjcdtttJCUl8cUXX+Sbcxesdu3a1KpVi6SkJFq3bk2VKv6t5fnggw9444038Hg8XHPNNWRnZ7N9+3ZUlccee4yoqChuvPFG9u3bx8GDzqpjTZs29Qed7du3z/cMAEaMGMGWLVsYPHgwK1as4Nprr+XHH509Crp27YrX6/V/BgwY4D8vKSmJ22+/HXAC/MTEvMve9ujRA4/Hw5EjR/LNv/Odk5ycTG5uLklJSXl6LRcsWEC7du1o27YtmzZtKtEcyo8++ogHHngAj8fDgAEDOHLkSJ55kRcrm5NnjDHmvBowYACPPPIIK1asIDs7259e0N7q7lqqVKxY0Z9WtmxZTp8+XWD5ghRVrqB6g8/1taEw4eHhrF+/3j9f7YsvviA5OZmlS/Mv1Vq+fHn69OnDp59+mqfHbsiQIbRr1474+HjKlCm+P+aOO+5g9OjRzJkzJ197X3jhBXr37p0nfc6cOWRlZZGWlkb58uUJCwvj5MmTBT6DgoZrARo2bMidd97JnXfeSUREBBkZxa9SlpiYyMGDB5k3bx4A33zzDdu3b/f3xvnm5BWmcePGhIWF8cknn7Bw4ULWrFkDwK5du3juuedYt24dtWrVIiEhwX8/gQJ/doH5ubm5rFmzhsqVKxd7DxcT68kzxhhzXt1555088cQT+YbnunXr5g8GVqxYQd26dalRo0ah9XTr1o0lS5bwww8/cPz4cRYvXkzXrl2pXr16nl6Zbt26MX/+fHJycsjKyuLTTz+lU6dOIbW1c+fOfPLJJ+zatQtw5g0G8wVbn33206pfP/zwQ4H1qSqfffYZzZs3z5N+5ZVXMmXKlJBfFomNjWX8+PH5grnevXvz8ssvc+rUKQC2bdvG8ePHOXz4MPXr16d8+fKkpKSwe/fukK7js2zZMn+dBw4cIDs7u9iXZrZu3crx48fZt28fmZmZZGZm8uijj+brxSxOXFwcY8eOpXnz5v55gUeOHKFq1arUrFmTgwcP8v77BW9v36BBA7Zs2UJubi6LFy/2p/fq1YsXX3zRf+z1ekvUpguV9eQZY4w5rxo1apRnKNNn8uTJjBgxgqioKKpUqcLcuXOLrKddu3YkJCT4A7aRI0fStm1bAK6//noiIiLo27cvU6dOZc2aNURHRyMiTJ06lV/96ld89dVXRVUPQL169Xj11Ve57bbbyM3NpX79+nz44Yd5yvzqV79i/vz5TJgwgX379lG/fn3q1q3LE0884S8zbdo03nrrLU6dOkVUVFSBwVzwm6dFqV69OhMmTMiXPnLkSDIzM2nXrh2qSr169ViyZAnDhg2jf//+dOjQwb+US0l88MEHjBkzhkqVKgHw7LPPFvsMExMTiY2NzZM2cOBAhgwZwu9///uQrz148GDGjBnDCy+84E+Ljo6mbdu2hIeH06xZM66//voCz3366ae55ZZbaNy4MRERERw7dgyAGTNmMHr0aKKiojh9+jTdunVj1qxZIbfpQiWhdm9fKjp06KCX6no6xphLx5YtW4pdX8wYc2Ep6M+tiKSpaoeCyttwrTHGGGNMKWRBnjHGGGNMKWRBnjHGGGNMKWQvXgTZlL3pZ9/epyihbP1jjDG/hFCWAzHGXBjO5B0K68kzxphLUKVKlcjOzj6jvziMMeeWqpKdne1/mzlU1pNnjDGXoEaNGrF3716ysrLOd1OMMSGoVKlSnv2CQxFykCciOUC6e84WIF5Vf3DzYoFFQGtV/UpEKgFeYLCqprtlxgPNVHVUEdcYCzwFNFDVw25aDJACjFTV2W5aW2A9MA5oClwPVHC/b3Wr+7OqJrvlHwGeBeqp6reh3rMxxpRW5cuX92/NZYwpnUrSk3dCVT0AIjIPGAU87+bFAauAIcBkVT0pIg8DL4lIN6AhcC9Q4DouAeKAdUAsMCcgPR24A5jtHg8BNgCo6mi3TWHAUl8bfUSkMXAT8HUoNxleJ5zUeFsnzxhjjDEXtzOdk7cSuApARKrh9KTdhRN8AaCqy4D9wHBgGk7w911hFYpIc6Aa8DucYC/Q10AlEWkgzizhPkDBe5bkNw0YD9jEE2OMMcZcMkoc5IlIOaAvTu8awK3AMlXdBhwSkXYBxR8GpuAMk75ZTNVxQCJOANlSROoH5ScDg4HrcIZqfwyhrQOAfaq6oZhy94hIqoik2vwUY4wxxpQGJQnyKouIF0jF6VnzDZ3GAb7dhZMI6IVT1W+A5cDLIdQ/BEhS1Vyc+X2Dg/IXuGm+YLBIIlIFeBx4oriyqvqqqnZQ1Q716tULoanGGGOMMRe2M5qT5yMidYAbgAgRUaAsoCIyXn96Lz/X/RRKRKKAFsCH7ppNFYCdwExfGVU9ICKncObXjcHp0StKc5wXMTa4dTYC1otIJ1U9UNhJ6fsOEzbxvWKqvnhlPt3vfDfBGGOMMefA2S6hMgh4Q1Xv9SWIyCdAF5xh11DF4czZeyqgnl0i0iSo3BNAfVXNKW4BT/etXv+Qr4hkAh3s7VpjjDHGXArOdjHkOGBxUNpCYGgJ6xlSQD2LCXiRA0BVP1PVJSWs2xhjjDHmkiO22nleFS9voZfHTz/fzfjF2HCtMcYYU3qISJqqFrhEne14ESTyipqkWiBkjDHGmIvcOQ3yRCQSCF5K5UdVveZctsMYY4wxprQ7p0Ge+zKEp9iCxhhjjDHmrJztixfGGGOMMeYCZEGeMcYYY0wpZEGeMcYYY0wpZEGeMcYYY0wpZEGeMcYYY0wpZEGeMcYYY0wpZEGeMcYYY0wpZEGeMcYYY0wpZNuaBdmUvYnIuZHnuxkGSI9PP99NMMYYYy5a1pNnjDHGGFMKWZBnjDHGGFMKhRzkiUiOiHhFJENE3hGRKgF5sSKiItLKPa4kIl+JSGRAmfEiMquYa4wVkZMiUjMgLcat+66AtLZu2iMiMtNt12YROeF+94rIIBH5k4hsdI8/EJGGod6vMcYYY8zFTFQ1tIIix1S1mvt9HpCmqs+7xwuAy4GPVXWym9YHeBzoBjQEPgU6qOp3RVxjLfAjMFtV57hpMcAM4ICq9nLTngF6A2+p6nNuWhiwVFUjAuqroapH3O8PAW1UdVRR99mhQwdNTU0N6ZkYY4wxxpxPIpKmqh0KyjvT4dqVwFVu5dWA64G7gCG+Aqq6DNgPDAemAZOLCfCaA9WA3wFxQdlfA5VEpIGICNAHeL+4RvoCPFdVILSI1hhjjDHmIlfit2tFpBzQF1jmJt0KLFPVbSJySETaqep6N+9hYC2wXVXfLKbqOCARJ4BsKSL1VfW/AfnJwGDgS2A9To9fKO2dghNoHgZ6FFLmHuAegCuvvDKUao0xxhhjLmgl6cmrLCJeIBWnZ222mx4HJLnfkwjohVPVb4DlwMsh1D8ESFLVXGARTkAXaIGb5gsGQ6Kqj6tqY2Ae8EAhZV5V1Q6q2qFevXqhVm2MMcYYc8EqSU/eCVX1BCaISB3gBiBCRBQoC6iIjNefJvvlup9CiUgU0AL40BmNpQKwE5jpK6OqB0TkFHATMAa4rgRtB3gbeA+YVFSh9H2HCZv4XgmrvjhkPt3vfDfBGGOMMefI2S6hMgh4Q1WbqGqY22O2C+hSwnricObshbmfhsAVItIkqNwTwARVzQmlUhFpEXA4APiqhO0yxhhjjLkone2OF3HA00FpC4GhOHPrQjUEZ55foMVu+he+BFX9rITte1pEWuL0JO4Ginyz1hhjjDGmtAh5CZVLRcXLW+jl8dPPdzN+ETZca4wxxpQuRS2hYnvXBom8oiapFgwZY4wx5iJ3ToM8dweM4KVUflTVa85lO4wxxhhjSrtzGuSpajrgKbagMcYYY4w5K2f7dq0xxhhjjLkAWZBnjDHGGFMKWZBnjDHGGFMKWZBnjDHGGFMKWZBnjDHGGFMKWZBnjDHGGFMKWZBnjDHGGFMKWZBnjDHGGFMK2bZmQTZlbyJybuT5bkY+6fHp57sJxhhjjLmIWE+eMcYYY0wpZEGeMcYYY0wpFHKQJyI5IuIVkQwReUdEqgTkxYqIikgr97iSiHwlIpEBZcaLyKxirjFWRE6KSM2AtBi37rsC0tq6aY+IyEy3XZtF5IT73Ssig0TkWbcdG0VksYhcFur9GmOMMcZczERVQysockxVq7nf5wFpqvq8e7wAuBz4WFUnu2l9gMeBbkBD4FOgg6p+V8Q11gI/ArNVdY6bFgPMAA6oai837RmgN/CWqj7npoUBS1U1IqC+XsByVT3tnoOqTijqPjt06KCpqakhPRNjjDHGmPNJRNJUtUNBeWc6XLsSuMqtvBpwPXAXMMRXQFWXAfuB4cA0YHIxAV5zoBrwOyAuKPtroJKINBARAfoA7xfXSFX9QFVPu4efA41CujtjjDHGmItciYM8ESkH9AV8r3veCixT1W3AIRFpF1D8YWAKUE9V3yym6jggESeAbCki9YPyk4HBwHXAepwev5K4k0ICQxG5R0RSRSQ1KyurhNUaY4wxxlx4ShLkVRYRL5CK07M2202PA5Lc70kE9MKp6jfAcuDlEOofAiSpai6wCCegC7TATfMFgyETkceB08C8gvJV9VVV7aCqHerVq1eSqo0xxhhjLkglWSfvhKp6AhNEpA5wAxAhIgqUBVRExutPk/1y3U+hRCQKaAF86IzGUgHYCcz0lVHVAyJyCrgJGIPTo1csEYkHbgF6aggTENP3HSZs4nuhVH1Ry3y63/lugjHGGGN+QWe7hMog4A1VbaKqYaraGNgFdClhPXE4c/bC3E9D4AoRaRJU7glggqrmhFKp+/LHBGCAqv5QwjYZY4wxxly0zjbIiwMWB6UtBIaWsJ4hBdSzmIAXOQBU9TNVXVKCel8EquP0EHqLW8LFGGOMMaa0CHkJlUtFxctb6OXx0893M35xNlxrjDHGXPyKWkLF9q4NEnlFTVItADLGGGPMRe6cBnnuDhjBS6n8qKrXnMt2GGOMMcaUduc0yFPVdMBTbEFjjDHGGHNWzvbFC2OMMcYYcwGyIM8YY4wxphSyIM8YY4wxphSyIM8YY4wxphSyIM8YY4wxphSyIM8YY4wxphSyIM8YY4wxphSyIM8YY4wxphSybc2CbMreROTcyPPdjAtKenz6+W6CMcYYY0rIevKMMcYYY0ohC/KMMcYYY0qhkIM8EckREa+IZIjIOyJSJSAvVkRURFq5x5VE5CsRiQwoM15EZhVzjbEiclJEagakxbh13xWQ1tZNe0REZrrt2iwiJ9zvXhEZJCKDRWSTiOSKSIdQ79UYY4wx5mJXkjl5J1TVAyAi84BRwPNuXhywChgCTFbVkyLyMPCSiHQDGgL3AsUFWnHAOiAWmBOQng7cAcx2j4cAGwBUdbTbpjBgqa+Nblpr4DbglVBvMrxOOKnxqaEWN8YYY4y5IJ3pcO1K4CoAEakGXA/chRN8AaCqy4D9wHBgGk7w911hFYpIc6Aa8DucYC/Q10AlEWkgIgL0Ad4vrpGqukVVt5bgvowxxhhjSoUSB3kiUg7oi9O7BnArsExVtwGHRKRdQPGHgSlAPVV9s5iq44BEnACypYjUD8pPBgYD1wHr4f+3d/dRltX1ne/fH5rHtgfGgZ5Mo0CrOIsE2nTMGY1ICMJcGkUNvQbGLu9cmyxZbWbGxehoCMRZ93LNUvFmLmLmZnA6Ywy4DB0f0srQEUZxiO2EYE6Twuo2iphm5MFZ6QDTijREm+/9o3bJsVLddar7VJ1Tu96vtWrV3r/9O7/93exV8OG3Hw7PzLX2A0myKUk3SXfPnj2DGlaSJGlo5hLyjksyDnSZnFmbunQ6BmxplrfQMwtXVY8CXwJu7GP8DcCWqnoW+CMmA12vTzZtU2FwYKpqc1V1qqqzcuXKQQ4tSZI0FId0T96UJCcC5wNnJSlgGVBJrqqqaro92/wcUJKXAS8FvjB5NZajgb8CfmeqT1X9zyQ/BP434N8wOaM3cBOP7GX11dvmY+hWePC6i4ddgiRJ6sPhvkLlUuDmqjqtqlZX1SnAbuCcOY4zxuQ9e6ubn5OBFyQ5bVq//xP49araf5h1S5IktdrhhrwxYOu0ts8Ab57jOBtmGGcrPQ9yAFTVn1bVZ/sdtHm1y8PAq4BtSe6YY12SJEmLUp67qiqAY1a9tFZtvGHYZYwsL9dKkjQ6kuyoqhlfUed3106z5gUn0DXISJKkRW5BQ17zDRjTX6XyTFW9ciHrkCRJarsFDXlVNQGsnbWjJEmSDsvhPnghSZKkEWTIkyRJaiFDniRJUgsZ8iRJklrIkCdJktRChjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgvNGvKS7E8ynuS+JPcmObtpX51kZ7N8XpLbZvjsXUk6/RaT5MNJHklyRE/b5UkqyQU9beubtkuTbG3qeyDJ3mZ5PMnZmfS+JPcnXSpwoQAAIABJREFU+cskV/ZbiyRJ0mJ2ZB999lXVWoAk64APAL806EKaYLceeAg4F7irZ/MEMAbc2axvAO4DqKr1zefPA95dVa/vGfNXgFOAM6rq2ST/cNB1S5IkjaK5Xq49HnhiPgoBXgPsBG5kMtD12g68IslRSVYApwPjfYz5L4H3VtWzAFX11wOsV5IkaWT1M5N3XJJx4FhgFXD+PNUyBtwCfA54f5KjquqHzbYCvgisA04AbgVe1MeYLwHelGQ9sAe4sqq+Nb1Tkk3AJoBTTz31cI9DkiRp6PqZydtXVWur6gzgIuDmJBlkEUmOBl4HfLaqvgfcA1w4rdsWJi/TbmAyDPbjGODpquoAvwv83kydqmpzVXWqqrNy5cpDOQRJkqSR0s9M3o9V1d1JTgIGnYQuYnKGbqLJj8uBp4BtPfv+apKzmAyd9/eZMx8GPtMsbwU+NtsHJh7Zy+qrt/1E24PXXdzPviRJkkbGnEJekjOAZcBjTAaxQRkDrqiqW5r9PA/YnWT6Pq4Bnp7DuJ9l8vLy7zH5sMj9A6hVkiRp5M3lnjyAABurav8MM2kXJHm4Z/2y5ve2JFP31t1dVZf1fqgJcuuAt021VdUPknwFeENv36r6fB/19roO+ESSdwJPAlfM8fOSJEmLUqpq2DWMlE6nU91ud9hlSJIkzSrJjubZg7/Db7yQJElqoTndk3e4mpcpf3Ba8+6pFxpLkiRpMBY05FXVHcAdC7lPSZKkpcjLtZIkSS1kyJMkSWohQ54kSVILGfIkSZJayJAnSZLUQoY8SZKkFjLkSZIktZAhT5IkqYUMeZIkSS1kyJMkSWohQ54kSVILzRrykuxPMp7kviT3Jjm7aV+dZGezfF6S22b47F1JOv0Wk+TDSR5JckRP2+VJKskFPW3rm7ZLk2xt6nsgyd5meTzJ2Ul+P8nunra1/dYiSZK0mPUzk7evqtZW1c8C1wAfmI9CmmC3HngIOHfa5glgrGd9A3AfQFWtr6q1wBXA9qbWtVX1p03fX+tpG5+P2iVJkkbNXC/XHg88MR+FAK8BdgI38pOBDmA78IokRyVZAZwOGNgkSZIOoJ+Qd1xzqfMbwH8GfnOeahkDbgG2Aq9PclTPtgK+CKwDfhm4dQ7jvi/J15J8KMkxM3VIsilJN0l3z549h1i+JEnS6Diyjz77msuhJHkVcHOSswZZRJKjgdcB76yq7ye5B7gQ2NbTbQtwJXAC8C7gN/oY+hrgfwJHA5uBXwfeO71TVW1utnPMqpfW6qu3Te8CwIPXXdznEUmSJA1XPyHvx6rq7iQnASsHXMdFTIa3iSQAy4Gn6Al5VfXVJlzuq6r7m36z1fvdZvGZJB8D3j3guiVJkkbSnEJekjOAZcBjTAaxQRkDrqiqW5r9PA/YnWT6Pq4Bnu530CSrquq7mUyElzB5z58kSVLr9RPyjksy9ZBDgI1VtX+GmbQLkjzcs35Z83tbkh82y3dX1WW9H2qC3DrgbVNtVfWDJF8B3tDbt6o+30e9vT6RZGVT9zjwq3P8vCRJ0qKUqhp2DSOl0+lUt9sddhmSJEmzSrKjqmZ8J7HfeCFJktRCc7on73AlWQd8cFrz7qpav5B1SJIktd2ChryqugO4YyH3KUmStBR5uVaSJKmFDHmSJEktZMiTJElqIUOeJElSCxnyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLXQgr4MeTHY9dgu1ty0ZmDjTWycGNhYkiRJ/XImT5IkqYUMeZIkSS00a8hLsj/JeJL7ktyb5OymfXWSnc3yeUlum+GzdyXp9FtMkg8neSTJET1tlyepJBf0tK1v2i5NsrWp74Eke5vl8ak6m/7/IcmT/dYhSZK02PVzT96+qloLkGQd8AHglwZdSBPs1gMPAecCd/VsngDGgDub9Q3AfQBVtb75/HnAu6vq9dPG7QB/v986zjzxTLobu4d0DJIkSaNirpdrjweemI9CgNcAO4EbmQx0vbYDr0hyVJIVwOnA+GwDJlkG/BZw1Sz9NiXpJunu2bPnkIqXJEkaJf3M5B2XZBw4FlgFnD9PtYwBtwCfA96f5Kiq+mGzrYAvAuuAE4BbgRf1MebbgVur6rtJDtipqjYDmwE6nU4d8hFIkiSNiLlern0VcHOSswZZRJKjgdcB76yq7ye5B7gQ2NbTbQtwJZMh713Ab8wy5snAZcB5c6ll4pG9rL5626z9Hrzu4rkMK0mStKDm9J68qro7yUnAygHXcRGT4W2imXFbDjxFT8irqq824XJfVd1/sJm5xs8xeVn3gakxkzxQVacPuHZJkqSRM6eQl+QMYBnwGJNBbFDGgCuq6pZmP88DdieZvo9rgKf7GbCqtgH/aGo9yZMGPEmStFTM5Z48gAAbq2r/DDNpFyR5uGf9sub3tiRT99bdXVWX9X6oCXLrgLdNtVXVD5J8BXhDb9+q+nwf9UqSJC15qfI5g16dTqe6XV+hIkmSRl+SHVU14zuJ/cYLSZKkFprTPXmHq3mZ8genNe+eeqGxJEmSBmNBQ15V3QHcsZD7lCRJWoq8XCtJktRChjxJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklpoQV+GvBjsemwXa25a8+P1iY0TQ6xGkiTp0DiTJ0mS1EKGPEmSpBaaNeQl2Z9kPMl9Se5NcnbTvjrJzmb5vCS3zfDZu5J0+i0myYeTPJLkiJ62y5NUkgt62tY3bZcm2drU90CSvc3yeJKzk3y0qftrST6dZMVsNZx54plMbJz48Y8kSdJi1M9M3r6qWltVPwtcA3xgPgppgt164CHg3GmbJ4CxnvUNwH0AVbW+qtYCVwDbm1rXVtWfAu+sqp+tqpcB3wHePh+1S5IkjZq5Xq49HnhiPgoBXgPsBG7kJwMdwHbgFUmOambjTgfGZxuwqr4HkCTAcUDN1C/JpiTdJN09e/YcxiFIkiSNhn6erj0uyThwLLAKOH+eahkDbgE+B7w/yVFV9cNmWwFfBNYBJwC3Ai/qZ9AkHwNeB3wdeNdMfapqM7AZoNPpzBgEJUmSFpN+Qt6+5nIoSV4F3JzkrEEWkeRoJoPYO6vq+0nuAS4EtvV02wJcyWTIexfwG/2MXVW/kmQZ8B+ANwEfO1j/iUf2svrqbQfrMqMHr7t4zp+RJEmaL3O6XFtVdwMnASsHXMdFTIa3iSQPAucw7ZJtVX0VOAs4qarun8vgVbUf+EPgnw2kWkmSpBE3p5chJzkDWAY8BiwfYB1jwBVVdUuzn+cBu5NM38c1wNN91hrgJVX1QLP8BuAbA6xZkiRpZM3lnjyAABurav9kbvoJFyR5uGf9sub3tiRT99bdXVWX9X6oCXLrgLdNtVXVD5J8hclgRk/75/uo98dDAzclOb5Zvg/4l3P4vCRJ0qKVKp8z6NXpdKrb7Q67DEmSpFkl2VFVM76T2G+8kCRJaqE53ZN3uJKsAz44rXl3Va1fyDokSZLabkFDXlXdAdyxkPuUJElairxcK0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFDHmSJEktZMiTJElqIUOeJElSCxnyJEmSWmhBX4a8GOx6bBdrbloz47aJjRMLXI0kSdKhcSZPkiSphWYNeUn2JxlPcl+Se5Oc3bSvTrKzWT4vyW0zfPauJJ1+i0ny4SSPJDmip+3yJJXkgp629U3bpUm2NvU9kGRvszye5Owkn0jyzSQ7k/xekqP6rUWSJGkx6+dy7b6qWguQZB3wAeCXBl1IE+zWAw8B5wJ39WyeAMaAO5v1DcB9AFW1vvn8ecC7q+r1PWP+feBfNKt/AFwB3HiwOs488Uy6G7uHdzCSJElDNtfLtccDT8xHIcBrgJ1MhrCxadu2A69IclSSFcDpwPhsA1bVH1cD+CrwwgHXLEmSNJL6mck7Lsk4cCywCjh/nmoZA24BPge8P8lRVfXDZlsBXwTWAScAtwIv6nfg5jLt/wH8mwNs3wRsAjj11FMPtX5JkqSR0c9M3r6qWltVZwAXATcnySCLSHI08Drgs1X1PeAe4MJp3bYweZl2A5NhcC7+I/Dlqto+08aq2lxVnarqrFy5co5DS5IkjZ45vUKlqu5OchIw6CR0EZMzdBNNflwOPAVs69n3V5OcxWTovL/fnJnk/2rqfVs//Sce2cvqq7fN3nGaB6+7eM6fkSRJmi9zCnlJzgCWAY8xGcQGZQy4oqpuafbzPGB3kun7uAZ4ut9Bk1zB5CXeC6rq2UEVK0mSNOrmck8eQICNVbV/hpm0C5I83LN+WfN7W5Kpe+vurqrLej/UBLl19My0VdUPknwFeENv36r6fB/19voI8D+Au5t6/6iq3jvHMSRJkhadTD54qinHrHpprdp4w5w/5+VaSZK00JLsqKoZ30ns15pNs+YFJ9A1sEmSpEVuQUNe8zLlD05r3j31QmNJkiQNxoKGvKq6A7hjIfcpSZK0FM31Gy8kSZK0CBjyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFDHmSJEktZMiTJElqIb/WbJpdj+1izU1rhl3GvJrYODHsEiRJ0jxzJk+SJKmFDHmSJEktNGvIS7I/yXiS+5Lcm+Tspn11kp3N8nlJbpvhs3cl6fRbTJIPJ3kkyRE9bZcnqSQX9LStb9ouTbK1qe+BJHub5fEkZyd5e9NeSU7qtw5JkqTFrp978vZV1VqAJOuADwC/NOhCmmC3HngIOBe4q2fzBDAG3NmsbwDuA6iq9c3nzwPeXVWv7xlzH3DbtLEO6swTz6S7sXuIRyFJkjQa5nq59njgifkoBHgNsBO4kclA12s78IokRyVZAZwOjM82YFX9RVU9OOhCJUmSRl0/M3nHJRkHjgVWAefPUy1jwC3A54D3Jzmqqn7YbCvgi8A64ATgVuBFg9pxkk3AJoBTTz11UMNKkiQNTT8zefuqam1VnQFcBNycJIMsIsnRwOuAz1bV94B7gAunddvC5GXaDUyGwYGpqs1V1amqzsqVKwc5tCRJ0lDM6T15VXV38wDDoJPQRUzO0E00+XE58BSwrWffX01yFpOh8/4B58wfm3hkL6uv3jZ7xz48eN3FAxlHkiRpruYU8pKcASwDHmMyiA3KGHBFVd3S7Od5wO4k0/dxDfD0APcrSZLUSnO5Jw8gwMaq2j/DTNoFSR7uWb+s+b0tydS9dXdX1WW9H2qC3DrgbVNtVfWDJF8B3tDbt6o+30e9vWNfCVwF/CPga0n+uKqumMsYkiRJi1Gqatg1jJRjVr20Vm28YSBjeblWkiTNpyQ7qmrGdxL73bXTrHnBCXQNZ5IkaZFb0JDXvEz5g9Oad0+90FiSJEmDsaAhr6ruAO5YyH1KkiQtRXP9xgtJkiQtAoY8SZKkFjLkSZIktZAhT5IkqYUMeZIkSS1kyJMkSWohQ54kSVILGfIkSZJayK81m2bXY7tYc9OaYZcxbyY2Tgy7BEmStACcyZMkSWohQ54kSVILDT3kJdmfZDzJziSfSrK8Z9v6JJXkjGb92CTfSLKmp89VST5ykPFPTfJfk/xlkq8nWT2fxyNJkjQKUlXDLSB5sqpWNMufAHZU1fXN+ieBVcCdVXVt03YR8B7gXOBk4MtAp6qeOMD4dwHvq6ovJFkBPFtVTx2ons7Jy6q7aUX/B3Dt3v77SpIkDVCSHVXVmWnb0GfyptkOnA7QBLJXA28FNkx1qKrbge8CbwE+BFx7kID3M8CRVfWF5rNPHizgSZIktcXIhLwkRwKvBaYe/7wEuL2q7gceT/Lynu7vAN4HrKyqjx9k2H8M/K8kf5TkL5L8VpJlM+x7U5Juku6ep4Y7sylJkjQIoxDyjksyDnSB7wAfbdrHgC3N8pZmHYCqehT4EnDjLGMfCfwi8G7gnwAvBi6f3qmqNldVp6o6K5fn0I9EkiRpRIzCe/L2VdXa3oYkJwLnA2clKWAZUEmuquduIny2+TmYh4G/qKq/asb9LPALPBck/46JejGrn76h/+qv3tZ/X2kEPHjdxcMuQZK0AEZhJm8mlwI3V9VpVbW6qk4BdgPnzHGcPween2Rls34+8PUB1ilJkjSSRjXkjQFbp7V9BnjzXAapqv1MXqq9M8kEEOB3B1KhJEnSCBv65dqp16dMaztvhrbfnrZ+eZ/jfwF42SGWJ0mStCgNPeSNmjUvOIGu9yxJkqRFrhUhr/kGjOmvUnmmql45jHokSZKGrRUhr6omgLWzdpQkSVoiRvXBC0mSJB0GQ54kSVILGfIkSZJayJAnSZLUQoY8SZKkFjLkSZIktZAhT5IkqYUMeZIkSS3UipchD9Kux3ax5qY1wy5j5ExsnBh2CZIkaQ6cyZMkSWohQ54kSVILDT3kJdmfZDzJziSfSrK8Z9v6JJXkjGb92CTfSLKmp89VST7Sx/jjSW6d36ORJEkaDamq4RaQPFlVK5rlTwA7qur6Zv2TwCrgzqq6tmm7CHgPcC5wMvBloFNVT8w2fj86Jy+r7qa+u4+Ga/cOuwJJkjQESXZUVWembUOfyZtmO3A6QJIVwKuBtwIbpjpU1e3Ad4G3AB8Crj1QwJMkSVqqRibkJTkSeC0w9RjnJcDtVXU/8HiSl/d0fwfwPmBlVX18lqGPTdJN8mdJLjnAvjc1fbp7nhruzKYkSdIgjMIrVI5LMt4sbwc+2iyPATc0y1ua9XsBqurRJF8Cbutj/FOb/i8GvpRkoqq+3duhqjYDm2Hycu1hHY0kSdIIGIWQt6+q1vY2JDkROB84K0kBy4BKclU9dxPhs83PQVXVo83vv0pyF/BzwLcP1H+iXszqp2840ObRdPW2YVegJebB6y4edgmSpFmMzOXaaS4Fbq6q06pqdVWdAuwGzpnLIEmen+SYZvkkJu/x+/rAq5UkSRoxoxryxoCt09o+A7x5juP8NNBNch/w34DrqsqQJ0mSWm/ol2tner1JVZ03Q9tvT1u/vI+x/xTwO8okSdKSM/SQN2rWvOAEut5vJEmSFrlWhLzmGzCmv0rlmap65TDqkSRJGrZWhLyqmgDWztpRkiRpiRjVBy8kSZJ0GAx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklrIkCdJktRChjxJkqQWMuRJkiS1UCtehjxIux7bxZqbFufX3U5snBh2CZIkaUQ4kydJktRChjxJkqQWGnrIS7I/yXiSnUk+lWR5z7b1SSrJGc36sUm+kWRNT5+rknxkln0cn+SRJP/f/B2JJEnS6BiFe/L2VdVagCSfAH4VuL7ZNgZ8BdgAXFtVTyd5B/Afk5wLnAy8DejMso/fBP6kn2LOfOZv6e7+ztyPYi6u3Tu/40uSpCVv6DN502wHTgdIsgJ4NfBWJkMeAFV1O/Bd4C3Ah5gMf08caMAkPw/8FPBf569sSZKk0TIyIS/JkcBrgalHRC8Bbq+q+4HHk7y8p/s7gPcBK6vq4wcZ8wjg/wV+bZZ9b0rSTdLd81QdzmFIkiSNhFEIecclGQe6wHeAjzbtY8CWZnlLsw5AVT0KfAm4cZax/xXwx1X10ME6VdXmqupUVWfl8hzCIUiSJI2Wkbonb0qSE4HzgbOSFLAMqCRXVdXUVNuzzc/BvAr4xST/ClgBHJ3kyaq6+kAfmKgXs/rpGw71WPpz9bb5HV8aMQ9ed/GwS5CkJWcUQt5MLgVurqq3TTUk+RPgHCbv2+tLVf3vPZ+/HOgcLOBJkiS1xShcrp3JGLB1WttngDcPoRZJkqRFZ+gzeVW1Yoa282Zo++1p65fPcT+/D/z+nIqTJElapIYe8kbNmhecQNf7hyRJ0iLXipDXfAPG9FepPFNVrxxGPZIkScPWipBXVRPA2lk7SpIkLRGj+uCFJEmSDoMhT5IkqYUMeZIkSS1kyJMkSWohQ54kSVILGfIkSZJayJAnSZLUQoY8SZKkFmrFy5AHaddju1hz05phl7GoTGycGHYJkiRpGmfyJEmSWsiQJ0mS1EJDD3lJ9icZT7IzyaeSLO/Ztj5JJTmjWT82yTeSrOnpc1WSjxxg7NOS7GjG35XkV+f/iCRJkoYvVTXcApInq2pFs/wJYEdVXd+sfxJYBdxZVdc2bRcB7wHOBU4Gvgx0quqJGcY+msljfCbJCmAncHZVPXqgejonL6vuphWDPMSl4dq9w65AkqQlJ8mOqurMtG3oM3nTbAdOB2hC2auBtwIbpjpU1e3Ad4G3AB8Crp0p4DV9/7aqnmlWj2H0jleSJGlejEzoSXIk8Fpg6lHNS4Dbq+p+4PEkL+/p/g7gfcDKqvr4LOOekuRrwEPAB2eaxUuyKUk3SXfPU8Od2ZQkSRqEUQh5xyUZB7rAd4CPNu1jwJZmeUuzDkAT1L4E3Djb4FX1UFW9jMkZwo1JfmqGPpurqlNVnZXLc1gHI0mSNApG4T15+6pqbW9DkhOB84GzkhSwDKgkV9VzNxE+2/z0paoeTbIL+EXg0wfqN1EvZvXTN8z1GHT1tmFXoCXiwesuHnYJkrQojMJM3kwuBW6uqtOqanVVnQLsBs6ZyyBJXpjkuGb5+Uze4/fNgVcrSZI0YkY15I0BW6e1fQZ48xzH+WngniT3AX8C/Puq8usZJElS6w39FSqjptPpVLfbHXYZkiRJs1pMr1CRJEnSAIzCgxeHrfkGjOmvUnmmql45jHokSZKGrRUhr7nPbu2sHSVJkpYIL9dKkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklrIkCdJktRChjxJkqQWasXLkAdp12O7WHPTmmGXoQGZ2Dgx7BIkSRoKZ/IkSZJayJAnSZLUQkMPeUn2JxlPsjPJp5Is79m2PkklOaNZPzbJN5Ks6elzVZKPHGDstUnuTrIrydeSvGn+j0iSJGn4UlXDLSB5sqpWNMufAHZU1fXN+ieBVcCdVXVt03YR8B7gXOBk4MtAp6qemGHsfwxUVX0rycnADuCnq+p/HaiezsnLqrtpxSAPcWm7du+wK5AkqbWS7Kiqzkzbhj6TN8124HSAJCuAVwNvBTZMdaiq24HvAm8BPgRcO1PAa/reX1XfapYfBf4aWDmfByBJkjQKRibkJTkSeC0w9TjkJcDtVXU/8HiSl/d0fwfwPmBlVX28z/FfARwNfHuGbZuSdJN09zw13JlNSZKkQRiFV6gcl2S8Wd4OfLRZHgNuaJa3NOv3wuSsXJIvAbf1s4Mkq4CPAxur6tnp26tqM7AZ4JhVL63VT98wvYsO1dXbhl2BdFgevO7iYZcgSYdkFELevqpa29uQ5ETgfOCsJAUsAyrJVfXcTYTPNj8HleR4YBvw76rqzwZbuiRJ0mgamcu101wK3FxVp1XV6qo6BdgNnDOXQZIcDWxtxvrUPNQpSZI0kkY15I0xGc56fQZ48xzH+edMPoV7efOalvEka2f7kCRJ0mI39FeojJpOp1PdbnfYZUiSJM1qMb1CRZIkSQMwCg9eHLbmGzCmv0rlmap65TDqkSRJGrZWhLyqmgC8106SJKnh5VpJkqQWMuRJkiS1kCFPkiSphQx5kiRJLWTIkyRJaiFDniRJUgsZ8iRJklrIkCdJktRCrXgZ8iDtemwXa25aM+wyJElDNrFxYtglSIfFmTxJkqQWMuRJkiS10NBDXpL9ScaT7EzyqSTLe7atT1JJzmjWj03yjSRrevpcleQjBxl/Y5JvNT8b5/doJEmSRkOqargFJE9W1Ypm+RPAjqq6vln/JLAKuLOqrm3aLgLeA5wLnAx8GehU1RMzjP0PgC7QAQrYAfz8TH2ndE5eVt1NKwZ3gJIkaWm5du+C7SrJjqrqzLRt6DN502wHTgdIsgJ4NfBWYMNUh6q6Hfgu8BbgQ8C1Bwlt64AvVNXjTZ8vABdN75RkU5Juku6ep4YbeiVJkgZhZEJekiOB1wJTjzNdAtxeVfcDjyd5eU/3dwDvA1ZW1ccPMuwLgId61h9u2n5CVW2uqk5VdVYuz+EchiRJ0kgYhVeoHJdkvFneDny0WR4DbmiWtzTr9wJU1aNJvgTcNsvYMyW2g07VTdSLWf30DQfrIkmt9eB1Fw+7BEkDMgohb19Vre1tSHIicD5wVpIClgGV5Kp67ibCZ5ufg3kYOK9n/YXAXYMoWpIkaZSNzOXaaS4Fbq6q06pqdVWdAuwGzpnjOHcAFyZ5fpLnAxc2bZIkSa02qiFvDNg6re0zwJvnMkhVPQ78JvDnzc97mzZJkqRWG/orVEZNp9Opbrc77DIkSZJmtZheoSJJkqQBGIUHLw5b8w0Y01+l8kxVvXIY9UiSJA1bK0JeVU0Aa2ftKEmStER4uVaSJKmFDHmSJEktZMiTJElqIUOeJElSCxnyJEmSWsiQJ0mS1EKGPEmSpBYy5EmSJLVQK16GPEi7HtvFmpvWDLuMkTexcWLYJUiSpINwJk+SJKmFDHmSJEktNOfLtUmerKoV09quBZ6sqn+f5PeBfw78VFV9v9n+YeBKYGVV/U2SJ4FXAR9vhjgV2Nv8/E1V/dMZ9rsa+Evgm0CAHwC/UlXf7OnzYeBS4JSqerZpuxzoVNXb+zm+M088k+7Gbj9dJUmSRtZ8zeQ9APwyQJIjgNcAj/R2qKqJqlpbVWuBW4Ffa9b/TsDr8e2mz88CNwG/MbWh2c964CHg3IEejSRJ0iIzXyHvFuBNzfJ5wH8HfjTgfRwPPNGz/hpgJ3AjMDaXgZJsStJN0t2zZ88AS5QkSRqO+Qp53wJWJnk+k4Fry4DGfUmS8STfBv4tcH3PtjEmw+VW4PVJjup30KraXFWdquqsXLlyQKVKkiQNz3y+QuWPgA3AK4G3DWjMbzeXd0nyJmAzcFGSo4HXAe+squ8nuQe4ENg21x1MPLKX1VfP+WNSXx687uJhlyBJWiLmM+RtAe4FbqqqZ5MMevxbgY81yxcBJwATzX6WA09xCCFPkiSpDeYt5FXVd5K8B/jiPO3iHODbzfIYcEVV3QKQ5HnA7iTL52kJMA0bAAAEhUlEQVTfkiRJI+1QQt7yJA/3rF9/oI5V9Z8OYfyDeUmScSZfofK3wBVNkFtHzyXhqvpBkq8Ab2iaLk9ySc84v1BVvccgSZLUKqmqYdcwUjqdTnW7vidPkiSNviQ7qqoz0za/8UKSJKmF5vPBi0OSZA3PfRPGlGeq6pXDqEeSJGkxGrmQV1UTwNph1yFJkrSYeblWkiSphQx5kiRJLeTTtdMk+T7wzWHXIQBOAv5m2EUI8FyMEs/F6PBcjIalfh5Oq6oZv5N15O7JGwHfPNCjyFpYSbqei9HguRgdnovR4bkYDZ6HA/NyrSRJUgsZ8iRJklrIkPd3bR52Afoxz8Xo8FyMDs/F6PBcjAbPwwH44IUkSVILOZMnSZLUQks25CW5KMk3kzyQ5OoZth+T5A+b7fckWb3wVbZfH+fh3CT3JvlRkkuHUeNS0ce5+LdJvp7ka0nuTHLaMOpcCvo4F7+aZCLJeJKvJPmZYdS5FMx2Lnr6XZqkkviU5zzp4+/i8iR7mr+L8SRXDKPOUbIkQ16SZcDvAK8FfgYYm+Ffkm8Fnqiq04EPAR9c2Crbr8/z8B3gcuAPFra6paXPc/EXQKeqXgZ8Gvh/FrbKpaHPc/EHVbWmqtYyeR6uX+Ayl4Q+zwVJ/h5wJXDPwla4dPR7LoA/rKq1zc9/XtAiR9CSDHnAK4AHquqvqupvgS3AL0/r88vATc3yp4ELkmQBa1wKZj0PVfVgVX0NeHYYBS4h/ZyL/1ZVTzWrfwa8cIFrXCr6ORff61l9HuDN1fOjn/9WAPwmk2H76YUsbonp91yox1INeS8AHupZf7hpm7FPVf0I2AucuCDVLR39nActjLmei7cCn5/Xipauvs5Fkn+d5NtMhosrF6i2pWbWc5Hk54BTquq2hSxsCer331H/rLml5NNJTlmY0kbXUg15M83ITf8/4X766PD4z3h09H0ukvwLoAP81rxWtHT1dS6q6neq6iXArwP/bt6rWpoOei6SHMHk7TzvWrCKlq5+/i7+C7C6uaXkizx3NW7JWqoh72GgN+G/EHj0QH2SHAmcADy+INUtHf2cBy2Mvs5Fkn8KvAd4Y1U9s0C1LTVz/bvYAlwyrxUtXbOdi78HnAXcleRB4BeAW334Yl7M+ndRVY/1/Hvpd4GfX6DaRtZSDXl/Drw0yYuSHA1sAG6d1udWYGOzfCnwpfKlgoPWz3nQwpj1XDSXpf4TkwHvr4dQ41LRz7l4ac/qxcC3FrC+peSg56Kq9lbVSVW1uqpWM3mv6hurqjucclutn7+LVT2rbwT+cgHrG0lHDruAYaiqHyV5O3AHsAz4varaleS9QLeqbgU+Cnw8yQNMzuBtGF7F7dTPeUjyT4CtwPOBNyT5v6vqzCGW3Up9/k38FrAC+FTzDNJ3quqNQyu6pfo8F29vZlV/CDzBc/9DqgHq81xoAfR5Lq5M8kbgR0z+d/vyoRU8IvzGC0mSpBZaqpdrJUmSWs2QJ0mS1EKGPEmSpBYy5EmSJLWQIU+SJKmFDHmSJEktZMiTJElqIUOeJElSC/3/K0/fVJlvXSsAAAAASUVORK5CYII=\n", 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340.0400080.7151383.2618913.4509540.7241380.7398150.7661100.7936840.0316130.138065226.189099245.095388
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560.1000190.5433842.8594633.1219840.6347990.6017940.6930790.6871010.1427960.312258185.946339212.198445
670.1500050.3662372.2242932.8228490.4937920.4469510.6266710.6070760.1111830.423441122.429306182.284922
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10110.5002860.1514310.7014181.5595370.1557140.1613350.3462160.3304550.0703230.780215-29.85824955.953665
11120.6003060.1312140.6192371.4028700.1374700.1407090.3114360.2988410.0619350.842151-38.07634240.286982
12130.7006590.1147940.5593141.2820500.1241670.1228170.2846140.2736300.0561290.898280-44.06856828.204987
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groupcumulative_data_fractionlower_thresholdliftcumulative_liftresponse_ratescorecumulative_response_ratecumulative_scorecapture_ratecumulative_capture_rategaincumulative_gain
010.0111550.8150103.2950553.2950550.7227720.8398580.7227720.8398580.0367570.036757229.505549229.505549
120.0205430.7955753.7007643.4804600.8117650.8056310.7634410.8242170.0347430.071501270.076417248.045999
230.0300420.7835503.6047213.5197490.7906980.7924410.7720590.8141700.0342400.105740260.472142251.974853
340.0400930.7431923.0058763.3909270.6593410.7613350.7438020.8009250.0302110.135952200.587630239.092657
450.0500330.6977023.4445123.4015730.7555560.7230910.7461370.7854610.0342400.170191244.451158240.157260
560.1012810.5531933.1047773.2513940.6810340.6147360.7131950.6990750.1591140.329305210.477654225.139444
670.1503200.3835642.1870462.9041710.4797300.4660670.6370320.6230610.1072510.436556118.704581190.417123
780.2000220.2969151.5804232.5752440.3466670.3278170.5648810.5496980.0785500.51510658.042296157.524427
890.3013030.2035391.1335142.0906160.2486370.2506480.4585780.4491740.1148040.62990913.351366109.061561
9100.4034680.1769700.9610681.8045950.2108110.1871900.3958390.3828360.0981870.728097-3.89319880.459549
10110.5002210.1520280.6557341.5823820.1438360.1635660.3470960.3404240.0634440.791541-34.42660358.238248
11120.5999560.1330090.5553491.4116510.1218160.1416510.3096470.3073810.0553880.846928-44.46507641.165144
12130.7022310.1150620.4923231.2777570.1079910.1235490.2802770.2806070.0503520.897281-50.76768527.775745
13140.8019660.1023800.3534041.1628020.0775190.1078340.2550610.2591210.0352470.932528-64.65959416.280206
14150.9053460.0918610.3799091.0734050.0833330.0975850.2354520.2406750.0392750.971803-62.0090637.340501
15161.0000000.0348100.2978991.0000000.0653440.0768840.2193510.2251720.0281971.000000-70.2101410.000000
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timestampdurationnumber_of_treestraining_rmsetraining_loglosstraining_auctraining_pr_auctraining_lifttraining_classification_errorvalidation_rmsevalidation_loglossvalidation_aucvalidation_pr_aucvalidation_liftvalidation_classification_error
02020-01-19 12:17:2722.452 sec0.00.4155910.5294270.5000000.0000001.0000000.7780010.4138150.5261050.5000000.0000001.0000000.780649
12020-01-19 12:17:2722.464 sec1.00.4078220.5118640.7161310.5347173.4749120.2363700.4055380.5074960.7267310.5371253.4442640.187652
22020-01-19 12:17:2722.474 sec2.00.4014830.4987460.7446460.5321723.5297060.2287310.3988080.4936980.7529090.5345883.4223070.232825
32020-01-19 12:17:2722.486 sec3.00.3964710.4890130.7481890.5356213.5297060.2286360.3933940.4832730.7564480.5356923.4223070.214491
42020-01-19 12:17:2722.497 sec4.00.3924420.4814300.7501210.5353583.5297060.2107800.3890300.4751350.7585110.5360953.4223070.217915
52020-01-19 12:17:2722.508 sec5.00.3891410.4753750.7500580.5351983.5297060.2450590.3854530.4686300.7585050.5356593.4223070.214270
62020-01-19 12:17:2722.519 sec6.00.3863990.4703320.7569860.5350243.5297060.2439610.3824470.4631570.7647220.5360393.4223070.229843
72020-01-19 12:17:2722.530 sec7.00.3841910.4663160.7570050.5354183.5297060.2439610.3800450.4588340.7646340.5364113.4223070.220013
82020-01-19 12:17:2722.541 sec8.00.3823410.4627600.7611060.5401763.5143590.2474460.3780630.4550490.7703400.5420433.4575240.204330
92020-01-19 12:17:2722.552 sec9.00.3807010.4595890.7625150.5408803.5182790.2356540.3761840.4514640.7723580.5435223.4575240.223548
102020-01-19 12:17:2722.565 sec10.00.3792020.4567050.7625220.5414243.5182790.2356060.3745830.4483800.7729820.5438933.4575240.226309
112020-01-19 12:17:2722.577 sec11.00.3780520.4544670.7616480.5415053.5213320.2310230.3733540.4459730.7729250.5445533.4608820.228960
122020-01-19 12:17:2722.590 sec12.00.3770430.4524200.7627670.5416583.5213320.2299720.3721990.4436700.7734120.5431953.4608820.224542
132020-01-19 12:17:2722.603 sec13.00.3761370.4505170.7647950.5432643.5258990.2343170.3713690.4419320.7741610.5436323.4480380.227413
142020-01-19 12:17:2722.622 sec14.00.3753570.4489630.7651460.5431133.5258990.2356540.3705490.4403350.7741760.5432023.4480380.228076
152020-01-19 12:17:2722.644 sec15.00.3746990.4475430.7661230.5440443.5284170.2332190.3699990.4391610.7745920.5437093.4480380.228297
162020-01-19 12:17:2722.673 sec16.00.3740980.4463410.7665300.5438953.5607130.2296380.3693900.4379260.7750210.5448513.4248550.226751
172020-01-19 12:17:2722.700 sec17.00.3735340.4451150.7663110.5442043.5683700.2314520.3688100.4366690.7749270.5459573.4429290.225425
182020-01-19 12:17:2722.730 sec18.00.3731210.4441710.7667770.5447093.5683700.2293520.3684960.4359090.7752560.5455863.4429290.226530
192020-01-19 12:17:2722.760 sec19.00.3727220.4433600.7671520.5450643.5683700.2274420.3680470.4350060.7754740.5459223.4429290.224652
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" + ], + "text/plain": [ + " timestamp duration number_of_trees training_rmse \\\n", + "0 2020-01-19 12:17:27 22.452 sec 0.0 0.415591 \n", + "1 2020-01-19 12:17:27 22.464 sec 1.0 0.407822 \n", + "2 2020-01-19 12:17:27 22.474 sec 2.0 0.401483 \n", + "3 2020-01-19 12:17:27 22.486 sec 3.0 0.396471 \n", + "4 2020-01-19 12:17:27 22.497 sec 4.0 0.392442 \n", + "5 2020-01-19 12:17:27 22.508 sec 5.0 0.389141 \n", + "6 2020-01-19 12:17:27 22.519 sec 6.0 0.386399 \n", + "7 2020-01-19 12:17:27 22.530 sec 7.0 0.384191 \n", + "8 2020-01-19 12:17:27 22.541 sec 8.0 0.382341 \n", + "9 2020-01-19 12:17:27 22.552 sec 9.0 0.380701 \n", + "10 2020-01-19 12:17:27 22.565 sec 10.0 0.379202 \n", + "11 2020-01-19 12:17:27 22.577 sec 11.0 0.378052 \n", + "12 2020-01-19 12:17:27 22.590 sec 12.0 0.377043 \n", + "13 2020-01-19 12:17:27 22.603 sec 13.0 0.376137 \n", + "14 2020-01-19 12:17:27 22.622 sec 14.0 0.375357 \n", + "15 2020-01-19 12:17:27 22.644 sec 15.0 0.374699 \n", + "16 2020-01-19 12:17:27 22.673 sec 16.0 0.374098 \n", + "17 2020-01-19 12:17:27 22.700 sec 17.0 0.373534 \n", + "18 2020-01-19 12:17:27 22.730 sec 18.0 0.373121 \n", + "19 2020-01-19 12:17:27 22.760 sec 19.0 0.372722 \n", + "\n", + " training_logloss training_auc training_pr_auc training_lift \\\n", + "0 0.529427 0.500000 0.000000 1.000000 \n", + "1 0.511864 0.716131 0.534717 3.474912 \n", + "2 0.498746 0.744646 0.532172 3.529706 \n", + "3 0.489013 0.748189 0.535621 3.529706 \n", + "4 0.481430 0.750121 0.535358 3.529706 \n", + "5 0.475375 0.750058 0.535198 3.529706 \n", + "6 0.470332 0.756986 0.535024 3.529706 \n", + "7 0.466316 0.757005 0.535418 3.529706 \n", + "8 0.462760 0.761106 0.540176 3.514359 \n", + "9 0.459589 0.762515 0.540880 3.518279 \n", + "10 0.456705 0.762522 0.541424 3.518279 \n", + "11 0.454467 0.761648 0.541505 3.521332 \n", + "12 0.452420 0.762767 0.541658 3.521332 \n", + "13 0.450517 0.764795 0.543264 3.525899 \n", + "14 0.448963 0.765146 0.543113 3.525899 \n", + "15 0.447543 0.766123 0.544044 3.528417 \n", + "16 0.446341 0.766530 0.543895 3.560713 \n", + "17 0.445115 0.766311 0.544204 3.568370 \n", + "18 0.444171 0.766777 0.544709 3.568370 \n", + "19 0.443360 0.767152 0.545064 3.568370 \n", + "\n", + " training_classification_error validation_rmse validation_logloss \\\n", + "0 0.778001 0.413815 0.526105 \n", + "1 0.236370 0.405538 0.507496 \n", + "2 0.228731 0.398808 0.493698 \n", + "3 0.228636 0.393394 0.483273 \n", + "4 0.210780 0.389030 0.475135 \n", + "5 0.245059 0.385453 0.468630 \n", + "6 0.243961 0.382447 0.463157 \n", + "7 0.243961 0.380045 0.458834 \n", + "8 0.247446 0.378063 0.455049 \n", + "9 0.235654 0.376184 0.451464 \n", + "10 0.235606 0.374583 0.448380 \n", + "11 0.231023 0.373354 0.445973 \n", + "12 0.229972 0.372199 0.443670 \n", + "13 0.234317 0.371369 0.441932 \n", + "14 0.235654 0.370549 0.440335 \n", + "15 0.233219 0.369999 0.439161 \n", + "16 0.229638 0.369390 0.437926 \n", + "17 0.231452 0.368810 0.436669 \n", + "18 0.229352 0.368496 0.435909 \n", + "19 0.227442 0.368047 0.435006 \n", + "\n", + " validation_auc validation_pr_auc validation_lift \\\n", + "0 0.500000 0.000000 1.000000 \n", + "1 0.726731 0.537125 3.444264 \n", + "2 0.752909 0.534588 3.422307 \n", + "3 0.756448 0.535692 3.422307 \n", + "4 0.758511 0.536095 3.422307 \n", + "5 0.758505 0.535659 3.422307 \n", + "6 0.764722 0.536039 3.422307 \n", + "7 0.764634 0.536411 3.422307 \n", + "8 0.770340 0.542043 3.457524 \n", + "9 0.772358 0.543522 3.457524 \n", + "10 0.772982 0.543893 3.457524 \n", + "11 0.772925 0.544553 3.460882 \n", + "12 0.773412 0.543195 3.460882 \n", + "13 0.774161 0.543632 3.448038 \n", + "14 0.774176 0.543202 3.448038 \n", + "15 0.774592 0.543709 3.448038 \n", + "16 0.775021 0.544851 3.424855 \n", + "17 0.774927 0.545957 3.442929 \n", + "18 0.775256 0.545586 3.442929 \n", + "19 0.775474 0.545922 3.442929 \n", + "\n", + " validation_classification_error \n", + "0 0.780649 \n", + "1 0.187652 \n", + "2 0.232825 \n", + "3 0.214491 \n", + "4 0.217915 \n", + "5 0.214270 \n", + "6 0.229843 \n", + "7 0.220013 \n", + "8 0.204330 \n", + "9 0.223548 \n", + "10 0.226309 \n", + "11 0.228960 \n", + "12 0.224542 \n", + "13 0.227413 \n", + "14 0.228076 \n", + "15 0.228297 \n", + "16 0.226751 \n", + "17 0.225425 \n", + "18 0.226530 \n", + "19 0.224652 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "See the whole table with table.as_data_frame()\n", + "\n", + "Variable Importances: " + ] + }, + { + "data": { + "text/html": [ + "
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variablerelative_importancescaled_importancepercentage
0PAY_02794.4448241.0000000.693347
1PAY_2307.2373660.1099460.076231
2PAY_3215.1528930.0769930.053383
3PAY_4155.4344480.0556230.038566
4PAY_AMT1127.9863130.0458000.031755
5PAY_5127.5386280.0456400.031644
6PAY_6102.3516010.0366270.025395
7LIMIT_BAL82.4323500.0294990.020453
8PAY_AMT258.9341350.0210900.014623
9PAY_AMT458.8580470.0210630.014604
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" + ], + "text/plain": [ + " variable relative_importance scaled_importance percentage\n", + "0 PAY_0 2794.444824 1.000000 0.693347\n", + "1 PAY_2 307.237366 0.109946 0.076231\n", + "2 PAY_3 215.152893 0.076993 0.053383\n", + "3 PAY_4 155.434448 0.055623 0.038566\n", + "4 PAY_AMT1 127.986313 0.045800 0.031755\n", + "5 PAY_5 127.538628 0.045640 0.031644\n", + "6 PAY_6 102.351601 0.036627 0.025395\n", + "7 LIMIT_BAL 82.432350 0.029499 0.020453\n", + "8 PAY_AMT2 58.934135 0.021090 0.014623\n", + "9 PAY_AMT4 58.858047 0.021063 0.014604" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mgbm_models[4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Calculating partial dependence and ICE to validate and explain monotonic behavior" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Function for calculating partial dependence" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def par_dep(xs, frame, model, resolution=20, bins=None):\n", + " \n", + " \"\"\" Creates Pandas DataFrame containing partial dependence for a \n", + " single variable.\n", + " \n", + " Args:\n", + " xs: Variable for which to calculate partial dependence.\n", + " frame: Pandas DataFrame for which to calculate partial dependence.\n", + " model: \n", + " resolution: The number of points across the domain of xs for which \n", + " to calculate partial dependence, default 20.\n", + " bins: List of values at which to set xs, default 20 equally-spaced \n", + " points between column minimum and maximum.\n", + " \n", + " Returns:\n", + " Pandas DataFrame containing partial dependence values.\n", + " \n", + " \"\"\"\n", + " \n", + " # turn off pesky Pandas copy warning\n", + " pd.options.mode.chained_assignment = None\n", + " \n", + " # determine values at which to calculate partial dependence\n", + " if bins == None:\n", + " min_ = frame[xs].min()\n", + " max_ = frame[xs].max()\n", + " by = (max_ - min_)/resolution\n", + " # modify max and by \n", + " # to preserve resolution and actually search up to max\n", + " bins = np.arange(min_, (max_ + by), (by + np.round((1. / resolution) * by, 3))) \n", + "\n", + " # cache original column values \n", + " col_cache = frame.loc[:, xs].copy(deep=True)\n", + " \n", + " # calculate partial dependence \n", + " # by setting column of interest to constant \n", + " # and scoring the altered data and taking the mean of the predictions\n", + " tframe = frame.copy(deep=True)\n", + " tframe.loc[:, xs] = bins[0]\n", + " for j, _ in enumerate(bins): \n", + " if j+1 < len(bins): \n", + " frame.loc[:, xs] = bins[j+1]\n", + " tframe = tframe.append(frame, ignore_index=True)\n", + "\n", + " # return input frame to original cached state \n", + " frame.loc[:, xs] = col_cache \n", + " \n", + " # model predictions\n", + " tframe['partial_dependence'] = model.predict(h2o.H2OFrame(tframe))['p1'].as_data_frame()\n", + " \n", + " return pd.DataFrame(tframe[[xs, 'partial_dependence']].groupby([xs]).mean()).reset_index()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate partial dependence for the most important input variables in the GBM\n", + "The partial dependence for `LIMIT_BAL` can be seen to decrease as credit balance limits increase. This finding is aligned with expectations that the model predictions will be monotonically decreasing with increasing `LIMIT_BAL` and parsimonious with well-known business practices in credit lending. Partial dependence for other important values is displayed in plots further below." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "glm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n" + ] + } + ], + "source": [ + "par_dep_glm_PAY_0 = par_dep('PAY_0', test, best_glm) # calculate partial dependence for PAY_0 for glm\n", + "par_dep_mgbm_PAY_0 = par_dep('PAY_0', test, mgbm_models[3]) # calculate partial dependence for PAY_0 for glm" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PAY_0partial_dependence_glmpartial_dependence_mgbm
0-2.0000.0822800.172167
1-1.4750.1058740.172167
2-0.9500.1351360.172167
3-0.4250.1708040.172167
40.1000.2133870.172922
50.6250.2630120.252675
61.1500.3192620.252675
71.6750.3810880.536813
82.2000.4468150.536813
92.7250.5142840.538050
103.2500.5811140.538050
113.7750.6450210.538050
124.3000.7041100.538050
134.8250.7570640.538050
145.3500.8032020.538050
155.8750.8424170.538050
166.4000.8750470.538050
176.9250.9017180.538050
187.4500.9232000.538050
197.9750.9402960.538050
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" + ], + "text/plain": [ + " PAY_0 partial_dependence_glm partial_dependence_mgbm\n", + "0 -2.000 0.082280 0.172167\n", + "1 -1.475 0.105874 0.172167\n", + "2 -0.950 0.135136 0.172167\n", + "3 -0.425 0.170804 0.172167\n", + "4 0.100 0.213387 0.172922\n", + "5 0.625 0.263012 0.252675\n", + "6 1.150 0.319262 0.252675\n", + "7 1.675 0.381088 0.536813\n", + "8 2.200 0.446815 0.536813\n", + "9 2.725 0.514284 0.538050\n", + "10 3.250 0.581114 0.538050\n", + "11 3.775 0.645021 0.538050\n", + "12 4.300 0.704110 0.538050\n", + "13 4.825 0.757064 0.538050\n", + "14 5.350 0.803202 0.538050\n", + "15 5.875 0.842417 0.538050\n", + "16 6.400 0.875047 0.538050\n", + "17 6.925 0.901718 0.538050\n", + "18 7.450 0.923200 0.538050\n", + "19 7.975 0.940296 0.538050" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "par_dep_PAY_0 = pd.concat([par_dep_glm_PAY_0, par_dep_mgbm_PAY_0['partial_dependence']], axis=1)\n", + "par_dep_PAY_0.columns = ['PAY_0', 'partial_dependence_glm', 'partial_dependence_mgbm']\n", + "par_dep_PAY_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Helper function for finding percentiles of predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def get_percentile_dict(yhat, id_, frame):\n", + "\n", + " \"\"\" Returns the percentiles of a column, yhat, as the indices based on \n", + " another column id_.\n", + " \n", + " Args:\n", + " yhat: Column in which to find percentiles.\n", + " id_: Id column that stores indices for percentiles of yhat.\n", + " frame: Pandas DataFrame containing yhat and id_. \n", + " \n", + " Returns:\n", + " Dictionary of percentile values and index column values.\n", + " \n", + " \"\"\"\n", + " \n", + " # create a copy of frame and sort it by yhat\n", + " sort_df = frame.copy(deep=True)\n", + " sort_df.sort_values(yhat, inplace=True)\n", + " sort_df.reset_index(inplace=True)\n", + " \n", + " # find top and bottom percentiles\n", + " percentiles_dict = {}\n", + " percentiles_dict[0] = sort_df.loc[0, id_]\n", + " percentiles_dict[99] = sort_df.loc[sort_df.shape[0]-1, id_]\n", + "\n", + " # find 10th-90th percentiles\n", + " inc = sort_df.shape[0]//10\n", + " for i in range(1, 10):\n", + " percentiles_dict[i * 10] = sort_df.loc[i * inc, id_]\n", + "\n", + " return percentiles_dict\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find some percentiles of yhat in the test data\n", + "The values for `ID` that correspond to the maximum, minimum, and deciles of `p_DEFAULT_NEXT_MONTH` are displayed below. ICE will be calculated for the rows of the test dataset associated with these `ID` values." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n" + ] + }, + { + "data": { + "text/plain": [ + "{0: 14136,\n", + " 99: 17863,\n", + " 10: 9632,\n", + " 20: 29179,\n", + " 30: 25974,\n", + " 40: 5795,\n", + " 50: 9033,\n", + " 60: 7620,\n", + " 70: 9808,\n", + " 80: 15816,\n", + " 90: 16549}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# merge GBM predictions onto test data\n", + "yhat_test = pd.concat([test.reset_index(drop=True),\n", + " mgbm_models[3].predict(h2o.H2OFrame(test))['p1'].as_data_frame()],\n", + " axis=1)\n", + "\n", + "yhat_test = yhat_test.rename(columns={'p1':'p_DEFAULT_NEXT_MONTH'})\n", + "\n", + "# find percentiles of predictions\n", + "percentile_dict = get_percentile_dict('p_DEFAULT_NEXT_MONTH', 'ID', yhat_test)\n", + "\n", + "# display percentiles dictionary\n", + "# ID values for rows\n", + "# from lowest prediction \n", + "# to highest prediction\n", + "percentile_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate ICE curve values\n", + "ICE values represent a model's prediction for a row of data while an input variable of interest is varied across its domain. The values of the input variable are chosen to match the values at which partial dependence was calculated earlier, and ICE is calculated for the top three most important variables and for rows at each percentile of the test dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n", + "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", + "gbm prediction progress: |████████████████████████████████████████████████| 100%\n" + ] + } + ], + "source": [ + "# retreive bins from original partial dependence calculation\n", + "\n", + "bins_PAY_0 = list(par_dep_PAY_0['PAY_0'])\n", + "#bins_LIMIT_BAL = list(par_dep_LIMIT_BAL['LIMIT_BAL'])\n", + "#bins_BILL_AMT1 = list(par_dep_BILL_AMT1['BILL_AMT1'])\n", + "\n", + "# for each percentile in percentile_dict\n", + "# create a new column in the par_dep frame \n", + "# representing the ICE curve for that percentile\n", + "# and the variables of interest\n", + "for i in sorted(percentile_dict.keys()):\n", + " \n", + " col_name = 'Percentile_' + str(i)\n", + " \n", + " # ICE curves for PAY_0 across percentiles at bins_PAY_0 intervals\n", + " par_dep_PAY_0[col_name] = par_dep('PAY_0', \n", + " test[test['ID'] == int(percentile_dict[i])][X], \n", + " mgbm_models[3], \n", + " bins=bins_PAY_0)['partial_dependence']\n", + " \n", + " # ICE curves for LIMIT_BAL across percentiles at bins_LIMIT_BAL intervals\n", + " #par_dep_LIMIT_BAL[col_name] = par_dep('LIMIT_BAL', \n", + " # test[test['ID'] == int(percentile_dict[i])][X], \n", + " # xgb_model, \n", + " # bins=bins_LIMIT_BAL)['partial_dependence']\n", + " \n", + "\n", + "\n", + " # ICE curves for BILL_AMT1 across percentiles at bins_BILL_AMT1 intervals\n", + " #par_dep_BILL_AMT1[col_name] = par_dep('BILL_AMT1', \n", + " # test[test['ID'] == int(percentile_dict[i])][X], \n", + " # xgb_model, \n", + " # bins=bins_BILL_AMT1)['partial_dependence']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display partial dependence and ICE for `LIMIT_BAL`\n", + "Partial dependence and ICE values for rows at the minimum, maximum and deciles (0%, 10%, 20%, ..., 90%, 99%) of predictions for `DEFAULT_NEXT_MONTH` and at the values of `LIMIT_BAL` used for partial dependence are shown here. Each column of ICE values will be a curve in the plots below. ICE values represent a prediction for a row of test data, at a percentile of interest noted in the column name above, and setting `LIMIT_BAL` to the value of `LIMIT_BAL` at right. Notice that monotonic decreasing prediction behavior for `LIMIT_BAL` holds at each displayed percentile of predicted `DEFAULT_NEXT_MONTH`, helping to validate that the trained GBM predictions are monotonic for `LIMIT_BAL`." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PAY_0partial_dependence_glmpartial_dependence_mgbmPercentile_0Percentile_10Percentile_20Percentile_30Percentile_40Percentile_50Percentile_60Percentile_70Percentile_80Percentile_90Percentile_99
0-2.0000.0822800.1721670.0452710.0929010.1027040.1153310.1327310.1521900.1736560.2008360.1847100.4634080.739221
1-1.4750.1058740.1721670.0452710.0929010.1027040.1153310.1327310.1521900.1736560.2008360.1847100.4634080.739221
2-0.9500.1351360.1721670.0452710.0929010.1027040.1153310.1327310.1521900.1736560.2008360.1847100.4634080.739221
3-0.4250.1708040.1721670.0452710.0929010.1027040.1153310.1327310.1521900.1736560.2008360.1847100.4634080.739221
40.1000.2133870.1729220.0452710.0929010.1027040.1153310.1327310.1521900.1736560.2008360.1847100.4690810.743592
50.6250.2630120.2526750.0743760.1494590.1715480.1809360.2156690.2354900.2709280.3031540.2914980.5517630.797646
61.1500.3192620.2526750.0743760.1494590.1715480.1809360.2156690.2354900.2709280.3031540.2914980.5517630.797646
71.6750.3810880.5368130.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8008630.933519
82.2000.4468150.5368130.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8008630.933519
92.7250.5142840.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
103.2500.5811140.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
113.7750.6450210.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
124.3000.7041100.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
134.8250.7570640.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
145.3500.8032020.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
155.8750.8424170.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
166.4000.8750470.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
176.9250.9017180.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
187.4500.9232000.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
197.9750.9402960.5380500.2740990.4882630.4031520.5274090.6093000.5575240.5981770.4821380.4601940.8108010.937355
\n", + "
" + ], + "text/plain": [ + " PAY_0 partial_dependence_glm partial_dependence_mgbm Percentile_0 \\\n", + "0 -2.000 0.082280 0.172167 0.045271 \n", + "1 -1.475 0.105874 0.172167 0.045271 \n", + "2 -0.950 0.135136 0.172167 0.045271 \n", + "3 -0.425 0.170804 0.172167 0.045271 \n", + "4 0.100 0.213387 0.172922 0.045271 \n", + "5 0.625 0.263012 0.252675 0.074376 \n", + "6 1.150 0.319262 0.252675 0.074376 \n", + "7 1.675 0.381088 0.536813 0.274099 \n", + "8 2.200 0.446815 0.536813 0.274099 \n", + "9 2.725 0.514284 0.538050 0.274099 \n", + "10 3.250 0.581114 0.538050 0.274099 \n", + "11 3.775 0.645021 0.538050 0.274099 \n", + "12 4.300 0.704110 0.538050 0.274099 \n", + "13 4.825 0.757064 0.538050 0.274099 \n", + "14 5.350 0.803202 0.538050 0.274099 \n", + "15 5.875 0.842417 0.538050 0.274099 \n", + "16 6.400 0.875047 0.538050 0.274099 \n", + "17 6.925 0.901718 0.538050 0.274099 \n", + "18 7.450 0.923200 0.538050 0.274099 \n", + "19 7.975 0.940296 0.538050 0.274099 \n", + "\n", + " Percentile_10 Percentile_20 Percentile_30 Percentile_40 Percentile_50 \\\n", + "0 0.092901 0.102704 0.115331 0.132731 0.152190 \n", + "1 0.092901 0.102704 0.115331 0.132731 0.152190 \n", + "2 0.092901 0.102704 0.115331 0.132731 0.152190 \n", + "3 0.092901 0.102704 0.115331 0.132731 0.152190 \n", + "4 0.092901 0.102704 0.115331 0.132731 0.152190 \n", + "5 0.149459 0.171548 0.180936 0.215669 0.235490 \n", + "6 0.149459 0.171548 0.180936 0.215669 0.235490 \n", + "7 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "8 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "9 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "10 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "11 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "12 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "13 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "14 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "15 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "16 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "17 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "18 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "19 0.488263 0.403152 0.527409 0.609300 0.557524 \n", + "\n", + " Percentile_60 Percentile_70 Percentile_80 Percentile_90 Percentile_99 \n", + "0 0.173656 0.200836 0.184710 0.463408 0.739221 \n", + "1 0.173656 0.200836 0.184710 0.463408 0.739221 \n", + "2 0.173656 0.200836 0.184710 0.463408 0.739221 \n", + "3 0.173656 0.200836 0.184710 0.463408 0.739221 \n", + "4 0.173656 0.200836 0.184710 0.469081 0.743592 \n", + "5 0.270928 0.303154 0.291498 0.551763 0.797646 \n", + "6 0.270928 0.303154 0.291498 0.551763 0.797646 \n", + "7 0.598177 0.482138 0.460194 0.800863 0.933519 \n", + "8 0.598177 0.482138 0.460194 0.800863 0.933519 \n", + "9 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "10 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "11 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "12 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "13 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "14 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "15 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "16 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "17 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "18 0.598177 0.482138 0.460194 0.810801 0.937355 \n", + "19 0.598177 0.482138 0.460194 0.810801 0.937355 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "par_dep_PAY_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Plotting partial dependence and ICE to validate and explain monotonic behavior\n", + "\n", + "Overlaying partial dependence onto ICE in a plot is a convenient way to validate and understand both global and local monotonic behavior. Plots of partial dependence curves overlayed onto ICE curves for several percentiles of predictions for `DEFAULT_NEXT_MONTH` are used to validate monotonic behavior, describe the GBM model mechanisms, and to compare the most extreme GBM behavior with the average GBM behavior in the test data. Partial dependence and ICE plots are displayed for the three most important variables in the GBM: `PAY_0`, `LIMIT_BAL`, and `BILL_AMT1`." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_par_dep_ICE(xs, par_dep_frame, ax=None):\n", + "\n", + " \n", + " \"\"\" Plots ICE overlayed onto partial dependence for a single variable.\n", + " Conditionally uses user-defined axes, ticks, and labels for grouped subplots.\n", + " \n", + " Args: \n", + " xs: Name of variable for which to plot ICE and partial dependence.\n", + " par_dep_frame: Name of Pandas DataFrame containing ICE and partial\n", + " dependence values.\n", + " ax: Matplotlib axis object to use, default None. \n", + " \n", + " \"\"\"\n", + " \n", + " # for standalone plotting\n", + " if ax is None:\n", + " \n", + " # initialize figure and axis\n", + " fig, ax = plt.subplots()\n", + " \n", + " # plot ICE\n", + " par_dep_frame.drop('partial_dependence_mgbm', axis=1).plot(x=xs, \n", + " colormap='coolwarm', \n", + " ax=ax)\n", + " # overlay partial dependence, annotate plot\n", + " par_dep_frame.plot(title='Partial Dependence with ICE',\n", + " x=xs, \n", + " y='partial_dependence_mgbm',\n", + " color='grey', \n", + " linewidth=3, \n", + " ax=ax)\n", + " \n", + " # for grouped subplots \n", + " else: \n", + " \n", + " # plot ICE\n", + " par_dep_frame.drop('partial_dependence_mgbm', axis=1).plot(x=xs, \n", + " colormap='gnuplot',\n", + " ax=ax)\n", + " # overlay partial dependence, annotate plot\n", + " par_dep_frame.plot(title='Partial Dependence with ICE',\n", + " x=xs, \n", + " y='partial_dependence_mgbm',\n", + " color='red', \n", + " linewidth=3, \n", + " ax=ax)\n", + "\n", + " # add legend\n", + " _ = plt.legend(bbox_to_anchor=(1.05, 0),\n", + " loc=3, \n", + " borderaxespad=0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# setup 3-way figure\n", + "fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, sharey=False)\n", + "plt.tight_layout()\n", + "plt.subplots_adjust(left=0.0, right=2.5, wspace=0.2)\n", + " \n", + "# histogram \n", + "test['PAY_0'].plot(kind='hist', bins=20, ax=ax0)\n", + " \n", + "# comparison of partial dependence\n", + "_ = test[['PAY_0', y]].groupby(by='PAY_0').mean().plot(kind='line', ax=ax1)\n", + "_ = test[['PAY_0', y]].plot(kind='scatter', x='PAY_0', y=y, ax=ax1, s=3, alpha=0.1)\n", + "_ = par_dep_PAY_0[['PAY_0', 'partial_dependence_glm','partial_dependence_mgbm']].plot(kind='line', x='PAY_0', ax=ax1, )\n", + "\n", + "# partial dependence and ICE #\n", + "plot_par_dep_ICE('PAY_0', par_dep_PAY_0.drop(['partial_dependence_glm'], \n", + " axis=1), \n", + " ax2)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "# comparison of partial dependence\n", + "_ = test[['PAY_0', y]].groupby(by='PAY_0').mean().plot(kind='line', ax=ax)\n", + "_ = test[['PAY_0', y]].plot(kind='scatter', x='PAY_0', y=y, ax=ax, s=3, alpha=0.1)\n", + "_ = par_dep_PAY_0[['PAY_0', 'partial_dependence_mgbm']].plot(kind='line', x='PAY_0', ax=ax)\n", + "\n", + "_ = plt.legend(bbox_to_anchor=(1.05, 0.5),\n", + " loc=3, \n", + " borderaxespad=0.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Partial dependence and ICE plot for `LIMIT_BAL`\n", + "The monotonic prediction behavior displayed in the partial dependence, and ICE tables for `LIMIT_BAL` is also visible in this plot. Monotonic decreasing behavior is evident at every percentile of predictions for `DEFAULT_NEXT_MONTH`. Most percentiles of predictions show that sharper decreases in probability of default occur when `LIMIT_BAL` increases just slightly from its lowest values in the test set. However, for the custumers that are most likely to default according to the GBM model, no increase in `LIMIT_BAL` has a strong impact on probabilitiy of default. As mentioned previously, the displayed relationship between credit balance limits and probablility of default is not uncommon in credit lending. As can be seen from the displayed histogram, above ~$NT 500,000 prediction behavior may have been learned from extremely small samples of data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Partial dependence and ICE plot for `PAY_0`\n", + "Monotonic increasing prediction behavior for `PAY_0` is displayed for all percentiles of model predictions. Predition behavior is different at different deciles, but not abnormal or vastly different from the average prediction behavior represented by the red partial dependence curve. The largest jump in predicted probability appears to occur at `PAY_0 = 2`, or when a customer becomes two months late on their most recent payment. Above `PAY_0 = 3` there are few examples from which the model could learn." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_par_dep_ICE('PAY_0', par_dep_PAY_0) # plot partial dependence and ICE for PAY_0" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_ = train['PAY_0'].plot(kind='hist', bins=20, title='Histogram: PAY_0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Partial dependence and ICE plot for `BILL_AMT1`\n", + "Monotonic decreasing prediction behavior for `BILL_AMT1` is also displayed for all percentiles. This mild decrease in probability of default as most recent bill amount increases could be related to wealthier, big-spending customers taking on more debt but also being able to pay it off reliably. Also, customers with negative bills are more likely to default, potentially indicating charge-offs are being recorded as negative bills. In a mission-critical situation, this issue would require more debugging. Also predictions below \\$ NT 0 and above \\$ NT 400,000 are based on very little training data." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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BNpuNLVmypPfgwYONIpEIcrl8cVpaWptOp/PNy8trkslkjvT09DCz2SyQSCRcp9MZFQqFY/PmzbPOnj2rsFqtbOPGja0ZGRntxcXFiuzs7JlKpdJmNBqnqdVq89GjR+t37dqlam1tFWs0mmh/f397aWlpTWhoqLqsrKx6xowZN2UkO3bsCC4qKlJarVaWmpp6fc+ePZ8PtS1nzpyZHh4e3h8bG2sFgNWrV187fPjwQwkJCVfvZJ9ONOpZIIQQMmFsNhtOnjzpq1ar+86fPy87fPiwsqyszGAwGKoEAgHft29fAAD09fUJEhMTe41GY5VGo+lNS0uLzM3NbTIajVVardbo4+PjyM3NDfTz8xuoqKioLi8vrz5w4ECQwWCQAEB1dfW0/Pz8S7W1tZVNTU3SU6dO+WRmZraqVCqbVqutKS0trRkuxiNHjvjW1tbK9Hp9dXV1ddWFCxfkJ06c8Bmq7aVLlyShoaHWwfezZs2yNjc3S8Z7v91r1LNACCEPsLH0AIyn/v5+QUxMTCwAJCYmmrZu3dq+e/fuwIqKCnl8fPwCALBYLAKVSmUHAKFQiPXr13cCgF6vl6lUKptGozEDgFKpdADA6dOnfQ0Gg/zYsWP+AGAymYRVVVUyiUTC1Wp1b2RkpA0A4uLizHV1dV4fwEtKSnx1Op1vbGxsLACYzWaBwWCQpaSk9IzfHpncKFkghBByzw3es+A+jXPO1q5d25Gfn9/s2V4ikThEopEPWZxzlpOT07RmzZpu9+nFxcUKqVR647kSQqEQdrudeRsr5xzp6elXMjIy2kdrO3v27Jt6Ei5fvnxTT8NURZchCCGETArJycndxcXF/s3NzSIAaGlpEdbU1NzSA7Bo0SJLa2urWKvVygGgs7NTYLPZsGrVqq6CgoKg/v5+BgB6vV7a3d094nFu+vTpA11dXSO2SUlJ6T506FDgYLv6+nrxYIyeNBpNb0NDg8xgMEgsFgs7cuSIcs2aNde92wOTF/UsEEIImRQSEhIsmZmZzStXrox2OBwQi8U8Ly+vKTo6+qYzc5lMxgsLC+u2bNkSZrFYBDKZzKHT6Wq2bdvW3tDQIFWr1Qs450ypVNqOHz9eN9I6161b156cnBwdHBxsHe6+hdWrV3dXVlbKli1bFgMAcrncUVhYWB8aGnrLsAyxWIycnJym5OTk6IGBAbz00kvtS5cutdzJfpkMGOcT88TPpUuX8rKysglZNyGETFWMsXOc86V3sozy8vKG+Pj4UbvUyYOnvLw8MD4+PsJzOl2GIIQQQsiI6DIEIYQQMkZXr14VJiUlzfecfubMGWNISMhtP9RpsqJkgRBCCBmjkJCQAc/RHPczry5DMMaSGWNGxlgtY+yHQ8wPY4x9yBj7G2NMzxh7cvxDJYQQQshEGDVZYIwJAeQDSAEQC+DrjLFYj2aZAN7jnC8G8CKAveMdKCGEEEImhjc9C18EUMs5/4xzbgXwWwDPerThAHxdf/sBGPKZ2YQQQgiZery5ZyEUgPvjQC8DSPRokwXgT4yx7wKYDuCxcYmOEEIIIRNuvIZOfh3ArzjnswA8CeAQY+yWZTPGNjHGyhhjZW1tbeO0akIIIVONUChMiImJiY2KiopLSUmZazKZJmQof3Z2tsp93RqNZl57e7sQAORy+eLbWebatWsjlEplfFRUVJz79JaWFuHy5cujwsPDFy5fvjyqra1NeGfR3zujPpSJMfYlAFmc8ydc738EAJzzf3NrUwkgmXN+yfX+MwAPc85bh1suPZSJEELGbrwfyvTzDX+e3VTRMa4lqsMWBpi/+87KEQtUyeXyxWaz+W8A8Mwzz8xZsmSJOSsrq8Wb5dvtdoxWJ8Jbw5Wk9oxxLE6cOOGjUCgc3/rWt+ZcvHixcnD6q6++OkupVNp37dp1dfv27SGdnZ3CgoKCW+pgTKQ7eSjTpwCiGGNzGGMSOG9gPObRpgnASgBgjC0AIANAXQeEEEJGtWLFip7a2lopAOzdu1epVqsXxMTExL700kvhdrvzGC6Xyxdv3Lhx1vz582P//Oc/+2i1WvnixYtj5s+fH6tWqxd0dnYK7HY7XnnllVkLFy5cEB0dHfvTn/40EHAWkvriF784Pzk5ee6cOXPinnnmmTkOhwNvvvmmqrW1VazRaKITExOjAWfycOXKlVsykR07dgQPLnfbtm0zR9qelJSUnqCgoFuSj5KSkodeeeWVDgB45ZVXOk6cOOF/xzvvHhk1NeOc2xljrwE4CUAI4B3OeSVjLBtAGef8GIB/BvA2Y2wbnDc7rucT9RxpQgghXhutB+Bus9lsOHnypO/jjz/eff78ednhw4eVZWVlBqlUyl9++eWwffv2Bbz22msdfX19gsTExN633377ssViYfPmzVtYWFhYp9FozNeuXRP4+Pg4cnNzA/38/AYqKiqq+/r62LJly2KefvrpbgCorq6eduHChc8iIiJsCQkJMadOnfLJzMxsLSgoCNZqtTVD9SwMOnLkiG9tba1Mr9dXc87x2GOPzTtx4oTPWEtUd3R0iMLDw20AMHv2bFtHR8eUedaRV4Fyzo8DOO4x7XW3v6sAfHl8QyOEEHK/6u/vF8TExMQCQGJiomnr1q3tu3fvDqyoqJDHx8cvAACLxSJQqVR2wFlWev369Z0AoNfrZSqVyqbRaMwAoFQqHQBw+vRpX4PBID927Jg/AJhMJmFVVZVMIpFwtVrdGxkZaQOAuLg4c11d3S3VLIdTUlLiq9PpfGNjY2MBwGw2CwwGg2ysyYI7gUAAxryukj3hpkxWQwgh5P4hlUodnk9A5JyztWvXduTn599yHV8ikThGu0+Bc85ycnKa1qxZ0+0+vbi4WCGVSm/0dguFQtjtdq+P1JxzpKenX8nIyLij4lsBAQH2xsZGcXh4uK2xsVGsVCqH7c2YbKiQFCGEkEkhOTm5u7i42L+5uVkEOEcP1NTU3NIDsGjRIktra6tYq9XKAaCzs1Ngs9mwatWqroKCgqD+/n4GAHq9Xtrd3T3icW769OkDXV1dI7ZJSUnpPnToUOBgu/r6evFgjGPxxBNPXN+/f38AAOzfvz8gOTn5+liXMVGoZ4EQQsikkJCQYMnMzGxeuXJltMPhgFgs5nl5eU3R0dFW93YymYwXFhbWbdmyJcxisQhkMplDp9PVbNu2rb2hoUGqVqsXcM6ZUqm0HT9+vG6kda5bt649OTk5Ojg42FpaWlozVJvVq1d3V1ZWypYtWxYDAHK53FFYWFgfGho6ZM/A008/PeeTTz5RdHZ2ioKDgxf98Ic//Hzbtm3tO3fuvPL8889HhoeHB4aGhlqLiopGjG0yGXXo5N1CQycJIWTsxnvoJCHu7mToJCGEEEIeYHQZghBCCBmjq1evCpOSkuZ7Tj9z5owxJCRkYCJiupsoWSCEEELGKCQkZMBzNMf9jC5DEEIIIWRElCwQQgghZESULBBCCCFkRJQsEEIIuefu1xLVtbW14sTExOjIyMi4efPmxf34xz9WDc6byiWqKVkghBByzw0+7vnixYuVYrGY5+TkBHn72cFKlONh//79wT09PTeOhVqttjYwMPC2RzOIxWLk5ORcrqurq/z000+r/+u//kt17tw5GQC88cYbM5KSkkyNjY0VSUlJptdffz1kPLbhXqDREIQQ8gD77T9aZl+tdMjHc5khcQLzi/8l87qa5YoVK3r0ev00wFmiuqCgINhms7ElS5b0Hjx4sFEkEkEuly9OS0tr0+l0vnl5eU0ymcyRnp4eZjabBRKJhOt0OqNCoXBs3rx51tmzZxVWq5Vt3LixNSMjo724uFiRnZ09U6lU2oxG4zS1Wm0+evRo/a5du26UqPb397eXlpbWhIaGqsvKyqo9q1Du2LEjuKioSGm1Wllqaur1PXv2fD7UtoSHh9sGK0v6+/s7IiMj+5qamiQJCQmWkpKSh7RarRFwlqjWaDTzAdxSB2Myop4FQgghE2awRLVare5zL1FtMBiqBAIB37dvXwAADJaoNhqNVRqNpjctLS0yNze3yWg0Vmm1WqNniery8vLqAwcOBBkMBgngLFGdn59/qba2trKpqUk6WKJapVLZtFptzXCPegZuLlFdXV1ddeHCBfmJEyd8Rts2o9Eoqaqqkms0mh7gAShRTQgh5P40lh6A8XS/l6ju6uoSrF69OvInP/nJpcH43FGJakIIIWQU93OJ6v7+fpaamhq5du3aa+vWrbtRWZJKVBNCCCF36H4oUe1wOPDiiy+GR0dHW7Kyslrc51GJakIIIeQO3Q8lqk+dOuVz9OjRgKioqL7Byyw7d+5sfuGFF7qoRPVtoBLVhBAydlSimtxNVKKaEEIIIbeFLkMQQgghY0QlqgkhhBAyIipRTQghhBDihpIFQgghhIyIkgVCCCGEjIiSBUIIIYSMiJIFQggh95xQKEyIiYmJjYqKiktJSZlrMpkm5HiUnZ2tcl+3RqOZ197eLgQAuVy+eKzLM5vNTK1WL5g/f37svHnz4rZt2zZzcJ7BYJAsWrQoJiwsbGFqaupci8UyZYpDULJACCHknhusDXHx4sVKsVjMc3Jygrz9rN0+fiUV9u/fH9zT03PjWKjVamsDAwNve+ijTCbjH330kdFoNFZVVlZW/fnPf/b985//PB0Avve978167bXXWpqamir8/Pzsb731VuB4bMO9QEMnCSHkAVayyTK7vcohH89lBsYKzMm/8L6a5YoVK3r0ev00ANi7d6+yoKAg2GazsSVLlvQePHiwUSQSQS6XL05LS2vT6XS+eXl5TTKZzJGenh5mNpsFEomE63Q6o0KhcGzevHnW2bNnFVarlW3cuLE1IyOjvbi4WJGdnT1TqVTajEbjNLVabT569Gj9rl27VK2trWKNRhPt7+9vLy0trQkNDVWXlZVVz5gx46aMZMeOHcFFRUVKq9XKUlNTr+/Zs+fzobZFIBDAz8/PAQBWq5XZ7XbGGIPD4cDHH3+s+OCDDz4DgA0bNnRkZWXN/MEPftB2+3v63plyycJATyUc3ecmNgjhNAh94sHk88AYdc4QQsjtstlsOHnypO/jjz/eff78ednhw4eVZWVlBqlUyl9++eWwffv2Bbz22msdfX19gsTExN633377ssViYfPmzVtYWFhYp9FozNeuXRP4+Pg4cnNzA/38/AYqKiqq+/r62LJly2KefvrpbgCorq6eduHChc8iIiJsCQkJMadOnfLJzMxsLSgoCNZqtTWeyYG7I0eO+NbW1sr0en015xyPPfbYvBMnTvgMV6Labrdj4cKFsU1NTdJ169a1Pvroo71XrlwRKRSKAbFYDACIiIiwtrS0eF0me6JNvWSh/Tj6a78/0WE4CX0h9F0CgSIBQt8ECH2Xgk2LpASCEDJljKUHYDz19/cLBgstJSYmmrZu3dq+e/fuwIqKCnl8fPwCALBYLAKVSmUHnGWl169f3wkAer1eplKpbBqNxgwASqXSAQCnT5/2NRgM8mPHjvkDgMlkElZVVckkEglXq9W9kZGRNgCIi4sz19XVeX2gLikp8dXpdL6xsbGxAGA2mwUGg0E2XLIgEolgMBiq2tvbhampqZGffvqpbNasWVOmHPVQplyyIA7dCJFqzYTGwO1dcJj+hgHTOQx0l8F2+T9hc/Q7Zwp9IfRNgMA3AULFUgh9E1wJxJS5j4UQQu66wXsW3KdxztnatWs78vPzmz3bSyQSh0g08iGLc85ycnKa1qxZ0+0+vbi4WCGVSm9UTRQKhbDb7V7/o8w5R3p6+pWMjIwxFd8KDAwc+MpXvmL6wx/+4JeVldViMpmENpsNYrEYDQ0NkuDgYOvoS5kcvEoWGGPJAN4CIATwS875T4Zo8zUAWQA4gHLO+UvjGOff1yN+CEz80N1Y9JgIfRdDjA0AAO6wwdFbiYHuc3AMJhBNebBx1+9A5AehwpVA+C6FULEUbNocSiAIIcRNcnJy9+rVq+dt3769JTQ01N7S0iLs6uoSepaoXrRokaW1tVWs1WrlGo3G3NnZKfDx8XGsWrWqq6CgIOipp54ySaVSrtfrpREREbaR1jl9+vSBrq4uwYwZM4Ztk5KS0p2VlTVz06ZN1/z8/Bz19fViiUTChypR/fnnn4skEgkPDAwc6OnpYR9++KFr9ypgAAAgAElEQVTvv/zLv1wVCAR4+OGHTe+++67/pk2bOt95552Ap5566vpt76x7bNRkgTEmBJAPYBWAywA+ZYwd45xXubWJAvAjAF/mnHcyxlR3K+DJiAnEECq+AKHiCwD+EQDAHVY4eioxYDoHR3cZBkznYGt660YCwcSBEPg9DOHgy3cZmMh3AreCEEImVkJCgiUzM7N55cqV0Q6HA2KxmOfl5TV5JgsymYwXFhbWbdmyJcxisQhkMplDp9PVbNu2rb2hoUGqVqsXcM6ZUqm0HT9+vG6kda5bt649OTk5Ojg42FpaWlozVJvVq1d3V1ZWypYtWxYDAHK53FFYWFg/VLJw6dIl8fr16+cMDAyAc86effbZa1//+te7ACAnJ+fyCy+8EPnmm2+GxsXFmbdu3TplyoQzzvnIDRj7EoAszvkTrvc/AgDO+b+5tfkPADWc8196u+KlS5fysrKy2wp6qnImEBUY6P4rBrpK4ej6BA6zwTWXQTA9DkK/h28kEYLpC+j+B0LITRhj5zjnS+9kGeXl5Q3x8fFT5kBF7p3y8vLA+Pj4CM/p3lyGCAXgfgPMZQCJHm2iAYAxdhbOSxVZnPMSzwUxxjYB2AQAYWFhXgV+P2ECCYS+SyD0XQLMehUAwG2druThEwx0fQJb6/vA566cS+gLod8Xb/Q+CHwTIZBMmWG5hBBC7hPjdYOjCEAUgCQAswDoGGNqzvlN12M4578A8AvA2bMwTuue0pjYH6KAJyAKeAIAwLkD3HzxRvIw0PUJrPW7ADic7afNc17uEIz3iBvP+ydGeU/3W0xyo30/9P1NJPGMb0KkfGSiwyB34OrVq8KkpKT5ntPPnDljDAkJue2HOk1W3iQLzQBmu72f5Zrm7jKAUs65DUA9Y6wGzuTh03GJ8gHCmABs+nwIps+HeOY6AAC39zhHXnR9AkfXJxjo0QPcMY5r9czbRnk/yqUrMtFG+37o+5toIuXKiQ6B3KGQkJABz9Ec9zNvkoVPAUQxxubAmSS8CMBzpMNRAF8H8C5jLBDOyxKfjWegDzIm8oHIXwORv2aiQyGEEPIAGvXuOc65HcBrAE4CqAbwHue8kjGWzRh7xtXsJIAOxlgVgA8BZHDOO+5W0IQQQgi5d7y6Z4FzfhzAcY9pr7v9zQF8z/UihBBCyH2ExuURQgghZESULBBCCLnnhEJhQkxMTGxUVFRcSkrKXJPJNCHHo+zsbJX7ujUazbz29nYhAMjl8sW3u1y73Y4FCxbEPvLII/MGpxkMBsmiRYtiwsLCFqamps61WCxTZlgSJQuEEELuucHaEBcvXqwUi8U8JycnyNvP2u3jV5Np//79wT09PTeOhVqttjYwMPCOhz6++eabwfPmzetzn/a9731v1muvvdbS1NRU4efnZ3/rrbemzINzplwhKUIIIePn7Gsfzr5efU0+nst8aIHS/OX/fMTrapYrVqzo0ev10wBg7969yoKCgmCbzcaWLFnSe/DgwUaRSAS5XL44LS2tTafT+ebl5TXJZDJHenp6mNlsFkgkEq7T6YwKhcKxefPmWWfPnlVYrVa2cePG1oyMjPbi4mJFdnb2TKVSaTMajdPUarX56NGj9bt27VK1traKNRpNtL+/v720tLQmNDRUXVZWVu1ZsnrHjh3BRUVFSqvVylJTU6/v2bPn8+G2p66uTnzy5Em/H/3oR1f27NkTDAAOhwMff/yx4oMPPvgMADZs2NCRlZU18wc/+EHb7e3le4t6FgghhEwYm82GkydP+qrV6r7z58/LDh8+rCwrKzMYDIYqgUDA9+3bFwAAfX19gsTExF6j0Vil0Wh609LSInNzc5uMRmOVVqs1+vj4OHJzcwP9/PwGKioqqsvLy6sPHDgQZDAYJABQXV09LT8//1JtbW1lU1OT9NSpUz6ZmZmtKpXKptVqa4arCwEAR44c8a2trZXp9frq6urqqgsXLshPnDjhM1z7zZs3z/6P//iPywLB3w+xLS0tIoVCMSAWiwEAERER1paWlvF+ut5dQz0LhBDyABtLD8B46u/vF8TExMQCQGJiomnr1q3tu3fvDqyoqJDHx8cvAACLxSJQqVR2wFlWev369Z0AoNfrZSqVyqbRaMwAoFQqHQBw+vRpX4PBID927Jg/AJhMJmFVVZVMIpFwtVrdGxkZaQOAuLg4c11dndcH6pKSEl+dTucbGxsbCwBms1lgMBhkKSkpPZ5tf/Ob3/gFBgbav/KVr5iLi4sVd7KPJhNKFgghhNxzg/csuE/jnLO1a9d25Ofnez4lGBKJxCESjXzI4pyznJycpjVr1nS7Ty8uLlZIpdIbjy4VCoWw2+1e31zIOUd6evqVjIyMUYtvffTRRz6nTp16KDQ01K+/v1/Q29srePbZZ+cUFRXVm0wmoc1mg1gsRkNDgyQ4ONg62vImC7oMQQghZFJITk7uLi4u9m9ubhYBQEtLi7CmpuaWHoBFixZZWltbxVqtVg4AnZ2dApvNhlWrVnUVFBQE9ff3MwDQ6/XS7u7uEY9z06dPH+jq6hqxTUpKSvehQ4cCB9vV19eLB2P0lJ+f39zS0qJvbm7+v1/96lefPfzww6YPPvigXiAQ4OGHHza9++67/gDwzjvvBDz11FPXh1rGZEQ9C4QQQiaFhIQES2ZmZvPKlSujHQ4HxGIxz8vLa4qOjr7pDFwmk/HCwsK6LVu2hFksFoFMJnPodLqabdu2tTc0NEjVavUCzjlTKpW248eP1420znXr1rUnJydHBwcHW4e7b2H16tXdlZWVsmXLlsUAgFwudxQWFtaHhoaOaVhGTk7O5RdeeCHyzTffDI2LizNv3bp1ypQJZ3yCigItXbqUl5WVTci6CSFkqmKMneOcL72TZZSXlzfEx8dPmQMVuXfKy8sD4+PjIzyn02UIQgghhIyILkMQQgghY3T16lVhUlLSfM/pZ86cMYaEhNzxQ50mG0oWCCGEkDEKCQkZ8BzNcT+jyxCEEEIIGRElC4QQQggZESULhBBCCBkRJQuEEEIIGRElC4QQQu45oVCYEBMTExsVFRWXkpIy12QyTcjxKDs7W+W+bo1GM6+9vV0IAHK5fPHtLDM0NFQdHR0dGxMTE7tw4cIFg9NbWlqEy5cvjwoPD1+4fPnyqLa2NuGdb8G9QckCIYSQe26wNsTFixcrxWIxz8nJCfL2s3b7mB6cOKL9+/cH9/T03DgWarXa2sDAwDse+qjVamsMBkNVRUVF9eC0N954Y0ZSUpKpsbGxIikpyfT666+H3Ol67hUaOkkIIQ8w/fc3zDYZK+TjuUzF/IXmRf/xjtfVLFesWNGj1+unAcDevXuVBQUFwTabjS1ZsqT34MGDjSKRCHK5fHFaWlqbTqfzzcvLa5LJZI709PQws9kskEgkXKfTGRUKhWPz5s2zzp49q7BarWzjxo2tGRkZ7cXFxYrs7OyZSqXSZjQap6nVavPRo0frd+3apWptbRVrNJpof39/e2lpaU1oaKi6rKysesaMGTdlJDt27AguKipSWq1Wlpqaen3Pnj2fj3W/lJSUPKTVao0A8Morr3RoNJr5AG4pmjUZUc8CIYSQCWOz2XDy5ElftVrdd/78ednhw4eVZWVlBoPBUCUQCPi+ffsCAKCvr0+QmJjYazQaqzQaTW9aWlpkbm5uk9ForNJqtUYfHx9Hbm5uoJ+f30BFRUV1eXl59YEDB4IMBoMEAKqrq6fl5+dfqq2trWxqapKeOnXKJzMzs1WlUtm0Wm3NcHUhAODIkSO+tbW1Mr1eX11dXV114cIF+YkTJ3xG2q6VK1dGxcXFLfjZz34WODito6NDFB4ebgOA2bNn2zo6OqbMCfuUCZQQQsj4G0sPwHjq7+8XxMTExAJAYmKiaevWre27d+8OrKiokMfHxy8AAIvFIlCpVHbAWVZ6/fr1nQCg1+tlKpXKptFozACgVCodAHD69Glfg8EgP3bsmD8AmEwmYVVVlUwikXC1Wt0bGRlpA4C4uDhzXV3dLdUsh1NSUuKr0+l8Y2NjYwHAbDYLDAaDLCUlpWeo9h999JFhzpw5tubmZtGjjz4aHRcXZ/FsKxAIwJjXVbInHCULhBBC7rnBexbcp3HO2dq1azvy8/Nv6ZqXSCQOkWjkQxbnnOXk5DStWbOm2316cXGxQiqV3qiaKBQKYbfbvT5Sc86Rnp5+JSMjw6viW3PmzLEBQGhoqD01NfX6xx9/PD0lJaUnICDA3tjYKA4PD7c1NjaKlUrl+N18cZfRZQhCCCGTQnJycndxcbF/c3OzCHCOHqipqbmlB2DRokWW1tZWsVarlQNAZ2enwGazYdWqVV0FBQVB/f39DAD0er20u7t7xOPc9OnTB7q6ukZsk5KS0n3o0KHAwXb19fXiwRg9dXd3Czo7OwWDf3/44Ye+ixYt6gOAJ5544vr+/fsDAGD//v0BycnJ10fbJ5MF9SwQQgiZFBISEiyZmZnNK1eujHY4HBCLxTwvL68pOjra6t5OJpPxwsLCui1btoRZLBaBTCZz6HS6mm3btrU3NDRI1Wr1As45UyqVtuPHj9eNtM5169a1JycnRwcHB1uHu29h9erV3ZWVlbJly5bFAIBcLncUFhbWh4aG3tIzcPnyZdHzzz8/DwAGBgbYmjVrOr761a92A8DOnTuvPP/885Hh4eGBoaGh1qKiohFjm0wY53z0VnfB0qVLeVlZ2YSsmxBCpirG2DnO+dI7WUZ5eXlDfHy8V13q5MFSXl4eGB8fH+E5nS5DEEIIIWREdBmCEEIIGaOrV68Kk5KS5ntOP3PmjDEkJOSOH+o02VCyQAghhIxRSEjIgOdojvsZXYYghBBCyIgoWSCEEELIiChZIIQQQsiIvEoWGGPJjDEjY6yWMfbDEdqtYYxxxtgdDeshhBBCyOQxarLAGBMCyAeQAiAWwNcZY7FDtFMA2AqgdLyDJIQQcn8RCoUJMTExsVFRUXEpKSlzTSbThPR0Z2dnq9zXrdFo5rW3twsBQC6XL76dZba3twuTk5PnzpkzJ27u3Llxp0+fng44n0i5fPnyqPDw8IXLly+PamtrE47PVtx93nw5XwRQyzn/jHNuBfBbAM8O0e7HAP4dgGUc4yOEEHIfGqwNcfHixUqxWMxzcnKCvP2s3T5+JRX2798f3NPTc+NYqNVqawMDA+9o6OOmTZtmP/7449319fWVVVVVVV/4whcsAPDGG2/MSEpKMjU2NlYkJSWZXn/99ZA7jf9e8WboZCgA96pklwEkujdgjC0BMJtz/kfGWMZwC2KMbQKwCQDCwsLGHi0hhJBxVfvmhtnmugr5eC5THrnQPC/T+2qWK1as6NHr9dMAYO/evcqCgoJgm83GlixZ0nvw4MFGkUgEuVy+OC0trU2n0/nm5eU1yWQyR3p6epjZbBZIJBKu0+mMCoXCsXnz5llnz55VWK1WtnHjxtaMjIz24uJiRXZ29kylUmkzGo3T1Gq1+ejRo/W7du1Stba2ijUaTbS/v7+9tLS0JjQ0VF1WVlY9Y8aMmzKSHTt2BBcVFSmtVitLTU29vmfPns+H2paOjg5haWmp4vDhww2A89HUMplsAABKSkoe0mq1RgB45ZVXOjQazXwAtxTNmozuuNuHMSYAsBvAP4/WlnP+C875Us750qAgr5NIQggh9ymbzYaTJ0/6qtXqvvPnz8sOHz6sLCsrMxgMhiqBQMD37dsXAAB9fX2CxMTEXqPRWKXRaHrT0tIic3Nzm4xGY5VWqzX6+Pg4cnNzA/38/AYqKiqqy8vLqw8cOBBkMBgkAFBdXT0tPz//Um1tbWVTU5P01KlTPpmZma0qlcqm1WprhqsLAQBHjhzxra2tlen1+urq6uqqCxcuyE+cOOEzVFuj0ShRKpX2tWvXRixYsCD2hRdeCB8sZtXR0SEKDw+3AcDs2bNtHR0dU+ZZR94E2gxgttv7Wbg5E1IAWAjgjKs2dwiAY4yxZzjnVPyBEEImsbH0AIyn/v5+QUxMTCwAJCYmmrZu3dq+e/fuwIqKCnl8fPwCALBYLAKVSmUHnGWl169f3wkAer1eplKpbBqNxgwASqXSAQCnT5/2NRgM8mPHjvkDgMlkElZVVckkEglXq9W9kZGRNgCIi4sz19XV3VLNcjglJSW+Op3ONzY2NhYAzGazwGAwyFJSUno829rtdlZdXS1/6623mh599NHeb33rW7N37NgR8tZbb93UEyEQCOA6Zk4J3iQLnwKIYozNgTNJeBHAS4MzOeddAAIH3zPGzgD4F0oUCCGEDGfwngX3aZxztnbt2o78/PxbuuYlEolDJBr5kMU5Zzk5OU1r1qzpdp9eXFyskEqlN6omCoVC2O12r4/UnHOkp6dfycjIGLX4VkREhDU4ONj66KOP9gLACy+80PmTn/wkBAACAgLsjY2N4vDwcFtjY6NYqVSO380Xd9molyE453YArwE4CaAawHuc80rGWDZj7Jm7HSAhhJAHQ3JycndxcbF/c3OzCHCOHqipqbmlB2DRokWW1tZWsVarlQNAZ2enwGazYdWqVV0FBQVB/f39DAD0er108BLAcKZPnz7Q1dU1YpuUlJTuQ4cOBQ62q6+vFw/G6CksLMweEhJiLS8vlwLAn/70J9/58+dbAOCJJ564vn///gAA2L9/f0BycvL10fbJZOHV9RLO+XEAxz2mvT5M26Q7D4sQQsiDJiEhwZKZmdm8cuXKaIfDAbFYzPPy8pqio6Ot7u1kMhkvLCys27JlS5jFYhHIZDKHTqer2bZtW3tDQ4NUrVYv4JwzpVJpO378eN1I61y3bl17cnJydHBwsHW4+xZWr17dXVlZKVu2bFkMAMjlckdhYWF9aGjokD0DP//5z5vS0tLmWq1WFhYW1v+b3/ymAQB27tx55fnnn48MDw8PDA0NtRYVFY0Y22TCOOejt7oLli5dysvK6EoFIYSMBWPsHOf8jh58V15e3hAfHz9qlzp58JSXlwfGx8dHeE6nxz0TQgghZERTZtgGIYQQMllcvXpVmJSUNN9z+pkzZ4whISF39FCnyYiSBUIIIWSMQkJCBjxHc9zP6DIEIYQQQkZEyQIhhBBCRkTJAiGEEEJGRMkCIYQQQkY05ZIFzjk4d0zwa2KeTUEIIfcLoVCYEBMTExsVFRWXkpIy12QyTcjxKDs7W+W+bo1GM6+9vV0IAHK5fPFYl1deXi6NiYmJHXz5+Pgszs7OVgHOJ1IuX748Kjw8fOHy5cuj2trahOO3JXfXlHsok+njn6L7z9+/CxF5T+AzA4FppyEOip3QOAghD5775aFMcrl8sdls/hsAPPPMM3OWLFlizsrKavHms3a7HaPVifDWcCWpPWO8HXa7HSEhIfF/+ctfqqOjo62vvvrqLKVSad+1a9fV7du3h3R2dgoLCgomVYnq4R7KNOWGTkpmfRmKf9g5gRFw9J7bi473noVqw18hmOY/gbEQQsid+Xzfhtn9lyvk47lM6ayF5pmvel/NcsWKFT16vX4aAOzdu1dZUFAQbLPZ2JIlS3oPHjzYKBKJIJfLF6elpbXpdDrfvLy8JplM5khPTw8zm80CiUTCdTqdUaFQODZv3jzr7NmzCqvVyjZu3NiakZHRXlxcrMjOzp6pVCptRqNxmlqtNh89erR+165dqtbWVrFGo4n29/e3l5aW1gyXPOzYsSO4qKhIabVaWWpq6vU9e/Z8PvTW/N2xY8d8w8LC+gcfV11SUvKQVqs1AsArr7zSodFo5uPmKs6T1pRLFqSzl0M6e/nExjDnMbQfegTXil5EwIt/BBNMud1ICCGTgs1mw8mTJ30ff/zx7vPnz8sOHz6sLCsrM0ilUv7yyy+H7du3L+C1117r6OvrEyQmJva+/fbbly0WC5s3b97CwsLCOo1GY7527ZrAx8fHkZubG+jn5zdQUVFR3dfXx5YtWxbz9NNPdwNAdXX1tAsXLnwWERFhS0hIiDl16pRPZmZma0FBQbBWq60Zqmdh0JEjR3xra2tler2+mnOOxx57bN6JEyd8hipR7e43v/mN8qtf/WrH4PuOjg5ReHi4DQBmz55t6+jomDIHjykT6GQinf1lPJScj+vHN6H7f34Iv8d+NtEhEULIbRlLD8B46u/vF8TExMQCQGJiomnr1q3tu3fvDqyoqJDHx8cvAACLxSJQqVR2wFlWev369Z0AoNfrZSqVyqbRaMwAoFQqHQBw+vRpX4PBID927Jg/AJhMJmFVVZVMIpFwtVrdGxkZaQOAuLg4c11d3S3VLIdTUlLiq9PpfGNjY2MBwGw2CwwGg2ykZMFisbDTp0/77d69+/JQ8wUCARjzukr2hJtyyYK1rQHWKxcnNAaRbxCmL9kIW2s5ej7JgVgVD/mib0xoTIQQMpVIpVKH5xMQOeds7dq1Hfn5+bd0zUskEsdo9ylwzllOTk7TmjVrut2nFxcXK6RS6Y0b9IRCIex2u9dHas450tPTr2RkZHh9n8fhw4f9YmNjzbNnz77RYxEQEGBvbGwUh4eH2xobG8VKpXLY3ozJZsolC6ZPfo/W/57YGxwB4KFHvg1V2k9ha6tE5x83QhQYA8nMZRMdFiGETFnJycndq1evnrd9+/aW0NBQe0tLi7Crq0voWaJ60aJFltbWVrFWq5VrNBpzZ2enwMfHx7Fq1aqugoKCoKeeesoklUq5Xq+XRkRE2EZa5/Tp0we6uroEM2bMGLZNSkpKd1ZW1sxNmzZd8/Pzc9TX14slEgkfrkQ1APz2t79Vfu1rX7vmPu2JJ564vn///oBdu3Zd3b9/f0BycvJ1L3fNhJtyyYLvl1/CtOiJvWeh53wxOv7w7zAbdAj5dj4Grn8bHb9/DqoNZRAqhv/BEUIIGV5CQoIlMzOzeeXKldEOhwNisZjn5eU1eSYLMpmMFxYW1m3ZsiXMYrEIZDKZQ6fT1Wzbtq29oaFBqlarF3DOmVKptB0/frxupHWuW7euPTk5OTo4ONhaWlpaM1Sb1atXd1dWVsqWLVsWAwByudxRWFhYP1yy0N3dLfjoo498Dxw40Og+fefOnVeef/75yPDw8MDQ0FBrUVHRiLFNJlNu6ORk0Vt1Bp/nfwP2rqsIePo7sDf8EiLVIgR94wyYSDrR4RFC7lP3y9BJMjkNN3Ryyj2UabKYHpuEuf9eDsXS59BxNA926xzYmj/B9RP/RA9tIoQQcl+ZcpchJhOhjxKhW99Dl/ZXuPqr70IaKAPK34U4+Avw+eKWiQ6PEELIXXL16lVhUlLSfM/pZ86cMYaEhAxMREx3EyULd4gxhoeSvgX5/BVo/s+vw9Z9Dl1/SofQby6mzX9qosMjhBByF4SEhAx4jua4n9FliHEimRGFiJ0fY9oXvocBC0fH755FT/nRiQ6LEEIIuWNTq2ehpgbYtm2iowAUCuDll4HUVMDtoRpMJEbwSzno/usSdJd8A9d+/zz66rIQ+FwmmGDK1AshhBBCbjK1koWuLuD48YmOwul3vwMSE4HsbGDVqpuSBt8vpkHkMw3XjqxB7ydZMFd+iJnf+TXEAbMmMGBCCCHk9tBliDtRWgo88QSg0QBa7U2z5LGr4bdqN8S+wEDnX/DZDxahu/T9CQqUEEIIuX1Tq2chKgooLp7YGDgHTp0C9u0DrK7nhPzv/wJJScDKlcCPfwx86UsAAJ8vpsPeogf0v4LAR4nm3K+i55F/RMg3cyGQ+UzcNhBCyAQTCoUJUVFRfQMDA2zevHl97733XoNCoXDc6ziys7NV27Ztax9ct0ajmff+++/XBwYGDtxuieqdO3eqDh06FMQYQ0xMjPl3v/tdg1wu5waDQfK1r31t7vXr10Vqtdr8/vvv18tksikx1p4eynS7Ll8G/vVfgV/+ErB7PMTrySedlycSEsDtFrQdSoK9tQLCkBdx7eQ7EAeGw/fLaVAkPA3Z3GVgAurgIYR45355KJP7gfiZZ56Zs2TJEnNWVlaLN5+12+0YrU6Et4YrSe0Zo7fq6+vFK1asiDEajRU+Pj78ySefnJucnNy1ZcuWjieffHLuc88917lp06bOl156KSw+Pr7vBz/4Qdu4bMg4Ge6hTFOrZ2EymTULKCgAvv99Z2/CwYPAgGto7fHjztdzz4FlZyPgq0VofWcpuOl/MCujCNeKc9Fx7CfoOPqvEPoFQ7HkafgkPIPpC1dCIB3XsvKEEDKizj9smG1rqxjXf3jEQQvN/k97X81yxYoVPXq9fhoA7N27V1lQUBBss9nYkiVLeg8ePNgoEokgl8sXp6Wltel0Ot+8vLwmmUzmSE9PDzObzQKJRMJ1Op1RoVA4Nm/ePOvs2bMKq9XKNm7c2JqRkdFeXFysyM7OnqlUKm1Go3GaWq02Hz16tH7Xrl2q1tZWsUajifb397eXlpbWDJc87NixI7ioqEhptVpZamrq9T179nw+3PYMDAyw3t5egVQqHejr6xPMmjXL5nA48PHHHys++OCDzwBgw4YNHVlZWTMnW7IwHDqlvVNz5gDvvANUVwNpaTfd6IijR4H4eAg3bkPAF/ZgwPQ5LBVvIWz7nxC9vxUzN/8a8gUadH/yHi7/7BnUbArEpZ8+g87/+SXs169O3DYRQsg9YrPZcPLkSV+1Wt13/vx52eHDh5VlZWUGg8FQJRAI+L59+wIAoK+vT5CYmNhrNBqrNBpNb1paWmRubm6T0Wis0mq1Rh8fH0dubm6gn5/fQEVFRXV5eXn1gQMHggwGgwQAqqurp+Xn51+qra2tbGpqkp46dconMzOzVaVS2bRabc1wdSEA4MiRI761tbUyvV5fXV1dXXXhwgX5iRMnhryWPGfOHNvmzZuvzpkzZ5FKpYpXKBQDq1ev7m5paREpFIoBsVgMAIiIiLC2tLR4XSZ7olHPwniJigJ+/Wtg+3YgKwv4/e+d0zkHfvc7SH7/ewQ/+TDaoz7EdeVm+CRug++X1sJvRRq43QpztQ6mc8fQc+4Yes7/AVcByOYl3nF+/7wAACAASURBVOh1kM5eOKVqnxNCpoax9ACMp/7+fkFMTEwsACQmJpq2bt3avnv37sCKigp5fHz8AgCwWCwClUplB5xlpdevX98JAHq9XqZSqWwajcYMAEql0gEAp0+f9jUYDPJjx475A4DJZBJWVVXJJBIJV6vVvZGRkTYAiIuLM9fV1Xl9oC4pKfHV6XS+sbGxsQBgNpsFBoNBlpKS0uPZtq2tTfjHP/7xodra2v8LCAgYSE1Nnbt3717l888/333rkqcOShbGW2ws8N57QHk58MYbwAcfOKc7HBAV/wXBAgaz9m2Y5ryNAaUAmDMHgjlxEKkWQLE4Af6PvYQBiwPm//sQpnPH0PZeJtrey4Q4KAI+Cc9AseRpyBf8A5hoyiSkhBByC6lU6vB8AiLnnK1du7YjPz+/2bO9RCJxjHafAuec5eTkNK1Zs+amA3NxcbFCKpXeuEFPKBTCbrd7ffbFOUd6evqVjIyMUe/z+MMf/uAbFhbWP3PmTDsAPPfcc9f/8pe/+Lz66qvXTCaT0GazQSwWo6GhQRIcHGwdbXmTBSULd0t8vPMyxKefAq+/DpSUAACYg2P6eWD6eQBwAKgDF30G+0PHMOAP2PyBAX9AOOMhPBQZBTz1ddg4R39zA7q0+9FZkgcIRWCCu/zVjdaLcdN8twdTeX5umHajLp+Q+1jwy7vx0CMbJjqMSSc5Obl79erV87Zv394SGhpqb2lpEXZ1dQk9S1QvWrTI0traKtZqtXKNRmPu7OwU+Pj4OFatWtVVUFAQ9NRTT5mkUinX6/XSiIgI20jrnD59+kBXV5dgxowZw7ZJSUnpzsrK+v/t3XtcVHX+P/DXZ+4M95GrXEUwBBEBL0W2WKihopWX1tK0XYsuy0+hb+5vt6wtatvaXYtwFa3Wfj+LXXclNZe8pJlSbplohiCgIGCS3O/MDMzl8/1jDjYidwcH8P18PM6DM59zzucyZ3Tec87nfD5jExIS6h0dHY2lpaVSmUzGu5ui2t/fv+PMmTN2LS0tIltbW+PRo0fto6Ki1CKRCHfeeWfLhx9+6JyQkNCwffv2MfHx8Y2DfrNusX594zDG4gC8C0AM4APO+Ztdtj8H4AkAegA1AH7NOS+/IaPb0bRpwIEDwH//C7z0EnD06A27MD2HtBaQXhezNgI4BeAUjApALwQR+jFiGJwVgKivL9u+vuy7Wzet8K6Hsq7Z8Z9fD/ZhGjYinhYiZEhID+4HIpcAjo7WrsqwEhUVpd2wYUNFbGzsBKPRCKlUytPS0i53DRYUCgXPyMgoWbt2ra9WqxUpFApjdnb2heTk5NqysjJ5WFjYRM45U6lUuv3795f0Vubq1atr4+LiJri7u3f01G9h8eLFzfn5+Ypp06YFA4BSqTRmZGSUdhcs3HfffW0LFy5smDx58kSJRILQ0FD1c889VwMAGzduvPLLX/5y/Ouvv+4VGhqqXrdu3YiZJrzPRycZY2IAFwDMAXAFpm+wRzjn5832uRfASc65mjH2DIBZnPNf9pbviH90crCys4HPPgMuXQJKS01Lfb21a0UIudUKCoDg4AEfNloenSTD0808OjkdQDHn/BIAMMZ2AngAwLVggXP+pdn+3wJYeVO1Hc1+8QvTYq6p6efAobT0+kCitBTQaq1TV0IIIQT9Cxa8AJj3lr0CYEYv+68BcOBmKnXbcXQEpkwxLV1xDlRWXh88XL1qSh+szmM5v369p789bbvZ8gm5nTk4WLsG5CZUVlaKZ82adUfX9GPHjhV5eHgYrFGnoWTRXnKMsZUApgKI6WF7AoAEAPD19R1UGY3fn0Tdt1/2veMQktg5wH3ug1C4jx36whgDPD1NS3T00JdHCCGkTx4eHoauT3OMZv0JFioA+Ji99hbSrsMYmw3gRQAxnPP27jLinL8H4D3A1GdhwLUFUPvVQRSnvTKYQy2GcyD/lf8Dl5lz4LV4FTzmPgixDY28SAghZHTqT7BwCkAQY2wcTEHCcgCPmu/AGIsAsA1AHOe82uK1NKNwUsDRZShL6B+puz/aLp7BD0mHkG9nD495S+G1eDVU0++huR4IIYSMKn0GC5xzPWMsEcAhmB6d3M45z2eMpQDI4ZzvA/AXAHYAdgnP2V/mnC8aigqr7lkEhcfgbmFYiubHi6je+x5kxhooJ3qAOXqjcv+/cWXXh7Dx8oPXQ4/Ba/Eq2I4Lsmo9CSGEEEugWScHyajXoSH7U1Tu+huavz8OJlNAGTwDmsZ21J3+DjAa4RR5F7yXrIbngochdXS2dpUJIaMAPTpJhlJPj07S9fJBEkmkGHPfUoSmH8Pkj3PhtmA1NBdOwXj5W3jfEwm/hx+BvqUReS8+jS+me+DMs8tQdeQ/MOp6HUyMEEJuC2KxOCo4ODgkKCgodN68eQEtLS1W+T5KSUlxMy87JiYmsLa2VgyYpqgeTJ6vvfaaW1BQUGhgYGBoSkqKW2d6VVWVODo6OsjPz29SdHR0UE1NjfjmW3BrULBgAbaBYQj4v1sR9Z8K+Ce9A0NrI5qP/xNKST2CnngS3kseQ/13x3H6yUU4epcX8jY8g9Ltqag8uBuNuTlor62Gta7wEEKINXTODXHx4sV8qVTKN27c6NrfY/X6GwZOHLRt27a5t7a2XvsuPH78eLGLi8ugH308deqUYseOHa5nzpwpKCgoyD948KBTXl6eHAD+8Ic/eM6aNaulvLw8b9asWS0vv/yyhyXacCvQ3BAWJLF3gufyJHg8vBaN3x5CZebfUJv1AZhIDJ+5D0EREIXa06dQsecjGNRt1x0rkitgM9YXirG+sPHy/Xl9bOe6D8RyhZVaRggZrTTnf+1jbM2z6ONcIrtJapuQ/s9mOXPmzNbc3FwbANiyZYsqPT3dXafTscjIyLYdO3aUSyQSKJXKiBUrVtRkZ2c7pKWlXVYoFMakpCRftVotkslkPDs7u8je3t74m9/8xvvEiRP2HR0d7Mknn6xev359bVZWln1KSspYlUqlKyoqsgkLC1Pv3bu39I033nCrrq6WxsTETHB2dtafPHnygpeXV1hOTk6Bp6fndRHJSy+95L5nzx5VR0cHW7BgQeM777zzU3dtOXfunE1ERESrvb29EQDuvvvulp07dzq9/vrrVQcPHnQ6fvx4EQA89dRTdTExMXegm6cLhyMKFoYAE4ngHD0PztHzoPmxGFW701GdtR31R3dBGRSOsJdeh1jpCL1GA31LCzqam9BRXwttbQ3aK39CzbEDaK++ekO+Mhd32Iz1hdzNE0w8xFev+pjoiXWde6LXCaT6mmCKkNuL98Nr4HrPHGtXY1jQ6XQ4dOiQw9y5c5vPnDmjyMzMVOXk5BTK5XK+cuVK361bt45JTEys02g0ohkzZrS9//77V7RaLQsMDJyUkZFREhMTo66vrxfZ2dkZU1NTXRwdHQ15eXkFGo2GTZs2LXjhwoXNAFBQUGBz9uzZS/7+/rqoqKjgw4cP223YsKE6PT3d/fjx4xe6Bgfmdu/e7VBcXKzIzc0t4Jxj9uzZgQcOHLDrborqKVOmaFJSUrwqKyvFtra2/PDhw47h4eFtAFBXVyfx8/PTAYCPj4+urq5uxHwHj5iKjlQ2PoHwX7cRPgkpqD30D1Tu2oTy1OQe92cA7JS2cAzxhEimACQygInAjYBRp4NBW4n2C2VDWmc+0Nmh+rqFQrdYCLlOa+ikYRMsDOQKgCW1t7eLgoODQwBgxowZLevWrat9++23XfLy8pTh4eETAUCr1Yrc3Nz0gGla6ccff7wBAHJzcxVubm66mJgYNQCoVCojABw5csShsLBQuW/fPmcAaGlpEZ8/f14hk8l4WFhY2/jx43UAEBoaqi4pKZH1t64HDx50yM7OdggJCQkBALVaLSosLFR0FyxERkZq161bVxkbGzvBxsbGGBoaqhZ38+NOJBLd+CNqGKNg4RYR29jC/cEn4fbAE+iovgJDaxMMbc3QtzXDqG6Bvq0Zhs5F3fLzunmaQQPwNsB4w+eTEDKCKD3c+t5plOvss2Cexjlny5Ytq9u8efMNl+ZlMplRIun9K4tzzjZu3Hh5yZIlzebpWVlZ9nK5/NqvFrFYDL1e3+9vas45kpKSrq5fv75fT5AkJyfXJicn1wJAYmKil7e3dwcAjBkzRl9eXi718/PTlZeXS1UqleU6XwwxChZuMcYY5O4+gLtP3zsTQshtJC4urnnx4sWBL7zwQpWXl5e+qqpK3NTUJO46RfXkyZO11dXV0uPHjytjYmLUDQ0NIjs7O+OcOXOa0tPTXePj41vkcjnPzc2V+/v79/oImq2traGpqUnk6enZ4z7z5s1rfuWVV8YmJCTUOzo6GktLS6UymYx3N0U1AFRUVEi8vLz0Fy9elH322WdOp06dKgSA+++/v3Hbtm1j3njjjcpt27aNiYuLaxzE22QVFCwQQggZFqKiorQbNmyoiI2NnWA0GiGVSnlaWtrlrsGCQqHgGRkZJWvXrvXVarUihUJhzM7OvpCcnFxbVlYmDwsLm8g5ZyqVSrd///6S3spcvXp1bVxc3AR3d/eOkydPXuhun8WLFzfn5+crpk2bFgwASqXSmJGRUdpTsLBo0aLxjY2NEolEwlNTUy93Pl3x6quvXn3ooYfG+/n5uXh5eXXs2bOn17oNJyNuUKYTv7+E4n9cGoIa9Z9ILof3oskImG8Lr2gxJIqRc9+JEDKy0aBMZCj1NCjTiLuyYFS3QtRu3SdNeLMG5R+U4NyWO2GUecN7phi+94nhHyuGa5gITETBAyGEkNFjxAUL97w7Gfe8O9mqdag/V4uvnzoKUcGXUAYFoeXHqch+QYJsADauDH73iuF3nxh+sWI4+NC4V4QQMtpUVlaKZ82adUfX9GPHjhV5eHgMelCn4WrEBQvDgSrMBQuOLUHun3OQl/o97N1/wj07YqBtH4vyLwwo/9KAwn+bbmWpJjD4xUrgd58YPjFiyB3oqgMhhIx0Hh4ehq5Pc4xmFCwMklgmRsSGGfCZPw4nnj2K/z61HxN+FYK5f4uGxFaO2nwjyr8woOwLA879Px2+T9eBiQHPaSJ4zxTD1oPBZoxpUagYbFSmdZlDNwMYEUIIIVY04oKFc3v1yNlh3cmYpEoGvztFGH+PGB7hrog/thTf//E7nN/8A346+iOiN98Hj7vHwnWSGFPXAfp2jp++NZiuOhw14NTbOnBj93mLJIBCxaBQ4VoAoVBdH1TInRgY3d0gZMRymyyCUwD9IyYjx4gLFjSNHHWl1h0RUF1vxPf/NN1mUDgC4+4WI+AXUzHlz34o3nwMny/8FBOfnoyIDdMhUUohkTP4xkjgGyPBPSmA0cChbQC09RyaOn7tr6aeQyv8Na0DjaUc2tNGaOo4DO1WbTYhxEJmb5JjCgULZAQZccHC9MelmP641NrVQH25EaVfGXDpKwNKvjKgYL8BgCNkNvMR6HUWBem5KP20HDO33oex91w/sZhIzKB0AZQu/b/dwDmHTg1o6zjamzkGOiIzIWT4sBtLgQIZWUZcsDBcqPxEUPmJELXSFLi0VBlR+rURl76SouSrGWgu84bPlW9weOFetLuGwvOhKATOksH/bjFsHAfeJ4ExBpktILOl/gyEkJFPLBZHBQUFaQwGAwsMDNT8+9//LuucqfFWSklJcUtOTq7tLDsmJibwk08+KXVxcTEolcoItVr9/UDzfO2119x27NjhyjnHqlWral5++eVqAPjmm29snnnmGT+1Wi3y9vbuyMzMvNQ5r8VwN+IGZRopNI0cJUc0yN/4DdoLL0BrdMJldTS0fAzcghkUjgxSJYPMxtQHQqYEpObr19K6vFYySBVA10kfCSEjh7OvCHaug/tHPFoGZTL/Il60aNG4yMhI9SuvvFLVn2P1ej36mieiv3qakrprHfvr1KlTikcffXT8mTNnChQKhTEmJmbCe++9Vz5p0qT2SZMmTXzrrbd+XLBgQWtqauqY0tJS+bvvvtvtVNfWMmoGZco9egU5/ym1ah1kNhL4TR6D8VFu8BjvCFE3gzDZODFMWqrEpKWxqDgciP+uPQabmgNQTpuCFkUY2jUMOg1HcyPQoTZCpwY61Fz4a4VGEUJumSWb5Yh+2vq3U01+7QPkKS2b5yQ10P/ZLGfOnNmam5trAwBbtmxRpaenu+t0OhYZGdm2Y8eOcolEAqVSGbFixYqa7Oxsh7S0tMsKhcKYlJTkq1arRTKZjGdnZxfZ29sbf/Ob33ifOHHCvqOjgz355JPV69evr83KyrJPSUkZq1KpdEVFRTZhYWHqvXv3lr7xxhtu1dXV0piYmAnOzs76kydPXugpeHjppZfc9+zZo+ro6GALFixofOedd7r9kj937pxNREREa+eVirvvvrtl586dTq+//npVeXm5vHOmyvj4+Ob7779/wnALFnoy4oKF8nN1+GJ7gVXr0KHRQ68zXTlSOsgwLsIF46PcMD7KFeOj3OAZ5HRdAOE1xw+LvlmOU7/7Gpf+9T0cAy7BYbwjpN4yyOxlkDrIILWXQeZgWpfYSSFWyMCkMnCpFBDJwEVS6PVi6NQcOo21Wk4IsQTPMOqz0Emn0+HQoUMOc+fObT5z5owiMzNTlZOTUyiXy/nKlSt9t27dOiYxMbFOo9GIZsyY0fb+++9f0Wq1LDAwcFJGRkZJTEyMur6+XmRnZ2dMTU11cXR0NOTl5RVoNBo2bdq04IULFzYDQEFBgc3Zs2cv+fv766KiooIPHz5st2HDhur09HT348ePX+juykKn3bt3OxQXFytyc3MLOOeYPXt24IEDB+y6m6J6ypQpmpSUFK/Kykqxra0tP3z4sGN4eHgbAAQGBmozMjKcHnvsscaPP/5YVVlZ2e9psq1txAULC9eFY+G6cKvWQa8z4Mf8epScrkHJ6WqUnK7Bgc3noGs3DdqlsJMiIMIV46NcERDlhvGRrhh7hxNmbo2F78IAFH2QB22NBs2XmqBr7oCupQMGbd8DfolkIkjtZZDaSgEaUpqQEcvmhWlQ+U+wdjUE/b8CYEnt7e2i4ODgEACYMWNGy7p162rffvttl7y8PGV4ePhEANBqtSI3Nzc9YJpW+vHHH28AgNzcXIWbm5suJiZGDQCd9/2PHDniUFhYqNy3b58zALS0tIjPnz+vkMlkPCwsrG38+PE6AAgNDVWXlJT0+4v64MGDDtnZ2Q4hISEhAKBWq0WFhYWK7oKFyMhI7bp16ypjY2Mn2NjYGENDQ9VisRgAsH379rLExESfN9980zMuLq5RKpWOmK7qIy5YGA4kUjHGTXHFuCmumL0mBIApgLhS0HAteCg5XY1D2/LRofkBAKCwlcJ/igvGR7nCd34AHOykkMjFkCnEkCokkIgZREYjmM4I6IxgHQbwDgO4Vg+j1gBDqw4dLTroWjqgb9PR0xCEjGA2bha+6j8CyeVyY9cREDnnbNmyZXWbN2++YQIgmUxm7KufAuecbdy48fKSJUuazdOzsrLs5XL5tf81xWIx9Hp9v39xcc6RlJR0df369f3q55GcnFybnJxcCwCJiYle3t7eHQAQERGhPXHixEUAyM3NlX/++edO/a2DtVGwYCESqRj+k13gP9kFsb8ypRn0RlwpNAUQl4QA4sgH59Gu7vFqV49EYgaZQgKpQgyJTESjPBIygq282wue1q7EMBQXF9e8ePHiwBdeeKHKy8tLX1VVJW5qahJ3naJ68uTJ2urqaunx48eVMTEx6oaGBpGdnZ1xzpw5Tenp6a7x8fEtcrmc5+bmyv39/Xsdxc/W1tbQ1NQk8vTs+YzMmzev+ZVXXhmbkJBQ7+joaCwtLZXKZDLe0xTVFRUVEi8vL/3Fixdln332mdOpU6cKzdMNBgP+8Ic/eK5Zs6Z6EG+TVVCwMITEEhH8Jo2B36QxuG/1RACAwWBEfUUbOjR6dGgN0LUboNPqodMaTK+1eujaf143/f15v85jCCEjl6ufvbWrMCxFRUVpN2zYUBEbGzvBaDRCKpXytLS0y12DBYVCwTMyMkrWrl3rq9VqRQqFwpidnX0hOTm5tqysTB4WFjaRc85UKpVu//79Jb2VuXr16tq4uLgJ7u7uHSdPnrzQ3T6LFy9uzs/PV0ybNi0YAJRKpTEjI6O0p2Bh0aJF4xsbGyUSiYSnpqZednFxMQDA9u3bVX//+9/dAGD+/PkNa9eurRvM+2QN9OgkIYSMIKPl0UkyPPX06CR1ySWEEEJIr+g2BCGEEDJAlZWV4lmzZt3RNf3YsWNFHh4eo+5eMQULhBBCyAB5eHgYuj7NMZrRbQhCCCGE9IqCBUIIIYT0ioIFQgghhPSKggVCCCGE9KpfwQJjLI4xVsQYK2aM/a6b7XLG2L+E7ScZY/6WrighhBACAB999JHT6dOnFZ2vk5KSxu7du7fXka6WLFni/+GHHzr3J/+srCz7e++9N/Bm6zlYSqUywlpl96TPpyEYY2IAmwHMAXAFwCnG2D7OuXkv0DUAGjjngYyx5QDeAvDLoajwuXPnYO3BnBhjUCqV1y22trY3pEmlw2UKWkII6QFjUUOWN+enLZ2lTqfD3r17nfR6fVNUVJQWAFJTU0fENM8jWX8enZwOoJhzfgkAGGM7ATwAwDxYeADAK8J6JoC/McYYH4LhIQ8ePIjf/va3ls52SAhzsN8QUCgUij7ndrjZ7YSQ4SspKQkLFy60djWspqioSBYXFxcUFhamzsvLU06YMEGza9eusldffdX94MGDTu3t7aKpU6e2ZmRklItEIkyfPv2OSZMmqb/77ju7BQsWNBw5csTp22+/tX/rrbc8P/nkk5KXX37ZMz4+vulXv/pVw/PPP+/ZXR59yczMdFi/fr2PjY2Ncfr06ddmk2xubhatWbPGt7Cw0Eav17MXX3zxp5UrVzampaWN+fTTT51aWlokVVVV0qVLl9Zt3LjxKgBs2bJFlZ6e7q7T6VhkZGTbjh07yoXvg4g1a9ZUf/75544KhcKYlZVV7OPjoy8sLJQtX748QK1Wi+Li4hrN6/XSSy+579mzR9XR0cEWLFjQ+M477/xUVFQkmzdvXtD06dNbc3Jy7Nzd3TsOHTpUbGdnx/Py8uQJCQl+dXV1ErFYzHft2nUpNDS0vbt8BnLO+hMseAEwn8L0CoAZPe3DOdczxpoAjAFw3XCijLEEAAkA4OvrO5B6XpOQkICHH354UMdaisFggEajgVqt7nVpa2vrNl2j0aC3OKqvGMtaQ3QTQizDYBh1Y/YMWFlZmWLbtm1lc+fObVu2bJn/X/7yF9f169dX//Wvf70KAA8++OC4nTt3Oj766KNNANDR0cHy8vIKAKC4uFjRGRx0zbe3PHqiVqtZYmKi/+HDh4tCQ0Pb4+PjAzq3vfDCC5733ntv865du8pqa2vFU6dOnbho0aJmAMjNzbU9d+5cvp2dnTEiIiLkgQceaLKzszNmZmaqcnJyCuVyOV+5cqXv1q1bxyQmJtZpNBrRXXfd1bpp06aKp59+2nvTpk2uf/7zn68+++yzvk888URNYmJi3Z/+9CfXzrJ3797tUFxcrMjNzS3gnGP27NmBBw4csAsICOi4fPmy4uOPP74UHR1dPn/+/IAdO3Y4P/vss/WPPvrouOeff75y1apVjWq1mhkMBtZTPt1Nsd2TWzooE+f8PQDvAaa5IQaTh6OjIxwdHS1aL0IIuW0Nwa2C/vDw8OiYO3duGwA89thjdWlpaW4BAQHtb7/9todWqxU1NjZKQkJCNACaAOCRRx6p70++Bw4csO8pj56cPXtW4e3t3R4WFtYOACtWrKj74IMPXAHg2LFjDocOHXJKS0vzAID29nZWXFwsA4CZM2c2d47WuGDBgoZjx47ZSSQSnpeXpwwPD58IAFqtVuTm5qYHAKlUypcvX94EAFFRUW1HjhxxAIAzZ87YHThwoAQAnnrqqbrXXnvNGwAOHjzokJ2d7RASEhICAGq1WlRYWKgICAjo8PLyao+OjtYAQEREhLqsrEze0NAgqqqqkq1ataoRAJRKJQfAe8rH0sFCBQAfs9feQlp3+1xhjEkAOAIYMbNpEUIIubW63kpljOF//ud//E6ePHk+MDBQ99xzz43VarXX7h/Y29sb+8pTrVaz3vIYDM45MjMzi8PDw9vN07/++mvb7trAOWfLli2r27x5c9fvSUgkEt55S0QikUCv11/LQCQS3fADmnOOpKSkq+vXr7/uKn1RUZFMJpNd218sFnONRtNjO3vKZyD68yaeAhDEGBvHGJMBWA5gX5d99gFYLawvBXB0KPorEEIIGR2uXr0qO3LkiC0AZGRkqKKjo1sBwMPDQ9/U1CT6z3/+0+OTC3Z2dobm5uYbvr/UarWov3mYmzJliraiokKWn58vB4CdO3eqOrfde++9zRs3bnQ3Gk2xyokTJ2w6t3399dcOVVVV4tbWVrZ//36nmJiY1ri4uOasrCzniooKCQBUVVWJL1y4IOut/MjIyNb3339fBQDvv//+mM70efPmNX/00UcuTU1NIgAoLS2VdubbHWdnZ6OHh0fHRx995AQAGo2GtbS0iAaaT3f63Fnog5AI4BAAMYDtnPN8xlgKgBzO+T4AfwfwEWOsGEA9TAEFIYQQ0i1/f3/tpk2b3BISEpRBQUHa559/vqahoUE8ceLEUFdXV314eHhbT8euWLGi/plnnvHfunWre2ZmZklnuouLi2HFihU1/cnDnFKp5Js2bSqPj48PtLGxMc6YMaO1tbVVDABvvvnmTwkJCb7BwcEhRqOR+fj4tH/55ZfFADB58uS2RYsWja+srJQtXbq07he/+IUaADZs2FARGxs7wWg0QiqV8rS0tMsTJkzo6Kn8LVu2XF6+fHlAamqqh3kHx8WLFzfn5+crpk2bFizU05iRkVEqkUh6/DH+8ccflz755JN+r7322lipVMp37dpV0lM+Xl5e+v68PwDArHUBYOrUqdzaj0ASQshIwxg7zTmfejN56kBSvgAACK9JREFU/PDDD2Xh4eGDviR9s4qKimTx8fFBFy9ezLdWHW5WWlramJycHNsdO3ZctnZdLOmHH35wCQ8P9++aTiM4EkIIIaRXNEU1IYSQW+qOO+7osNZVhTlz5oz/8ccf5eZpf/zjH68sWbKkeSD5rF27tg63UUd+ChYIIeT2YzQajay7Hvij3eHDh0v63uv2ZDQaGYBunzqh2xCEEHL7yaupqXEUvhwIgdFoZDU1NY4A8rrbbrUOjoyxGgDlAFzQZaTHUep2aCe1cXS4HdoIjNx2+nHOXfverWenT592k0gkHwCYBPrRSEyMAPL0ev0TUVFR1V03Wi1YuFYBxnJutmfvSHA7tJPaODrcDm0Ebp92EmIJFFESQgghpFcULBBCCCGkV8MhWHjP2hW4RW6HdlIbR4fboY3A7dNOQm6a1fssEEIIIWR4Gw5XFgghhBAyjFGwQAghhJBeWSxYYIz9hTFWyBjLZYztYYw5mW37PWOsmDFWxBi73yw9TkgrZoz9zix9HGPspJD+L2FqbDDG5MLrYmG7f19lWFtPbRxOGGM+jLEvGWPnGWP5jLF1QrqKMXaYMXZR+OsspDPGWJrQplzGWKRZXquF/S8yxlabpUcxxs4Jx6QxYSL4nsoYwraKGWPfM8ayhNcW+6wN9PM8RO1zYoxlCv8WCxhjd42288gYSxY+p3mMsX8yxhSj7TwSMuxwzi2yAJgLQCKsvwXgLWE9BMAPAOQAxgEogWmqa7GwHgBAJuwTIhzzbwDLhfWtAJ4R1p8FsFVYXw7gX72VYam23cR70mMbh9MCwBNApLBuD+CC8J7+GcDvhPTfmZ3T+QAOAGAA7gRwUkhXAbgk/HUW1p2Fbd8J+zLh2HlCerdlDGFbnwPwDwBZlvysDebzPETt+/8AnhDWZQCcRtN5BOAFoBSAjdl7+/hoO4+00DLclqHJFHgIQIaw/nsAvzfbdgjAXcJyyCz998LCYBpVrTPwuLZf57HCukTYj/VUhtXf3B7aaO169aPenwKYA6AIgKeQ5gmgSFjfBuARs/2LhO2PANhmlr5NSPMEUGiWfm2/nsoYonZ5A/gCwH0Asiz5WRvM53kI2ucI0xcp65I+as4jTMHCjzAFMhLhPN4/ms4jLbQMx2Wo+iz8GqZfHeb/uDtdEdJ6Sh8DoJFzru+Sfl1ewvYmYf+e8rK24VqvHgmXaSMAnATgzjm/KmyqBOAurA/0nHoJ613T0UsZQyEVwG/x80QplvysDebzbGnjANQA+FC41fIBY8wWo+g8cs4rAPwVwGUAV2E6L6cxus4jIcPOgIIFxtgR4T5h1+UBs31eBKAHkGHpypKhxRizA/AJgCTO+XXTtXLOOYAhfc52KMtgjMUDqOacnx6K/IcJCYBIAOmc8wgAbTDdErhmFJxHZwAPwBQYjQVgCyBuKMoihPxsQFNUc85n97adMfY4gHgAscJ/GABQAcDHbDdvIQ09pNcBcGKMSYQo3nz/zryuMMYkMF12reujDGsarvW6AWNMClOgkME53y0kVzHGPDnnVxljngA6JxfpqV0VAGZ1ST8mpHt3s39vZVja3QAWMcbmA1AAcADwLiz7WRvo59nSrgC4wjk/KbzOhClYGE3ncTaAUs55DQAwxnbDdG5H03kkZNix5NMQcTBd4l3EOVebbdoHYLnQK3kcgCCYOkmdAhAk9DCWwdT5aJ8QZHwJYKlw/GqY7qF35tXZM3spgKPC/j2VYW3dttHKdbqB0KP97wAKOOdvm20yf7+7nodVQm/6OwE0CZegDwGYyxhzFn4BzoXpvu5VAM2MsTuFslah+3NqXoZFcc5/zzn35pz7w3QejnLOV8Byn7XBfJ4t3cZKAD8yxu4QkmIBnMcoOo8w3X64kzGmFOrQ2cZRcx4JGZYs1fkBQDFM9/rOCstWs20vwtTDuAhC72khfT5MPe9LALxolh4A0z/cYgC7AMiFdIXwuljYHtBXGdZeemrjcFoAzITpsnGu2fmbD9N92i8AXARwBIBK2J8B2Cy06RyAqWZ5/Vo4P8UAfmWWPhWmedJLAPwNP48e2m0ZQ9zeWfj5aQiLfdYG+nkeorZNAZAjnMu9MD3NMKrOI4BXARQK9fgIpicaRtV5pIWW4bbQcM+EEEII6RWN4EgIIYSQXlGwQAghhJBeUbBACCGEkF5RsEAIIYSQXlGwQAghhJBeUbBACCGEkF5RsEBGFMaYgTF2ljH2A2PsDGMsWkj3Z4zlCeuzmDAFdZdjjzHGpg6grFTGWAVjTGSW9jhjjDPGZpulPSikLWWm6dnPCtMYNwnrZxlj0YyxRCGdM8Zcbu6dIISQW2dAwz0TMgxoOOdTAIAxdj+APwGIsXQhQoDwEEwDjcXANHpfp3Mwjex3RHj9CExTGYNz/pBw/CwAz3PO483y1MA0S+IxS9eXEEKGEl1ZICOZA4CGIcp7FoB8AOkwBQPmvgIwnTEmFSbfCoRp1Mtecc6/55yXWbiehBAy5OjKAhlpbBhjZ2EaxtcTwH1DVM4jAP4J0/j/bzDGpJxznbCNw3RV4X6YJibaB9MsiIQQMirRlQUy0mg451M458EwTU28Q5hQyGKECYTmA9jLTVN1n4QpMDC3E6ZbEcthCioIIWTUoisLZMTinH8jdBR0tXDW9wNwAnBOiEOUADr7G3SW/R1jLAyAmnN+wcLxCiGEDCsULJARizEWDEAMoA6mL3RLeQTAE5zzfwrl2AIoZYx1LeN3ALQWLJcQQoYlChbISNPZZwEwTbG8mnNu6OaXfSxj7IrZ62XC388YY519D77hnC8zP0gICOIAPN2ZxjlvY4x9DWCh+b6c8wMDqThjbC2A3wLwAJDLGNvPOX9iIHkQQog10BTVhBBCCOkVdXAkhBBCSK/oNgS5bQmDOr3VJbm0c2AlQgghJnQbghBCCCG9otsQhBBCCOkVBQuEEEII6RUFC4QQQgjpFQULhBBCCOnV/wLUFDyt4b3lEwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_par_dep_ICE('BILL_AMT1', par_dep_BILL_AMT1) # plot partial dependence and ICE for BILL_AMT1" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = train['BILL_AMT1'].plot(kind='hist', bins=20, title='Histogram: BILL_AMT1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Generate reason codes using the Shapley method \n", + "Now that the monotonic behavior of the GBM has been verified and compared against domain knowledge and reasonable expectations, a method called Shapley explanations will be used to calculate the local variable importance for any one prediction: http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions. Shapley explanations are the only possible consistent local variable importance values. (Here consistency means that if a variable is more important than another variable in a given prediction, the more important variable's Shapley value will not be smaller in magnitude than the less important variable's Shapley value.) Very crucially Shapley values also *always* sum to the actual prediction of the XGBoost model. When used in a model-specific context for decision tree models, Shapley values are likely the most accurate known local variable importance method available today. In this notebook, XGBoost itself is used to create Shapley values with the `pred_contribs` parameter to `predict()`, but the `shap` package is also available for other types of models: https://github.com/slundberg/shap. \n", + "\n", + "The numeric Shapley values in each column are an estimate of how much each variable contributed to each prediction. Shapley contributions can indicate how a variable and its values were weighted in any given decision by the model. These values are crucially important for machine learning interpretability and are related to \"local feature importance\", \"reason codes\", or \"turn-down codes.\" The latter phrases are borrowed from credit scoring. Credit lenders in the U.S. must provide reasons for automatically rejecting a credit application. Reason codes can be easily extracted from Shapley local variable contribution values by ranking the variables that played the largest role in any given decision." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find the index corresponding to a particular row of interest later, the index of the `test` DataFrame is reset to begin at 0 and increase sequentially. Without resetting the index, the `test` DataFrame row indices still correspond to the original raw data from which the test set was sampled." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "test.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select most risky customer in test data\n", + "One person who might be of immediate interest is the most likely to default customer in the test data. This customer's row will be selected and local variable importance for the corresponding prediction will be analyzed." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "decile = 99\n", + "row = test[test['ID'] == percentile_dict[decile]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a Pandas DataFrame of Shapley values for riskiest customer\n", + "The most interesting Shapley values are probably those that push this customer's probability of default higher, i.e. the highest positive Shapley values. Those values are plotted below." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# reset test data index to find riskiest customer in shap_values \n", + "# sort to find largest positive contributions\n", + "s_df = pd.DataFrame(shap_values[row.index[0], :][:-1].reshape(23, 1), columns=['Reason Codes'], index=X)\n", + "s_df.sort_values(by='Reason Codes', inplace=True, ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Reason Codes
PAY_01.526435
PAY_50.511360
PAY_60.328183
PAY_20.289542
LIMIT_BAL0.287896
PAY_40.227838
AGE0.225641
BILL_AMT10.197412
PAY_30.178575
MARRIAGE0.171177
PAY_AMT30.133252
PAY_AMT10.107046
PAY_AMT20.082654
BILL_AMT30.068008
EDUCATION0.060491
PAY_AMT40.060251
BILL_AMT20.057245
BILL_AMT40.039342
PAY_AMT50.036137
PAY_AMT60.025210
BILL_AMT60.005666
BILL_AMT50.002853
SEX-0.096203
\n", + "
" + ], + "text/plain": [ + " Reason Codes\n", + "PAY_0 1.526435\n", + "PAY_5 0.511360\n", + "PAY_6 0.328183\n", + "PAY_2 0.289542\n", + "LIMIT_BAL 0.287896\n", + "PAY_4 0.227838\n", + "AGE 0.225641\n", + "BILL_AMT1 0.197412\n", + "PAY_3 0.178575\n", + "MARRIAGE 0.171177\n", + "PAY_AMT3 0.133252\n", + "PAY_AMT1 0.107046\n", + "PAY_AMT2 0.082654\n", + "BILL_AMT3 0.068008\n", + "EDUCATION 0.060491\n", + "PAY_AMT4 0.060251\n", + "BILL_AMT2 0.057245\n", + "BILL_AMT4 0.039342\n", + "PAY_AMT5 0.036137\n", + "PAY_AMT6 0.025210\n", + "BILL_AMT6 0.005666\n", + "BILL_AMT5 0.002853\n", + "SEX -0.096203" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot top local contributions as reason codes" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = s_df[:5].plot(kind='bar', \n", + " title='Top Five Reason Codes for a Risky Customer\\n', \n", + " legend=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the customer in the test dataset that the GBM predicts as most likely to default, the most important input variables in the prediction are, in descending order, `PAY_0`, `PAY_5`, `PAY_6`, `PAY_2`, and `LIMIT_BAL`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display customer in question " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The local contributions for this customer appear reasonable, especially when considering her payment information. Her most recent payment was 3 months late and her payment for 6 months and 5 months previous were 7 months late. Also her credit limit was extremely low, so it's logical that these factors would weigh heavily into the model's prediction for default for this customer." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTH
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" + ], + "text/plain": [ + " ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 \\\n", + "5399 17757 10000 2 3 1 51 3 2 2 \n", + "\n", + " PAY_4 PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 \\\n", + "5399 7 7 7 2400 2400 2400 2400 \n", + "\n", + " BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", + "5399 2400 2400 0 0 0 0 0 \n", + "\n", + " PAY_AMT6 DEFAULT_NEXT_MONTH \n", + "5399 0 1 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row # helps understand reason codes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To generate reason codes for the model's decision, the locally important variable and its value are used together. If this customer was denied future credit based on this model and data, the top five Shapley-based reason codes for the automated decision would be:\n", + "\n", + "1. Most recent payment is 3 months delayed.\n", + "2. 5th most recent payment is 7 months delayed.\n", + "3. 6th most recent payment is 7 months delayed.\n", + "4. 2nd most recent payment is 2 months delayed.\n", + "5. Credit limit is too low: 10,000 $NT.\n", + "\n", + "(Of course, credit limits are set by the lender and are used to price-in risk to credit decisions, so using credit limits as reason codes or even in a probability of default model is likely questionable. However, in this small, example data set all input columns were used to generate a better model fit. For a slightly more careful treatment of gradient boosting in the context of credit scoring, please see: https://github.com/jphall663/interpretable_machine_learning_with_python/blob/master/dia.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Summary\n", + "\n", + "In this notebook, a highly transparent, nonlinear, monotonic GBM classifier was trained to predict credit card defaults and the monotonic behavior of the classifier was analyzed and validated. To do so, Pearson correlation between each input and the target was used to determine the direction for monotonicity constraints for each input variable in the XGBoost classifier. GBM variable importance, partial dependence, and ICE were calculated, plotted, and compared to one another, domain knowledge, and reasonable expectations. Shapley values were then used to explain the model predictions for the single most risky customer in the test set. These techniques should generalize well for many types of business and research problems, enabling you to train a monotonic GBM model and analyze, validate, and explain it to your colleagues, bosses, and potentially, external regulators. " + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}