{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CS-109A Introduction to Data Science" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Lab 9: Decision Trees (Part 1 of 2): Classification, Regression, Bagging, Random Forests\n", "\n", "**Harvard University**
\n", "**Fall 2019**
\n", "**Instructors:** Pavlos Protopapas, Kevin Rader, and Chris Tanner
\n", "**Lab Instructors:** Chris Tanner and Eleni Kaxiras
\n", "**Authors:** Kevin Rader, Rahul Dave, Chris Tanner" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## RUN THIS CELL TO PROPERLY HIGHLIGHT THE EXERCISES\n", "import requests\n", "from IPython.core.display import HTML\n", "styles = requests.get(\"https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/cs109.css\").text\n", "HTML(styles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning Goals\n", "\n", "The goal of this lab is for students to:\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# imports\n", "%matplotlib inline\n", "import numpy as np\n", "import scipy as sp\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import tree\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.utils import resample\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "import matplotlib as mpl\n", "import matplotlib.cm as cm\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "pd.set_option('display.width', 500)\n", "pd.set_option('display.max_columns', 100)\n", "pd.set_option('display.notebook_repr_html', True)\n", "import seaborn.apionly as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "Let's do a high-level recap of what we've learned in this course so far:\n", "\n", "Say we have input data $X = (X_1, X_2, ..., X_n)$ and corresponding class labels $Y = (Y_1, Y_2, ..., Y_n)$ where $n$ represents the number of observations/instances (i.e., unique samples). Much of statistical learning concerns trying to model this relationship between our data's $X$ and $Y$. In particular, we assert that the $Y$'s were produced/generated by some underlying function $f(X)$, and that there is inevitably some noise and systematic, implicit bias and error $\\epsilon$ that cannot be captured by any $f(X)$. Thus, we have:\n", "\n", "$Y = f(X) + \\epsilon$\n", "\n", "Statistical learning concerns either **prediction** or **inference**:\n", "\n", "**Prediction:** concerns trying to learn a function $\\hat{f}(X)$ that is as close as possible to the true function $f(X)$. This allows us to estimate $Y$ values for any new input data $X$.\n", "\n", "**Inference:** concerns trying to understand/model the _relationship_ between $X$ and $Y$, effectively learning how the data was generated.\n", "\n", "Independent of this, if you have access to gold truth labels $Y$, and you make use of them for your modelling, then you are working on a **supervised** learning task. If you do not have or make use of $Y$ values, and you are only concerned with the input data $X$, you are working on an **unsupervised** learning task.\n", "\n", "
\n", "
Q1: Using the above terms, what types of problems are linear regression, logistic regression, and PCA?
" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q1.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Q2: What is a decision tree? Why do we care to make a decision tree?
" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# discussed in lab." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding Decision Trees\n", "\n", "My goal is for none of the topics we learn in this class to seem like nebulus concepts or black-boxes of magic. In this course, it's important to understand the models that you can use to help you with your data, and this includes not only knowing how to invoke these as tools within Python libraries (e.g., ``sklearn``, ``statsmodels``), but to have an understanding of what each model is actually doing 'under the hood' -- how it actually works -- as this provides insights into why you should use one model vs another, and how you could adjust models and invent new ones!\n", "\n", "\n", "### Entropy (aka Uncertainty)\n", "\n", "Remember, in the last lab, we mentioned that in data science and machine learning, our models are often just finding patterns in the data. For example, for classification, it is best when our data is separable by their $Y$ class lables (e.g., cancerous or benign). That is, hopefully the $X$ values for one class label (e.g., cancerous) is disjoint and separated from the $X$ values that correspond to another class label (e.g., benign). If so, our model would be able to easily discern if a given, new piece of data corresponds to the cancerous label or benign label, based on its $X$ values. If the data is not easily separable (i.e., the $X$ values corresponding to cancer looks very similar to $X$ values corresponding to benign), then our task is difficult and perhaps impossible. Along these lines, we can measure this element in terms of how messy/confusable/_uncertain_ a collection of data is.\n", "\n", "In the 1870s, physicists introduced a term ``Gibbs Entropy``, which was useful in statistical thermodynamics, as it effectively measured uncertainty. By the late 1920s, the foundational work in Information Theory had begun; pioneers John von Neumann and Claude Shannon conducted phenomenal work which paved the way for computation at large -- they heavily influenced the creation of computer science, and their work is still seen in modern day computers. Information theory concerns [entropy.](https://en.wikipedia.org/wiki/Entropy_(information_theory)) So let's look at an example to concretely address what entropy is (the information theoretic version of it).\n", "\n", "Say that we have a fair coin $X$, and each coin fliip is an observation. The coin is equally likely to yield heads or tails. The uncertainty is very high. In fact, it's the highest possible, as it's truly a 50/50 chance of either. Let $H(X)$ represent the entropy of $X$. Per the graphic below, we see that entropy is in fact highest when the probabilities of a 2-class variable are a 50/50 chance.\n", "\n", "
\n", "\n", "
\n", "\n", "If we had a cheating coin, whereby it was guaranteed to always be a head (or a tail), then our entropy would be 0, as there is no **uncertainty** about its outcome. Again, this term, entropy, predates decision trees and has vast applications. Alright, so we can see what entropy is measuring (the uncertainty), but how was it actually calculated?\n", "\n", "#### Definition:\n", "Entropy factors in _all_ possible values/classes of a random variable (log base 2):\n", "
\n", "\n", "
\n", "\n", "#### Fair-Coin Example\n", "In our fair coin example, we only have 2 classes, both of which have a probability of 1/2. So, to calculate the overall entropy of the fair coin, we have Entropy(1+, 1-) =\n", "

\n", "

\n", "$H(X)$ = -1 * (P(coin=heads)*log(P(coin=heads)) + P(coin=tails)*log(P(coin=tails)))\n", "
\n", "\n", "

\n", "

\n", "$ = -1 * (\\frac{1}{2}log(\\frac{1}{2}) + \\frac{1}{2}log(\\frac{1}{2}))$\n", "
\n", " \n", "

\n", "

\n", "$ = -1 * (\\frac{1}{2}*-1) + \\frac{1}{2}*-1)$\n", "
\n", " \n", "

\n", "

\n", "$ = -1 * (-\\frac{1}{2} + -\\frac{1}{2})$\n", "
\n", " \n", "

\n", "

\n", "$ = -1*-1 = 1$\n", "
\n", "\n", "\n", "### Worked Example\n", "\n", "Let's say that we have a small, 14-observation dataset that concerns if we will play tennis on a given day or not (Play Tennis will be our output $Y$), based on 4 features of the current weather:\n", "

\n", "

\n", "\n", "
\n", "

\n", "Completely independent of the features, we can calculate the overall entropy of playing tennis, Entropy for (9+, 5-) examples =\n", "\n", "

\n", "

\n", "$H(X) = -1 * (P$(play_tennis=yes)*log(P(play_tennis=yes)) + P(play_tennis=no)*log(P(play_tennis=no)))\n", "
\n", "\n", "

\n", "

\n", "$ = -\\frac{9}{14}log(\\frac{9}{14}) - \\frac{5}{14}log(\\frac{5}{14}) = 0.94$\n", "
\n", " \n", "Okay, **0.94** is pretty horrible, as it's close to 1, which is the worst possible value. This means that a priori, if we use no features, it's hard to predict if we will play tennis or not. There's a lot of uncertainty (aka entropy). To improve this, could we segment our data in such a way that it's more clear if we will play tennis or not (i.e., by more clear, I mean we will have lower uncertainty... lower entropy).\n", "\n", "Let's start with looking at the ``Wind`` feature. There are 2 possible values for the Wind attribute, **weak** or **strong.** If we were to look at the subset of data that has weak wind, we see that there are 8 data samples (6 are 'Yes' for Play Tennis, 2 have 'No' for Play Tennis). Hmm, so if we know that the Wind is weak, it helps inform us that there's a 6/8 (75%) chance that we will Play Tennis. Let's put this in terms of entropy:\n", "\n", "When we look at ONLY the Wind is Weak subset of data, we have a Play Tennis entropy for (6+, 2-) examples, which calculates to:\n", "

\n", "

\n", "$H(X) = -1 * (P($play_tennis=yes$)*log(P$(play_tennis=yes$)) + P($play_tennis$=$no$)*log(P($play_tennis$=no)))$\n", "
\n", "\n", "

\n", "

\n", "$ = -\\frac{6}{8}log(\\frac{6}{8}) - \\frac{2}{8}log(\\frac{2}{8}) = 0.811$\n", "
\n", "\n", "A value of 0.811 may seem sadly high, still, but our calculation was correct. If you reference the figure above that shows the entropy of a fair coin, we see that having a probability of 75% does in fact yield an entropy of 0.811.\n", "\n", "We're only looking at a subset of our data though (the subset for Wind is Weak). We now need to look at the rest of our data (the subset for Wind is Strong). When the Wind is Strong, we have 6 data points: 3 have Play Tennis is Yes, and 3 are No). In short-hand notation, we have (3+, 3-), which is a 0.5 probability, and we know already that this yields an Entropy of 1.\n", "\n", "When looking at this possible division of separating our data according to the value of Wind, the hope was that we'd have very low entropy in each subset of data. Imagine if the Wind attribute perfectly aligned with Playing Tennis or not (the values were identical). In that case, we would have an Entropy of 0 (no uncertainty), and thus, it would be INCREDIBLY useful to predict playing tennis or not based on the Wind attribute (it would tell us the exact answer).\n", "\n", "We saw that the Wind attribute didn't yield an entropy of 0; its two classes (weak and strong) had an entropy of 0.811 and 1, respectively. Is Wind a useful feature for us then? In order quantitatively measure its usefulness, we can use the entropy to calculate ``Information Gain``, which we saw in Lecture 15 on Slide 40:\n", "\n", "

\n", "

\n", "$Gain(S) = H(S) - \\sum_{i}\\frac{|S_{i}|}{|S|}*H(S_{i})$\n", "
\n", "\n", "Let $S$ represent our current data, and each $S_{i}$ is a subset of the data split according to each of the possible values. So, when considering splitting on Wind, our Information Gain is:\n", "\n", "

\n", "

\n", "$Gain($alldata)$ = H($alldata$) - \\frac{|S_{windweak}|}{|S|}H(S_{windweak}) - \\frac{|S_{windstrong}|}{|S|}H(S_{windstrong})$\n", "
\n", "\n", "

\n", "

\n", "$ = 0.94 - \\frac{8}{14}0.811 - \\frac{6}{14}1.00 = 0.048$\n", "
\n", "\n", "Okay, using Wind as a feature to split our data yields an Information Gain of 0.048. That looks like a low value. We want a high value because gain is good (we want to separate our data in a way that the increases our information). Is 0.048 bad? It all depends on the dataset.\n", "\n", "

\n", "

Q3: Using our entire 14-observation dataset, calculate the Information Gain for the other 3 remaining features (Outlook, Temperature, Humidity). What are their values and which ones gives us the most information gain?
" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q3.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Q4: Now that we know which feature provides the most information gain, how should we use it to construct a decision tree? Let's start the construction of our tree and repeat the process of Q3 one more time.
" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q4.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Q5: When should we stop this process?
" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q5.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Q6: Should we standardize or normalize our features? Both? Neither?
" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q6.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Q7: What if we have outliers? How sensitive is our Decision Tree to outliers? Why?
" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q7.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Connection to Lecture\n", "In Lecture 16, Pavlos started by presenting the tricky graph below which depicts a dataset with just 2 features: longitude and latitude.\n", "\n", "
\n", "\n", "
\n", "\n", "By drawing a straight line to separate our data, we would be doing the same exact process that we are doing here with our Play Tennis dataset. In our Play Tennis example, we are trying to segment our data into bins according to the possible _categories_ that a feature can be. In the lecture example (pictured above), we have continuous data, not discrete categories, so we have an infinite number of thresholds by which to segment our data.\n", "\n", "

\n", "

Q8: How is it possible to segment continuous-valued data, since there are infinite number of possible splits? Do we try 1,000,000 possible values to split by? 100?
" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q8.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary:\n", "\n", "To build a decision tree:\n", " \n", "\n", "\n", "## Sklearn's Implementation\n", "\n", "Our beloved `sklearn` library has implementations of DecisionTrees, so let's practice using it.\n", "\n", "First, let's load our Play Tennis data `(../data/play_tennis.csv\")`:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
playoutlook_overcastoutlook_rainyoutlook_sunnytemp_cooltemp_hottemp_mildhumidity_highhumidity_normalwindy_Falsewindy_True
0no0010101010
1no0010101001
2yes1000101010
3yes0100011010
4yes0101000110
5no0101000101
6yes1001000101
7no0010011010
8yes0011000110
9yes0100010110
10yes0010010101
11yes1000011001
12yes1000100110
13no0100011001
\n", "
" ], "text/plain": [ " play outlook_overcast outlook_rainy outlook_sunny temp_cool temp_hot temp_mild humidity_high humidity_normal windy_False windy_True\n", "0 no 0 0 1 0 1 0 1 0 1 0\n", "1 no 0 0 1 0 1 0 1 0 0 1\n", "2 yes 1 0 0 0 1 0 1 0 1 0\n", "3 yes 0 1 0 0 0 1 1 0 1 0\n", "4 yes 0 1 0 1 0 0 0 1 1 0\n", "5 no 0 1 0 1 0 0 0 1 0 1\n", "6 yes 1 0 0 1 0 0 0 1 0 1\n", "7 no 0 0 1 0 0 1 1 0 1 0\n", "8 yes 0 0 1 1 0 0 0 1 1 0\n", "9 yes 0 1 0 0 0 1 0 1 1 0\n", "10 yes 0 0 1 0 0 1 0 1 0 1\n", "11 yes 1 0 0 0 0 1 1 0 0 1\n", "12 yes 1 0 0 0 1 0 0 1 1 0\n", "13 no 0 1 0 0 0 1 1 0 0 1" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tennis_df = pd.read_csv(\"../data/play_tennis.csv\")\n", "tennis_df = pd.get_dummies(tennis_df, columns=['outlook', 'temp', 'humidity', 'windy'])\n", "tennis_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Normally, in real situations, we'd perform EDA. However, for this tiny dataset, we see there are no missing values, and we do not care if there is collinearity or outliers, as Decision Trees are robust to such." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# separate our data into X and Y portions\n", "x_train = tennis_df.iloc[:, tennis_df.columns != 'play'].values\n", "y_train = tennis_df['play'].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can build a DecisionTree classifier as follows:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "dt = DecisionTreeClassifier().fit(x_train, y_train)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tree_vis = tree.plot_tree(dt, filled=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "

Q9: Is this tree identical to what we constructed above? If not, what differs in sklearn's implementation?
" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q8.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example, we did not use the tree to do any classification. Our data was too small to consider such.\n", "\n", "Let's turn to a different dataset:\n", "\n", "## 2016 Election Data\n", "We will be attempting to predict the presidential election results (at the county level) from 2016, measured as 'votergap' = (trump - clinton) in percentage points, based mostly on demographic features of those counties. Let's quick take a peak at the data:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
statefipscodecountypopulationhispanicminorityfemaleunemployedincomenodegreebachelorinactivityobesitydensitycancervotergaptrumpclinton
0Colorado8117Summit County2723915.1734.91845.9962.5683525.448.18.113.146.046.2-27.63231.53059.162
1Colorado8037Eagle County5365330.0405.16947.2313.17666110.147.39.411.831.047.1-19.89736.05855.955
2Idaho16067Minidoka County1922634.0705.61149.3183.74633224.111.818.334.280.061.854.14871.13516.987
3Colorado8113San Miguel County755810.1544.74746.8083.7596034.754.412.416.75.762.6-44.76923.89268.662
4Utah49051Wasatch County2160013.2444.12548.8123.4652079.534.413.923.0257.868.325.35750.47125.114
\n", "
" ], "text/plain": [ " state fipscode county population hispanic minority female unemployed income nodegree bachelor inactivity obesity density cancer votergap trump clinton\n", "0 Colorado 8117 Summit County 27239 15.173 4.918 45.996 2.5 68352 5.4 48.1 8.1 13.1 46.0 46.2 -27.632 31.530 59.162\n", "1 Colorado 8037 Eagle County 53653 30.040 5.169 47.231 3.1 76661 10.1 47.3 9.4 11.8 31.0 47.1 -19.897 36.058 55.955\n", "2 Idaho 16067 Minidoka County 19226 34.070 5.611 49.318 3.7 46332 24.1 11.8 18.3 34.2 80.0 61.8 54.148 71.135 16.987\n", "3 Colorado 8113 San Miguel County 7558 10.154 4.747 46.808 3.7 59603 4.7 54.4 12.4 16.7 5.7 62.6 -44.769 23.892 68.662\n", "4 Utah 49051 Wasatch County 21600 13.244 4.125 48.812 3.4 65207 9.5 34.4 13.9 23.0 257.8 68.3 25.357 50.471 25.114" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "elect_df = pd.read_csv(\"../data/county_level_election.csv\")\n", "elect_df.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# split 80/20 train-test\n", "X = elect_df[['population','hispanic','minority','female','unemployed','income','nodegree','bachelor','inactivity','obesity','density','cancer']]\n", "response = elect_df['votergap']\n", "Xtrain, Xtest, ytrain, ytest = train_test_split(X,response,test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(ytrain)\n", "Xtrain.hist(column=['minority', 'population','hispanic','female']);" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3066, 18)\n", "(2452, 12)\n", "(614, 12)\n" ] } ], "source": [ "print(elect_df.shape)\n", "print(Xtrain.shape)\n", "print(Xtest.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression Trees\n", "\n", "We will start by using a simple Decision Tree Regressor to predict votergap. We'll run a few of these models without any cross-validation or 'regularization', just to illustrate what is going on.\n", "\n", "This is what you ought to keep in mind about decision trees.\n", "\n", "from the docs:\n", "```\n", "max_depth : int or None, optional (default=None)\n", "The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.\n", "min_samples_split : int, float, optional (default=2)\n", "```\n", "\n", "- The deeper the tree, the more prone you are to overfitting.\n", "- The smaller `min_samples_split`, the more the overfitting. One may use `min_samples_leaf` instead. More samples per leaf, the higher the bias." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "x = Xtrain['minority'].values\n", "o = np.argsort(x)\n", "x = x[o]\n", "y = ytrain.values[o]\n", "plt.plot(x,y, '.');" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(np.log(x),y, '.'); # log scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "

Q10: Which of the two versions of 'minority' would be a better choice to use as a predictor for prediction?
\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q10.txt" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(np.log(x),y,'.')\n", "xx = np.log(x).reshape(-1,1)\n", "for i in [1,2]:\n", " dtree = DecisionTreeRegressor(max_depth=i)\n", " dtree.fit(xx, y)\n", " plt.plot(np.log(x), dtree.predict(xx), label=str(i), alpha=1-i/10, lw=4)\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsvXmUHPV57/35VVVv09OzaDRaBy2DBMYSmySMiLENwfJrOw7YEIy3YMcLSV77XOc6570v1/cNccg5Pjg3viH3hJOYOA6+vpgLNnbAxCQYA8FgI9BILCNAaEEjzYxm33rvWn7vH7V0VXX1LNKgjf6eI81MVXXVr6q7n9/ze57v832ElJIGGmiggQbOfiinegANNNBAAw2cHDQMfgMNNNDA2wQNg99AAw008DZBw+A30EADDbxN0DD4DTTQQANvEzQMfgMNNNDA2wQLMvhCiO8JIUaEEL2+bUuEEL8QQux3frY724UQ4n8KIQ4IIV4WQmxZ7ME30EADDTQwfyzUw78H+GBo263AL6WUG4FfOn8DfAjY6Py7Bfj74x9mAw000EADJ4oFGXwp5dPARGjzdcD3nd+/D3zUt/1/SRvPAW1CiJUnMtgGGmiggQaOH9oinGO5lPKY8/sQsNz5fTVw1Hdcv7PtGHWwdOlSuW7dukUYUgMNNNDA2wc9PT1jUsrOuY5bDIPvQUophRAL0moQQtyCHfJhzZo17Nq1azGH1EADDTRw1kMI0Tef4xaDpTPshmqcnyPO9gHgHN9xXc62AKSUd0spt0kpt3V2zjlBNdBAAw00cJxYDIP/MPBZ5/fPAg/5tt/ssHW2A9O+0E8DDTTQQAMnGQsK6Qgh7gOuApYKIfqBPwfuAB4QQnwB6AM+7hz+c+DDwAGgAPzBIo25gQYaaKCB48CCDL6U8pN1dl0TcawEvnw8g2qggQYaOBHouk5/fz+lUulUD2VRkUwm6erqIhaLHdfrFzVp20ADDTRwOqC/v59MJsO6desQQpzq4SwKpJSMj4/T39/P+vXrj+scDWmFBhpo4KxDqVSio6PjrDH2AEIIOjo6TmjV0jD4DSwKevomuevJA/T0TZ7qoTTQAMBZZexdnOg9NUI6DZwwevom+fR3n6NiWMQ1hXu/uJ2ta9tP9bAaaKCBEBoefgMnjOcOjVMxLCwJumHx3KHxUz2kBho4LbBu3TouvPBCLrnkErZt2wbAxMQEO3bsYOPGjezYsYPJSXtVLKXkP/2n/8SGDRu46KKL2L1796KPp2HwGzhhbO/uIK4pqAJimsL27o5TPaQGGjht8OSTT/Liiy96KgJ33HEH11xzDfv37+eaa67hjjvuAODRRx9l//797N+/n7vvvps//uM/XvSxNEI6DZwwtq5t594vbue5Q+Ns7+5ohHMaOH3wnfe9tef/w/9Y8EseeughnnrqKQA++9nPctVVV/Gtb32Lhx56iJtvvhkhBNu3b2dqaopjx46xcuXiaU42PPwGFgVb17bz5as3NIx9Aw34IITgAx/4AFu3buXuu+8GYHh42DPiK1asYHh4GICBgQHOOaeqRtPV1cXAQI0azQmh4eEvAnr6Jut6t7PtO5vwdrnPBhpYCJ555hlWr17NyMgIO3bs4B3veEdgvxDipLKJGgb/BDEbQ+VMYq+ciMEO3KeqcPe1F3HR2nYOlspv+4mwgbc3Vq9eDcCyZcv42Mc+xvPPP8/y5cu9UM2xY8dYtmyZd+zRo1VF+f7+fu/1i4WGwT9BRDFUXAM2277TCSc6Mfnvc2tOoefHB+kBHpdFXtOMM3oibOAMx3HE2BcL+Xwey7LIZDLk83kee+wxbrvtNq699lq+//3vc+utt/L973+f6667DoBrr72Wv/u7v+MTn/gEO3fupLW1dVHj99Aw+CcMl6GiG1YNQ2W2facTwhPTg7v7F+R9eyydisW5usqkUUFK2ILK3oxxwhNhYzXQwJmI4eFhPvaxjwFgGAaf+tSn+OAHP8hll13Gxz/+cf7pn/6JtWvX8sADDwDw4Q9/mJ///Ods2LCBpqYm/vmf/3nRxyRsjbPTA9u2bZNnYgOUMz2G73rcumGhKgKEwDAX5n339E3y3J4hKr8eZSJf8bb/IFMmril843c3MVmoeJOee73YHNc40dXAmfD8G1h8vPbaa1xwwQWnehhvCaLuTQjRI6XcNtdrGx7+CWIug7J1bftpb2j8tMrBqSL3PX9kXt53+N7Xx+I8/OIUk/kKrhshgPed18ntj+wNGO17v7idB3f3M1e66kTCYu5kUdbtiez26zbzqcvXzHkfDTRwtqJh8E8A9QzKmWhA3Impp2+SB3f3zxmGivK818fiNMU1VrWlGJwqIoFETGFZJlFjtLd3d/CT3f1UnBDSbR+prgD8z8wNF1V0CyEE7U3xed/Tc4fGKesWEjAsyW0P9XL+ikzg/I18QgNvJzQM/gkgyqAANd7smWRA5ltE9ZPd/d69u0Z8/fl2gmlJOk4yppAvm9z7xa0AgUmkvSnOnY+/4U0CFd3itod6saT0nhngjeG2j2zy9t/+yN4ao10P7U1xhAA3amlJWbNCON4VxMme1M9EJ6KB0w8Ng38C2Lq8hQ2GCpbEEDCsWTzae+yMYObMhrnCUD19k/xo11EvbKOqtSuBprhGU1zzzuNOIvuHs/y3n77ivVYRdtjHsOwtbtLY9f7jmsINW7qwpMSSUNbt/XPlSnr6Jrn9kb04p0UA8YgViz+xriqCwakiPX2Ts97/D3ceqZmg3Gu+FbmcxiqkgcVCw+AfJ7ITJYb+rZ8bEhkGpooAlBTJxRuX8cLhiQUzc6IMghteEcD1W7pOmy/5c4fG0U3bkgrg97baY5sYzNd9zda17ewbyvIvLw4GtncvTXN4ogDO+VRVQUBg0pSApggqpkQCP+7p5wbf84gKrU0WKlQMC7DLyd+9cSl/8v7zap6hu6J5cHc/P+7p577nj/Dg7v66RrWnb5LbHur1JqiKM6nvG8pGTgJv9Ozi9YND/JeXBBWLE6a9nqlORAOnBxoG/zgxuH+KSskMbEtJwYqKWLCuTJQHB/DJu39DxTGEP+rp574vze1Jngy0N8U9D10Cm1e1Rh5XqBjc9eQBb5xPvTDA+wsxJLA7YTCpSkq6iWVVmWLvO6+T67d0BUJAN2zpQmB71hIwzVqaZzi0dvt1m73Yv6IINq1s8VQ8o4z+c4fGMcy5jepzh8YxfeNVnLxC1CQw8shPGXhBQUrBH4h+/n7FJcdlsM8Uem8Dpz9O2OALIc4H7vdt6gZuA9qALwGjzvavSyl/fqLXO12gl00KFYNBx7sHO1a87+g0X37/uoV5cC8Ps64gyAmFY7IqL+x60RCUHX4rlvcLWU1MFiooAixph2QmCw4N00e5KVQMDo3l+d+PTRDXFP7sd97J6iNlDNOWb2ouxXgoXWFwuoSiCCznXv/jjVH+6H3nRk6aUcnknr5JBqaKNbH6yULFi/2bluQfnj6EIqI9bPccmqpgGLMnh7d3d5CIVScSdzURngS2d3ew5/uqN6YlsouVlREm0ysWbLAb4nRnJo4ePcrNN9/M8PAwQghuueUWvvrVrzIxMcFNN93E4cOHWbduHQ888ADt7e1IKfnqV7/Kz3/+c5qamrjnnnvYsmXLoo7phA2+lHIfcAmAEEIFBoCfAn8A/I2U8q9P9BqnJaQkXzaRvk0CWL+kaUGnGevP0bxnineVNaSEg0nLMwgxVXgevmvk6mnPz2YM5loR9PRN1l1NRJ1jPh5nvmwiJV5S9i//pZcb8wlvf4slUKT90DatbOHl/umA9+4Ksf1w5xHufPwNNq1s4XrH03cnJH8ox/8+aGr1WVlSevuivHf/6koAUoBp1U8ORxnfnr7JmkkAqhOQiyszJT7x+eOboM8Eem8DQWiaxre//W22bNlCNptl69at7Nixg3vuuYdrrrmGW2+9lTvuuIM77riDb33rWwF55J07d/LHf/zH7Ny5c3HHtKhng2uAg1LKvrOxvZgfUkI6oXqepQBWtaVYnUkC80/SHXl1nJSm0r00Tb5scm5C9Y6/75YrIr1uv7Ftb4rPyjWfT8LPH5OHoFF0Pf8f9/QHirGijN5zLw6Rrhg0xTXv2ajCFoiyzJD1w14daJrCFeuXUDmco9UQ9KWkN4H8cOcRvv7TVwD41f4xBDbN8/otXd64K4ZVM+m6OQX3WVV0C8u5XniC8k+gflRmmUzDxjdqErjryQOE1whXnNt5XEb7VIfwzmTc9MhNb+n57//I/XX3rVy50pNGyGQyXHDBBQwMDJxSeeTFNvifAO7z/f0VIcTNwC7gT6WUNQ1PhRC3ALcArFlTWxRzukJKSVNc8wx1OqHSFNcwdWverIqevkmee30UNW+HBNxzuIjy6sLGZS6ueb2En9+ItDfFbe/WuYY7kXz9p6/w455+L/kJ1XP4pZDd+20uSz5ciNO9NE1TXGNpOkFnxo75Hx7JeecQAla1pvjPV6+lPZPg3gdfY1tRQwBXZVJsXpYB4NHeY8FnTvAetnd3eMlcF27M372/2z6yicnRAsmBEiUsLrtmbeCZtjfFUYRA+lYCUI3Nzzd8Fn6vtnd34O9XJASsbk9FvnY2NBg6ZwcOHz7Mnj17uPzyyxcsj3xaGnwhRBy4Fvivzqa/B/4S+3v6l8C3gc+HXyelvBu4G2xphcUaz1sNd7nu0g9dGLrFC/MwsmDH4rfNKKw3VMA2Ct1L03Ne22/Ms0Udv7UOc82jaIc/3HmE2x/ZS1m3UIT9UtekKwI+/1vrvP3hN0REUBerk4pASjucU9ItRnNlhkSZoZkySQEtyepzKuomV3Yv4fm+Sbbk7fuXgGlI3tg5xIVXdbFpZQu/2j9WvTbUUCdv3HaOl8x1vXv32brqnX/W2klMl2QAsW8GNlTj/7c/shfTkigCFEVgmtILy+wdnK6pNTgeYysEc1YU10ODoXPmI5fLccMNN3DnnXfS0tIS2HcmyyN/CNgtpRwGcH8CCCH+EXhkEa9Vg5O97JXhGAB2ovKFA+O0X7GsrpH1c8vtuHG1B41rLOdCvdg12EbLbxD9tMMHdh3lhzuPeAlXiceGDIxh77GZmlCJf/8Pdx7h/heOeuEjd1JRTGmHcRTBaK4ceJ1p2bF1v+zC7//T83zu3eu8+cp97dMvD/FKyuKe3xy270nAtRevoimhedTJH+06yo3bzmHTqlYSMSXA6PEbydayZGqiRGfGzh+8+dIYl/3OeiBYPAZw47ZzWN2W8ibkbzzcG2AjzbfK182J/D++Z4aAwrSCUTHR4uq8zgMNhs6ZDl3XueGGG/j0pz/N9ddfD3DWyCN/El84RwixUkrprsk/BvQu4rUCOB2WvS4rZWDS4tnxUW77yCZ6B6c9A6UI4RUP+bnl/rldCDsvsOvwBDvfnKg7eYUNlR9SEuCSg+0ljs2UOKckWGaq9Gkmw1r0YkoI+NDmlV4tgXAnB2lPJpYlvfDR//cvdnz9U5evscNMLw6hPj/B4HSxJmEZUwVGOThm3bC4+1eH+DQJBLA0nWBwusihfI7nhkawZJXrv3F5xnuNBCqm5Ic7j5CIKXzuinXsPTbjUS/bm+KekUyqCumESqFieKE3iC4e27yq1WMc/WR3fyBUZEn4xs/mV+Ubzom478ve3XDkzV186OYLWLa2pc6rg2gwdE4Ms8XY32pIKfnCF77ABRdcwNe+9jVv+xkvjyyESAM7gD/0bf4rIcQl2M7R4dC+RcWpWPZKK/i3y0pRnTFMFiqsbkt53G6kHSoQSFTH0F91/jIqPePeOVwP8jPf3UklpFbprmDam+IBQxWGa5B1w+LJH/0a+WIf44pg/9I1XFm2vdyNusrD6Qp5jQCdEGwD86nL13D+iox3vW883ItuSgT2PbivsSSBnEF3Is53nx4OGPsVLQku6mrjfd1L2XffgdrxOsdK7DCPlLBeV2nJCsY0yZ6EgerkFJ7cNxK4b4nNAPruM2/SqsPw3gkGNQsjoXjaPJsSSQ4/dpRDY3k7uS6qq0GXNy+Ai7tavcIpTRGE3l5g/p+t7d0dxNTaZfp0UWekkuXpfz/M791y0azn8KPB0Dkz8eyzz/KDH/yACy+8kEsuuQSAb37zm9x6662nTB55UQy+lDIPdIS2/f5inHs+OBXL3rCstMtKiVFlguwbyqIIO2ChKYKrzreXbk+9Mcp9zx9BUwTvFfZbIAS0N8XIl010abNKorj3ihA1RtqFG+OWUtJhVVjSW8BQWmkFlmQNsvGEd9yWssqqK1YBcP8LRzAdC/fS0Wl27hvlXect9dgmuul69bCkSWOyoPuojtWcwSMvH2OmZATGdN7yDG8MZ8lmy2yY45mmYir5ioGU0GEpdFQgq0re99613P7IXkp60AwL7FVHRwV2FO3Es16WPCwqTBYqfPnqDQwdmmZPQfcmISnh9p/t5abL1gTCbruPTHnPNeydu5jvZ2vr2nbuu+UKdt/2cGC7M+9z9MAUYIfGHu09xoc2r4xU8WzgzMaVV15ZYydc/PKXv6zZJoTgrrvuekvHdFZU2p6KZW/4jXQZOy3S5IZLWtk3lOX2R/ZiWBKBHSt//LXhQGjHtCQbl2VozpoBhk7CUCibViT3HikDRUYuFIE3qXRmErxn7+sMjSi46di2co5svJoQbpGKJ0/gVrEmLXh/XuWp7+zlzbUtfPQLFwaqagEmCnrgum6RUk/fJPe/cIRLQ8/paSfpOjBWYAMJZsNYrszS5gRF3SRXtieObUWN3zhMpPD9vv+C5XQvTTP0aFVmOYbgoooWMMyGGXztS/3TvHasl29caxdNuTkW/7lVVcE07ZDWuo403Z3N/OH7zj2uOocwJvIV/uT/7PFkJn61f4wj43lu/fD89Nvnc70wQaAREmoAzhKDDyd/2es3uG58WFUEQ9kiP3l+OuCJS3yhEykRikBIiaoqnNuRxqKa4GyKa3z/5nfxfN8k7U1xj4njrhTimh2zvvtXhwLCYBeubuW1oSyPvzZMXFO4wtSRs3BDlmUS3urBlTI4LydosxRyZYNX908w8IOX+dFkDZM2ANOSXnKzuQKX1rDPg2hJapiWJF+pTU5LbKO/qi1Fvmx4E83L/dM1k5wl4al9Izz1huAmK+ZtF8Bvrwh+FjS1mhh3UTElvYPTfPNjF3q1Bv7CqfNXZLz6g0Njefqnivzh+86tOU+9/NFzh8ZnfRIPhTSF7v7VIXZsWgHMXUQ3Ww9lNwznEgS042xo08DZibPG4J90ONbHTda6xVceAybCExeApvriw1JGLvkuPacNRVVqmDiqIrjtI5v41OVrWNORDoh1bV7dyisD014eY7qoo4ggv96PkWyZ7z22j7hmx7vfs7GTlbumcdnoUsJo7wRDmUrEq4OoJjftCcZ9BgAXl1UMAW9qtoHXVIVsqeI7OvRYsWP5miq80IobClF9SWNwQy/Vu2tOaCxvSbCiPVjt3N4UC7CDXLjXr7dC9OvrVHSLOx9/o0aAzb/68h/T3hTHLyVnM++qdxwei5R4E4wbmgxXO0O0LLW/6tgN+3kEAecZnSi1tIGzAw2Df5xw7bSbrAX7S6z5fr/u4lU88vIxLxF44za7qOK+5494Xv/IdIlOlJpzR1WRmpZk7+A0YDNjVlsKvU/1syQZZ2C4wrqKQp9moaqCtqYYE1lBXFMwTIkZmlhcg1DRLf7sX17BlPD7ZjDkEjtO9rjwzXQXVeyP2EpDwaUb10s4g20S/S0S/ehqS1E2TEZzFZDSa8doXxOWtyScsFj1Cq8PZcmXTVa1pSjqJpP5ihP7h96BaX648wifunxN5ArR33zFAp7ZP8bOQ+Pcd8sVNXUO7jHPHrCPsYD/NzT+JekYQ0V7dMmYwgc3reChFweR2CymsWzZK3KrGBY/CclAh5lF+DR//BOB5TwbleozMs355bcaVb1nNxoG/zjh8vDD8goqdiWTothUwvv/cF2NBIFfBGxpcwKm7bi4Gxra0zfpGZJwotL9sv/6lWF6Hz5MOqEynSsyPVXk3cRYr1o81WyQK5sYlrDj0U5BUfg8btKzXhI4DFUBfzhcU0VAB8dF+HwtSY0VjtxC+H7C0FQF3Yw+pm+i4P3+gXcu56rzl9E7OM2S30zS3hTzciDu3NbTN8mfP9zLe7OaV9TW3hTj/d1t/OLVYV7qn+al/iq1NAzX87/9Z3t5ybnHiikDevzuMXc+/gbPHhjzvOooD745GaO7uSnQGObnvUPohuVNXIHXhP72M4vAfs63PdTLkfE8P9p1lDZT8N5SjJQFLyVNtly52pOhmE87yZNFby7MVMiOl1iyOk1sATUJDZw4Ggb/OOGvtHXlFVRFMDhdJCFAON7UXLorE78cZHJaD4SGvvD9F7jnS7ZezT/8x0Eef3UYiR0q2byqlZ6+Sf7xey/RVbG95qRW/dKsMhWEKdk/kmWZaHXCTPZXvbWcYzrRXD22LcmXr97IbQ+9gjG7HUZgX3t5S9Lb1plJsGlVK/uG99otCOu8VhGCzkyCQsWo672716hn7MMYnil5cerPFBK0N1Xj+K6ltPnw9vncorbOTIKSHswf3P/CEY+Gur27g01Lmzm4e4R4SuPSLcvYvLqVl/qnA+N04U7gqZiK5iR6VUUEDLOLsm6Sq9iT++gr4xxM2wlliS0atzSTIO6EsmKqYPOq1oC8tOsE+MN8hiW5+1eHANhajtFi2aPbVtIYnSx5AnQPvHAUS8pZtf7nojcvhvc/cSzPI9/rJZvTae9MceNXLmkY/ZOIhsGPwHw+2P7Yu19eIRlT+FRXO1IV5AbzDFsqyWaNlqUpr4TaHyPuyJZRCIaGDOfLdsmSZqZeHGMDCvtjdsjgGz/by41bu0ib7jjsicCP/6sQQxG2AZTghXNaKnmy8SYsYYeQBqZKANx02Rru9ckTRN4vdvI0Ecty20c2BaqG3SKzx3b2R752qqgTmy4xli/XsIv88Mft58J4ruwZPteYVz18+xzbuzt4wEnYukVtQI1kwysD03zyH5/DMC00Ibgl1sJSYbOmCtMVrt/SxY98sXXXaw6rjKoKfOJda7h+Sxc/+M1hGKxeA2B4pkxZscfz6387zPmf3BCgE9+wpcurFPYnXv3etls1ff8LR6ukACe/sdIMhgb3vTrGHT9/je8+86Y3AdXrGOY+r3r0Zr/374YnNzmFaguZAB7/yX4OHMsiJQxnS/zyF4f54O/UJsPPBnz+85/nkUceYdmyZfT22nWnp1IaGRoGvwbzXdbOZrjMFyeREn7ZM8GbjpDY2s0dXH5td801ri3EuaS9ORAaUoVgcLxA7tEB3lG23yINg9fipldpqrqxa6oJUhdLLIVkbCnoBcJISpOCqBqGv/r317nqvE6bzTEHJHZsOdzGsXdwmqMThZqwkR9hqQWA83WVkqi+Zr7GHqDfmaxcqL7xu+/N1rXt/MW1m9nzL7YHXNItJgtFvvfroCG2JF7svMWA6ekSM47A2zOP97F9wybu+9J2jzF15+Nv8KHNK+2uWr4xm5adDN+6tp07H3+jZsz+u5MSXu+b4votXYxly570g7sivOvJA9VksM/bdv9tXtUaSNrf9pFNDD94OLCCktJm//g/q5LajmEuZqM3B5LTpuReh8Zar8dAPYz25wI1EQf3jsFZavA/97nP8ZWvfIWbb77Z23bHHXecMmlkaBj8Gsy7areObQokcX2eZ1/vOBdfcw7JdCzIq7dsz295S4LupWkmCzpQYdevB3hXuRqm2FbWeC1ughPWEet1hgZzTOYrFPVo/R1F2BOD7psREjGFgu/wqYJe03bQj4/n4vTGTV6Nm845RUB6QVWEV7jVtsAk75Zy9McvzCyqxzTyo6ibjGbLpBMq6RDff7JQCRi9ilIbNnKZRQlp34OUMDhVRAI3fec33H7dZtqb4vz3f98H2Nz5j168khjgr0x44vURevom2bSyVjoh/HT+9eVBJh0GjSLsOLtbIZwt6t5Ebkm8WgfXGPuroV3jfM+jA+RKBhVfWMySdn2G6WM3hTuG+VGP3hwVTnLPPxf7xz/uQM5LwLJMMvI1i4U3b/i9t/T86x/8cd19733vezl8+HBg26mURoaGwa/BfKt2o8TTgJoPtBtGAOg5MM6LEzlP66WiWwgJubJBfszwlDINU5KStdxx+8Jw+yN7+fMly9w/60JTFJdk6W0rVkw6W5vIVcre+TYYCu2mEmmuE1KwtaxxWDMpqQT09h/tPcZYtsxrQ9lZRhFEbJakrO8WA7hgZYZXj1WvoSoCaUksqkbUpV0KAbFme6LMTpTY+2jfrKsxsENJ6zrSHPBJOPvH4cpOb1pVNeLNFli/HuczJHldNfhNwgBhS1u4Tdj/NHSdVFyl7CtE1i1w32Y/Y8rtJOaJygE/3dPPniNTNT1zXSO76/AEh0bzNc9Oc+oKntw3whOvjyClrOkYNp+4vD+c9OOefgyjfo8BP8Ir5lvVtoCk+NLm+QnSnS04ldLI0DD4NZhv1W74i5VuS9DclmA0W2Z4MospYZkpKOkWTXGbgfMn9+9hXFbj3ve/cATxut0iUUoYzZbRVAUVuLAS/da4YZWJ3Oz8+HRCQy3XMnAktje8LJOESplLKyqb61zLj2WmwsbNnXzq8jWerHDFsGaPyXvJS4kiTSyhzTsp64cIhXp+96KVbFyeIVvU+e4zb3r3BfZznHLCGr/56UHUiMtpCkhsTr9rWMPGvimmUvCtnEzLNrQuLi5rtFoCA8kGS+VAzGRUreokVSKy4O6+eqsXy3cjpmP0pbQ3vXC4WgBXifCod74yEjn5//Y7bMfgV/tHsSzp1XKEufv+Xsr1Pvv+vwXMK4YfXjHnDTtx7pcUf7viZEsjQ8PgR2I+VbtSyoAC429ds4Gu8+3Y62Nv2loz1+bjDE4VScYUWyPHlFiKI2y2b4ThmRIbfeecKRmsbkuxuejo6zj/+Y1qa6XEmlIeLd5Guk5BEbilNtErgFzZYNLQEU2wTo9mSPiLp9yxLMsk6Omb5Hs/eY33TKtMKQp7EgZGxGfW1gaKMz0zRWdxAlVCWVMZTXRiiYWxMt7Zb/B6DEznOo+8fMyju1qhGUcIe2Vz1xP7UQ9NMV0MSkGkYio3XbbGq4XAXgREAAAgAElEQVSwJJHduAqhMJkEXjw65f3dbQTv4byKymjK4ItXrmfHphU8uDs6gd0UVwNVxs0JlVyoVaaLVa1JBqZKtRLYTs9cPy5clmGY4PutCMHjrw3zxOsjXqtH6fT7hVpj/ODufn6866jHEvLXG4Atv+HPG8zV+xhqV8xp9e3NyDmV0shwFhj8Z/72j5h5/Ndv+XWklFhSojiz8njLR6nEu4kBFeC5//otEuUDrEXwN46lHF/6eUxtCfoRiAN/mn8J1ZiwT/gGXA3kWq72rmEpAqvUye9tXM3IkawXDpos6EwWKuwYGeQyPQVChVwfrFxBJqnVCJaBnQAtGxb1gj6Wo96ZkrYxDxuicMRKEfYK5A++8xzXTscQKKwwoSQk516+nPF8hQ+s6cB8dpTJgk5ZN5kqVmirzKA650oYJmktS1ZrW9Cz75AKyyzBMedErmCba0xcpOMqqiI4MJLlx/8+zmdytdo9JcNk06rWgHCaZPaEsbAsPnPsKCtMjWHV4N6V50Qet9QUZEYrnJtKcO8Xt7Pnz23xNHdOypYMKkpwIsnO0v/g2EwpIEntiuPdft1m9g1lvV6/mVSMNaMG3Z1pRrNlz2CDHqj6FlS1j6DWGI9ly14SOlxv0NM3yW0P9c6L7eNHeMV88N6gYur80/THh9li7KcCp1IaGc4Cg6/ncySytQZvMWEvq6UXUBUIlJRE8WnKxwoGyYKdVE1iK1ZOt0uk06lbAOXkxf6zAqD4LKtiSaypUZak1yMzVWPVFNdob4rRdaQVhO69vjw+QTYRbTwrhkVijuD1ljVtdB4ymMxXyFdMT/qhnvH7xavDbCmpgVj/JRWN4YrJn7z/PL73b/tZPlEIvD5lBN+bpFEmG/Gp0+pw1wHyFdNmETkG321SvnVtO5+7Yh35fx3wjgOIz5I8lhJ6B6e9ePRYtswT+0a8/VGv/NDoIOeIVoQGXcCHRwYgbWt/xpzntcJU6C6oqG/keHzoVa769Dt40XfSqLeitjyLgByH6cR4NEXwxSvXk0nF2N7dwS/2DvEPT9vMI7fX77XFOJe2N7O2I02hYjBZ0FGphpA8yq8l+cbP9vL8m+O8eHSKD25awcblGbZ3d/CtR18LjGUsa+d5evomufPxNwLhwdnYPmH4V8wHZz3y7MInP/lJnnrqKcbGxujq6uIv/uIvTqk0MpwFBv+kQfp+CiAkhyD8+goCX2yu2uTE/RnxNfd+UwwJoXAR2GyftDTR/UPRK8gIAcr5UCzBVo18l5kK3Fo9Y+/GkqPkFob2jnPDq8O0mYLfNY8vCVfP2LsITDJdrQB8/aevcP8LR/lUSKZMCR0ffu4P7DrK5lWtszaS8WNzRSEet4vcTEuyyYgxQJVlMjhVpMlh9wxO2yG8g7ujY+puP99sSQdqqaoKoDpet8eqsSSZVIwvX72Bnr5Jr9DKhcROFk8WdEazZW/FZ6UkUbn/imF5zKzD4wU+eskqBqaK7OqrFcq78R9+za6+ycgJaza2z3xxciPYJxf33Xdf5PZTJY0MDYM/f/gpE+CUwvs+ri6fPPAJjtarFKE9/mMk8HLvCJNFw5NrcL9ra0xHD9/tBFXHUs1lPL3jDCtStTIK7y7F6NatmuIegM05hbGUwhJzYV/fNsUkkZ1GlSa6ojGRbMFU5o7x7uqb5JP/+FzAKNaDIuy4PVRXAIYp+Z+/fCO6Z2/EOWJqytanEYKYqqAqTaxoSZJOqDYN13esS8Xd98porZEUglWtKZak43Yf3YiEsilhS1cru3xJWgl272JszZzIt1eImnzOPD8GnnJnMP4Pv3x9mHCO/eIuW5V1vto89eA6NOTeXiydU40z3uBv+8Lt5K49MveBJ4j9IzleH5rhHSta2LismcpTFgxX98c/8hXe1KdoTmj8cOcRdMvistJ6ujMtJHzSBy0d0Nphm5X+AxJpQfHoEYR0v3CSqXwZKaqNvV24tMOwlUpoihOvXxikrNW9mQ1Rxh5s/aBrirHIfS5qNfwl6ZkJFKd1mGpWaC9nGUtFh6j8htjfJtK/P6yl7x4bNakNzdR616qAbWvb4fUQPVPak6hh2eEVVRFeoZR7bf9YVEUwlquQCZ1fSsiWdEzLFTdTQFo1r3cbzHSYgitLMeISBl6boGeT3XMgCq2pGDIXDJ8JYYfojDkK2mqTwnavgcdeHa7ZftvvbgJOTF/fLyOyd1+Rc53+yw289TjjDX5meTeZ5d1v+XVWSMkVFcsr20/t2U9upmoYLr1sGx9c18JdTx7gVTWNpcA7RJwiKZpTVePQvXUVm95jZ99/8tc9GBWLQugbr2BhUY89I4irgoppIZx1QjqhUTZCNM05nO1VisZNucVfUMesEm2VabRQD8iwUVOk9Iy9i7hZPxezuaLSF6ser6nCoxm2JO2PsSklrakY+crx5XRMCW+O57lwlmNcwz8xVfQE29xkqRtKGZwqosSVGoMPNhNrpmQgAKPZwtHaC+DweB5VgS0FzdPGOXda8OCuo3U1j9wonv9cn/2tdWzeusITTssktEAfhXqwpC00pykErnflhqWeof/y1XYO43j0dfzFiZYlTzgsdCLIlw3yZYN0QiOdOOPN4ZxYtDsUQhwGsoAJGFLKbUKIJcD9wDrsvrYfl1LO3lHjNMTkUJ5f/+Qg+amgV+iPs7shez/zwV945R57ZLLIpsirVL+FrmJ+kBpp/2JYFpqiVJNxSMq6Oa9qVABFWghpK2ker/zxbOgsTaBFWBRvdRLYEoSY5Q6WWMHVxVXnL2NkpkRCU8i+Ugi8srM5wfsvaIUXplkobP783M9lIm+3UXSL5fy3LKmldYbhUkKjLiUtyU3vWkP7M+PoimRJOs6SZIzXhos1xzZZcHkphjTtSaQlqaGpCu1NMc41NC5a0cLWj9lT2F1PHgisssLUWz/+5cUBbr/uQn7wm8NeYd3T+8d45sBYgLPv5/H/2e+8k6mBPBee08p7LlsVyTEvVAwKvglZUu3lfLKRLxu8OZZ3WEyC9UvTZ73RX+y7u1pK6RcquRX4pZTyDiHErc7fYZnw0x69/zEQaez9jbFfH8qybG1LgIa25KUZ1IIZOPbxXx1k2cWONyTt84S/94oTvA/w4F2PSBIonRd1QhZ+CGHiGtjOkt3lSlcEo6kODDF728GFQEgr0thDtfLY1bwRAcMj0BQR0KUJn7e9lOW/5HUsfYY3YhY/ozplnB+SUhjNlenu6KCtrcJEvoJEopsyOnxlWWwsHwUEh5vO4erzOpnZWf0IdzYn0PIC06qdotxiuWzJmNdkO+/pVdhKmVqHrRdUqBgMTBV5dbLAVRWNuBTsSRi0rkyz9nCZVU6oTWIzulxl0pf2DHNo/yS///XL2H1kioGpIjFNwTDszl5fvHI9dz99KLJh+9BMmdse7q3JQ1iySslc3ZbyePxl3eKn97/GBRWNYQY5sHecz//BRZTyOrv/rY/sRAllXdr7HvifiVsXcLKRLxtIpz4BKT1P/2zGW3131wFXOb9/H3iKM9DgZydKNdvCmjmvTOZ4r7PPpaH92/5eZgrFoBKmKb3WgsemilR0i6aQJViWn0KGWUB1TIpA1kkNu5BEedMxS9JamWY8sYzVTkvBKV+RUjKmoABKPo8mgyESU6jkY0lPdXO+cJPQYVhSIqVdAdsUUyiENPPbynnShvMeqGk2W3BwZoK9LUvqXusfn3mTr9JCTBXMlOpPiH808jId0la/LOReQbtkHQknF9CasvMSFsLJQQSfo4DIGgio5mOqR0ImqZFsSmFazuQja/MIYOdVbn9kL7eqdj7DNZLbfV/X9xZj/Gw0xxWhpjUV02IiX2FwuoiUMDRT4sa/epo907YnqymCT16+xiuaOjSWr4nVu6gX+3cpmf/t0nV8LB9HWrA7YXCBU7EtgTd3jXCLuotrmzKM9U6QL5sUXh+r7cWs1BaRnSykE5rXrEcIcdYbewhzC08MEnhMCNEjhLjF2bZcSnnM+X0IWL6I1ztpqIQMhhZXyDTHMRXIKZKetMn2C5bVfb0/5KOoduHLp7/7HINTRSYj9OEVpPcvrth/u5yg8D9Vmggs75/i/MQ7pr7v6cbZi7pZw+zRFIVleo728gyZSiHwr62cpbMw5YUQ0gvQM683Gt2JjZci4u8xq3bbmmJQCTQZC0+QtqdfzyADdOgTnrEHaDJW8cSvX2Esb4vZAQxMFbEsWVPRC3axWj0sN6e85y+cSTdbNkjGFDoziZoOZGHohkW+bAachcC1pWBjufb6E/kKA1PFwGuGRwqYlt3hzLAkq9pSXsz8ExvjfHZ0N18eepFPjO0mbtY6NwKb6vuude3VCduwyO0e58IlzSyPx3hPqTZp/9irwzz/5FEOjeUZmikF3gtVGnTEyry7K+PJPNz15AF6IqihbxXSCY31S9Msb0m+JeGco0ePcvXVV/POd76TTZs28bd/+7eALZG8Y8cONm7cyI4dO5ico2/0YmIx7/BKKeWAEGIZ8AshxOv+nVJKKUQtkdCZHG4BWLOmtuvQqYaUEj1kNK77z5eiqoqXsPrMHAkrf5OUK69cyYFCxWtfKAFLFajenCJRhQnS9Bg59d4kIWCFFWJSOJWZqmXP5lJANHlReJNBrmTUFAGlYir6eD7SIxBAShq0xhWmy0ZdPZ1aZg7EKzqr9UFUn7svpYr7h4hcBsxuHJek48RVhSG91lhFYUtZpQIoZglCHP6LKxl0xV7B+dk+UJt4ni2U1lwp+I52gnYOZRNssTfS0a8VEJAh8HdUyyQ1ss4zb5snDTYQLgzJMhy95wm6DPt716bD58Ze4+7ll1aPB969cSl/8v7zAPjk3b9BNyUZBOmY3TOgNWUFms77Ib3/qojJMiuK46gFSSE7wVM7m/mjR46+5Z22ovBWJms1TePb3/42W7ZsIZvNsnXrVnbs2ME999wTKZF8MrBodyqlHHB+jgghfgq8CxgWQqyUUh4TQqwERiJedzdwN8C2bdvmzxE8STD0oECYFlNQnaYa89HcceE2SVm/LM3SZQnimoJwQgWltIJimp7RNwkZvcD3OlhGpJTC++yVgaSqPRMNieKsBCohsnVns+2BijqWXDhNsnMlHTkLb77eRKDW6N+YSOnqBy3sI6ApgpSq1khEdxn1F6+bnNBDTK4ExoNjQXrJdlURAaMfoIFKiZxF+EozallKCPtZVwr16wdUUW2icvDeAxQqhpfUdFlBrhc/H3OvSIOPzUwgKkfZm4arP/sp7zNbKuTQy8HP7xKztfpaYWvdu43be/omq7xg594n8hVPRnq+aKtM2XkqQDEt9v6fR6kk3xnID4S/V8fbbeuBb76wgJEtHB//+mV1961cudKTR8hkMlxwwQUMDAzUlUg+GVgUgy+ESAOKlDLr/P4B4HbgYeCzwB3Oz4cW43onE3oonBNLzi980dM3ycHRHGldBpQBhRDVxO539pKOq0zlNSbb7axgjUesQ2t2finB+TJ1qq+IRkk3UPRKjfGVzqogKrzhHhH8Wf+64SM07Oe8ujhWc7xqhZq5o3CpkWRtfx8r25IM6RY5LRG4o6gQgx8tSY20kkAUap+52zJySTpuV8RGqD+vyo8x3NSOocz/KySlbSAFTsWtqdAXk16Fs6oI/tInP/1KiBjgtnFcSO1ER3nckbdYyeUFSD3xCMaWL6HFYliRyqWCd67M8Jnt6zxdfn/DF7clo2Xalb2ztawMo7M5gaoIEsfsSdS9i+RUE9oqO2nv5gf8kg0nq9fuW4nDhw+zZ88eLr/88roSyScDixXDXw48I4R4CXge+Fcp5b9hG/odQoj9wPudv88oVELhnNg8ln/uB3Tf0AyHRvMMTBU9Kpq/zeEyRyY2lWwDobjqVl6oRwKW6xp6/1yEo/nMsq/ecbVQpEXzxAgt07WGN8qjFdjhn7ZULBCznu0fEcepWKhYxC2j+k8aJKTh7fOOwUBTNZZpbZi5JO3FadIR4ZwohYlUTGV1W4rOTILxOi0Xi7rJobE8hYoRKLAKP6eoa84HLhXxo8kM37h2c/VdkZIj43kvlh3VTAeqOSFX2nk2hd2wltHEvjQP3jZ7Cf8bw3Z9yYtHp/iHpw/xq/1jfP2nr5At6sQ1BVWApgmMBUpd+/MWUlb/Ady47RzvObiSDS6imhKdScjlctxwww3ceeedtLQEG+OcbInkRfHwpZSHgIsjto8D1yzGNU4VjsfDdz+gEhWJw9nOV2hPx2kby7MhdHx7qgNVjZGrlByNeVvp0rAk0jKRBPnXbigo76vgTWgKqiIo6xaGlMRNC80KhRUkIKr3I5He33EnTBUrldCsoKRw4HgnHi2A5oTGtC5RCjnS5Rwt0qqv9+CDlhGY2WDWwBTR0xbYeQjfIOzchGVhUm3akjQq5GOpyNf7UdRNBqeKxDVlVi1/18B2ZhIITcUwrZpEqzLbCWaBEPbEY1qS1w9OIA07hGdK+IenD1VDKUpLZDMdNye0vD1GetJ+rxbibeeHmgAwrVqDLdUWzikLvvnTXnKhddjeYzMe5fiSJc08f29tG8e690y1Sc2aCKrn9Vu6eHB3f2Tjofk2JTodoes6N9xwA5/+9Ke5/vrrgfoSyScDZz8P6QShhxJ38cTcBj8s2wt4hv9vfrGPjne0hZakAo00EzPB5NxMxUCaFaSs1JxLV2KUW5d5RTZu2KhvPM9MySBmGazIh+PTtV/wWGLCE1szLImqGggR7bkpqunNF6pQMSw7v9FeylJdl8wN4f1fPb5+VDsUABLBRiE+n7HmlfUUBSRQNixmD/oE++T6w1huy8CFBdBs6QepJVEV4dEmxbMlbhQJnkhWGHLUV11P1kQGukP5Q4NNcY3z17RzND/h/Q3MavSlkzvGyZe8fCRaFO3KUoxSWfKvTRUKvo/xhzav9PJWz740NG8PPxVTUUT9JLclJT/xtXcMx+nn25QoCrPF2N9qSCn5whe+wAUXXMDXvvY1b3s9ieSTgYbBnwNhSuZ8PHz3A/rEP79KdixIkdNlVWc8kY4xMWHz9CtmNTkssfnd/pVeTBFYsros9o7xXbcpbldZzhcxA1aNV4IMnQgD4EaV/PdhSgNNz9ImhBd/VwQEa36jU7DjsoUWZmr2jbYGx94541Y7uYqjsub/wBgjsCRtJzwN0/LYLfOFGyt3m4e4mK84XRiZpEYiZXdF87/X7QmNiyoWQ1p1ZaWqCmlN9ZL9EKzsboprHH1tInD+9lka4rhwVws9fZP83/f28JU6x6Wk4BxT4aBmsa4jTXdnM+evsMUievom+ep9e/iQL0/S2ZxgLFdGsUwylQIKkg+1GkCibs/l6pjspuhxTeG+L0XH5xdCkDhd8Oyzz/KDH/yACy+8kEsuuQSAb37zm3Ulkk8GGgZ/DuihBhXx5Pwe2da17YytbuUYCoNTJe9D7zpZAMrKFIfeGKtbkJRJaJQN2wgYliSmKlimDPjSXsjIKfNvb4rZTbvne4OOeFcQ4bSq8IgZfhPepsygSftYN0NgeX9J37mCmCRDs8wFVhISKKvBZ2uh+5JMofHME4WKgUDQnLAnw7JuzlGZ7OQYnBBKobKwSaIWwjufi3Df485MArLwrKWTd1I5v7e1C17KeY3ZgUACt3tpuqZNYFNcY1VbymPN1HtKUtqqm4ZR+xw0S7K8NIQVj3Px8uWsXZHmiX0jHBzN88Trw9x0mZ1QDstoq4qgPR0nPnLMq5u49KjJQMaad4Gevxn68bJyTidceeWVNcV6LqIkkk8GGgZ/DlSK4aTtwlq02V/CpO/LKti0yqa+9bUrvBg3WK0rLLUUMimNXNGRWhB2ow83LSixPc6YqmBatdW1/phzazJGoTA/2eNooxBtrMPHFhSbYpk6rpyTgpQ268dx4pFmU2gUOe8LI4QZ2DNflPRqcVkYWoQdSho6iWaFZa0pSrrF4FSRrohYtzPkeSF8nL8uwzXm2ZLBx0jwREpnLAmbV7Xy+i+HfQydeE0Ctymu1Xj9S9Jxr6VmOqFi5KPosZIf7TpaN6SVMEwwiqzt3cn63gTbSGFR5pXUDPfutKmjzaG7SidU0paCbvn4+EIlbuqUtLnkO+xXuPH5s4GVc7qiYfDnQK2Hb39B5+WBON+JprjGqlbb87KQfOPhXvYOTrNpVStvpCV7DR1VEdy4bTkdh4roAwVaUzH7i+ujBJpSYpoykn0Ctpc1NF1iqqijzdMt1WT0ydwwjn9vmJOSzEosRWAep/ftcnUAkAJpNgf2SlkCJ1wksGqKw+aCKiu06dOoVvTkFzdrt7eVi8SxkM0rGYzoJzsXmhOanb/xKZHO5m0Dnqa+AM7TFa569yp7leYz8EBgVaAqgoGponecAFa12Vr7ruc/WdBpjrgBibNinONe4qJaGK+S4JJSC8/pE4zHaiUtSrrFkqRKLtS5bD6fBlURfNon93DXkwdqWDkNg784aBj8ORA2+LGkdlweiOn0JbXFzyQ/3HmEREzhto9sondwmh/39PPDnUf4raJGt6GSrxh0L03T1d6EWZgMMEQsGU2RNC3JVNFO2hmKiiWUGgniYOGWZKa5yaaEAppukixVk36KED4PW6BaViDtKyROsVjtWKpBnlq4e6TvZxTKaoymiLBD5DkjJrjO0jjxBVIHY6qCYpkUZnLIiK/HXLUOubIBZciEaxicidVtP+g31Jmk5hnzlFC4YUsXUkoeFocDHPz2phj5shlI+nrnx5aBKOomqZjqhXWCU6h7D7ZY3WfG9gPR/XnrYV15lPHYkpp3dnCqSHJJCnWWVpVREML+bK1qqzKs2pviKE6B14mwclwVzLMJ9UJE80XD4M+BSskMLJtjSTWSFxxl8CfzFcadGKxfTwccpo1hMVmosLot5RW0ePudZfvSuEpJFZhGyIBEjdW0m3Lb8VVBb1OKi/P5iGOrW8ZoxXI+BudkJIoxhuUYoop/TNL2xKScnwGeDd2daSgoXiMQS0r8emluQ5eJZAZZFqT1sCzwfL7EcsHG3uW2AyRUm7FaI/bl6zi2UJR1kyOT+RpD7Sbfl6TjXNTdyiWrW3n1mcEaho5by1HUo/V1YHaWjnuPAvjEOljaf07NPm9cdUlTAiFhhVnLQitUDDKO8ul8jb7bXObbj+0jrtkO0O2P7PU+G7d9ZNNxeffJZJLx8XE6OjrOGqMvpWR8fJxkMnnc52gY/DkwOJoPJMvWTxbmxQvu6ZvkN4fGaTGEl2TrXppG6Yrzw8MjNS3i4ppCJaQSmS3ppGNxVEUQV5WABEJUhWf4y34kmWRrsWDz+b2t0V/ElqRGSwrKk/YHy5QRR0rFrgauc456dM7gMfZP3VexqYZiVO4xUihMJpojDL49+bihdYH9/Jak46hCMJor173P+SKhCrqXppksVCttbdkFW3JCAk1GAbVUritBEcZ4voKUfu0eSXOlSMK0E/NpGaOtpPDKU/28+MxAjbEPSwsfD6Tz/nXJiPJh5l7BrDOX885couZYIapicgv18iW2sJuuWzz6yqCnM2VZkkd7be3F3sFpBHhhn7nQ1dVFf38/o6Oj8x7HmYBkMklXV9fcB9ZBw+DPgZGJYiCW2juS5ctXdM3JC37u0DiW0+HCn1D94nu6+d0d3TWvdc83/twIer+tBJmvmBwcL7Im0048N4MmK5hSUFRiZONNNdcMQ2CHf1SPN14fuimZKVmIOfrE+kM1JTXOdKIaNGg2p2iu1PcwXe8yXza9+LEbT/Z77SV9rolDIqRJEtPLMyjSZIlhyw0v0SzKZm3M3/LpUFuGRKtpEuZUE0hJtmRgxS0qhuVonFkgLJzFk/cUUrOGnILms2KYgdr2lnKB1krOOzJeqjDUW2BgxOTQjB5g5NRTzTweSODxvUN8QLTVnNO+/9Bd+Ci57bKZsoCYKsgkY14BmaoICmWdtJROVzbHQZnnoFebgivKMTYcNkmWYjwd1ykLeGb/GL/aX636/lFPf13qph+xWIz169fP69pvJzQM/hxoj2sM+5Jll3bbCavZeME9fZO2uJWolhi5XqyiRL/W3Xbrr47VJNOOVFRItHPv0jKfmUl4nPgofrwfEoe37yb9ZrnPom4yUqowW81f+DKGolJRq6OVc6g3uoU/5jwShlW4zzBovt3bEs45pSXRy4YdkpESYckag59QfZOKv3rLvYKUTkWtYKaoM23ZK4t25rd6WSgSZnXGURVhx60llHN5byXgOgvphBpFlp01D2LHx/HOE4CzWgxHO6KMfegIwNbKn8hXbE2g1hSD00VU0yTm5LVURdQlBETh/aRYvyRJU1zj3ZpCc8rkoanpmo5cjSTuiaFh8OdAk6IEYqlbN8yeQPIndH+HGC5zfXC66Gi2z/4lOO+cVt4crhEVRUonWQssabLlgNMJlXPet4qXH+3zwjkuW2O6qCMwkJaBqAn/VMcQVxXH+Lnee30cT8IoLJEspa1TP99U4dLmBFpB1Fg007X03gYJufrjC99ZPUMmnVnprYr6+lPmVV2hYFVvQlMQVtXJcMM6AY69Y2hd79qfxG1LxVAU58HnoicE9xPh7qvrNERsd3Ms7v6xnFtIJj36sCXm+qQH0SaqlcRNcY2r17TxaC5LRQ8SBc40aYXTDQ2DPwtsr9EMVDvOxcP3J3TDhi5fNhH1OJUObrzuPO56fZJ8LqhnI4TNf/brsJR0i9dHc6RiQe8vGVNIxhJouTJvKgW6aam5jjMqe9ktqi3yZkN4/2x34oZvjjcKEdcUOpsTLGmKUR5X0A33itUzVumg9Xzf+Y62CgULBUkTRU8+WsOsIYUqBCehSkyh6PtstOTdPEL1upmkRltrmpJuMZYrowqBKgSa6nj3DhKqEimpEObY+wuv/NvBLdKSNi1T1E6879vYidiz8AS8wA7ljOXK3u2XDcsLcQncEN3C3nlXCdTFypakF+Zsb4ovOIbfQDQaBn8W6JVg3FSLK7bnNAv8CV1FCO+L5lq5DFMAACAASURBVHpqcxEGMkuSXPOHm/nZ/9gT4Fcn4ypfe+861MeHA97cky/McHXBF1YBBqdKrGpL8oX3dPNmai3mI7/BmCyTjM0gzGV1DfFs+u5+KMKmLybSceLppNdW70SgKsFndU67TdMbzZVpdpKk9rirgQyJ8IUsqmMXTqzL/95ZCPrkCu/vNFmWkCOsAWR3F5M0UaZJVOxG30iswPpH1NQ5aMKkSan6otXpujopuY02XC+92knLZipZ0mUJ1TeWrvORLxsMjowgTJ1kTCWmKqQAy4BC2SBtGIHTCO8/Oww2ki3SKeNzfh5rIRnLl4n7vHwATRW0JmIohYWzmITWwkzRYHCq5OUGRg+Ns31zG1++Oiw1ePza+A00DP6sqFHKnIc0sl/oqeOVLKWJUsAjm40i5v8ghz08IeBzV2/grseGalYOYbjyvjKbYcCSPJBZjtkk+WjxWTZN1UbphYCl6QSTM26Rk3PuOuNUhe2Rul93d4UR5UTP18vXfMlJv5eKlMQNK2SYhDduAegxBaTEwjZmMVNGPBe7aaQLax7K4NUwS3j1cHwo6RYVESxTc0Mgbp2GwBbsO1LIe393d6YDVbWqIiiOHKW5Uu2J6w+hJIFExEP3wjfAvsEsnXR4E2zUx3K2ME8iZPB1UzKjG5G8/7lgoTjMKijqNk31zVyOv/nOCLf7+gPA2aGNfyrRMPizoF6V7VxwE7CPHdrLVM4Iap7UsRfhD/KtalukFrtfhwVs0keUUbUk/HXvEfwsfMuL1YPf88wkNFpSGi3xNLI0YYugOT1mI+F0vJrIV5gwS/aE0ZxAP4HWnGVLkgDvnsMCYyJkdO24t4aVTmG0pMlXTPJlA9WyWDkTpByqQkEoSuA5uf56KBNQhQWmHhWasNvAKL73YL7+bCqmoPmSr+49ubkZ91y5koGMJby/Jwv2eqFKy5ScowcboHtJbEHN5Oufttzj1NCoI5+B7x5F6PfOTIJMMsZItuTp6syW41loeM8NDf3ZQ72cvyLjGfX51sA0EI2GwZ8F4eYn8zX4s6FeSCj8Qc4bZo04FgR1WFRF8ImLV5B+NVujh/7rlE4hwlhFYaZkkC0bdC9JVaWS63x5bRKMzdPHeRxS2hWm/unJ9Rzni4KWxPDpw2RL0Zr8fqhCRYg4I9Mqdl/cONIygXzgOFOCNKse9NLmBEqljCjZoSD/nZpuJNrA/udNDEGWji6DK4QycUo+jn2G6ZrxJjSVRFyjPR2fNQRmRWj3+GmZQsq6bQdcJpT/0UclZcNvzVxrGPf9bEnFyLSkndCSXSuim/42kBLVV5zmP1dLUpu1qbwf7tM1Lcl3/uMgF5/TxvbujjNaG/90wBlv8KdHi0wO5ec+8DgwNVwI/B2bp1LmrKhjBMMfZLeBtYt8xeSuJw+QrhiBJPKVl67mmQMHALxy/X9v1vmtS1dw6MXBwDki4/buTwlTRYMOiGxhGFOEF34wLMc/9g2xqJuE1yOugY1qZh6FdEL1+rVWYSc2gy+v+uUVw2IB7D/bu1UEzam4b0VSPYFVN4ZePUYCBsH3J0eKGdJeGz9yMzXnyRfHKJpTJJA0awaaKKMKm7deLd4SNFFG8XFpO5UESKiIsu/WZcBzL8er41EQxJAoulmHV69yobUaQcm7LRmy+PV4SplkUK7Zr5opwKnSljUevXT2zYWYqiCl9LW2gV+8Nszjrw17IZzj1cZv4Cww+McOTvHyE/0n5VoLVcqMQr0YfrjJw8F7D3j73CrL//3YOJ/JJupK47qe///4vU28NJOP5K7YiP7m6ZbEUjXKpWq4QGDLCSiOHr/LVZHgrQbqwfU255vDc9UpoxEdFIilm+y+tL5tihPT8E9cHgXSTZ7rxx+Lj0JZsxPnubLhaPDXPvmcUcDrYKACwqExSonfZU9SoqlSAtPOl1DKIiUspToZ677zCwGZRHVVoCqCiglu35yorgRpYUvhCTcU5D9GOlUPodiQlFDRTZqYvXm5hICX7yIVU8mXozx84f3vSoyEZR4k9uT+3KFxvnz1hoahP06c8Qb/ZGK+WvizYbYwh78g66Bve75sYkk8qqcrjRuG6/m/c1ULRxwFTn8cfi67my0biGQLcX2apFMU5Boky7Clmf1mV69x2YI3t5CQjsRm5ESN0b6P2u1m+xJIp5GF4ArPcuLcqpNr8J+zKWYnwcuG4tmz8IRU1qpJYcWUaDXXFhixOLoTDipqScqqHc4p6iYDU0XW1twdBJYiokpjrLlnz1CDFMEEtJc7CMds8B/jk52OhLB1keoeUR1veHU2litjxWuNfSqmYpm13CQ/VEXQ3ZlGz4XHCytagmyvqJS6EGLOEE6DwTM7TtiCCSHOAf4XdiNzCdwtpfxbIcQ3gC8BrpjF16WUPz/R651KrDi3dUHHp5pjTIUa0qcy8eiDQ9iwbRkHdo1QqBhUTIv9Scvj4ad9K40o7/nVwRluf2SvF3pxD4lyyJviGnndTbpBAZXppnaSRpnO4lT1OlTDIe4kEj6dpga3LLROqxKy6jFV0JaKY+Xdq1dhCSjFmiCUWDcVBUsIFCkj6YHeHFVnNjIUhRFfsrylZNBa9OcTBFKqDCdakUKZcxL1oyVHwMgq9Yp3BdUKYmeTa+Ols382WFJ6t2e/pnZaEYhQW8lZZpDQnolQVy0BrGpLUslLKFTHEIbLOJsJTSKKECzJJGwlUefcUbf42+9YNmt1+4O7+/lxTz+G2WDw1MNiePgG8KdSyt1CiAzQI4T4hbPvb6SUf70I16iLlqUp1m5+axM3iipYeW4rKxdo8C949yrGB3NUiiZCwIZty2lqmZ/BP/fSZfS+MMyhsTwVJPvTBptXt7LqmAh69xHfzZf6pyg7ejR+Y18t7ql+nZa1JDg8XvK+ZG2puEeRC0NgJ51dCiHYre2KuklrKgYzc4e8hIBzO9PIguLEfGWkcQC7fH80VyZpRfcAUBVRM0mAYDzZGpis/HBbHmqxOLVpU4GUMaRe1XuXRh4pczVHyshASe35/GyomKVUaZC+QyTS678rsTsAmOL/Z++9w+S4zjPf36mqrk6TegLCIAwwDKAIJgMQCUoMpkjFlU3lQFqy1pcKu9J6fWXvWrJ9aS93bd9dP/bdu1pdyzQty7YoyZRok4omRYmKJigSzAAIEhjkMJgcuqdDVZ37x6mqrqquDoOZIQBi3ucZYKa7wqlTVd/54vu57j+ptHx/c/ceBqdMoLqI+UFdB9oNB0R13KE6BQRlLYlG1X3mF8o1uai+/CxWqUJHoDYiYxokRmdxZgvUC8l2uNbxyEwpNtbjdfYa7MsyMlPiVKX27ty0KZ74w8twK1WqS1gwg6c8ZzFxskBnX5pUW+ukHq9GLFjgSylPACfc32eEEHuANQs9bqvov7CL/gu7XqnTzQu9a9v41f/4SxSmypgpHTPd+nR39qWZ2dLJj38wxinNYU7AxNEprpxNUracqpCNwZVru2o08ddf2MvNlZWcfKy6nQSmCgHtVUBHWmXJ2DFvbsXVnExDw5FKeKY7q1StJ8aVf7cePKGSMQ1KrgTXEVTq0Bg3E6jHJueIGBVoAopGktFUJ73F2kyZmWKFVEIjY5o4bRmYqQbmpdTmlWbfPNUwrAdbjkqGFFJVEnsLsO3YSGmF9hKiao2MtxngFqYJIFl2yBSdwJFBZnqrmT8SDFEi7WYrRV1XEpioGPRE/PMiJmDtfR4qQLTKblqscg1ploNVqZ/G653FSytdH9nMdiQnp4s+UdxAT5aETPLQ9DgdjiAlYVKXTBTiM5u8DLfq0lqlYCjmKzzyt7sZHS2Qdxx+6T0X8rorVtYc43xxBbXe8boFCCE2AL8EPO5+9CkhxHNCiC8KIWJnUQjxMSHEk0KIJ19tVKagNOK2XDJW2O88NMHnH93HzkMTsX9vv3QFw2koundJgs9FM1uyODY5xyN7qj6jQtliZEZp53fdehmGpvLFkwmN37rlYvraqrqVF3qdKFTDf1KqnO9C2caJcXlIcKtBFed5ItAwvVC2mjJyBnjcQtA14WeFeFk9rSLSWpV0orGVMV20GBrJUyhbdPYPoAsDKXWkNJiPtE+bOv1daV9zDUIE/vUQpHLz5tFD3LQFBexcQlAwBHldMKsLilq1itg7Sy6TqLpxBCQT9ZUL6f8T83kd1JCsoawNb9GyojcCxdPUnjJAqHlvZj148alC2eLl4VleVzS4tWDy5jmT988mMXdOcODZEWRkwrYP9mC48SVDF9x2zXrfnbP38ZOMjhYYGs1zcmyOL/7ts/775cGzEP784b3cfs+Omu9fTVi0oK0Qog24H/gtKeW0EOIvgf+Kejb+K/DnwG9E95NS3g3cDbBt27Z5en3PXUQLrbzGD8G/JwplvyPW1588gmVLhnWHlYHmEz88Ncm/QQ/xpX/5G8/yvz9xDf/48WtDWssvftp4TEJUsyR+lJa8b7aMrqteqhP2FDm9M9a1Mmc7DI3m6TiNu+dRCrRnDMz2lKoirdhYtsNMSQmJimaQlM2pG7KmzqrOFEMj1SBuXLaIRC1sGdOITT+M09y1gMYtUF2Zol2nBJDLmuQyCaxaLxAns1XXY3fGRNMEGVNHK85hjw7HnNUdb511KBi8jfbJ1Sp5KtFtI9cYf8B4+Ak7MpBuiyqO0kT8yCVKALfaM8dr3Tg8XSLtQLcML95HDkzx0H0v8fqJEpf/coQT3h27Rphv5/Cu8VANw+qyVlOs1Uox16vFAlgUgS+ESKCE/b1Syn8CkFIOB77/a+Dbi3GuVwuCD1m54nD3T/aH/r7zwRdwpMQ0ND5y7QaXWx+eTllcV0iQlrAzafPrV/bDD4ZDD7Vl16av7Tw0wa7jU0gi3XJEVVB5rfEAKprG13vTfLpdoqcTVK66AfMbz4dIvjzMbuviu4+O8fbSETrpa3kOHCl9U3x2zsLISoZnir4bob9TsX6+4OhsLTQ9HIWKzfScFRI+jpQkNBGTUeRaJTFCrr8rTdly/FiGAN+a8fhuZmUtfYP3Z8Y0mKE2WFoJsJYOuy4ZUbBZqUsywsCWEhnwjRu6CgqvSPf78wIgnRm86Kj3mdcNy4PQ6hvvQkJHubZ2JS5YKgISXrlxbCodKQQwY9n+dwlNw5wOx36EI0hODLMm0i0+GP0ITqGU+ItoKmZJkig33q5nT4UE/o6hMb/Jj+3IkMAOclh5z1U006dZMderic5hMbJ0BPA3wB4p5V8EPl/t+vcB3gm8sNBzvZrgPWQe/euhsUL1hRPVHrjFisNf/XTIf7HHdMns1TlGKza/ftlqbrtmPff9YDj0UBt6+KH1Hti3TE5yWYjxXpDLmOSySTKmoegMAt9efeNm3vbOy/2//+H++FvY1WYyqkmi7Q+DjJm+sJJKOOkQCv4CoWCxlOr7lR1JfmS0s7Xg0SWEhaguqm4dz93lbYW3pRCYAWe/xzg6NJpnbY28VxbGmq40HWmD0ngZ0wkLe00oIjQhSjVCf6JQJpdJBKoVGkOi3B0JP96i3GUeP77QNTQzSS6tKaunaMWq/KE+uQLWt4dtlxo/fEP3m6j9y93cQTKeUIuP1MPHWOEHl4W7raPiCZWoil+9hwJCHcOirqqudILJuXDV9f7hWT7/6D5f224ksFUnrrD142v/u8c48MwoqfYEX7ptGztPTsVq8K8mOofF0PBfD3wIeF4I8Yz72e8BHxRCXIW6sweBjy/Cuc5JxJmDXqHV/3zkJX6+r9pH1nsxo5qPB00IPnHjBbEPXC6jMlD+7D2bY03WWkgmC2VyWeXbDy4auia4rL8zNP42qapCPTeMY6tuuJNu4LdR5ncQ+ZJdl7DZEwUC1VM3i877t63D+o5XNRw+RzZp+K6fekFULxuoI2Vg6Bq5TELFLur4scfzZT8WUCzb6HZY89Y1gUaV7K1QtnzKAC8OEm1i3iqivnFbilCLzf6uNKWpGSjG7BtY+IqWQzrwXTRw2witRDKaB6xbPYvEiKGS8FCKeW5HZkr8TaAH7gvHp7jhoj762pNs7u9kx9AYoN4xL/gQrE4HVaG/44Eh/+/VBYtPvv/i2DG8mugcFiNL52fEPyPndM79YqGRObh1IMdv3XIxTxwcp2I5iEChUNwLZWiCu269rEbYB/33cQFP74EVMQQsUqoXaKBHvRD9narJhiMld317F4AfW/is5ZDQNb8fbT5fJp0vt8R7E0TZdrBsJ5SOqQSdcuPMVWwm8mXG82UmCmX0abNu2ldfe5K+9iT5ko3tyBBPe/B84DYLd7X7iSZ0zh69Q3vMOC1HcnQ0T3+nEqntqUR10ZlHwDl4TA+CcL2ERViQew1PorBsJ+S20LUoPZp7/IZCfx5ivMU0zvCxw79FdxUoniPvHgqgWGMdVIPe5YrDH/zz8z7LkeorEM7Dr4dTh6ZDf5/YX5vR5SFaBX+uavewXGm75GhmDgYfplzG5K5v76JiOeqlFgLbVr+/d9u6UDAqaDUE/fdSwsO7T3Ld1tU153juH5/Fnqp9qYN8KJ6bRbrj/d4LJ6oNXSDSEF0Jxide8jT8KoK55lHtcjxfJhtxKaQSOj0uZcSxQBVnEcmk4cQK/DVd6ZDWNjSabyqupISpucoCtVPX3xzTfcorimuNIsyFu0rENUMx9LD/OZvUKRVrBb5nYSQNjY5UglOzefpjLlIISK1bR8myKU+NkygUazeoN0z3jqYTKvnWdqQfO1CnKvuHUPdabV9J6cgAtXhy2otHhAeYy5p0pI1QXCVIMpdO6LQlDWYKRTT3+QomB6lMIfWB965trHM5dqzFWx+NWpqeS1gW+EuMVszB4MO0aVW7L8iBWK0iajX8ltYR0s0e2jXMrYcmahYWu7+D53aFzy2pFiNB1a2jCZXL/NbLVvPEwXHKXiFXjEp3cDSvBHogLTD4nrUiXDWNkPAuCckx3WG3afPWzhQ1RqQIj7tek+/o8iYEdKYT5MtVN1DCDY4iJa3aKsHjepp3kM56uq4eW0VXOsGU65/2NHvLluhalYvfMPSa3giOpuHU0dRLbrBZazDpyUw7SWDWKuHMFWuOE4y5VOW/wNASaEKwumNAVcUWKiR1h5LluEqBytPQCfMYVRI5SokMFVvSnTWR0y/XjEkgaavMMX50HM/GTSU0uioOnpOvU0swOimRCRNNCC5a0caekzM1xwLQ3TjWqb3x3Exy8VsUnxNYFvhLjPmag1FNIm77qNVgI0OUu47TemApmdDdXrsKXoBr5SXtbL92jX+M/+uB54E64qtWFvsICo9GcJxqtWUuk+BfZIGXdJuELnj3lrU888DO0PmjipvK5Y93g3lj6HZTJr2FZWquQqKgBVwkgraEUcPrEoUuBO0pI+TGycYS6zW+8FRCp6fNpJDXMUpTfiaRbUtM1LhEwqjxP/vX38A9I4WOrQn0BsHZiu2EFoZq97DAcfzfpa9gFMqqliF45F2mxTqqMQ7HrlqChbJN3i0sOzY5x0CMdr+6MotRKNIWLMIrE2p2r1mCbkciKzp2W3ts5hXuGN6zVVnD3xP1iBUXauOdm1gW+K8AFssc9Nw4uYzpWw26JihXHNV1qk6WTiOULZVDH2TgzJgGt1zVz1p3zBOFcmxxUNLQWNOVxnDKfqZFKz7sVR0pjHyY2K1k2UwEqi1/582X8JJu+YvkM3ivqPev8N0JfoZKzLmCQiHtEqcVypaf/tdtO0ipmEB1TZDQVHP4ciHaZasKW0oVD6B2EQEvRbJ2NEEh7QlGtV+KqakM6WLB3892JEYqid5V/7nxg/zRzwX0d2awkiswxodr9vHnxnZqaA4awfOdF0rhrl3fzJSZ0iVvQSUV6JrAkaJ+A50YZOxSNS5QZxvPaugo55G0MzQyi65BtFA7YWi8e4tK26x3D+fL8/RqwbLAP0cQV6j1wvEpvrHzKOP5csiP/Gfvic/iAe+FqrobfpYssb2U9AuRQAmsB585zva08NPeElEOA5QrxEZxvwRctg3hdUsqndLQNekygAaqUN0sl/zJabZfv46tAzl2HpqoTXOU4VTEVnB8cs5v9h2kGQhq1qWSxTEvAI5buRtPLYREVZMG2w9mkzr5Um1vAG/7bEKnUFYC8/iUcjccn5pDJtoQRtbPUe9pS9Le1YioogqB4ieaKFSwbAdD1xR9RLaHqelRNKs28NlKZXTceTTXmvEE84TmMBVIz/S4kfQI51Kjo6q0T/UsJHQNR8bTNPiFb+7NcySxjWBuvLgv8PzXk/hNB/aqxKJSKyxj6RB140wUyqzpSmPZnm+96ke+tL9e0qNC8BU4ZNj8U1ZlwxTKlp/xc9+TR7j9nh185fHD7Bga449+9TLFzR44hiaUEPUER5QEzXe/iMBP4HuPoiFKrTBRKPPA08e4/Z4d/N/f3cP7/+oxat5Q4VYF135cFxK1SJTt+tp78JjVEGAVuqjmtwsBHR3KlTY0kufkdJGh0XxsFo2HfNkOLW4js4FWjkLgCA1HaOiBBjgeZYZ3fwplq2ZcGdMgl1HZQuP5MkOjij7CS6ENbu/d42AGTPTehD4XKlVUCWP1+WBflu6syUBPljddujIUsfDoN0xDI6Fr9LQlyZr1KC/UolC2HCq2+hHAVLLN/5lJ1F/44uT2zkMTfOXxw3X32XlogkdfPFVTrHY+YFnDP0dQL/hrulWM9f3I8fA0fYmgqHqAk3ephqVUP9GK38/qAgPhFyCVZbQGqPpH1HdfT6CAEg4d6QSVhIGUknzZxsGtQP7pUN1OSTNumqWnjacSOppQQrXeNfvFSSh3TNS1FNpeQNrQ/esReBTQglUdKTq7kqy9bjU//MqLNQFco0HQNui2iKuPCN7LUMqtu3NbxVZNyiNzGs3WGpkp0eao1u0ysH00wN3o3njjBfx03KOuC3BNV5qulRnecGM/P3l5xN/WW+80obic0qkEZZmoe1+CsyQBS8K0mfW/SzgW7ZXaUuv1FY3Lyzo6gr0Jm72mOv54vszv/fPzHB7L0z4yS7YiQ9br7ffsYFNecGXZiG0m9GrGsoZ/jsAL/n76TZv8XH7vs1UdqZYe3LiXWgY+1TURytJRXa6kb1UEidM0ITB1TW2LCo6u687UnGNEq69peXCkZHquwmzJCgkFKRq3xZOoYrPurAlCNR+pJ1R0IchlzZD2DjCjhZ0v+USKVELz57QtZfiaapDhEqD3sm6XrqIKAVz53guUf93/RP32D20lLCHpbUvSlgzfq7akwZqudM29jLqf6hWLjcyUlKUUyJSq2GGXirevd4/ng6C1E1QOhKg+m9E58lwzRcv2g+pV1B9A9Jv2VC0rrCHh+mKCbkej0xFcXTL40EySD80k2VLS0STc/dMh9pycZmgk7wfg8yVbLbIyfB3nC86fpe1VgLjg79aBHPvb5xN6C0MTAu+Rtx0ZytLJbezw6wIShlZDnJYwBP2ZNNvXt/Gmq/oxHznCVKThSyNXqe9ukBIZo3pEy+yjIT3Pxz41F1c1G/5A14RbYVulHwCYNjOkrBICiS10CkYKx3L8FEh7rqqpOlJSsR1sR/L31jRTj83we1cPMhywMvq70ly3ZTV7/RG4/8o5EDAtJIl8if7OdDU1VMDKjmRN4NcrJgsiKgy9ReDkdDH8nVSLSPBD75q9ezw7PosohY8p/H/C5/OWLUnEmnQ32DqQY7erIHiNdzyLYHyqSCFRtZKaIeoazGUSyIkqH5IAuhB1tdXNZYPVlsb3MhV/ofLiN9mkjmmrZ3m+VvGrAcsC/zxCnPBNJHRwfdrew58xDdav7mCPy9Y5USizfbCH3Xc9FNq3Yjkcn5pjx4ESO45O8FlRLcIJ5nHHuQyCxGmNhIAGvP6iXjhRS53d25asZax0g9fFyXKIfsB2JDpVOgSPlbOiJ/iXrixGaZxTmR5uKOsg4fhkkbSpk7Md9Mh4AYZ1iW5LZkpWTZ58da6rA3s5ewwYYEaTdFvKGgn5MagKeV0T/nVF5yaV0EkJ3RfUIQ0+8vtcxaZTuM67yD3ImAaOWacaN+4zgd8Hob8zTb5kU6w4jJy06XZrPqJz5I9FUmNZBZMHaq8jPILR2TK9orqVNNpISUHW1H2LzrvvM8UKFVsyaCTotcM9BiYKFdZ0pbn39u3seOgg2ePF88qdA8sC/zxDrbb45Tuu4fED4+iPDIfM2z/57m6GNDtEB7E7crSyLX23i205TNtWbQOUOip+MJAogYypk+qo0iMb0kYXyrL4rVsu5pmf7HG3rQoDxS5ZPWY6oZM2VV1BKmNSmamew5aSQyP5KitoYKGw2zIUB3NUThb8frJzFVstCOUSK2MCnwKV/re5v4NTLxf8TJ2RmVIsn/pUQrklnk7adKLRYTuh6x+ZKcVyAnkLovf3XMXGsBs3mvEwW7Jol2A08dHPB1LK0NyNzTh87p4d3HvH9po58uEu+r5l5V6QH+UQ+M3U45AvW3QEcy8F3JRP0N6RoCtjMjVXoTOdoDtr+sV4IzMlUuVwVzKP2G7rQA7z4jx7Rk9wvmFZ4J9PiHEAbx3IIYTgwamD/guZy5hUHIkjGrMDmrpASMVYqRkaHWaihlLAU8yCmh3gl8Z7Ai1jGqQDrqn/dPV69gby8J/XTmE7HtOnpGIoPpwgN0zRFdIThTKDaaWRWna1t62E2GKqmZLFgZNl/v2V65BPjDNbCl+FF6QOjve2a9bzri1r6bc0DpaPhlJEb79nB78TM/1Xru3ksjWdvGvLWn7y+edD31VsyRNJi21FIyTkhYDebJKpYoVj0qLP1pqmFJqGVocs7/QhpeIkisYRPAqOHUNjZCP7JFy/yerONGQyvhVkx/QKCKJBklMIuiZ8eot8yXI7mSmRlk3q6Hb4QFLC8LRakBPnaSL+ctD2fEKdZ3zH0FgouwNUA3Fd0JAdMKFrDPZm+dQbLuLeO7bTnlIuIUHVldPtBkr9H3ffYNqel2nkpR4CXLiizefz33logvHXJMGPNlTY9+ZEJAAAIABJREFUn0wigfZkNR9mQnPYk1BBzmLFVgHmmPqBetg3mmdlRzKsDbuC3ssR98b7x++8nK0DOb6/66QfFPTmruIL2+qRDE1w569s5o8DdNO+f9ydJy+d02PFXNWRor8zzWi+RNlyeMa0sIQXE4i/nUKoPsOnQ+LWCMGgbfiE1Wek5ns3wJ9q0oVMKQTeGSSaBkKzQKgfIWwIfC+QdCYs5uZm0WUJQ5bQZYmJ6WnKlSJlu4yhO1y6ViK1kjqOO7rZksrSOTzWuMGC40gqJTuWSuRcxrKG/ypHkGQt7uEVQrB9sIcHRVDDT/B7N13KHqfclA4iYxrcdv0gZtrgUCRPH5Tvu55WEQyGjs6WmKwk/Erb4Pg/ePdjlO0Lub7rGS4pjvNkup8UA1xYCXPqfytbIePApVaYLiIOUb8xwPUX9ZKcmWTQLWICaCtbVMZnfK07mIWy89AE9/x0iBsJZ5GIkLRVv197QQ87hsbYe3KGu769i/fmEzWVur95w0W8/KPjoVjAscmq++SkIXm4T3LLsQPkWOHfL2/BAPxjpRIaE4UKWgyN8ulAE5ovcgXVhjmrOgzu+MDFbB3I8ULECvI09aLlMDRVZXNdDzGLatWd6GCTSk2q4K0A3ZJohWq2kiFtuiaHkRLagsfJw9w4zKZ15toMTuR+Qn92E9qcasrjWJ3gJKlYDofG8nVb9RSmyzz4ty8wfrLAqgs7ecdHNqPrrw7deFngv4oRrc7989XKnREVdFsHcjwfCTxevLKNN1zS3fD4FdthYqbE00cmuebi3litMpXQmzJHhvz50k2Vc4+1Y2jMZ/P8afoqfuqSvF/rCrJ82a5y+Au4cVMfg0dsUqU5KsSnda7pSvsVt7YjuWxFmo9et443r+vmB09NhlIii+h+1ayk6t7xxhbTytXNrAl7/P91/yj/cmyvP9ZxzaHb0UKVuiPDsyFhXyhbNTTOY3MVrEjpr3CvKYqJQpmkFzBdgB9f13R0YaALFcsY7KumjfasbvMVAs9qCzaKAWVtSbdgQ7pTE3TxRTODgnEaDeHPd/D58vap0WEkZAs200lB0tAI3gdNz+M4SRKGxkB3lsLYNHH44UMH2PPyuHIBPV2ka7CDm28aaHW6zmq8Opat8wTRJufNEK3OPTVdDGSPqB/vWBnToK892XLWgiNVA/ST00Xu+Lsn3OPUipSUy/FTL1sH8FPkoDZVrh6tQxCGKxEcCT/dN1p7bPd3TzB2Z03/eld1pvjtN23itmvW+9W+XrHTyekiIxWBpVVdVUH/siqGq2aaeA3N45wAHq20t7jtNW3/Wr3zPfjsMb9CFtwc/IZXXivwvMDxyExtN67TgaEl0IW6Lq8GI/iMRO9MsE7DQyqhx9x39fxFBb+7LHAkeYyKLSlZDnPSUfQdMv6cUQhAc6BUcUgH0y6FQ3fW5N47trM2V7tAejj01EjIxfniY6+e4O6yhn+O4HT6akarc/vaTE5Gtrn/qaOnRezmSPCITLweumvqvInNXlBNCHrbkqRSKV/Yf+e5E2zvUO3ovvqxa/nCj/fzyO7hWAHoWQAS1Wt2RDisCxzb0zqDmmmQ+2bf8CzfGZ3kig6V+xLshiWBcqaN9NxMSGsFZRnd+fbN7P7OIX/cMyWrJS1q3UU5Bk+qkwxPK+HsyKqFkzGNUAeyMMIfOLJaeBVNU7U0jUQgw0UG4gRCtNqjrD7ae1JNt0kldNpTms/ZH4eoG0zaKaSsVjmPZTU6ihYJ8Ck+HCn9OgXdiSRzSh3d6aR9upfgstlKMDvaAzdIw32uY1ngnyM4nb6aUWrm8g/31gj8b+w86jMLRhHXmtGDFrDDPXbOI0+q705HiJiGRl970td2fz47yedePOovbH3tyZq0yHoYWpvgoiMln9veixUkA8I+SFfw1Yf38rJusRKdjyc7a45Xrjg1wt7DplXtjASyiwZ7s/z2jRvgSyPUJNq70DXBx28Y5Jl7X/azTKBqBQXrIYJ1A1euTvHs0fjOTF7hVXTuZ4w20pXJqpXTpa7vbf/uCh790jMMj7QuzLxCOWFAm6GTW53lshvr9SKr7jNRKIOorZb1oKFhaNVx6LoBc6uQFeG7buaAYhq/Cnk8Xw7N3ar8GAknkHdf6WLd/huw9bA1PFuy+MDdj/EXVw7WHU+0B25XTKVvPTR6Z84GLLlLRwjxFiHEXiHEPiHEZ5b6fK9WeNp6s8yZKLYO5Pxsl2ohT1Vw2a52HsVLw7Pcfs8O/vzhvdx+z44aN5ImVOXqqo4U9/z6a/30Ti9tz/spVlpLDxRGmEogmO4XHrHCYF8b3VkzNm6Q605zUf33uYauQLVbhILjkC/Z5DLhF9xzxcQi8nnGNPjkTRfWPzmwZX0XQEhgAVzS31FDkeG5nrqzJu9/7fqGx40bYsnIYvdPUDF0SpkMc2ZOuYxacOhLVDzCY8D0CM7+28wohdf38sIqnd1j+br7e/uM58uK9yj0rXLgxA3D7M5x82tWqoB21qS/M0w5UShboa5oHgxNBAj+JFk7XrxVbMmO/bXPfBBBF6cTF6iJgWeF13tnzgYsqYYvhNCBzwNvBI4CTwghvimljNbwLKMJFqWvZsxz6+UyZ8tWSNDsOTFdY1FEOTgTutLKf2mdEmBxXLXFik2cDmn09GCNqZcuodlobe1Abcctb2F715a1fH3nUd899doNOUqH8n5D8ol8WRVDuQ1T+PGputMQMtmBKRN0oGQIcl0pjLKkK51gcq7a/2qhWY4ywB2Ry5j81U+GiNpVZpNMkB/tPeWOpf5ovAwaXagCtnRC53/pm3h30l3EZsqcmi3zXB1LwR8van4sKbEd1XfBu7tRUr167sVocV131lTXmK99VBL9/Wr8iYS7+MsQDYaXuRVM3a0Zs6w2XxFE3UTeKBRmixYkW7NuWk3NPB0r/JXGUrt0rgb2SSmHAIQQXwNuhZqizWW0gMVqpKJM/2qU9Ku/OMyvTSf9VDvbkbQl2zE0QcVW3ObbB3tauGm1AkurU0Wjd3WqvoaVCm/5xGU8/HUVGPO5fC5qY/t11R6+WwdyfPWj1QXPeWGSA4fyfjeoXCbB79y4wV8MdzV4SYMme/9rctx6Y79/3AtSSR74y2f91oPefDlSIh3qunY8FMoWn390n5vZU91O16u21SN7hknZsDbCmv/0kUmypUQsEV6hbPGDPePqjwbyx/tqNF9y4wEW5bawlSUlPHV4gs65CtJdjhstaJ6F47mMoqR69QRbNWAu/HTfjGkwExMD1dLhIOqp6VIocNqs94FEYEuJY0sSukZvW5JkNsnJwrg/1mCx7kB3hsKEVUOJEXvsFjX8VtqZnmkstcBfAxwJ/H0UuGaJz7mMFuC9vJ47QxKuQv35YweRnmejxSoePwsnEGgcnS2xkjiBIiilMuT7E+w3wzWaGdPgl69YzYYYojhPsDzxwmTNPh8JuFKavaL9167i5VKRdVesDB13enQuNjvGclMtBVVK6iC8Hq8ThTJffnic33Oz0b3rvu7CXh45pppu1yua8rRqL2gb5NaZmqvgNFBI/fx394Qhhs3otkL19X3q0DivkVn/AI3usiPVtRua4K5bLwuR6tUTbF7AvLvNJN0+PxriFe3JKild5JoAjhkOa6za++Bdb3cmgTR1tKJAQ2DZjp/p/5qyTqlsV+M4Ilz7UXPtLTaKWRQrfIlxxoO2QoiPAR8DWL++sY9yGfNHs8Ir09AoxfjZLVtiaSqLw/PzN26rwrz8Hl7gdGjS5nP3jPAZvWtBRFaeZu2/aDHXuv3WQYYPTjOd0fjkD3erjKcnDoZcElLWunyShuZ3vfL82lXIUBAY4v39xYrdkOrZOzYot0T0mADSFfhx629/l+p25gV862nDpqHR15ZUC0hdbdk7zyyCJI5M+58LIbjtmvVsWtXekmBTsR4TrYV7G8ycGmhvDwVOo9f0tGlREjoXVFwCOM9gdX+GZ4pYRdtn3gxe62tLBqmRMoWIBVEPch6dwRbLCl8qLLXAPwZ+hhzAWvczH1LKu4G7AbZt27bQLLFlBBBN5fwfPeEiHgHc+fbNfoPyIAxdkDA0bLuqxTVz6dT6TKuI3thgcLZUcZgoVUICf9+pWb7z6CS5jOmzddZ7kTzh+OWHx32fcjLmSVq/uYf1m3v4/X9+nlJF8cLEuSSiWRqF0fhm5gCZjmRtQ5GY7dZ0Z9BHwMsKbPSg246sOWYQwnWRBL8P8siA0liPTMzVpCGWXYbTizNmbMaGBCrOBLszs+zs2MB/mKowO1etnhXgL6zNgtPzQShzSkB2VaameXt/Z5pjk3OhsebcuEDaMhBlG4EqCJyeq1AqSzQzeP3VVSGdqLZpBLWY7HRZP6NoVcM/F7DUAv8J4CIhxEaUoP8AcNsSn3MZLqJBpLHZ2uas0ebfSUMjmzT4g39zMZ0bwlpcq4GXQMYm/V0Z7PGwcCqUrVBfVEmVydBzZfzNQ3t5SVOt/DRBw+CgJxyDPuUb64xt56EJvv7kkao2HWn47llBIWFj6gRFfrCFYdfKDH3r2hjeXURKeD5lc9s16+GBkZDgX92R4v2vXc+9DVrveS4ZT6ONz8EHqG3t6LmBgkjVIVGTEl48OV2zMnkupf3JGb7VtQVNgO2c8usYPGH65w/vbbkWRF1I802i3bpOTRfpiyxJQcvKmyvvmanM6DiW8Avc4k8s/eOP58u0pwy/NkBK6sYi5qPhn+1YUoEvpbSEEJ8CHkIlQnxRSrlrKc+5jCqiQaSebIIoq7y3jYeS5VC2lXibj3m689AEB8fypOkIVEwqzTOauJcv2fS1qyCxKCmNLei7zpdsKo7jN0VpFhz0XDDBlNXh5/ZQcaN0eqDyc8fQmN/SUADv2bq2+TWaZogjJmWEzf93ffRycj8+wkvjs9x6dT/bNnTzNw/srDnMu7as5etPHqEcEwQUwBtes4KBo2V/0WtPGlRsSVvSYK7LwMxXsCwHYjT8YHVynDsodC735jSTY5oQ6v7Z3vbVauFgyqynFNSDSDTPY48WO63oSCHHy3W30TXBGy5ZSWYsfC+8QHFcc/MgFP01oXPWu4ZW0zLPBSy5D19K+V3gu0t9nmXUIhpEmntoT43A97Z55P991qcFllKlZb6hxfM8fWSSX/+HJ3jTyCyXy46mAUBPOOUyCXSriB4pOMomdRK2hoaDQ22KZhSeC+bTbpYOwDefPc5mR1Fr2Y70F7XoIlhTdBbzbueFgdQTmLbqoFTa0MbOQxPc/9RRBEqQv/GWDbyxyTxtHcjx3m3rarR8j0Dtlus28q//tJ/xfDnkuihaNje/YyPrtDXc+eALSGQomOnNAShhPzwdplXwqCcEquq1LWlwiuY0xHdctxHxnZFQoxpBdWHNZcyQy/AzrnYdzGQy+noReoMMGPenVHHIZaoEcL1tSUYiAj/oavtf77+MFx8/yYHRPJ3pBO3utXiB4pXWLNIW2O4zXdYEExmNqL3TnjQwmqTDxi2cZ3uBVT2c8aDtMpYWQS39p3UUla0DOZ7vSJIfrbbce83q+iFaP81N0ymXLf7yR/tiOx0BdG/MURgmRGjjCaeMafDurWu5pt8k++OR0Of/6YZN7BVWXR9+9CUMZul8/tF9yMCbHQy01suk8F7gKztrszWySYMDmRyn7EleTDl89OZtLoOnOubXdx7lqx9t5t5QE/CuLWu5/6mjaCUHgQq2eqX7wg3WHg8Ie4CHU2VWFhVbnCMlBIjIgvxEcZq9EHDnr2zm1AOHmCiU/cYu//TUUd4iq9sE/dkClWDbnk4w124gZ6rzaJpFPv2mTWwf7Am5DMsVh3Ig79HUNZIretE7aiuXPbgOFqR0mJsc9sdiaEkYOQWF2rSkDJDRwX7u6wy/qJMur2KuBOlKGc2x3GuXmCiuorJUz6UhQUgHywwTpqVsnZLrTnrwb/4b1uX9zExcRFLXw9lYT/6t/+uh8QIP/mwIy5Y8qAt6rxtkoLuVljQtIJGGKz+wOMeKwbLAP5/QwDKNBiovXtlWs83Vb17Djn85StlysIXgRFFDlvI8NlfCCTruA7jp336Qe5+4B8duUy/eypUtDfWiFe3cctn88pg9oZ3LmET51mynShQXJ+w9TbVXanzQyfq+YQ9SCPZm2ziWhl3Hp3z+Hgi7m7wxJGtmI7Lg7DlFdsd4KP3yYKHERLESuk27ExbjKeFbLtGUUI8JE6gJ9KYTOv1dKT7w2nV84VtHQt95vYTjCO0EYCaUReV86CZ++KcP0ClTlChz+Ydu4Nobq8Faz1qKBuzLtgOW01JnLgF0ixn/b21uFkaHobDaHauq9FUUGeo8XUMPcoV2FWNCLdDCkchoP3JRXVQAkhZ0pWZCrizDEVioAMUt/ACeB7P4H7ClgRNsWr/zS/4+6ZkS76WI1NXY08/9HBbQVzqEdG5Z4C9jcdCMKiuaFRHFpvddz78e/w4TuyzV7Fto7nGVRtjTZiKKYQFiJpO863+8nx9/+VGmRntJpqsLSaFs8a2dR/n5CxV+bSYZLjiaR4qnd6zb79lBqaKqQj+45QJ4otpRvWIXuP+po/zTU2rBMjTBe7et411b1oY0VcuWjBfKqmuWOx4/KIxKUZWoil5Pw/fcTd7CUao4/D4SWacmdutAjs29bXzzqemQVv6prz1FuyO4VNdJOYIR3aH3yh6++oYL/cXp3ju28+0/ewZiEoeCgXBQrqDQdxG/f1RIe2v2yo4U796ylr0nZ3jh+BT3DWzAtiUJXXDdhtWh6/CspVzGZPbzPw0dr9SiwI8iWNjmSMWYqSBJukJYuakCF2MKZEX6Hy3M6+74544rsssmDTV3UiKEIJs8d8TouTPSZSwcixB7uuKdr+Nvh54k57aPmxWSCpBMaFzU005xtHafzp5eXvfut/LDv98T+tzjo4+yRLaEiE8nX7Ipuea75UjuPV7iw84k/VoX0inzULvJyplS1QVhS77y+GHuf+ooH7l2A5oQoTqFONZKz3d9WX8nI5tKHBiZZbCvjY/feAFbB3J8/tF9Ee6gsNAP+n0397b54/ZOazmSw5rkcMYNNgu4LyDsPRTKUVVWIWMa5LKmX0Dn9xagasF5NBRA3YK6UzNF7nv8sO+392alYssadtWgy/BLX9B8t46yEsKB5GpOvTqq969A0XRAWIsH/Dz64N+aEPRkTQ4E2CGEDrJduE3R3IVY1yjPVO+HkLWBasuR/rn7u9KcmCoiXIFfr6I6a+ps7M2SL1lkkwZZM9zRK1+26353prEs8M8rzF/iB90kni/96rdu4MnvHURIeCptcdt21d/11He+xfF5nCOb1NFLoiZo28qYnjg4TjbAG69rAl0KPwNHSvj7dRtoLxUpaW2UDIMPtif9QjMvWFiuONzzswPYjlTB4QB9cHA8uYzJjRfmuPDKPv7omy/42v2BsQK/vGkFWwdyftAxDidmynw6EOD8u1/bRqFsUbYdX/OOBhQltamCO4bGGqYJKm6hKgeNrgm+8OP9PleSV0UsoW6aTrASOLiFpMquGhevUP53NX+6JjANNX/RHPv13sY+wYeG0d7rH6d/VZFK5WIYM3Esh4nZkr9vbzoJhkZm6/tIJUysw20kDY2K7TBZqPjsHu0pQ3Wpmhn1zyKBcdkeXsWAjkSC9pRBzw0fZXasgPXoCqRtkJcqTdk0NNj666Frzbo/URwaK/DFnx/AsiWGLviN129koGcedo5Rn6d/MbAs8M8jzLchRtBF4WVfePnXmy/tZcfQGB8O+MK/N8/xZEyD92xdx/b+RChoC/U9Ot6Yts5oXFDRfRoHIeCOt23knp8d8Em9PnLtBv/vpJuR8243aPqNnUexbeV79tINAd582Spyz1f9yUFh9fj+AqU+M+S/tx3JnQ++wKZV7UwUyrEUxQDHxguhmohvPnscPUDRnI40CRGouY5mJm0f7OFwg6SSKKXy8ak5vvrIS3xwVrnMfHI6DUSd4wih5KaXISXA7+zlVV1HBf7OQxOhbJ5grUI0x776HHo5lBpkejESGhuu6GXz9f38/P59MDeLCXSlqtaB6T4j4sqb6CqepH1W5Z2ZQMksMzVXoTOdoCNrcnyiQJYwK+Y47WQTBoWy7Y8VzUQ3ErDtrQwAiWefZO/hyTDtwra31p/0AL796D7+obIXR4LuwApzE5/ctngFagvFssA/j9CM9S9sdlcLt7y9gvnXHuVyEPUESCP0d6W49aaN3PfY/Lp4SVSPVQKCpD2d4B8/fm0oKPvGzatCeeI7hsZ8we9ZLkFemIv62ni5MOaW25fJZUxfQNm2rPHfq3mR/jmSCY2y79YR/gIwU7Yw9Grl8thsmb6Avzmhi9BC8cZLV/quoiC2DuTYtbqD0kH3DK5cHZkp+SRg3o/X9SroMutrV4L/U69bT+cTByi9HF6gBHBZfyfrb9zkW3XROfLiFcF53jE05nf2lahYyORMiaRerokt1MMNH9xE79raZIH42JKoiUGkEhq2Y/j9jKdTIqSFC/eB0TXh9y2eKJQZz6uYzXcffJk3v/0CJouV0ALlucVawdlOoLYs8JcB1JrdLw3P+g9vudJaPvzphAjqsjHU+dwbk+Zt0qRwxvMxx3UM86gBgrwwO546EXrZvTFKqRgvvcXiCz/ezw9fPIV0rQlP8HlBTL40EhBykheOTUGb5ANXr2dzfyd/8sALvNNjqhSqiYynFGsCrnQppz//6L6a1NT2VIKKu610x3lyuuhro55wDMYeoo1Vbrt+kEdeSnOciOUnlDvkQxHahOAcATVzuX2wh6eoPgO2lMxVbEYn51jTlQ7FFhYD0ecm+vwO9mZJm0ZViw9gpmjR157E1LXQvT6wY5hdHWlybSbDpwI0Dy26GuHsJ1BbFvjLACJt/aQqvPrkjetDWRjNOG20aObEEsB/ob65n6xbZRm0Suq1gWzEVR4MPOZPzTEsjvsvey6TIJdJkC/Z/O6b1vnb/fWHt8UW33jHuudLPwvPhFTun/6uNBOFMpWAlM1lVOGVUdHQHSdU1BTnTjPb4lMAo4Fvz73zH68bQH/0VCiAi4Drbn8n9z3xAKoIvoqZYoSILjJHn390X81cfvKmC3nBpWDwmqZ4mJqrsLIjWUPoVq/HcUuI7Bh1Gw1Pl+hek1UlC8Fzurlq3jMTrLTNJnVGjszQ3ZakvzPtu4cypjGvQquzmUBtWeCf49h5aCJkzjeCrKVV8Y8xEdC+goVX83t4T+f1nf8+Wwdy2Bu6OTij/LPedTcS6q2a2peubmcoUI8QFJ4XrQi7G4LWQ1BAqnz/8MInItbR5xIak5pDTmrkMgn6VmT567dt4vED46Gipjh32q+++TpO7vw5nqDOm1VLQddEjXtn88ZuvvXAoRoq4I5cN53XpZj8WZie4IXjU3y9AV9OLmO6GSwydE1eK0mByoKy3TC0JzS92ILIL0DQ+/MZXi2iaaezJYvDRya5KpKLKqQELVjtXa3uBXjx2BQJTfN7A+fdxIA759lP+mzFssA/h+G5Kd47nagx5+MRr33vGBoLfZPLmLGFV83QiC3TQzBO4Gmai4VGQj3O1I7T2qRsXo8QRJyryCu8qkKwfWMP//6dVUFx7x3b2bHrFOtnJWs601x89Uracim2bej296rnTls3kGPL7UfZ+c0JbE1Hmjm6hUY6ofuCKvg8/OLAeI1P2rtX7/3obdzz878LjTZKRBcUbjsPTXDXt3f5WU03XNRXMye6JjDRsLsL9MlubEdRSHvzGtcApQYtGIo1VQ6RfRyX6kEXVddXVyZBrk1lzQRdQN7cjQm1yPa4RE5SKgulnDy7O1m1imWBfw7D0wKhtTx2Uect2j7Yw4PiYMiN0QinwyMiRLyf9XQRdyXN/KdBayVOUCsefbVtzcIEsYtTnFWxfbCHpyPbvXZDjqvq5K/HIVrUFHWn/dIbb+bZnz/G4dE8smApn3YgwBx8Hq7e2M23OBTKoLn7J/vZfnGf6kUcmddgzUEuY4asl6DlYUv4/u5hfvLyCPfesZ1VfQ4nR5Sg1DUHZ/MFjD4+W7MALQXimtYITVQLpNzrWtmexEhUA9reXJ2aUWynUX4hr1mMqZ29gdj5YFngn8MIMl22Elyql6SzdSDH8zFujDjUFZQQ1+EwhKifdT7ZD62iVRdUUFCXKk6ooChuYao3J3FWxdaBHE8HxGijFNNGC2f0WqKuo3wlPJ9AjU86NAh3m+NTc3ztBy/zuZ/s5947tofOGc3Suevbu/x7fefbN3Nscg5DV9cr3UN6C90d//ntPPPFHzIymudv8pJndo3xazLpj6+eQiJRWUa7T0xzw1pFTLfnxDTZshPb6jFfsnnu2BTJOi4dgeLJv2JjJ5NPnoycTNZsD/iptjqCdsUTgmlorMulKVYcNq1sZ2VHKjZz6lzCssA/h+FrgV/Y1ZoPv4GZ3Kobo5GfvJlLJy5ItlRoJky3D/ZgaCq9UlItKNrc2xa7MMXNjXeOO9++uUYDr5kJTa/Zt+7CWed6otu/5s3rOXHfSwgJL6Uc3hoIMAefh5BLx/3HkWC79y8VOZeXpRMMzgYblxua4I2XruRHL42EGuQkuzu45nfewecf3cezD++tCdDG3W8JOI7DyekiX/mn5/ikY3HXt3dx05ROn6OFFltvIZ4Vks/d+yR/dsVG/zhRLqiMadDbZjIZuRFzZYtiScU4BnuzDE+XfJZYwBf2oPoJTM9ZjMyWeNYuAVN+kd25imWBf45j60CO/VHipjqCfTHyZxoGP5v44+NeSm+NiHWhBDAfN1IrwtSjKv6KSyEQLCga2NLH8I+P1CxMLw/P8q3hibqpidFzNJrvRgtnq9t/8qYLya5I8+TQOO/YvIL99+7z5zmIqzd28y1xyNd+8VI13fv3TJ1zBu+1V6DmZeFcua6Lj994Qew9Ce2H0rZryOi8/wOTZFkO33vhhOsy0msW23wuwcFJm+cSNhVbcnisQDCCUKOyxoWTAAAgAElEQVS0CEHwoZTA7PAYNjolAT1Zk66KgxEQ+F49hO1IZBEqQBfwzuHHABj72ycZ2z2gDm8myF59NcmLLqozg2cflgX+MuaFRn5y0ULlVZwlsfPQRI0LJRiQixPgjaRpq8LUoyqOLl5vf+8mKgWL4y9O+AtQoWzx1/+yh/2ajelW7TY6hwz8C3Biao6rAueeb4FOve2vuaSPay5RYm+/u2108dyyPseuSH/Yd1+2AhGlFI0gGkeIFl/Vc58F9/MqqAtly88e8gKoURiGxlsvW80TB8d9n3vQKtj+jgv43D0jlCuKTyed0IHGbkEZuEQpIVMp4i0CVmUOISXtge01S2UaWRHaiRuPPoNAsnY2zfRItSXo9De/Rf+f/Q/Mc6Qf97LAfzWi3ns8D26FzhX1+T8WO894x9BYQ99+nADf1iBg0KowbbR4XbC+k8qxgv93vmRj2RJH4Puv5yOwDyb7Wz73fMcaRL3AeHChLZQtHnjmGEVbxS5+h/hHJmpVtdq43Bvv1oEc9z02UTOmATRkgDlIFzqrOlL86bsGueG1/eo8X3uJ7FzYh791IMedb9/su5Z+sGeYN7a313dFCnCEqPukxL0NnhXjhTwEih+oLWWSS2l+7wL/GJbF3NNPLwv8ZZyNqC/wr33XBTz+4BCOLRm4vIf27qhnV6GRa6VZB6U4CCFqsoSyST0kgeIEuPVsfSqG+QjTVhevbFLHcISfvRKkZ4g7R8fgIaaH1gGCkvEi2695h/9dcA6jjcAbzW8rY20lMJ4v2ZSlSvesxPS89cZRY1W1iGA3sItcayMUXBYGyAogQWroukY2qfP80UmyK9JsHcgxubqDsaOzNceeKJSrriVbNsxMG50pMW0Icm4rZ7d0ICTIK3Y8abhwKZi97l2p/ssZs3VOuPORNDQ0adNdOUHX3FzMEc5OLEjgCyH+DPgVFDv3fuDfSiknhRAbgD3AXnfTHVLKTyzkXMtYWqy7pJvu1VkqRZvOFfGMfU1949r8yXSOTsxxZKhCf2ca25GxPvw4Af54A4Hv7bMQKyQutvCZt7yGF6nUVKDG4X1/+If84NvfYf+Rk2x/22eap4M2+a5VtBIYzyZ1TEtDugHXOEStqmAvgUZj23loItQN7MOzqmo1OCYjYYClAseekjA0mudf/nWCzz11yHXZ1U8h9hZ/QxMNA/8HRvPkTZ2K7mBaikOny+ymYgvMhBLaWA6Fkh0K3HpY0ZH0s+D2Wb/sp3Km7DKdVNCE4Ej6UtqnKpwrYdyFavjfBz7rNiv/78Bngd91v9svpbyq/q7LeKUhZWMVPNuZhPod6Zr6xluouwqhULZ44InD7DBjGqBE8EqXq6faEjWuiHVQo5E3ws1v/zfcHPksTpD6PD7zDOTGIS4wHrfN3334an5xSAWgn/nDb4a+ny5afvqll4UjoKWx7RgaC7GJSjfIGxyTfnIS6VblgvKXe5TM3rEvqXN9wcV/7YkKpYO1VgCoZ2v/xCwrBZQNjbLhUh13dNKmV90yGYCyxcmRfI2mnzVTZNyEiPxMiWmX31+Wod2qoAmw0Tk1Johz6JyNfW8XJPCllA8H/twBvGdhw1nG0mJheTpNfeONJH7MVwtqgLLE2HhFLw/f/3LIFfHiyekaAT5fBOdQ1wTf2HkUy67muS+UabFZtpP3/UaEv3gFs3QksPv4FPeVDmNogg9crXodALEB7rjrC7KJtpJ+q4kqJbN37P27D9WlDPEW/ye+c4ADxAv8fMkjdlAPnq4Jn+spOkexRVuRcYfy9jUt5L6sxDSkWQxrbSmwmG/XbwD/GPh7oxDiaWAa+AMp5U/jd1vGK4X58uFHsdhMgNmkjl6efwOUVwKGqde4Ry5p0Ni9VQTn8PjkHF/9xWFfa54olOc1v1ENslnBWPD7D3/xcb5cp/G6R63gkb2F6CCajG3rQI6vfuxa34c/+KwSyEMB7v/1ZZuE293K88f3d6b58LZ+tm9RLRR//NII3ZZoWPjW6HnOJnW0asxdKRaOpFCxODpepZ/Y2Jt1PZHhgxmaxshMid52ScY0SJs6G3sz5Es2q9okc0PVhaZStqnYYT6in+8/SdkquzUM8BeP7OY333ARW9Y3f2cSeuNK94WgqcAXQjwCrIr56vellA+62/w+YAH3ut+dANZLKceEEFuBB4QQm6WU09GDCCE+BnwMYP05Euk+Z7EIifiNXCv1WsLVQ8Y0+ODV63n9CqOmAcpicuzA6ZnXNY3dV7Q336kFBEnXolpzq66rOA0ynwnT/Q5Pl1gZWKOCwdNGbpkgtUKUj6iVsQW3u2/PE2EaA6BiO0jpkNBVpyqJqv69Ze1Gtg6oVpGKbVM0rtBtIPEzpsEFK9qYHZn0P3Ok5FThBDNtE3SUFP30sVnVk1Mzq8d6rm8nV4xsZdaB2SllHRi+hQDPsYP1dtVbvffUS3z+e78WOn+hZJPeMOtf99MV+I2HYbC3jUwDxabT7OTuN91d9/uFoqnAl1Le0uh7IcRHgLcDN0v3DkgpS0DJ/X2nEGI/cDHwZMzx7wbuBti2bdti1AYt40yhQdA2mFfvmdS6JrDHC2zfvpH9LTZAOR0sxLyeD5HafLEQiynO33/lLesZ/uKuEGNkftTiqcNqboMWi1HHLSOAS/s7+fSNmxbN96zHpG/5xU3e3xKeH5vhBpRbaI84oMbTyPJrIi0G+9p4fqT6tyYEp/oOUpjRfIFfVVKqB5s2p5hMjdNVVER2tiOxHbeBuiaYoxKyLqRdO75MUmewt43hmSKzRcu/xnzZaijwlxoLzdJ5C/CfgRullIXA533AuJTSFkIMAhcBQwsa6TIWAUu7njbkjHn6pN+D1jPvAfYWZvnc8WE+o3ctmWBdjGDo6WK+fDmtoh6Hj2NLHvvqS37WyRNJi5UHxukjbLF8+Y4tdc/bnjT48DyC080wV6n1cXskbo6bFvl0xua/vGYFoOZk30V9jB6bbUgZ0qyD24r2JF3pNPnKrKJu1gRT6w7Qe2ATiTnhfwb4fXEdCZrUsGMKuixHYmqCGS3SyMWJH18mqbOSFPlSlUAue4ZjVAs9+/8GksD3XR4VL/3yBuAuIUQF1RrzE1LK8QWeaxkLxFKbT3GFtp523VaSvK1ghloGAhzUbSqWJG+FzfbF9OicqbZzp8OXs9BCrOu3rSZ/ssBDjxzglHA4nJZcvbGbA79Qrg3PYpkPDcRCUChb4V4LqPx3zVH++4SuWlX+l09dHRpTT5uJcDNkPItQ9Rmg2nmr2aCFYGW2l1NzDiVLJeMbWgIzoZPQw1q27jJrFis2Quo4ovbg0k0uMLLhfQ1MElrV754vWcwULdpTBp3pFBevMPy/s8nGIncp/fdqrAuAlDJWDZBS3g/cv5BjL2MJsNCo7WnA064nAsyEnlvhsGEzaqgWgVm9NTPXE4prJkstj2HRgs3zXIXmY1m0sjhEF4Q460AIwVt/5UJWXKHSPH97sIer+js50GCcUaqDmWKl7rbzRT6t+XJ5RkjWJBPYs1Jp51JRdpuGVjsv7k7BQPMH734MhPCzmv70wiYxPykxNIP+bLXK+XUXXsdzJ46RTVt1s5i03CbSlRIp22ZktvqcOYBdhvdt/k0OPPOMX0fQZazgy2/7LHD2Zud4ODty4JbximAx5H0jLVTE+PCD2vWTWZsPtCfp6UgyszHDJRd0cPWcKmTyiL+andt7ma4vJbipo0FZfQSvdB4/zM+yaLY4zFeQBK/XinOpNAiwT8/VFiHVQzOr5Ko3D/DlLz0PjuS5rEPv2i7aR6vfS1RGUD2EAs22BLdFYcVyOD4xR237lfoolC3+8zee4+KCYMDWY7N/MqbBr28f4PDucU7un6IjbYQYNaWErz13kmtQlgqAHShWPpPuw1awLPDPI8wUK762dTouk2ZCJ06ItKpd74/9NIzgy+Q4jcvqzwbMx7JotjgsSJDMc6HvSJ9+t6/oIvXMxCzXf3ATE4Uydwz2IKXkyV9UF3dBY0qOYKA54ebvesVgq7tSWIXWaQ3yJVv56WOYOIP40Ysj7Ns9wlpLpztrsrID8qOWv/D867EZfsm9Zk0IbKeq6Jwp92GrOHvflmUsKlRTiRlW+zyONQ3imqK50Ik/4mJp18GXSWtSVn+6CGqsi4H5pDI2WhwWIkiaurr9fxTaU611PDs+OVf3eWi0GLyYkJStQEFUA2sjGGj+o1+9mBeOTyFQTKelJ8c4SusCP5vUMUsaRa1+9s/JqSLf3DFKv6UhLPV9d9akvzPNsUl1rqLQfMtEVdtqSMtCGMai16osNpYF/nmCICPl6aKZ0GmBHbkhghWQcVW7wZdpzZES5aOFmKOcPqJCaikzh+LQaHFYakHS6uIfnCNDEyH6heDz0Eg50DWhtPUW4d2DYPetd21Z6+bqt46MafDf334Fz52cpv2ZKVIRF2ShrJqdiCQ+l+fUXIXurIkd8DtZmghRQ9gigVMsobepcZ4J92GrWBb45wm2D/bwvSbvWCsphA2FzgJSa6JVogem51gbQ0nlvUw7HtzP4UUW+FEhFc0cWozUoYXwq5y2IFngQh8cc3CObEfy/qvXsaYr3bAJSnQxiON0ajYv+ZJNmUjdQZPrivv60v4Ort/WT+kNFR78n+HWLx6zqKAq8DvTytrJJnWfMtlCWcigirksKXh63wm2XbV4qaxLhWWB/2pEzJO+dSDH0Mp2pobUgxp95VoNCjYSOq00QKmH/No0ciQPwIRweG4mz/Ut7BeyChaIqJBqNXOoVZztGRw+Ag9HdMxRvp93b1nbsnLgCfUOR1XRerQKSMn7/+oxHCnrUjFnkzqmHV5A5kZGa7ZrCj9TrHbR8eIFGgIpoK8t6fPfZ0yDXNZkPF8GAVLa2I7mF4/99t8/wV/kzj4XThTLAv88QlvSoIbbwsViZBecmC6etjJ5zS3r+f2XhjEsyeG05CMX9DbcXje0sFUA/P4/P8+76gigVhAVUq1kDs0HZyqDo1mBkpGcwCpVxzGw5WL/9+iY58P3E1QOggvH71Zskrru0ypAtcNU2Z2XiyNPUsY0uPf28Hl/umOE00Yday2XMbluoJMta7qY3V2lZSiULSzboSgkmsBt4FJNOU0Uy/zPR17it25Rc7fsw1/GK4fTcD0sNLtg56EJ7nvyCDfL7prPW3not23o5k8+eXXLL8qlr+/nFz875sclnjItdj1+mPufOrogzTkopFrJHJoPznQGRz1r6JrfuJbHv/gYTsWkc9Dhyptvajjm03EtBRcOZJhWIQjNbYgzfvB4zXfR8y5WVYnXfnHapUB4/uAcN//yAPL4HPnJklIsRvI4wLNJCyEE7WkdpyL868g4DgdeHOczLz6uMokch29q+/lPb9nERSva0RMaK9a3Y7aYAbVUWBb45xMaaHoLDQruGBrjuF7LJjkfLXY+giTbleTiN6zhxW/vY0w47DXtEJ/62aZZwdIHXutC1sZIggvxJddcwyXXXLOkYw43RA/TKgRxx3WKQO371Ar8GjRK4G8RnjAPHqngODx1app/94nLmRkr8ucPvci38hPkNUlZgHCUhq8JHUMTWI7ktbKPciGY3aQW1b3fP8q4WzGc6TC55d9eSiq7tNW0jbAs8JfhYyHZBdsHe/hctp+ZsWdoR1U27kse5n2Dv7qYQwzhjW/cSPfFXdz/1FGe3HkUPSZbZDExX8MpLhC5lBkc9QKfuqHVtBlcqoW40Zi8hSP15ScQCBwpapqFt7tB0lbU96abNNrA/S7KhW8hOWRKtg/2oGmCfYUiX9tzgrJe3SphaGSSOrNlScUdv/e1l+Ia7DjmW1ZlixP7p9h4RWN35VJiWeAvY1HgaYKPvbQB57l/pZhI8r5bP7XkWqwnjBr1lz0TeKUDtI3Opyc01rwmx/AvTiAl7E85vCOyKC5Fd6a4MX3ypgu59ytP4jhKyw+6dsz5LtaL4NMJZt8MGTYvpRz+8B2X+XOwY2gstChdubaTO39lMyN/+X2mpqoVyRqShC5oTyVIJ3S/XScQsqxWHVkW+Mt4lcDXBN/4mjN37rMEr3SAttn5fvXXLqV9XRsvnpzh1mv7F0Tb0OriUHdMrvzUhMA0VKbL7desn3fAvVkwOn6f8N8Z02CwL8tEoYJzbY5PR8YQjWHc+Sub2TqQ45GEphYsr7OXlFRsyXi+TEmTvO61/azqTLHzhZGQZbX/5My8x7yYWBb45xHOAHdaDU5Hkzwbe4M2wysdoK13vuDc3XzTQGyLxlYWJ+84uYwZKoBqtDjUG1PwMdSEQNMFf/zOy0P7tvKstvo8N0vd9RhE/8/IGKB+DMMwdXTNwkQtWClD85stjgjJL9od/uTWC5jAYfhElR55Q3emtUEvEZYF/vmEMyzxT8fNsdSukaVaTF7pAG29vPdW5q7Z4hQ8juKOUW6YcqWx5VJvDhwpsR1FSzDfLmkhtPA8R4PVu09Mc8PatnkFZOKsRz2hak50TaBrAjNQgiKF9A+/cWUbw4GuaWs70q2feAmwLPDPI5xpBf903BxL6RpZ6sXklXYzRc/X6tw1W5yCx5Gy6nN3UHnr8xnTzkMTFCvV9uIeAdnpoBX9JRqsfv7oJDe8tr/xTi0gEbEWEpqolue617Pz0AQdhhbqmmZbDmcSC2Q/WcY5hTMo8XcemuDY5ByGrqGhKh2bCQuoap9xPVYXijiB+GrCfOZu60COT950YUMLQHfTKT3xrAmYKJRrtm+E4Bw3o0ZuhlZ29apnQcnhy9d2nf4JAzACjUwcKZmZq86DI+CrvzjM7ffs4OBEmP7jTAv8ZQ3/fMIZEvhhlwAITZXV3/XtXWxa1d60WnOpXCPN/N7ZcnyTjHMFizV3weN4PvzTjU1sH+xhp/t7M2rkpmhhtYg2or909eI0oh8v21iOqrr16CE82I70lYiXTs2GOPsd+8za2efu07yMeWOpHrVmfvCwS0CNZD5FUkvlGmnk9y5VHD40k6S/K+3zqZyLWKy5Cx5n06r2BRHAvWBoPrXwQnz4rT7Pi92IfuehCR7cNcyVdhIBJHSNtFWib05RMeSKx7hsajeaJrjMuoRjpQv8facnX2J41311j61lM/T95m8u2lijWBb4y1gQWvGDBzVpXQs3sTjTDSLi/N6liuJ4kcDxyTlSCe0V1/TP5sykRotIK+PWAtTCC8IZUpZ3DI3hOWq85yStQ1JW0ATk7CnaZg8rps0jCezsKn/f8uQpCvufBCBftsmXLLJJg6ypYgJ6Z+eSjn1BT7EQ4o+AjwIei9HvSSm/6373WeD/AGzgN6WUDy3kXMtYBCzBC9JKYDCqSXv7nY3CbPtgD7pWrQCVVGlzH3zmONvTYsnHfM6wakZwuuM+XdnfLA8/7tvFSFTbPtjDz5IpKEnfLeUtYsqfX4KSRb5s0ZOpQLa6ryOUYM+XbQ6MVtM1N/a2+UJ/KbEYQdv/R0p5lfvjCftLgQ8Am4G3AP+fEGLpr2YZTbD4Er/VwGAwKNgoQHimsXUgx123Xub3K/V4X4ZG89z35BFuv2cHOw9NLOkYztVg8umP+/SeyzOVZbx1IMcnbr8ePZUKZRk5UmLZEhzLbwxfKoeD2o7LsZMvqZaJ3nb5Uut9hBeCpbJTbwW+JqUsAQeEEPuAq4HHluh8y2gBcjE6eERwxgjBlhC3XbNe+am/sAtdE0zNVZCSV6xq9kyzap4u4sZ92q6ps6FKsAGu27aG6V8MUhibZa5UYaZYYbb4/7d3r8FxlWUAx//Pbi5tQqG1hKZtekuBlrZgM02bYqkIVKzaseIoFBXvoCMqjowXBu2MoF+YwfELo1MVdUZFUdQyfFHQKo5DoY0UKLex1Lb0QlvalJa0ZLt7Hj+c3eRks7dkT/bd3fP8ZjKT7G7Ovuec3ec953lvSbxGZWd7J2+2zyQWE27qmk/jgaHBVs3nzWDau1Zw4vApfvHICyRTHg3xGN9Zt4hp0yZBw/imDsPY+pdE5BPAduB2Ve0DZgJbA6/Zn35sBBG5BbgFYPbs2SEUJ5pKWwgkvC9R9he5HgJ90LI5U3iuOT44aAf81EOp3UnLfW/XlehYAnWu1F2xFI+ninpeydNoB41laoWwvgLxxhjXfHoxjz/+Kn/4xy6SMSXVEudgg8exeMdgV9B7XhW+2NQ81AZ03kRali+hC7jz0q7BY9VVoXNcNOCLyGNAe46n7gR+BNyNfxjvBu4FPjOaAqjqJmATQHd3d3VX61VqxGjCgye5YnrriNeFddFUqznm0QoO2gE4GdeSu5OWy2UlWs75DZb7vi27CrbveKokkh6qHh/76dZRf4403aU9eLFTycb1lnObeKVV6W1M4gXeNi5DYwwGPKV/YGipTC811A/fxTkumsNX1TWquiTHz2ZVPayqKVX1gJ/gp20ADgCzApvpSD9mxkH2aMJnjo3vBE21mmMerQXXDF2p7W/00gG/vvcZwju/xdp3PB264E5kvU9p1yY6eLHz2sm32P16P6cTgVx4oLbOLHLy4qF8a76NTWYfg4FU0ou7xwViDTLsrtv1wKuyGm1FZHrgz+uAnenfHwY2iEiziMwDLgKeKue9TH4Lr51FZl3olyamWLmgLfcLQ1gwAsZ39Gs1WbNmLu/53BLar57Bmg0LIrHPEN75zaR4vnbtgpxX78EWJU+LT9OQ7dIrO0Zc7GR6VAUFK4U7//RcqI3umX1cddH5g11N1VM+vKyDr127gB9/onvYXYfrgF/u/c89IrIUv0LeA3weQFWfF5EHgRfwF3m/VVVHngkTimuumsM501vYvvs4H1x8QYHbxHAabashx1wpq5a2s2qpn9FcOP3cSOxzmOe3UNpi2KyZjH6ahvYLz2PGwikc3naIIzGPC7xYzjasYKWQTPl3EpdOC2fELfj7+NU1F7Ntz/ERi7ufTaTYy57B13rJGh5pq6o3FXju+8D3y9m+KV3PwjZ6Fua5sh+lUhrs6rGhtpgo7XMl9jUmpBcfUZoas+4k0nGxUGeEeDzGdZ9czOx3zfCnwvjn0Zw5/Mz8Pwo0xMfn7ixfJRlvGJ5ESSU9VBUpZ5bQMthI2wgZvPUt8JrfPLmPjZt34qnWdYOsS9U8iraSMgugqKcjPmdTZ57DoQOnBjsjnIlr3p48mcrpwSeGp2oUv8I4+MYZFL9yuXl1J8vmTCHxVnj93oPn89arLhy+jzFBYoJmBvKpn/KRuJuAb7NlRszQ+FH/J5jP7N3bx8bNO0mmJ3/Kbkgz5cv0gLn3ry9XZBBXtcsE/exAvuiKGfQnvcHBSf9uPjumz+KwHD9winBz6KWcz3jD8OC+/X993Ldll5NzbwE/Qt7xqesh8IE/G/vvsC/R1t3HSAUadmMidd046UJUejgV46mmLyyUXNmNlnObWPqR+TzbkuKxlgR9E8b2WZy/qn1w+681eaxcMq3Mkg9XyvkMpnVOJ5Lc/POnnFX4ltKJkLbOS5g067e8ua8N5RR/m9rGHYEv0crOqTQ3xkic9YjFhLvWL4l0ymE81Ooo2jAN9r/HT7Pky26sens7Gyc3l5X+WrvuQiZOncDOvSdY3zMj9M9zKeczFh8K+P0DKVKehyeVGbWdzQJ+EfWWb73he98d3Kc7svYpSr1vXKmlYzxen/2GiW8xcMrvgqmATMqfTx9t43GuRt4rL+/gyss7xlzeQko5n8GUTmtznAmJGGfUTYVvAb+Aeh1RWuhLFKWeKK7UwjEez89+5/U9PPeTJyHWhHoJ5m+4IpTtZo84nzTrnMHnxvPCrdj5DKZ0Wpoa+OH6pTxzot9JhW8Bv4DxXE/VmHyq4a5yPD/7l1+5iFhLMy888TKLVi+ip2tuKNvtn9eCHu0H/N4wr13QCOSuvMLsh19MdtfMS6ZN4oqu6XlePb4s4Bdg+VZTadVyVznen/2e5fPpWT4/7/NjqfRWrO7g3mcPM/ks7Jug3H2Z30Cbq/K6rP3cUPajFLGsXjouB19ZwC+glvKtpj5Uy12ly8/+WCu95Z1T+fpXlrN19zFuTldQ923ZxZSWpmErrh08cYan91Wud0w8PnLwlSsW8IuohXyrqR/VdFfp6rNfTqWXKXN2pbFx3WJ2HnyDP/Tu54Gn9vFw735ub5hcdHbNMNJr2Smdk8fOMKG1MedrJQbntbXkfC4MFvCNqSJ2VxlOpZddafSdTjBz8kSSqaHH+lOpggE/uKB9PN1N+aM9o1+zI9Ygw3oP7Xj01byvndDawAdu6xr1e5TKAr4xVaYe7irLuTIOo9LLV2kMPhaP0dpQeNXV4IL2SU/ZuHnnmNZBONKfGNZ7qPP81orO2x9kAd8YE6owGp7LrfTyVRqZx5Z3TGbP7/9XcBvZC9p7qmNqUzngnR0xhbMFfGNMXaimhufs9808lngrGZi0OP//37V+ybDJBMeSXup55yzuee412hJCXITF01uZ3Jp77v+midW/pq0xxgyqpobncg0uaF9Geql73tv45pdXVEW7jAV8Y0yoaqHheTTz0YfRplIt7TIW8I0xoauWAOdCsQZrlyOpLeAbY0xIijVYux5JXe4i5r8TkR3pnz0isiP9+FwRORN47sfhFNcYY6pXsfnxXa+HUO6atjdkfheRe4E3Ak+/oqpLy9m+McbUkmIN1lNamoiJv8JuzU6PLH4LyPXA1WFszxhjxsL1TKOFGqx79/Zx1yPPk/KUeEzYuG5xzebwVwOHVfW/gcfmicjTwEng26r6r5DeyxhjRnCdH8/I12CdSecooKr0nU5UvGxFA76IPAa053jqTlXdnP79RuCBwHOHgNmqekxElgF/FpHFqnoyx/ZvAW4BmD179PNUGGNqXxhX5tUy4CtbZt+Cs3a6Gp9QNOCr6ppCz4tIA/AhYFngfwaAgfTvvSLyCnAxsD3H9jcBmwC6u7vdTRRtjHEirCvzahzwlWvWzr7TiZzpnkqkosJI6awBXlLV/ZkHRKQNOK6qKRHpBC4CdofwXsaYOhPWlUy3zcoAAAQjSURBVPloBnw9va+Po6cGaG2Oj+u8Nrlm7bz1qguHvaaSqagw9nQDw9M5AO8E7hKRs4AHfEFVj4fwXsaYOhPmlXkpA7569/bxmV9u47qTjeM+e2Up+1bJVFTZe6mqn8rx2EPAQ+Vu2xhT/yo9FUMmwEJ4s1fmS8mUsm+VTEXZSFtjjHOVnIphZedUfpRehUoEWpsLz4tfTLGUTLF9q2SFZwHfGBMpy+ZM4f5PLmfb/S+FksMPIyVTqQrPAr4xJnK6Zk9hz6TmULZVjb2D8rGAb4yJntJnRy6qFqaDzrCAb4wxZaqV6aDLmi3TGGNM7bCAb4wxEWEB3xhjIsJy+MaYyDqdSNI/kCq7L36tsIBvjImk04kku1/vR9UfgNW7t68mGl7LYSkdY0zkNDbF6W/wp1YAOBar/HKDLljAN8ZE0mVr53C8UTka9+ht9ap6wFRYLKVjjImk1d0zaGmbyNbdx7i5ygdMhcUCvjEmsmplwFRYLKVjjDERYQHfGGMiwgK+McZEhAV8Y4yJCAv4xhgTERbwjTEmIkQzQ82qgIgcBfaGuMnzgddD3F6tsuPgs+Pgs+Pgq6fjMEdV24q9qKoCfthEZLuqdrsuh2t2HHx2HHx2HHxRPA6W0jHGmIiwgG+MMRFR7wF/k+sCVAk7Dj47Dj47Dr7IHYe6zuEbY4wZUu9X+MYYY9LqMuCLyFoReVlEdonIt1yXxxURuV9EjojITtdlcUlEZonIFhF5QUSeF5HbXJfJBRGZICJPicgz6ePwXddlcklE4iLytIg84roslVJ3AV9E4sB9wHuBRcCNIrLIbamc+QWw1nUhqkASuF1VFwErgVsj+pkYAK5W1bcDS4G1IrLScZlcug140XUhKqnuAj6wAtilqrtVNQH8FljvuExOqOrjwHHX5XBNVQ+p6n/Sv5/C/5LPdFuqylPfm+k/G9M/kWzEE5EO4P3AT12XpZLqMeDPBF4N/L2fCH65TW4iMhfoAp50WxI30mmMHcAR4FFVjeRxAH4IfAPwXBekkuox4BuTk4icAzwEfFVVT7oujwuqmlLVpUAHsEJElrguU6WJyDrgiKr2ui5LpdVjwD8AzAr83ZF+zESYiDTiB/tfq+ofXZfHNVU9AWwhmm08q4APiMge/JTv1SLyK7dFqox6DPjbgItEZJ6INAEbgIcdl8k4JCIC/Ax4UVV/4Lo8rohIm4hMTv8+EXg38JLbUlWeqt6hqh2qOhc/PvxdVT/uuFgVUXcBX1WTwJeAv+A3zj2oqs+7LZUbIvIA8ASwQET2i8hnXZfJkVXATfhXcjvSP+9zXSgHpgNbRORZ/AujR1U1Ml0SjY20NcaYyKi7K3xjjDG5WcA3xpiIsIBvjDERYQHfGGMiwgK+McZEhAV8Y4yJCAv4xhgTERbwjTEmIv4PQEuFR/QjGegAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(np.log(x),y,'.')\n", "xx = np.log(x).reshape(-1,1)\n", "for i in [500,200,100,20]:\n", " dtree = DecisionTreeRegressor(min_samples_split=i)\n", " dtree.fit(xx, y)\n", " plt.plot(np.log(x), dtree.predict(xx), label=str(i), alpha=0.8, lw=4)\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(np.log(x),y,'.')\n", "xx = np.log(x).reshape(-1,1)\n", "for i in [500,200,100,20]:\n", " dtree = DecisionTreeRegressor(max_depth=6, min_samples_split=i)\n", " dtree.fit(xx, y)\n", " plt.plot(np.log(x), dtree.predict(xx), label=str(i), alpha=0.8, lw=4)\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
populationhispanicminorityfemaleunemployedincomenodegreebachelorinactivityobesitydensitycancerlogminority
106612951834.66912.18349.1017.44502513.723.120.926.021.3209.32.500042
1267631910.22913.19349.5225.53264517.411.432.736.032.9218.02.579686
2318240701.9054.02950.3416.94485124.612.426.328.377.3265.71.393518
1247235303.4672.35250.1925.94374017.511.230.636.152.8217.10.855266
101834602.5871.89250.1252.54853511.714.422.829.83.5206.90.637634
\n", "
" ], "text/plain": [ " population hispanic minority female unemployed income nodegree bachelor inactivity obesity density cancer logminority\n", "1066 129518 34.669 12.183 49.101 7.4 45025 13.7 23.1 20.9 26.0 21.3 209.3 2.500042\n", "1267 6319 10.229 13.193 49.522 5.5 32645 17.4 11.4 32.7 36.0 32.9 218.0 2.579686\n", "2318 24070 1.905 4.029 50.341 6.9 44851 24.6 12.4 26.3 28.3 77.3 265.7 1.393518\n", "1247 23530 3.467 2.352 50.192 5.9 43740 17.5 11.2 30.6 36.1 52.8 217.1 0.855266\n", "1018 3460 2.587 1.892 50.125 2.5 48535 11.7 14.4 22.8 29.8 3.5 206.9 0.637634" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#let's also include logminority as a predictor going forward\n", "xtemp = np.log(Xtrain['minority'].values)\n", "Xtrain = Xtrain.assign(logminority = xtemp)\n", "Xtest = Xtest.assign(logminority = np.log(Xtest['minority'].values))\n", "Xtrain.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok with this discussion in mind, lets improve this model by Bagging." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bootstrap-Aggregating (called Bagging)\n", "\n", "

\n", "

Q11: Class poll: When did the movie Titanic come out?
\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# %load solutions/q11.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The basic idea:\n", "- A Single Decision tree is likely to overfit.\n", "- So lets introduce replication through Bootstrap sampling.\n", "- **Bagging** uses bootstrap resampling to create different training datasets. This way each training will give us a different tree.\n", "- Added bonus: the left off points can be used to as a natural \"validation\" set, so no need to \n", "- Since we have many trees that we will **average over for prediction**, we can choose a large `max_depth` and we are ok as we will rely on the law of large numbers to shrink this large variance, low bias approach for each individual tree." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.utils import resample\n", "\n", "ntrees = 500\n", "estimators = []\n", "R2s = []\n", "yhats_test = np.zeros((Xtest.shape[0], ntrees))\n", "\n", "plt.plot(np.log(x),y,'.')\n", "for i in range(ntrees):\n", " simpletree = DecisionTreeRegressor(max_depth=3)\n", " boot_xx, boot_y = resample(Xtrain[['logminority']], ytrain)\n", " estimators = np.append(estimators,simpletree.fit(boot_xx, boot_y))\n", " R2s = np.append(R2s,simpletree.score(Xtest[['logminority']], ytest))\n", " yhats_test[:,i] = simpletree.predict(Xtest[['logminority']])\n", " plt.plot(np.log(x), simpletree.predict(np.log(x).reshape(-1,1)), 'red', alpha=0.05)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(614, 500)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yhats_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
**Exercise 2**
\n", "1. Edit the code below (which is just copied from above) to refit many bagged trees on the entire xtrain feature set (without the plot...lots of predictors now so difficult to plot). \n", "2. Summarize how each of the separate trees performed (both numerically and visually) using $R^2$ as the metric. How do they perform on average?\n", "3. Combine the trees into one prediction and evaluate it using $R^2$.\n", "4. Briefly discuss the results. How will the results above change if 'max_depth=4' is increased? What if it is decreased?" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import r2_score\n", "\n", "\n", "ntrees = 500\n", "estimators = []\n", "R2s = []\n", "yhats_test = np.zeros((Xtest.shape[0], ntrees))\n", "\n", "for i in range(ntrees):\n", " dtree = DecisionTreeRegressor(max_depth=3)\n", " boot_xx, boot_y = resample(Xtrain[['logminority']], ytrain)\n", " estimators = np.append(estimators,dtree.fit(boot_xx, boot_y))\n", " R2s = np.append(R2s,dtree.score(Xtest[['logminority']], ytest))\n", " yhats_test[:,i] = dtree.predict(Xtest[['logminority']])\n", "\n", "# your code here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forests\n", "\n", "What's the basic idea?\n", "\n", "Bagging alone is not enough randomization, because even after bootstrapping, we are mainly training on the same data points using the same variablesn, and will retain much of the overfitting.\n", "\n", "So we will build each tree by splitting on \"random\" subset of predictors at each split (hence, each is a 'random tree'). This can't be done in with just one predcitor, but with more predictors we can choose what predictors to split on randomly and how many to do this on. Then we combine many 'random trees' together by averaging their predictions, and this gets us a forest of random trees: a **random forest**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we create a hyper-param Grid. We are preparing to use the bootstrap points not used in training for validation.\n", "\n", "```\n", "max_features : int, float, string or None, optional (default=”auto”)\n", "- The number of features to consider when looking for the best split.\n", "```\n", "\n", "- `max_features`: Default splits on all the features and is probably prone to overfitting. You'll want to validate on this. \n", "- You can \"validate\" on the trees `n_estimators` as well but many a times you will just look for the plateau in the trees as seen below.\n", "- From decision trees you get the `max_depth`, `min_samples_split`, and `min_samples_leaf` as well but you might as well leave those at defaults to get a maximally expanded tree." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "odict_values([[400, 600, 800], [0.2, 0.4, 0.6, 0.8]])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# code from \n", "# Adventures in scikit-learn's Random Forest by Gregory Saunders\n", "from itertools import product\n", "from collections import OrderedDict\n", "param_dict = OrderedDict(\n", " n_estimators = [400, 600, 800],\n", " max_features = [0.2, 0.4, 0.6, 0.8]\n", ")\n", "\n", "param_dict.values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using the OOB score.\n", "\n", "We have been putting \"validate\" in quotes. This is because the bootstrap gives us left-over points! So we'll now engage in our very own version of a grid-search, done over the out-of-bag scores that `sklearn` gives us for free" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "from itertools import product" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(600, 0.6)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#make sure ytrain is the correct data type...in case you have warnings\n", "#print(yytrain.shape,ytrain.shape,Xtrain.shape)\n", "#ytrain = np.ravel(ytrain)\n", "\n", "#Let's Cross-val. on the two 'hyperparameters' we based our grid on earlier\n", "results = {}\n", "estimators= {}\n", "for ntrees, maxf in product(*param_dict.values()):\n", " params = (ntrees, maxf)\n", " est = RandomForestRegressor(oob_score=True, \n", " n_estimators=ntrees, max_features=maxf, max_depth=50, n_jobs=-1)\n", " est.fit(Xtrain, ytrain)\n", " results[params] = est.oob_score_\n", " estimators[params] = est\n", "outparams = max(results, key = results.get)\n", "outparams" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "rf1 = estimators[outparams]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{(400, 0.2): 0.7539639690584528,\n", " (400, 0.4): 0.7677805365253206,\n", " (400, 0.6): 0.7699751622917039,\n", " (400, 0.8): 0.7675383680731207,\n", " (600, 0.2): 0.7547137231855866,\n", " (600, 0.4): 0.7683114062482946,\n", " (600, 0.6): 0.7707024796128402,\n", " (600, 0.8): 0.7695227029080896,\n", " (800, 0.2): 0.7543389426401441,\n", " (800, 0.4): 0.7675397515655356,\n", " (800, 0.6): 0.7704787398435397,\n", " (800, 0.8): 0.7691255683564636}" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8073817254966813" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf1.score(Xtest, ytest)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally you can find the **feature importance** of each predictor in this random forest model. Whenever a feature is used in a tree in the forest, the algorithm will log the decrease in the splitting criterion (such as gini). This is accumulated over all trees and reported in `est.feature_importances_`" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.Series(rf1.feature_importances_,index=list(Xtrain)).sort_values().plot(kind=\"barh\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since our response isn't very symmetric, we may want to suppress outliers by using the `mean_absolute_error` instead. " ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.679048688925084" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import mean_absolute_error\n", "mean_absolute_error(ytest, rf1.predict(Xtest))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }