diff --git a/notebooks/ce4.ipynb b/notebooks/ce4.ipynb new file mode 100644 index 0000000..bc628d5 --- /dev/null +++ b/notebooks/ce4.ipynb @@ -0,0 +1,503 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "pressed-massage", + "metadata": {}, + "source": [ + "# Computational Exercise 4: Exploring Overfitting\n", + "\n", + "**Please note that (optionally) this assignment may be completed in groups of 2 students.**\n", + "\n", + "---\n", + "In this assignment, we'll continue to develop models that predict malignancy in the [Wisconsin Breast Cancer Diagnosis Dataset][1], but this time we'll be exploring overfitting and taking a first look at some techniques for mitigating it.\n", + "\n", + "Goals are as follows:\n", + "\n", + "- Continue to gain experience training and evaluate logistic regression and multilayer perceptron models in `sklearn`\n", + "- Critically interpret and contextualize model performance\n", + "- Observe overfitting and understand its practical relevance\n", + "- See how early stopping and regularization can be used to mitigate overfitting\n", + "\n", + "We'll be using the same libraries as before:\n", + "\n", + "- `numpy` for efficient math operations\n", + "- `pandas` for dataframes and dataframe operations\n", + "- `matplotlib` for visualization/plotting\n", + "- `sklearn` gives us a convenient way to load our dataset, train our models, and evaluate their performance. Very soon, however, we'll start to work with `tensorflow`, which is much more customizable.\n", + "\n", + "[1]: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "conscious-ecology", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "latter-postage", + "metadata": {}, + "source": [ + "## Loading the data\n", + "\n", + "Once again we'll use `sklearn` to load the dataset without having to find and download a `.csv` file on our own.\n", + "\n", + "**Important**: if you encounter an error with `load_breast_cancer`, try upgrading `sklearn` by adding a code block with the following:\n", + "> `!pip install --upgrade scikit-learn`\n", + "\n", + "We'll also flip some labels to make the problem a bit more difficult just like in the previous exercise. Please refer to earlier assignments if you are not sure about the details here.\n", + "\n", + "Additionally -- and this is new -- we're going to *add 100 new predictors*, all of which are just random noise. In other words, we're making the problem even more difficult by adding predictors that are not informative with respect to the outcome. Our models now face the more difficult task of picking out the informative features from the noise. This is done in the last two lines of the block." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "missing-chemical", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_breast_cancer\n", + "df_true, y_true = load_breast_cancer(return_X_y=True, as_frame=True)\n", + "y_true = 1 - y_true # let's set benign to 0 and malignant to 1 to stick to convention\n", + "\n", + "def flip_some_labels(labels, flip_rate=.1, random_seed=0):\n", + " return (labels + (np.random.RandomState(random_seed).rand(len(labels)) < flip_rate)) % 2\n", + "\n", + "y = flip_some_labels(y_true)\n", + "\n", + "noisy_features = pd.DataFrame(np.random.RandomState(2022).randn(len(df_true), 100))\n", + "df = pd.concat([df_true, noisy_features], axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "latin-grade", + "metadata": {}, + "source": [ + "### Standard Logistic Regression\n", + "\n", + "Since we've done all this before, we can quickly partition our dataset and train a standard logistic regression model. We can consider this model to be our *baseline* model; it is a well known standard that we can use as a benchmark to compare subsequent models against." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cognitive-parker", + "metadata": {}, + "outputs": [], + "source": [ + "# Standardize and partition the data\n", + "\n", + "train_mean = df[:400].mean()\n", + "train_std = df[:400].std()\n", + "\n", + "X_train = (df[:400] - train_mean) / train_std\n", + "X_test = (df[400:] - train_mean) / train_std\n", + "\n", + "y_train = y[:400]\n", + "y_test = y[400:]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9a7eac8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Train our logistic regression baseline model\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "lr_model = LogisticRegression(random_state=0, penalty='none', max_iter=10000)\n", + "lr_model.fit(X_train, y_train)\n", + "\n", + "y_test_pred_proba = lr_model.predict_proba(X_test)[:,1]\n", + "y_test_pred_label = (y_test_pred_proba > .5).astype(int)" + ] + }, + { + "cell_type": "markdown", + "id": "professional-event", + "metadata": {}, + "source": [ + "### Defining some helper functions to display results\n", + "\n", + "In the next block, we're going to define functions that (a) plot the ROC and PR curves, and (b) print the accuracy, since we'll be doing both multiple times. Most of these functions are just fine-tuning the aesthetics of the plots. To review the ROC and PR curves, please see Activity 4.\n", + "\n", + "We'll be using a few different functions from `sklearn`:\n", + "- `roc_curve()` and `roc_auc_score()` to help us plot the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC), respectively\n", + "- `precision_recall_curve()` and `average_precision_score()` to help us plot the precision recall (PR) curve and calculate the average precision, which is similar to the area under the PR curve" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "minute-wages", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from sklearn.metrics import roc_auc_score, average_precision_score\n", + "from sklearn.metrics import roc_curve, precision_recall_curve\n", + "\n", + "def plot_roc_and_pr_curves(labels, predicted_probabilities):\n", + " \n", + " plt.style.use('seaborn')\n", + " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n", + " \n", + " auc = roc_auc_score(labels, predicted_probabilities)\n", + " ap = average_precision_score(labels, predicted_probabilities)\n", + " \n", + " fpr, tpr, _ = roc_curve(labels, predicted_probabilities)\n", + " precision, recall, _ = precision_recall_curve(labels, predicted_probabilities)\n", + " \n", + " ax[0].plot(fpr, tpr, label='AUC = %.3f' % auc)\n", + " ax[0].set_xlim([-.01, 1.01])\n", + " ax[0].set_ylim([-.01, 1.01])\n", + " ax[0].set_xlabel('False Positive Rate (1 - Specificity)', fontsize=14)\n", + " ax[0].set_ylabel('True Positive Rate (Sensitivity)', fontsize=14)\n", + " ax[0].plot([0, 1], [0, 1], 'k--', label='No information')\n", + " ax[0].legend(loc='lower right', fontsize=14)\n", + " \n", + " ax[1].plot(recall, precision, label='Avg Precision = %.3f' % ap)\n", + " ax[1].set_xlim([-.01, 1.01])\n", + " ax[1].set_ylim([-.01, 1.01])\n", + " ax[1].set_xlabel('Recall (Sensitivity)', fontsize=14)\n", + " ax[1].set_ylabel('Precision (Positive Predictive Value)', fontsize=14)\n", + " ax[1].plot([0, 1], [labels.mean(), labels.mean()], 'k--', label='No information')\n", + " ax[1].legend(loc='lower right', fontsize=14)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + "def print_accuracy(labels, predicted_labels):\n", + " acc = (labels == predicted_labels).mean() * 100\n", + " print('Accuracy = %.1f%%' % acc)" + ] + }, + { + "cell_type": "markdown", + "id": "distinguished-methodology", + "metadata": {}, + "source": [ + "### Logistic Regression Results\n", + "\n", + "Let's use the functions we just created to take a look at the performance of our logistic regression model. This may look familiar from Activity 4." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "major-bracket", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy = 68.0%\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_accuracy(y_test, y_test_pred_label)\n", + "plot_roc_and_pr_curves(y_test, y_test_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "id": "15041ea3", + "metadata": {}, + "source": [ + "Since we added random noise, it's not surprising that our results are worse than before. Undoubtedly, our model has identified some spurious relationships between the new, noisy predictors and our outcomes in the training set, but these relationships do not generalize to the test set. Let's look a little more closely by taking a look at what's happening in the training set:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cb3b1317", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy = 95.2%\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_train_pred_proba = lr_model.predict_proba(X_train)[:,1]\n", + "y_train_pred_label = (y_train_pred_proba > .5).astype(int)\n", + "\n", + "print_accuracy(y_train, y_train_pred_label)\n", + "plot_roc_and_pr_curves(y_train, y_train_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "id": "43e59a7e", + "metadata": {}, + "source": [ + "As we guessed, performance is much better on the training set that on the test set. **We are overfitting!**\n", + "\n", + "What can we do about this? As we discussed [in class](https://github.com/mengelhard/bsrt_ml4h/blob/master/lectures/on_n_vs_p.pdf), a common, simple, and effective way to mitigate overfitting is by penalizing large model parameters. This is known as *regularization*.\n", + "\n", + "In fact, regularization is so effective that `sklearn` does it by default for logistic regression; we removed it by specifying `penalty='none'`. Let's instead use an `'l2'` penalty, which means we'll penalize the squared magnitude of each of our coefficients. We can choose how strongly to penalize with the `C` parameter, which we'll set to `.01`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2317b34c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy = 86.4%\n" + ] + }, + { + "data": { + "image/png": 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xfXn5psuQSIEshEgRFi9ewKhRw8icOQvbt+8mT568+o6U4vj5+cVbFhQUpHn9YWYLIYQQ30YKZCGE3i1duojhwweTKVNmtm3bTd68+fQdKUXq0KFDgsstLCzInDkzBw4cSOZEQghhmKRAFiKZfHhq3sdSwlPq9C0mJoYNG9bi7JyRbdt2ky9ffn1HSrFu3rwZ571KpeLx48dMmDCBunXlfm0hhNAVmcVCiGTy4al5H0sJT6nTNzMzMzZv/p0dO/aRP38BfcdJVUxMTMiTJw9Dhgxh3rx5+o4jhBAG44uuID958oTAwECMjY3JkCEDWbOm3SdaCfE1HO0smN69or5jpAjr168hb958lC9fAQcHRxwcHPUdKdXy9/cnJESexCiEELry2QL53LlzrF+/nhMnThAcHKxZbmRkRPr06alSpQo+Pj6UKFEiCWMKIQzJ+vVr6NPnJ3LkyIWf3znMzMz0HSlVGDRoULxloaGhnDhxggYNGughkRBCGKZEC+R79+4xZswY3rx5Q/Xq1Zk9ezYuLi44ODigKApBQUHcvHmTc+fO0b9/f7Jmzcro0aPJly/hwTVqtZoxY8Zw69YtzM3NmTBhArly5dKs37lzJ8uXL8fY2Bhvb29atWql+9YKIfRu5cqV9OnzE46OjqxcuU6K4y9gYmISb1mGDBkYOXIkjRs3/uz+0g8LIYR2Ei2QR48eTY8ePahQoUKC6zNnzkzmzJmpVq0a/fv3588//2TUqFGsW7cuwe0PHz5MdHQ0Gzdu5OLFi0yZMoUFCxZo1k+bNo3du3djbW1N/fr1qV+/Pvb29t/YPCFESrJp03p69uyGg4MDmzfvpHDhIvqOlKpMnjz5m/aXflgIIbSTaIG8Zs2aLzpQ1apVqVq1aqLrz507h7u7OwAlSpTg6tWrcdYXLFiQkJAQTE1NURQFIyOjLzq/EPqQ0MwUiUnrM1bs2rWDXr1+xN7ens2bd1C0aDF9R0oVfv75Z6237d279yfXSz8shBDa0WqQXqNGjWjYsCH16tUjW7ZsX3Wi0NBQbG1tNe9NTEyIjY3F1PR9hPz58+Pt7Y2VlRW1atUiXbp0nzyeo6M1pqb/ft3o7Gz3VblSI2lrynH+TgBBoVFksLf87LYZHCypVDxbom1K6W39VjVquFOiRAkWLVpE6dKl9R0n2Xzrz/Xs2bNabadNMavrfhikL04r0kpbddlO89D3sxZZWJilyD+/lJgpqXxNW7UqkFu2bMn+/fuZM2cORYsWpUGDBtStWxcnJyetT2Rra0tYWJjmvVqt1nTKN2/e5NixY/zxxx9YW1szcOBA9u3b98l5PYOCwjWvnZ3tCAhIGyO4pa0pi0ql4GhrwZSu2j/BLKE2pYa2fq2IiAisrKywtk7Pvn1HyZgxncG29b8+/Fy/5YNo9erVOsuj634YpC9OC9JKW3Xdznfh0QBERcWkuD+/tPIzhbht/ZK+WOsCuWXLlrx+/Zr9+/dz4MABpk2bRtmyZalfvz61a9eOc1UiIaVKleLo0aPUq1ePixcvUqDAv/Od2tnZYWlpiYWFBSYmJqRPn553795p3QghRMq0a9fvjBw5lE2bfqdAgYLylb0OXLlyhbt376JSqQBQFIXo6Ghu3LjBhAkTPrmv9MNCCKGdL5oHOUOGDLRp04YKFSqwY8cOVq1axT///MO4ceOoU6cO/fv3J2PGhB96UKtWLU6ePEmLFi1QFIVJkyaxa9cuwsPD8fHxwcfHh1atWmFmZkbOnDnx9PTUSQOFEPqxZ88uunbtiKWlFe/evdV3HIMwd+5c5s+fT4YMGQgMDCRTpky8fv0alUpF7dq1P7u/9MNCCKEdrQvku3fvsn//fvbt28ejR4+oWLEi48aNo1atWgQFBTF27Fi6devGtm3bEtzf2NiYcePGxVnm4uKief3hKrUQKZU8Klp7+/btoXPndlhYWLJhwzbKlCmn70gGYdOmTYwdOxYfHx88PDxYuXIl9vb29O3bV6vxIdIPCyGEdrR61HT9+vVp1KgRp06donXr1vz1118sWrSIxo0bY21tTbZs2fDy8uLp06dJnVcIvZFHRWvnwIF9/PBDW8zNLVi/fivlypXXdySDERwcrJmFolChQpw/f5506dLRt29f9u/fr+d0QghhOLS6gty4cWMaNGjwyUdLV61alePHj+ssmBApkTwq+tMiIiIYOLAPZmZmrF+/hQoVtB+8KD4vc+bMPHnyhKxZs+Li4sL169dp3LgxNjY2BAUF6TueECIRarXCY/8QbjwK4sajIBxsLOhQz1XGZaRgWhXIGzdupHnz5vGWv3r1iiZNmuDn54eVlZXOwwkhUhcrKyvWr99KcHAQbm6V9B3H4DRv3py+ffsyefJkatasSbt27XBycuLvv//G1dVV3/GEEAm4+TiY3nP/IiwyNs7ytt8XxNRECuSUKtECee/evRw7dgyA58+fM3r0aCws4t5r+fz5c80UQUKItOvEiePkz1+ATJkyy9PxdOzYsWO4u7tjYmJCly5dyJw5M1ZWVhQrVoxhw4axYcMGHBwcmDhxor6jCiE+YmFqgrmpMaERMTils6RkAWe+y+XI4XNPuf9cZohJ6RKtbitUqMBff/0FvJ9GyNjYGBMTkzjbuLq6Mnjw4KRNKIRI0Y4dO4Kvrw/58hXg8OHj8foJ8W169OiBra0tdevWpVGjRjRq1EizrlmzZjRr1kyP6YQQibEwN2Fsp3IYAc4OVprbKU5eeaHZRlEUXgVFcOPhG/yDI6jvlhtbKzM9JRYfS7RATp8+PZMnTwYgW7ZsdOrUSW6jEAZJ28dFy4wV8R0/foy2bVsAMHLkWCmOk8CpU6c4ePAg+/btw9fXl8yZM9OgQQMaNmwYZwYKIUTKk8nROtF1y/fe4Obj4DiDv3NktKVikSzJEU18RqIFsp+fH2XLlsXU1JTSpUtz8eLFRA/i5iYDcUTq9WF2is8VvzJjRVwnThzH19cHtVrNypXr8PCoqe9IBildunQ0bdqUpk2bEhwczIEDB9i/fz+LFy+mYMGCNGrUiHr16iU6B70QImUx+/+j2f2uvcLWyowyrhkx4v1nkVqt32ziX4kWyB06dODkyZM4OTnRoUOHRA9gZGTEjRs3kiScEMlFZqf4MqdOnaBNm+bExsayYsVaatT4/EMqxLdzcHDQPNAjMDCQgwcPcujQIebMmUOpUqVYtmyZviMKIT7Dq2peCudJT/7s9mTPaIuxkREnLr/Q6ptMkXwSLZBv3ryZ4GshhFCpVJiamrFw4XJq1fpe33HSJCcnJ0qUKEFgYCD+/v5cuXJF35GEEFrI7mxLdmdbfccQn6HVFBTNmjWjfv361K1bl0yZMiV1JiFECufuXpVz565gb++g7yhpzuXLlzl48CAHDx7k1atXVKlShZ49e1K9enV9RxNCCIOhVYFcq1Ytdu3axfTp0ylZsiT16tXj+++/J3369EmdT6Rw2g5w+xomJkaoVEqSHPtjMvhOO//8c5oZM6awZMlK7OzSSXGcjM6fP8+BAwc4dOgQr169okyZMnTu3Jnvv/8eOzs7fccTQgiDo1WB3KVLF7p06cKTJ0/Yv38/27ZtY9KkSZQvX5769evj5eWV1DlFCqXtALeUTAbffd65c//g4+NFREQ4Z8/+Q/XqNfQdKc1wd3fn9evXFCpUCF9fX+rVqyff5AkhRBL7oqd85MiRg86dO9OsWTM2b97MggUL8PPzkwI5jUuqAW7OznYEBITo/Ljiy1y4cI7mzT2JiAjnt9+WSnGczJo2bUrDhg3JmzevvqMIIUSaoXWB/Pr1aw4dOsShQ4c4c+YMBQoU4KeffqJ+/fpJmU8IoUeXLl2geXNPwsJCWbBgCY0byy/Dya137976jiCEEGmOVgVyq1atuHjxInny5KFu3bqMGjWK3LlzJ3E0IYQ+hYS8o0ULL0JC3vHrr4vw9Gyq70hCCCFEstCqQC5dujSjRo3C1dU1qfOkeZ8b9JZcA9e0ldrvPxaJs7NLx8SJ04iNjcXbu7m+4wghhBDJJtEC+cmTJ2TPnh0jIyOaN2+uWZaQHDlyJE26NCi1DXqTAW6G5/79e2TPngNzc3O8vJrpO44QQgiR7BItkGvVqqV5kl6tWrUwMjJCUd5fuTQyMgJAURR5kl4S+NSgNxm4JpLS9evX8PZuQLlybqxYsVbzb12kHDdv3mTNmjU8evSIGTNmcOjQIXLkyEHVqlX1HU0IIQxGogXyH3/8gaOjo+a1EMKw3bx5g6ZNGxIYGEitWnWkOE6B/vrrL3r27EndunW5dOkS0dHRvHnzhilTpjB58mQaNmyo74hCCGEQjBNbkS1bNoyN368eOnQodnZ2ZMuWLc5/VlZW9OrVK9nCCiGSxq1bN/HyasDr16+ZMeNn2rRpp+9IIgGzZ89m6NChTJ48GRMTEwB69erF8OHDmT9/vp7TCSGE4Uj0CvKff/7JxYsXAfjnn3+YP38+VlZWcbZ5/PgxT58+TdKAQoikdefO7f8XxwFMnTqLtm076DuSSMT9+/epWDH+7VeVKlVi8uTJekgkhBCGKdEC2cXFhaVLl6IoCoqicPHiRczMzDTrjYyMsLa2ZurUqckSNCVKiscsp6YBesIw/PXXnwQE+DN58gw6dPhB33HEJ2TPnp2LFy/GGxh95MgRGSwthBA6lGiBnD17dlatWgW8v8Vi+PDh2NraJluw1CApZpyQWSFEcuvYsTNly5ajaNHi+o4iPqNPnz4MGjSIK1euoFKp2Lp1K0+ePOHAgQNMnz5d3/GEEMJgaDXNW/fu3QkKCiIoKCjBbdPylYukesyyEEnpwYP7bNy4lkGDhmNsbCzFcSpRs2ZN1q1bx7Jly8ifPz/Hjh0jT548rF27luLF5WcohBC68lXTvH1MpnkTInV5+PABXl4NePbsKRUqVKJaNQ99RxJa+vPPP6lcuTLTpk3TdxQhhDBon5zmLX369JrXQojU7/HjR5rieOTIcVIcpzIDBgzAxMSE2rVrU79+fcqVKyfT8QkhRBJItEDOli1bnNehoaGYmZlhYWHB7du3OX78OEWKFKFChQrJEjQl+TA4TwbUidTkyZPHeHrW5+nTJ4wYMYaePfvoO5L4QqdOneLEiRMcOHCAnj17Ym5uzvfff0/9+vUpWbKkvuMJIYTBSHQe5I8dO3YMd3d3zp07x5MnT2jdujWbN2+mW7dubNiwIakzpjgfF8cyoE6kBm/eBOLpWZ8nTx4zdOhIevXqp+9I4iuYmZlRvXp1pkyZwsmTJ5k4cSJRUVH88MMPeHjItwFCCKEriV5B/tjs2bPp2rUrbm5uzJkzhwwZMrB3717++OMPpk6dSosWLZI6Z4ojg/NEauLomJ7vv6+Ho2N6+vYdqO84QgeuXLmCn58fp0+fxtTUNMH5kYUQQnwdrQrkBw8e0LhxY4yMjDhy5Ag1a9bEyMiIQoUK4e+v23mAhRC6Exoaiq2tLUZGRowfP0XuV03l/Pz8OHjwIIcPHyY0NBQPDw+GDBmCu7t7nHnqhRBCfButbrHImDEjN2/e5ObNm9y5c4dq1aoBcOLEiTj3KgshUo6XL19Qs6Y7c+bMAJDi2AB07doVf39/hg4dip+fHzNnzsTDw0OKYyGE0DGtriB36NCBnj17YmxsjJubG6VLl2b+/PnMnz+fKVOmJHVGIcQXevXqJV5eDbh//x5hYWH6jiN05NSpU/LAJiGESAZaFcitW7emZMmSPH/+nMqVKwNQsWJFPDw8cHV1TdKAyeVLHhsts1eIlOzVq1d4eTXg7t079OjRh2HDRuk7kvgGgwYNYtSoUdja2jJu3LhPbivzIwshhG5oVSADfPfdd3z33XcoioJaraZYsWIAqNVqjI21ulMjRfuSadtk9gqRUvn7+9O0aUPu3LnNjz/2ZOTIsXJrRSpnYmKS4GshhGF7GxbNjYdvyJM1HZkcrfUdJ83RqkC+cuUKEyZM4OrVq6jV6njrDeVJejIzhUjtFiyYx61bN+natTtjxkyQ4tgATJ48WfPay8uLEiVKxLvnODo6muPHjyd3NCGEjgW+i2T/6cecvxPAvadvUYCS+TPQ07uYvqOlOVoVyOPGjcPCwoJ58+bJ/W9CpGDDho2iYEFXfHxaSXFsQNRqNYqi0LZtW44fP46Tk1Oc9bdu3aJfv35cvnxZTwmFELqw48QDAIwAl2z23H32lugYlX5DpVFaFch37txh06ZNFChQIKnzCCG+0Js3gfz9tx/16jXAzMyMFi1a6zuS0KENGzYwZswYjIyMUBSFKlWqJLhdpUqVkjmZEEJXcme2wymdJdmdbShZwJkS+TJgbWlKl+nH9B0tzdKqQHZ1deXp06epskDWdvCdDLwTqVFQ0BuaNm3MtWtX2LXrIOXKldd3JKFjLVq0wMXFBbVaTbt27Zg7dy729vaa9UZGRlhbW6fK/lkI8V72jLbxbvGMVcW/pVUkH60K5IYNGzJixAiaNGlCjhw54t3/1rRp0yQJpwvaDr6TgXcitQkODqJZsyZcvXqZtm07UqZMWX1HEkmkbNn3P9s//vgDe3t7QkNDyZw5M/B+PvpChQrJXMhCCKFDWhXIS5cuxdLSkv3798dbZ2RklKILZJDBd8LwvH0bTLNmTbh8+SJt2rRj2rRZBjGbjPi0gIAAvLy8aNasGQMGDADeD+J7+/YtixcvplChQnpOKIQQhkGrAvnIkSNJnUMIoaV3797i4+PJpUsXaNXKlxkzfpbiOI2YNGkSzZo1o3///pple/bsYfr06YwfP55169bpMZ0QQhgOrT9VX79+zYIFCxgyZAiBgYHs3buXW7duJWU2IUQCjI1NsLCwxMenFbNmzZPiOA25ffs2Pj4+8WYo8fHx4fr163pKJYQQhkerT9YrV65Qp04d/Pz82L17N+Hh4Zw+fZpmzZpx4sSJpM74xTYducvA+acYOP8UQSFR+o4jhE4oigKAra0t69dvZc6cX6U4TmOyZ8/OyZMn4y0/ffo0zs7OekgkhBCGSatbLKZMmUKXLl3o2rUrJUuWBGDs2LE4Ozszc+ZMzeOnU4qPB+bJ4DthCEJDQ2jfvg3du/fEw6Mm1tbyVKW06Mcff2Tw4MGcP3+ewoULA+8f1LR3797PPoZaCCGE9rQqkK9fvx7naU4fNG7cmMWLF+s8lC7IwDxhKEJDQ2nVqhl//32KLFmy4OFRU9+RhJ7Ur1+f9OnTs2HDBjZv3oyZmRm5cuVixYoVlCpVSt/xhBDCYGhVIDs5OXHv3j1y5swZZ/m5c+fImFGuzgqRVMLCwmjTpjl//32KRo08mT37F31HEnrm5uaGm5ubvmMIIYRB06pA7ty5MyNHjqRz584oisLJkyd58eIFq1at0kw1JITQrfDwcHx9fTh16gQNGjRmwYIlmJpq9U9WGJBBgwYxatQobG1tGTRo0Ce3nTZtWjKlEkIIw6bVp62Pjw/Ozs6a+ZBnzpxJnjx5mDhxIvXq1UvqjEKkSaNHD+fEiePUq9eQhQuXyYMg0igTE5MEXwshhEg6Wl+O8vDwwMPDIymzCCE+MmjQMKytrRk+fLQUx2nYx+M/EhoLIoQQQvc+WSBHRERw9OhRqlatio2NDQCrV6/m5MmTpE+fnrZt2+Lq6posQYVICyIjI3ny5DH58xfA2dmZsWMn6juS0LNfftH+vvMePXokYRIhhEg7Ei2Qnzx5gq+vL2/fvmXHjh3Y2NgwefJkVq1aRa1atTA2NqZ169YsW7aM4sWLJ2dmIQxSVFQUHTu24Z9/zrBnzyEKFCio70giBTh16pTmtVqt5uLFi2TIkAFXV1dMTU25ffs2r169wt3dXY8phRDCsCRaIM+ZMwdXV1dmz56NlZUVgYGBrFmzhu+//57Zs2cDkDdvXubMmcPy5cs/eyK1Ws2YMWO4desW5ubmTJgwgVy5cmnWX758mSlTpqAoCs7OzkyfPh0LCwsdNFGIlO9DcXz48EE8PGqSM2euz+8k0oSPHx89ceJEChQowKhRozQDNtVqNZMmTeLt27efPZb0w0IIoZ1EH8N16tQpevTogZWVFQDHjx9HrVbTpEkTzTaVKlXi8uXLWp3o8OHDREdHs3HjRvr378+UKVM06xRFYeTIkUyePJn169fj7u7Os2fPvrJJQqQu0dHRNGvWjEOHDlCtmgcrVqzD0tJS37FECrRlyxY6dOgQZzaTD9/mHTp06LP7Sz8shBDaSfQKclhYGOnTp9e89/Pzw9zcnAoVKmiWWVpaolartTrRuXPnNF8BlihRgqtXr2rWPXjwAAcHB1auXMnt27epWrUqefPm/eLGbDpyN85T9IRI6WJiYujcuT379u2matXqrFy5XopjkaiMGTPy119/kSdPnjjL9+3bR44cOT67f3L0w0IIYQgSLZBz5crFzZs3yZo1K9HR0Rw/fpzy5cvH+brtr7/+ivP13KeEhoZia2ureW9iYkJsbCympqYEBQVx4cIFRo4cSa5cuejWrRtFihT55GT4jo7WmJr+O+WRs7Md5+8EEBQaRQYHSyoVz4azs51W2VIbQ21XQgy9rU+ePOHy5QvUqFGDnTt3pplHSBv6z/VjumzrgAED6Nu3L4cPH8bV1RVFUbhy5Qo3btzgt99+++z+uu6HIeG+OK2QthqelNTOmNj3FyDNzE2TJFdKamtS+5q2Jlogt2jRgrFjx/Lo0SP++ecfgoOD6dChA/Dv7BY///yz1qOmbW1tCQsL07xXq9WarwkdHBzIlSsX+fLlA8Dd3Z2rV69+smMOCgrXvHZ2tiMgIASVSsHR1oIpXd/vFxAQolW21ORDW9OCtNBWS0sHduzYT6FCeQkLUxEWZtjthbTxc/3gQ1t19UFUq1Ytfv/9d7Zu3cr9+/cBKFOmDFOnTtXqYoWu+2FIuC9OC6SthieltTNW9b5AjomO1XmulNbWpPRxW7+kL060QG7dujUAW7duxcjIiKlTp2o6ymnTprFp0yZatmxJ27ZttTpRqVKlOHr0KPXq1ePixYsUKFBAsy5HjhyEhYXx6NEjcuXKxdmzZ2natKnWjRAiNYmNjWXs2BF06tSV3LnzkDNnLqytrdNEcSy+Xb58+Rg4cCDPnj0jc+bMKIqCubm5VvtKPyyEENr55DzIrVu31hTKH+vatSs9e/aMc4/y59SqVYuTJ0/SokULFEVh0qRJ7Nq1i/DwcHx8fJg4cSL9+/dHURRKlixJtWrVvrgxQqR0sbGx9OjRhW3btuDv/4qFCz8/A4wQH8TExDBr1izWrFmDSqXiwIEDzJgxA1NTU8aPH//ZW3SkHxZCCO0kWiD/+eefVK1aNcF1mTNnTnD50aNHqV69eoLrjI2NGTduXJxlLi4umtdubm5s2bLls4GFSK1UKhU9e3Zj27YtlCtXgZkz5+o7kkhl5s6dy4kTJ1i6dCldu3YFwNfXl5EjRzJlypR4fex/ST8shBDaSXSat71799KyZUt27dpFSEjiX/2GhoaydetWmjZtyt69e5MkpBCpnUqlonfv7mzduokyZcqxfv0WbG3TzgAJoRt79uxhzJgxlCtXTrOsTJkyTJo0Satp3oQQQmgn0SvIU6dO5ezZsyxYsIBhw4ZRuHBhXFxccHR0RK1WExQUxK1bt7h9+zblypVjwIABcaaAE0L8a+TIIWzatJ7SpcuwceM27OzS6TuSSIWCgoJwcnKKt9zKyorIyEg9JBJCCMP0yXuQy5Qpw9KlS3n06BEnTpzg6tWr3L59GyMjIzJkyIC3tzdVqlTRav5NIdKypk19uHPnNkuXrpLiWHw1Nzc3Fi9ezIQJEzTLQkJCmDVrllygEEIIHfpkgfxBrly5tJ7vWAjxnlqtJiwsFDu7dJQqVYbNm3foO5JI5UaPHs1PP/2Em5sbUVFRdO3alRcvXpAjRw4WLFig73hCCGEwtCqQhRBfRlEUBg/uzz//nGbLlp1kyJBB35GEAbCwsGDLli34+flx//59YmNjyZMnD5UrV8bYONEhJUIIIb6QFMhC6JiiKAwZ0p+VK5dSpEgxTEykcBG60bhxY+bPn4+bm9tnH+AhhBDi68kntxA6pCgKw4YNZPnyJXz3XRG2bNmBo6P284UL8SkWFhZER0frO4YQQhg8uYIshI4oisLIkUNYunQRhQoVZuvWXaRPH3/GASG+lru7Ox07dqRKlSpky5YNCwuLOOt79+6tp2RCCGFYtC6Qjx07xooVK3j8+DGrV69m06ZNZM2aFR8fn6TMJ0SqcevWTVasWIqrayG2bt2V4HRcQnyL27dvU6RIEd68ecObN2/irDMyMtJTKiGEMDxaFcg7duxg4sSJtG3blgsXLqBWq8mYMSNTpkwhIiKC9u3bJ3FMIVI+V9dCrFu3BVfX72RQnkgSq1ev1ncEIYRIE7S6B3nJkiWMHTuWHj16aEZKt27dmsmTJ7Nq1aokDShESqYoCmvXriI8PByAKlWqkTFjRj2nEoZm586d9OjRg379+rFnzx59xxFCCIOnVYH8+PFjihQpEm95oUKFeP36tc5DCZEaKIrC5Mnj6du3ByNGDNZ3HGGgFi1axNChQ4mMjCQ8PJzBgwcza9YsfccSQgiDplWBXKBAAf788894y7du3UrBggV1HkqIlE5RFKZOncCcOTPIkycvAwcO1XckYaA2bdrExIkTWbJkCb/99huzZs1i7dq1KIqi72hCCGGwtLoHefDgwXTt2hU/Pz9iYmKYP38+Dx484ObNm/z2229JnVGIFGf69MnMmjWd3LnzsH37HrJkyarvSMJAvXz5Ms6cxx4eHkRERODv70+mTJn0mEwIIQyXVleQy5Qpw4EDByhQoAAeHh68e/eOMmXKsHfvXipUqJDUGYVIUWbNmsaMGVPIlSs327fvIWvWbPqOJAxYbGwspqb/XsswNTWV+ZCFECKJaXUF+ZdffqFTp07x5tgMDQ1l6tSpDB4s91+KtCNjxkya4jhbtuz6jiOEEEIIHUu0QL579y4BAQEA/PrrrxQoUAA7O7t426xfv14KZJEmKIqCkZERbdq0w9u7OVZWVvqOJNKI3bt3Y2Njo3mvVqvZt28f6dPHfUpj06ZNkzuaECIZxcSqufbg/RzoJfLLdKJJKdECOTAwkA4dOmje9+rVK9421tbWdOzYMWmSCZGC/PLLz1y9eolfflmEqampFMci2WTNmpWVK1fGWebk5MSGDRviLDMyMpICWQgDpFIrXHvwhtM3XnH+VgDhUbEYGcEvfapgZSEPRE4qif7Jli9fnps3bwLvB4Vs2bIl3tUKIdKCBQt+Ydy4kWTNmo2AAH8ZkCeS1ZEjR/QdQQihRzcfB3Pz8UUAHO0ssDA3ISgkiliVWr/BDJxWv3p8qoN++fIlmTNn1lkgIVKShQt/ZfToYWTOnIVt23ZLcSyEECJZGBsbkSm9NRGRMZRxzUi5QpnIl92eBb9f5dytAH3HM3haFcj37t1j2rRp3LlzB7X6/W8siqIQHR1NcHAwN27cSNKQQujD4sULGDlyKJkyZeb33/eQN6+LviMJIYRII4yNjJjUuTyK8r5YFslLq2neRo4cydu3b+natStBQUF07tyZ+vXrExERweTJk5M6oxDJ7vz5swwfPpiMGTOxffse8ubNp+9IQggh0hgjIyMpjvVEqyvIV69eZePGjRQqVIjff/+dvHnz0rp1a/LkycOmTZto0qRJEscUInmVKlWGMWMmUqtWHfLly6/vOEIIIYRIRlpdQTY1NdVM8ZY3b17NLRUVK1bk9u3bSZdOiGR27tw/mkf4du/ek/z5C+g5kRBxRUVFsXPnTubOnUtwcDB///23ZkpOIYQQuqFVgVy6dGmWLl1KREQERYoU4fDhw6jVai5duoSFhUVSZxQiWaxdu4q6dWswY8YUfUcRIkGPHj2ibt26zJ07l0WLFhESEsKGDRto0KABV69e1Xc8IYQwGFoVyEOGDOHUqVOsW7eOxo0bExQURJkyZRgwYAAtW7ZM6oxCJLn169fQr19P0qdPT/36jfQdR4gETZgwAQ8PDw4dOoSZmRkAs2bNok6dOkyaNEnP6YQQwnBodQ+yi4sLBw4cICIiAisrK7Zu3cqZM2dwcHCgRIkSSRxRiKS1YcNa+vT5CQcHB7Zs2cV33xXWdyQhEnThwgWGDRuGkdG/g3aMjY354YcfaNy4sR6TCSGEYfnsFeTQ0FAiIiIANE8Ps7a2plq1amTNmpX+/fsnbUIhktDmzRvo3bs79vb2bNmyiyJFiuo7khCJsra2TvB+49u3b5MuXTo9JBJCCMOUaIH88uVL2rdvT9myZSlVqhRdu3bl7du3AKhUKpYsWULdunU5ceJEsoUVQtf+/tuPdOns2bJlJ0WLFtN3HCE+qUWLFowaNYrDhw8D7+eo37RpE6NGjZLHTAshhA4leovFuHHjePbsGdOmTcPMzIx58+YxefJk+vTpQ/fu3bl58yZNmzalb9++yZlXCJ2aPn02PXv2IXfuPPqOIsRnde/eHTs7OyZMmEBERATdunXDycmJDh060KlTJ33HE0IIg5FogXzu3DnmzJmDm5sbAK6urnh7e3P79m1UKhUbN26kaFH5OlqkPjt3bsff/xU//NANY2NjKY5FqhESEoKvry++vr6Eh4ejUqk0U3AKIYTQnUQL5Hfv3uHi8u+jdXPnzk1MTAzZsmVj1qxZmhHUQqQmu3btoGvXjlhb29CokRcZM2bUdyQhtObm5oa7uzsNGjTAw8NDimMhhEgiid6DrCgKJiYmcZaZmJjw008/SXEsUqU9e3bRtWsHLC2tWL9+qxTHItVZu3YtuXPnZubMmVSsWJE+ffpw6NAhoqOj9R1NCCEMilbzIH/MxsYmKXIIkaT27dtD587tMDe3YP36rZQrV17fkYT4YsWLF2fw4MEcOXKElStXkjVrVmbMmEHFihUZMmSIvuMJIYTB+OQ8yLt3745TEKvVavbt20f69OnjbCejp0VKdubMaX74oS3m5uasX7+FChXc9B1JiG/m6urK69eveffuHXv37uXChQv6jiSEEAYj0QI5a9asrFy5Ms4yJycnNmzYEGeZkZGRFMgiRStevAR16zagQ4cfcHOrpO84Qny1iIgIjh07xsGDBzl+/Dh2dnbUrVuXlStXyqBpIYTQoUQL5CNHjiRnDiF0LjAwECcnJywsLFiyZOXndxAihatQoQI2NjbUrl2b3377jbJly+o7khBCGCStHjUtRGpz9OgfdOzoy88//0qjRp76jiOETvzyyy9UrFgx3gBqIYQQuiUFsjA4f/55lHbtWqIoCunS2es7jhDfZMuWLTRq1Ahzc3NevXrF9u3bE91WbncTQgjdkAJZGJS//vqTtm1boCgKK1eup1o1D31HEuKbzJ8/nxo1amBubs78+fMT3U7GgwghhO5IgSwMxqlTJ2jTpjkqlYqVK9fh4VFT35GE+GYfjwf51NiQN2/eJEccIYRIE7SeBzkqKoqdO3cyd+5cgoOD+fvvvwkICEjKbEJoTVEUpk+fTGxsLMuWraZGjdr6jiSEzhUqVCjBQvjp06fUqFFDD4mEEMIwaXUF+dGjR3To0AFjY2NevnyJp6cnGzZswM/Pj6VLl1KkSJGkzinEJxkZGbFixVouX76Eu3tVfccRQme2b9/Oli1bgPe/CP7444+YmsbtugMCAuTJkEIIoUNaFcgTJkzAw8OD4cOHU6pUKQBmzZrFmDFjmDRpEuvWrUvSkEIk5syZ06jVKipUqIi9vYMUx8Lg1KlTh2fPngFw7tw5SpUqFe+Jph+mfhNCCKEbWhXIFy5cYNiwYRgZGWmWGRsb88MPP9C4ceMkCyfEp5w9e4YWLbwwNjbm3Lkr2Ns76DuSEDpnbW1Njx49AMiWLRv169fH3Nxcz6mEEMKwaVUgW1tbExAQQJ48eeIsv337NunSpUuSYEJ8yvnzZ/Hx8SIiIpxFi5ZLcSwM1sfTvKlUKnbu3JnotjKLhRBpT0ysimsPgnj48h0epbOTzlp+gdYFrQrkFi1aMGrUKAYMGADAvXv38PPzY86cObRs2TJJAwrxXxcvnqd5c0/CwkL57belNGzYRN+RhEgyMs2bECIhF+685sajIC7dfU1ktAqAdDbmeJTKrudkhkGrArl79+7Y2dkxYcIEIiIi6NatG05OTnTo0IFOnToldUYhNK5cuUSzZk0IDQ1h/vzFNGnire9IQiQpbad5E0KkLSv23QQgg70lLlnTce1hECqVoudUhkOrAjkkJARfX198fX0JDw9HpVJhZ2eX1NmEiCd9eicyZMjApEnT8PJqpu84QiS7kydP4urqipOTE9u3b2ffvn0UKVKEH3/8ETMzM33HE0IkseIuGXj9NpIiedJTpmBGcmay5fzt11x7GBRnO7Wi8NQ/lPTpLLG1kr7hS2lVILu5ueHu7k6DBg3w8PCQ4lgkO7VajbGxMdmyZefYMT8sLCz0HUmIZLdo0SLmz5/PihUrePjwISNGjMDT05O9e/fy7t07RowYoe+IQogkVrlYFioXy5LgOpVa4frDN5y/HcD52wEEh0ZTzMWJPs2KA/A2NIoLd19jaWlOBVfn5Iyd6mj1oJC1a9eSO3duZs6cScWKFenTpw+HDh0iOjo6qfMJwbVrV6latQI3blwHkOJYpFkbNmxgzpw5lChRgh07dlCyZEkmTJjAtGnT2Lt3r77jCSH0bPPRu8zYcJEj558RE6vGCHgVFMEev4dMXHWWfr+cZNX+Wyz6/QqBbyP1HTdF06pALl68OIMHD+bIkSOsXLmSrFmzMmPGDCpWrMiQIUOSOqNIw27cuE7Tpg25desmV65c0nccIfQqMDCQggULAnDs2DGqV68OgIODA5GR8mEnRFrl7GAJgL2tOR6lsjGgRQlm96yMqakxr96Es/XP+zx4EULBnA5kd34/j7pKrf6icyiKwqOXIew6+YANf9xBrTbs+521usXiY66urrx+/Zp3796xd+9eLly4oNV+arWaMWPGcOvWLczNzZkwYQK5cuWKt93IkSOxt7fXzJgh0q5r167h7d2AwMBAZs6cS/PmMmOKSNtcXFzYtm0bTk5O+Pv7U6NGDaKjo1myZAmFChX67P7SDwthmHJmsuPXvlWwMDfB+KNnVtQsnR3/4AhK5MtA8XwZsLUyY9neGzwNCNNs8+ZdJBfvvubhixDqueUic3przbqIqFiuP3zD5XuBXL4fyNvQf+8cqFgkMzkzGe4tt1oVyBERERw7doyDBw9y/Phx7OzsqFu3LitXrqRo0aJanejw4cNER0ezceNGLl68yJQpU1iwYEGcbTZs2MDt27cpW7bsl7dEGJTbt2/h5VWf169fM23abHx92+s7khB6N3jwYHr16sXbt2/p2rUruXPnZuzYsRw5coSFCxd+dn/ph4UwXFYW8Uu6ZtXzJbr9/tOPuff8HU/8QzXLMjhYUtY14/uC+F4gt58Eo/r/lWI7azMqFsnMm3eR3HwcjGLYF5C1K5ArVKigeZTpb7/99lUd57lz53B3dwegRIkSXL16Nc76CxcucOnSJXx8fLh///4XH18YDrVaTZcuHfD392fKlJm0by9TCQoBUL58efz8/AgJCcHe3h6An376iWHDhmk1g4X0w0KID1eYj118jqmJEUXypCd9OkuOX3rO7lOP+P2vB5ptc2e2o5iLE8VcMpA7ix3GRkZs+OMONx8H6yl98tGqQP7ll1+oWLEiJiYmX32i0NBQbG1tNe9NTEyIjY3F1NQUf39/fvnlF3755Rf27dun1fEcHa0xNf03j7OzHSYmRprXhszQ2wewceN6/Pz86Ny5s76jJJu08HP9QNr69fz9/Vm9ejX37t1DrVaTJ08emjdvjouLy2f31XU/DAn3xWmFtNXwpIV2ennkx8HeksJ5nChRwBlrSzOeBYRy/NJzzM2MKV84M2UKZaK0a0Yc01nG29/K6v2T+hwcrVPNn9fX5Ey0QP748aavXr1i+/btiR5Em6c32draEhb27z0varUaU9P3p9+/fz9BQUF06dKFgIAAIiMjyZs3L15eXokeLygoXPPa2dmOgIAQzQTZAQEhn82TWn1oqyG6f/8eVlZWZMmSlUyZctG5cxGDbet/GfLP9b/SYlt19SFy5swZunTpgqurKyVKlEClUnHhwgXWr1/P8uXLKV269Cf313U/DAn3xWmBtNXwpJV2prMwoXPjogQEhBAWEklYSCTmwM+9KmNlYYqpyfv5G2KjYggIiIm3f0TE+/uQg4PCCbD4+gunyeXjn+uX9MWJFsi6frxpqVKlOHr0KPXq1ePixYsUKFBAs65t27a0bdsWgG3btnH//v3PdsrCsDx4cB9Pz/pYWlpy5MhJbGxs9B1JiBRn6tSptG3bln79+sVZPnPmTKZPn86GDRs+ub/0w0KIxNhZm+s7QoqSaIGs7eNN37x5o9WJatWqxcmTJ2nRogWKojBp0iR27dpFeHg4Pj4+XxBZGJqHDx/g5dWAFy+eM3r0BCmOhUjE3bt3mTVrVrzl3t7erFq16rP7Sz8shBDa0eoe5EKFCnHy5EnSp08fZ/nTp09p2LChVlO9GRsbM27cuDjLErpnTq5YpC2PHz/Cy6sBz549ZcSIsfz0Uy99RxIixcqePTuXLl2KNzXbxYsXcXJy+uz+0g8LIYR2Ei2Qt2/fzpYtW4D3k0P/+OOPmnvVPggICCBjxoxJm1AYrCdPHuPpWZ+nT58wbNgoevXqq+9IQqRonTp1YvTo0dy9e5dixYoBcOnSJdauXUv//v31nE4IIQxHogVynTp1ePbsGfB+aqBSpUrF++r7w9RvQnyNZ8+eEhj4msGDh9OnjzyQQIjP+XBld82aNaxcuRJLS0vy5MnDlClTpC8WQggdSrRAtra2pkePHgBky5aN+vXrY24uN3AL3alQoSJ//XWGHDly6juKEKmGl5eX3AIhhBBJTKtp3lQqFTt37kz0INrMYiEEwIsXzxk7dgRTp87C3t5BimMhPkOlUrFo0SIOHjyIubk5NWrUoEOHDlo9GEQIIcTXSbZp3oR4+fIFnp71uX//HhUqVJIn5AmhhRkzZrBx40YaNmyIqakpS5Ys4fHjx0yYMEHf0YQQAoCYWBUvAsPJ4mSDmamxvuPoxDdP8yaENl69eomXVwPu379Hr179aNeuo74jCZEq7N27l1mzZlGtWjXg/fiQzp07M2bMmHgDp4UQIrk8ex3KjUdBXHv4hjtPgomOVdOsmgt1K+T6/M6pgNa968mTJ3F1dcXJyYnt27ezb98+ihQpwo8//ihf9YlP8vf3x8urAXfv3uGnn3ozfPhojP7/LHghxKe9fv2a7777TvO+TJkyxMbG8vr1azJnzqzHZEKItGzJ7hua107pLAh8F8W78Gg9JtItra6DL1q0iJ9++oknT55w7tw5RowYQcaMGdm7dy9Tp05N6owiFYuNjcXHx5M7d27TrVsPRo0aJ8WxEF9ApVJhYvLv41yNjY0xNzcnJib+I2CFECKpueZyJIuTNW6FM/NDg0LM6lGJ7p5F9R1L57S6grxhwwbmzJlDiRIlGDVqFCVLlmTChAlcvnyZbt26MWLEiKTOKVIpU1NT+vYdwLlzZxkzZoIUx0IIIUQqViJfBkrkyxBnWVBIlJ7SvH9WR8DbSB6/DCFfdnscbC10clytCuTAwEAKFiwIwLFjx2jXrh0ADg4OREZG6iSIMCxBQW+wtrbBwsKCRo08adTIU9+RhEi1Fi9ejJWVleZ9TEwMK1asIF26dHG26927d3JHE0KIJKcoCv7BEdx+HMztJ8HceBxEpSJZePkmnDtPgwkOfX9rR+ViWehYr5BOzqlVgezi4sK2bdtwcnLC39+fGjVqEB0dzZIlSyhUSDdBvtamI3c5fycAlUohKCQKRzvd/OYgvt6bN4F4ezcia9asLF++VubPFuIblC1blmvXrsVZVrJkSW7fvh1nmXw7I4QwFIqi8Px1GLefBHPryfui+EMR/MGuUw8BSGdjTjEXJy7fCyQiMpbHr0K48/Qtd5+95e7Tt2RwsGJwq5JfnEGrAnnw4MH06tWLt2/f0rVrV3Lnzs3YsWM5cuQICxcu/OKT6tI/N/0JCo3C0dYCRzsLyrrKo6/1KSjoDc2aNeHatSuUKVNOBnAK8Y1Wr16t7whCCKEzb0OjCImIIbuzrWaZWq3wxD9UUwzffhJMaMS/4yzS2ZhTxjUjBXM4kDm9NTceBZE5vTX5c9iT0cGKkPAY+sw7wbnbAZy7HaDZz9bKjIK5HL8qp1YFcvny5fHz8yMkJAR7e3sAfvrpJ4YNG5YiCqAM9pZM6eqm7xhpXnBwEM2be3LlyiV8fTswdepMuaolxDf6888/qVq16hftc/ToUapXr55EiYQQ4tOiYlTcffaWW4+DsLexwM7ajFuPg7n5OIgXgeEAtK/rSmhEDLefBHPnaTARUSrN/unTWeCWNxMFcjhQ4P9F8cf1ROE86eOcz9rSlFyZ7YiKVpEvuz35s9mTL7s9mdNbkzFjOgICQr64DVpP8+bv78/q1au5d+8earWaPHny0Lx5c1xcXL74pMLwvH0bjI+PJ5cuXaBNm3ZMnz4bY2PDmCxcCH3au3cvv/32G61ataJatWrY2dkluF1oaCgHDhxg/fr15MmTRwpkIUSyu/EoiClrz3P/+VtiVUq89RZm/87Is2LfTc3rTI5WlCn4vhgumNOBDPZW8fb9FFMTY0a3L/v1wRM6pjYbnTlzhi5duuDq6kqJEiVQqVRcuHCB9evXs3z5ckqXLq3TUCL1OXz4IBcunKdlyzbMmPGzFMdC6MjUqVM5e/YsCxYsYNiwYRQuXBgXFxccHR1Rq9UEBQVx69Ytbt++Tbly5RgwYAAVKlTQd2whRBry4el5j1+FYgTkzGRHoVyOPHr1/srtd7kdcc3pSK7MdjzxD2Xz0btkyWBDwf9fIdbVzBO6pFWBPHXqVNq2bUu/fv3iLJ85cybTp09nw4YNSRJOpB7e3s1Jn96JKlWqSXEshI6VKVOGpUuX8ujRI06cOMHVq1e5ffs2RkZGZMiQAW9vb6pUqUKOHDn0HVUIkQZly2BDp/qFsLIwpWBOB2wsE7/9Nk+WdAxqVSoZ030drQrku3fvMmvWrHjLvb29WbVqlc5DidQhNDSElSuX8+OPPTA2NqZ69Rr6jiSEQcuVKxe5cqXcx7jmzp0btTr+16rdu/eiU6cu/3/dmdOn/eJtU7p0GRYtWgHA6tUrmDNnRoLn8PM7j7m5OXfu3KZFC68Et5k1ax5Vq76/xaROnWq8fv063jbNm7dk8ODhAIwePZzdu3fE2yZnzlxs374HgH379jBixGDNOmNjI01bd+06QNas2QgODqJGDfcEMw0bNgpv7+YAtG7djJs3b8Tbpnr1msyYMQeAefPmsGLFknjbWFtb89dfZwA4e/YMXbt2TPB8y5atpnjx9yP3y5cvQWxsbLxtunT5ka5dfwKgT5+f+OuvP+NtU7Rocfbs2QnAhg1rmT59coLn+/PPv7G1teXhwwd4ezdMcJtp02ZRo0ZtABo0qM2LF8/jbePp2ZQRI8YAMGHCGLZv3xJvmyxZsrJ790EA/vjjIIMG9Yu3DcDWrbvInTsPoaGhVK2a8LcqAwcOpUWL1gB4eXlx9uy5eNu4u1dlzpxfAVi48FcWLVoQbxtTU1NOn74IwKVLF+jY0TfB8y1cuIwyZcr9/7jlCA8Pj7dN+/Y/0LNnHwAGDOjD0aOH423j6lqItWs3/7+dm5g0aVyC5/vjj79wcHDk+fNnNGxYR7P847+/EyZMpW7d+gB4etbn8eNH8Y7ToEFjxo6dCMDUqRPZtGl9vG0yZMjAgQPHAPjzz6P069czwUwbNmwjf/4CREdH4+aWcKHcp88AfH3bA9ClS3vOnTsbb5vy5d2YP38xAEuXLmL+/LkJHiuh9mhDqwI5e/bsXLp0KV7HfPHiRZycnL7qxCJ1Cw0NpWXLppw+7YeDgwOtW7fVdyQhhBBCCJ0wUhQl/q/7/7Ft2zbGjx+Pr68vxYoVA+DSpUusXbuW/v3707p16yQP+l8fRiQOnH8KExOjNDOLhbOz3VeNxtSlsLAwWrVqip/fSTw9vfn118WYmmo93lNrKaGtyUXaapg+tNXZOeGBdYbg459lWvzZpgVppa1ppZ2Qdtv6JX2xVlWNl9f7r7HWrFnDypUrsbS0JE+ePEyZMoXatWt/RVyRWoWHh9OmTXP8/E7SqJFnkhXHQgghhBD6onVl4+XlpSmURdoUFRWFr68PJ0/+RYMGjVmwYIkUx0IIIYQwOIlWNyqVikWLFnHw4EHMzc2pUaMGHTp0SBEPBhH6YW5uTpEixbC1tWPhwmXyd0EIPfjrr7+4cuUKsbGx/PcOud69e+splRBCGJZEC+QZM2awceNGGjZsiKmpKUuWLOHx48dMmDAhOfOJFCA2NhZTU1OMjIwYM2YCKpVKrhwLoQeTJk1izZo1uLq6YmNjE2edPLVSCCF0J9EqZ+/evcyaNYtq1aoBUKdOHTp37syYMWOkOEpDIiMjad++FaVLl2XgwKEYGRnJz18IPdm7dy/jx4/H29tb31GEEMKgJVrpvH79mu+++07zvkyZMsTGxvL69WsyZ86cLOGEfkVFRdGxYxuOHDmMsbGx5kqyEEI/VCoVpUql/An2hRAitUv0kWcqlQoTk3+fmW1sbIy5uTkxMTHJEkzoV1RUFJ06+XL48EE8PGqybNkaKY6F0LM2bdrwyy+/EBYWpu8oQghh0KTiEfFER0fTuXM7Dh7cT7VqHqxYsQ5LS0t9xxIizTt58iSXL19m3759ODo6xhsoe+zYMf0EE0IIA/PJAnnx4sVYWVlp3sfExLBixQrSpUsXZzsZOW1Yli9fzP79e6lSpTorV66X4liIFKJZs2Y0a9ZM3zGEEMLgJVogly1blmvXrsVZVrJkSW7fvh1nmYycNjydOnUlKiqKH37oFucXJCGEfnl6egLvH/X+6NEjVCoVuXLlwt7eXs/JhBDCsCRaIK9evTo5cwg9i42N5dSpE1SpUg1TU1N69eqn70hCiP+Ijo5m6tSpbNy4EZVKhaIomJqaUr9+fcaPH4+5ubm+IwohhEFIdJCeSDtiY2Pp3v0HmjZtxL59e/QdRwiRiKlTp3L8+HEWLFjAP//8w5kzZ/j111+5cOECs2fP1nc8IYQwGDJIL41TqVT06NGV33/fRvnybri7V9V3JCFEIvbs2cPcuXMpV66cZlnVqlWxtLSkX79+DB48WI/phBDCcMgV5DRMpVLRs2c3tm3bTNmy5Vm/fgu2trb6jiWESISiKDg6OsZb7uDgQHh4uB4SCSGEYZICOY1SqVT07t2dLVs2Urp0WTZs2IqtrZ2+YwkhPqFChQrMmDGDkJAQzbJ3794xa9Ysypcvr8dkQghhWLS+xeL169ds3ryZhw8fMmjQIE6fPk3evHlxdXVNynwiiURHR/Ps2VNKlSrNxo3bsLNL9/mdhBB6NWzYMNq2bUuVKlXImTMnAI8fPyZ37tz8+uuvek4nhBCGQ6sC+cqVK7Rv357ChQtz/vx5evTowZkzZxgyZAjz58+ncuXKSZ1T6JiVlRVr124mJiaadOlkiighUoNMmTKxe/dujh8/zv3797GwsMDFxYWKFSvKlJtCCKFDWhXIU6ZMoUuXLnTt2pWSJUsCMGbMGDJkyMDMmTOlQE4l1Go1w4cPokaNWtSsWQdra2vAWt+xhBCfoFarMTY21rw2MTGhevXqVK9eXbONoigoiqLZTgghxLfRqkC+fv06kydPjre8cePGLF68WOehhO4pisLgwf1ZuXIpFy+ex8OjlnyYCpEKFC5cmBMnTuDk5MR3332X4JViRVEwMjLixo0bekgohBCGR6sC2cnJiXv37mnuefvg3LlzZMyYMUmCCd1RFIUhQ94Xx4ULF2Xt2s1SHAuRSqxcuVLzpLxVq1bpOY0QQqQNWhXInTt3ZuTIkXTu3BlFUTh58iQvXrxg1apVDBgwIKkzim+gKArDhw9i+fIlFCpUmC1bdpI+vZO+YwkhtPTxnMcfXn+47SIgIICzZ89SqFAhcufOraeEQghheLQqkH18fHB2dmbp0qVYWloyc+ZM8uTJw8SJE6lXr15SZxTfYP78eSxZspBChb5j69ZdODlJcSxEanXx4kV69+7N1KlTyZcvH97e3oSGhhIdHc3s2bOpVauWviMKIYRB0HqaNw8PDzw8PJIyi0gCPj6tuHTpPBMnTidDhgz6jiOE+AZTpkyhRo0aFC1alNWrV2Nqaoqfnx87d+7k559/lgJZCCF0RKsC+eeff/7k+t69e+skjNANRVF4/vwZ2bJlJ0OGDCxatELfkYQQOnD9+nVmzpyJjY0NR44coUaNGlhYWFCxYkXGjx+v73hCCGEwtCqQz549G+e9SqXi6dOnvHv3Tm6xSGEURWHixLGsWLGUrVt3Urx4SX1HEkLoiIODAy9evEBRFK5cuaK5OHH16lWcnZ31nE4IIQyHVgXy6tWrE1w+depUYmNjdRpIfD1FUZgyZTxz584ib14XMmXKrO9IQggd8vb25qeffsLMzIyCBQvi5ubG2rVrmT59On369NF3PCGEMBha34OckFatWuHp6cnw4cN1lUd8g2nTJjF79gzy5MnL9u17yJw5i74jCSF0qHfv3nz33Xc8e/aMRo0aYWxsTPbs2Zk9e3acB4cIIYT4Nt9UIO/fvx9LS0tdZRHfYMaMKcycOZVcuXKzffsesmTJqu9IQogk8N+BeFWrVtVTEiGEMFxaFchVq1aN9/SmsLAwQkNDGTx4cJIEE9p7+zaYtWtXkTPn++I4a9Zs+o4khNCRatWqsX37dhwdHRPsiz927Nix5AsmhBAGTKsC+b/3thkZGWFmZkbRokXjPV1PJD97ewe2b9+DiYkJ2bPn0HccIYQO9e7dGxsbGyB+XyyEECJpaFUg79q1i+HDh+Pi4pLUecQXWL58CVWrVidvXhdy586j7zhCiCTg6ekZ5/XTp08JDw+nQIECAGzevBk3NzeyZ8+ur4hCCGFwjLXZ6MaNG5iaftPtykLH5s+fx+DB/ejRoyuKoug7jhAiGRw9epT69etz5MgRzbJ9+/bRsGFD/Pz89JhMCCEMi1ZVb4sWLejVqxc+Pj5ky5YNc3PzOOvd3Nw+ewy1Ws2YMWO4desW5ubmTJgwgVy5cmnW7969m5UrV2JiYkKBAgUYM2YMxsZa1e9pzm+//cKYMcPJkiUrv/666JP3JAohDMesWbPo378/bdu21SxbtmwZK1euZNq0aWzfvv2T+0s/LIQQ2tGqQF6wYAEA48aNi7fOyMiIGzdufPYYhw8fJjo6mo0bN3Lx4kWmTJmiOW5kZCRz5sxh165dWFlZ0a9fP44ePUqNGjW+pC1pwty5cxk1ahiZM2dh+/Y95MmTV9+RhBDJ5MmTJ1SrVi3e8urVqzNr1qzP7i/9sBBCaCfRAvn333+nXr16mJubc/PmzW8+0blz53B3dwegRIkSXL16VbPO3NycDRs2YGVlBUBsbCwWFhbffE5Ds3LlMgYO7EOmTJnZvn03efPKPeFCpCUuLi7s3r2b7t27x1l+4MABrQZMSz8shBDaSbRAHjp0KO7u7jg5OenkRKGhodja2mrem5iYEBsbi6mpKcbGxmTIkAF4/9S+8PBwKlWq9MnjOTpaY2pqgonJ+9sLnJ3tdJIzJatWrRKFChVi27ZtuLq66jtOskgLP9cPpK2GSZdt7d+/P126dOHkyZMULlwYeD9G5NKlS/zyyy+f3V/X/TD82xd/ID9bw5RW2ppW2gnS1s9JtEDW9cAvW1tbwsLCNO/VanWcgX9qtZrp06fz4MED5s2b99n7aoOCwgFQqRRMTIwICAjRad6UJDo6GnNzc3LlKsiVK1d48ybcoNv7gbOzXZpoJ0hbDdWHturqg6hixYrs2LGDLVu2cP/+fczMzChWrBiTJ0/WahYLXffD8G9fDGnzZ5sWpJW2ppV2Qtpt65f0xZ+8B1mXg79KlSrF0aNHqVevHhcvXtRMUfTBqFGjMDc3Z/78+TIo5CNr1qxk8eLf2LJlJ87OzpiYmHx+JyGEwXJxcWHgwIE8e/aMzJkzoyhKvIHTiZF+WAghtPPJAlmbr9cArQbp1apVi5MnT9KiRQsURWHSpEns2rWL8PBwihQpwpYtWyhTpgzt2rUDoG3btvEeqZrWrFu3mn79euLk5ERg4GucnZ31HUkIoUcxMTHMmjWLNWvWoFKpOHDgADNmzMDU1JTx48djbW39yf2lHxZCCO18skCePXs29vb2OjmRsbFxvFkwPn7wiC4GAhqSDRvW0rdvD9KnT8+WLbtwdS2k70hCCD2bO3cuJ06cYOnSpXTt2hUAX19fRo4cyZQpUxKcaehj0g8LIYR2Ei2QjYyMKFu2rM4G6Qntbdq0nt69u+Pg4MDmzTspXLiIviMJIVKAPXv2MH36dEqXLq1ZVqZMGSZNmkT37t0/WyALIYTQTqI3mcnT2fTD39+fgQP7kC6dPZs376Bo0WL6jiSESCGCgoISvGhhZWVFZGSkHhIJIYRhSrRA9vT0lDkw9SBjxowsX76WLVt2UKxYCX3HEUKkIG5ubixevDjOBYyQkBBmzZpFhQoV9JhMCCEMS6IF8uTJk+PMlymS1p9/HtVMv+ThUZPixUvqOZEQIqUZPXo0t27dws3NjaioKLp27UqVKlV4+fIlI0aM0Hc8IYQwGFo9alokrV27fqdLlw7UrFmb1as36juOECKFsrCwYMuWLfj5+XH//n1iY2PJkycPlStXlmnZhBBCh6RA1rPdu3fStWtHLC2t6NWrn77jCCFSsMaNGzN//nzc3Nxwc3PTdxwhhDBYcslBj/bu3U2XLu0xN7dgw4ZtlC1bXt+RhBApmIWFBdHR0fqOIYQQBk+uIOvJ/v176dy53f+L462ULy8DbIQQn+bu7k7Hjh2pUqUK2bJlizeQunfv3npKJoQQhkUKZD0JDg7C3NyCdes2U6FCRX3HEUKkArdv36ZIkSK8efOGN2/exFlnZGSkp1RCCGF4pEDWkxYtWlOzZh0yZMig7yhCiFRi9erV+o4ghBBpgtyDnIyOHDlMjx5diYmJAZDiWAihlZ07d9KjRw/69evHnj179B1HCCEMnhTIyeTYsSO0a9eSnTu3c+PGNX3HEWnAoUP7qVy5DOvXr4mzfOnShfz4Y6cE9/H0rMfevbviLPv771P07v0j339fjfr1azBoUB9u376p06znz5+lXbsW1KhRiR49uvD06ZNEt6tcuUyC/128eB6AFy+e079/L2rXrkrLll4cPLgvzjEuXbpAx45tqFmzMu3bt+LMmb912hZdW7RoEUOHDiUyMpLw8HAGDx7MrFmz9B1LiGSXWJ+mS//tV6pXd6N166bs3r0jSc73qf74Y3v37sLTs16SZPgaW7ZswNOzHrVqVWHSpLFEREQkuu2rVy8ZNKgvdepUpWnThmzYEPfnd+fObbp27UCNGpXo2LFNnBopNjaWJUt+o2nThtSuXZXevbvz8OGDJGvXx6RATgbHjx+jbdsWAKxcuV6ekCeSxeHDB8iePQf79+/+6mNs2bKB4cMHUr68G7/9tpy5cxfi6Jien37qorMi+dWrlwwe3I/ateuyZMlqnJycGDKkP2q1Ot62RYsWZ8eO/XH+q1jRncKFi1KkSDGio6Pp06c7iqJmwYKl/PhjL2bNmsbx48cACAp6w+DBfalevSYrV27Aw6MmQ4f25+XLFzppS1LYtGkTEydOZMmSJfz222/MmjWLtWvXxnmanhBpgS76NG2MGzdF07+sX7+NevUaMmXKeM0v4brUsqUvU6d+/hfeGjVqsWzZWp2f/2v8+ecRFi9eQP/+g5k37zdu3LjGL7/MTnT7UaOGYmFhweLFq+jduz9LlvzGkSOHAYiIiGDAgF4ULlyEpUvXULx4SQYO7EN4+PsHp61Zs4I9e3YyaNBwlixZScaMGenfv+cnC3JdkQI5iZ04cRxfXx/UajUrV66jevUa+o4k0oB3795x5szfdOzYhXv37n5VMfvs2VN+/fVnBg4cRqtWbcmdOw8uLvkYOnQUhQp9x8KF83WSddeu38mXLz+tW7cjT568DB06Gn//V5w790+8bc3MzHByyqD57+bNG5w9e5pRo8ZjamrKqVN/ERj4mjFjJuLiko8qVarRunU71q5dCcDly5cA8PVtT7Zs2WnbtiMWFpZcu3ZFJ21JCi9fvowz57GHhwcRERH4+/vrMZUQyUsXfZq27OzsNH1M5sxZaN26HTlz5uLPP4/q/FzW1takS2f/2e0sLCxxdHTU+fm/xqZN6/H29qFy5aq4un7HgAHD2LdvN+Hh4fG2fffuHdeuXaFt2w7kzJkLd/dqlC/vxvnz7/v3P/44iKmpKT169CV37jz06tUPW1tb/vjjEAD79u2mXbtOlCtXgZw5czNo0HDevXvLpUsXkrydUiAnoadPn9CmTXNUKhUrVqzFw6OWviOJNOLYsT8wMzPHw6MWOXLkZO/eL7/icvjwAdKls6d27brx1g0cOIzevfsnuF+PHl0SvAWiadOGCW5/7doVSpQopXlvaWlJgQIFP1u0qlQqFiyYR9OmLciWLTsAz58/I2fOXHE+cPLly8/Nm9eJjY3F3t6e0NBQjhw5jKIoHD9+jPDwMFxc8n/2z0NfYmNjMTX9dzy1qampzIcs0pzE+rQFC+bFu0VhzZoV/PBDWwDevg1m2LCB1KrlTrNmjfn99y1Urlzmi89vYmKCmZkZAE2bNmT+/J9p3Ph72rRpRmxsLPfv36NXr254eFTCx6cJ69evifMtz+HDB2jTpjk1alSic+e2XL16GYh7i0VsbCwzZkymQYNa1KhRiT59uvP48UMg/i0WDx8+oF+/ntSuXZXGjb9n6dKFmm/dli5dyOjRQ5k1ayp16lTF27sBq1cvT7Rtid22NnHimHjbqlQqbty4HqfPLly4CCqVijt3bsXb3sLCAktLS/bu3U1sbCyPHz/kypXLFCxYCIBr165StGhxzZNAjYyMKFq0uObPZ9Cg4VSpUk1zPCMjIxRFSZb+T2axSELZs+egX79BuLoWombNOvqOI77RpiN3+edm0ly1MzExQqWK/5V5WdeMNPfI98XHO3RoPxUqVMTU1BR396rs3buLHj36xCm0Pufu3TsULFgowUcY58iRM9H9Jk2arhmI+jFjY5MEtw8MfB1vwGr69E74+7/6ZL7jx4/y4sUzWrduq1nm6JiewMDXqFQqTEzen+/VqxeoVCpCQ0MpXrwk3t7NGT16KGPHGqNSqRgyZAS5c+f55LmEMETa9mmJ9U9fQ9d9Ws2atVm3bhWvX//bjxw9+ofmM3f06GFERUUyf/4SXr8OYMqU8V903qioKPbs2cnDhw8YMGCYZvmBA3uZOfMXFEWNShXLgAG9qFOnHgMHDuPp0ydMnz4JMzNTmjZtwdmzZxg/fhS9ew+gXLkK7Ny5nYED+7B1a9zxHlu3buTMmb+ZPn0OdnbpmD9/LhMnjmXhwrjFbXBwMD/99AOVKlVh0aIVPHnyiKlTJ2BlZUWrVu/7wz//PIqnZzMWL17JsWNHWLjwVypXrkqePHnjtXHHjv0Jtt3CwjLestDQEKKjo+L02aampqRLZ59gn21hYUH//kOYNWsa27ZtQqVSUadOPRo2bAK87/9z5swVZx9Hx/TcvXsbgNKly8ZZt3v378TGxlK4cJEEM+uSFMhJ4OHDB+TKlRsjIyN5fLRIdq9fB3Dp0gVGjhwHQJUqHqxbtxo/vxO4u1fT+jihoSE4Oqb/4vNr83XhxyIjIzEzM4+zzMzMLMEi+2O//76NunUbYG/voFlWoUIl5syZzsKFv9KpU1devnzBhg3rAIiNjSEiIoIXL57Trl0nqlSpxj//nObnn2eSO7cLRYoU/aLcyWn37t3Y2Nho3qvVavbt20f69HF/Pk2bNk3uaEIkuc/1ably5eb48aN4eTXj+fNn3Llzi0mTpvP48SPOnj3DunVbyZkzF/nzF6RDhy7MmDH5k+cbPLgfJibvLwxERkZiZ5eOHj36ULx4Cc02tWrVJV++99887d79O3Z26eja9Sfg/QWEzp1/ZPnyxTRt2oLff99KjRq18fJqBkC3bj0AhXfvQuKc98WLF1hYWJA5c1YcHR0ZMGAIT57EH7B86NB+zM0tGDRoOKampuTOnYfAwNcsWfKbpkC2tbWjR48+mJiY0LZtRzZsWMvNm9cTLJCdnLSfUSsyMhLgi/rsx48fUaFCRVq18uX58+fMnj2VTZvW0bx5K6KiIjVX5j8wNzdP8Arx5csX+eWXOfj6dviizF9LCmQdO3PmND4+nrRt24GxYyfqO47QoeYe+b7qyoc2nJ3tCAgI+fyGWjh8+ADGxsa4uVUG3n/9lSGDM/v27cHdvRqmpqaJDvBSq9Waq8z29g6EhLz74vP379+Ly5fj3x+WKVMW1qzZFG+5ubkFMTFxO8OYmJhPFtrBwcFcuHCWzp27xVnu6OjI+PFTmThxNBs2rMHRMT2tW7dj7tyZWFvbsH79aqKjo/nhh/f7FSjgyoMH91m1ainTps354rYmh6xZs7Jy5co4y5ycnNiwYUOcZUZGRlIgiy+ibZ+my/7pa3yuT/PwqMWff74vkI8ePUyRIsXIlCkzR48exsbGJs4VyiJFin32fAMHDqVo0eIYGRlhYWGBk1OGeA/iyZIli+b1w4cPefjwPrVquWuWqdVqYmJiiImJ4eHDBzRs2FizztjYmO7d4z/10tu7OcePH6VJk+8pVqwE7u5VqVevUbztHj16QIECBeN8I1ikSHGCg4MJDg7W5PvwLRq8v9dZpYpNsL0f5/5Y7dp1GThwWJxl5ubvn96ZUJ+d0BXn8+fPsn37ZrZv34eVlRWFChUmMjKCefNm4e3tg7m5ebzCOjo6GktLy3jHGTKkP5UqudOhQ+cE8+qaFMg6dPbsGVq08CIyMoIyZcrpO45Iow4fPkhsbCz16/87IFStVnPq1F8EBwdja2tHWFhovP0URSE0NARbWzsAChX6jjVrVqIoSrwPh1OnTnDgwB5Gjhwf77aNIUNGEBUVFe/4id3e4ezsTGBgYJxlb94EkjevS6JtPH36FOnTO1G4cPyrvuXKVeD33/cTGBiIo6MjZ8744eDggLW1NTduXIt33IIFC7Fjx9ZEz6VvR44c0XcEIfTqc31azZp1WLlyKW/fBnP06B/UrdsAABMTU/57LUCb2V8yZHAme/Ycn9zmQ6EI7+/LLVGiNAMHDo233ft7l7UrtXLkyMn69dv4++9T+PmdYNWqZezcuZ2lS+M+IMjc3Dzevmq1Ks7/TU3N4m2TWNOXL1+X4PKPv7X6wN7eHnNzCwIDA8mb9/0vV7Gxsbx79zbBZzvcuHGNLFmyYmVlpVlWsGAhQkND/79PRt68id//f3yF2M/vBMOHD8bdvQojR45P8La/pCAFso6cP38WHx8vIiLCWbhwWZzfFoVILk+ePObmzev06tUvzi9pr169ZODAPhw+vJ/8+Qvy5Mlj3r4NjnN7wt27t4mKiiJ//gIAVK9ek4UL57N//x7NBw68/2Bav341JiYmCRa9zs4Zvyhz4cJF40yfFBkZye3bt2jXrmOi+1y7doXixUvEK9wfPXrIjBmTmT37V01nfeLEcUqUKA28/+C7d+/uf/Z5QNas2b8osxAieWjTpzVt2gIXl3zs2vU7d+7cYtq091OO5c6dh/DwMJ48eawZN3Hr1g2dZ3w/w8URMmfOoukTjx49zJkzfzN48AiyZ8/J7dv/DmBTFAVf3+b06NE3znH27duNmZkZNWvWoWrV6vj7d8HLqz5378bts3LlysPRo3/EGcB79eoV0qWzx8Hhy2e6+NwvAx8zNjamUKHvuHz5ImXLlgfe98cmJibkz18w3vYZMjjz5MljoqKisLB4/0vFo0cPsLa2wcHBkcKFi7By5TLNhRhFUbh69bLmVpFr164yfPhgqlevwbBho+NcFU9qMouFDly8eJ7mzT0JCwtlwYIlNGrkqe9IIo06fPgAtrZ2NG7sTd68+TT/ublVpmjRYuzbt4eiRYuTL18Bhg8fxJUrl3j+/Bl+ficYN24ktWvX1RS4GTNm4ocfujF9+iTWr1+j+aAaPXoYt27d1Nn99fXrN+L69WusXLmUBw/uM3nyODJlykTp0u8/DMPDwwkKCoqzz/3798iTJ/4V5ixZsvL48SMWLZrP8+fP2LZtM/v27aFNm/YANGrkycWL51m7diXPnj1l9+7f2bt3Fz4+rXTSFiGEbmnTpwH/v4q8jJIlS5M+vRPwvnAtV86NqVMncOfObf755zRLly7UecY6deoSExPD1KkTePjwAf/88zezZk3Dzi4dAM2ateDIkUPs2vU7T58+YcGCebx79y7e7R5hYaH8/PNMzpz5mxcvnrN37y6srKzjDYquVet71GoV06ZN5OHDB5w48SfLli2kSRPvZLm66unZlA0b1vLnn0e4efM6M2dOoV69RlhbWwPw7t1bQkPff0tZuXIVbG3tmDx5HI8fP+Sff04zf/5cmjdviZGREdWr1yAiIpzZs6fx4MF95s2bRVhYGDVr1kZRFCZPHkeePHn58ceeBAcHERj4msDA10RFRSZ5O+UKsg5s376V0NAQfv11EU2aeOs7jkjDDh8+QK1a32t+U/9YkyZNGT9+FA8f3mfmzLksWDCPkSOH8PZtMBkyOFOzZh06duwSZ59WrXxxdnZm8+YNrFixGDMzM4oUKcZvvy3VfL32rbJkycrEidOYN28Wq1Yto3DhokyZMkvT0a9fv5p9+3azZcu/I76Dgt5oPnw+Zm5uzuTJM5g9+/2I6Rw5cjJ58gxcXd9PKfTdd0WYMmUmixf/xooVS8iSJSujRo2PN1JaCJEyaNOn3b9/lxo1arNgwTxq1qwdZ5thw0YzbdoEunbtQIYMGahXryHr1q3SaUZraxtmzpzL3Lmz6NixDXZ2dtSt24AuXboDUKxYCQYNGs7y5YuZPXs6BQsWZNq0Odja2sY5jpdXcwICApg0aSzv3r0lTx4Xpk2bTbp06f5zPmtmzJjHzz/PoGPH1jg4ONK0aQvatk38WzddqlmzDi9fvmDGjCnExERTpUp1evbso1k/bNhAsmTJyvDhY7CxsWXu3AX8/PMsunRpj62tHfXrN6Jdu/dT29nY2DJ9+hymT5/E7t07cHHJx/TpP2NtbcP9+3d5+PA+AE2axJ1udPDgEZqZMJKKkZJKH8f0YcDAwPmnMDExYkpXt8/skXTUajUXLpxLlg9ZfQ+WSE7SVsOUFtvq7Gyn7yhJ5uOfZVr82aYFqbWtkZGRnD17mgoVKmluRThy5DDz5/8c5xfuD1JrO79GWm3rl/TFcovFV7p27SrLli0G3t+TI1eghBBCiJTj/TdK41i2bBHPnz/j6tXLLF++iOrVa+o7mkgF5BaLr3D9+jWaNm3ImzdvqFixsubrWyGEEEKkDMbGxkyaNJNff53Dxo1rsbGxpXbtunTu/KO+o4lUQArkL3Tz5g2aNm1IYGAgs2f/IsWxEEIIkUIVL16CRYtW6DuGSIXkFosvcOvWTby8GvD69WtmzPg5ziNuhRBCCCGEYZACWUuPHj38f3EcwLRps2nbtoO+IwkhhBBCiCQgt1hoKXPmLJQuXZZq1Txo376TvuMIIYQQQogkIgXyZ0RGRmJpaYmFhQUrV66L9+QuIYQQQghhWOQWi0+4f/8elSqVYfv2LQBSHAshhBBCpAFyBTkRDx7cx8urAc+fP+Ply5f6jiOEEEIIIZKJXEFOwIcBec+fP2PUqPH8+GMPfUcSQitNmzaka9cO/PcBmefPn6Vy5TLExsZ+8TH37t2Fp2c9rbdftWoZtWpVwdu7wRefS9eCgt7wxx8HNe8rVy7DP/+c1mMiIcSXkD4tLunTko9cQf6Px48f4eXVgGfPnjJixBh69Oit70hCfJFr166wc+d2Gjf20snxatSohZtbZa22fffuHYsWzWfgwGG4uVXSyfm/xYIF84iNjaVGjdoA7Nixn3Tp7PWcSgjxJaRP+5f0aclHriD/x4QJo3ny5DFDh46kV69++o4jxBfLnDkLCxf+SnBwsE6OZ2FhiaOjo1bbhoeHAVCqVBkyZsykk/N/i/9edXJyyoCZmZme0gghvob0af+SPi35SIH8HzNnzuXnn+fTt+9AfUcR4qv4+LTG2tqa+fN/TnSbd+/eMXXqRBo2rE3t2lUZO3YE7969TXDbj7+OPH/+LJ6e9dixYxuenvWoV68G48ePJCoqkvPnz9K0aUMAWrb0YunShQCcPPkXHTu2xsOjEq1bN2Xfvn2aY/fo0YVZs6bi49OEJk3qcvPmdSpXLsOJE3/StGlDatVyZ9682dy7d5eOHdtQs2ZlBg/uS0REBACxsbH8+uvPeHrWo2rV8nh7N9AMql26dCH79u3m4MF9mlwffx0ZFRXFggXz8PKqT82alRk0qC8vX74A4MWL51SuXIZjx/7Ax6cJdepUZfDgvjr7gBZCaC+5+rQqVap8VZ925MhhzbFTS59WunRp6dM+Q26x4P1fnEePHlGhght2dulo2bKNviOJFKp06SIJLu/evRedOnX5/+vOnD7tl8C+ZTSPPF29egVz5szQrDM2NkKtfn9lwM/vPObm5ty5c5v8+Qt8cUZLS0t69+7PsGEDadCgMcWKlYi3zbBhA4iKimTq1FkYGRkxY8YUxo8fxfTpiX8AffDmTSBHjhxi+vSfefHiGWPGDKdIkeI0aNCYxYtX0rlzO377bTl587pw7tw/DB8+kO7de1GhQiX8/E4wYMAA5s9fwnffvf+z3Lt3FzNn/oKFhTn29g4ArFmzkilTZnHv3h3Gjx/FqVN/0b//EExNTRk8uC979uygadMWrFmzghMn/mT8+Kk4Ojqyf/8e5syZjrt7VVq29OXRo4eo1Sr69x8arx0zZkzmypVLjBgxFnt7B+bPn8vgwf1YtmyNZpvVq1cwatQEYmKiGTlyCOvWraJ7915f/DMRIqXSpk/z9fXlzz+PJ7Bv4n3ax1JLn7Zo0SKuX7/zxX3a2LHDyZw5c6rq02xsTOnZs5f0aZ+Q5q8gv3z5Ak/P+vj4NOHhwwf6jiOETri7V6NixcrMnDk13iCWu3fvcPHieYYPH8t33xWhUKHCjB49Hj+/k9y/f++zx1apVPTq1Z98+fLj7l6N8uUrcuPGNczMzHBweP+1pYODA9bW1mzduokqVarTvHkrcubMhY9Pa2rXrs26das1x6tQoSLFi5fA1fU7zbJ27TqSL19+6tSpR7p09tSsWYcyZcpRokQpSpYszaNHjwDImzcfQ4aMpEiRomTLlh1f3w6oVCoeP36EtbU1FhYWmJtbxPs69d27dxw4sJfevQdQqlQZXFzyMXr0eJ49exLnl5sOHTpTuHARSpQoRe3a33Pz5vUv/2EIIb5ZcvRprq6uX9WnVavmker6tLJly0qf9hlp+gryq1cv8fSsz/379+jTZwC5cuXWdySRwp07d/Wz28yfv/iz2/j6tsfXt73mvbOzHQEBIXG2+ZorLR/r02cgbdo0Y8uWDRQo4KpZ/ujRA6ytbcidO49mWc6cubGzS8ejRw/Im9fls8fOli275rWNjQ0qVcIjyR89ekDDhk3iLCtZsiTr1q3XvM+cOWu8/bJm/ff4FhYWZMqUOc77mJhoAKpUqcY///zNvHmzefz4Ibdv3wTef+B9ypMnj1Gr1RQu/O/Vs3Tp7MmZMxcPHz4gT5688dppbW3zVSPmhUjJtOnTVq9eHa9/+q//9mkJMdQ+rUiR4uzcuU3zXvo0w5BmryC/evUKT8/63Lt3l549+zJ06Eh5EIgwKFmyZKVt244sW7aY168DNMvNzc0T3F6tVqFWq7U6tqlp3N+t/zNu5JPnUqvVcc6T0DYmJiZx3hsbJ9xVLVo0n7FjR2BiYkKdOvVYuHDFZ5Infk4AlUqNWv3vB9F/B7/8d4CMECL5pNw+TSV9mgFKkwWyv78/3t4NuHv3Dt2792LEiDFSHAuD1KpVWzJkyMDixQs0y3Llyk14eFicW4oePLhPWFgYOXPm0un5c+XKw/Xr1+Isu3Dhgs7Os2PHVnr3HkD37r2oWbOOZqALvO/0E/t3nT17DkxMTLh27d+rZ2/fBvP06WNy5sytk2xCCN1LiX3a1atXpE8zQGmyQFarVahUKrp2/YnRo8dLcSwMlpmZGf36DebFi+eaZTlz5qZiRXcmThzNjRvXuHHjGhMnjqFYsRLkz19Qp+dv0aI1f/55hE2b1vHkyWM2bVrHoUOH8PJqppPjp0tnz6lTJ3j27CmXL19k/PhRAERHxwBgZWXFy5cvCAjwj7OflZUVTZp48/PPMzh//iz37t1l/PhRODtnpHx5N51kE0LoXkrs044fPyp9mgFKkwVy5sxZ2L//COPGTZLiWBi8MmXKUbNmnTjLRowYQ/bsOenduzt9+/YgT568TJkyS+fndnX9jtGjJ7JjxzbatvVhz56dzJkzh7JlK+jk+EOHjuL+/bv4+vowYcJoqlevSeHCRblz5xYA339fn2fPntK+fct4XyV2796LcuUqMGLEYLp164iZmTk//7wACwsLnWQTQiSNlNanjRs3Wfo0A2SkpNIbUD4MGBg4/xQmJkZM6frp35DevAmkS5eOjB07Mc5N7KlNQoO5DJW01TClxbY6O9vpO0qS+fhnmRZ/tmlBWmlrWmknpN22fklfnCauIAcFvaFp08YcP36Ubds26zuOEEIIIYRIwQy+QP5QHF+9epm2bTsyfPhofUcSQgghhBApmEEXyMHBQTRv7smVK5do06Yd06bNSnRqFSGEEEIIIcDAC+QePbpy6dIFWrXyZcaMn6U4FkIIIYQQn2XQT9IbNWo8Li75GT16vBTHQgghhBBCKwZXNYaGhmjmRyxQoCBjx06U4lgIIYQQQmjNoCrH0NAQWrTwplGj7/H39//8DkIIIYQQQvyHwRTIoaGhtGzZlDNn/qZUqdKkT59e35GEEEIIIUQqlGwFslqtZtSoUfj4+ODr68ujR4/irD9y5Aje3t74+PiwadOmLzp2THQkrVs34/RpPxo39uLXXxdjamrQt1cLIcQXS8p+WAghDEmyVZGHDx8mOjqajRs3cvHiRaZMmcKCBQsAiImJYfLkyWzZsgUrKytatmxJ9erVcXZ2/uxxY6MjObJ+NC8eXKJhwyYsWLBEimMhhEhAUvXDQghhaJLtCvK5c+dwd3cHoESJEly9elWz7t69e+TMmRN7e3vMzc0pXbo0Z8+e1eq4bwOfEvDsNvXrN+K335ZKcSyEEIlIqn5YCCEMTbJVk6Ghodja2mrem5iYEBsbi6mpKaGhodjZ/ft8bBsbG0JDQz95PEdHa0xNTfCsX42yRdYxtGt9zMzMkix/SvIlzxJP7aSthknaqh+67ofh3774g5TU3qQmbTU8aaWdIG39nGQrkG1tbQkLC9O8V6vVmqu9/10XFhYWp6NOSFBQOAANK+TEuWFhAgJCgEjdB09hnJ3t/t9WwydtNUxpsa0p5YNI1/0w/NsXQ9r82aYFaaWtaaWdkHbb+iV9cbLdYlGqVCmOHz8OwMWLFylQoIBmnYuLC48ePSI4OJjo6GjOnj1LyZIlkyuaEEKkCdIPCyGEdowURVGS40RqtZoxY8Zw+/ZtFEVh0qRJXL9+nfDwcHx8fDhy5Ai//voriqLg7e1N69atkyOWEEKkGdIPCyGEdpKtQBZCCCGEECI1MJgHhQghhBBCCKELUiALIYQQQgjxESmQhRBCCCGE+IgUyEIIIYQQQnxECmQhhBBCCCE+kqoKZLVazahRo/Dx8cHX15dHjx7FWX/kyBG8vb3x8fFh06ZNekr57T7Xzt27d9OsWTNatGjBqFGjUKvVekr67T7X1g9GjhzJjBkzkjmdbn2urZcvX6ZVq1a0bNmSXr16ERUVpaek3+5zbd25cyeenp54e3uzbt06PaXUrUuXLuHr6xtvuaH0Sx+klX4YpC9OiPTFqUta64t12g8rqciBAweUwYMHK4qiKBcuXFC6deumWRcdHa3UrFlTCQ4OVqKiohQvLy/F399fX1G/yafaGRERodSoUUMJDw9XFEVR+vbtqxw+fFgvOXXhU239YP369Urz5s2V6dOnJ3c8nfpUW9VqtdKoUSPl4cOHiqIoyqZNm5R79+7pJacufO7nWqlSJSUoKEiJiorS/LtNzRYtWqQ0aNBAadasWZzlhtQvfZBW+mFFkb74v6QvTn3SUl+s6344VV1BPnfuHO7u7gCUKFGCq1evatbdu3ePnDlzYm9vj7m5OaVLl+bs2bP6ivpNPtVOc3NzNmzYgJWVFQCxsbFYWFjoJacufKqtABcuXODSpUv4+PjoI55OfaqtDx48wMHBgZUrV9KmTRuCg4PJmzevvqJ+s8/9XAsWLEhISAjR0dEoioKRkZE+YupMzpw5mTdvXrzlhtQvfZBW+mGQvvhj0henTmmpL9Z1P5yqCuTQ0FBsbW01701MTIiNjdWss7P79xnbNjY2hIaGJntGXfhUO42NjcmQIQMAq1evJjw8nEqVKuklpy58qq3+/v788ssvjBo1Sl/xdOpTbQ0KCuLChQu0atWK5cuX8/fff+Pn56evqN/sU20FyJ8/P97e3tSvX59q1aqRLl06fcTUmTp16mBqahpvuSH1Sx+klX4YpC+Wvlj64tRE1/1wqiqQbW1tCQsL07xXq9WaP4z/rgsLC4vzB5KafKqdH95PnTqVkydPMm/evFT9G9+n2rp//36CgoLo0qULixYtYvfu3Wzbtk1fUb/Zp9rq4OBArly5yJcvH2ZmZri7u8f7TT81+VRbb968ybFjx/jjjz84cuQIb968Yd++ffqKmqQMqV/6IK30wyB9sfTF0hcbgq/tl1JVgVyqVCmOHz8OwMWLFylQoIBmnYuLC48ePSI4OJjo6GjOnj1LyZIl9RX1m3yqnQCjRo0iKiqK+fPna77eS60+1da2bduybds2Vq9eTZcuXWjQoAFeXl76ivrNPtXWHDlyEBYWphlAcfbsWfLnz6+XnLrwqbba2dlhaWmJhYUFJiYmpE+fnnfv3ukrapIypH7pg7TSD4P0xR9IXyx9cWr2tf1S/GvRKVitWrU4efIkLVq0QFEUJk2axK5duwgPD8fHx4chQ4bQqVMnFEXB29ubTJky6TvyV/lUO4sUKcKWLVsoU6YM7dq1A953XrVq1dJz6q/zuZ+pIflcWydOnEj//v1RFIWSJUtSrVo1fUf+ap9rq4+PD61atcLMzIycOXPi6emp78g6ZYj90gdppR8G6YulL5a+ODX71n7JSFEUJRlyCiGEEEIIkSqkqlsshBBCCCGESGpSIAshhBBCCPERKZCFEEIIIYT4iBTIQgghhBBCfEQKZCGEEEIIIT4iBXIy8PDwoGDBgvH+a9Cggdb7b968OclzFS5cmBo1arBo0SKdHL9gwYKcOnUKgMDAQPbu3ZvgOl36759xoUKFcHNzY+DAgV80v+P+/fsJCAj4piy+vr7cunUrzrLo6GgaNGigk7bv27cPb29vihUrRtmyZenWrRvXr1//5uNq4+OfX2hoKK1ataJo0aLMnj1b67+vvr6+zJ49G3j/57Jhwwatzj1jxgw2bdr09eGFSOX+23e7urpSrlw5fvzxR168eJHk5583bx4tW7YEYNu2bVSpUuWT2/v5+dGrVy/N++vXr9O5c2dKly5NiRIlaN68ebI9oOJT/c7H6z5lyJAhDBgwAABFUVi/fj1qtfqz+61fv16r44sUQhFJrnr16sqyZcsUf3//OP+9efNG6/03bdqU5LmeP3+ubNu2TSlUqJCyffv2bz6+v7+/EhUVpSiKogwZMkTp379/gut0qUCBAsrevXvjtOngwYNKuXLllCFDhmh1jKdPnyoFChRQHj58+NU5fv/9d6Vv375xlkVGRio//fSTUqBAAeXkyZNffWxFUZSjR48qJUuWVH7//Xfl0aNHyo0bN5QhQ4YopUqVUp49e/ZNx9bGxz+/5cuXKxUrVlTu3r2rBAUFKYGBgUpERMRnjxEUFKSEhoYqiqIo27ZtU9zd3bU699u3bxUPDw+t//0IYWj+23e/fPlSOXHihFKjRg3F19c3yc8/d+5cpUWLFoqiKMrWrVs/+W83OjpaqVevnnL37l1FURTl5cuXSunSpZU5c+Yod+/eVR48eKAsX75cKVSokHLo0KEkz/6pfufjdZ/y7t075d27d4qiKMrp06eVAgUKKDExMZ/dLyYmRvn++++V+/fvf2V6kZzkCnIysbW1xdnZOc5/jo6O+o4VJ1eWLFnw9PTEzc2NgwcPfvOxnZ2dMTc3B97/lp3YOl2zt7eP06ZatWrRvn17Dh8+rNX+/836pRRFYcGCBbRq1Uqz7O7duzRv3pzHjx9/07E/2Lp1K02aNKFx48bkzJkTV1dXJk6ciL29Pbt379bJOT7l459faGgoOXLkwMXFBQcHB9KnT4+lpeVnj+Hg4ICNjQ3wZX/m6dKlo3Llyqxbt+7rwgthAD7uuzNlykSlSpXo1asXp0+fJiQkRN/xNPbv34+zszMuLi4AHDx4kMyZM9O7d29cXFzInTs37du3p0GDBqxfvz7J83yq3/l43afY2dlpHlX8JX2Xqakpnp6eLF68+AsSC32RAjkFiImJYerUqVSpUoXChQtTvXr1RD/8b926RevWrSlRogSVKlViypQpxMbGAu//oc6fPx93d3dKly5Np06dePjw4RfnMTU11RQ/arWaJUuWULNmTYoVK0abNm24efOmZtv9+/dTr149ihYtSu3atdm6datm3Yev4efNm8f27dvZtWsXHh4ecdatX7+eqlWrxulk9u7dS6VKlVCpVERHRzNx4kQqVKhA+fLl6d27N69fv/7iNpmbm2NiYqJ5f+HCBVq1akXx4sUpUaIEnTp14tWrVwDUqFEDgNq1a7Nt2zYADh8+TP369SlevDienp6aR3cm5O+//+bt27eUKlVKs+zs2bNUqlSJjRs3fnH2hBgZGXHp0iVCQ0M1y4yNjVm1ahXNmzcH3n8N2qtXL4YNG0bx4sWpU6dOnF8SPvf3JSgoiP79+1O6dGnc3NyYOHGi5u/ah5/fkCFDmDdvHhcuXKBgwYI8ffo0zi0WKpWKuXPn4u7uTqlSpfjxxx/x9/cH/v068/Tp0wwdOpRXr15RsGBBLl68SKFCheLc4nL//n2+++47AgMDgfdfMW/cuFGrrzWFSCs+9NvGxu8/2kNCQhg8eDClS5emUqVKjBw5Mk6fcf36ddq0aUPx4sWpUaMGW7Zs0aw7evQonp6eFC1alNKlS9OnT584+2pr/fr11KxZU/PeyMiI58+fc//+/TjbDR48mAkTJmje37lzh7Zt21KsWDFq1arFsmXLNJ8T8+bNo2/fvowbN47SpUtTvXp1Fi5cqNn3U5+TifU7T58+1ay7d+8eBQsWjNMf+vv7U6hQIe7cuaO5xeLp06e0bdsWgMKFC7Nr1y4KFSrE5cuXNfuFhoZStGhRrl27Brz/fNmzZ49BPtLZ0EiBnAIsXryYI0eOMHfuXPbv34+npycTJkzQFGwfGzhwIHnz5mXXrl3MmTOHHTt2aDq1NWvWsGPHDqZNm8amTZvIlSsX7du3JyIiQqscMTExHDx4kJMnT2oK2V9//ZVly5YxdOhQtm/fTvbs2fnhhx8IDQ0lMDCQAQMG0L59e/bv30/Xrl0ZMWIE9+7di3Pcjh07UrduXerUqROnAwaoU6cOr1+/jtOh7N+/n++//x4TExNmzZrFxYsXWbhwIatXr0ZRFLp27fpFv7VfvnyZ5cuXU6dOHeB9h9W1a1cqVqzI7t27Wbp0KU+fPmXBggUAmuJu48aN1KtXj5s3bzJw4EA6d+7Mrl27aN68OT169ODGjRsJnu/48eO4ublpPqQAWrRowaBBg7CystI696e0atWKmzdv4u7uTq9evVi3bh3Pnj0je/bsODg4aLY7cuQIKpWKbdu20bRpU3r16qW5L/pzf1969OjBs2fPWLlyJb/++iuHDx+Od+Vj+PDhdOzYkWLFinHixAmyZMkSZ/28efPYtGkTEyZMYPPmzURFRTF48OA425QsWZJhw4bh7OzMiRMnKFq0KNmyZePAgQOabfbu3YubmxtOTk4AVKhQgdevX3P79m2d/HkKkdo9fPhQ88voh6ugw4YNIygoiLVr17Jw4UIePHjA0KFDAXjz5g3t27cnb968bN++nb59+zJmzBjOnj3LkydP6NmzJy1atGDfvn38/PPP/P333198hTckJIQLFy5QsWJFzbK6detibW1NgwYNaNu2Lb/99htXr17FyclJ039ERkbyww8/UKJECXbu3MmIESNYuXIla9as0Rzn0KFDmJiYsHnzZnx8fJg1axZ3794FPv05+cF/+52P+y4XFxdcXV3jfJN68OBB8uXLR/78+TXLsmTJwrx584D3/X6dOnUoW7ZsnPupDx8+TNasWSlcuLDm2Pb29pw5c+aL/ixF8pMCOZmMGzeOkiVLxvnvw9WwAgUKMHHiREqUKEGOHDno1q0bKpWKBw8exDvOs2fPcHR0JGvWrJQtW5bFixdTuXJlAJYsWcKAAQNwc3PDxcWFkSNHYmJiEqfQ+FSuYsWKMXjwYNq1a0ejRo1QFIU1a9bQo0cPatSogYuLC+PHj8fU1JQdO3bw6tUrYmJiyJQpE9myZcPb25tly5aRIUOGOOewsbHB0tISc3Nz0qdPH2dd+vTpcXNz02QMDw/n+PHj1KtXj4iICNasWcPYsWMpXrw4BQoUYNq0ady9e5dz584l2qZu3bpp2lSkSBE6d+5MnTp1NB8MERERdO3alZ9++okcOXJQunRpateurelcP2R0dHTE0tKSpUuX4u3tTZMmTciZMyctW7akfv36rF69OsHzX716lbx58yaaTxcqVKjA+vXrcXd356+//mLs2LHUqFGD/v37ExUVpdkuXbp0jB8/HhcXFzp37kypUqU0HxSf+vty584dzp49y9SpUylSpAilSpVizJgxODs7x8lhZ2eHtbU1pqamODs7x7lKrygKGzdupHfv3lStWhUXFxfGjBlD0aJF41z5NTc3x87ODmNjY80x6tevz/79+zXb7Nu3j/r162veW1hYkCNHDq5cuaLzP1shUoOP++6iRYvi5eVF4cKFmT59OgCPHz/m0KFDTJv2v/buPaapsw/g+LdyM0qxU6uLMEFHUJCgMrIwhzqMRrCAoaDDu2J0sukUnSBTYahDYRlEcEDK2CAk2/AS53VGRWAGdZlicF64VcAGnC4FMsUMWPH9w3BCadG6y6vO5/MXOZznnOe0p8/zO8/tpDB27Fg8PT1JTk7m5MmT3L59mx9++IGBAweSkJDA6NGjCQoKYtOmTXR1dWEwGNi8eTPvvvsuTk5O+Pn5MWnSJKmMtNT169exsrLC2dlZ2jZkyBAOHDhAREQEN2/eJC0tjbCwMMLDw9HpdAAcOXKEQYMGsX79elxcXJg6dSrr1q0jPz9fOo5cLmfTpk2MHj2aVatWoVAouHr1KvD4erKbuXKnJ5VKZRQgd/eW9mRlZcWgQYOk67K1tSU4ONjk4b73hHxXV1dRdr0ArJ91Bl4Wq1evJiAgwGhbd0vf9OnTKSsrY9euXdy8eVNaicBc9/H69evZsWMHhYWFTJkyBZVKhaenJ21tbfz666989NFHRi2X7e3tjx1m0TNfdnZ2RgWFXq+ntbWV8ePHS/vb2Njg6emJVqtl/vz5BAUFsXLlSkaOHIm/vz9qtVoqMCylUqnIzMwkJiaGkpISFAoF3t7e1NTU0NnZyYIFC4z2b29vp66uDh8fH7PH6644WlpaSElJwdbWlrVr10rjYpVKJaGhoeTl5XHjxg1qa2upqqrCy8vL7PG0Wi3V1dVGw0c6Ozv73F+v1/+t8eUqlYqmpiYARowYwbFjx8zu5+XlRXp6Oh0dHZSXl3Ps2DH279/PkCFD+PjjjwHw8PAwGuvt6elJTU3NE+8XOzs77O3tjSq2qVOnPtV1tLS00NzcLLWcAIwcOZL169c/MW1wcDAajYa7d+/S2trKrVu3mDFjhtE+CoVCesgUhJdNd9n94MED9uzZQ1NTE9HR0VLZo9VqefjwIf7+/iZp6+vrqa2tZezYsUaB4cKFC6W/bW1tycrKoqamhpqaGmpra40eUi2h1+uRy+Umwefw4cOJj49n69atVFZWUlRURH5+Ph9++CEHDx7k5s2b1NbWMnHiRClNV1cXHR0ddHR0AODo6Gh03IEDB9LZ2Qn0XU8+DZVKRVpaGrdv38ba2ppLly6RlJT0xHQzZ84kMTGRiooKXFxcOHfunEmvmUKhoLm5+anyI/z/iQD5/2Tw4MFGwUZPaWlpFBYWEhYWxuzZs0lISJCGOPS2YMEC/P39KSoqoqSkhPfff5+oqCiWLFkCQGpqKq6urkZpuicTPG2+7OzszG43GAwYDAZkMhmff/45kZGRnDlzhjNnzvDNN9+QnZ1t8rT+ODNmzCAhIYHKykpOnDhBYGAgMpkMg8EAQEFBgck19G6J7mnYsGE4Ozvj7OxMZmYmwcHBxMTEkJ2dDcCdO3cICwvD3d0dPz8/5s6dS0lJSZ+t0gaDgeXLl6NWq4229zXJUCaT/a2JfhqNRhovZ21t+hNta2sjNTWVZcuW4eTkhK2tLb6+vvj6+mJvb8/Zs2elfXun7/7euj/bvu6X8vLyv5z/bjY2Nn85raurK25ubpw6dQq9Xs/UqVNN7gGDwWAU3AvCy6Rn2Z2WlkZ4eDgffPABe/fuxcbGBoPBwIABA/j+++9N0iqVSoqLi/s8dmVlJfPmzcPf35833niDpUuXGrXeWkomk5k09Gg0Gtzd3Zk8eTIymQx3d3fc3d0ZN24cq1atorm5mT///JM333yTxMREk2N2l2mPK1/6qifXrFljcd4dHR0ZP348J0+exNraGg8PD0aOHPnEdA4ODkyZMoUTJ05IQzK6Jyh2E2XXi0F8Q8+B7777ji1btrBx40ZUKpU0BrR3kNXe3s6OHTuQyWQsWrSI3NxcVq9ezfHjx3FwcGDIkCH89ttvUnDo5OREamqqyVq8lpLL5SiVSioqKqRtnZ2dXLt2jVGjRqHVatm5cyfjxo1jzZo1HDx4EB8fH06dOmVyLJlM1ud57O3tpQLl7NmzUivFa6+9hpWVFS0tLdI1DR48mJ07d9LY2GjRNSgUCrZs2UJxcbG0DvOpU6cYOHAgOTk5LFmyBB8fH3Q6nfR5987rqFGj0Ol0Uh6cnZ05dOiQ2esEGDp0KC0tLRblzxxHR0fpPI6Ojib/79+/P4cPHzZaV7qbXC43ar2urq42qqCuXr3KmDFjnni/uLi4cP/+faNVN/bt2ydNSLGEXC5n8ODBRmsz19fXM2nSJFpbW432NXd/qFQqzpw5Q0lJidmWq9bWVpPhPILwMrK1tWXHjh1UVlby9ddfA4/KrQcPHmAwGKTfOMDOnTu5f/8+zs7OVFVVGZUPcXFx7N69m0OHDuHt7U1qaioLFizAy8uLhoaGp37wVyqV/P7779IDOUB5ebnZYFsul0s9V6NGjaK+vt6oLLxx4wY5OTlPDCwfV0/29rh6CWDWrFkUFxdLk7TNMXeMoKAgiouLKS0tFWXXC0wEyM8BhUJBcXExOp2OS5cuERMTAyB1JXWzs7OjvLyc7du3o9Vqqaqq4scff5S6sJcuXcru3bs5ffo0DQ0NJCYmcu7cub81HjYyMpI9e/ZQVFSEVqslPj6e9vZ2goKCcHBwoLCwkIyMDHQ6HRcuXKCqqspsV9aAAQNoamoyO/EQHgVD+fn5KJVKKb29vT1z5sxh+/btnD9/Hq1WS2xsLNXV1bi4uFh8DTNnzuTtt98mOTmZBw8eoFAouHv3LmVlZeh0OjQaDSdPnpQ+7wEDBgCPWlHa2tqkSYh5eXk0NDTw7bffkp2d3WdrgoeHx19+KLGElZUVUVFRpKen88UXX6DVatFqtezdu5evvvqK5cuXS/s2NjZKQ3e6J8PMmTMHePz94urqyqRJk9i8eTOVlZVcvHiRrKysp+oZAFi8eDEZGRmUlZWh1WrZtm0bHh4eRhMJ4dFnfu/ePerq6qTW86CgIH7++WcaGhpMuonv379PY2PjU3ebCsJ/lZeXF+Hh4WRlZXHnzh1ef/11Jk+eTExMDBUVFVRWVhIbG4ter2fYsGGEhITQ1tZGUlISdXV1HD16lKNHjzJ58mQUCgXV1dVUVFRQX1/Prl27+OWXX6QhDJYaM2YM/fr1o6amRtr23nvvceHCBWJiYrhy5Qo6nY7Tp08THx/PokWLsLW1JSQkhI6ODmnSd1lZGdu2bbNo+N6T6smezJU7PQUGBnL58mUuXrxIYGCg2fN11xfXr1+X5n9MmzaNO3fuUFpaajJuGR41XIiy6/knAuTnQFJSEtXV1ahUKmJjYwkICGDChAlm34qWlpZGe3s7c+fOZf78+Tg5ObF161YAli9fTkREBImJiYSEhFBdXU1ubi7Dhw//y3lbunQpERERJCQkoFaraWpqoqCggKFDh6JUKsnIyKCoqAiVSsXGjRuZN28e4eHhJseZPXs2t27dkib/9dYdAPUuTOLi4vDz8yM6Oprw8HDa29vJzc21aJ3dnjZv3oxerycrK4vAwEBCQkJYt24darWaCxcuEBcXR11dHX/88QevvPIKarWaDRs2sH//fiZMmCC9vU2lUpGXl0dSUhLvvPOO2XNNmTKFy5cv/6tLkEVGRrJ9+3ZKS0sJDw9HrVZz4MABUlJSjPLl6enJvXv3CA0N5fjx42g0Gunh4kn3S0pKCgqFgoiICNauXYtKpTIKvi2xYsUKZs2axYYNG5g7dy5yuZzk5GST/Xx9fRk9ejQhISHSMoIjRozAw8ODadOmmXzf5eXlvPrqq7i5uT1VfgThvyw6OhobGxvpN5aSkoKzszORkZEsXLiQYcOGkZmZCTxqsdVoNFy5coWQkBAyMjJISkrC29ubRYsW4e3tzbJly4iIiKCxsfGxK/f0RS6XM2HCBKPhaxMnTqSgoICWlhZWrFhBYGAgn332GWFhYdL8BHt7e7788ksaGxsJDQ0lNjaW0NBQoqOjLTrv4+rJnsyVOz0plUppEmTvFXq6ubm54efnx/z586XlP/v378+0adMYN24cI0aMMNpfq9XS1taGr6+vRdciPDuyh3/3rQiCIBjp6uoiMDCQTz75hLfeeuuZ5SMjI0Naa/pFNX36dLZu3WoyQTA2NhYXFxeioqKeUc4EQbDEoUOHKCwsfOle7LN48WICAgKMXhgFkJ6ezt27d43WfBaeT6IFWRD+Yf369WPlypUvdGD6rJWWlrJr1y66urpMhnU0Nzdz/vx55s2b94xyJwiCpVQq1Uu1ZvlPP/2ERqPh2rVrJsu7dXR0cPjwYSIjI59R7oSnIQJkQfgXqNVq9Hq92W474cny8vI4cuQIn376qckSUTk5OURFRZmMYxYE4fljbW1NfHy89EKN/7ojR46Qk5NDQkICDg4ORv/bt28fAQEB//o6+cI/QwyxEARBEARBEIQeRAuyIAiCIAiCIPQgAmRBEARBEARB6EEEyIIgCIIgCILQgwiQBUEQBEEQBKEHESALgiAIgiAIQg8iQBYEQRAEQRCEHv4HXapueq/6W7cAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train our logistic regression baseline model\n", + "\n", + "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n", + "\n", + "reg_lr_model = LogisticRegression(random_state=0, penalty='l2', C=.01, max_iter=10000)\n", + "reg_lr_model.fit(X_train, y_train)\n", + "\n", + "y_test_pred_proba = reg_lr_model.predict_proba(X_test)[:,1]\n", + "y_test_pred_label = (y_test_pred_proba > .5).astype(int)\n", + "\n", + "print_accuracy(y_test, y_test_pred_label)\n", + "plot_roc_and_pr_curves(y_test, y_test_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "id": "17ee9542", + "metadata": {}, + "source": [ + "As we'd hoped, test set performance is now substantially improved, and is now similar to the values you may remember from our earlier exercises. Now, let's turn to the multilayer perceptron and see how all this plays out." + ] + }, + { + "cell_type": "markdown", + "id": "alternate-radar", + "metadata": {}, + "source": [ + "## Multilayer Perceptron\n", + "\n", + "We'll now follow the exact same steps as before, but using a multilayer perceptron with a single, wide hidden layer, which we can specify using `hidden_layer_sizes=(1000,)`. If we instead wanted to use two hidden layers with 100 neurons each, for example, we could use `hidden_layer_sizes=(100, 100)`.\n", + "\n", + "Let's fit this MLP, then take a look at the results. We'll also specify `alpha=0`, which tells the model not to penalize large parameter values (i.e., regularize). This has the same effect as specifying `penalty='none'` in the logistic regression model; the conventions are just a bit different between the two models." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "accepted-adapter", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy = 77.5%\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "\n", + "mlp_model = MLPClassifier(hidden_layer_sizes=(1000,), random_state=0, max_iter=10000,\n", + " alpha=0.)\n", + "mlp_model.fit(X_train, y_train)\n", + "\n", + "y_test_pred_proba = mlp_model.predict_proba(X_test)[:,1]\n", + "y_test_pred_label = (y_test_pred_proba > .5).astype(int)\n", + "\n", + "print_accuracy(y_test, y_test_pred_label)\n", + "plot_roc_and_pr_curves(y_test, y_test_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "id": "35d78198", + "metadata": {}, + "source": [ + "Interestingly, even though we haven't regularized, the MLP does *better* than our original logistic regression model. There are many different perspectives on findings like this, which we can discuss in class. For now, suffice to say that neural networks often perform better in situations like this than we might expect.\n", + "\n", + "If we look at performance on the training set, we'll find that the MLP is *just as overfitted* as the logistic regression model; and yet it generalizes better to the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ac35b6f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy = 100.0%\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_train_pred_proba = mlp_model.predict_proba(X_train)[:,1]\n", + "y_train_pred_label = (y_train_pred_proba > .5).astype(int)\n", + "\n", + "print_accuracy(y_train, y_train_pred_label)\n", + "plot_roc_and_pr_curves(y_train, y_train_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "id": "51ceecc8", + "metadata": {}, + "source": [ + "As expected, the plots above show dramatic overfitting on the training set.\n", + "\n", + "Now, let's explore an approach to mitigate overfitting that is very common when working with neural networks. We'll set `early_stopping=True`. This tells the model to (a) set aside some of the data as a validation set, (b) continuously monitor performance on the validation set during training, and (c) stop training when performance on the validation set stops improving. This is a common and effective method to prevent the model from overfitting, and it also reduces the total training time." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "074f335b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy = 85.8%\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "\n", + "mlp_model = MLPClassifier(hidden_layer_sizes=(1000,), random_state=0, max_iter=10000,\n", + " alpha=0., early_stopping=True)\n", + "mlp_model.fit(X_train, y_train)\n", + "\n", + "y_test_pred_proba = mlp_model.predict_proba(X_test)[:,1]\n", + "y_test_pred_label = (y_test_pred_proba > .5).astype(int)\n", + "\n", + "print_accuracy(y_test, y_test_pred_label)\n", + "plot_roc_and_pr_curves(y_test, y_test_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "id": "ready-gross", + "metadata": {}, + "source": [ + "It was super effective! Accuracy, AUC, and Average Pecision all improved. This strategy is all the more important for very large datasets and very large neural networks.\n", + "\n", + "## Returning to the true labels\n", + "\n", + "It may be interesting to go back to the true data and labels (`df_true` and `y_true`) on your own to see how well logistic regression performs on the original, unmodified prediction task. We modified this problem quite a bit in these exercises to illustrate important concepts related to measuring performance and mitigating overfitting. As it turns out, the unmodified problem can be solved quite well with logistic regression. However, this is *not* the case for computer vision tasks, which we'll be covering over the next few weeks.\n", + "\n", + "## This exercise does not need to be turned in." + ] + } + ], + "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": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}