{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Risk Models Using Tree-based Models\n", "\n", "Welcome to the second assignment of Course 2!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outline\n", "\n", "- [1. Import Packages](#1)\n", "- [2. Load the Dataset](#2)\n", "- [3. Explore the Dataset](#3)\n", "- [4. Dealing with Missing Data](#4)\n", " - [Exercise 1](#Ex-1)\n", "- [5. Decision Trees](#5)\n", " - [Exercise 2](#Ex-2)\n", "- [6. Random Forests](#6)\n", " - [Exercise 3](#Ex-3)\n", "- [7. Imputation](#7)\n", "- [8. Error Analysis](#8)\n", " - [Exercise 4](#Ex-4)\n", "- [9. Imputation Approaches](#Ex-9)\n", " - [Exercise 5](#Ex-5)\n", " - [Exercise 6](#Ex-6)\n", "- [10. Comparison](#10)\n", "- [11. Explanations: SHAP](#)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1id9x6FmKclN" }, "source": [ "In this assignment, you'll gain experience with tree based models by predicting the 10-year risk of death of individuals from the NHANES I epidemiology dataset (for a detailed description of this dataset you can check the [CDC Website](https://wwwn.cdc.gov/nchs/nhanes/nhefs/default.aspx/)). This is a challenging task and a great test bed for the machine learning methods we learned this week.\n", "\n", "As you go through the assignment, you'll learn about: \n", "\n", "- Dealing with Missing Data\n", " - Complete Case Analysis.\n", " - Imputation\n", "- Decision Trees\n", " - Evaluation.\n", " - Regularization.\n", "- Random Forests \n", " - Hyperparameter Tuning." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "F6k2pItifWeK" }, "source": [ "\n", "## 1. Import Packages\n", "\n", "We'll first import all the common packages that we need for this assignment. \n", "\n", "- `shap` is a library that explains predictions made by machine learning models.\n", "- `sklearn` is one of the most popular machine learning libraries.\n", "- `itertools` allows us to conveniently manipulate iterable objects such as lists.\n", "- `pydotplus` is used together with `IPython.display.Image` to visualize graph structures such as decision trees.\n", "- `numpy` is a fundamental package for scientific computing in Python.\n", "- `pandas` is what we'll use to manipulate our data.\n", "- `seaborn` is a plotting library which has some convenient functions for visualizing missing data.\n", "- `matplotlib` is a plotting library." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "V5s0iQ82okBv" }, "outputs": [], "source": [ "import shap\n", "import sklearn\n", "import itertools\n", "import pydotplus\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from IPython.display import Image \n", "\n", "from sklearn.tree import export_graphviz\n", "from sklearn.externals.six import StringIO\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.experimental import enable_iterative_imputer\n", "from sklearn.impute import IterativeImputer, SimpleImputer\n", "\n", "# We'll also import some helper functions that will be useful later on.\n", "from util import load_data, cindex" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YckMl2bwg5Hb" }, "source": [ "\n", "## 2. Load the Dataset" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xrqlr_ZQhnr4" }, "source": [ "Run the next cell to load in the NHANES I epidemiology dataset. This dataset contains various features of hospital patients as well as their outcomes, i.e. whether or not they died within 10 years." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 122 }, "colab_type": "code", "id": "iM2qfgvUs9c_", "outputId": "53895f4d-48f8-429f-b447-e175a80472d9" }, "outputs": [], "source": [ "X_dev, X_test, y_dev, y_test = load_data(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset has been split into a development set (or dev set), which we will use to develop our risk models, and a test set, which we will use to test our models.\n", "\n", "We further split the dev set into a training and validation set, respectively to train and tune our models, using a 75/25 split (note that we set a random state to make this split repeatable)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X_train, X_val, y_train, y_val = train_test_split(X_dev, y_dev, test_size=0.25, random_state=10)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "L6ijpFDIx_I6" }, "source": [ "\n", "## 3. Explore the Dataset\n", "\n", "The first step is to familiarize yourself with the data. Run the next cell to get the size of your training set and look at a small sample. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 275 }, "colab_type": "code", "id": "V4gvn20Gx-pF", "outputId": "b8e98069-70a6-425c-b26e-fc18571c2233" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train shape: (5147, 18)\n" ] }, { "data": { "text/html": [ "
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AgeDiastolic BPPoverty indexRaceRed blood cellsSedimentation rateSerum AlbuminSerum CholesterolSerum IronSerum MagnesiumSerum ProteinSexSystolic BPTIBCTSWhite blood cellsBMIPulse pressure
159943.084.0637.01.049.310.05.0253.0134.01.597.71.0NaN490.027.39.125.80300734.0
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50034.080.0385.01.077.79.04.1197.064.01.747.32.0NaN376.017.08.230.74348930.0
\n", "
" ], "text/plain": [ " Age Diastolic BP Poverty index Race Red blood cells \\\n", "1599 43.0 84.0 637.0 1.0 49.3 \n", "2794 72.0 96.0 154.0 2.0 43.4 \n", "1182 54.0 78.0 205.0 1.0 43.8 \n", "6915 59.0 90.0 417.0 1.0 43.4 \n", "500 34.0 80.0 385.0 1.0 77.7 \n", "\n", " Sedimentation rate Serum Albumin Serum Cholesterol Serum Iron \\\n", "1599 10.0 5.0 253.0 134.0 \n", "2794 23.0 4.3 265.0 106.0 \n", "1182 12.0 4.2 206.0 180.0 \n", "6915 9.0 4.5 327.0 114.0 \n", "500 9.0 4.1 197.0 64.0 \n", "\n", " Serum Magnesium Serum Protein Sex Systolic BP TIBC TS \\\n", "1599 1.59 7.7 1.0 NaN 490.0 27.3 \n", "2794 1.66 6.8 2.0 208.0 301.0 35.2 \n", "1182 1.67 6.6 2.0 NaN 363.0 49.6 \n", "6915 1.65 7.6 2.0 NaN 347.0 32.9 \n", "500 1.74 7.3 2.0 NaN 376.0 17.0 \n", "\n", " White blood cells BMI Pulse pressure \n", "1599 9.1 25.803007 34.0 \n", "2794 6.0 33.394319 112.0 \n", "1182 5.9 20.278410 34.0 \n", "6915 6.1 32.917744 78.0 \n", "500 8.2 30.743489 30.0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"X_train shape: {}\".format(X_train.shape))\n", "X_train.head()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "c_q2vf4XECvg" }, "source": [ "Our targets `y` will be whether or not the target died within 10 years. Run the next cell to see the target data series." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 374 }, "colab_type": "code", "id": "drkvmcbQD9S5", "outputId": "b7178c14-f6b1-4d24-e8d1-7fb77c241b9f" }, "outputs": [ { "data": { "text/plain": [ "1599 False\n", "2794 True\n", "1182 False\n", "6915 False\n", "500 False\n", "1188 True\n", "9739 False\n", "3266 False\n", "6681 False\n", "8822 False\n", "5856 True\n", "3415 False\n", "9366 False\n", "7975 False\n", "1397 False\n", "6809 False\n", "9461 False\n", "9374 False\n", "1170 True\n", "158 False\n", "Name: time, dtype: bool" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.head(20)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "P0OwPz5xh-uS" }, "source": [ "Use the next cell to examine individual cases and familiarize yourself with the features." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 374 }, "colab_type": "code", "id": "mms5-w98ykxM", "outputId": "485fe24d-947c-4ae0-aaa9-63ef3f1eb318" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age 67.000000\n", "Diastolic BP 94.000000\n", "Poverty index 114.000000\n", "Race 1.000000\n", "Red blood cells 43.800000\n", "Sedimentation rate 12.000000\n", "Serum Albumin 3.700000\n", "Serum Cholesterol 178.000000\n", "Serum Iron 73.000000\n", "Serum Magnesium 1.850000\n", "Serum Protein 7.000000\n", "Sex 1.000000\n", "Systolic BP 140.000000\n", "TIBC 311.000000\n", "TS 23.500000\n", "White blood cells 4.300000\n", "BMI 17.481227\n", "Pulse pressure 46.000000\n", "Name: 5856, dtype: float64\n", "\n", "Died within 10 years? True\n" ] } ], "source": [ "i = 10\n", "print(X_train.iloc[i,:])\n", "print(\"\\nDied within 10 years? {}\".format(y_train.loc[y_train.index[i]]))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1VTEfpTXpamz" }, "source": [ "\n", "## 4. Dealing with Missing Data\n", "\n", "Looking at our data in `X_train`, we see that some of the data is missing: some values in the output of the previous cell are marked as `NaN` (\"not a number\").\n", "\n", "Missing data is a common occurrence in data analysis, that can be due to a variety of reasons, such as measuring instrument malfunction, respondents not willing or not able to supply information, and errors in the data collection process.\n", "\n", "Let's examine the missing data pattern. `seaborn` is an alternative to `matplotlib` that has some convenient plotting functions for data analysis. We can use its `heatmap` function to easily visualize the missing data pattern.\n", "\n", "Run the cell below to plot the missing data: " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 719 }, "colab_type": "code", "id": "SPNsph0HirU-", "outputId": "14cc7dc6-49af-4e42-b9fe-7d4a935dd135" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(X_train.isnull(), cbar=False)\n", "plt.title(\"Training\")\n", "plt.show()\n", "\n", "sns.heatmap(X_val.isnull(), cbar=False)\n", "plt.title(\"Validation\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each feature, represented as a column, values that are present are shown in black, and missing values are set in a light color.\n", "\n", "From this plot, we can see that many values are missing for systolic blood pressure (`Systolic BP`).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Exercise 1\n", "\n", "In the cell below, write a function to compute the fraction of cases with missing data. This will help us decide how we handle this missing data in the future." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " Hints\n", "\n", "

\n", "

    \n", "
  • The pandas.DataFrame.isnull() method is helpful in this case.
  • \n", "
  • Use the pandas.DataFrame.any() method and set the axis parameter.
  • \n", "
  • Divide the total number of rows with missing data by the total number of rows. Remember that in Python, True values are equal to 1.
  • \n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "zLfV7B8OyWBR" }, "outputs": [], "source": [ "# UNQ_C1 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n", "def fraction_rows_missing(df):\n", " '''\n", " Return percent of rows with any missing\n", " data in the dataframe. \n", " \n", " Input:\n", " df (dataframe): a pandas dataframe with potentially missing data\n", " Output:\n", " frac_missing (float): fraction of rows with missing data\n", " '''\n", " ### START CODE HERE (REPLACE 'Pass' with your 'return' code) ###\n", " return sum(df.isnull().any(axis=1)) / len(df)\n", " ### END CODE HERE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your function by running the cell below." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 170 }, "colab_type": "code", "id": "0U6tjKIkxaGy", "outputId": "cf128645-a131-4891-9aa8-7cd5934b432d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Example dataframe:\n", "\n", " a b\n", "0 NaN 1.0\n", "1 1.0 NaN\n", "2 1.0 0.0\n", "3 NaN 1.0\n", "\n", "Computed fraction missing: 0.75, expected: 0.75\n", "Fraction of rows missing from X_train: 0.699\n", "Fraction of rows missing from X_val: 0.704\n", "Fraction of rows missing from X_test: 0.000\n" ] } ], "source": [ "df_test = pd.DataFrame({'a':[None, 1, 1, None], 'b':[1, None, 0, 1]})\n", "print(\"Example dataframe:\\n\")\n", "print(df_test)\n", "\n", "print(\"\\nComputed fraction missing: {}, expected: {}\".format(fraction_rows_missing(df_test), 0.75))\n", "print(f\"Fraction of rows missing from X_train: {fraction_rows_missing(X_train):.3f}\")\n", "print(f\"Fraction of rows missing from X_val: {fraction_rows_missing(X_val):.3f}\")\n", "print(f\"Fraction of rows missing from X_test: {fraction_rows_missing(X_test):.3f}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OBXhCGRZjCzM" }, "source": [ "We see that our train and validation sets have missing values, but luckily our test set has complete cases." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "DvYzlx7rlG-R" }, "source": [ "As a first pass, we will begin with a **complete case analysis**, dropping all of the rows with any missing data. Run the following cell to drop these rows from our train and validation sets. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", "id": "C1Ev-eq83hP_" }, "outputs": [], "source": [ "X_train_dropped = X_train.dropna(axis='rows')\n", "y_train_dropped = y_train.loc[X_train_dropped.index]\n", "X_val_dropped = X_val.dropna(axis='rows')\n", "y_val_dropped = y_val.loc[X_val_dropped.index]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VDVUZ810pFNQ" }, "source": [ "\n", "## 5. Decision Trees\n", "\n", "Having just learned about decision trees, you choose to use a decision tree classifier. Use scikit-learn to build a decision tree for the hospital dataset using the train set." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 119 }, "colab_type": "code", "id": "q7qeLJyEpUxF", "outputId": "44f1e245-de92-4028-af9a-87d4bf4c5285" }, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, presort='deprecated',\n", " random_state=10, splitter='best')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt = DecisionTreeClassifier(max_depth=None, random_state=10)\n", "dt.fit(X_train_dropped, y_train_dropped)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "t5EGqsTCp3OV" }, "source": [ "Next we will evaluate our model. We'll use C-Index for evaluation.\n", "\n", "> Remember from lesson 4 of week 1 that the C-Index evaluates the ability of a model to differentiate between different classes, by quantifying how often, when considering all pairs of patients (A, B), the model says that patient A has a higher risk score than patient B when, in the observed data, patient A actually died and patient B actually lived. In our case, our model is a binary classifier, where each risk score is either 1 (the model predicts that the patient will die) or 0 (the patient will live).\n", ">\n", "> More formally, defining _permissible pairs_ of patients as pairs where the outcomes are different, _concordant pairs_ as permissible pairs where the patient that died had a higher risk score (i.e. our model predicted 1 for the patient that died and 0 for the one that lived), and _ties_ as permissible pairs where the risk scores were equal (i.e. our model predicted 1 for both patients or 0 for both patients), the C-Index is equal to:\n", ">\n", "> $$\\text{C-Index} = \\frac{\\#\\text{concordant pairs} + 0.5\\times \\#\\text{ties}}{\\#\\text{permissible pairs}}$$\n", "\n", "Run the next cell to compute the C-Index on the train and validation set (we've given you an implementation this time)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "colab_type": "code", "id": "mg9WsT-pqgjZ", "outputId": "6470e060-3efe-487a-e107-85d0fd0f1b92" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train C-Index: 1.0\n", "Val C-Index: 0.5629321808510638\n" ] } ], "source": [ "y_train_preds = dt.predict_proba(X_train_dropped)[:, 1]\n", "print(f\"Train C-Index: {cindex(y_train_dropped.values, y_train_preds)}\")\n", "\n", "\n", "y_val_preds = dt.predict_proba(X_val_dropped)[:, 1]\n", "print(f\"Val C-Index: {cindex(y_val_dropped.values, y_val_preds)}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "HJ4UtZgQqng-" }, "source": [ "Unfortunately your tree seems to be overfitting: it fits the training data so closely that it doesn't generalize well to other samples such as those from the validation set.\n", "\n", "> The training C-index comes out to 1.0 because, when initializing `DecisionTreeClasifier`, we have left `max_depth` and `min_samples_split` unspecified. The resulting decision tree will therefore keep splitting as far as it can, which pretty much guarantees a pure fit to the training data.\n", "\n", "To handle this, you can change some of the hyperparameters of our tree. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Exercise 2\n", "\n", "Try and find a set of hyperparameters that improves the generalization to the validation set and recompute the C-index. If you do it right, you should get C-index above 0.6 for the validation set. \n", "\n", "You can refer to the documentation for the sklearn [DecisionTreeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " Hints\n", "\n", "

\n", "

    \n", "
  • Try limiting the depth of the tree ('max_depth').
  • \n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": {}, "colab_type": "code", "id": "RhbNgoVpzNod" }, "outputs": [], "source": [ "# Experiment with different hyperparameters for the DecisionTreeClassifier\n", "# until you get a c-index above 0.6 for the validation set\n", "dt_hyperparams = {\n", " # set your own hyperparameters below, such as 'min_samples_split': 1\n", "\n", " ### START CODE HERE ###\n", " \n", " 'max_depth': 3,\n", " \n", " ### END CODE HERE ###\n", "}" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kzO-2H3fzCJ2" }, "source": [ "Run the next cell to fit and evaluate the regularized tree." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train C-Index: 0.688738755448391\n", "Val C-Index (expected > 0.6): 0.6302692819148936\n" ] } ], "source": [ "# UNQ_C2 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n", "dt_reg = DecisionTreeClassifier(**dt_hyperparams, random_state=10)\n", "dt_reg.fit(X_train_dropped, y_train_dropped)\n", "\n", "y_train_preds = dt_reg.predict_proba(X_train_dropped)[:, 1]\n", "y_val_preds = dt_reg.predict_proba(X_val_dropped)[:, 1]\n", "print(f\"Train C-Index: {cindex(y_train_dropped.values, y_train_preds)}\")\n", "print(f\"Val C-Index (expected > 0.6): {cindex(y_val_dropped.values, y_val_preds)}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "H2ofWJB76j9B" }, "source": [ "If you used a low `max_depth` you can print the entire tree. This allows for easy interpretability. Run the next cell to print the tree splits. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 551 }, "colab_type": "code", "id": "GY-15i0b4MKw", "outputId": "c902a2fc-f679-46d3-a4e2-900c0e3f7c17" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dot_data = StringIO()\n", "export_graphviz(dt_reg, feature_names=X_train_dropped.columns, out_file=dot_data, \n", " filled=True, rounded=True, proportion=True, special_characters=True,\n", " impurity=False, class_names=['neg', 'pos'], precision=2)\n", "graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) \n", "Image(graph.create_png())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Overfitting, underfitting, and the bias-variance tradeoff**\n", ">\n", "> If you tested several values of `max_depth`, you may have seen that a value of `3` gives training and validation C-Indices of about `0.689` and `0.630`, and that a `max_depth` of `2` gives better agreement with values of about `0.653` and `0.607`. In the latter case, we have further reduced overfitting, at the cost of a minor loss in predictive performance.\n", ">\n", "> Contrast this with a `max_depth` value of `1`, which results in C-Indices of about `0.597` for the training set and `0.598` for the validation set: we have eliminated overfitting but with a much stronger degradation of predictive performance.\n", ">\n", "> Lower predictive performance on the training and validation sets is indicative of the model _underfitting_ the data: it neither learns enough from the training data nor is able to generalize to unseen data (the validation data in our case).\n", ">\n", "> Finding a model that minimizes and acceptably balances underfitting and overfitting (e.g. selecting the model with a `max_depth` of `2` over the other values) is a common problem in machine learning that is known as the _bias-variance tradeoff_." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3FQOUmmwtNtX" }, "source": [ "\n", "## 6. Random Forests\n", "\n", "No matter how you choose hyperparameters, a single decision tree is prone to overfitting. To solve this problem, you can try **random forests**, which combine predictions from many different trees to create a robust classifier. \n", "\n", "As before, we will use scikit-learn to build a random forest for the data. We will use the default hyperparameters." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 153 }, "colab_type": "code", "id": "OKQco4cItPY2", "outputId": "996f244e-5d96-4562-b17c-834986380dfe" }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n", " criterion='gini', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, max_samples=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100,\n", " n_jobs=None, oob_score=False, random_state=10, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf = RandomForestClassifier(n_estimators=100, random_state=10)\n", "rf.fit(X_train_dropped, y_train_dropped)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RYu0LeEww2lV" }, "source": [ "Now compute and report the C-Index for the random forest on the training and validation set." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "colab_type": "code", "id": "5ehZ4LRbw8Un", "outputId": "78624185-6943-405f-e2e6-c25627be66f4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train C-Index: 1.0\n", "Val C-Index: 0.6660488696808511\n" ] } ], "source": [ "y_train_rf_preds = rf.predict_proba(X_train_dropped)[:, 1]\n", "print(f\"Train C-Index: {cindex(y_train_dropped.values, y_train_rf_preds)}\")\n", "\n", "y_val_rf_preds = rf.predict_proba(X_val_dropped)[:, 1]\n", "print(f\"Val C-Index: {cindex(y_val_dropped.values, y_val_rf_preds)}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "qkcdnwYgw80C" }, "source": [ "Training a random forest with the default hyperparameters results in a model that has better predictive performance than individual decision trees as in the previous section, but this model is overfitting.\n", "\n", "We therefore need to tune (or optimize) the hyperparameters, to find a model that both has good predictive performance and minimizes overfitting.\n", "\n", "The hyperparameters we choose to adjust will be:\n", "\n", "- `n_estimators`: the number of trees used in the forest.\n", "- `max_depth`: the maximum depth of each tree.\n", "- `min_samples_leaf`: the minimum number (if `int`) or proportion (if `float`) of samples in a leaf.\n", "\n", "The approach we implement to tune the hyperparameters is known as a grid search:\n", "\n", "- We define a set of possible values for each of the target hyperparameters.\n", "\n", "- A model is trained and evaluated for every possible combination of hyperparameters.\n", "\n", "- The best performing set of hyperparameters is returned.\n", "\n", "The cell below implements a hyperparameter grid search, using the C-Index to evaluate each tested model." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 249 }, "colab_type": "code", "id": "eHX56jeLzvA7", "outputId": "24f2082c-7e52-4713-d83e-4cf77db767eb" }, "outputs": [], "source": [ "def holdout_grid_search(clf, X_train_hp, y_train_hp, X_val_hp, y_val_hp, hyperparams, fixed_hyperparams={}):\n", " '''\n", " Conduct hyperparameter grid search on hold out validation set. Use holdout validation.\n", " Hyperparameters are input as a dictionary mapping each hyperparameter name to the\n", " range of values they should iterate over. Use the cindex function as your evaluation\n", " function.\n", "\n", " Input:\n", " clf: sklearn classifier\n", " X_train_hp (dataframe): dataframe for training set input variables\n", " y_train_hp (dataframe): dataframe for training set targets\n", " X_val_hp (dataframe): dataframe for validation set input variables\n", " y_val_hp (dataframe): dataframe for validation set targets\n", " hyperparams (dict): hyperparameter dictionary mapping hyperparameter\n", " names to range of values for grid search\n", " fixed_hyperparams (dict): dictionary of fixed hyperparameters that\n", " are not included in the grid search\n", "\n", " Output:\n", " best_estimator (sklearn classifier): fitted sklearn classifier with best performance on\n", " validation set\n", " best_hyperparams (dict): hyperparameter dictionary mapping hyperparameter\n", " names to values in best_estimator\n", " '''\n", " best_estimator = None\n", " best_hyperparams = {}\n", " \n", " # hold best running score\n", " best_score = 0.0\n", "\n", " # get list of param values\n", " lists = hyperparams.values()\n", " \n", " # get all param combinations\n", " param_combinations = list(itertools.product(*lists))\n", " total_param_combinations = len(param_combinations)\n", "\n", " # iterate through param combinations\n", " for i, params in enumerate(param_combinations, 1):\n", " # fill param dict with params\n", " param_dict = {}\n", " for param_index, param_name in enumerate(hyperparams):\n", " param_dict[param_name] = params[param_index]\n", " \n", " # create estimator with specified params\n", " estimator = clf(**param_dict, **fixed_hyperparams)\n", "\n", " # fit estimator\n", " estimator.fit(X_train_hp, y_train_hp)\n", " \n", " # get predictions on validation set\n", " preds = estimator.predict_proba(X_val_hp)\n", " \n", " # compute cindex for predictions\n", " estimator_score = cindex(y_val_hp, preds[:,1])\n", "\n", " print(f'[{i}/{total_param_combinations}] {param_dict}')\n", " print(f'Val C-Index: {estimator_score}\\n')\n", "\n", " # if new high score, update high score, best estimator\n", " # and best params \n", " if estimator_score >= best_score:\n", " best_score = estimator_score\n", " best_estimator = estimator\n", " best_hyperparams = param_dict\n", "\n", " # add fixed hyperparamters to best combination of variable hyperparameters\n", " best_hyperparams.update(fixed_hyperparams)\n", " \n", " return best_estimator, best_hyperparams" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Exercise 3\n", "\n", "In the cell below, define the values you want to run the hyperparameter grid search on, and run the cell to find the best-performing set of hyperparameters.\n", "\n", "Your objective is to get a C-Index above `0.6` on both the train and validation set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " Hints\n", "\n", "

\n", "

    \n", "
  • n_estimators: try values greater than 100
  • \n", "
  • max_depth: try values in the range 1 to 100
  • \n", "
  • min_samples_leaf: try float values below .5 and/or int values greater than 2
  • \n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def random_forest_grid_search(X_train_dropped, y_train_dropped, X_val_dropped, y_val_dropped):\n", "\n", " # Define ranges for the chosen random forest hyperparameters \n", " hyperparams = {\n", " \n", " ### START CODE HERE (REPLACE array values with your code) ###\n", "\n", " # how many trees should be in the forest (int)\n", " 'n_estimators': [100, 150, 200],\n", "\n", " # the maximum depth of trees in the forest (int)\n", " \n", " 'max_depth': [3, 4, 5],\n", " \n", " # the minimum number of samples in a leaf as a fraction\n", " # of the total number of samples in the training set\n", " # Can be int (in which case that is the minimum number)\n", " # or float (in which case the minimum is that fraction of the\n", " # number of training set samples)\n", " 'min_samples_leaf': [3, 4],\n", "\n", " ### END CODE HERE ###\n", " }\n", "\n", " \n", " fixed_hyperparams = {\n", " 'random_state': 10,\n", " }\n", " \n", " rf = RandomForestClassifier\n", "\n", " best_rf, best_hyperparams = holdout_grid_search(rf, X_train_dropped, y_train_dropped,\n", " X_val_dropped, y_val_dropped, hyperparams,\n", " fixed_hyperparams)\n", "\n", " print(f\"Best hyperparameters:\\n{best_hyperparams}\")\n", "\n", " \n", " y_train_best = best_rf.predict_proba(X_train_dropped)[:, 1]\n", " print(f\"Train C-Index: {cindex(y_train_dropped, y_train_best)}\")\n", "\n", " y_val_best = best_rf.predict_proba(X_val_dropped)[:, 1]\n", " print(f\"Val C-Index: {cindex(y_val_dropped, y_val_best)}\")\n", " \n", " # add fixed hyperparamters to best combination of variable hyperparameters\n", " best_hyperparams.update(fixed_hyperparams)\n", " \n", " return best_rf, best_hyperparams" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1/18] {'n_estimators': 100, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6772273936170212\n", "\n", "[2/18] {'n_estimators': 100, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6771110372340425\n", "\n", "[3/18] {'n_estimators': 100, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6712599734042554\n", "\n", "[4/18] {'n_estimators': 100, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6732047872340425\n", "\n", "[5/18] {'n_estimators': 100, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6697972074468085\n", "\n", "[6/18] {'n_estimators': 100, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6693317819148936\n", "\n", "[7/18] {'n_estimators': 150, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6797207446808511\n", "\n", "[8/18] {'n_estimators': 150, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6803523936170213\n", "\n", "[9/18] {'n_estimators': 150, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6743184840425532\n", "\n", "[10/18] {'n_estimators': 150, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6769614361702128\n", "\n", "[11/18] {'n_estimators': 150, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.673936170212766\n", "\n", "[12/18] {'n_estimators': 150, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6719082446808511\n", "\n", "[13/18] {'n_estimators': 200, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6809175531914894\n", "\n", "[14/18] {'n_estimators': 200, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6812832446808511\n", "\n", "[15/18] {'n_estimators': 200, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6752659574468085\n", "\n", "[16/18] {'n_estimators': 200, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6782912234042553\n", "\n", "[17/18] {'n_estimators': 200, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.6745844414893617\n", "\n", "[18/18] {'n_estimators': 200, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.6736868351063829\n", "\n", "Best hyperparameters:\n", "{'n_estimators': 200, 'max_depth': 3, 'min_samples_leaf': 4, 'random_state': 10}\n", "Train C-Index: 0.7791152740424743\n", "Val C-Index: 0.6812832446808511\n" ] } ], "source": [ "best_rf, best_hyperparams = random_forest_grid_search(X_train_dropped, y_train_dropped, X_val_dropped, y_val_dropped)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-hTrzOWH3zl2" }, "source": [ "Finally, evaluate the model on the test set. This is a crucial step, as trying out many combinations of hyperparameters and evaluating them on the validation set could result in a model that ends up overfitting the validation set. We therefore need to check if the model performs well on unseen data, which is the role of the test set, which we have held out until now." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "sjkGhi9n32tS", "outputId": "204242c9-993e-4133-bcf4-c7040f72d3c3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test C-Index: 0.7000063288468841\n" ] } ], "source": [ "# UNQ_C3 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n", "y_test_best = best_rf.predict_proba(X_test)[:, 1]\n", "\n", "print(f\"Test C-Index: {cindex(y_test.values, y_test_best)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your C-Index on the test set should be greater than `0.6`." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WsHviHfFKiD8" }, "source": [ "\n", "## 7. Imputation\n", "\n", "You've now built and optimized a random forest model on our data. However, there was still a drop in test C-Index. This might be because you threw away more than half of the data of our data because of missing values for systolic blood pressure. Instead, we can try filling in, or imputing, these values. \n", "\n", "First, let's explore to see if our data is missing at random or not. Let's plot histograms of the dropped rows against each of the covariates (aside from systolic blood pressure) to see if there is a trend. Compare these to the histograms of the feature in the entire dataset. Try to see if one of the covariates has a signficantly different distribution in the two subsets." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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RSV4yQX8RcKDVel20LWGZKNiPAYMBzGyMme0BdgELWgV/CzObb2YxM4sdOnSo460QEZEzSvtdN+6+xd2vAkYBPzOz7ARlHnf3EncvGTJkSLqrJCLSqyQT9AeBYa3Wc6NtCctEY/CDgPrWBdz9HaABKOxsZUVEpOOSCfqtwOVmlm9m/YEZQEWbMhXA7Gi5DKh0d4+O6QtgZhcDVwK1Kam5iIgkpd07YNy90cwWAa8CWcBT7r7HzJYDMXevAJ4EVptZDXCE+JsBwDeBpWZ2EmgGFrr74XQ0REREEkvqVkd3Xw+sb7Pt3lbLx4HpCY5bDazuYh3Pmkvf+23iHVlfObsVERFJIU2BICISOAW9iEjgFPQiIoFT0IuIBE5BLyISOAW9iEjgFPQiIoFT0IuIBE5BLyISOAW9iEjg9LQnyYzYqs4fWzI3dfUQ6QUU9CIiHbRmy3tpOe9tY4an5bwauhERCZyCXkQkcBq66e26MlYuIj2CevQiIoFT0IuIBE5DN92Bhk9EJI0U9Bm05d0jaTnvmHw9+lBEPqegF5FuIV33povG6EVEgqcevcjZomkfJEPUoxcRCZx69NLzdPUuJfWOpZdR0EvS0nGXkO4QEkk/Dd2IiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROQS8iErikgt7MJpnZXjOrMbOlCfYPMLO10f4tZpYXbb/RzLaZ2a7o9/Wprb6IiLSn3aA3syzgEWAyUADMNLOCNsXmAR+5+2VAObAi2n4Y+K67FwGzgdWpqriIiCQnmR79aKDG3fe7+wngeWBqmzJTgWei5XXADWZm7r7D3f9ftH0PMNDMBqSi4iIikpxkZq+8CDjQar0OGHOmMu7eaGbHgMHEe/SfmQZsd/dP276Amc0H5gMMHz486cqfLel6tquIyNlwVi7GmtlVxIdz/j7Rfnd/3N1L3L1kyJAhZ6NKIiK9RjJBfxAY1mo9N9qWsIyZ9QUGAfXRei7wEvB37r6vqxUWEZGOSSbotwKXm1m+mfUHZgAVbcpUEL/YClAGVLq7m1kO8Aqw1N3fTFWlRUQkee0Gvbs3AouAV4F3gN+4+x4zW25mt0TFngQGm1kN8CPgs1swFwGXAfeaWVX089WUt0JERM4oqUcJuvt6YH2bbfe2Wj4OTE9w3H3AfV2so4iIdIG+GSsiEjgFvYhI4JIauhGRSGxVpmsg0mHq0YuIBE49epHQdfVTSMnc1NRDMkZBHyBN2SAirWnoRkQkcAp6EZHAKehFRAKnMXrJqHRdTxiT/5W0nFekJ1LQS++je+Gll9HQjYhI4NSjlyBpSEjkc+rRi4gETj16kQxL5tPHvqb3Onze28Z0v+cvS2aoRy8iEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROX5gS6QEufe+3HT8oS9M1SJx69CIigVPQi4gETkEvIhI4jdGLdEC6pj8WSSf16EVEAqegFxEJnIJeRCRwCnoRkcDpYqyIdMiaLR1/2pVklnr0IiKBSyrozWySme01sxozW5pg/wAzWxvt32JmedH2wWa2wcwazOyfUlt1ERFJRrtDN2aWBTwC3AjUAVvNrMLdq1sVmwd85O6XmdkMYAXwfeA4cA9QGP2IyFmSqnv+O/NgculekunRjwZq3H2/u58AngemtikzFXgmWl4H3GBm5u7/6u6biQe+iIhkQDJBfxFwoNV6XbQtYRl3bwSOAYOTrYSZzTezmJnFDh06lOxhIiKShG5xMdbdH3f3EncvGTJkSKarIyISlGSC/iAwrNV6brQtYRkz6wsMAupTUUEREemaZIJ+K3C5meWbWX9gBlDRpkwFMDtaLgMq3d1TV00REemsdu+6cfdGM1sEvApkAU+5+x4zWw7E3L0CeBJYbWY1wBHibwYAmFkt8CWgv5ndCtzU5o4dEZHTdOqpWpF9w6ensCY9X1LfjHX39cD6NtvubbV8HEj4X9bd87pQPxER6aJucTFWRETSR0EvIhI4Bb2ISOAU9CIigVPQi4gETkEvIhI4Bb2ISOD0hCkR+UL64lLPpx69iEjgFPQiIoHT0I2IpE1Xhn0kddSjFxEJnHr0IhIcXUA+lXr0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0IiKBU9CLiAROjxIUEWmlqw80746PIlSPXkQkcAp6EZHAJTV0Y2aTgH8EsoAn3P2XbfYPAJ4FrgXqge+7e22072fAPKAJWOzur6as9iIi3UyXhn7G/MfUVaSVdnv0ZpYFPAJMBgqAmWZW0KbYPOAjd78MKAdWRMcWADOAq4BJwKPR+URE5CxJZuhmNFDj7vvd/QTwPDC1TZmpwDPR8jrgBjOzaPvz7v6pu78L1ETnExGRsySZoL8IONBqvS7alrCMuzcCx4DBSR4rIiJp1C1urzSz+cD8aLXBzPZmsj5dcD5wONOVyAC1u3dRu9Pmx105+OIz7Ugm6A8Cw1qt50bbEpWpM7O+wCDiF2WTORZ3fxx4PIm6dGtmFnP3kkzX42xTu3sXtbvnSWboZitwuZnlm1l/4hdXK9qUqQBmR8tlQKW7e7R9hpkNMLN84HLgj6mpuoiIJKPdHr27N5rZIuBV4rdXPuXue8xsORBz9wrgSWC1mdUAR4i/GRCV+w1QDTQCd7t7U5raIiIiCVi84y2pYGbzo2GoXkXt7l3U7p5HQS8iEjhNgSAiEjgFfSeYWbaZ/dHMdprZHjP7h2h7vpltMbMaM1sbXbwOjpllmdkOM3s5Wg++3WZWa2a7zKzKzGLRtq+Y2Wtm9n+j31/OdD1TzcxyzGydmf1vM3vHzMb2knZfEf1bf/bzsZn9h57adgV953wKXO/uI4BiYJKZ/TviUz+UR1NBfER8aogQ/XvgnVbrvaXdE929uNUtdkuB1939cuD1aD00/wj83t2vBEYQ/3cPvt3uvjf6ty4mPofXvwEv0UPbrqDvBI9riFb7RT8OXE98CgiITwlxawaql1Zmlgt8B3giWjd6QbvPoPXUH8G128wGAeOJ31WHu59w96ME3u4EbgD2ufuf6aFtV9B3UjR8UQV8CLwG7AOORlNAQLjTPfwK+CnQHK0Ppne024H/aWbbom9yA1zg7u9Hy38BLshM1dImHzgErIqG6p4ws3MIv91tzQD+R7TcI9uuoO8kd2+KPtblEp+o7coMVyntzGwK8KG7b8t0XTLgm+4+kvgsrneb2fjWO6MvCIZ2C1tfYCTwmLtfA/wrbYYqAm13i+h60y3AaXMP96S2K+i7KPoouwEYC+REU0DAGaZ76OGuA24xs1ris5heT3wMN/R24+4Ho98fEh+rHQ18YGZfA4h+f5i5GqZFHVDn7lui9XXEgz/0drc2Gdju7h9E6z2y7Qr6TjCzIWaWEy0PBG4kfpFqA/EpICA+JcTvMlPD9HD3n7l7rrvnEf84W+nuswi83WZ2jpmd99kycBOwm1On/giu3e7+F+CAmV0RbbqB+Lfcg253GzP5fNgGemjb9YWpTjCzq4lfiMki/mb5G3dfbmaXEO/pfgXYAdzu7p9mrqbpY2YTgB+7+5TQ2x2176VotS+wxt3vN7PBwG+A4cCfgb919yMZqmZamFkx8Qvv/YH9wFyiv3kCbje0vKm/B1zi7seibT3y31xBLyISOA3diIgETkEvIhI4Bb2ISOAU9CIigVPQi4gETkEv0oqZ3WpmbmbBf9NZeg8FvcipZgKbo98iQVDQi0TM7Fzgm8SnWZ4RbetjZo9G87G/Zmbrzaws2netmf2vaKKzVz/7arxId6OgF/ncVOJzr/8foN7MrgX+BsgDCoAfEJ/TCDPrBzwMlLn7tcBTwP2ZqLRIe/q2X0Sk15hJfJI2iE/pMJP4/yO/dfdm4C9mtiHafwVQCLwWn5KfLOB9RLohBb0I8ccCEp+Ns8jMnHhwO5/PcXPaIcAedx97lqoo0mkauhGJKwNWu/vF7p7n7sOAd4EjwLRorP4CYEJUfi8wxMxahnLM7KpMVFykPQp6kbiZnN57fwEYSnxe9mrg18B24Ji7nyD+5rDCzHYCVcC4s1ddkeRp9kqRdpjZue7eEE1R+0fgumiudpEeQWP0Iu17OXrQTH/gvyjkpadRj15EJHAaoxcRCZyCXkQkcAp6EZHAKehFRAKnoBcRCZyCXkQkcP8fq/y/1MrmRWcAAAAASUVORK5CYII=\n", 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\n", 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\n", 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eLwIOufTSzMxKx9+MNTNLOQe9mVnKOejNzFLOQW9mlnIOejOzlHPQm5mlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYpd8Sgl3S3pK2S1jaYd6OkP0l6WdKjkrrkWLdW0hpJNZKqi1m4mZnlJ59P9POB0Y3mLQb6RcQ5wL8C/3SY9UdGREVEVDavRDMzK8QRgz4ingHebjTvqYjYmzz9A9CzBWozM7MiKEYf/WXA73IsC+ApSSslTT/cRiRNl1QtqXrbtm1FKMvMzKDAoJf0P4G9wP05mnwqIgYCY4CrJA3Pta2IuCMiKiOisqysrJCyzMysgWYHvaSpwDhgckREtjYRsTn5uRV4FBjc3P2ZmVnzNCvoJY0GvgN8ISL+mqNNR0mdD0wDo4C12dqamVnLyefyygeB54EzJW2SdDlwK9AZWJxcOjkvadtD0qJk1ZOBFZJWA38EHo+IJ1rktzAzs5zaHalBREzKMvuuHG3/AoxNpjcCAwqqzszMCuZvxpqZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaWcg97MLOUc9GZmKeegNzNLOQe9mVnKOejNzFLOQW9mlnIOejOzlMsr6CXdLWmrpLUN5p0kabGk15KfJ+ZYd0rS5jVJU4pVuJmZ5SffT/TzgdGN5s0Bno6IM4Cnk+cHkXQScB0whMyNwa/L9YZgZmYtI6+gj4hngLcbzb4I+EUy/Qvgi1lW/SywOCLejoj/ABZz6BuGmZm1oEL66E+OiC3J9L+TuRl4Y6cCbzZ4vimZdwhJ0yVVS6retm1bAWWZmVlDRTkZGxEBRIHbuCMiKiOisqysrBhlmZkZhQX9W5JOAUh+bs3SZjPQq8Hznsk8MzMrkUKCfiFw4CqaKcBvs7R5Ehgl6cTkJOyoZJ6ZmZVIvpdXPgg8D5wpaZOky4EfARdKeg24IHmOpEpJdwJExNvA94AXk8cNyTwzMyuRdvk0iohJORZ9JkvbauCKBs/vBu5uVnVmZlYwfzPWzCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaVcs4Ne0pmSaho83pH0zUZtRkja1aDNdwsv2czMmiKvO0xlExHrgQoASW3J3PT70SxNl0fEuObux8zMClOsrpvPAK9HxL8VaXtmZlYkxQr6icCDOZYNlbRa0u8knZ1rA5KmS6qWVL1t27YilWVmZgUHvaTjgC8Av86yeBVwWkQMAG4BfpNrOxFxR0RURkRlWVlZoWWZmVmiGJ/oxwCrIuKtxgsi4p2IqEumFwHtJXUrwj7NzCxPxQj6SeTotpHUXZKS6cHJ/nYUYZ9mZpanZl91AyCpI3Ah8NUG82YARMQ8YDzwNUl7gfeAiRERhezTzMyapqCgj4j/BLo2mjevwfStwK2F7MPMzArjb8aamaWcg97MLOUc9GZmKeegNzNLOQe9mVnKOejNzFLOQW9mlnIOejOzlHPQm5mlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Qrxs3BayWtkVQjqTrLckm6WdIGSS9LGljoPs3MLH8F3WGqgZERsT3HsjHAGcljCHB78tPMzEqgFF03FwH3RsYfgC6STinBfs3MjOIEfQBPSVopaXqW5acCbzZ4vimZdxBJ0yVVS6retm1bEcoyMzMoTtB/KiIGkumiuUrS8OZsJCLuiIjKiKgsKysrQllmZgZFCPqI2Jz83Ao8Cgxu1GQz0KvB857JPDMzK4GCgl5SR0mdD0wDo4C1jZotBL6SXH3zSWBXRGwpZL9mZpa/Qq+6ORl4VNKBbT0QEU9ImgEQEfOARcBYYAPwV2Bagfs0M7MmKCjoI2IjMCDL/HkNpgO4qpD9mJlZ8/mbsWZmKeegNzNLOQe9mVnKFWsIBLMPhAde+HPW+V8e0rvElZjlz0Fv1gQf/fOvsy8Yck1pCzFrAnfdmJmlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp1+ygl9RL0lJJ6yS9IukbWdqMkLRLUk3y+G5h5ZqZWVMVMqjZXuCaiFiV3Dd2paTFEbGuUbvlETGugP2YmVkBmv2JPiK2RMSqZPpd4FXg1GIVZmZmxVGUPnpJfYBzgReyLB4qabWk30k6uxj7MzOz/BU8Hr2kTsDDwDcj4p1Gi1cBp0VEnaSxwG+AM3JsZzowHaB3b9/EwcysWAr6RC+pPZmQvz8iHmm8PCLeiYi6ZHoR0F5St2zbiog7IqIyIirLysoKKcvMzBoo5KobAXcBr0bET3O06Z60Q9LgZH87mrtPMzNrukK6bs4DLgXWSKpJ5v0z0BsgIuYB44GvSdoLvAdMjIgoYJ9mZtZEzQ76iFgB6AhtbgVube4+zMyscP5mrJlZyjnozcxSzkFvZpZyDnozs5Rz0JuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcg56M7OUc9CbmaWcg97MLOUc9GZmKeegNzNLuYJvDm5mZk3zwq9/knX+kAnXtMj+Cr05+GhJ6yVtkDQny/LjJS1Ilr8gqU8h+zMzs6Yr5ObgbYGfA2OAcmCSpPJGzS4H/iMi/gswF/hxc/dnZmbNU8gn+sHAhojYGBHvA78CLmrU5iLgF8n0Q8BnJB32PrNmZlZchfTRnwq82eD5JmBIrjYRsVfSLqArsL3xxiRNB6YnT+skrW9mXd2ybR/+sZmbK5ocdbU619U0Pr6axnU1yT8WUtdpuRYcNSdjI+IO4I5CtyOpOiIqi1BSUbmupnFdTeO6muaDVlchXTebgV4NnvdM5mVtI6kd8GFgRwH7NDOzJiok6F8EzpDUV9JxwERgYaM2C4EpyfR4YElERAH7NDOzJmp2103S5z4LeBJoC9wdEa9IugGojoiFwF3AfZI2AG+TeTNoaQV3/7QQ19U0rqtpXFfTfKDqkj9gm5mlm4dAMDNLOQe9mVnKHTNBL+luSVslrc2xXJJuToZbeFnSwAbLpkh6LXlMybZ+C9Y1OalnjaTnJA1osKw2mV8jqbrEdY2QtCvZd42k7zZYdtihLVq4rm83qGmtpH2STkqWteTr1UvSUknrJL0i6RtZ2pT8GMuzrpIfY3nWVfJjLM+6Sn6MSeog6Y+SVid1/a8sbXIOGSPpn5L56yV9tskFRMQx8QCGAwOBtTmWjwV+Bwj4JPBCMv8kYGPy88Rk+sQS1jXswP7IDBfxQoNltUC3Vnq9RgCPZZnfFngdOB04DlgNlJeqrkZtP0/mSq1SvF6nAAOT6c7Avzb+vVvjGMuzrpIfY3nWVfJjLJ+6WuMYS46ZTsl0e+AF4JON2swE5iXTE4EFyXR58hodD/RNXru2Tdn/MfOJPiKeIXPlTi4XAfdGxh+ALpJOAT4LLI6ItyPiP4DFwOhS1RURzyX7BfgDme8btLg8Xq9c8hnaolR1TQIeLNa+DycitkTEqmT6XeBVMt/sbqjkx1g+dbXGMZbn65VLix1jzairJMdYcszUJU/bJ4/GV8LkGjLmIuBXEfG3iHgD2EDmNczbMRP0ecg2JMOph5nfGi4n84nwgACekrRSmSEgSm1o8qfk7ySdncw7Kl4vSf9AJiwfbjC7JK9X8ifzuWQ+dTXUqsfYYepqqOTH2BHqarVj7EivV6mPMUltJdUAW8l8MMh5fEXEXuDAkDEFv15HzRAIaSdpJJn/hJ9qMPtTEbFZ0keAxZL+lHziLYVVwGkRUSdpLPAb4IwS7TsfnweejYiGn/5b/PWS1InMf/xvRsQ7xdx2IfKpqzWOsSPU1WrHWJ7/jiU9xiJiH1AhqQvwqKR+EZH1XFWxpekTfa4hGfIZqqFFSToHuBO4KCLqh4CIiM3Jz63AozTxz7FCRMQ7B/6UjIhFQHtJ3TgKXq/ERBr9Sd3Sr5ek9mTC4f6IeCRLk1Y5xvKoq1WOsSPV1VrHWD6vV6Lkx1iy7Z3AUg7t3ss1ZEzhr1exTzq05APoQ+6Ti5/j4BNlf0zmnwS8QeYk2YnJ9EklrKs3mT61YY3mdwQ6N5h+Dhhdwrq68/cvzA0G/py8du3InEzsy99PlJ1dqrqS5R8m04/fsVSvV/K73wv87DBtSn6M5VlXyY+xPOsq+TGWT12tcYwBZUCXZPoEYDkwrlGbqzj4ZOy/JNNnc/DJ2I008WTsMdN1I+lBMmfxu0naBFxH5oQGETEPWETmqogNwF+BacmytyV9j8zYPAA3xMF/qrV0Xd8l0892W+a8CnsjMzrdyWT+fIPMgf9ARDxRwrrGA1+TtBd4D5gYmaMq69AWJawL4EvAUxHxnw1WbdHXCzgPuBRYk/SjAvwzmRBtzWMsn7pa4xjLp67WOMbyqQtKf4ydAvxCmRs2tSET4o8pjyFjIjO0zL8A64C9wFWR6QbKm4dAMDNLuTT10ZuZWRYOejOzlHPQm5mlnIPezCzlHPRmZil3zFxeadZSJO0D1pD5//AGcGlkvtRilgr+RG8G70VERUT0I3P98lWtXZBZMTnozQ72PMmAUZI6SXpa0qpkjPL6ERYlfUWZMeBXS7ovmVcm6WFJLyaP81rpdzA7iL8wZR94kuoiolPyrcVfAXdFxBPJeCP/EBHvJGO0/IHMoFzlZMZBGRYR2yWdlHw79gHgtohYIak38GREfLy1fi+zA9xHbwYnJF+XP5XM+OWLk/kC/rek4cD+ZPnJwPnAryNiO2SGQEjaXwCUJ1+hB/iQpE7x93HIzVqFg94s6aNPxid/kkwf/c3AZDKDUX0iIvZIqgU6HGY7bcjcNWh3Sxds1hTuozdLRMRfgauBaxoME7s1CfmRwGlJ0yXABEldAZTcbxR4Cvj6ge1JqihZ8WaH4aA3ayAiXgJeJnOLufuBSklrgK8Af0ravAL8APh/klYDP01Wvzpp/7KkdcCMUtdvlo1PxpqZpZw/0ZuZpZyD3sws5Rz0ZmYp56A3M0s5B72ZWco56M3MUs5Bb2aWcv8fIvskzMAG4IoAAAAASUVORK5CYII=\n", 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\n", 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lrDLGIvGhEb2ISMwp0YuIxJwSvYhIzCnRi4jEnBK9iEjMKdGLiMScEr2ISMwp0YuIxJxumOpBLnr71yfdvmfIlC6KRES6kkb0IiIxpxG9NDluxJ911okdimd2XTAi0mEijejNbIKZ7TazKjNbmGJ7XzNbFW7fYmZ5YfsoM6sIX9vN7BsdG76IiLSm1URvZlnAg8BEoAC42cwKmnWbBXzo7hcDZcCSsH0nUOzuRcAE4P+amX5FiIh0oSgj+lFAlbvvdfejwFPA5GZ9JgOPh8urga+Ymbn7f7l7XdieDXhHBC0iItFFSfTnA+8krdeEbSn7hIn9MDAQwMxGm9kuYAcwJynxNzGz2WaWMLPE/v372/4pRESkRZ1+1Y27b3H3y4CRwA/NLDtFn0fcvdjdiwcNGtTZIYmI9ChR5sv3AYOT1nPDtlR9asI5+AHAweQO7v6mmdUCw4DEKUfciVq7zrwn2fLWBye07an/9GElejCJSOaIMqJ/DRhqZvlmdhowFShv1qccmB4ulwLr3N3DfXoDmNkFwKVAdYdELiIikbQ6onf3OjObB7wIZAGPufsuM1sMJNy9HHgUWGFmVcAHBH8MAL4ELDSzY0ADMNfdD3TGBxERkdQiXero7muANc3a7k5aPgKccP+8u68AVrQzRhERaQeVQBARiTndvNSFdLJXRLqDRvQiIjGnRC8iEnNK9CIiMadELyISc0r0IiIxp0QvIhJzSvQiIjGnRC8iEnNK9CIiMac7Y6XjJJadfLseLi7SLTSiFxGJOY3o2yjVAzlERNKZRvQiIjGnRC8iEnOauomT8GToRW9reklEPqURvYhIzCnRi4jEnBK9iEjMaY5eOkWqy1D31L/dtHzL6CFdGY5Ij6ZEn4F0Lb+ItIWmbkREYk6JXkQk5pToRURiTnP00i1Wbnm71T46YSvSMTSiFxGJOSV6EZGYi5TozWyCme02syozW5hie18zWxVu32JmeWH7tWa21cx2hP9e07Hhi4hIa1pN9GaWBTwITAQKgJvNrKBZt1nAh+5+MVAGLAnbDwBfd/dCYDqwoqMCFxGRaKKM6EcBVe6+192PAk8Bk5v1mQw8Hi6vBr5iZubur7v7f4Ttu4B+Zta3IwIXEZFoolx1cz7wTtJ6DTC6pT7uXmdmh4GBBCP6RjcB29z9k+ZvYGazgdkAQ4boSou4uujtX590+54hU7ooEpGepUtOxprZZQTTOf8j1XZ3f8Tdi929eNCgQV0RkohIjxEl0e8DBiet54ZtKfuYWW9gAHAwXM8FngX+u7vvaW/AIiLSNlES/WvAUDPLN7PTgKlAebM+5QQnWwFKgXXu7maWAzwPLHT3VzoqaBERia7VRO/udcA84EXgTeAf3X2XmS02sxvCbo8CA82sCvgu0HgJ5jzgYuBuM6sIX5/t8E8hIiItilQCwd3XAGuatd2dtHwEOOFMmrv/GPhxO2MUEZF20J2xIiIxp6JmkhkSy06+vXhm18QhkoE0ohcRiTklehGRmNPUjWSs5GfnJj94vJHq2YsENKIXEYk5JXoRkZhTohcRiTklehGRmFOiFxGJOSV6EZGYU6IXEYk5XUcvsbVyy4nX1ifTdfbSU2hELyIScxrRS2THPfM166zuC0RE2kQjehGRmNOIXk5Jcp0ZEUlvGtGLiMScEr2ISMwp0YuIxJwSvYhIzCnRi4jEnBK9iEjMKdGLiMScrqOXWDjurt0U9gyZ0kWRiKQfjehFRGJOiV5EJOY0dSNp44TpFxVOE+kQkUb0ZjbBzHabWZWZLUyxva+ZrQq3bzGzvLB9oJmtN7NaM3ugY0MXEZEoWk30ZpYFPAhMBAqAm82soFm3WcCH7n4xUAYsCduPAHcB3+uwiEVEpE2ijOhHAVXuvtfdjwJPAZOb9ZkMPB4urwa+Ymbm7n92900ECV9ERLpBlER/PvBO0npN2Jayj7vXAYeBgVGDMLPZZpYws8T+/fuj7iYiIhGkxclYd38EeASguLjYuzkckcj0XFrJBFES/T5gcNJ6btiWqk+NmfUGBgAHOyRCka6QWHby7cUzuyYOkU4QJdG/Bgw1s3yChD4VuKVZn3JgOrAZKAXWubtG5tIunf0Uq+TR+EVvp36v0fm6xFMyX6uJ3t3rzGwe8CKQBTzm7rvMbDGQcPdy4FFghZlVAR8Q/DEAwMyqgTOB08zsRuA6d6/s+I8iIiKpRJqjd/c1wJpmbXcnLR8BUhYTcfe8dsQnIiLtpBIIIiIxp0QvIhJzSvQiIjGXFtfRi3S21urVi8SZRvQiIjGnRC8iEnOauhHpRCqRIOlAI3oRkZhTohcRiTlN3YhE0ULRs8YaOXuGpLwxXCQtKNGLdKPW5vBB8/jSfkr0Il2gtev42/OLQCd8pTWaoxcRiTmN6EVirr0jfk0vZT6N6EVEYk4jepEOoFo6ks6U6EVOorMfZxhFlKkTkZNRohdJA515VU6rx886Sw8/jznN0YuIxJxG9CIx0J5zBFve+oA99SefHjrZ8XVXcPpTohfJAJ19slcnk+NNiV5E2k1356Y3zdGLiMScRvQi0i6dfcWQtJ8SvYh0Ok3tdC9N3YiIxJwSvYhIzGnqRkS6naZ2OlekRG9mE4C/B7KAX7r7z5pt7ws8AVwJHAS+6e7V4bYfArOAemC+u7/YYdGLSNpr7zX6e4ZMUankdmp16sbMsoAHgYlAAXCzmRU06zYL+NDdLwbKgCXhvgXAVOAyYALwUHg8ERHpIlFG9KOAKnffC2BmTwGTgcqkPpOBReHyauABM7Ow/Sl3/wR4y8yqwuNt7pjwRSTuov4iaGnQH/XyzhZ/EYQPhm+pkmnj8aP8ouiuKaooif584J2k9RpgdEt93L3OzA4DA8P23zfb9/zmb2Bms4HZ4Wqtme2OFD2cDRyI2DfdZHLskNnxZ3LsoPjb6HuRek2LdrAUsX+vLft3RAwtuaClDWlxMtbdHwEeaet+ZpZw9+JOCKnTZXLskNnxZ3LsoPi7U6bGHuXyyn3A4KT13LAtZR8z6w0MIDgpG2VfERHpRFES/WvAUDPLN7PTCE6uljfrUw5MD5dLgXXu7mH7VDPra2b5wFDgXzsmdBERiaLVqZtwzn0e8CLB5ZWPufsuM1sMJNy9HHgUWBGebP2A4I8BYb9/JDhxWwfc6e71HRh/m6d70kgmxw6ZHX8mxw6KvztlZOwWDLxFRCSuVAJBRCTmlOhFRGIuIxO9mU0ws91mVmVmC7s7ntaY2WNm9r6Z7UxqO8vMXjKzfw///Ux3xtgSMxtsZuvNrNLMdpnZX4XtmRJ/tpn9q5ltD+P/27A938y2hN+hVeGFBmnJzLLM7HUzey5cz6TYq81sh5lVmFkibMuI7w6AmeWY2Woz+zcze9PMxmRS/I0yLtFHLMmQbpYTlIBIthBY6+5DgbXhejqqA/6nuxcAXwTuDP/3zpT4PwGucffhQBEwwcy+SFCmoyws2/EhQRmPdPVXwJtJ65kUO8B4dy9Kuv48U747ENT4esHdLwWGE/z/kEnxB9w9o17AGODFpPUfAj/s7rgixJ0H7Exa3w2cGy6fC+zu7hgjfo7fAtdmYvzAXwDbCO7sPgD0TvWdSqcXwb0na4FrgOcAy5TYw/iqgbObtWXEd4fgfqC3CC9aybT4k18ZN6IndUmGE8oqZIDPufu74fJ/Ap/rzmCiMLM84ApgCxkUfzj1UQG8D7wE7AEOuXtd2CWdv0O/AH4ANITrA8mc2AEc+Bcz2xqWOoHM+e7kA/uBZeHU2S/N7HQyJ/4mmZjoY8eDoUFaX+dqZv2Bp4HvuPtHydvSPX53r3f3IoLR8Sjg0m4OKRIzmwS87+5buzuWdviSu48gmGq908zGJm9M8+9Ob2AE8LC7XwH8mWbTNGkef5NMTPRxKavwnpmdCxD++343x9MiM+tDkOSfdPdnwuaMib+Rux8C1hNMd+SE5Togfb9DVwM3mFk18BTB9M3fkxmxA+Du+8J/3weeJfhDmynfnRqgxt23hOurCRJ/psTfJBMTfZSSDJkguWzEdIK577QTlpt+FHjT3f8uaVOmxD/IzHLC5X4E5xfeJEj4pWG3tIzf3X/o7rnunkfwPV/n7tPIgNgBzOx0MzujcRm4DthJhnx33P0/gXfM7JKw6SsEd/lnRPzH6e6TBKd4kuR64A8Ec61/093xRIj3H4B3gWMEo4RZBHOta4F/B34HnNXdcbYQ+5cIfpq+AVSEr+szKP7LgdfD+HcCd4ftFxLUXaoCfg307e5YW/kc44DnMin2MM7t4WtX43+rmfLdCWMtAhLh9+c3wGcyKf7Gl0ogiIjEXCZO3YiISBso0YuIxJwSvYhIzCnRi4jEnBK9iEjMKdFLlzOzvwkrSb4RVjUc3YZ98xqrgJpZsZnd13mRgpkVmdn1be1nZjd0dWVVM/uOmf1FV76nZIZWHyUo0pHMbAwwCRjh7p+Y2dnAKZXZdfcEwTXOnakIKAbWtKWfB4/Y7NAb+cKb18zdG1ro8h3gV8B/deT7SubTiF662rnAAXf/BMDdD7j7fwCY2ZVm9v/CAlgvJt1mfmVYT347cGfjgcxsXFKN9kVm9riZbTSzP5rZX5rZz8Na6C+EZRxO9h4bzGxJWLv+D2ZWEt55vRj4ZvjL45tmNsrMNodFrl41s0ta6DfDzB4Ij51nZuvCXzBrzWxI2L7czO4Lj7PXzBrvdiXpM+ZZ8OyFJwhu+BpsZg+bWcKOr68/HzgPWG9m68O268JYt5nZr8N6RdITdfcdW3r1rBfQn+Du2j8ADwFfDtv7AK8Cg8L1bxI8iB6CuxLHhsv3EJZ75vi7RRcBm8LjDCcY1U4Mtz0L3NjKe2wA/k+4fD3wu3B5BvBAUvxn8mmJ4K8CT7fQr2kd+Cdgerh8G/CbcHk5wZ2tvQierVCV4n+vPILKlV9Majsr/DcrjPvycL2asCQwcDbwMnB6uP7XhHcF69XzXpq6kS7l7rVmdiVQAowHVoVz2QlgGPBSMENBFvBuWKcmx91fDg+xgqASYir/7O7HzGxHuP8LYfsOgoR5Sar3SNq/sWDb1rB/KgOAx81sKEFpiD4RPvYY4C+T4v950rbfeDAVU2lmLZW7/aO7/z5p/b9ZUPK3N8EvpAKCP4bJvhi2vxJ+1tOAzRFilRhSopcu5+71BCPRDWFSnk6QXHe5+5jkvo0FySJqnA5qMLNj7t5Y36OB4Ltuqd6j+f5APS3/t/G/gfXu/g0L6vNvaEN8J3tPwvhS+XNTB7N84HvASHf/0MyWA9kp9jHgJXe/uZ3xSQxojl66VDinPTSpqQj4I8FTewaFJ2sxsz5mdpkHpYUPmdmXwv7T2vH2Kd+jlX3+BJyRtD6AT8sCzzhJv2SvElSfhCD+jW2IubkzCRL/4fAXQPKvm+QYfg9cbWYXQ1Mlyc+3430lgynRS1frTzD1UWlmbxBMLyxy96MEpXeXhCddK4Crwn1mAg9a8JSolka9rWrlPVqyHihoPMlKMO3yUzN7neNH/c37Jfs2MDP8vN8ieAbsqX6G7QTVOP8NWAm8krT5EeAFM1vv7vsJ/hD9Q/i+m8mQB65Ix1P1ShGRmNOIXkQk5pToRURiToleRCTmlOhFRGJOiV5EJOaU6EVEYk6JXkQk5v4/XIILw409WHkAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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FRGJKCV5EJKaU4EVEYkoJXkQkppTgRURiSgleRCSmlOBFRGJKCV5EJKaU4EVEYqpNpgZm1g1YAHwScOAhd/9pvTYG/BQYBfwNqHD3yuyHK7GUmJd6eemk/MYhEjMZEzxwCLjF3SvN7HRgg5mtcPfqpDYjgfPCnzLgwfC3iIgUSMYpGnffeXQ07u7vAa8CZ9drdiWwwAMvAu3NrEvWoxURkcgaNQdvZj2A/sD6eqvOBt5Kur+d498EREQkjyIneDNrBzwBfMvd323KzsxsspklzCyxa9eupmxCREQiipTgzayIILk/6u5LUjTZAXRLut81XHYMd3/I3UvdvbRTp05NiVdERCLKmODDM2R+Drzq7v+WptlS4GsW+Cyw3913ZjFOERFppChn0VwMXAtsMrOqcNn3ge4A7j4XWE5wiuRWgtMkdX6bRLb+jb0pl5eV5jkQkZjJmODdfS1gGdo48I1sBSUiIs0XZQQvUhjpLoASkUhUqkBEJKaU4EVEYkoJXkQkppTgRURiSgdZRRph0fo3Uy6/uqx7niMRyUwjeBGRmFKCFxGJKU3RSIuV7gpXEYlGI3gRkZhSghcRiSlN0Yg0Qq83H0u9ouyW/AYiEoESvEgK6U6H7JXnOESaQ1M0IiIxpRG8SAppp2JETiAawYuIxJQSvIhITCnBi4jElBK8iEhMKcGLiMSUEryISEwpwYuIxFTG8+DN7BFgNPCOu/dJsX4Y8BTwRrhoibvfkc0gJSYS8wodgchJJcqFTvOB+4AFDbRZ4+6jsxKRiIhkRcYpGnd/DlBhbhGRE0y2ShUMMbONwH8D33b3LVnarsSIvsBDJL+ykeArgXPcvdbMRgG/Bc5L1dDMJgOTAbp315cUi4jkUrPPonH3d929Nry9HCgys45p2j7k7qXuXtqpU6fm7lpERBrQ7ARvZp3NzMLbg8Nt7mnudkVEpHminCb5K2AY0NHMtgO3A0UA7j4XGAt83cwOAe8D493dcxaxiIhEkjHBu/tVGdbfR3AapchJK903QF1dpmNNUji6klVEJKaU4EVEYkoJXkQkppTgRURiSgleRCSmlOBFRGIqW7VoRE5qvd58LPWKslvyG4hIEo3gRURiSgleRCSmlOBFRGJKCV5EJKaU4EVEYkoJXkQkppTgRURiSgleRCSmlOBFRGJKCV5EJKaU4EVEYkoJXkQkppTgRURiSgleRCSmlOBFRGIqY4I3s0fM7B0z25xmvZnZPWa21cxeMbMB2Q9TREQaK8oIfj4wooH1I4Hzwp/JwIPND0tERJorY4J39+eAvQ00uRJY4IEXgfZm1iVbAYqISNNkYw7+bOCtpPvbw2UiIlJAeT3IamaTzSxhZoldu3blc9ciIiedbCT4HUC3pPtdw2XHcfeH3L3U3Us7deqUhV2LiEg62UjwS4GvhWfTfBbY7+47s7BdERFphjaZGpjZr4BhQEcz2w7cDhQBuPtcYDkwCtgK/A2YlKtgRUQkuowJ3t2vyrDegW9kLSIREcmKjAlepNES8wodgYigUgUiIrGlEbxIDi1a/2bK5VeXdc9zJHIyUoKXrFv/RkMXPotIvmiKRkQkppTgRURiSgleRCSmlOBFRGJKB1lFcqjXm4+lXlF2S34DkZOSRvAiIjGlBC8iElNK8CIiMaUELyISU0rwIiIxpQQvIhJTSvAiIjGlBC8iElNK8CIiMaUELyISU0rwIiIxpQQvIhJTSvAiIjEVKcGb2Qgze83MtprZzBTrK8xsl5lVhT83ZD9UERFpjIzlgs2sNXA/cBmwHXjJzJa6e3W9povdfVoOYhQRkSaIMoIfDGx1923u/iHwa+DK3IYlIiLNFSXBnw28lXR/e7isvjFm9oqZPW5m3bISnYiINFm2DrL+O9DD3S8CVgC/SNXIzCabWcLMErt27crSrkVEJJUoCX4HkDwi7xouq+Pue9z9g/Duw8DAVBty94fcvdTdSzt16tSUeEVEJKIoCf4l4Dwz62lmpwDjgaXJDcysS9LdK4BXsxeiiIg0RcazaNz9kJlNA34PtAYecfctZnYHkHD3pcB0M7sCOATsBSpyGLOIiESQMcEDuPtyYHm9Zbcl3f4e8L3shiYt3aL1b6Zc3ivPcYhIapESvJzkEvPSrPh8XsMQkcZRqQIRkZjSCF4yWv/G3pTLe/FYniOJj/WP/STl8rJxt+Q5EokzjeBFRGJKCV5EJKY0RSPSgqQ7M+nqsu55jkTiQCN4EZGYUoIXEYkpJXgRkZhSghcRiSkdZBVpQXq9mebagjKdHy+NpwQvdVRbRiRelOClTtrRo4ickDQHLyISU0rwIiIxpSkakROAipNJU2gELyISU0rwIiIxpQQvIhJTmoM/CaWbzxWReFGCFzmBqbywNEQJXuQElu7itEWMS7lcif/kEinBm9kI4KdAa+Bhd7+z3vpTgQXAQGAP8FV3r8luqCISlWraCERI8GbWGrgfuAzYDrxkZkvdvTqp2fXAX939U2Y2Hvgx8NVcBHyySvdRPB2VHRCRKCP4wcBWd98GYGa/Bq4EkhP8lcCs8PbjwH1mZu7uWYz1pKaELdnQlAumcn6RVWJe6uWlk7Kz/ZNYlAR/NvBW0v3tQFm6Nu5+yMz2Ax2A3dkIsr6GzgLJVqdLW1kxTaJt7H51Jou0JE3pj7nuw2Wl2dlOrv+XG1LoK5DzepDVzCYDk8O7tWb2WtLqjmTlDeHbzd9Eeg3EmNP9RpWl1zCnWnqMiq/5Wvr/cob48vG/nHEfDcV4TtS9REnwO4BuSfe7hstStdluZm2AMwgOth7D3R8CHkq1EzNLuHuW3rNzo6XH2NLjg5Yfo+JrvpYeY0uPD7IXY5QrWV8CzjOznmZ2CjAeWFqvzVJgYnh7LLBS8+8iIoWVcQQfzqlPA35PcJrkI+6+xczuABLuvhT4ObDQzLYCewneBEREpIAizcG7+3Jgeb1ltyXdPgBprqyILuXUTQvT0mNs6fFBy49R8TVfS4+xpccHWYrRNJMiIhJPqiYpIhJTeUnwZjbCzF4zs61mNjPF+jlmVhX+/NnM9iWtO5y0rv7B3WzF94iZvWNmm9OsNzO7J4z/FTMbkLRuopn9V/gzMdXj8xDfhDCuTWb2gpn1TVpXEy6vMrNELuKLGOMwM9uf9Le8LWldg/0jT/F9Jym2zWG/+3i4LuevoZl1M7NVZlZtZlvM7Jsp2hSsH0aMr6D9MGKMBeuHEePLbj9095z+EByYfR04FzgF2Aj0bqD9TQQHco/er81DjJcAA4DNadaPAn4HGPBZYH24/OPAtvD3meHtMwsQ39Cj+wVGHo0vvF8DdGwBr+EwYFlz+0eu4qvX9ksEZ4Ll7TUEugADwtunA3+u/zoUsh9GjK+g/TBijAXrh1Hiy3Y/zMcIvq7Ugbt/CBwtdZDOVcCv8hBXHXd/juDsn3SuBBZ44EWgvZl1Af4BWOHue939r8AKYES+43P3F8L9A7xIcK1CXkV4DdNpbP9okkbGV4g+uNPdK8Pb7wGvElwhnqxg/TBKfIXuhxFfw3Ry3g+bEF+z+2E+EnyqUgcpn5SZnQP0BFYmLS42s4SZvWhmX85dmA1K9xwiP7c8up5glHeUA38wsw0WXElcSEPMbKOZ/c7MLgyXtajX0Mz+jiA5PpG0OK+voZn1APoD6+utahH9sIH4khW0H2aIseD9MNNrmK1+2NLqwY8HHnf3w0nLznH3HWZ2LrDSzDa5++sFiq9FM7PhBP9Yf5+0+O/D1+8TwAoz+89wNJtvlQR/y1ozGwX8FjivAHFk8iXgeXdPHu3n7TU0s3YE/9Tfcvd3c7GP5ogSX6H7YYYYC94PI/6Ns9IP8zGCj1Lq4Kjx1PtI4u47wt/bgNUE73r5lu45NOa55ZSZXQQ8DFzp7nVlIpJev3eAJwk+iuadu7/r7rXh7eVAkZl1pAW9hqGG+mBOX0MzKyL4x3/U3ZekaFLQfhghvoL3w0wxFrofRnkNQ9nph9k8iJDmQEEbgoM+Pfno4MWFKdpdQHAQwZKWnQmcGt7uCPwXOTgAF26/B+kPEH6RYw9u/Slc/nHgjTDOM8PbHy9AfN2BrcDQestPA05Puv0CMCKHf+uGYux89G8bdsw3w9czUv/IdXzh+jMI5ulPy/drGL4WC4C7G2hTsH4YMb6C9sOIMRasH0aJL9v9MOdTNB6t1AEE71i/9vAZhD4D/D8zO0LwaeNOP/aLRrLCzH5FcHS9o5ltB24HisL45xJcxTuKoPP+DZgUrttrZv9KUK8H4A4/9iNVvuK7jaA88wNmBnDIg0JFnwSeDJe1ARa5+zPZji9ijGOBr5vZIeB9YHz4t07ZPwoQH8BXgD+4+/8mPTRfr+HFwLXAJjOrCpd9nyBptoR+GCW+QvfDKDEWsh9GiQ+y2A91JauISEzpSlYRkZhSghcRiSkleBGRmFKCFxGJKSV4EZGYUoKXnDKz/xNWznslrIJXVsBYKszMzewLScu+HC4bW6i4wjimmNnXChmDxE9LK1UgMWJmQ4DRBBX0PgivGDylEY9v4+6HshzWJoJrLv4Y3r+K4KKWgko6B1okazSCl1zqAux29w8A3H23u/83gJkNNLP/CAsn/T6sioiZrTazu8N61980s/nJo2szqw1/Dwsf/5SZbTOzOy2oR/6nsGZ2rzQxrQEGm1lRWBPkU0BV0vZvM7OXwlrcD1l4ZYmZDUr6FHKXhXXlw08FS8zsGQtqsf/fpG1dbmbrzKzSzB4L90cYa3W4vdnhsllm9u2k16A0vN3RzGqS9vVbM1thQW3waWZ2s5m9bEExvo838+8lMaMEL7n0B6CbBV/i8oCZfQ7q6nHcC4x194HAI8APkx53iruXuvtPMmy/LzCF4Irna4FPu/tgglooN6V5jBOM3v+BoBxs/S+Ruc/dB7l7H6AtwScQgHnAP7l7P+Bwvcf0A74KlABfteCLHToC/wx8wd0HAAngZjPrQHCl4oXufhHwgwzPsb4+wD8Cgwhes7+5e39gHaApHjmGpmgkZzyo2DcQKAeGA4st+KacBEGiWhEOkFsDO5MeujjiLl5y950AZvY6wRsKBNMwwxt43K+B6QQ1P24huFz8qOFm9l3g7whqvGwxszUEdUDWhW0W8VHiB3jW3feHcVQD5wDtgd7A8+FzPIUgCe8HDgA/N7NlwLKIz/WoVR7UEn/PzPYD/570nC9q5LYk5pTgJac8KP28GlhtZpuAicAGYIu7D0nzsOQaHIcIP2maWSuOncP/IOn2kaT7R2igb7v7n8yshGD0++cwAWNmxcADQKm7v2Vms4DiCE8zOY7D4b6N4Es4rqrf2MwGA58nqIsyDbi0XpO655xi/016znJy0hSN5IyZnW9mybW2+wF/AV4DOoUHYQnnwy9MtQ2CCqMDw9tXEBYIy4KZHDtyh4+S6e5wvnwsgLvvIxgxHz0DaHyE7b8IXGxmnwIws9PM7NPhds/woFTtDIJppvpq+Og5F/TsHjmx6R1fcqkdcK+ZtScYlW4FJrv7h+GB03vM7AyCfng3kKp638+Ap8xsI/AMx47um8zdf5di2T4z+xmwGfgfPqrOCMEXWPzMgsqm/0Ew1dLQ9neZWQXwKzM7NVz8z8B7BM+nmGCUf3OKh88GfmPBt/Y83agnJpJE1SRFIjCzdh5+UUR4HKGLu3+zwGGJNEgjeJFovmhm3yP4n/kLUFHYcEQy0wheRCSmdJBVRCSmlOBFRGJKCV5EJKaU4EVEYkoJXkQkppTgRURi6v8Dj1evLNxtunYAAAAASUVORK5CYII=\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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dropped_rows = X_train[X_train.isnull().any(axis=1)]\n", "\n", "columns_except_Systolic_BP = [col for col in X_train.columns if col not in ['Systolic BP']]\n", "\n", "for col in columns_except_Systolic_BP:\n", " sns.distplot(X_train.loc[:, col], norm_hist=True, kde=False, label='full data')\n", " sns.distplot(dropped_rows.loc[:, col], norm_hist=True, kde=False, label='without missing data')\n", " plt.legend()\n", "\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4OFcONx8TbNn" }, "source": [ "Most of the covariates are distributed similarly whether or not we have discarded rows with missing data. In other words missingness of the data is independent of these covariates.\n", "\n", "If this had been true across *all* covariates, then the data would have been said to be **missing completely at random (MCAR)**.\n", "\n", "But when considering the age covariate, we see that much more data tends to be missing for patients over 65. The reason could be that blood pressure was measured less frequently for old people to avoid placing additional burden on them.\n", "\n", "As missingness is related to one or more covariates, the missing data is said to be **missing at random (MAR)**.\n", "\n", "Based on the information we have, there is however no reason to believe that the _values_ of the missing data — or specifically the values of the missing systolic blood pressures — are related to the age of the patients. \n", "If this was the case, then this data would be said to be **missing not at random (MNAR)**." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WwGi6hXjUCO8" }, "source": [ "\n", "## 8. Error Analysis\n", "\n", "\n", "### Exercise 4\n", "Using the information from the plots above, try to find a subgroup of the test data on which the model performs poorly. You should be able to easily find a subgroup of at least 250 cases on which the model has a C-Index of less than 0.69." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " Hints\n", "\n", "

\n", "

    \n", "
  • Define a mask using a feature and a threshold, e.g. patients with a BMI below 20: mask = X_test['BMI'] < 20 .
  • \n", "
  • Try to find a subgroup for which the model had little data.
  • \n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "colab_type": "code", "id": "TzoMtJL604Ni", "outputId": "551875f2-8deb-497f-dd47-4edf86ca635f" }, "outputs": [], "source": [ "# UNQ_C4 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n", "def bad_subset(forest, X_test, y_test):\n", " # define mask to select large subset with poor performance\n", " # currently mask defines the entire set\n", " \n", " ### START CODE HERE (REPLACE the code after 'mask =' with your code) ###\n", " mask = X_test.Age < 40\n", " ### END CODE HERE ###\n", "\n", " X_subgroup = X_test[mask]\n", " y_subgroup = y_test[mask]\n", " subgroup_size = len(X_subgroup)\n", "\n", " y_subgroup_preds = forest.predict_proba(X_subgroup)[:, 1]\n", " performance = cindex(y_subgroup.values, y_subgroup_preds)\n", " \n", " return performance, subgroup_size" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QcMz-ved01uW" }, "source": [ "#### Test Your Work" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Subgroup size should greater than 250, performance should be less than 0.69 \n", "Subgroup size: 586, C-Index: 0.6274714828897339\n" ] } ], "source": [ "performance, subgroup_size = bad_subset(best_rf, X_test, y_test)\n", "print(\"Subgroup size should greater than 250, performance should be less than 0.69 \")\n", "print(f\"Subgroup size: {subgroup_size}, C-Index: {performance}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Expected Output\n", "Note, your actual output will vary depending on the hyper-parameters that you chose and the mask that you chose.\n", "- Make sure that the c-index is less than 0.69\n", "```Python\n", "Subgroup size: 586, C-Index: 0.6275\n", "```\n", "\n", "**Bonus**: \n", "- See if you can get a c-index as low as 0.53\n", "```\n", "Subgroup size: 251, C-Index: 0.5331\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "JJ0kcNcXU0Yy" }, "source": [ "\n", "## 9. Imputation Approaches\n", "\n", "Seeing that our data is not missing completely at random, we can handle the missing values by replacing them with substituted values based on the other values that we have. This is known as imputation.\n", "\n", "The first imputation strategy that we will use is **mean substitution**: we will replace the missing values for each feature with the mean of the available values. In the next cell, use the `SimpleImputer` from `sklearn` to use mean imputation for the missing values." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 252 }, "colab_type": "code", "id": "TS1jmsZ2611M", "outputId": "c6e4aabc-7d7a-4ceb-f781-18d95a7048b9" }, "outputs": [], "source": [ "# Impute values using the mean\n", "imputer = SimpleImputer(strategy='mean')\n", "imputer.fit(X_train)\n", "X_train_mean_imputed = pd.DataFrame(imputer.transform(X_train), columns=X_train.columns)\n", "X_val_mean_imputed = pd.DataFrame(imputer.transform(X_val), columns=X_val.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Exercise 5\n", "Now perform a hyperparameter grid search to find the best-performing random forest model, and report results on the test set. \n", "\n", "Define the parameter ranges for the hyperparameter search in the next cell, and run the cell.\n", "\n", "#### Target performance\n", "Make your test c-index at least 0.74 or higher" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " Hints\n", "\n", "

\n", "

    \n", "
  • n_estimators: try values greater than 100
  • \n", "
  • max_depth: try values in the range 1 to 100
  • \n", "
  • min_samples_leaf: try float values below .5 and/or int values greater than 2
  • \n", "
\n", "

\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Define ranges for the random forest hyperparameter search \n", "hyperparams = {\n", " ### START CODE HERE (REPLACE array values with your code) ###\n", "\n", " # how many trees should be in the forest (int)\n", " 'n_estimators': [100, 150, 200],\n", "\n", " # the maximum depth of trees in the forest (int)\n", " 'max_depth': [3, 4, 5],\n", "\n", " # the minimum number of samples in a leaf as a fraction\n", " # of the total number of samples in the training set\n", " # Can be int (in which case that is the minimum number)\n", " # or float (in which case the minimum is that fraction of the\n", " # number of training set samples)\n", " 'min_samples_leaf': [3, 4],\n", "\n", " ### END CODE HERE ###\n", "}" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1/18] {'n_estimators': 100, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7355381411780544\n", "\n", "[2/18] {'n_estimators': 100, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7351896364911549\n", "\n", "[3/18] {'n_estimators': 100, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7433713540004212\n", "\n", "[4/18] {'n_estimators': 100, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7431542171238483\n", "\n", "[5/18] {'n_estimators': 100, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7494088448535303\n", "\n", "[6/18] {'n_estimators': 100, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7476066087779754\n", "\n", "[7/18] {'n_estimators': 150, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.737671510990383\n", "\n", "[8/18] {'n_estimators': 150, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7375510000238851\n", "\n", "[9/18] {'n_estimators': 150, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.745231131348268\n", "\n", "[10/18] {'n_estimators': 150, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7450291940530552\n", "\n", "[11/18] {'n_estimators': 150, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7483622451084491\n", "\n", "[12/18] {'n_estimators': 150, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7477325481663877\n", "\n", "[13/18] {'n_estimators': 200, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7396604847797906\n", "\n", "[14/18] {'n_estimators': 200, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7393901493684574\n", "\n", "[15/18] {'n_estimators': 200, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.745559008031893\n", "\n", "[16/18] {'n_estimators': 200, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7454830101250925\n", "\n", "[17/18] {'n_estimators': 200, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7495499838233027\n", "\n", "[18/18] {'n_estimators': 200, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7489767424691502\n", "\n", "Performance for best hyperparameters:\n", "- Train C-Index: 0.8109\n", "- Val C-Index: 0.7495\n", "- Test C-Index: 0.7805\n" ] } ], "source": [ "# UNQ_C5 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n", "rf = RandomForestClassifier\n", "\n", "rf_mean_imputed, best_hyperparams_mean_imputed = holdout_grid_search(rf, X_train_mean_imputed, y_train,\n", " X_val_mean_imputed, y_val,\n", " hyperparams, {'random_state': 10})\n", "\n", "print(\"Performance for best hyperparameters:\")\n", "\n", "y_train_best = rf_mean_imputed.predict_proba(X_train_mean_imputed)[:, 1]\n", "print(f\"- Train C-Index: {cindex(y_train, y_train_best):.4f}\")\n", "\n", "y_val_best = rf_mean_imputed.predict_proba(X_val_mean_imputed)[:, 1]\n", "print(f\"- Val C-Index: {cindex(y_val, y_val_best):.4f}\")\n", "\n", "y_test_imp = rf_mean_imputed.predict_proba(X_test)[:, 1]\n", "print(f\"- Test C-Index: {cindex(y_test, y_test_imp):.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Expected output\n", "Note, your actual c-index values will vary depending on the hyper-parameters that you choose. \n", "- Try to get a good Test c-index, similar these numbers below:\n", "\n", "```Python\n", "Performance for best hyperparameters:\n", "- Train C-Index: 0.8109\n", "- Val C-Index: 0.7495\n", "- Test C-Index: 0.7805\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "u8eMe7MY7UJk" }, "source": [ "Next, we will apply another imputation strategy, known as **multivariate feature imputation**, using scikit-learn's `IterativeImputer` class (see the [documentation](https://scikit-learn.org/stable/modules/impute.html#iterative-imputer)).\n", "\n", "With this strategy, for each feature that is missing values, a regression model is trained to predict observed values based on all of the other features, and the missing values are inferred using this model.\n", "As a single iteration across all features may not be enough to impute all missing values, several iterations may be performed, hence the name of the class `IterativeImputer`.\n", "\n", "In the next cell, use `IterativeImputer` to perform multivariate feature imputation.\n", "\n", "> Note that the first time the cell is run, `imputer.fit(X_train)` may fail with the message `LinAlgError: SVD did not converge`: simply re-run the cell." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 252 }, "colab_type": "code", "id": "TS1jmsZ2611M", "outputId": "c6e4aabc-7d7a-4ceb-f781-18d95a7048b9" }, "outputs": [], "source": [ "# Impute using regression on other covariates\n", "imputer = IterativeImputer(random_state=0, sample_posterior=False, max_iter=1, min_value=0)\n", "imputer.fit(X_train)\n", "X_train_imputed = pd.DataFrame(imputer.transform(X_train), columns=X_train.columns)\n", "X_val_imputed = pd.DataFrame(imputer.transform(X_val), columns=X_val.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Exercise 6\n", "\n", "Perform a hyperparameter grid search to find the best-performing random forest model, and report results on the test set. Define the parameter ranges for the hyperparameter search in the next cell, and run the cell.\n", "\n", "#### Target performance\n", "\n", "Try to get a text c-index of at least 0.74 or higher." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " Hints\n", "\n", "

\n", "

    \n", "
  • n_estimators: try values greater than 100
  • \n", "
  • max_depth: try values in the range 1 to 100
  • \n", "
  • min_samples_leaf: try float values below .5 and/or int values greater than 2
  • \n", "
\n", "

\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# Define ranges for the random forest hyperparameter search \n", "hyperparams = {\n", " ### START CODE HERE (REPLACE array values with your code) ###\n", "\n", " # how many trees should be in the forest (int)\n", " 'n_estimators': [100, 150, 200],\n", "\n", " # the maximum depth of trees in the forest (int)\n", " 'max_depth': [3, 4, 5],\n", "\n", " # the minimum number of samples in a leaf as a fraction\n", " # of the total number of samples in the training set\n", " # Can be int (in which case that is the minimum number)\n", " # or float (in which case the minimum is that fraction of the\n", " # number of training set samples)\n", " 'min_samples_leaf': [3, 4],\n", "\n", " ### END CODE HERE ###\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1/18] {'n_estimators': 100, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7329770117188772\n", "\n", "[2/18] {'n_estimators': 100, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7325264526999885\n", "\n", "[3/18] {'n_estimators': 100, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7406224011430085\n", "\n", "[4/18] {'n_estimators': 100, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7401512141208454\n", "\n", "[5/18] {'n_estimators': 100, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7439022536636419\n", "\n", "[6/18] {'n_estimators': 100, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7433290123094896\n", "\n", "[7/18] {'n_estimators': 150, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7338140743780657\n", "\n", "[8/18] {'n_estimators': 150, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7336707640395276\n", "\n", "[9/18] {'n_estimators': 150, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7409926195175653\n", "\n", "[10/18] {'n_estimators': 150, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7403889790006927\n", "\n", "[11/18] {'n_estimators': 150, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7430380488948819\n", "\n", "[12/18] {'n_estimators': 150, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7422932694082369\n", "\n", "[13/18] {'n_estimators': 200, 'max_depth': 3, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7356792801478268\n", "\n", "[14/18] {'n_estimators': 200, 'max_depth': 3, 'min_samples_leaf': 4}\n", "Val C-Index: 0.735444772321128\n", "\n", "[15/18] {'n_estimators': 200, 'max_depth': 4, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7429316518253611\n", "\n", "[16/18] {'n_estimators': 200, 'max_depth': 4, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7425451481850615\n", "\n", "[17/18] {'n_estimators': 200, 'max_depth': 5, 'min_samples_leaf': 3}\n", "Val C-Index: 0.7453787844243376\n", "\n", "[18/18] {'n_estimators': 200, 'max_depth': 5, 'min_samples_leaf': 4}\n", "Val C-Index: 0.7451247342787473\n", "\n", "Performance for best hyperparameters:\n", "- Train C-Index: 0.8131\n", "- Val C-Index: 0.7454\n", "- Test C-Index: 0.7797\n" ] } ], "source": [ "# UNQ_C6 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n", "rf = RandomForestClassifier\n", "\n", "rf_imputed, best_hyperparams_imputed = holdout_grid_search(rf, X_train_imputed, y_train,\n", " X_val_imputed, y_val,\n", " hyperparams, {'random_state': 10})\n", "\n", "print(\"Performance for best hyperparameters:\")\n", "\n", "y_train_best = rf_imputed.predict_proba(X_train_imputed)[:, 1]\n", "print(f\"- Train C-Index: {cindex(y_train, y_train_best):.4f}\")\n", "\n", "y_val_best = rf_imputed.predict_proba(X_val_imputed)[:, 1]\n", "print(f\"- Val C-Index: {cindex(y_val, y_val_best):.4f}\")\n", "\n", "y_test_imp = rf_imputed.predict_proba(X_test)[:, 1]\n", "print(f\"- Test C-Index: {cindex(y_test, y_test_imp):.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Expected Output\n", "Note, your actual output will vary depending on the hyper-parameters that you chose and the mask that you chose.\n", "```Python\n", "Performance for best hyperparameters:\n", "- Train C-Index: 0.8131\n", "- Val C-Index: 0.7454\n", "- Test C-Index: 0.7797\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9IhZVEMnnmTe" }, "source": [ "\n", "## 10. Comparison\n", "\n", "For good measure, retest on the subgroup from before to see if your new models do better." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C-Index (no imputation): 0.6274714828897339\n", "C-Index (mean imputation): 0.5974334600760456\n", "C-Index (multivariate feature imputation): 0.5903992395437262\n" ] } ], "source": [ "performance, subgroup_size = bad_subset(best_rf, X_test, y_test)\n", "print(f\"C-Index (no imputation): {performance}\")\n", "\n", "performance, subgroup_size = bad_subset(rf_mean_imputed, X_test, y_test)\n", "print(f\"C-Index (mean imputation): {performance}\")\n", "\n", "performance, subgroup_size = bad_subset(rf_imputed, X_test, y_test)\n", "print(f\"C-Index (multivariate feature imputation): {performance}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8fvmd2o76yma" }, "source": [ "We should see that avoiding complete case analysis (i.e. analysis only on observations for which there is no missing data) allows our model to generalize a bit better. Remember to examine your missing cases to judge whether they are missing at random or not!" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CFM27SfS1QSD" }, "source": [ "\n", "## 11. Explanations: SHAP\n", "\n", "Using a random forest has improved results, but we've lost some of the natural interpretability of trees. In this section we'll try to explain the predictions using slightly more sophisticated techniques. \n", "\n", "You choose to apply **SHAP (SHapley Additive exPlanations) **, a cutting edge method that explains predictions made by black-box machine learning models (i.e. models which are too complex to be understandable by humans as is).\n", "\n", "> Given a prediction made by a machine learning model, SHAP values explain the prediction by quantifying the additive importance of each feature to the prediction. SHAP values have their roots in cooperative game theory, where Shapley values are used to quantify the contribution of each player to the game.\n", "> \n", "> Although it is computationally expensive to compute SHAP values for general black-box models, in the case of trees and forests there exists a fast polynomial-time algorithm. For more details, see the [TreeShap paper](https://arxiv.org/pdf/1802.03888.pdf).\n", "\n", "We'll use the [shap library](https://github.com/slundberg/shap) to do this for our random forest model. Run the next cell to output the most at risk individuals in the test set according to our model." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 258 }, "colab_type": "code", "id": "emlK-wlyJEel", "outputId": "5de57232-6d03-4c0f-d438-ba4ad393d6af" }, "outputs": [ { "data": { "text/html": [ "
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AgeDiastolic BPPoverty indexRaceRed blood cellsSedimentation rateSerum AlbuminSerum CholesterolSerum IronSerum MagnesiumSerum ProteinSexSystolic BPTIBCTSWhite blood cellsBMIPulse pressurerisk
549367.080.030.01.077.759.03.4231.036.01.406.31.0170.0202.017.88.417.02947090.00.619022
101765.098.016.01.049.430.03.4124.0129.01.597.71.0184.0293.044.05.930.85885386.00.545443
205066.0100.069.02.042.947.03.8233.0170.01.428.61.0180.0411.041.47.222.12949880.00.527768
633769.080.0233.01.077.748.04.2159.087.01.816.91.0146.0291.029.915.217.93127666.00.526019
260871.080.0104.01.043.823.04.0201.0119.01.607.01.0166.0311.038.36.317.76076686.00.525624
\n", "
" ], "text/plain": [ " Age Diastolic BP Poverty index Race Red blood cells \\\n", "5493 67.0 80.0 30.0 1.0 77.7 \n", "1017 65.0 98.0 16.0 1.0 49.4 \n", "2050 66.0 100.0 69.0 2.0 42.9 \n", "6337 69.0 80.0 233.0 1.0 77.7 \n", "2608 71.0 80.0 104.0 1.0 43.8 \n", "\n", " Sedimentation rate Serum Albumin Serum Cholesterol Serum Iron \\\n", "5493 59.0 3.4 231.0 36.0 \n", "1017 30.0 3.4 124.0 129.0 \n", "2050 47.0 3.8 233.0 170.0 \n", "6337 48.0 4.2 159.0 87.0 \n", "2608 23.0 4.0 201.0 119.0 \n", "\n", " Serum Magnesium Serum Protein Sex Systolic BP TIBC TS \\\n", "5493 1.40 6.3 1.0 170.0 202.0 17.8 \n", "1017 1.59 7.7 1.0 184.0 293.0 44.0 \n", "2050 1.42 8.6 1.0 180.0 411.0 41.4 \n", "6337 1.81 6.9 1.0 146.0 291.0 29.9 \n", "2608 1.60 7.0 1.0 166.0 311.0 38.3 \n", "\n", " White blood cells BMI Pulse pressure risk \n", "5493 8.4 17.029470 90.0 0.619022 \n", "1017 5.9 30.858853 86.0 0.545443 \n", "2050 7.2 22.129498 80.0 0.527768 \n", "6337 15.2 17.931276 66.0 0.526019 \n", "2608 6.3 17.760766 86.0 0.525624 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test_risk = X_test.copy(deep=True)\n", "X_test_risk.loc[:, 'risk'] = rf_imputed.predict_proba(X_test_risk)[:, 1]\n", "X_test_risk = X_test_risk.sort_values(by='risk', ascending=False)\n", "X_test_risk.head()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "aefDv0PDKrfv" }, "source": [ "We can use SHAP values to try and understand the model output on specific individuals using force plots. Run the cell below to see a force plot on the riskiest individual. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 276 }, "colab_type": "code", "id": "elJX1FqWKzYm", "outputId": "7c97d958-9383-4d7a-bb2b-e61af5ecf47f" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "explainer = shap.TreeExplainer(rf_imputed)\n", "i = 0\n", "shap_value = explainer.shap_values(X_test.loc[X_test_risk.index[i], :])[1]\n", "shap.force_plot(explainer.expected_value[1], shap_value, feature_names=X_test.columns, matplotlib=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Kak4kR4yQ8Tk" }, "source": [ "How to read this chart:\n", "- The red sections on the left are features which push the model towards the final prediction in the positive direction (i.e. a higher Age increases the predicted risk).\n", "- The blue sections on the right are features that push the model towards the final prediction in the negative direction (if an increase in a feature leads to a lower risk, it will be shown in blue).\n", "- Note that the exact output of your chart will differ depending on the hyper-parameters that you choose for your model.\n", "\n", "We can also use SHAP values to understand the model output in aggregate. Run the next cell to initialize the SHAP values (this may take a few minutes)." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": {}, "colab_type": "code", "id": "Q9pwTohdRlFB" }, "outputs": [], "source": [ "shap_values = shap.TreeExplainer(rf_imputed).shap_values(X_test)[1]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1NpathrHHH3q" }, "source": [ "Run the next cell to see a summary plot of the SHAP values for each feature on each of the test examples. The colors indicate the value of the feature." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 558 }, "colab_type": "code", "id": "ZsqthyxCDfY1", "outputId": "d7f397c7-2487-4700-b4f5-8076cf3fada5" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap.summary_plot(shap_values, X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly we see that being a woman (`sex = 2.0`, as opposed to men for which `sex = 1.0`) has a negative SHAP value, meaning that it reduces the risk of dying within 10 years. High age and high systolic blood pressure have positive SHAP values, and are therefore related to increased mortality. \n", "\n", "You can see how features interact using dependence plots. These plot the SHAP value for a given feature for each data point, and color the points in using the value for another feature. This lets us begin to explain the variation in SHAP value for a single value of the main feature.\n", "\n", "Run the next cell to see the interaction between Age and Sex." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 341 }, "colab_type": "code", "id": "RA3YOaJhEkDZ", "outputId": "1bef0e51-3659-4bd3-d728-ee4d10894cd4" }, "outputs": [ { "data": { "image/png": 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OAzPX8vJn3/NR95PotXktj0fHwunn0CG+iMGLZjF83jR2JCbzfZdewJ8AmL/dpmxl8oYcG5/fxuuxWL/XvX+JH7blQQvZ7CkiJCALIUQETV7n55zP/JT6oXkSzBnhpVG9ioPyYzP8PPaHnxZJMOFCL52c7FdtUy0+OG/fSVNvLPBzw/d+bKBXOugRXizLcnqvblA+JsX53FVbGfn7ZEb+PtkctzobgIIpK3jhi7fIj44lobiI9Ny9lAVknxuPwaY8IP+ll8WPG2wKS2FgK4vjQjJ3iUMnQ9ZCCBFBHy+3KXVSUG7Kgd82Vfzsd3W2zf1T/eSVwLLdcPcUf4X1Af7yk7887M7ZAT9tMNfklwbXK+sx+8/ozvrGZsOK3Ng4dl1sclQ3bRrP/WcNo/FDr9N49GtsSG1Yfm3jPLcrnFBcSIwTLQa19rDmei9zRnj55mIPUR4Zso4UCchCCBFBx6W7ASrGC8c6vV3btvnrTz5avVLK0C99FDjBsjQkF+XmnMonb5WExOyy3ZyyCoPP73KGrO9fXI9ut/2bATc9SMe7nqP/2rYAzLRTeOGUcwDIi41j9JmXlV+r/3s/p69YwClrlqCfvzdo8lfTRIvjGlt4JRhHlAxZCyFEBN3c08JjeZi/0+bPHa3y4efPVtq84Mya3rDcpldjm7tPsCgKSV8ZHcay3gvawviVphznhfPbmr7VlcdavDDXDCen14PBzraIizMhJ64ev7TrCkBxWbrLjIZBuap3JrqzyJpnZ/LDa48c0t+BODTSQxZCiAiyLIuRPT28PNDLgJbuT2xeSXC9XGcLxIwESAyYHd0zvfJe5z0nekiMNo94r+8OCTHmmk9XQqEzbL0jHyavN+VHkzfgCdiC8c7cJQC0Tyzlv5+9Ro8t6xi4Yj4P/zLe/ZB2AXsyN0oGryQAqWrSQxZCiCPgzx0t3l5s8dMGm65pcNtxJlin1bOYONjDQ9P9tEmxeLp/cD/pj602FnBCUzdQX/+tv3wt8Utz4fbj/bRN9VAybx3Qsrxe8cpt0D6DbvMXMfu5F3jn+P6cunYpg5sUwb09iNKruHnGD9w84wcAljdqAlxhLh5/N9z+JhSXwpMj3AblF8E/3oe1O+CWs+CMHhH+m6q7JCALIcQREBdl8eOfveQU2yTFuMG1xGfz4HQ/0zbD1M02A1vbXHGsef+2H3yMmWd60n873uKZAaaXuio7+N7r99i0TYW/rp3Blz4/C5u24uylc7goLR/IgNQEem7dQM+v3jUXHNsXgEZ2EX4sPM4UsRbZAXmqOzeH7x7c94vc9x48/7UpT5oDq/8LzRruW08cNBmyFkKII+TeKT56v+fjmm98FJWaILh0N0zbbN63gadmuUPLL893J1K9EJC1K780eFbXjlzn2fSgk1jTsDG2ZbGweRsyT+lpKpx1HKQ5i4W9Hrj+DFOOjykPxgD17MrTc/LdPLdcVALLN1d+jQiL9JCFEOIImLjKzxMzTfBbvtuma5rN33tbbNwbHFwDe7+BE7CDJmPblrtGGFi7eg90acj7BWk89P0nnLBxFeO7nci3eedyFUBGA5j7FPy4ELq3guOOMRe2bxLcSDtk+vamXVDqh9bp7rl6scF1wpmFJsIiPWQhhDgCskOWJGUXmQgb4w2exJVwgG5SRYuhGqWblJd/euYtumzfyM9tuzBi9hSavfalW6l5Glw1wA3GAK9MDr6RL+BTnv8KWtwIbUbC/e+554ef6pYz6kP31hW0TBwMCchCCHEEXNrRopfT0WyVBLf0ND+/J2XYBC7n7ZYWxs1CJmJvt2MA2ByfxNnX38/oMy+j360PU1gQMAQ97lc45xEY/SH4nPONK0iQfc87bvmxz9xr/nKuCcp9OsArN0NKQhgNFuGQIWshhDgCtubBamc4ekueGZpumgjfrbXwByTdmLMj8KqQ/JWOKMuitOzQtunVyNRZ0KETE956khM2rmZ8txPZNMxM3uL3ZXDF86b8zRyz7vihy+D83nDb6+7HBQ5HFx/gefJ978G4KaY89GlY8zKkh7HzhaiU9JCFEOII+GKVnz3Fplzih3cWm+e1x4b0iNPrBRzY+y/fMfVrTl21mPY7t/DEl+/SoYF5c9CUXxm8WJOxN4tR076l0xe/mAs+mxH8IZ//YV7zi4LPB3bVvQcID59Md8t5RTBz1f7riYNWa3rISqkOwNtAQyATuFJrvTKkziDgMaAb8KLW+s6A97zAC8BZmH/6j2utX0cIIQ7Su4v9LNhpc0lHDyc664dXZgU/BV7s7DGcFhd8bf2Q4/1ZldSQKe26ADD67KEc++ki2t54HAkE92q9ic7N+nQMvsHxJnUmq7cFn88NeNDtD5ngVZYYZG9B8Pko6ddFSm36mxwLjNFadwDGAK/sp84a4HrgP/t5bzjQDmgPnAQ8pJRqXTVNFULUVq/MLWHuvV/Q5c6XeHj0bFY5gXh1yNrhbU7+6Q+WBQe+WSExcn+mtelUXi6MjmF7Y/NwumX3dHJizLDz5qRUup3X3lRSbSEmoP81sLt5XbP9wB8SkmObQidYhwbkGSsqb7AIS60IyEqpdKAX8IFz6gOgl1KqUWA9rfUqrfU8IGRPFAAuA17TWvu11juBz4FLq7DZQohaKPs/3/DMV+9w9exfmfDGE/z6nclfOah18EysvhnmNTmmgptZ1n7Le+sFT6RKaG/2QGw/bzFJxWYYullONqkzl5oK05ebjFtlJs83r91ahXzeAcoAcWVd95BAnZZUwRcQB6NWBGSgBbBZa+0DcF63OOfD1RJYH3C84SCvF0IIGq/fWF6O9vvYM98kzvh7by/DO1s0ioc/tbR4+2zz86uaemiWncm9P01g+OwpZCRUvtvT6e3d5NfRFpzrbC5hr9sZVM+eu84UQp8V78oxr6n1gs9XlK+6bJZ1UnzweZllHTG15hnykaSUuhG4EaBJkyaV1BZC1CU/9O3H0JlTiCstYU2DdAr6dSl/r38Li+wiOCnDwuP0eIuzC5g+5gFaZmcC8GzeDhg5tMLP+PL0IsZ/8x3bc/xcdE1nkmLNZ+yNiSOF/PJ62+ol0hTM5hCB2jsbR2zfE3w+8Llx6P8XlM0E79MBvpnrnu/TocK2ivDVloC8EWimlPJqrX3OBK0M53y4NgCtgFnOcWiPuZzW+lXgVYARI0ZU/r+zQog647rbutHT+xTtdmxheeeOzD7DbGn48wY/N35vAt7Xa2zS63m4obsFa7aXB2OAE5YtrvQz/H95g4vf+cWUv4qDDa9AgyS+HHU1w0c/gwWUWhaz776a8wDOPR6G9oOPp0PP1nDXYHOjZg2Cbxy47MmygvZALjd2JJz2D9i5F0aeCe0zwvuLEZWqFUPWWusdwDxgmHNqGDDXeRYcrk+AG5RSHufZ82Dg08i2VAhR2w1o5eGXfzTnvodPYP7tKSTHmp7w7O3BwW36ZnNc2qYJqxs0Lj8/tUv38nJ0wC90TMBoctbkJeVlT14h+Wt3AXBO34aUeEzFvMREBnV2nvt6vfDBHVDyMcx+yk0I0rEZdGrm3vj289zysQHnm6RClNN/u+89s9NTbiE89yVsCdiQQhyWWhGQHSOBUUqpFcAo5xil1CSllHLK/ZRSm4A7gJuUUpuUUmc617+LmYW9EpgB/EtrvfZIfwkhRM2XFA1NEiA2IIhuzwsOyJudDSHikmM5+daHuf2Cq7h4xB28eOYQt1JxwCbKhcXlxVw7+Fnv1gIT9BtMmkGM3zzrTcnJIeaXhcEN84T85EdHwSd3wdm94LrTYfRl7ntNA3rPjVPc8g8B9yz1w08hnyEOWW0ZskZrvQw4cT/nzwkoTwWaH+B6H3BzlTVQCFEnLN7pp8/7Zr/irmkw6wovcVEWhaUWgQ9mC51Jz+v2WmxPTuX5U84FIDUgBqcW5rMz0QTDhgV5gJmEtSfanZpdAqSnl020ChliTgxY1DxvLXwxC3odA+cppxHFcN6jsN4ZTKwXCy9cb8pzA/ojCzaYSV1er+lRbw9Yw9WjdXh/MaJStamHLIQQR91V35hgDLBoF7wy3/RYN+UEB8tteea1U8hWwmkBk5jfmvQmrXfv4JjMbfzvu7fKz3fZ6E5viQayxjtTX9aHPKVb7+ThXLUV+txr8lif/xh8ONWc37I7+Jrfl7vlC09wy+ce787AHne7eQ5dPxGeuGLfpVPikNWaHrIQQlQH6/cGH8/ZboaTV4UkBtnhTIYu8gUv+G0ZMCE61xvDx+8+g9+y2JThPtMN7UmlWU53e8mm4DfmrTOv42eYvYvLjP3WTPJqkWYC6kInwJ/f263z5AjIzDHrl8fc4J5fuB4WbYRSH3w2E/5+YcXLpUTYJCALIUQEhc5LTok1Z1okwaJM8Pp8+Lze8hSZXdLggrYWX6y2ifXCHce74fba864jL9ZUTCnI42LnfGlSPaJz3OVN9c7oagotGsCyze6Hd3RmQIds8UhpwPKmAyUDGfasm0Dkwsdh7tOmfOtrJhgD/LECvp9vnkGLwyZD1kIIEUHevODUklu3meNRXUuZ8+xdlN47jHWP3swdrU1yDo9lMSF/GvM/e5K1Pz7PuYluFzvG52bXiva5eaqXpLu95VLLYnVT5zjLDdIAZOaa15SQBCDpziStjbtgQcDqzomz3PKcNW55/jo3MUhpyC5QxftLfCgOhQRkIYSIoItWzClfvxvlK2Vopsn1fOqb4zluiwl+rbIzufqZl80FG3bgGfEcbeYsotE3v8Olbqr9/IDJWwXRbnauLmtWl5ejbJv670w2B1uyghuz2EnFkBiyY0WhM3yd0QBaBWQY7huwCcWwfm750pPcYel/DnV70m0bw5k99/fXIA6BDFkLIUQEDSrZRpcJb/BDh+787dcvKRkzEoAZKwuYMPhavjhWoTat5i+LfuU0gNlrGHXBNbzU72wa5u1l0sfPUzadKqGkiCInKNcrdZ8Be+3gDSkaeJxeaq82weuCBzt3GvdbcCP/cDaEiIuBKY/Aq5PN0qabz3LrvHiDmcxVXGpey1z9J7NZxYZdcFpXcw8RERKQhRAiglbaCfzf4CH4PR5mtOrAhxvXAa346xlDWVxgeqob66cx9dhu7ACWde3MS/3MMqTMhGQevHA43zr3yolxe7Z7A8pWbBQUBQwVn+ak59waMnNs/jrz2q0lfD3bPR+YSrNlI3jk8v1/mbMO8Gy4ayvzR0SUDFkLIUQEfZPRAb+TgGN7UiqTY0y+++LY4GHjvFjzXLde42Q8AVPBkgJ2fS31un2m4oByaYJ7LxugyHmu2zYkt34XZ3+cwCVMAMNOCffriCOo0oCslIpSSi1USoWxbbYQQtRtezu2dA/8fhp2NUEytzi4XonTwW2ZbDE0JZsov49kXxF/6+IOTb/76cv0XbeMfmuW8u74l8vPr491M2f5gezmTurNt26DkztBaoJZ1jTKJBthesD6YpDsWtVUpUPWWutSpVQq+87mF0IIEWJbUcDPqsdDtrPz4Z6QgOx3dlZav3oPH2SlYHs87MXL0+PW0PdfZgel4TN/YfjMXwKu+isAbbZuKT/jBfa+9Rup/xoM8bEw9bF9GzVrZfDxsk371hFHXbhD1s8Djyql5JmzEEJUYFvIyqPPnFj45z2rgs4P2mp6rbu35GAH5JjelRs8YWt//CHLiv2VTaxKTwk+TpY9jKujcAPyTZj/NdujlFqllFpR9qcK2yaEEDVOaA6OFonm9ZXmm7n+jx9Jy93L+Ys1r3vNsHHpMU1IKcgrr1+aEhAsA28W5f5cz+rft7y8NyaWtNtOr7hR/3cxJDhbK1oWvHBd+F9IHDHh9ngfqdJWCCFELRHayylLo/HRtnie/vIlXvv0FXJjYplw0RBGAD7Lw554NwhvatI04OKAJ4UB2bVOXLG0vJxYUownPw+SnYC7aqtJb9m3k7vNYpP6sHIM/LIYuraU/NPVVFgBWWv9dlU3RAghagOPBzPTypHs5PNYvzaHtAdfJ7GkiD1x9bhj5vcAxO/JoUGen90JSQCc9PsMuKkfFfEErDX22Db891v41+UwYzkMGG12cWqSavY+znC2UWzaIHKzq+etNZtSnN4NEuMrry/CEvYzYaVUb+BaoAWwEXhTaz2r4quEEKJusWw4btXuEZ0AACAASURBVNMaLNtmb1w8pW1NPumirHxKYmLIijHPezfHmbHsqCc/4+fv5vNKn4G0yN7FX6d8BW9VHJAJ3skReh5jXj+Z7u6bvC3b5KK+akAEvx3wwW9wxfPg95utF3//t5lMJg5bWM+QlVKDgSlACjAXSAZ+VUoNqfBCIYSoY/qvXMjc5scwp0Vb1jZoTJud2wAojooOqldclhYzr5DuWzcwZsIb3PvzRDyBgfYAz5BJDZmU1czpBXdv7Z7zeNx1yJH03q8mGINJPBKYC1sclnB7yKOBi7XWk8pOKKXOBh4HJlRFw4QQoibK8biB1+f1krp4NdCchBOOCerV1m9oepVZzYKTeSxp3Jzjym+w/2fIRIf8dMc6x1cNMNsszlwJg08E1e7wvsz+dGsFk+aYclI8tE6P/GfUUeEG5NZQns2tzHfABxFtjRBC1BDLMm2u/MZHZgE80s/DsM6mB5sVH7yzUqkzRH3O8M689+xGvH4/NhaDbjBh96RhXZn4XS/OWT6PvbH1WJ+R4QbkA2nbGHbscY8Dn+PeOMj8qSoPD4PkeFi7A24Y6E4cE4ct3IC8HjgD+D7g3OnAhoi3SAghaoCbf/Azy4xGc9U3fs5uY5EaZ5FYUMBTX7xNQnERixs3J6+ZCVg9GkGjHhlM3wLtU+GM1iaAez0WFyydgwU0LMhl8Kr57oec0hl+c2ZUn97NPf/klXDmvyC/GK47HdoFzMyuatFRcN8lR+7z6pBwA/LDwESl1KfAWkyP+WLgqipqlxBCVGt5Je5wcokfip31Tf/+5n0GrHGXJf3cqTtwKh8vt7n2uZeZsGQ2s1q0ZUy7u3ngtFh8vyzGG3Dfkrxiyge9SwL2Hi4OKH83zwRjgB8XQn4R1JOJVTVdWJO6tNbjMT3ifEABBcBArfWnVdg2IYSotp481UNKjJnw/M++HtITzASsfmuXBdU7rngXAA1fmMB1s34mPW8v5y6bS5/RJjf1ql5dgyZMZ9cLmLCl3X2PmRmQ/vLnRW553Q7zR9R4B+whK6XGa60vdsrXaK3/B0w/Yi0TQohqrMRv5lnZQFHA5CuvHZz2Pz7J9Fxbzw7e0KHtKhNsG8XYBCb3sgN7ul4LynZZ9ATUGtQTpjmBv11TaCMTq2qDinrIgbnYnq/qhgghRE1y329+8pxg+dgfNtvzTCD+pmOP8jp7YuJYkO8E2A7NyvOF2MCepo0AaJAY/DPcMDZgNnXgZK3kgMliD/4ZPvo7PHsNTHtU1gHXEhU9Q16slPoAWAjEKKXu218lrfV+thYRQojaLSXWzc4R64U459f0H2cNY3z3k+i3dilj+wyid/52egOFMTFsTG7A5tQGNMzLJa5swfHGzKD7enflugdv3grX/df0jt+4JbgBfz65ar6YOGoqCshXAPcCAzA7fA3cTx0bkIAshKhzxg70cO13Pnblw8P9PE6Ahnunf80Fc2cQV1rCgNVL2DnqQgCSiws554b7WdKkBU337Obrya+ZG4Um+UgJ6AlfcALsPOFIfB1RDRwwIGut12J2eUIpNU9rHeH8a0IIUXO1q28xZei+P6GX6KkmvzTQJmsnzd+YAP88h1/Sj2FJE5M5a2tKAz7peqJZb9y7nVnXu7fAuUGf8BqQlQvZedCmcQS+jagOwp1l3bOqGyKEEDWJz2/zynw/j83wlz8/BvARvP/irjjTA26YHhd0vkGU86x4+WY3GAMsDCO9w3dzodn1cMzNcKVM8aktwt5cQgghhOtvP/t5ca4JxG8vhkVXe4n2Wvi8HqID0lzGOdsipi1cxTObVvB5l96ctH4Fx+9dDwyCVumQlgy79poLwkl3+e/PoMBZh/zur/DApdAhI5JfTxwFYfWQhRCirvpshZ9mY0tp93opUze5PeFfA8orsmB7vinHhPyq1s8w2yoWX9SXW6d+w69jH+Kxb94n+4zjTYXUBHjuGujQFE7rAg+EkQWrcYpbjo2G1HoHritqDAnIQghxAD6/zbCv/GzJhdXZcMUkN1vWWa3doekejaCJMzfL4wkesjYbJMOAgq3E2Kbn7AEuKNxk3t+1F0a+Aiu2wi+L4Z53K2/YC9fDRX2gTwez/Cld8knXBjJkLYQQwPwdNu8s9tM21WJkTwuPZeG3bYoDlgWv3+uWHz/VQ6/GNrsKYHhni6iyQFxYEnzjFVvM60/BiUG8Py0whS27IbfQfWP55sob2zgVxt8d5jcTNUVYAVkp5QX+D5O7Ol1rnaKUOhNoo7UeW5UNFEKIqrY9z6b/Rz72FAHY7C708MBJFnuK7ANek1kA362zySyA4xtb9Cl7hBsf4z7fBffZbk5B8A32OGPczRqYDRtKnCwjSfGIuincIeuHgQuAe3B39FyBsyxKCCFqspVZOMHYmLXN/Myl1fMSH9BtaRqwZPjGyX7+t8jmi9U2Z4/3kV+22cQtZwXf/NHLzWtZT7nMtizzuinTDcYQPONa1CnhBuTLgQu11p9Befa3dZhdn4QQokbrkQ7tAh7DXtTefQ688xYPV3SGm3tYbLrJ/clcu8ftPWcXQVbZqHPgPsVgng0DxIQMSJYdd2wGx7Zwzw858VC/hqjhwn2GXA8I3U4kBijcT10hhKhRkmIsZgz3MmmNTdtUi77N3ICcEOPh3XP37bvc3stk6vLbFpd1tGiW5Fyzcmtwxd055vXfV8Cgf7nn7x5iXuNiYOqjMHEmNG8IZ/RA1E3hBuQ5wDXA6wHnLgdmRrxFQghxFDSMtxjRxaq8oqP3muV03F6P3XH1OGf2cji/n3kj9FnxBrP9IgN7wg+j4a1f4NxeMPQUt079RLj6T4f3BUSNF25AvhP4RSk1FKinlPoSsy+ypNMUQtRJt0z1sjS9GQDX+RQXbMwmtUUqnNMLFm8EzIQb64Le7kX9u8IxTaBp/fA/aNF6sCzo0jKCrRfVUbipMxcBnYFvML3kKUBPrfWyCi8UQoga4uvVfs7/zMftPwVM0AJ25NlcNcnHrT/4yC12zxfEuFselnqjKImOBmBCWnue7H8+33bowS1DrmMzTsrMnAI48R6T7rLTKNiws/JG3T8Ouv0Nut4Ooz+MzBcV1VbY65C11juBp6uwLUIIcVSs32MzZKKfEmfKqsfy88wALwCd3yhld7EZyv55XSlLrjeB96nzEhj8Uz7ZUXE80nALjZq0BmBSTjKvnzei/N5nLFvFxb2BT6fDnDXOB+6EV76HR4dX3LBnvgwofwH/HHrY31VUX+GuQ97vXsgg+yELIWq+FVl2eTAG+Hmj6QnvLfSXB2OA5VluD/mUPmnsOtGm1A/R3tbu+aSC8sk2CUWF9OrmTN9uFJDuEiA95Hh/2qTDUiejl+zqVOuF20MO3Qs5A2gDTEX2QxZC1HBRIQ/v4kznmGTLR3xxcfnwdJPcPUAjABbttLn8ax+ZhfBoPw9XdzU3ufLjj0ks+pH5Ga0YsmgmbaJPhZ5D4Nzj4aSOMHs1tGoE14YxiWvivXD/++Cx4LFKetOixgsrIO9vL2Sl1G2U/cusBpRSHYC3gYZAJnCl1nplSB0v8AJwFma+xeNa69ed9x4CbgHKVu9P01rfemRaL4Q4mrqmWdSPhSwnOcj5bZ0IHRvNH3Pf4Ob0k4krKeGduMWAGY6+9UcfmzbkklhUyA3fp3FhO4v6cRbER3PR7JlctMhZhJLgPGv+/A/4fbkpr9wKY7+HuwZX3LD2GfDxnZH9sqLaOpxc1i9jgtfoCLXlcI0Fxmit31NKXQG8AoT+L+hwoB3QHhO45yqlftBar3Pef0drLf/6hahjGtWz+G2Yl1cX+OjUwGJkD3eYutu465g6fgbERcPgK8rPn/jHTL576XniSksY0/dMSkfeaN544Xrodx/kF5u0mWXLmUKXQ4UeizrvcHZ76gGEv2ivCiml0oFewAfOqQ+AXkqp0B78ZcBrWmu/M0ntc+DSI9dSIUR1ZNs2//7Dzwtz4KHpNvMDJ0DHRMOwU2BIH7P8yPHgj58RV2o2krh1+nc02uOkwjzuGNjwGsx5ChY8CwnOLOtL+0L/LqbcuTncevYR+GaiJgl3Utdk3BzWAAmYAPhMVTTqELQANmutfQBaa59SaotzPvA/rZbA+oDjDU6dMkOVUoOAbcBorfXvVdtsIUR1MGsbjFtqfuJ25MMjv/v59EJvhdckNkuBsg2c6sVCYsCmEA2TzJ9AXo/ZahEgK9c8FxYiQLg95KnAtIA/44GztNb3h3OxUipFKXW5Uupu57iJUiqjsuuOsLGY3au6A/8BJiqlGu6volLqRqWUVkrprKysI9pIIUTkJccED/eluEuMeXexn6TnS2k0ppTJ6wKmYr96M5yvzEStz+6ufJem0R+WJwxhWzZc+1LE2i+qB8uyTj/A+WvCuT7cSV3/PJhGBVJKHQ98C2zFzMx+EuiO2Snq4kO9b4iNQDOllNfpHXsxM8E3htTbALQCZjnH5T1mrfW2skpa68lKqY1AV+DX0A/TWr8KvAowYsSIA+/PJoSoETo1tHjxdA8vzvXTNsXi8VNNX6XUb3PD936KfJBbAiMn+1l9g9OPaZEGXxxwRei+svOCj3NkK4BaaLxlWc/Ytv0vAMuy4jGdvdOB/1V28QEDcrg9WK31lkqqPAfcrbX+n1KqrDs5PZzGhUtrvUMpNQ8YBrznvM51nhMH+gS4QSn1GWZS12DgFAClVDOt9Wan3BOzk9XySLVRCFE9rMyymbbZpk9Ti04N3X5xrBewwW/bJEbbgIUFqJVLeeS7jyiIjuHRsy7FJC107NprJmeFs0b4n0Ph/amwN9/s9PRsWJ0mUbP0AT6xLOtkzJLg/2I6hseFc3FFPeRNBD83DmU571f8oAW6AG85ZRtAa52rlEo44BWHZiTwtlLqQSALuBJAKTUJeFBrrYF3gROBsuVQ/9Jar3XKjzm9eR9QDIwI7DULIWqOvGKb23/2s2y3zQ3dPVzZxfRqF++yOXGcj7wSiIuCacO89GpsMXe7nxu+N8PRy7Ng0Kd+fhvmoTS/mB9efZg4n9mvuM+GFfDce+ZDnp4Id75tyv06wW+VpGRIT4Utr5th63ZNoEFSxfVFjWPb9jLLsk7APNr9CXjTtu0bwr2+ooDc5nAb59hJyGQqpVQ7YHOE7g+Ak1d7n41EtdbnBJR9wM0HuP6qSLZHCHH0PDDVz+sLTX9i2mY/qrHFsWkW362zyTMToykshW/W2vRqbPF7yDjf0kzzGhNtgROMAVIK8t1K941zy1OXmU0guraquGEJcXBC+0P9WqKasywrFngRaOK8XmNZ1m+2bb8TzvUHDMha6/UHeu8gvQ18qJS6C7CcXujTwGsRur8QQgT5ao07uGcDM7baHJtm0btJ8Mzm3k3Ma7v6Nn+eN51bfv+O1Q0bM+bSy4E0SgpKiDnQh5T6g49z5ZmwYCawCzjOtu3tlmVNAN63LKu/bdvXVXZx2IlBlFKdgNMw2bnK/1Vrrf91oGscT2CWSU0CEoGfgecxGbOEECLi8kuCj7MK/YCHjERIioGcYkiIguaJ5qfsmG9/Z9wHLxDl99N/zVJa5WXDXQ8Sk1LPbKHo3McfHeUuTRnQBX501j0lxJr1x6LasyzrKcyE4tZAN9u2F+2nzkOEZG60bTuczI0TgYds2/YD2Lb9q2VZvTBzmyoV7jrkYZjnwAswM6QXYBKDTKnsWmeY+H7gfqVUmtZ6VzifKYQQh6p3E4uJq91e8glNTBj9YpVNTrE5l1cKE1aZnnO7jevB7/Z41abVplBcGrQcKio5zj348j54aiLsyoFR50BsdFV9HRFZn2M6hb9VUu8d27YPKnOjbdsP7ufcdsuyBoVzfbjrkO/HTHLqDeQ7ryOBOWG3FJBgLIQ4El4e6OGEJpAcDXf3tjilhfmp65IWXK+rc7z0lkv4tkMPAHJi4/jvNdeaN2Kj4Yzu7gVD+rjl+Fj4x5/h+eugXdOq+ioiwmzbnmrbduiS2IixLKufZVmvWpb1pXN8PM5qnsqEO2TdErNkKNA7mOncd1d0oVLKz/5naxdjJnq9j9nkoTjMtgghRIUKSmF3IeSUQGbAo91BrT28dw58v85mQEuLC9uZQJ1b4GfE0Nu4ZtbPzM9oRXKb5u5FX94HH/wGcTFw2cnu+eISeOFryMyFm8+EltVmrx0RGUOdnu02YLRt25VmbrQs63LMZK5xwKnOaRv4F+aRb4XCDcjZQIrzul0p1Rmzo1I4S5duB24AnsUE4FbAXzEBPRe4y7nPPWG2RQghKvTw735WZZvyGwttrupic0pzM/g8/FgPw48Nrt9r1XJmvvgybbJM6oJpp/SFvzqjlXExMLCHWTvsCRhUvO11eG2yKX84FZa/aPJei6PC+s9H+5w7jKxNY4FHbdsusSxrIDDRsqzOtm1nVnLd/cCZtm1ry7JGOOcWYZb/VircIesfgCFO+WPneCbwTRjXXgOcr7V+U2v9o9b6TUxCjiu01q8AFwF/DrMdQghRqW15wT/Fpb6Kf5qX7fTRJmsnRV4vpZZF/LaA392//w9a3AhNroWXJrnnpyx2y+t2wI49kWi6qAZs295m23aJU56MGQ3uGsalGbZt67LbOK+lVJ6vAwg/dea1AYejgWVAMmZJU2WOwZ2pVmYL0Na594L97MokhBCHLC7kl620kq5SSkkBpR4PsT4fAA2yne51cQk886Up28Cdb8FtTmqDEp97A8ty9z0WNZ5lWc1s297slA8mc+Nqy7L62rY9PeBc3zCvDa+HrJRqWVbWWtta6/e11mO11uFs6DkXeEIpFevcKxb4t3MepdQxmOFvIYSICDtkZ9jUgFjpX7COnGcn4Z+7pvzchjnbiAqYZb0t3smi5Q+J5MVukhAaJAZ8oA3FPkT1Z1nWC5ZlbQKaAz9YlrXYOT/JsizlVHvMsqxFlmXNx+TMGGHbdjiZGx/BDG8/AERblvV34EPMM+RKhfsMeY1S6mfgDWCC1roozOvAPD/+EhiplNoBpGOeJV/gvN8UeX4shIigGE9wIM1yfrFyZq1lwsjP8Xk8WONWMuSZs0k5tQOr27ahpE1n+q9dSkFUND+260of2HeLxLiANCEPD4NLnoK8QrhnCDROrdLvJCLDtu2/AH/Zz/lzAsqHlLnRtu3PLcvKc+6/HhgAXOMMe1cq3IDcHrga07P9r1LqQ+BNJz90hbTWK5VSXYCTMDswbQb2ALcBf9FaTwuzDUIIEZYzWll8ssIE5ZQY6NnIBNZPX5rHFfOmE+334bMs3nqpMded2oETt63ll0YZ/LfvIOJLirn71y+AK80krVvPgjHfmhs/crn7IWf1gp3/g4JiyUtdx1mWFQVYtm2XOMF3srPlYg8g7H8cYQ1Za63Xaq1Ha63bAJfiZNxSSs0P83ofZsvDOMz2i/OBXuE2UgghDsb2fLdnW+BsnQiQuHUH0X4ztOy1bVK2m6dlbU9tw40zf+Sjcc/z1scv0zwqYGj6pRthxuOw8Fm44wKCxMdKMBYAH2EmMANgWdb9mJna/YBxlmVdH85Nwp1lHegXYAKgCWPWmVLqWKXU85iJXK8ACjhba93vED5bCCEqtSTTHbIu9sGaPea4f5cEirxmYLDE4+WkDvEAeJPjy6fE+oHkJgHPh1/4GvreB73vgfGVLkUVdZMCvgo4/gtwg23bCriCA2xqFCrsgKyU6q6UehYTWJ/HbC/VsYL6I5RSv2HWYPUHHgKaAbuBeeF+rhBCHKyrulhEO79uXRpCn6amx5zeIhmfZbEppQHFXi/NWjiBN7+ofBqYB6DIyVNUUgp/f8uk1SwsNmUh9lXftu0tAJZldcbk7fjYee9zzCztSoWby3ouJvg6D1aYrLX2V3wVb2NmT5+rtS5fr6yUOvAVQggRAWe18bBkWAkbN+TSu2d9EmOccNswiXqlJdTbs9scp6eY11YhKy97O1skej1QLxb2OtsuJsVXfeNFTZRnWVaibdu5mN7yItu2y3LEWYQZa8Od1PUa8L7WOvsgGvgP4Hrgc6XUJOBN4OuDuF4IIQ7ND/Npd9bDtPP5oVUarH4ZvF7wWvuv36k5vHQDvDgJ2jaGJ5wkSx4PfHIn/O1/EBcNr4U18ijqnt+Ahy3LegW4Cfg24L2OwNZwbhLupK7/HmQwRmv9KCYpyGDMkvrxmBnWqZjZ1kIIUTWGPws+ZxBv/S74z+em/OmM4HrjA45PPRYu6G3+pCW75wf1hMXPw+ynoFfbqm23qKnuAc4ClmCSZj0T8N5wYGo4Nwl7P+RDobW2Mek1v1FKNcX0mK8FZimlJmitJWWmECLyCkM2RN6VY17P6gkTZ7rnB5kdntiRDac+ANl5bv37L6n6dopawbbttUBny7Ia2La9O+TtJzGbKVXqUGZZHxKt9Vat9cOYXvOFQEwllwghxKG5/Ty3HOWBuwab8lUDoKWz52JGfbNLE8CKrW4wBpi58si0U9Qq+wnG2Ladbdt2fjjXV2kPeX+cXvMk548QQkRc5r1DuarxQJblRXFTF7irLIvW4xNgg7Mt+5YseOgjeOpq6NEajmkMa7ab9wafcDSaLeq4SgOyUqod0A2Yr7VeU1l9IYQ42kZP8/N1fipYcPcSOP14m16NLZgT8hM2Z615TYqHP56ArzS0bQKnhOzPOGslxMdA11ZH5guIOqnCIWul1EXAUsyErCVKqXMqqi+EENVBTvEBjps3CH4j8DgtGa7+077B+OZX4IR7oNvf4InPIt5WIcpU9gz5AeA+TC7O0U5ZCCGqtXtP9NDMyfnx544WpzR33hjYM7jimSHHoYpKYOx37vEL8qRNVJ3KAnIb4GmtdR5mGne7qm+SEEIcns4NLdbf6CXrNi8fne/FYznrjy/qAzcMNIlArjwNhvev+Eax0e4kMIB2TaqszUJUFpC9ZRm5tNYlyMxoIURNkFuAd+BDpHa8CR4f756fthTe+BHW74R3f4UfF1R+r28fhMtONjO0P7ij6tos6rzKJnXFKKUCh6njQo7RWj8W+WYJIcRhGPIE/LTQlP9vHJzUEfp3hT9WmrzUALYNM1bA6d0rvlfn5vDh36u2vUJQeUCeAQwMOP4j5NgGJCALIaqXFVuCj2esMAF5YA8zW7qgGGKiKn+GLMQRVGFA1lqfdoTaIYQQkXPVafDwp6YcHQUjTjPlbq1A/wd+WwJ9O5ljIaqJQ0oMopSygHOAm7TWF1RWXwghjqiLT4LnvoacAji5IzROcd87toX5I0Q1c1CpM5VSGUqpB4F1wARgb1U0SgghDstTX5hgDPDLYpiy5Oi2R4gwhJOpywLOxmwpdTawC7Nj0/Fa64VV2zwhhDgEaUnBxw2T9l9PiGqkskxd/wDWAp9jJnBdDLQE9gDbq7x1QghxKAafYCZtAXRuJkPUokaorIf8TyATGKy1Lk9Ro5Sq0kYJIcRhee0HKC415aWbzZD1n7od3TYJUYnKAvII4EbgS6XUAuBNYBymtyyEENVT4CQuy4L05KPXFiHCVOGQtdZ6nNa6P9AV+AWTz3ozkAZIN1kIUT09NNRk1jqhPbx2s+zSJGqEsGZZa62Xaq3/BjTD9JhnAF8ppWZWZeOEEOKQJMXD386Duy40+auFqAEOah2y1roIeBd4Vyl1LCY4CyFE9fL5H3DJf8Dnh3ZNQT8JKQlHu1VCVOiQEoMAaK2XALdHsC1CCBEZH041wRhg1VaYuXLfrReFqGYqDMhKqZVUMoFLa90hoi0SQojD1bMNfDTNlONjoEPG0W2PEGGorIf8SEDZAsYAt1Rdc4QQIgLuHmwC8dJNMKI/tEo/2i0SolKVbS7xduCxUuqZ0HNCCFHteDzw1/OOdiuEOCiH/Ay5ulFKdQDeBhpikplcqbVeGVLHC7wAnIUZin9ca/16Ze8JIYQQVe2gNpeo5sYCY5xn2mOAV/ZTZzjQDmgPnAQ8pJRqHcZ7QgghRJWqFQFZKZUO9AI+cE59APRSSjUKqXoZ8JrW2q+13onJ0X1pGO8JIYQQVepgZ1knK6VWBNapJrOsWwCbtdY+AK21Tym1xTm/M6BeS2B9wPEGp05l7wVRSt2Iswa7SZMmkWi/EEKIOu5gZlkLh9b6VeBVgBEjRkhebyGEEIftoGZZV2MbgWZKKa/TO/YCGc75QBuAVsAs5ziwV1zRe0IIIUSVqmzIOgqwtNYlAeeuBnoCU7TWn1Vt88Kjtd6hlJoHDAPec17nOs+CA30C3KCU+gwzG3swcEoY7wkhhBBVqrJJXR8B15QdKKUewAzV9gPGKaWur8K2HayRwCjnGfco5xil1CTlbuD8LrAGWInZIONfWuu1YbwnhBBCVKnKniErTHArMwq4Xmv9jlLqYuA+oFqs1dVaLwNO3M/5cwLKPuDmA1x/wPeEEEKIqlZZD7m+1noLgFKqM5ACfOy89znQuuqaJoQQQtQdlQXkPKVUolNWwCKtdaFzbFGLMn0JIYQQR1NlAfk34GGlVCfgJuDbgPc6AlurqmFCCCFEXVJZQL4Hk9t5CZAMPBPw3nBgahW1SwghhKhTKluHvBborJRqoLXeHfL2k0BxlbVMCCGEqEPCega8n2CM1jo78s0RQggh6qZasbmEEEIIUdNJQBZC1Fx782DI43D6g7Bs89FujRCHRZYtCSFqrL3H3UvyGhOIc9W9JO59GzzSzxA1k/zLFULUWDm78svLiXl5lKwLTV8vRM0hAVkIUWP90L5befnXYzpTFBd3FFsjxOGRIWshRI1V6PHye8v2JBYX8mvrzpxQXHq0myTEIZOALISo/rJy4X8/QUo9uHoAeL0AxPj9XHPZLeTExvPQdx+Rk5pI/FFuqhCHSgKyEKJ68/vhT6NhnrMbql4NL98EwH8GXMDy9GYAjBpyHQP3FkNq7NFqqRCHRZ4hCyGqt925bjAGGP97eXFrUv3ycok3ij1x0j8WNZcEZCFE9eb3Bx9n5ZYXfSmJePx+LNvG7/HQJc17hBsnRORIQBZCVG97VeZRpAAAIABJREFU8oOPfW6APqU5+D0ebMuieSJ4rSPcNiEiSAKyEKJ6a5CIHXBY6nV7wUsDsuxvyoW9st2NqMEkIAshqrWNOTaBHd/tCcnl5VZJ7vnG9SAlVrrIouaSgCyEqNaSU2Mp9ri94vwYdxb1hhy33vZ82FMU2JcWomaRgCyEqDqZObBsU/DErKISGPo0ZFwH140Bn6/CWyTm5RPjd+vElrrJPzo3dHvEzZMgKSZyTRfiSJOALISoGr8uhlY3Qee/wAX/dgPvy9/CR9Ngaxa8+SO8/1uFt8nKKeXJ/ucDUOzx8siAIeXvvXO2h9uOs7iqi8UPl3rxWDJkLWouCchCiKrx9ETIKzTlr2fD7DWm/NuS4Hq/La3wNjEtGnDclnWm7PfROntX+XsN4i1ePN3LW2d76dhAgrGo2SQgCyGqRkYDtxzlhfQUU+7cPLhe4PFjn0KbkXDeo7DbPCDO3pbLKWuX8X7Pk/m603FcsnBGFTdciKNDUmcKIarGEyOgsATWbIdR50DrdHP+5rNgzLeQnWeC9FWnmfMzlsP975vyuh1w51vw5ihaNI6j78jRzGjVAYARc3/jnSP+ZYSoetJDrg1yC452C0Rdl1NgAmygwhLYng079pjJXWW+mePW3bEHpjhD2NOWBV8/bTkA/9/eWYdHcbV9+J7djW0SoiQhCcHd6WDFS9EWirZQoEbdqOv3tm+dtvStu7sLVWiRQgUb3IsHAoEYcVmZ748zuzO7aFskJOe+rlw7c3Z25oxkfuc8z3Oek1fs8osxwOKMpse79hJJtUAK8qlgx3644iW4/nXYf+Cf76fSBQP/C9EToe1U8fKTSE4km/cc/Mx+MB8SLhZ/z3xnlt/5PsxcAZuy4JpXxW8BvgoyOX+9WHx2axZY3qEhAHF2ncZ5+/zFZ+zfdRxORCKpfkhBPhUMfhDemA0v/gTj/2eW7z8AE5+GQQ+ICNWj8fmfMHu1WF63C1748cTUVyIBuOR5aH49pF8BX1tE9Y73wOUWQ5tuf8+Mpt6WHfh7X6/4rz2B5RsMgQ0LCSyPcYrPhCgINcchK/UT/uWJSCTVEynIJxu3x+wpACzbai5f9JwYAvLLKhj6kDADHonIoGnmIsOPXz0lEis798O788SyywO3vmN+V2HJV6ko/rmKOcwQpNXOugHrG+xG5q3VOwNSZKJtAaCgUmFbdKK/eGl6k39wAhJJ9UcK8slGgYC3jlV0F/5lLpdXQWZO4HabsoSg+2iUBDbLS6+h5UW3Yx/c8Do8/S3oMnuR5F8SPMFDgcVfXFZpLrvcZg+5RZpZbrdBohDeGd17837n3uyMTeTVbmfzS7eeAFTY7AEpMg+4xOspIQJ6WXY1spkc3iSpmcgo65ONJ2gqOatWRoZBkeXF5zPZrd8FXe4QL74WqbDiKYgIExO1ey07WLwZxveGolJoPVWIuq/8k1tPyOmcMuashvd+FUNmbj/P7JVJTgyNkyEqHEqMccX92pjfVboP/Zuz2wnXjK5DRiI0SgagkVLOZeOuweVwEOaq4us80fNeXx5OZ8vPM0OiiAUURWHWWDufb9KJCYORzWQ/QlIzkU/2ycZuCzTlWc3OwUFZPkG98H9mL2TTHjNwZsf+wO3XZorPOWvM3wLMWvHv612d2LIXhj0sBPnuD+Dxr091jUx25x6f4LpXZkKfe+E/Hx1s4cgrDjQTH439B2DD7n9nKYmKgK/ugLYZcFY7eOv6w29b6RKfN79Nlc1GfkQkbN8vYh6A9d5IXA7RF6gMCWVdufgfqNOvBXkRkZSGhKEDO87p7d+lM0Th4rY2KcaSGo18uk82djskWKaoSbcEqHiDXpi/rhWfu/ICy1fuEJ978gPLfUNL2mYElh+Lb7mgBC59XgSUHauAF5fDvDVChKy88QukXAbtboJ1mce2r7/D1myosvTKlm8zl79cCE2ugc63wuodx7a/7zV48hux32COkmc5gAc+hfpXQtrl4hpY+WMDfLvEFKsjMWMxXPOayGD18Bdivz4G3A+JF4vI+hmLzfKvF0HsJIibDN9Yyr9dAqmXQ+sbYdhDxybK//kIGl4Fwx81z9/lhglPi0bf3DVw3WuH/71TPG+aM4m0/3uVhAff5pILroViYf3ZFpdEuKuKFvuzCK+qZGNiPQAat4pj7HMvE/Xo+yQ9+T7Nbu5/9LpKJDUIKcgnm4pKyC0y1zftOfy2GUYgS2xkYLnPN1cR9HL39YqDhaUgaHzoobjpLXhnnggoG/X40YdjFZQIYTjrfiFCP2qiPLcIrnpF9BLXZsJlLwb+7o8N4oX+d3pr7iBRDA3ytLiM78sr4cKnRSKKFdvhypfNbapcIgr9sS8Dx8S+OVsIzx3vQfe7zN7tgRKoMxEc4yBqAmQXBNZn1fbAnrDLDf81hNPjFUN+fDw1A3rdC+dNE8F6vokWSsphwv+g1Q0iQ5WPF2cGnt87RjDV/LXi2vnqcNWr5jaTnhV+3gOlMOkZs/yS5003ycyVsNgSp/DXHtFr3Wtp2H3yu2gE7MwRDZXRT4jyJZsDr9sXCzksheJ5u2XQheRGCb/xu2o/ZjhFQ7GwXhKbnpjKxidvZuXTd1DQIBWAeZk68/NDCa+qpNBt5/El3kPvXyKpoUhBPtn8HSEqMgK+gntVOYagb9jtd0HrAFlGT9oq+CDEyIfbIwTiqRmBQrfOMrazvApyLS/fQ3HRc4G91PFPi8+svMCe/srt5vLIx4QwDbgfut0ZuL+t2WKol/VcC0sh6RIIGQfplwvBBdhXGPjbbEMY3d7AOq2wHLv9LXDDG3DPh2JfvvtgmFEBcd18ve2xT5oBd6WVcN5jxjE80Pte6HgrZFwpfNkgXBFWrI2gDxaYy/PWQpYhgNO+FgK4MUtkqPLleG6eGrgvnxUl2IJifZasgVWlluUDQcFYvmsyexW0uB7Onw4ZV5kBhB8tCNzel7RD1/FYXC16cCyElRjRgNwanxxQvLJIDGu6bMkcMg6IZ7VF7l4mLROTS2QfcKMAFaFhuBwOtq0vQCKpTUhBPtkE9/aOhC9QKfhFbBcvxrJtuf6oVAWoKjPELDoicHvr7zvdCre9K/46WQK9soNefr5eXEEJtLkR7GOEkPrqXxz0oveZNsuDfJvWKPAZS83lpVtErxLgu6XQ/Dro9x/RS/WJ8shpZuMjKx8mGj2/4OFevkOsDTKPW8V5U5a5XOGCNTvE8rqgJBO+sbPbgvzzmYZZfuZyWPSXuf8phgXgIFO05Zq3s7gQkmKgrjHMJzizVUGJ+BzRJbB8ZFfx2b8dNE0xyx+awNEJenbqGM/GpS+YZW6PSFIDgdcM/I25qpho7JYGwJakoEaDlUrxDMS6KwKKW7YVua3drkAxd5WLYxT/sAqvzXwl2bcfwoUgkdRgpCCfbBRbwCvyiP1ll/GSdwe9JBVx2w6EBwpvpcNIrGCkHPRjFWSraFmX84J6xJv2is9rXoX1u8U+5q4Rw6gAnpkSuP2kPuKzc+PA8lgnh8Un+lPfNOu4crvpO88K8pH7enHBvuGdRrntbwyH8Zm5HUHR2T5BaJgYWF7PmBhB2xxY7vOfH24/EGglyC82e7Y3D6cqXRynakAHGGrEGO8K8sn7GiVb9sIWi0g99S1HJTwo2UZ8lPgsCmpQ+a61M6ixEyHWl0SlsLi+mbLyvU5mwBV1zHush9ghTExKXJBuaTzoOuF1RexEQvfGLE9tSFlIKH9kNKdhrwZim6Bxy7Yj/3dIJDUOKcgnGfffGUK5x+i1BpuPZ68EwDGsc0BxURPDt5wae/h9Wk2r1uVgMTNengFmXzCn0PtwfmD5AmMKve+0wPJsi3l5THdzuXtz/4vb3/v0sdgQvbtGB5bfN058Br+nfRHHKUc472B8fvhgi4XvOmzICizfbtRx497Acs9hRMMqyNZeuNsLf4qczbMdScTe+BxJ979Ok7H3kOMyRH1IJ3Aa18Zug9HGdZu5PPAYW4LqcijiowPX2xi99WA/fIIh1BN7B5arooHljFA46+r7mDT+egZdfi/TBpj35pZnn+SlHoN4s0t/rpj2hL+8QazlGigKdQ3dTl28ms57duB0VdEz8y+cy8X9bt0rPcAM70w4QmNOIqmBSEE+yTiKjyHAysefhsgFv/OrhIgk7QoMCEsrMnypyUcQJqvvz7ocbHL1NwaC/NG+AKB1uwPLfUOwso8QDLbAMg+utdfoDfJH+qLHc4J8xVlGnbYGCVGZIcj14gLLY4JM91Y2GvXfH3QMX718AuXDZ+rNP4xvPcQReHyrmTo4JWQDkcDls00645cu4P9mf0nD1RtZsNu40V8vMs/J44XXfhbLEaGHP5/DEZztzRehHxIkyG7jHiwImpt4i8ghraBQFhrOh2f04ZcWHQjTTavN11vgjW5n8VyvYSzItuM2rB0PNCrE7hXPakd3Pt3qicZO5I7A5zZ8k2iwlOwtDuglR7n/xtAuiaQGIAX5ZBMZweE6yQf1tbYLU6weEmQOrWcIbvsGgeXGiz4gkvZYCe7pbTZ6iMF+Tp8QB5uNfRHf7YLqZCXHIu4VLjNIK9jc27a++NyRE1juE+pgi4Gvlxs8/rfwCKlHfWbW4Jvh84EHH9vnY0+OOfT+dD3wWlkbMm9cay4PV6Gp8L9euGA2b33+Cjf+MZNfXn+YjvmGUGmWdKoAawzXQvO0wPLg6PtDERfUsGgvGgqbbp3gf968QNZjhgsi2G9rNFjCSsuJLTfPr8t28xkLr6xkRVpjVqc2oDDCrNMdM8vw2MS9XWmPY91C8UzF/GcULsOCUGWzk/zAKADa9konymX6nXu1C+rdSyQ1nNM+U5eqqk7gbeAMwA3cpmna94fZ9grgTsRr+CfgRk3TvKqq9gN+BHxvmUpN07qdiPq6sWHnYB04FLndW5MIrM1oRLutW/zlW9u3pgmQ3zidwri6NCjIId8ZRUHTJjSD45OYosLoAQUHlPlEtFPjQB+vL9Dq70wF6TMPO2ymTxfMYLalQf7ahYZvPDWoJ+ww2pUb/sYsQPsLhcA1ShZRzj4m9RWfwUPKfIFI7RoAv5nlvsZSlSswoM3qB95uCRDLLRIBcHY7ffeZwhvudtEkaxeQDnFBQus7hjWgC0TqVB+RYWZ0tXXceUqc6WMH/zbvOJswoEkb1F1b+ap9N2xFUVwCEBE0Zj1C9O4P7C9h5uv/44n+55FUUsiYVYuA9gDkRNYxT88ZheLxgM3B+khLKldF4aWtYbzSE7Z37cCZ0z4ktqSYwuho1rYKIRrISI1gwYRyPvstl9b1w5jcrz4SSW2iJvSQbwOKNE1rCgwH3lBVNSp4I1VVGwH3Az2AZsbfJMsm6zVN62j8nRAxFugBYlxpM3uH3qCglpBOjQDIiQrslWXbxEtz5S87aFSQgw1ILCth6zoxlMTrcmM1ApeE/ANT55qd4tMe1HTwJTV5+crAIJynLhGfLYKib4ODiqz4hLfSzfude/PAwLFsSUiGvUZvtCrIv+sT7aQgk3y4cX7O0GMPmPOboEsCy30+8qb1AsvTjEZAcI/TFwQVPJzNsqo//wMfdezJcz2Hkr8yC3aL+6QosD+yDosymlHuCDEbGsFDlXyNomVBPWeL0C9KbsjvDVvwW8MWLEo2rRQVlmhxHfy+4/GffM7ZW9cRW1XBZdp8+nwtZgorv3UkbsV8LWRfOkxUoX5dlqc14sv3nuJ/377Lc33O8W8T4TYbL5GuKiqMxlxyWaC7Y0iM6GHf9ZtOlWJnf3Qsldi59zfzYnVqGMFjk1OY3C+o0SWR1AJqgiBfALwKoGnaZkADhh5iu7HAN5qm5Wia5gVeN357clFsAeM53XbTSFEcEejzjOopJmVPLizwC2y5I4TWBcKvF14Q6P8MM4Yire5yRsCN1S0v2GMWrF6txGdwjmhfb+2PjYEi5BsKFDzDT9QR/LiGqfmpnsMI8Xg4Z/0yHu0/kvytRu9yb1CU9U5DXILN6MZ+3LGBDZfDZFgWpIgXvp5f4r+2HkWBFYbotQ/KdmaYmeneIvC6+bKi2Wy4bHY+a9+DGW1UdIuP9s6uI5g4cSpTR15K32sewJ0rGgHan9k0u/M5etzwCN1ufJTiV+aIH/zfWDPoSlHgv8Zj2r5hYJ3amD3I5IJ8eu3YRO8dm0g6YA5hcxebJmAF2LVImL/rRwQ2dpJShAn/TUcjzrv0Dt45oy9TR1zCxZ2FOTnJqXDtmCvIuOclUv/zKr93OsP/2wKLmbo0NIxww1rya/4vNMjbR3hVJbfM/46RncR29YIMAPXNDjYPLfTS4FU3Q7/wkFcuo6wltYvT3mQNZAA7LeuZwKFsXUfbrrmqqssBF/CSpmnvHu+KAnh1D6BgN17rbouATRs6jkkLfqFOZQUzm7djcEoKDYCme3f7BTbC7aIySwiWI2joSnyJ6JHojZLYGxVDvZJC3IqN1Sn16WlsU2WzUR4qetgRleX4B7lYTZ4Agzse7gTEZ3Aw1AbhW/YoNma07crotUsoCwllVhuVUcYmOqapXgcUQ0j7bV3HGXt2APDGF6+yZFAfusPByTZ8jYPgRoLRMChKTuDPlp04Z+MKvMD0fsO5+xDHBkQvPCmW3XXiWJHSgA1J6YxZs4jwhLqkA/ywLPAYRoBd/pZcwhwhRBq9wvkVUfQFCA1hzJQ7+K55JwCuXbMAX46yz9qZBpe19TLYE5ZPBvDUGQMpihBCuKZeA2YUpjIJqGqYwlnPv0XFuiyqMpKY2zuWRICMuvDWdfDg51A/ET66yb/fuqVFh1wuiY4msqICBXDZbISd2QyATY4YelhOb5VSh55AXoXOj60682MrEcHfxbDCJ0cqRDhgV5wYpjU83fxtZXioeC4UBa8lWr/Jo6PZcds76No+lOuHifoDbwxSWJ+nsyEPOifDE33FfV60R+e+P0TzKLNY58GFXp49S04aIqk9VHtBNkQy4zBfJx+m/O+yHKivaVqhYdqerapqlqZpsw9TpyuBKwFSUlIOtclhcYSFgm4alCNdpt9xwp/zyI2OZUO9OkxYtYhda0dBrwzWJabSOWuHf7u/KsLoCmREBkYnp7pEz7EiLJyVqQ25oucQMg7k0nP7Rr8gd7hlOpuSxdu0xb7dbPT92CrGAL+shg6NhBnVGuDUTbzQD/y8FqvhuGzDXpzArjrxjLn4NlIL8ykJDadpSIVfkHfGJtLwgGhMFIVFEB0egg1olWNG3dqA+lUiaGvPDaOo93/v+Ges3H/PBHHDM4OSdhimbY/Lw8Nnj2HUJbdj83jpuHfHYQW5NDaKSOCh/qN4vftAAJ7sN4KHcpdyDZATHsXWxATmNG1L7+0baZG/h2RgxdYyBrhdFIeF46yqJHavuDZVFW6/GAN82aiDX5DLwwL9sroRxLatiWXMtq6T3U8F4PHFXv4oCof6TUCHK3/x8NV5xr/qpQPEXxAb66bS2WjUbEqsh2qU1ykppiwkjOLwcFKKC4lcsw2aJJCaHzjULDVXnMdFbRQeXKj7212XtxNXLSZMYcZIG48s8pLkVHhugNlY8mKzXFzFb0GYnRfGOS2uoKoZnFkKfxjloQ4b2uSDjXOlLj1o/aBNJJIaTbUXZE3TOh/pe1VVM4EGgE81MoB5h9jUtx2W7XYZx/B3KTRN266q6jdAT+CQgqxp2mvAawCTJ0/+W3Y1m81GTmQ0dUuF6GxNTKGl8d23rVXuGzIegO47NnHV2iJa94IPOvchtbCAlJJCfmvUkk1Nm9EVWN+qFcl/rvG/C9c1aUo/oKLExYSJN9JhbyaLMpqyql4DJhrbbEoyI3Wtywfhy6JVHJhtyTfRRW6RJ0CQq7zgBJLr2Bm3+nfunPsNOZF1eO/CCYAIPup+w8NUhIThttmJKytmq0dYZsMsjRIdiGsqelJZP6zH55FWgJ2frSR5Sh88DjvWfpNbUXAAq7aVsriBMPNjhwKn6e99qccgrl34Mzbgkw49GJFVAA3q8kfDlv5t8iKjseeIRs4zZw7miV7Dcdsd2Lxebl/wLdOA7D1lTBl3NW91PYvk4gO8+OXrdABCwx20zs1kfaKoccfifYBIeam68vgRYZd1VlUSX0/Ua/hZSSxZZJ7fwEGiobRmUxFgRhjv2ZAH5x257XnR+OvYmlAPFGiau5d1RvnCtKaMuvg2isOdTFk8h3vLFRoB9YMCmBvFiSu6Oicwjm+jxWswsKGNgQ0PFlK7Evgb3/N42SwvVUab8c898OVfXsY0P7yXrH+GwviWCp9s1GlYB+7qWhM8ahLJsVPtBfkY+By4CtBUVW0GdAEOlVPwS2CBqqoPAHnAFcBHAKqq1gOyNU3TVVWNBwYB/3ciKuv26oy8+HYe++ljKh0OHhowGl/24Gd6m4Eyixq24JYWwhc4t2lb3lT7k1BeQlFYBP1CDnA5kBwXEtDri4gWwU1rHDE0PJDHgiZtULxezt68xr9Ny31ZXL50LgBvdD0LaCi+sCuBQ59SDjO8xwjyShzYChaYSUBc9Q0R/X0rH3z4HKHG+NOEN4rh/ukA5ETF+lMjloaG+YOjFzdqzpnGMBoF2DagK22BunsCxxsn7RLR0Jvbtabl5+bkBvlxcSQBPUNKqFPu9JuBU4sK8BlXpvUfybL0xsSXl/LimYPZ10ocfMJmjf+kCM9FnfIymncVjZSZbVS/f99rszGjXVemAeUdm/BWqrhm+6JjeaXnYMYA6Dr3//AxP7Q+A4fXw/DNK4HWALx+bRrXv5FJrsfBfX1DiI4RAWPlXoWumZtx22xU2RwUVYke86Dta/nGqeJyhKDoXoZuXgkMPvT9MNiQYhqR1luWHxw4luJwwz/cbQBT7WIsuKdJClkxCdQvzGNzQjLN6ovGQ0TQGyHcsl7p1vllp05dp+IfUwzwaG+FO+br6MCtqoL9MBnTjpZIzaYofHyunTcG6ThDxDzIEkltoiYI8pPAO6qqbgE8wJWaphUDqKr6ILBH07RXNE3bpqrqQ4DRJ+Fn4ANjeQxwjaqqLsQ1eVfTtBknorKKy0PznD30vfYB7B4PU5bMBToAUOGw89DMT6hbUsQzvYdRGiH8ddFeF0XOSIqckdg8bsJjRI/yPVs6d4c7ia0ow2Wz83q9DnQDUnZlsSpVGC11m42cKDNqZvXTtxFiJOK48fcf4Ukxy5Crf3tCZq8ChADZRhp+T8PPW2l3EOZx+yej14OmfgwxZvjJX5dNU68ZMNQ5y0wAEhBFrihUuMEZAi90H0x8SQmN8vfzTO9hpK0upO14SCoM9FMnFooGiq1zk4Dyv7p2JAmoCgvni3en88Dg84mpKOPpr9+Cl18B4LYF33HT7z8BcO76ZXguE8bsrIsGc+9rX1AWGkaZI5SIy0VUcWFEJF++O53Bm1bxa5PWXHf+lQD0SgUsFvPkMHEtKyvc9N++nvPXiqkP1yabTtbUek6++k9QSlGg3Rc/88jzbwBQZbfz26CnID2D4rZNcGWK6HRdsbG9bSv/b/631MMji3WSnTBzrJ2MOuKahrpdVBmpU8NdVfj+tffWtzh7dZ2wfmJfLwwdzS2NL8Pu8eCx2/m5SwUDsRqcBZlFYt2r6wz90su8XWL9ubNs3NBZNGpu62Ln0rY6bq/wNft4d6iNYV96qfBA73QYdYxzGUeGSiGW1E5Oe0HWNK0UGHeY7+4LWn8VIyI7qPwF4IXg8hOBzRnKrBYdGbfyD1x2BwubmCbTl75+i4uXif7yeeuWstGYuKB9wR5+T2sKioINGFq8E0jh57BUvpg6jbO2rkVLb8KeWJG8P3lQa2JmlPqTNMRUmWODHZasWNblq2+5l4beT2ian820oeczJyyKRCAnxMmgm+5nZVojhm5Yzjd/fkgokLt8FwEDU4yJEdomBw5zUiyR2JOWLWBus3ZUOhyMWrMY520iGP689Rrtbp2O2+6ga+Zm5uybB/QnOyWZxgfMAKW9aWk0AyoapfBY//O4fMlc1ienM6djF3oBIW1S8Srw20v3oQPP9RmGYcBmxHozSKv/tvXk55dCciQ7cbK5QXNa5uzhx9ZncJnhbZ64bAGj1y4B4JyNK1ixeA5wIS2jPdw+bwavdh9ISnEBj6+eBajgchFfZg6hSiw9OKOXV9exWRolQwt3+JdDPR76FO8GMviJpIDfzYkQZvC/8r3cOl9cz/wK6PK+h33XiX/hh2Z+wl3DhGPigVmfwt2XAhDvLocQo0GmKLgqXEAIK+1xgI7HCJDb6I5gIPDs8kBB/sloT2UW4RdjgBcWVnBDZzO1ZULEwSLaP8NG+c3S7CyRHCunvSCfbiiKwqdDvNzyY0PC8PDBRFPW1N3b/MspJYXsyj0AOPm8ZRf/cCK33cFHia2ZCAyILuEpmrItMQV0nQt2rQIScNaNJK4sm+Y5e7DpOltbNvfvd3dMPPUL881lo3zebtg+dLx/u51FOolOhf+7cAor08R46J9adeblVqFMBQrTUoDV/u1LnU5igbJebQizRJHn1In1R969+fkrflO2DrirBuIIddAlbxfrpt/KrtgEeu7YxN6fHiQKKHr+GlyDRY++0u7A/dJVAOz7djX3DJvIPYYATd4keqV5+8tpk7MHBWH6bp1tpvfMjYymcb7o2u6KiScuTZjkE779k3Vx9fioUy/GrfqTbxd2p+voeHqVBZrLuxWJwLNtVaG82l1ERxdFOHmiVV+eBVxhYcxq2YkRG5bjttmY17i132+ycH4Wo+ZHkB/m5BHnDm6/QTTCYvq3RH9vtghaC7ET0kf0XrOCRnUVGy7255cHBvHtt+Rg6b91PXseFtdnZ6yZkGPC73NYPPBCANrv2UHTdSWQ0ZHFewOFV8sW6y3j4AdLeaTRvnLvKwRMn3zBgRMTcZVbpjM7U6dFnEKnZNlTltQupCCfAtSuyQzyenGGQOuWZg9ibUp92uwTIvJng+a0dR0AUomp6yTHknyrYxMxtvfBuL1c8MCbfNa+B2dtWUuXIY3jLSRzAAAdpklEQVQBFU+lhx0JyexIEFJYv7IcEJG+dwydyLufvehf/tjYZ3jQ6BKH8S78qtkZYInr+jKtLVOBnf27oH71s7/8T7UL44D9WSUkWsyee6NNQQ7xBo599fkUixLi6LhqDc1zhQhWGgHfHeu48Npt4PUSEmqnVYRQpr7tojjr2zXMbdaO+NJibspZBfQkvryUyELTlN7JEpk+cdJURq9aRHRlBW926cdGY7rIZUkZbDDMy6/1GMSNW7cC8ay65Dw6LtSIKy+lMNzJ0knnMRD4JbQeRRZH6+wkYYqODLUxfvItxJcW47Lb0R12vyDfPLOSfca9uLO8MZdkFVM3LRpe/MmcPtPlgbfnwl1juLQN3G6ZlniIaA/RwZL4Kpixk2/mjl+/RVcUpvcdju/Mr9yp0f6VzWTFxDNinUbYVY+LaxyUc6XAuMfCb2vevzpGzpX4CHC43bgd4tw7FGYDh4kzOAbeWuNlwW6dIY0Uxhv/AwUVOl0+8LCjSIQqfHWejRFNZQ9bUnuQgnwK6PxCMcPmzqHQEULPcweweIoQ2BEVu3i8z3AKIqO4fPEc6nQSwTmP9IQLjG5LiA2mdhbq6UyPpcvubXQxetbeq0VAetO8fTgr4/xiMnDfekD4hF/+5k1CDVP1SzPeAvoD4mW/wdCyCDs0MUKowwqLIMz0QbvzS4BYCvp04v6zx9KoIAeX3cZPEy5gHJDihKw6caQVCX/v9sRkDjOiWYxDDnGQptaHVSLwzOVw0LKdML0zcwU2I9rbVl4Jv6yCNhmEntOZLr8tZUVZCS2Lc0h76SIAIpsm8VvXrrzYrAcxFWVMjdiDT8NyndE80X+k/9hlIRAGZMfEB9SpsL4IuApplU6b2/9H593bWZXagGs6iu3q7twNegO/xaLYKwTD4/JQZbOTFSuCo5KKzRaUx5JxzKbrlFUY657AHq8vE9mtXey8v97DujxIiYTXB4ljdEqxgSUHW6JlNNXe6FiuH305AOFV5hC2O6+9mesenk6XXVt4+Owx3JCaRhpwZQeFeywZsm7rIs6ngSVJB0C8cYz4+rF8ELmC+/ckkuQq45Vxx5BH+zB8+ZeXKbPEeby7TifJCWdl2Fi4R2eH4aHw6PD5Xzojmh5hRxJJDUMK8kmm3OXl9eceo9cOkZf50+0bYMrtAPS/bRpLCkLQFYW3epzNtggnUcBsy7TFLi8s2K0ztoXCnFYdmT10AqPXLGZhg+Y4eg3hWiCxaTzfv/0Y/beupzg0nM39uuIT5Mgqs7sbVWnaPPdZcoyUeyCzWKF1GKQV5pOVZL6l6+fsA2IJtes8OPh8f/lIY5P42BAqLP7Ts/PMoC6PouAwfMo6YDPm2q377CS84TqerfsIuX6IP4GEt1tz7h42kfmNWzHkr9X8t6sYA71wdSGPJ4oxv386o3jgiyxeujEKl0dn3MW3+c8lq7GCL6l5ctEBDoQLEalbUghlERDl4Lzda3mnaVcAQl0uzg/PBerg1UXK0lktRXPCF/G7alu5aT4AyhyiC+lxuQl3uyk1fLIRlqFc4ZEhRFWWU2kPod3enYQkGy6EN6/D0+Nu7JUuqjKSCDWmm5y1Q2e1MUw4qwTeXgc3doZ1uYECXlhhriuWeHvd4qf+Lrw+z9/xrH99fJlOWrTi7xH72FkkflPXGdhDbmYJFLggqZQxL76FkhSD/drrORZKqnTyK8QwK981XBs02+baXDgrA1rFi+Qj5caIu85J0mQtqV1IQT7JROD1izHAWVvX+ZdXl4SiG++gnIho1uZB91Q4UBn4kvQlsMraX8l1f8wivSifFjl7eHnrudAllXXfb6L/VjG8JbqqgrItZliwLakOlTki+CikrjkYtU6YeYwQGyQaGS9joi1BWrpOWn3hR9wdFLO0y1h3VbgJ95hJKwvDnf4RtXZLgJcNoLQcIiMgMhzbC1cclMf1ndT2PNG/LQCLGzSnbYKNsUDVX9mAGWldvkdEYxdXBTYs1uWax3tu5gdcNmIK5SFhPPH9+8TdKvytaQ7TF2rXvTSyid7loIYKkSEiOUWYHYY1EjdmSDM72z6fzw1/zmRbfDIv9hoMxGO32ahwmNfKZUmJujw+nTLjkiyv34Qylwgc+yisARMf+gCH10NImION5XYyQiA7yIecXSrOw7k3F0TOLgDiSorxjXWucpjHq7TUI8afik3gMPwEKUEdXN96lxSFqBAoMS7LYN+44/xi3JOexeH2wKYsci99jcT593EkFu7RGfqlh8JKGNtc4dPhNmyKwphmCo8vEcJbJxSGNxZ1ahSrMHucnY83emmdoHB1BynIktqFdNCcZLwOO/OatfWv/9amg3+5vfmuJdQO7Yz1+KB00L4kDOe//QHpRcLOXKeyghteeBmA3KS6VFnSS1onp7j0yacJn/YR4dM+4pLp//OXV7hN8XJ5zV5K4whL8I6i0DzcZdQv8GVpTApEVnxdvjPSLnoVhbd6m+Nni8LNE6m02XEHzywUxMHCJD4T2qQE5NGuMuYhdjoCA5XyLb3A/xs+iazYRPIjo7l2zBV+0dra3cyuVR4axq62ohfeJlFh2WQ7bwy2sWyynTNSxPmu8Nbhnc9eouuurYxf9SeXavMBcESEct9uMTba7vHwoNucVzg4A6hv6uFpS0S6SbfdQbkb3l8nTNbBuZ7jDFEd69rDCCPyO8xVxSs/vuPfpv6BPP9ygwIzs1r/+uZ9qhthuiI25AVeK9+1bRyrsPBCO4/2tvHjaBvnG/7dPTmVQowN9u45+rze0xZ7KTSs51/8pbPMmNkxq0T3P18lrsBG1JlpCs8PsHNNR5schyypdUhBPgWMvfxOrhl1OZeNu5o7LzHnyv15nJ1L2igMaaiw8EIbkaHi9lzU2obT6AA1i4OzMsSLKjwsMBLLGSbKW/ZIY/KEG9DSG/NNG5XXp1zi3+bzXPNt/0WOuawEZnrGp8+XHdhEhOGTTCkqYHiVSM5xWVvFLxwhNpjWW9Q1NVrh9pvvoM81/6XtrU9ROPls/z5veekZ5jVuzZ8Zzbho2nR/b+1wDNm8ikZ5YiKNVvt2M3C3SPT5SlZkwCQWMxB+371BGlFumV2iJCXBv1zhCMVlNFgu6BaFTREn2ygGuqaa17SkSgQ7FVtmVaxXWRzQ069bZpoK7nu+H3vPLWDfuFKmPNbXXz66mfn7FCe0TBDX6kBQttK9JeKcdgdNQJXr8yx0bcYlq/7gjMwtDN2wgl4tzWFHs95+jFFrFjN6zWJ+evdxf/lWy1DunHJzXyuCso/O322eU6hdJAixJgmJaJDA9P4jAJH29KOJh8q9E0hds3rYFUgw2mOLLAHsXp2DIr4lktqKNFmfZGyKwgvDI7g6fBBhdvhgiNkmqhOm8PbQg5Ppn5mmsOEyO38V6HRNUQzzMvD4ZAo/X0qdrH2U14ki7O0bsQM55QqfdTiTzzqcCUBfy7DWjkmwyEgd3cFSPq2PjXV5HvaWwt1dFZrEimN0vaILa899mDV6NN3j3CTfIfzd8RGiTov3iiEqDWLE9g4b2O02fmssslSdH2oZuzopkZfbPUilB149BnNk0YK/WP/ONHbFJpBRkMsifTItRrQ+KELYZzFIcgaWWwXlydmfcX638yl3hPL47E+JunE8EEJsmJn6McIhxAhg+T6dMz/2UOUR388fb6dnmkLdPs34uk0XRq1byoFwJzNGn8dw4xgVbp2fXAmE6XCBV/dnrFptSQWeXQa7i3XSoxXUZNPUDzCwoTiRYY0U0qKE/zg6FCYYWcX2hkUxftJNVOkKyzKaEtm8iz+zTatu9fjqvafEypju/n0G54f2pbK8qr3Clb+I7xRjHWBnoU7XD4WZWQG+Og9GNrMRF67Q+JWLOHP2aGJiQnn53CBb+CF4oo+Nokov2wp1pna20dh4poY0Unh0sUhB7nTA2Q1kT1giASnIp4QJrWz+l+yxklFH8Wdl8jFnr52zb3rev/5Cno3rMqB5PHRJgaWGiXBSa/NYByxm3ELLcockhcyrHAclr6BBEo21B2mcXQDpCQEzLcWEKQxqGFintTk66y1JvJ5eBv81ZraICFG4RT32l2/5OV1wv/cNzXKzKYiIRB8qTOFTz7DxxhozoGmcESPlDerlp1umLj5n80oOfP8TLrsdp9sFrrEQFsKDPxbj8oqu/vo8+GmLhzGtHMzfpfunY/boMDdTp2eaQpN4O0Muv43E/TnkOaO5sa/ZChjxtZdfdgqRm7NT4c0h4lo1jFFYsV83rplpgo6PCIwNwJgms16UwqqL7WjZOm0SFdKjxXkVVECVbp5jdoWl8fbprfDBAqGiE/v4ixPCzWOE2yHB8BKc19TGo4vFEKMzU8X9B1i0V/ebmXXg5x06I40e/ujmNkY3DwrDPgLxEQoP97Kxq1hcOx/d6iksnSQac73TFFomSEGWSECarE9rMgPnf2dXsXjxhtoVru2o0LCOeNme09h84VknC9gQNN0wECjGPsJCoEHSwdMeer2wKxcqLRPUBybq8vc4fews1NlScGwmyqEjG/Ppp//j8dtv4buvnqLfAJFnum2ije9G2RjaSOHurqbwRYcqPN7HhgJEhcBT/S2P9z1jCFF0nK4qmHqOf57mjBVmUJ3D44almwHona74c23bFOibLq5LvSiFd4faSWyVxDkdnPz3TLFRlUf3izHA99vM5dcG2rikjcK5jRV+HG33p4YM9hUL8TSWIxQGN7L5xRigVQJMaCnWI0Pgrm6WexUaApcZM0GFmjchu8ysR4VH9LoBnl3u9Q8x+mMPfLVZbKemKAH3sE/9fy6WX2/20uYdD2d/7qXvJx4qLXEK7esqXNHeJsVYIrEge8inMSObKTy5VAhrkhMua2sE4JToXPGzyC28owhumufl0+FCtMLsZlKIsIOt48dOpQsGPwjz10FaPMx7EJql0iTOxsVtdN5dpxNigzcHm6L4/HIvU+d60YF7uik80vvoFZgysh6MrHdQ+blNbJzb5ODt7+hq44ZOCiF2An3UHy4wx/1+8jtMvxjsdq4p/YuUuXvYkJzGJUt/pcdrIluZmqLw23g7czN1eqcr9DYEOb9cZ8osL4VVsGyfToe6Xu7ubifUrgRYJXpZeoSJzkO7Iu7qamNHkZdV+3UmtbbRK938jdurk1kEqVEQbgyzUhSF5nHCZ58QDslOc/tfM71cPVvYCF4ZaKevIaQXtbbx+25xzXukQnNjGFOEI7B37jPvN4lV+HG0jddW6/RNx5+045/w5hrdH8C2JBtW5kC3g2+lRCIxkIJ8GhMXLiKBNxWIgKQYw7dcVGlG8gLkWVIsnpVh5icecLhZpo+FH5cJMQbIyofnfoDnrwDgnaF2nuqn43QIM7WPRxd7/RIwbYnOAz31owZ2/ROsx/Sz2RJJlH0ASiogJpJuL51PwoRXCP9jId4pZ5NyZiP/Zt1TFbqnBu5rbqZOoSXI67XVOncbLttZY+28tFIn3A7Xdjz6eUWGKrw/7GChLq7Saf+OMCfHhcGKi+w0iFHYUqDzwEJxBTOL4a4FXn4YI34/4Xsv2Ua08oXfe8i6RvxrX97eRudkhT0lOmc3UAgxouNvPkNh2T6FJdk6o5spjGgiynPLdC76ycvOIvhyM7RK0AMaCn+HlvHwg5EN1uk4eMpHiUQSiBTkasSybJ0x33rILYdHetmYesbReycRIQodA+cioGWCwpR2Cm+u0akTCvf1MPfz6y4Oufy3Saxz2PXXV3u4a4FOYjh8NdJOm0TxQk+LMofXpERyQsSYmcvh7g8hxgmvXQPNjRmVrx8KN78tlif1hRjDXlzHSdMfbjnm3duVQHO7y9LwiQtXuLf7vz+nxxaZ5uSCSrhujofvRzsIGmkWMJwq1xIPkFMeuF3nZIXOQXmhI0MVvh55cGNgbqbOTuPYFW74eKOXXun/zJTySC8bYXYv2wrh6g42UqOkeVoiORJSkKsRN83z+F+GN8/zMqGlQlLkP3uJvTHYzsO9hCA7LT3G1gmwTIwkolXCYX58LPRuDY9Pho9+g46N4A6RljKn1MuVP5szEo2d4WHDFPGYfTrczi3zvFR64LHeJyB8oaIKxk6HUkOdLnsBfn9ULN80HAZ2gOJy6Nb88PuwkFWsszRbp3OyGVAXHIiVEXn8h+yUugPXyw0XfaNYhel9bTy62Et6NEzva15D61Pyb9o5LeKVgKmxW/8LH2+Y49jcEhKJRCAFuRoRNF0w/zYvQsohxHzGSDsPLRLduv90/5eieMco8WdhZ3AGL8uY2iaxCjNGncAXdJXbFGOA/KABvW2O3Ua/pUCn24ce8ivE0KOFF4qefpvyPBJKHeRFCvvriE3L8aUlPV7c3c3GO2s9FFUJf/HjFuG9tYuNW7scfN96pyvMzdT9y/+UDkkK34y08fkmnU7JyjGZ3iUSyfFBCnI14tn+ds7/zkOOYbKuawna+XaLl5X7RSBX+7r//CWZFq3wysATJ4otg/IR96h3El/odZxw3/nw4GcQHgqPTvR/tWSvzqQfPRRXwVP9bFx4lGFn32/T/Zm+iqvg681iCFJiXj6/v/g8H3TuTZO8fVySVsbxFuSUSIXtV9jR9um0SVBIiz76NfxmpI0XVugowHWd/t01P1zAnEQiObFIQa5GdEpW2Hz5wbfkow1eJv4gerVPLIXVF9v9SRaqG1GhCr9eYOfxJR6SnQqPngjT9JF4YDzceA6EOfxDmwCu/sXDZjEBFZfO9DK6meKPXj4UbRMD19v7po1Sm9CybTwPz/oUIkLh0buO8wkI4iMOHuN9JJZl67y7TkRZ90i107f+UX8ikUiqGVKQTwPm7zL9lKUukUWqugoyQNd6Cl+edwofrYSDw3mtwVcer5nd63Cc3cDGJ+eKmZf61VfMeXlDHPDz/bBqB6TGQb34I+7nZHHhD15/6tCJP3jYfbW4/vnlOjfP85JVIqZYHNJIph6QSKor8r/zNGCwpacUGyYyHUn+Hs+dZSMxAsId8MIAW0Cg2+G4oKWNt4bYuahN0L+Jww5nNKk2Ygz4Z5MKXr5xrpf31uvMydQZNcNLTtnxD0KTSCTHB9lDPg0Y3dzGvPNhVQ4Ma6xQv44U5L9L/wwbOdfZ0HX9tJhF6PZfPXyzRad7qsLrg2wB5vXtB3TiwiHWktnrhQE2Lp/l9S/72F1sydTlFpNL1A3K+S2RSKoHUpBPE/pl2Oj3bxJ5GCzZq3P9HJGq64UBdrrWst726SDGM7Z4ma4JId1yQKd9os7tXRV0XWfiD14+3qgTGQIzRtoY0ECI76TWNi5ooaAogeO7b+1iY9FeMdRsdDOFltWnUy+RSIKQglzLGPeth0xjaNL533nYcaV8BKobhUHTMhZWCXHekAcfbxTLpS54bLHOgAbmdiHBmUOA4U1s7LhSIbcM2iSeHg0SiaS2In3ItYwCy8s+v+Lw20lOHeOaK/ROF8st4uG6juLfNC5cjEv2kRx5iB8fgpRIhbZ1FSnGEkk1R3aPahlP9rVx3Wzha7RmepJUHyJCFBaMd5BXLnzFvhm46kUpfHKujSeXekmPVnimv7x/EklNQgpyLeOqDjbGG1P4+SajkFRPEiIOvj9iTmIpxBJJTUQKci1ECrFEIpFUP2RTWyKRSCSSaoAUZIlEIpFIqgFSkCUSiUQiqQZIQZZIJBKJpBogBVkikUgkkmqAFGSJRCKRSKoBUpAlEolEIqkGSEGWSCQSiaQaIAVZIpFIJJJqgBRkiUQikUiqAVKQJRKJRCKpBkhBlkgkEomkGiAFWSKRSCS1BkVRpiuKsl1RFF1RlLaH2cauKMqLiqJsVRRli6Iol5+MuklBlkgkEklt4hugD7DzCNtMBJoCzYAewH8VRWl4oismBVkikUgktQZd13/XdX3XUTa7AHhd13Wvrus5CBEfd6LrJgVZIpFIJJJAMgjsQWcC9U/0QR0n+gA1nQ0bNuSqqnok00d1JRHIPdWVOAXI865dyPOuvszUNG3I8dqZfptDOV77OlVIQf6XaJpW91TX4Z+gqqqmaZp6qutxspHnXbuQ5y35h2QCDYClxnpwj/mEIE3WEolEIpEE8jlwhaIoNkVR6gIjgS9O9EGlIEskEomk1qAoynOKouwG0oHZiqKsM8p/VBTFZ1V4H9gGbAYWAQ/qur79RNdNmqxrL6+d6gqcIuR51y7keUsC0HX9RuDGQ5QPsyx7gGtOZr0AFF3XT/YxJRKJRCKRBCFN1hKJRCKRVAOkyboWoKrqN0AjwAuUADdomrZSVdXmwLtAApAHXKRp2uZTV9MTg6qq9wP/BdppmrZWVdXuwKtABLADmKRp2v5TV8Pji6qqO4AK4w/gTk3TZtWC8w4HngbORpz7Qk3TrqzJz7mqqg0RSSt8xAJ1NE2Lr8nnXVORPeTawcWapnXQNK0TMB14yyh/BXhR07TmwIuIl3WNQlXVzkB3jCELqqragA+A64zzXgBMO3U1PGGM1TSto/E3q5ac9xMIIW6uaVo74D9GeY19zjVN22G5zx0R4vyR8XWNPe+aihTkWoCmaYWW1RjAq6pqEtAZ+Ngo/xjorKrqaTmu+lCoqhqGeBFZgzPOACo0TfvdWH8FOP9k1+0UUKPPW1XVKOAi4D+apukAmqbtqw3PuQ9VVUMROZjfqk3nXZOQglxLUFX1DVVVM4FHgIsRaeCyNE3zABifezgJ6eFOIg8CH2iatsNSFjDAX9O0XMCmqmr8Sa7bieZDVVVXq6r6kqqqsdT8826CMMver6qqpqrqr6qq9qJ2POc+RiDOdTm167xrDFKQawmapl2uaVoGcA/w5Kmuz4lGVdUegAq8dKrrcgrorWlaB6ALoAAvnOL6nAzsQGNghZGh6k7gKyDqlNbq5HIZpjtKchoiBbmWoWna+0B/YDeQpqqqHcD4TAWONgvK6UJfoBWw3QhySgdmIaZUa+DbSFXVRMCraVr+qajkiUDTtF3GZyWiQdITMxUgUCPPOxNwY5hoNU1bjMjlXE7Nfs4BUFU1DfHMf2gU7aIWnHdNQwpyDUdV1ShVVetb1ocD+cB+YCUwwfhqAqJ3kXPya3n80TRtmqZpqZqmNdQ0rSGiATIYYR2IMMyZAFcj0uTVCFRVjVRVNcZYVoDxiPu8jBp83oYJfh4wEMCIME4C/qIGP+cWLgZ+0DQtD8CInq8N512jkMOeaj6RwOeqqkYCHoQYD9c0TVdV9WrgXVVV7wMKEEExNRpN07yqqk4GXjWGyewAJp3aWh1XkoEvjR6RHVgPXFsLzhtEI+MtVVWfAlzAZE3TDtSS5/wSDs4+VRvOu0YhM3VJJBKJRFINkCZriUQikUiqAVKQJRKJRCKpBkhBlkgkEomkGiAFWSKRSCSSaoAUZIlEIpFIqgFSkCUSiUQiqQZIQZZITkNUVb1XVVVdVdWLT3VdJBLJ8UEKskRymmFMpXgFIsnLlae4OhKJ5DghM3VJJKcfg4E0YCTwvaqqbTVNWwv+lJGvA52A7YjJBp7RNE0xvncAdyAyOyUB64CpmqZpJ/skJBJJILKHLJGcflwJ/KRp2g/AauAq8Ivtd8AqRArNUYietJUHgPOAIUACQrBnqqoad3KqLpFIDocUZInkNEJV1VTgXMxp9t4EJqmqGgF0BxoCd2qaVq5p2jbgactvFUS+49s1TdumaZpH07Q3gb3AOSfxNCQSySGQJmuJ5PRiCsJ3/L2x/gHwBHABYqrB/ZqmlVu232lZTkTMD/ydqqrWJPYhiOkpJRLJKUQKskRymmAEc00BYoHdqqr6vrIjzNa3A3VVVY2wiHKGZRe5QClwtqZpS09OrSUSybEiTdYSyenDEKA+cCbQ0fJ3LsJcXQhkAo+pqhquqmoj4CbfjzVN04FngemqqjYD/3zZgw1TuEQiOYXIHrJEcvpwFfCNpmnLgsqzVVVdaHw/AngNyAG2Ae8DD1u2vR/hR56hqmo6ose8CLjhBNddIpEcBTkfskRSg1FV9SrgVk3Tmp/qukgkkiMje8gSSQ1CVdVeiKjpbUA7xJjjD05ppSQSyTEhBVkiqVnUBz5CRFTnAJ8Dj53SGkkkkmNCmqwlEolEIqkGyChriUQikUiqAVKQJRKJRCKpBkhBlkgkEomkGiAFWSKRSCSSaoAUZIlEIpFIqgFSkCUSiUQiqQb8P5amOnMbqHOMAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap.dependence_plot('Age', shap_values, X_test, interaction_index='Sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that while Age > 50 is generally bad (positive SHAP value), being a woman generally reduces the impact of age. This makes sense since we know that women generally live longer than men.\n", "\n", "Let's now look at poverty index and age." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 340 }, "colab_type": "code", "id": "tXcUiJ_ZFtcl", "outputId": "3427d6ba-0334-4fc5-baa3-44af277e033c" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap.dependence_plot('Poverty index', shap_values, X_test, interaction_index='Age')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the impact of poverty index drops off quickly, and for higher income individuals age begins to explain much of variation in the impact of poverty index.\n", "\n", "Try some other pairs and see what other interesting relationships you can find!" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-Qj31_mFiSB_" }, "source": [ "# Congratulations!\n", "\n", "You have completed the second assignment in Course 2. Along the way you've learned to fit decision trees, random forests, and deal with missing data. Now you're ready to move on to week 3!" ] } ], "metadata": { "colab": { "collapsed_sections": [ "-gc0vttU7Dkf", "CFM27SfS1QSD" ], "include_colab_link": true, "name": "C2M2_Assignment.ipynb", "provenance": [], "toc_visible": true }, "coursera": { "schema_names": [ "AI4MC2-2" ] }, "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }