{ "cells": [ { "cell_type": "markdown", "id": "4d1e7188-e3fa-43eb-bf79-23afc40b5a8d", "metadata": {}, "source": [ "# Model Evaluation\n", "\n", "In this tutorial we go through the **Model Fitting and Evaluation**, to perform after the **Feature Selection** step. If **PCA** is selected as the Feature Selection method, it is performed right before the Model Fitting.\n", "\n", "\n", "Initially, the feature set is splitted into train/test set. \n", "Then, the train set is used to perform a *5-fold CV*, including **Feature Selection** and **Model Fitting**.\n", "The CV classification performance (*ROC-AUC* score in this tutorial) is then analyzed to select the best model. In this tutorial we refer to *model* as the combination of a *classifier* (i.e. Random Forest) and a *number of selected features* (i.e. 5 features).\n", "\n", "Finally, the best model is used to perform feature selection and model fitting on the train set. The test set is used to evaluate the classification performance of the model.\n", "\n", "In case of *model ensembling* k-top models are selected, and the CV classification scores (ROC-AUC) are using as weight factors for the single classifiers predictions.\n", "\n", "The *classification performance* are reported as **YellowBrick** plots and in a summary table.\n" ] }, { "attachments": { "CV.png": { "image/png": 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mX/TQ7o9tjsCDbw61pTmBau49Ubp7ef/S3cv7d3btxppKP+/OAAAAAADg1BKeOKJ27JWPZfbk2IqcNWGVB7aFNdZWBhzcaIxkSV6rb8YoHM5QjyRZTZ5eX/HDERTeNOjsH+UNOvtHefI2rc369PG0bfMemOBpcDnXvPTdM2MGnflh22WiGlzFwZJUXZgZs+HV26Z3enHLMrWlOYFh/YafVIPAODqR4fK0fP/O41o8bbSqj+Q6YSFq+ud92vLP+7RlzTaFLVqr2MVrlfDhCqXv2qfoL9+l6Vtf1WddXcM0/wYfURfzXnT7N7Tv0Zc1al+hIlZuUpx0KGCw2aTAAHnq3XLceY22/OomdbjMW0/YTpp5HscGAcMpzlWYGbzm5e9NKNm5NOXE+7XuW0PPvzvH5gjybnj1jul1Ffnh+754pV/69G8faNnfUFN+8B85bZsetRWVMo4GSAAAAAAAdMmr7e8/OHDX4r+PaKgqCO3rak4YNrs1dO5PcyISR7g+/9uX5ngaapyZHz08cMI3/nJYT01Pg8spSYFh8bUBwZHtelS0sDtDGoMikzodw8CpaWiaao2RLEtatVlRRxowtDZ5pFyTR8r10+uU88hLKrrrCZ2xLVtxn29UxIwOehi0CA3y3f1fV6+ApiaZ1s2S+1pspOr2FSoiwNF+BkJCtGpzCxWxebc6XaqsJ0KDD81+KCqTc3jGyTXjw18EDKcwd3VxwKLHzp5TX1kQJkmhMemVwbHp1QHBUW5JaqwtDSzZ9Xm7KXWnk8Fn/zBv89s/b2xy1wVU5G2KSJcOBgyBYXEHm9gMv+Du7SmTrjyKBk/G90baPCMCAAAAAIDTzapnvzl+35r/DZUkmz3AGxSR6AoIiW4wdrtXkir2bUg4nf+/OXHMJaUR/ceUVOZtSKjM39iuD4TdGdzoqa92pky+aveEq/+8vaNr9ISxmUODvafx632qSUmQOz1JlXv3K/KF9zXw9m9o37G8/pdmqeiuJ3zf5xYouKuAIT3RN6De5JXZlq2QMUPU7eorx8PuPAVt36M4SRo7VO2am08aqeLcQkXMX6G04gptOdIG14OSVdcS9uzIUeisifJ72biTCU2eT2E75j88oL6yIMxmD/BOueH5pRf9bu/HZ/9k8fIZt76zZsat76wZfsE9O/q6xhOBMTZLkpyhsYel+2FxA+sdztBGSSreuST2aB4jICjCI0lNjfUOT301TWAAAAAAAKeV0uwVEfvWvDpUkhJHX7zn4t9mv3vx73M/mnvfxoXn/WLd4vN+sW6xMfYT5i7nvhIQHOmWJHtAcLv134Mi+tdIUmX+5uijeYzWPTjrq4r6pN8FescNl2qnJK3foX7fvFfjGxraj/0eKJGz8ggaFD/0nP6fvfuOk6q6/z/+OtO2910WdukdBQTEgiAKgr1FikZ/JtFvoiYxlqixJdaYaEw1Gls00VjBrrFiBxECCIL0svSyhe1l2vn9MSwssJUtd9l9Px+PebBz77l33ped2Zm5n3vOGQCR4Y9GDKy/d8Qpx1EQ5Y1cxX/f0wxs6mMdqp0FeAuKa7+g/sOvSZn8c8ZXBXCnJ1P+y4vZeGCb6y9mrTHY4jKizvgFJ2zewUHzsZSU4d6yi3pfN1mRYk8hwGOvMsB/GE2OfSjUg6EDK8tdmwAQl963sNeYH21vqH1ntHrWH3sGq8p8GGMzh0zaf6Jsl9sm9xq9M2/N5903/++lfkee9Zu1voQuh1S5TOx2RGn1z3nrvkzqeuSZBc2MLiIiIiIiInLYyF83JwUsGGOP//ELi2qe5JaIsvwN0YUbF2QCJPUYedB5g/R+Y3YVbVncpWD9V92Kty+LTew2tPxQHic2ObvK5YkKhYNV7pKdK+KBnc2MLu3Ery9nw/tfkT1vGd1e/ICBH82jx/AB5PbqStnuEnwrNpCyZjMpJ41iyyePMffA7f/1Ft0ef43+yQlUpSbiT02kKq+Q6Hnf0SVnW2TYoImj2TSkD/U+93bmNsgAACAASURBVFITCU49hbXPv8+glz9iwOrNJA7vz+7iMrwLV5A+5RRy/nw9jZqvpSnG/R8n52wj6Yg+5PfOoiQ5Af+uAqLXbiFp7WZSABJi8T//W+YkxB08ifOJIyn66VSW/WMmwxauIHPQFM4aOZid/bIpqfTjXr+VhKVryUiOp2rze/zX56l76KefT2PVTX/juG9WkjlkCpPHDGNnMIRryRpSe3ej5L2/M7+lj98pKjB0YAldhxTDm5TmrkveMPvJrD7jfrLN6UxtbdP85zJ9camBrkNO241r35UQZblro1e89/sBG79+ZhBAt6Fn5qT2Of6g6uvgU29aNXvtF9n+srzYT/944kmjLnliQcbAk/Z2oQpWlri3LX4zIya1R2XN5QdKH3BikS8uvcJflhez7M1fD0vtc/wcX2xqEKCyZKfX44sLe6Li9eFKREREREREOqT4jH6RIVKsNWs/f6zH4NNuznE2Udsry10XHQpWuGorDGxZOLPL0td+NTLoL/N6Y1MrjzjjtoMmmB146k3rN8x5ekg4WOWe8/ezTzz2xy/OTes7Zr9haiqLtvkqC7f5knuNLj1w+2rG7bPJ3YfvKsj5X7eNc5/pP+Dkqzcd6gWV0rpcrsg8AfWdyK7J48F+8ihzfvxbhs+cRf+8QmI++R89D2xXWlH7OeHn3qPXvGV0q2v/k49n46sPsKAxWZ64naXb8yOP/81KMr9ZSWb1utc/pXdLFxj8QUyVH3cwhOvbtWR8u5aMA9ucOIItD/2KJSMG1j1k0yM3syIzlao//ofhJeX4vlpC9ldLDm6XX4i3Wzp1znVy46VsXL2J+H++wRHrt5K8fivJ1evWbialtJyF8bEHFzkORyowdGCDT7t5w6b5L/QrL9iUuPC5K8Z9985dJXEpPUtc3qhQqKrMW1WSG1Pf9lsWvNRlwXNXnICtOedJ5O/ZklduOHbp6zePrl7abfg5Ocf934uLW+dIDk15fk7U//592Yk2HHR5ouL8UfEZ5W5fTLCqJD/WX54fY8MhA5DW74Stx/9kxsLa9tF12Fn5AyffuGT1h38cUbJzdernfz75VF9cWkVMcnZpsLLYV1G0LT4c9LvjUnsVnfG7nA/qymJcHjtw8i+XLnvjtmMLN32T+e6tPc+OS+9bFKws8ZUXbErMHDJ547hr3u8wlUsRERERERGRmrJGnJeXlD18V9HWb7sse/2WYzfO/Xe/pKyhBZ6YxEDIX+EJVBR5rQ3VOYxIsKLQ8+kfx58Y8lfsPZdVUbglAWDn8vd7vv+bAenVy+PSe5eceO1HX7fuETXdnEfPH1O8bVlGTHJ2SWxar2JfbIrfX5ofVVaQk1g9f6YnOqFqzBUzvvTEJAcP3D4urU/lsO/9/n9LXrnhuLKCjUmfPjj2tIQuA3fHpfUutjZsygpyEsp2rUtxeaJCZ/1u49u+hIw6iwaDT7tl5dwnpnUtL9iU9P5dR56e0nPELjAUb1+e6olO8J9214qPW/P/Qhrnydv5es0WYn98Hlsbu01sDOEX7mPxnVew6oX3yFqeQ2JZOd60JKp6daNs6iS213WC/bdXsfyDkezalU90QTFRRaV4o6MI9e9OydRJbD+uCRNHx8YQ/vhRvn79U9a+8RlZubuJTozDf/QQdl94Kjsau5/G8nmwm/7L+7MXk/jOl2Ru2kFcUSnepHgCg3tTPG0SOxrqeVHtjp+w/uoL2fTM22QtWEHK7mKiEuMIdEun/PQx5E4+ngJXIyYeeOJ2vrvsbDY/81+6b9pBfFwsgcG9KJ46iR0dpbgAKjC0KW9schAMYPHFphz0RnEgT1RCEMDjjTmkKrInJjl48k1ffrrwP1eM2LVyVs/Kwm0JlYXbEg5s5/L4QtVX09dUVVbgC1aW1DqmWDhY5QkHq/be95fkRh/YxhudFARwe2PqPFZ3VNyeY4xt8P+jqVwen03rN3ZbwYavuwarynzBqrL9jiUurXdRv4m/WDHwlOs21TcdyfAL/rA6qduRxcvfuWt4WX5Osr8sP8Zflr+3OOONSapK7Temwe6Eg0+/NScUqHSv+ehPw4NVZb6irUv3VlIrinfEHuJhioiIiIiIiBwS4/GEAdzeqAZPdHl80XvbuL3R4fra1s7FSdfNmjPv6YuP3rni454lO1amlexYWet8h96o+IPOEVQW7/TW/B5dU6CiKCpQUbR3rPSq0tyDvmO7fDHh6nMyrhrHUlP1cRm39xCOr2HeqISAMS5bUbg1oaJw637nZ4zLbbsMnrRp5IV//TY+c3BFXfsYcMr1m6ISMquWvvarURWFWxNKdq5KLdm5ar8JoaMTupT5Kwo99RUYskZekDvsgvvnf/fWnUf7y/Jidq6Y1WtfFk+4LH9DdFxan8rmHK8037TJ7DrUbQf1ouLuq1jXlG3GHEXxmHombz4U35tA3vcmkNdwy5YxbgTF40Y0/xhSEwlefwmbgE3N2c+e/9Plzc3TnhlrD+5hM/0Jfm8tfR3I0+GV5W+IJhwmLqNfw3+kwyFTtH15bExKVpUvNq1ZJ+D9Jbne7d+9l1ZRtD0aGzJR8elVUQld/NGJXavi0/tV1vWmU7x9WawNBxuciCQuvX9lbUP8lOxYEeuNSQpGJ2XV2mXIhvymeMfy2Li0fpWe6IRWqdwFq0rdu1Z9mlyelxMbqCzyRCd1q0rtfWxRUvawJs9gX5AzL6Fg/dfJgYpCb3Ri16q49L7lXQZNLKw5/FJD/OUFnu1L3sooL9gY44vP8Kf3P2F3UvaIJmeR/RmYOeNKXnE6h4iIiIiItKxpj/MD4Cync3RE+evmJFbs3hzd9cjTC2q7Yr4mGw6arYvfTPd4o8Ndh52V35zHLd6+LHbrN29kVuzeHBsKVLo90YmB6IT0Kl98hj8mqVtl1vBz82v7nr1tyVvpwarSBienjU3tUZne/8SiA5fvWPrftGCg0pU94rw84/IctP+Qv9y1/du3030JGf4ugybWOQxyc1QWbvFtXfx6l7LcnLiqsrwoX0ySPz5zQGn2yKm7opO61TncykHCIbPt27fS8tbMTqsqy4vy+GKDMak9KroeeVpuco+j6xwe6UAVuzdH5cx9Jqssd228yxMVju86qKT7qKk7Y1N6VjW8tdTFZVj28hXcW+vKheYs4Adtm0jaPcvrjLYvOR3jUKjAICIdggoMIiIiIiIdkwoMInK4UYFBmuwwLjA0YrQoERERERERERERERGR/anAICIiIiIiIiIiIiIiTaYCg4iIiIiIiIiIiIiINJkKDCIiIiIiIiIiIiIi0mQqMIiIiIiIiIiIiIiISJOpwCAiIiIiIiIiIiIiIk2mAoOIiIiIiIiIiIiIiDSZCgwiIiIiIiIiIiIiItJkKjCIiIiIiIiIiIiIiEiTeZwOICIiIiIiIiIiItIpGFYRZobTMZz29hf0n/8do4cN4Nvpk1judB7HuVnhdIRDVWuBwRrmYFnd1mFERA6VC9Y4nUFERERERFqeK8y2sJslTueQjqWyaHtC6a613aKTuhXEd+mf53Qe6WBcbKhz3Si7FljbdmHap3OPMVcBU4El02+xrzqdRw6dsdY6nUFERERERERERKTNGGMuAZ4D/mCtvdnpPCKdjTHmKuBR4DfW2t86nUcOneZgEBERERERERERERGRJlOBQUREREREREREREREmkwFBhERERERERERERERaTIVGEREREREREREREREpMlUYBARERERERERERERkSZTgUFERERERERERERERJpMBQYREREREREREREREWkyj7mSh50O0aYMr9vH+BhgxkIedFlinI7UZly8P3UU7wDMXMjdxpLmdKS2EjJ8fuHRzASYsZBbXZZspzO1Fetm3rSR/Adg5kKuNZYBTmdqKyFYeuFoHnc6h4iIiIiIiIiISEfkwZDhdIg2FSau+kcXZGA6UYEhTHz1jxbSjSHdyThtyWNJqP7ZBamd6nkfIqn6xzCkujvRsRtIdjqDiIiIiIiIiIhIR6UhkkREREREREREREREpMlUYBARERERERERERERkSZTgUFERERERERERERERJpMBQYRERERERERERERaUsrgKeBxU4HkebxOB1ARERERERERERERDoPa+3nwOdO55DmUw8GERERERERERERERFpMhUYRERERERERERERESkyVRgEBERERERERERERGRJlOBQUREREREREREREREmkwFBhERERERERERERERaTIVGEREREREREREREREpMlUYBARERERERERERERkSZTgUFERERERERERERERJpMBQYREREREREREREREWkyFRhERERERERERERERKTJVGAQEREREREREREREZEmU4FBRERERERERERERESaTAUGERERERERERERERFpMhUYRERERERERERERKTNGGOyjDETjTG9nM4izaMCg4iIiIiIiIiIiIi0pXOBj4FLnQ4izaMCg4iIiIiIiIiIiIiINJnH6QAiIiIiIiIiIoeDmcYkhSHLA3EBiPFATFjnVg5LY2HEHKAv9J1hzJlO5xHpbEbB0EXAIBio12DdXBAEKgxUBKEsCrafb22h07lq0pugiIiIiIiIiEgdnjAmNgkGu6GHhQQDhIgMCRF2OJscuiiIA/BBDJDqcByRTicaYgGi9BqsV833GQP4gVeMKXHB5jxYeYW15Q5F20sFBhERERERERGRAzxhjDcNBibBMAMeFRNERKQ9CENCGI5IgcGvGrM6DN9Os9bvVB4VGEREREREREREanjHmPQUODkE0aaONiZyMakcplx7fn8G/S5FnGD2vQaNXoMNs2BrWeYKwWADvWca8/k0a3OdyKZJnkVERERERERE9njJmN7lMNlCdM3l1SfBdDJMRETaWn3vQXverya/bEw/J7KpwCAiIiIiIiIiArxgzAAXjAPc1ctUUBARkfbmwPcmCy4DY142ZmBbZ9EQSSIiIiIiIiLS6b1oTKYXjqk5BkVDhQVXZMiKUOsmk9Zg9/zeLIRdEHQ6j0hnY/fNX6zXYP3c4XreiwyYmsMnuWD0W8YUnWvtzraJpwKDiIiIiIiIiHRy/zEmLgbGh2uM9FBbccEN/mTY3gVys6EsBQLUMi62tH+3QApALmyaAp86nUeks/kl9ADYAev1GqyX2Q3erRC3CzIKoVsIfPs1qFFksODyw/j/GPPupdaWtUVAFRhEREREREREpFOLghFhiKq+f2BxwUA4FTaMgY0x6rEgIiJtx6aAPwX8wO4KWDcXehVAH3tAUby6yBCGqDgYAcxpi4Cag0FEREREREREOq0XjElxQe/q+7UUFwKDYdFEWK/igoiIOCkGQhNhfV9YZCK96Paq+f4Vht7/NSalLTKpwCAiHUphwU7v3+/4wfAfTUg998LR5n5jTIUx5kNjzFins4mIiIiISPvjg6PqGt/aQHgYfDMUdrd1LhERkbqMgt1DI0WGcG3rw2Aq4Ki2yKIhkkSkw1i/YmH8PT+bPLGyvMQXCgWrC6jRwERgkjHmGmvtww5GlEM1deoA3O6RhMMDgb5AEsbEAfEOJxMRae9CWFuCMUVALtauxuVaQWHhfD74oE3GZBURac+eMMabAlnV9w/svdANVg2CorZPJiIiUr/BUJwPq7bBkOplB8zHkLXQGO/R1gbq3kvzqcAgIh1CMOg3f7jxgnEVZcVR4XDowKuP3Hv+/Zsx5htrbZuMQSfNcPfdLpYtGwtcDEzCmCzCBxTlrebSExFptJp/M8NhSEwMMG3aAqx9C5/vZV54Yadz4UREnJMI2baO0R18UDoWtrZ1JhGRziAaSt2wKUpF3GYZCVtzoXsAEg5cZ8G1BrKPhpzWzKACg4h0CJ+8+XRWQe6WBBsO19q1eQ8L3Amc2kaxpKmmT4/B2h9gzDXUGAdXRERanBcYgzFjCAR+y7Rpb+F2P8hLLy1xOpiISFtyQXZd69JgE3uuAhURkZa1Cl4jcpNmiAXbFTZthiNrW++LvM/ltGYGzcEgIh3Csv990sUY09CHfzcw3hhTXxFCnGCMYfr0i7H2O+DPWNvb6UgiIp2IG/ge4fAcpk17ge9/v4fTgURE2oqFxOqfaw6P5ALbD3KdSSUiItJ4AyHXVaMgfsBkz4m1b9VyVGAQkQ6horTYGw6FGvM3LQr13mpfpkzpxbRpH2Dtk0Cm03FERDotaw1wHqHQIqZN+wUqyItIJ+CKzNl2EDdUdAN/W+cRERFpqlQIuKGijtUxrf34KjCISIeQkdWrzO3xhhrRtNC28uQ20gRTp56DyzUXa8c6HUVERPawNha4n2nTZnDJJSlOxxERaWW1nnhxQ1VbBxERETlU9bxvxbT2hUMqMIhIhzD+rB9sDgUD7oZbkmyMmWuMOafVQ0n9pk69CmNeAJKcjiIiIrWw9kz8/o81ZJKIdFR3G+Opa4JnoDEXL4mIiLQLFoK1LQ+DeaKVR/JQgUFEOoTBR40tPv6UqWtcbne4jiZBoAD4FjgeeMsYM9sYM7HNQso+06bdhjF/Qu9DIiLt3SCCwY+ZOrWv00FERERERKT90TjkItJhXHffi4v/etv3zdefvNLf7fGGQ8GACwgQmdxmLXAOsA6YCtwDjAU+NsbMAW6z1n7hVPZOZfr0nwK3N3GrTcB7WDsHj2c54fBmZswobYV0IiIdz/TpbiAD6E84PAqXawLWTgC8jdxDNi7Xm1xwwSm89tqu1gsqIiIiIiKHGxUYRKTDcHs89oY/zFy0cvHstZ+982zPDSsXBdavXPgZ8AXwlrW2urvYTGPMq8AU4D4ihYbPjTGzgFuttQscOYDOYPr007H2D03YYhbwF4YO/YI776yrd4qIiNRnxowQsGPPbTbwEBdfnE4g8CPgaiLFh/pZ2xePZybTp09mxgxNeioiIiIiIoAKDCLSAQ0eMa548Ihxy8KwaPrRPFBbG2ttmEih4Q3g+8AdwCTgFGPMf4E7rLXftF3qTmD69GysfZLGDYu0GpfrWl5+Wb1KRERawwsv5AF/ZPr0x7D2Noz5OdbW/93A2tFEegDe0hYRRURERESk/fMQ5lWnQ7QpD6uqf7SGN12N7xp+2HN5WV79sw3xnvEQ62SethSMDI9T7SNjSHYsTBuzHnKqfzbwmTF852CcNmUs2xpqY60NAM8aY14ELiNSaDgbOHNPL4ffWGtX1bcPaSRr/wGkNtjOmGeAG3j55YpWzyQi0tlFhpu7jWnT3gGeAbLqbW/M1Uyb9l9mzvyyLeKJiIiIiEj7Zqy1TmcQEWk3jDE+4EfAXUA3IAy8CtxurV3jXLLD3PTp38Pa5xrR8k5mzvxjq+cREZGDTZnSHbf7Tawd3EDLFaSmjuHxxwNtkktEpJXcbYxnCFxUfd9E5m4DIBryzgH1aO7ABsAFa+GRNHgkD37rdB4RkeZ4C0ZU1Rj61MLek/6F8PIVkQtsW0VjhqkQEek0rLV+a+0TwADgOiAXmAZ8Z4x51hjT19GAh6MJEzw07gP73SouiIg46NVXt+DxnIkx6xtoOYT8/B+1RSQREREREWnfVGAQEamFtbbMWvs3oD+RsaZLgUuBFcaYx40x2Y4GPJxkZEzH2t71trH2RWbObMrkzyIi0hpeeGEnLtcUIu97dXO5rufKKzvNUKMiIiIiIlI7FRhEROphrS211j4A9CJSaKgArgDW7yk0dHU04OHA2msbaLEBl+u6NskiIiINe+ml1cBN9baxthcFBee3TSAREREREWmvVGAQEWkEa23JnkJDP+BuoIpIoWGtMeZvxpgujgZsry666ChgaL1tjLlxzySjIiLSXrzyyn8w5qsGWl3SJllEREREpEMqrzH3jRy+PE4HEBE5nFhr84G7jDGPADcA1+y5Xb5n2f3W2kInM7YrweBFmHo/L8xlxoz32yqOiIg0krWW6dPvBj6os40xE5g+PYMZM3LbLpiIiEjTzQHfU9Ct5rJySAcIQeLlkR7re02E/P/X0HCBItJo/4aEf8DImsvWwORC+HEivDwIXqu5bios/RXsbtOQcshUYBAROQTW2lzgFmPMw0SGkbgSuBn4mTHmH8DvrbVFTmZsF1yuU7C27vXGPNR2YUREpElmzJjNtGkLgaNrXW+tBzgZmNmGqURERJrMB/ZZeDEEfQ5cVwiX/isy3x4ABsqTYKwKDCItZxKUXwH3BGDQgeuK4cL/wYXV992w7R4Y27YJpTk0RJKISDNYa7fYyBwDA4EngBgihYZ1xpibjTGxjgZ00gUXpAFH1LnemHxAvRdERNoza1+od70x49soiYiIyCE7BgJHwl2NaBrqAg/9BXa0diaRzqQ7hEbB7UA9VyACEBoAd50OlW2RS1qGejCIiLQAa+0m4EpjzO+BW4H/A+4HbjDG/An4m7W2c71But0jsLbu8ZGsncXMmf42TCQiIk3lcr2LtX+qp8XIetaJiMgh2pra33P/qQ/235A8sG8gKjop5PLGOJ3pcJcBdF305LpAZWFva627libW7faVDR7904pJnqjL2zpfR+MKh6uig+VFXcq2bbn889+tOGHDJ1VOZzqsXEEGYzmCJNLwkICL2p6zh5Wvgd+9xcodhQy09uDjMYZwcizb75lKBnD4vwYtYUIUU8RuFrKCv3bcwqUKDCIiLcham0Ok0PBXIoWGS4gUGq42xvwReMxa2zk+WBkzsIHhkea0XRgRETkkM2ZsYtq0TUDPWtdbOwBjDLa+P/giItJYHw+bEvuX8fedlhefeULYuGNwAVaToLaUzJE/Xp+3aXbf2i+iNiRlDlteHp95VJsH66hMqt2R0J3rL3zdn1i1+5vz5z/17s/n3JvvdKx27WGGMZCziKY7FoMhTJhyIOh0tJbwkzNYNuMrBtXxydF19mgWEUu/No7VOixu3MSShJteWM5mB6t5jxtZRHmDPTkOKyowiIi0AmvtCuAHxpg/AHcAU4G/Ar80xtwHPG2t7RAfEOpkbe8GWqxqixgiItJMxqzE2toLDBDPtGnpgCZ6FhFpph//aNaIZV2Pvjjscscaa0PRwYoNsWUlG2Orykq8tqqCkNMJO4Zdaz8MlZRum2QjcwkBYDAhny9+zdjkAX9h50on43UYfp8vqsodHV8eldijPCquV1FM6nHPjL9x1MfDLnjntcdGfux0vnbnDOK4lsuIZTAhSihlLnlsIJ8CAh3nZHQG8PEn+NcWcGHY7jsv7TIEshL44Op0/kyOc/lanMGQTjLp9CWeIxjKZbzFeB7naWbSYebtVIFBRKQVWWuXAdONMcOBXwPTgMeJTBB9P/CUtbajflVIqndtKLS5jXKIiEhzRIYBrFs4nIwKDCIizXLBlUtO3Zra5xxrMXGVJUv75q2cH1dRVO50ro5oXLejH377m0UnWhuueU7MjOoz6Y+ZJVt2Ohas41pe6Y31bsgcPLIoKuXoLcn9vjf5+pysFx8f/Xx6eV7Y6XDtwhVkcBE/x0Uau5nLWhYT6hg9Fmrz4Ck8N/UVzg7b/c4ZhG8dy9OOhWotFksuu8llIYbFDGAoKYzlZ9xEb/7Bg2xzOmJL8MxYxItOh2hLLnh+6ijeATBX8hTQeSZgtbxun2AGwIyFPIQhw+lIbcXAB9NG8W+AGQt4AFcd3fw7IJfly6lH8w+AmQu5wxqGOJ2prZgwi6aN5kGncwBYa78lUmg4nsjERmcTKTRct6fQ8Jy1tqN9uIqrd63bXdJGOUREpHlKG1gf3yYpREQ6qEsvn33slpQ+54INZBZt+bBP3qr1TmfqyLomdi/NSur95NbC9dcALoMJJMSkvn9Ur5PWOp2to4oOlAeGbFk0f0d81obNXQadVRSVetyPLptd+s4jg193OpvjxhPDdK7CTQLbeYNNbHE6Umvrl0rFmB7844uN3Ay4jCE4oiv/PKkXBU5na1WWEKtZQhY7yeYczuAq1vJHXqfY6WjN5XLZznWzYVw1jt+NwdVpbux/7E7/Ltr0VuP37jLtIE9bPuftvmMPd7LfuzX7PefbBWvt19bac4ATgU+BIcAzwBJjzDRjTEcaX9XXwHpN8Cwicjgwpv65g6yNaqMkIiIdzv1nPNRtVeaw7xsX4e55699RcaFtnD7s/73lcfu2EpnhInTioPP+6XSmzqBr6bbc3rnfvY61VbmJWROvvOTD4U5nctzNXISPLmzlw85QXKj20Kl8GO9jHWB9bvL+fiqdp9i0jR1s4b94SeJKfuB0nJbQ7k6+iYh0Btba2dbaicBkYD4wFJgBLDbGTHM0nIiIiIiItIkPhkw5D4wvuTh3bvfCDZ3m5KLT3G5veFDmiL8AJjOx1z+zk/t0mLHQ27uMkl1F3XfnfGjBtSxr1Pn5Md3dTmdyzF30JoFRFLOYLR1q5oEGedzYi47kLwCn9+OvSTEdd0ioWm1nG0XMJ5bBPMgRTsdpLhUYREQcZK2dZa09jkihYSEwHJhhjPnKGHOKs+lERERERKS13HvWP7LLoxKOdIdDRX3zv1vidJ7OZuzAcxalxnd97vSh33/D6SydTffd63J8ocpNAbevy/UXPjfK6TyOOZbJWPxsZoHTUZxw3XEsH9+L+++bwNdOZ3HEOhYTpJzhTHY6SnOpwCAi0g5Ya2cBxwDnAouBMcAsY8xsY8zJTmYTEREREZGWN6/XySOswSRWFiz1BoMhp/N0RlNH//yfPm9s57pyup3oUrh9MQazJbnvCKezOOJIfMQxmHLWU0Kl03Gc8o8z+MDpDI6pIkgZq4miP98j0ek4zaECg4hIO2Ej3gaOBqYDq4CxwKfGmI+MMcc4GlBERERERFpMUVzaECwmrWjnBqeziLS1zJKNm8H6S2OSBpXHxnakuQgb54f0wRBFGXr9d2alrMeFYTIDnI7SHCowiIi0M9basLV2JnAEkULDWmASMH9PoaHzdiEVEREREekggi5fqrGE0kp3FjqdRaSteYPBkCcYLDKYqPcGTo93Ok+byyAFi6EEvf47x0q7agAAIABJREFUs90UYTEkkex0lOZQgUFEpJ06oNDwQ2A9kULDAmPM28aYoxwNKCIiIiIihyQvNt0VNu54CJUbY63TeUSc4LbBMmsxy7OPPayHhzkkMXuGxCmn3OEk4qQyyjCEidUQSSIi0oqstQFr7bPAEOBKYDtwNrDIGDPDGDPQ0YAiIiIiItIkO5L7uDEYl0Xj/0unZfY8/6tcMV6ns7Q5N24ALJp/pTOzWMDCnufDYUoFBhGRw4S11m+tfQLoS6TQsAuYBqzYU2jo72hAERERERERERHpVFRgEBE5zFhrq/YUGvoDtwCFRAoNy40xzxpj+jgaUEREREREREREOgUVGEREDlPW2jJr7QNALyKFhjLgUmClMeZxY0yWowFFRERERERERKRDU4FBROQwZ60t3VNo6AfcDVQCVwBrjDF/M8Z0dTSgiIiIiIiIiIh0SCowiIh0ENbaAmvtXUQKDQ8ABrgGWGuMud8Yk+JkPhERERERERER6VhUYBAR6WCstXnW2luA3kQKDW7gZmDjnkJDspP5RERERERERESkY1CBQUSkg7LW7tpTaBgIPAT4iBQa1hlj7jLGJDoaUEREREREREREDmsqMIiIdHDW2s3W2muBQcATQCJwJ5FCw83GmBhHA4qIiIiIiIiIyGFJBQYRkU7CWrvRWnslkR4NTwApwP1Azp5CQ7SjAUVERERERERE5LCiAoOISCdjrd2wp9AwHJgJZBApNKw2xlxrjIlyNKCIiIiIiIiIiBwWPE4HEBERZ1hrlwPTjTHDgN8AU4G/AtcbY34HPG2tDTqZsZHMZ5/hdjqEyOEk92TsNAjVXGauZBSWHzkUqfPJ5kZ7J36nY4iIiIiIiDSHCgwiIp2ctXYpkULDccCvgbOBx4kUGn4PPG+tDdW3Dye9Pp8BoQTudTqHyOHELASO5sL9FrqIwZLpTKJOKB/jdAQREREREZHm0hBJIiICgLV2nrX2HGAs8DEwGHgGWGKMmWaM0ckwERERERERERHZSwUGERHZj7X2K2vtJOBE4HPgSGAGTSg0GGOOaeWYIiIiIiIicpgLh4Mderjb/jA9E26cAz6ns4i0Fg2RJCIitbLWzgZONsZMAn4HHEOk0DDPGHOftfbt2rYzxsQD7z65YcPKn/Tp03aBRUREREREGuHzxf86avn6D28CXInxXb+45NS/POZ0ps7AHyj3fL7m7bG5xVtOrPCXDosffF66N3WA+09zfjfpQVifDB+PhTdfg1VOZ20pAYjbBTecBN8bAPevgFq/RwvMziHxJ6/wQ5+bqoXX8KTHja2r7TVvcdRT87nJgqt/Gp9/ez2Pt2VW2Z8KDCIiUi9r7Sxg1p5CwwPAccBbxpivgN9Yaz85YJNrgPQbv/32mOk9eoSTPJ46PxSIiIiIiIg0xpK17/TaXbQtvTFte3QdtrFf9pi8utYXlmzuHgxVdQeoqNg9uKUySt1mrZg5blPeiquCoUD36mXWRr4qhm043g/Dd8Hw1+HaRHj9/+C3f4EdjgVuYSHouxKeiIWPz4E7Xz6EfdzzIQO3lZJQfT8llvIRXck/sS+7sxIJtGBcR8z4lj4rdzEFYE4OL53Uj+K62q7cQc9yP90Bthej17DDVGAQEZFGsdbOMsaMJjIJ9L3ACcDHxpg5wO3W2s+NMUnALQDlwaD3hiVLwv88+mi/c6lFRERERKQj+HLxvx+3NhzdmLZrt8xe0i97zLV1rXcbz76TsS7T5BOzm3csSgmEQ66+WcfkN3XbziYUCrheW/TYlbvLdl3YyE1cJTDlIRi/GC7/FBa0asA2VgGnzIDxA17n09svpyqmkdu98R3pd87iidrWuQxVWYnMO3sIbzx6AYtaMG6bsiEaPe9jlGdfQcUNwdZJVLcPV5FSHsR9/pHUWcjsTFRgEJEOJVBV5XrtX/f1mTvrld75O7eccWFFyU+A94E/W2vXO53vcGcjl5i8bYz5LzAF+C2RSaE/M8bMAjYAUQBBa11Pb9jguqhHj9CkLl1CjoUWEREREZHDnrVhH4DL5dntMu6K+trGRKesrG/9uKN++EV8XHoeYUy3tEFbm5Jj845FKW98+dvXADNx9M8uPrLPpG1N2b6zmbHg4V+WVBSc3dTtwpDxOcycCFM+4fA9aV4H7/LtnPrO/6ia/TlrHziRt6O9hOvbIK903xwO0R5yjStygt0fJDkUJnZLEeMf+5rxH67mv4uu4c9JsXTo7+B/OY/PeiazKwTmhN406TXcXAu2EHfaU7wKuB44k8t/dTKd/lyTCgwi0mHk7dgcdccVJ52Uv3NTYjgUqp7EPgH4CfBjY8z3rbWvOxixw7DWhoGZxpjXgYuBO4BJB7YzxvB/Cxb4Vp52WkWMu0PP3SUijZW/OI73zhpPOBz5Ox2bWcbUxZ85G6oeK5/KYv6vR+69n3XSNia99E2T9jH/9v6se7EvF67+CJeGjRMREWmOvlnH/u2MMTd+1px9JCdkV5488opDOmldXFEQA5ErrSsqixp7AXqn9M7ip6YcSnGhmoXoz+HpO2DyPZDbktnaA3+QqNk5XH9yDmefN4iHbx3LksZsd/skbv31RNYCBEOYP3xB38fncsmmQiauL+Cs4x/Bv+Im/ta66Z3VP43KRxzqrbGtmGjABZBXSqwTGdobV8NNREQOA+Ewv7v2jLEFOzfXLC5U8+25zTDGDHUgXYdlrQ1aa58FjgA+hP0nYQpby47KSnPn8uVeRwLKIVu5eHbizZeOPunPt140suHWh4dn/nrjwIfv/OHwh+/84fBH7vrRsIJd23wNb7VPMOg3j97746ELvng7rbUythtL/96DfyWfS9438bU3CMOLfU/lo+lNf35EpwfIHLOTLsfuBCzFa5ObF3aPVf/uxtMJ5/N0fO23WYf4XE7oW06X43bQ5didBIp8FK9LaHijAxSvj6d4QzIhf6O7fYuIiIgczjYXrEnZXrTp8ubuJwyZf4ebWiJTe1XqZ8DzS/nbyc/y+9dX0a0p23rc2NsmsG7jbdxzVDeeA1iZy3kPf0WP1kkrcjD1YBCRDuHLD17K3LJ+eZq1tq6TN4bIye+7gKltFqzzSAROhIPHTPSHw/xp9WrvtO7dQ8ekpNTb7bMzm/Hk3X03rFiUWl8bl9tt/++mh5emdslq9XkttmxYEb9+xcLMovydbX5FxpMP/HxIwc4tcbWtc7nd9srbH/82MTmjyWPlLpn7QXZJYV5sVWW5p6KsOGrsqRdtTe2SVdDY7YsLdnk/efOpIwBGjz+nY4+3mzywDH9RNBteyyR9ZOlB67fPSaJ4QzJ9puY0ed9x3f1MfiVytdE7k45n19ddmxsXgIqdUQRKfXQ/bSOxmQcPm9Dt5EO76i17QiHZExYC8J+sLs3KKCIiItJJfL32vUusDdf6mb6pCuH7V8FDj8GWlthfe5VXzpg7P2PUs9/y6gMTeW5gGuVN2f7zK/lX5r2cXhUi/ZkFTLj6BJ5taJvKAK6GhmdqbcEQxuPGsV6+Tj9+R6ACg4h0CIvm/Lery+W2oVCwvqtDvcDpxhizZy4BaTm3AHWOgeQCLp0/37d08uRKr0ud52qzfeOahK0blqdU3y8s2BVbUVYc1a1H/93Vy4zLbSsrStpkrKkRY07LP/uS65d0ye5X1haPV9PXs2b2q6oo86ZmZB10Ytu43LayvMyVmJzR5P3++eWlnwK8+vTv+rz0j9uPaYGoHVf2KbvxxAXYMacLsO6g9Zvfi/wCup/W/rqqD716Lb3O7tgFIBEREWmyUCjgeva9n/0mZAOJNZenxGfPnzLhvpcb2v7596+5uiJQ3CccDkdVL1u0+o0bFq99u8aFDSY8auAFj4wadE5OC0Y/7IRCAVdx5e6TW3CXnrfhrMfg8RbcZ7sUtkStyefii17j1OO78+RDp/JhY09+J8USSotj1bZi0vPKyaq5rjSAa9iD3FpQQd+KIKnBEEl2z8g2LkNljJed2YksvukkXv7xcdQ7r8jn60j8xVtctDaXUyqCZHjdFGcnMe+G8Tx39QlsbihnaQDXpS8wYU4OZ+yuYEgwTJzbRbnXRb3fPSsDuAb8gTuqguzXo3hQF+Z9+VNmNPS41YIhzGUzOeHjNZyVV87QQIhEl6Eq2kt+l3i+HdOT2U9P46sDCy/D/8IVO4oZFLLsHaHhiflc/e+F++UOXz2GJ+84ldWNzdMRqMAgIh1CSWFudCgUbMyZ6zgihYZWvwK8szDGdAWuJjIMVRgIASGfy+V1GeMOW0vIWlaVlLj+tGaN95ZBg5p85XlncO1vn9tvvM0/3jRl1LxPX+v/0OtrPnIiT3rXnlU/vP7Pq5x4bIBeA4bn3vevubOdevxOz+WxpA7LJW9RRuRlfcCf1x1fdcEb5yfr5MK9ywLlLrZ9nELp5hgCRR4SBpbR55w8XL72W9ANhwzrZ2ZQuiGWtKOK6XFmo3u0NCh3QTw75qbg9oap2K7xmUVERBxWXlnsKa3IP4kDP9iEcQMNFhiKyneeGAoFMmsuq/KXHXFgu50Fq3sBOc0Ke5hbsmVOv1A4mN6S+8yD88dB0+bBagdKoNehbOcPkf7FRm498VnOv/AI/h4PhQ1vBf5QZE6AaDf79ej9cj1JOYVMrm2bsCW6zE+v1Xn0uup1JueV84tbJtRykRHwxNdkX/8OD5b79xUwAiGScwo47bq3GL9oC3c9PZ15deWbnUPi957h7rwy9hu+NBQmNhSufz6D7SV4txQxngNew3YXLmhcgWFtPtEnPcrt24o5sebysCWq3E9WTgFZOQWcPjidKw4sEqzPZ2yZf//fZ1Elgw98jP9t4WNQgUFE5LCTkp5V7vZ4w6FgoDFFhhXGmPeAd4FPrbUHD6UhTTEZ+BQoAXbv+bf05/37nz8wPn5Iksdj47xe4j0euvja8YnGw1hVRbkrHA6ZmLiEUENtiwtzvVEx8aGoqJgW7QZbUVbkjolJCOFgD5XS4kKP1+sLR8XEtngX39Liws73mSlzTC67vs5ix9xEuo4t3rciDPnfZJB6VB4u977X9Mons/nqujH77SO+ZzGnvjqbjNEHD7PktMI1Mbx3+okUr983B0Tm2K2c9eFcvM14DpVujuKj6cew6+ushhuLiIhIW0mIS/MPH3DO9eUV+SkA2/K+O6O8sui4xm5/zOCpv84r2phdWVWcsiV32bUAWWmDHo2NSdtZ3cZjvIHjhk5f0PLpDy8FZbtaZgjMGvwwYg682dL7be9Kqhjyz294OMnL5w21/fdCuuWXMxSgT2rdJ7inDueu6cP4rnsylf4gZslOkl9ezJi5G/lxKEzsI1/xg1smcOeB25UGcP3qXe4o95MV5SbvnCN55PJjWPzFBro+NY+f5JYx6j+L+PVZg7lsynDyDty+MoDre//m3rxyjjIQHpLJqz86mncvH83m3Co8t7zDiW9+x+115e6TStW147h+azEpAF9u4MydJRzb0P9LTRMe49Y9xQXbO5UPzjmC90Zlsyu/lKjF28lesJnR5QESbjuFNQdu++tTuGfhVnoUlZPw0VpuABjdnad6p+7rteF1EbzjNDrd34DO92VZRDqkMZOmbfvsnWeGNNAsROQEeB/g53tulcaY2cAs4C1r7YrWTdrxWGv/A/znoBXTpg0BBrR5oE7iX3+6bvAX7z0/oLKsxBcMVLkBoqJjA4OOOmHbT3/z9OL0rj2qarb/zY/Hn7D2u/ndgoEqt3G5bGZW78KLfv67JWMnX7irZrvCgp3emy8ZNSkcDrsAuvboX3jvP7+cU1uGB3553uiVS2Z3qywr8QWDAbfb7Qknp3Utm3DuZasvvOqeWq94aQ3fzvso5Z8P/Pzo7ZvWpGIMg4aP2XrNvc8v6JLVu6rhreuWs2ZJ3L//dN3w1d/O7RbwV3k8Xl+DBZwOpcdpuSz9C2x+r8t+BYYdXyXiL46i6wn7PXcY+KNtpI34kIReFYSDhlVPd2fx/SOZ+8vhnPvFV20dv0GfXHIMZVsTGPfQV2Sfns839/Vn9TNDmHfTIMY9cojvBWF494yxFK1OZegvvmXIFZuI6+7nk/83kk3/7dOyByAiItI5VVYVp3y3YVadhfxoX6K/X/axB53cBDhpxGV7ew0///41w5pUYDhi2hpgzXcbZmVtyV0GQK9uoxeMHjKlzT73Hi6qAuUpDbeSJnDlljOhvgavfkv6tW9yj7V4vW4KHzqfT+tq2zOZvGlHsXeo05P6UXzNCWwa/heSl27n+3llHNQzB+DSF5hYVMkglyFw16ncXN3L4YxB7P7FCdw85EEeLa6i/70fc/6U4fzzwO1/OINxeeUcBXD+kfz2tR/ySfW6tHiCPRJpcPjVv57L3tfwkAc5amdJQ1vsc/3bDN9SxEkA4/rwjy9/yswDmuQAtX73BdhzvOveWk5adYFhQl+++cPZLGt8io5JBQYR6RBGjTsrf/DI8VvWfPtVVh1DJYWBCmAUUAWcDpwNnApM2nO73xiznkix4R3gQ2tts05QirSW7ZvWJJSX7I6a8n+/XuTx+sLBgN+1ZunX6YvnftD7kbsvM3c+Ouvrmu1HnHD69oHDj89LTE6r2rllQ9zsD14c8PBvLj2xZ//h7/XoM2Tv5GExMQmhEcefviUUDprFX73fM3dbTuLBj743Q5LLuOz3Lr/tG68vKlxavNs3/5PXer3yz3uPTsnIrjx1ypVbW/P/AGDbxjUxf7jh/JOjY+ID5//wlm9252+P+fLd5wb97pozxv3p5WWfuN3uQ+o1s37FN/F3XXXSKcGA333cxKnreg88qrCwYGfUO8/9aURLH0O7lX3KbjyxAXbMzgDW7l2+6b3IRMfdT93/C0BUUoisk/Z13T72d2vZ/H42hava9gvm3BtGsuCO/YdiM27LiY8tIuPoSE+K/CVx5P6vKwN/uJIjfxGZLHDCv5ey/fNurH+t7yEXGDa8kcHu79IZ+otvGfvQyr3LvQkaGk5ERKSFbMlddm31Cf66TDj6p5cM7Tu51T+LSu28bl+bz+PW0Xld5ANpAF+s5YirCkmqCODNLSVlTR5HbtjNxFCYWGMI/OQY7umfRmVTH2N0NouXbuf7YXvg+KgR8zdHihyZ8cw/cAilrEQCo3vwzidruW5tAePg4ALDVzlMBEiNZVnN4kJbeXdlJH+sj62fXsErbf34HZkKDCLSYdz217fm3f2zySesX76wKwZsOFw94bOfyLA9Z1trq7uuPQs8a4xxA2OIFBsmESlAXLHnVm6M+YpIseF1a+2mtjwekYa4XG47/Yq71tdYtP7Gi46K27RmyUHjnU65/LYNNe/3HXJ04eP3XTFu0ZdvZfToM2Rj9fKomNjwT+98ainATRePTC0pzKt37PjY+MSqmhnOuui6nJ+f2/ucRV++k9WcAsPu/B1xM564q++By7v3ObL0hMnT9l45P/PJuwZWVVV4f/3wBx8PHjGuGCAqKjb44auPDv3645kZY0+9aNeB+2iM5x/+1ZH+ygrvXY9/+mH1fgt2bfF1qgJDZB6GPPIWd9lvHoadczLwxAXImlD3OLBV+R6CfhdRaZUULGvR8Xcb5I0N4ks8eJ4dU+N70rYvUgHoftrO/dp0OX4X614aSFW+h6i0YJMfe/ucyH57nnlIzzsRERFpmDGuSoOps3hvjKsyNjq5ySdXpeXERyXlt/Q+XbAzBV5v6f22tjIYWgnjDnV7l6GqXyqvjs1k1k0beRrgo7X8ssblP3vF+dj4s+N58FCvqH96OvOuH8eZafHU+vrKK4vMN1BcRY8e93HXgevLAqQDVPipdYis/D3b903hf4eSr7lySyKP3z2RBY2dOFsaRwUGEekwYuKSQvc/M//LT976V7evPnyx5+Z1yz0FuVuXEumR8JS19qCTYdbaEDB7zw1jTG/29Wo4nX29G/66p3fDO8DbwBfWWk0ULe3ORT+779td2zbUOzkWwNBjJhQA7NqaE9eSj5/aJcuflJpZnr9rS7P2m7stJ2nmE3ePPnD5oOEnbKtZYFi9dG5mtx79C6qLAACnXfiznA9ffXTodws+PeQCw+ql87r17Dc0t+Z+O6XME3axa143dnyVSNdxxRCGvMVdSBuei8uz/4fynV8nMO9Xw9g1vxuhyLBdALi8LT4nRr1G37uUXmfX/6XWX+AFIDZz/15q0WmR+xW53kMqMARLIvtN6K25fURERFpJv+zj7z9jzI2fOZ1D6nZE9nErv9s2r8paG9VS+0yBV/Pg3pbaX1vpBZdtOsQCQ3osc68/nr+dN5Ad/5y3b1LlOB8bPS6qXC4C0W4KU2LYPr4vXz14DovjvTTrs/ewLMprW14ZwBUIkwxQ5qdnmZ+ede0jbInKL8WTFs9+n6f9ocj2GfEND4XUGirDJAEkxzrz+B2ZCgwi0uFMPPey7RPPvWx7GBZNP5oHmrKttTYHeAJ4whgTTeSDwCTgHOAI4Jo9tzJjzKdEig3vWmu3tOQxiByq0ePPzgcOOrm6fsXC+Hdf/nvf7RtXJQYCVe5wMOQCCIVCLT4rs9vrCwWDVc3a78Bhx2+7719zZzfUrrSoILpn/2H7HW92z8EVxhhbtDs3+lAeu6qqwlVZXuJLzeyurt09To3Mw7Dp3S50HVccmX+h6OD5F8q2+Hj3tIlYa+h34RoS+0SGIlo3szdFa1KdiF6v6NRIgbh8x/5feivzIvdjuhzakEbVPSdKNsaQPFhFBhEREemUkmPTK2O88QvK/SVjW2qfw+C9ltpXexfvY815g3j41rH75huo6ZaJ3PvribX1YWg9VQGM3TN0Us9kPspOZENdbTMT2HlgcQHAggEwpnlFkEMWxg1gcOjxOzAVGERE6mCtrSTS+2EWcIsxpi/7ig2TiAyrdDaAMWY5kWLDLOBza63G25Z2Y85HL3f5+28uHW+My2b3HpQfn5hSFQwE3A1v2f55fVEhv79yv2OpKC91W2uN1xd1SJMyezw+a4yxVZUV+pyUPWk3npgAO+Z0Aday+f3I/AvZB8y/kPN2Bv7iKE7461yGXbt57/JdC9PqLTC4fWHCtc6b07q6TSgAA5vf7cqAi/cNk7Rzbldis0qJSq2994LLGyIcqDtvt/EFLPkTrHm+Oz1OK2jx3CIiIuI4j9nXOzNo/fq8WId+mcNfXLp5TosUGKJh3qewoCX21Z55XRQflcmzj53Ja9HN7InQ0pJiCXlclAfDxPVJYcVnP+W1pu7D56akMkhUfjmOXIDk9VBUESSzqLJ5jx/t3ve7qdxTtOjs9IdQRKSRrLXr2de7IQYYS6TYcB6R3g1HADcD+caYT4gUG9621m53KLIIAG/86/4jfFExgT88v+iDrt37VQLk7dgc9dOze57ndLbmSsvsUbJr6/qkYNBvPB6fBVi24JMUgMysvqW1beNyGQsQtrV/Zne73TatS4+SrRuWp9bcb6fk8ljSRuSSt7AL279MYuPb3fHG+8meuP+QcxmjijHGsvrf/UgeUEby0DICJW4Cxd5695/Qt4TQe/+fvTuPj6o62Dj+OzOTfQ+QAAkQ9lV2BEQFFRURragortW3ldrWavva1lZf61br0trWtSJtba11Q4sLbkBVRHBH9kV2CFsIW/ZkZu55/5iwJyEhk9xk8nw/n/mQzNx75xlgstznnnN8LPxdZzqNz6NkWwxZY/fiaeC/8/R+JWSO2Ma6V7qRPmAfHc7J55sHu1O0OZl+Ny+pdr+EDkXsXpjBupczSO5aQkWhj6zD1qLodGE+rfrnsfaFnhiPpdsVW/HFBSnZdtxpy0RERKR5SE7OPDjKtah4V5qbWZqykV3HLVuft/Tj4vKC0+t5qMBIuDcsoZooYwjkpPDGA2fybL8MqvwdpilIjWVtfgkDvs1nCNS9YEiNY+2OQlpv3MtA4IXwJ6xZ63jWFpTRY9t+6rWuXq9MigELmO0F7pQlTU3jXzEmIhIBrLWl1to51tpbrLU5QFfgp4RKhSRgEjAVyDXGfGWMedAYc6oxRl93pdF5fVGOdYKmrKQw4q6uGHHWJZuL9u+J+/fjv+oBodELr027t6/H63VGnXfFtqr2aZ3ZqRRg/aqvU6o97thLNuzN35742J3XDiwvLWnZ79sBv1hF0O/lzdPPZffSDPrcuOKY9Rcyhhcy9Ldfsm91Gu+cP5YXOn2H6f0msP3jbDy+6keSDLp9HYkdCvjyjmG8Ouh83jl/LCv/1r7a7WtifKHGyBNVu3LizH9/RXKXfXz+y+G8OvB81r3Ug/ajt3Dyg99Wu8+QO1difA5zJo/hP8PGM/Osc9i36siF0M97dwFtT93Kt8/15p1xY3lz9Lls/zibhKxCvC24rBIREYkQ7dJ7FXo9UfkAu/Zu6O92nqbsrL5XPOzzRm85/pbV6wD3fgALw5WpqWmTwBe3j+L6tybzeFMuFwB6ZYbWrtxeyMifzKj7Sfr+7UP77yxk6M/eotHfO6fkMB+goJxul/+bEy6+OqZSER/NVoBl2xv/dTRFGsEgIhIGlaMbHgUeNcYkAGcSmj5pPDCk8nYbkF+5dsNMQqMb9roUWVqQyTfeu+wPv7xk9C+vHjK+dduO+6OiYoJlJYXRVW17941njFz+1UcdDr9v0lBzGUCHrn3z//jysg8aI3NtXXjNLzd9NffN7Jn//tOA+bNe7lpWUhhVWlwYc97lP1nSsUu/KhcoG3r6Bfmp6ZnFr0y9e+h/Z/ytR0VZcdSocVeu/Z+fP7rqwDZX/+ThbzevXZr66eyXu3/54X+6JKdnlJQU7Y8B8Pmim9Rw5QbXeWI+l696m20fpJPWt5DMEYVVbjf49o2c9JMt5M5Op2RrDDFtKojLqCC5W/VrESS0r2Dymlnkzk6jeFsMSdlltB2zr9rta9Lvplyyz3mbtD5V/rsfI7lzGZev+C+b3m5Fwbp4Wg0qpP3omp+7w7l7uGrD2+R+lEb5nihSe5SQ2uPI15fQvoILP1rAvlVx7JifCh4DqIIgAAAgAElEQVRI71NExvCq/95ERESkUazdOr/N3v1bDo44qAiUpQEEbTD+yxUv9Thwf2xMeslJXc+pcY29lITMuXsKcy/ZXbDlovc+e2TVaYO/92m0x+d8ueKNnnixp/S9amXDvZLmo21ydtGo7hN+OX/NzAcDwYpOddzdZsAfN8O0Bgnnsvhodo7oybqrruduSqhwO09tTL2ImUMf55LSAG2f+owHv9zKc/8zlLlXDmH7nmJ8zy2k4zsrGBYXTcl/p/DG0fs/O4n3ezzM5cUVdHh8Pg8tzOVvd57FnLE92LdqN7Gb9pNR0/NPX0ybZTs5+B4uqggtGu0PEn/XLA6+h7OSKZ4yIlQAHPX8C95ewYq9ZfR5dQl3DtvNc3ecxbsX9SV/WwFRry6j3YylDCkqI/nLW/hnTVm6teLjJdu58tt8xk/8J8uenMjHCT6c339Mj4oA3ocnsKw2f6eRQgWDiEiYWWuLCa3H8BaAMaYvobJhLDCa0OiGSUDQGLOIyrIBWGit1dWtAsCZF31vY4euffdX9/jYiTds6NFv+DGLOVdl4Cnj9vzxleXvzHr1Lx23bV6d7ASCnoTktPKklFblp4678oir/M+YcP2Gjt36V1l8tevY44gras6/6qcrnWDQHL3dhdf8fLnX6zvh/8sXXXvbktTW7cprs63X67X3TJs7/63nH+m0cuG8DK8vyhl+5sW5YyZ8d0d1+8QlJAUfeO7LOW8+/4fOeVvXJ8bFJwW69Rl2xGv2+nz2zidnf/7Zf19b/8VHr7cvKtwbnZCYUtGj/4jdp427ptpjR6zkzmUkf6/KESFHiEoK0vniXcfd7nDeGIdOE2r1f/m4x2l1Uh0X5vZApwuqXBi9WjGtAnS95PivMbVXqRZ6FhERaTpmffbo1KATOGY6kwp/cY/Plr/yzGF32dSE1hd3aDu42ovBxgz6wXNvzL9vVDBY0XbNlvn3rt2yIGCxXsB4jLewT8cxk1KTssoa4nU0Nz3bDtqeHt/mx7NXvPzTorJ9Z1G50G9NPLCzJ9yxAt5uhIiNygMFmfD4M9ewKT6TceS5naj2+rSj9J5z+PVvZvFQWYCMzzcz5fPNTLlxBn5rOTg1qjH4F+UyZ2A2R/xs3j4Z/+/Hc/vP3+Ghkgraf7yBm87+Kzd5DBWO5YgL4LxVjL2/6iWe8Qc5Zlqy/WX0vHcOR7yHu6QzcWwPjrh4yOfF/mMyd179Ig8XltP1q1y+N/GffK+q5//9R3z8izHVL2Q97VJeOGMqp5dUkP36cu58YwVBazGAx2sou2wglww96vVHMhUMIiINzFq7HFgOPGSMSQfO4tAi0QdGN9wF5Blj3idUNsyy1lZ7clki3+BR4/cMHjW+2kViTx5zUf7JYy7Kr+3xMtrnlF9980Nrjrfd6AnX7hg94dpanUA/e+KUKk84n3vpD4+5WqQuLrjm1s112d7ni7YTr/v1xonX/Xpjbfdp3bZD+eEjFqoz4qxLdo04qxYnk0VERESkQcVEJawJBMvbpCa3P6GLPaJ9cVvK/SU1rw8FeDy+woS4VjVe7JKV2Xf/uBG3/njuwmk/Li7dfZrFRgEY4ymNjU1eHAxWRNzUpPXRJjm76MoRt/72640fTF+945tJJeUFIxzrJB69XTQsbwUznoBnL4bajUptPoKp8OL/woN3wm5iOK8uOw/MYn+cjx0WzMD2VPt7YnX6ZlIcH02u4xA7oD21/j3yaL8Yw4aROXz/xzOY/O0uzikL0PpAuWAMwdbxLD6pHXN7ZVLlhTY/PIUtwzpyw5TXuGzlTsaVBcg8cHLfgJMWz9K+mcw7NYeCo/dNimFLQdnxz2VH+SjMTKx6VMiFfdi9+Gf86KoXuHjxDs4vqSD7sHLBpsWyqlcG8y7tT40XV53cgaI3ruWmG17jR5v3MebAMTyGsjaJfFNc3rIWf1bBICLSiKy1e4DpwPTK9RgGESobLgBGAtdU3gLGmM8JlQ1zrLVfuxRZRERERETEdVMu+tcP6rP/97/zz1vClQWgS/thu7u0H3bvvsKtsZt2LGwbF5Vamp01YHd8VHIgnM8TSYbknLl6SM6Zv63wl/hWbvsyZ0/HU89zEjKzs9oNfXb43nVfPAbb3c7YEKJh4Si4sz5rSQzNprjkd0w+0f07plJR/FuuPtH9D3dqDgWLf8YzwDNvrqDVml0kdW1N0Rmd2ZsST/Xrr1Uamk3xwlt4Fnj23dWkrdhBSk4aRWO6sK9VItW+f3bfzc3hyN85nfIFN/Ei8OKHa0hZuI20jqkUj+7Mvoxk/LU9ztge7Nvwa363djd/fHsVmWlxlI/rxu66HCNS+BwbmXOZVSc+nkML9zn8A9OCShYf6w9+bHnBgbgato4oUQ4Hr4YNwKseS5KbeRqT8R5qXT2GNx2HeW7maUzWS5O+6tha6wBfV94eMsa0Bs4gVDZMAEZV3jDGbABmE1pE+j1rrebyFhERERERcVlqUlZZalLWRrdzNCfRUfGBAZ1Gr12UOXBdWVQcZ3cdt/i3K16OyHLhZphxKzzrdo6GcmEf6jbd6FHO68ne83ri2tqUZ3Rn/xndqdfsEd1aUXbLKDaFK1Nz5LtsKHPcDuEWO42P3M7glsuGhlZOb4kmD+FztzO4ZdJgdBV8E2atzefQ6AYvMJBDZcNgYErlrcwY8wmhsuFNa60WEBMRERERERFpYm7lyHUARCKRx+0AIiJyLGtt0Fr7tbX2bmvtUKAt8F1CBUQ5oWmVHgRWGGPWGWOmGmMuMMbEuBhbRERERERERERaEBUMIiLNgLU2z1r7nLX2MqAVcBrwEKHplToTGtnwJrDHGDPbGHOLMaaje4lFRERERERERCTStZz1B0REIoS1Ngh8UnnDGJMDnENoVMO4yj/HAn82xqwHZhJaLPpja22FC5FFRERERERERCQCqWAQEWnmrLUbgWeAZ4wxscCphAqGC4A+wM2Vt2JjzIeEyoZ3rLW57iQOr0AQBy8lbueQY/nLy6I8Xq/j9UUF3c4iR7IGe8ydDl/g5UYX4py414aPpmDdX7g+v4/bUerscSp4zO0QtbR7dzdKSqqfgi85Oa0R04iIiIiISBOigkFEJIJYa8sILf48B/iVMaYLh8qGsYQWjJ4AYIxZQahsmAPMtdb6XQldT5NGsha43u0ccixj4uYBn1hrf+12Fjk+OxU/sNftHHVh/vJFAMi0TzWv3AA85XaAOigvb0VBgbfaxxMT4xsxjYiIiIiINCFag0FEJIJZa9dba5+x1l4ApANnA48BmwiNbrgNmA1sN8a8YoyZYoxpV5/nNMYMrW9uERERERERERFp+lQwiIi0ENbaUmvtHGvtLdbaHKAr8FNCIxiSgEnAVCDXGPOVMeZBY8ypxphaf6+onKLpQ2PMVQ3wEkREREREREREpAlRwSAi0kJVjm541Fp7NqHRDRcSWsthGzCE0OiGecCOytEN1xpjjjfP9mggAXjuqXXrshswvoiIiIiIiIiIuExrMIiICNbaYkLrMbwFYIzpS2ithrGESoNJlbegMWYRMLNy24XW2sMXiz0PqACif7Jo0cjs+PiKC9u10wK/IiIiIiIiIiIRSCMYRETkGNba5dbahypHN7QFLiM0umEnodENdwFfERrd8JwxZpIxJgWYCMQAxrHWXLJgQcw7O3ZUvzCoiIiIiIiIiIg0W74XPuF4011ElLS2lJzXjXKA6V+T4i9tOSVLRTal1+VQBmCuJplkWs5Jv1jK7B8pBXhzNUlFu1rO6J3kOMonDKEEYPpHJPp9RLmdqbEEk6m4pj/Fbudo7qy1e4DpwPTK9RgGERrZcAEwErim8haEI7+uBK1l4oIFMe+fdlrZmDZtnMZNLiIiIiLNUvfuycTFndjvq9HRRXz1lT/MiURERKQavug4nnY7RGMq2se/gTcBDDwaHUecy5EaTXQ+M8jhJQASeQCH1i5HajxFvA/8HaCiiN9Ex9HR5USNptQyD3gCwEniF9HQy+VIjcbxsxB4yO0ckcRa6wBfV94eMsZkAOOA8YQKh/gjtgcC1jL+k09iPxw9umx4erpKBhERERGpWVTUaQSDrxEaGVs3JSUL6NVrHKtWFYY/mIiIiBytxVy9LyIi4WetzbPWPmetnQx8RqhTOIJjLeWOw9iPP45dvG+fvu+IiIiISM1WrHgbay+B0OwDdWLMKXg879GrV1L4g4VPWtneAz836+djabEcgwHAG2x5F6LZyt+dD/wdSMvlYDDHnktpTvSNTERE6s0YEw+cBlX/cOQzhqJAgPM++SRmR3m5foASkXBZCtzkdggREWkAEV4yZO1ZGzBOsCzo8bSYWRVEjmG88cZgc3Z82/JGHJVVTuccS6zLScRNScRi8FBOkdtR6kMFg4iIhMMZcMTaJhUGrMcYTkpJcW7u3t0/89RTy1ePG1fWNiamWTfzItJ0WGs3WWufcjuHiIg0kAgvGXxOcD+Y6KLEJJ1glBbHWmMCXm8yFjtx8QsFbudpdAXsw2BJoMl+jZJGEF/571/CfpeT1EuLWehWREQa1HhCoxeCwCJg1j+GDRt1cVbW0ESfvtWIiIiIyAlaseJt+vS5BGPqvibDoZKhSa7JkFCxf12Fr027/LjsnMSilavcziPSmHaltG/jGG9CbEXp2laluUG38zS6eWyiP5YEOgK5bscRl6TRAYNlMRvdjlIfGsEgIiLhsAQ4B0ix1g611t5+badOO1QuiIiIiEi9RehIhs753y7BYvMT2vR2O4tIY8tLyeqFwbYp3r7M7SyueJkCythEIl21DkMLlkB3/Oziz+xwO0p9qGAQEZF6s9ZOtdbOttYWu51FRERERCLQihVv4/FcCfjrvG8TLRnu/88Nq6Kcip0BX3SHLa26dHQ7j0hjyU9ok1wck9TPOMGyqz998nO387gml3l4SaUbKhlboo50IYoMtjPP7Sj1pYJBRERERERERJq+pUv/g8czmRMtGbzed5tSydCqNDfYa8fimVjsttROZxXHpcS7nUmkoVV4o3yb2/Q+11rjzSzYNmfi4mktb/2FA27iS8rZRhojSSbZ7TjSiBKIoy2nEWAPv1TBICIiIiIiIiLSOOpTMsCoplYy/O25s75JKdvzmcWbuLLtgAsLY1MT3M4k0lAqouN8y7NOPqciKrptrL903Z9euvy/bmdyVQkO/+VfGLz04HyS0ILvLUEiUfRiPIYEvuA5tp3Q97MmRQWDiIiIiIiIiDQfEVYyPP3chJfj/EVrAr7oNivbD7o8N61rjtuZRMJtZ1J25pL2J08qj47r5gtU7PrZnP/7W9c9y5r9idV6e5AtLOd5okijF5PIoLXbkaQBpZBGTy7DSzvW8hK/Yq3bkcJBBYOIiIiIiIiINC9hKBmu79ixSZQMXfcs8//92XOeSi3e86ljvAm5rTpf+HXn0y9e36Zv34L41ES384mcqKLYpNh1Gb26L+o4avyGtr0uC3ijWseXF62+850f/6FFT410tB/xDZ/zJJZoujCZfpyloiHCtCGNfpxOH67ARzzLeIbv8anbscLF53YAEREREREREZE6W7r0P5x00mQc5yUgqo57j+qQkDBzWVTUs0V+f1lDxKuLrnuW+Wc9mvPvG65+f8nKdkO+U+GNzspLaZedl9IOgw1gnVKPxXE7p0htBY0nHmNC70uL9QaD+3LyVr/3xEuXftqqNDfocrym5+d8y9Xcz+WcTwojSKAvOeyjnD04FBFs/tPotDgefESRSBRpGNKxOOznK95gJtPY7Xa8cFLBICIiIiLNkjGmFTDAWvuB21lERMQl9SgZjDGnnNm5c+sPNmz4fVMoGQCmPX/u0t1x2Svu/s7j3b/NOOmkopjkrkFPVLL1eJMc43Y6kdrzOMESbzCYFx8o2tJp79qlP5x194oh2z6pcDtXk/Y8+3meF7iB9zmD/qTTl2ha4yMbW+cStUn79xwy7n+OPgc+/9FFrL3pYnLdzBR2Bj8BCiglnz18zMcsYSo73Y7VEFQwiIiIiEhzNRJ4E037KSLSstWjZIj1+Xqc2bnzLz7YsOH3xX5/eQMlrJNWpbnBx1+auApY5XYWEXHBNHYzjQ+BD92O0lDuhYu/hScPfP7wo/z5pkf5q5uZ5MTplzERERERERERad7qsSbDgZIhMSoqtgGSiYiIRDQVDCIiIiIiIiLS/NWzZBiTk6OSQUREpI5UMIiIiIiIiIhIZFi69D8YcwUnUDLERUV1V8kgIiJSNyoYRERERERERCRyLFv2Wr1LhujouAZIJiIiEnG0yLOIiIiIiJy4goIu9O49xO0YIiJH2Ygxd2DM76jjuY+4qKjuo3Jy7mPHju9QUFDUQPlEREQiggoGEYk4c2c+13beey90yt2wfOTlO3PPAGYBf7fW7nM7m4iISMQpKnoYjwZGi0hkifb5+tC+/QvAlSoZREREqqeCQUQiRmlJkfe3N50zcs3Sz9oZY3AcxwAdgDHA7caYCdbaz9xNKSIiIiIizYLHM0wlg4iISM10qZGIRIyH/veCk9ct/7KttdZUlgsHxABpwGxjTEeX4omIiIiISHPj8QwjK+vfJCcnuh1FRESkKVLBICIRYeH8d9KXf/VRh2AwUN3XNQ8QDfymEWOJiIiIiEhzZ8zJZGX9zu0YIiIiTZEKBhGJCJ/OeTXL64tyjrNZNDCpMfKIiEijeA9IdTuEiIhEvFz27v2D2yFERESaIq3BICIRYd+ubXHBgL82pWmyMcYCZcDeE7jtttaWh/8VNH/GGA8Qba0tczuLiLQM1toAUOB2DhERiWi57NlzCTt2bHY7iIiISFOkgkFEIkJiSnqF1+tzapgi6QAH2EFoTYZ2lbc6McYUAfs4VDrU+mNrbUldn68ZaQ18ZIz5Hy2mLSIiIiIRQOWCiIjIcahgEJGIMGjU+O3zZ73U/Tib+YE3rbWXHrjDGBNHqGyo660dkF3XnMYYOPHRE3mVV+s2VWlAb2C+MeaRwosu8ib69G1GRCTixcZOpbx8odsxRESq0AFrfwYk1HXHoOPkefftU7kgIiJyHDrzIyIR4fRxV+6c8Y8Hdm/f9G16NaMYLGCAu46409pSoBTYVtfnrEc50YoTHz1xouVEvrW2oq7PV0dplX96gFu6vPtuYPrIkZ7RrVsfb20MERFpztLTZzN79mtuxxAROUK/fgOx9necWLmQ/83Wrb8eWliockFERJoFTw1rLaeFZvNoMCoYRCQyeDz8+s9vz79rypgxe3dtTTqqZKggVDBcYa1dHq6ndKmcqM/UTidSTpQCZdba2rzGNELftDxA9O6Kiqgz5841P+ve3X9/v37+GE9tlsgQEREREamnULkwh9CFPXUSdJz8T7dseWBXcfG+oQ0QTUREpCEEIaaq+y2UX2ZtsCGfWwWDiESMjPY55Y+9tnr2q3+9t8tnH7yWk79jc2xFeekO4H3gD9ba9W5nPCBM5UQqh0qH2n5c33KipjUmenCoYMCx1gA8vnZt1IytW30vDB9ePjw9XaMZRERERKTh1KNcCDjO7s+2bHlge3HxLi/EloAnvoGv+hQREamvEvAEIa6ah0sb+vlVMIhIRImKiXGu+PH9a6/48f1rHVh42RAecjtTuNWnnIB6jZ5ozQmUExWOw6aSEnPKhx/G/rxHD/+9fftqNIOIiIiIhF/9y4XfbS8u3gUQBO8KSB8K+WHPKSIiEkYrID0I3qoeM1Dc0M+vgkFEpIWp5+iJVKofITEeOI1qvrdYa5m9c6f3kqys4MkaySAiIiIi4VTPcuHTLVvu315cvMuE1m0DYCdkoYJBRESauF3Q/vDPbWia8AMf72jo51fBICIitWat3UdoOqSNRz9mjMkERh1+n8cYsJb+KSnOXX37+i9s1y5ojt5RRERERKQ+6lEuAFs+3bLlTwdGLhyuBDKWQEp/2F//kCIiIuG3GlKKILO6xz2wpaEzqGAQEZFwSeXQ95WgAe/A1FTnN336+L/Trl2DLigkIi2HMaY1kFz5aW/gOuC2wzbZYa0taexcIiLikvqVC5sJBsfsKC7uQ2hELhbs4aMY1kO/bPgiHfxhSiwiIhIWeyBqJfQ7/L7DRy8Y2DvJ2qKGzqGCQUREwqU1oV/GLPDlCyNGlE3Ozj7Z5UzSiIwxBvia0EnfCiABONkY8yNCC049Ya39XxcjSmToD8wGdgExhMrN04BoQgtxdnYvmoiINKowlAusWrUhaIz1wKlVbeSH+PkwYBQsVskgIiJNxR6Img8D/BBf3TZBWN4YWVQwiIhIuKQA84H/s9bOZdKkl90O1JQYML+dRbZTRmvHQ5TbeRrC3W9ZPp/5+J+Xzn3xCesED1xh7gWiPV7f3vE3PvH2PTOJ3NLJoaw8yNbfTWS321HCyYDhJjripxUeknFwd6azKdYya+JiKvYP5NAVppkYbwlthjzFyQ8ONT9wMyDgJYBDAT5y7ROR9f9BRKTJCFO5ADDZ2o2vGtPbqTzW0aMYyiDtYzi5FyztBQXheQEiIiInZhUkL4eTnKPKhcNHLwB7Jlu7sTHyqGAQEZFwmWKtXet2iKbmmWeI2t6es++2nBvwkI4Hh9DV/RFp+ISfkNqm457i/fmJod/NwePx+tt07LuyXZdBk93O16AMsTHRcPdbbPbC67+5gK+O+gGvWTE3E0M55wHnA1EYHIJ43c4FwOl/20Due31xAtEH7/PFBeh8SSaW69wLVimIxeDgJ8rcyA48vGSf4mu3Y4mIRIwwlgsH+GFhFIx1KouFo0sGP8QvheHrYUdHyO0XWpes2X6fFxGRZscsg9TNkF0MbY9+8PDfPT1go2m83z9UMIiISFioXDjW7TNoFd2O/zWQgYcv/UFWeUzkX80ctOZfn7312JvWCaYAeH3RO7774LwbgxbH7WwNyovHBGjr9dLfsdx091t8/sx2pk2Z0vymUzA/JAOHX2NphYeYgw94XAx1uLj0AMmdN1G8tQvW8WK8fjJHfIXHE3P8nRtR6LRURwLcbG7gC9Yy1X5IwOVUIiLNWwOUCwBXWLtzhjGLHRh44L6jSwaAYmi7EtquhkAUFAMVUUT4zzgiImGWAR2+PezzVtD+3dBUqHIUf+i3sGg/JDjVnMs/+sI2C0sutHZnowREBYOIiEiDuGcm8TE+foXBBj38lSBFHncnlmk0PYdfuPvr955+sTB/y3UYT3nOgDPvjI5NiPxfvIM41rAt4LDNGpb6PFy8NYsbDTzRnEYymO+RRBS/wZKOp4mMWKhK6yFLKdnREWs9RKfkk5CV73akanmIxTKcbgA86XIaEZHmq1+/AQ1RLhww0dpl041JtZBz4L6qSgYAB3zloSlCKT+BMCIiLVls5dfPA2IgqQgy3crTnFXxu+bmSdYubcwMTeU6NBERkYjiOFxlPMTg8ApBitzO09jGTXlsmjGekqiYuBVnX/+Hr9zO09iMZSvwqgcG3z2TUW7nqRMv1wGpTbpcAPBGB0jtuQSDJWN40/8/ZojBwzBzI0PdjiIi0izVt1zwes+oqVw4zKcObDr8Dgu2OV0sICIika+a702bTWhtzEalEQwiIiJh9sC7tPF5ONWxzAgaSt3O44b0dt3Ks3uNvDOr54g1bmdxS9Bhu/Gw0Gu42MD85nBiwvyQDGAYNJOFyNP6bMATU0F0UvN4n1nisFwBNP1CRESkKTlULrQ+gb1D5cKSJetrs/Eka4PAvOnG7LMw4PDHDnwvr2pEg4iISGOo7vfKICy5wtoljZ0HVDCIiIiEXVmAoV5DcYVhrbfJn1JuOON/9PRctzO4zWP5xhpOvu8tcriA2lw16a4AQ5vd+NaUrlvdjlBrHsDSxtxApp1Go82JKiLSrDViuXC4SdYunW7MLmCIhbTDH2sOFw2IiDRlVcyfq5FiJ8jCvij4+jJrt7uVQQWDiIhImHk8dAC2eh39gNTSBS17vVBkoSM0g4LBkIMlRtdlNiBDAA9ZoIJBROS4XCoXDphk7Q5jzDuvQecg9LOQfKLHEhERCScDBV5YdglssNa6eu5BBYOIiEj4JQcNJW6HkKbBOpQEPM3khIQhTeVCA7N4cJrJ/wcRETe5XC4cUHnSZj2wfroxKQ508EA7CwlAHDTxNYtERCQSBA2UAUUObPfAlknW7oemMexDBYOIiEi4BfF4vASCbueQJsEYHI+nmUw8FJrCRxqSxeBRjSMiUqMmUi4crfJkzn5g2YH73jUmpqi5rF0kItJEfAnRh3++DJYaeN2tPE1ZIvjPs7bc7Rw18VmHB9wO0Zh8frYd+NjCIzgt52oDr+eIofhP4hz5Zo5olvyDH1r+iiXOzTiNKS6BPQc/NvyrPEiim3kak89Q4HYGERERERGpo2DQhzFXnODey1mypNHmoa486dOkT/yIiDQ1lxlTdvjnxVA+ydoit/JI/fgmDWOR2yHcMmkIS93O4Bb7NCvczuCWSUNZ7XYGt1w4mLVuZxAREREREanRypVfux1BREREaqd5DNcXEREREREREREREZEmRQWDiIiIiIiIiIiIiIjUmQoGERERERERERERERGpMxUMIiIiIiIiIiIiIiJSZyoYRERERERERERERESkznxuBxARERERkSbM6y0lOtpW+7gx/kZMIyIiIiIiTYgKBhEREQm7irJiz+alHyZmduhXmtQ2p8WefNy29quE5JQ2rQr3bss0558Sba2tcDvT4YwxPwdmWGvXuZ2lwZXvCf3cG5MecDlJ85OZuYSMjPOqfdxx8hoxjYiIiIiINCEqGERERCTs/vHLkVft3Lj0tvjUNm/f+lzebW7naUzBQIV5+8kfnLNr8/Jr/OUl/QeddZ1n88pPrgf+ZIx5G7jfWrvY7ZyVzgPuN8b8EfgdN1Z/kXqTt/iRjiz5Yz8u/nwOCdlHFjnl+738s81ErDVcvfV1Eto1qaJHRERERESkuVLBICIi9WaMyQF6A7OstUF300i4PTQ55W/+suLeVT1mwJLDTAAAACAASURBVBl12R1Xj7nyno2H32+tDa3z5GCOd/z3//7z7l1PGrOz27AJBeHI66Zta79KeP+Zmx8uLykYDVg44vXHA5OAS4wxd1hrH3Ql5LGigV8B17H53fl0OK/69/CWd9KZffnpoZcGeOMDJHfZT7crNnLST7a4urxX8ZZ4SrYlUrY36piCAQhlDnOBsm9NHIEiL60HFYX3wCIiIiIiIs2DCgYREQkHB3gHyDPGTAOetZde6nIkCZdgoKKNx+vbn9a2y6vHPmqCnfuN3XX0vdf//uN/bV46f0ZK+15lNR1705J5CV+8/siMNZ+/PvWmYRMeD1/qxle8P8/33tM3PVZRVjS88q7qyhUDPGCMCVprf99I8WpyoAjJZMf8S7DsodWAb0hov/uYLcv2ROEviib73E2k9thPyY44dn2RwYKfjmTjG9mcP/szPN6mNwwiJiXId3fNwHEMcW3CN2XXR9cNpmBdKtfueDtsxxQREREREWlGfC9/TVe3QzSmGC+7LxrIPoDXF5FTHsTrdqbG4olj76Q+7AGYuYKOxaVEuZ2psTiw/4oh5AOYG8jGQ4zbmRqNh0L7F/IApn9KlhNNrNuRGksUlFw8hO1u52ghtgBlQAbwC+D2vrNm7f5ljx6+yzp0CMR5W8yX2ojljYrZ/sMnV/yttttHx6Y5tRmREAwUHRjp4OKl7+Ex84kbrjmsXKiJIXRS/wFjzExr7coGjlZboULEX5jOjk/OIi5jC60HLyY6ueSYLXO+k0vfH24FwAkaPrquP2ue78mih3Yw+PYNjZq6tmJahX/tBesYrHPcUToiIiIiIiKRyueF37kdojH5A/wbeBMgEORuL8S5HKnxlDADeAmgpJTbvNDa5USNxmd5H/g7AF5uATq6GqgxOcwDngBwopnihV4uJ2o0AVgIPOR2jpbAWmuNMeuAvoSmW2FlQUHrGxYu5IfffBN9Ufv2getycoLnZGZq+qQIV7hjY9S8/zzcHUL/1G27DMkfPG5KlQvAfvjc7Z33blvbGsBfXtT6nad+0AfAGxvvP/d//rSm0UKHQfH+PN++nRu/z7HTIlXHAF7gNuC6Box24krzOpA7qz1JOatoNXAVHl/V71+P1zLmH0vYOKMzm9/OOlgwOBWGrR+mUbozmtL8aAKFPuIyy2k7ei/pvY8tLQ7Y8Hpr9q9OJK1vIZ0mHDuK4nB5XyaxfW46Ngj71yVVuU3+N4mU7gpdVOGJsmSdse+4r33b3FTyPkvBeCG1VxEdxu3B4zs0MqNwcwz7VsXjL4rCCXjYMivt4GMZQwu1kLSIiIiIiLQUmiJJRETCZTGhdRg8EDrL6ncc/MBrW7f6Xtyyxdc7Kcn59/DhFYNSUx03g0rDmfWPXwxaseDVvx/4PD651fuDx025taptF/zn4X85TjAVoGjfzolfv/fMxMqH7OBzfzykTVa3ZrMQ7/xXHxxsnWDKCew60RjzCGFfHKDWEmp81DpeCtb3pSi3C6k9lwKlVW7n8Vq8CQGCpYeGK636Rzvm/eDUY7Y1HkuvG5Zz+tMrjri/dGcU74wbRf6ijIP3tRm6g/PfW3DM6IPSXVHMvmQY2+dlH+8F8v6Fp1GUe6h8uGrzGyR2KK9y24J1scy+dMQRGQCiU8q4OvdtohJDJcvnv+zDupe7H3z8nXPPPvjxsPu+ZPD/Nc1RHCIiIiIiImGmgkFEROrMGOMjNBKoC6FRC32A4VRz5bbfcfAYw/e7dAmoXIhs477354XxKRmTrA2YhbP+No0aVv09dfKdV+3euDxj+YLpzyamZs7oOeI7LwL44pIrmlO5AFCYn5tzgrsmA0vCGKVhOBVx7Fl6MoUbq776f80LmZTlxZMzYePB+6w/9G9/2tPz6XxxHr5Yh/xFiXx8w1BW/bUvg+9Yd8SJ/g+uGcyeZa0Z8chndLtsB6ufy+Lru4fw4fcGMO71r494vvcvGk7eZ+3oPWU5/X++nuTOZXzyoz6snNb3mGzjZn5Myc5olj/VhU1vdMUJVDPCxIF3xp9K4YYUBty6iO5Xb8WXGCTvixQK1iYcLBcARv5hOT3/ZyOf3jqQ4q1JjH1p3sHHMoY3+8XKRUREREREaksFg4iIVMsYkwj0JDS1Vu/KP3sB3amcCul4ojweYj0eXh05slxTJDVf/rLiXr+fnDLt6Ptjk1qt+Mm09X868HlCm6zAeT98ciXAN7P/XuM0MaMn37Vp/cJ39yxfMJ2omMT88T+auqKm7Zsya52WsdCIUxEa8VCyPZa8L5MoWBfH1jkZrHmhB7EZJQy+69ipraKSAwcXVm532n56Xr+Oz3/Viv1r4g4WDIWbY9j63450mbSWAf+7GYDBt29gz7IUNv6nK/6iRQdP8Od9nsTOBe3pesW3nD51+cHn8cVX/fWl1YBiWlHMhtdqXHCcta9ksP/bdPrcuIwRf/j24P0p3Y7dLyG7goTsCr5K9OPxOXQ4Z2+NxxYREREREYlQKhhERARjTBpHjkY48GcOVV+BvhdYCqwAlgPrgQrg9cM3ivF46Bgf77x72mnlXRMS3JoCRsLAGBPw+KKOuTLbGxVb5EaepiYuqdWJLigfAG4B3BrZ81NCJeJxGEti9lqCpXnAKBbeN4SF94Ue8vgc2o7aymlPLyKpY9VTDx1u4C820vO6rcRl+g/et/W/6VjHsPPTTF4bPPrg/aV58QTLvexbHUebIaH/a9s+Tgcg+6wq1/Y4YTs/DR23y6RtYT2uiIiIiIhIBFPBICLSghhj2nNkgdAFOAnIrGaX7RwqEA6WCdba9VUcO5rQyr5eAK8xzug2bewrI0dWpPh8KheaOV9M/Npbn8+vci0FgQFjr/9qy6oFfqz1UbtFng9431r7VEPlOh5jzCSgBzVljknbSauBi4hrs5+CdaGvFb2nLKfd6Hzi25XTun/RMWsk1MjDEeUCQPnu0IiomNQyopMPTY8VnVxBSo99xLY5tL1/f2jB5oSsmkck1JW/qPK41azPICIiIiIiIsdQwSAiEmEqT/R351CBcKBM6A3EV7GLn6MKhMqPF1lra311urW2whiTC3QC7Pe7dFn5l0GDOtflTKu0PAmtsyoAa20wyu0s9ZHdc0RRQnKbGcX78y6r466PNkigcPDGFpHebxHJXY69or/VwH10v3Jn2J4rJi1UKnS7ciMDb9tY47bR6aFtCzfHhu35AaJTKo+7IZbU7lUvZn00b4yDE6h2nREREREREZFIp4JBRKSZqmJaowMf96LqaY32ASs5skxYAayy1oZrbYRlQAZwxdODBl0JdA7TcSVCZXbsX+7x+nZXlBVmu52lvkZcdOsTHz5/x+lOMJBJ7UYxPG+tnd3QuerMeAMkdVpNq0Er8XgbZ+qmrLP3YLwO66Z3YuAvNtawNjhkn70bYyzrXu5Inynhm84o64x8lv4J1vyzY63XVEjMKmbHJ1mU7/bVbRSHiIiIiIhIZFDBICLSxB02rdHhZUI/oG01u9R6WqMG8AbwS2vtCiZNurIRnk+amP/84aohRXu3twaw1sYEyssyn7vjzHMBsrsPW3vmdQ+tO3qf2IS0L0oK88/5+60nX5zZZeDqnZuWde014pKvTrn41mY1F373Yefvyc9deePSj/79FyfobwdYji0aDtz3BjClsTNW40BGh6SchXQ8byu++MadJiipYzmdJ65n/avdeP30UfS5cR0J7cvYsySJXV+nc9ozS4mKC5UdrU4qJvvcTWx5P4d3LwjS/apNxKT72b826Zjj7lsdx5ZZrQHY/20yAGv+1Z7oND/e2CB9bjj0f6zTBbtpPWgna1/qgWMNXS/dii8+yK6FKRRvieO0vxy7CHn7M/NY80JP5lwxhP63rqFwcyy+GIce1+5oiL8mERERERGRpkYFg4hIE1DDtEa9gIQqdgnLtEbhZq2d5tZzS8PxYAIePLW6OnvVgtfuDwbKD45GqCgv7r9p6YePAORvWfHWmdc99Ouj9xl7/e8fem/aza23rvny3q1rvgSgrGD3E6dcfOvTYXoJjWbkxJ+vzew8YNL8Vx/8ccn+vIuttTFHbbIVuA/4q7XWrYWdqzIPuIXe3x+PpX+1W3liQuupeKOPn91UbuOLqd3rPPP5RXhjg6x7uRsfzs86eH9sRgkFq9fSamDxwfvOnv41H10fYOPrXdg889BIqbi2xcRnHlrDYcXTHVn65wFHPM9Xdw2tDAidL5pB3GHrO4x/bwEfXD2Y9S91Z92LPQ7en5SzH3/RaqISjxzt1et728n7chmr/tqH3NmdAEjIKqTHte/W6jWLiIiIiIg0cyoYREQaUQ3TGvWkcnHko+wHvqZhpzUSqdGlt71ynS8qoVYnib/zixcm781dlVjVY50Hjd1d1f0Dzvru7n6jr/jevBfv6bRzy6p27bsNyj3tsv/bUp/Mbuoy8Ox9XQaefX9+7qpHvnrnqcHZPYZf7PF5V6767I3ngW+stU1p0fOtwHeBf1lrrfkh42vcuusleaStmEl6z5LjHrnX/2yn7am12xZC6xmc+a/FjPrzcnI/SsP6DUk5pWSOKDxm26jEIGdPX0jpzqVs+zgVHENy1xLaDD2yYB3+4Bp6fje3yufzJQaPKBcA4jL8nD/rcwo3LiLv8xTwWFJ7ldDqpOIqjwFw+tMrGHT7OvI+TyY6IUj7s/bV6vWKiIiIiIhEABUMIiINoJppjfoC7arZZS9HFggHRiVsaGInI6UF6jZsQkFtt+078uJ9hNb7qBOvL9qOueb+jcDGuu7bVLXO7lU2bspjC7zQo1P/0z5595mfLXQ7UxW+W7evMR5I7127wsDjtbXe9nAxrQJ0vWRXrbaNy/TTdVL123pjnCNGPtRWUk45STl5td++YzlJHWuXWUREREREJIKoYBCRiLN68afJc9/+Z/aGbxd1u3zZ5x2AD4A3rbVhXYCzclqjbI4sEE5kWqPF1tpjr9AVEWlgKjBFRERERESkPlQwiEjECAYC5rE7rx6wYM4rPbxeXzAY8HuB/sAPgHXGmAnW2rV1PW6YpjVaDyy31padwEsTERERERERERFpclQwiEjEePyuawd89sGr3bCWynIBDn2d6wrMM8b0s9ZWOQ98NdMadam8VeXwaY0OLxM0rZGIiIiIiIiIiEQ8FQwiEhFWL/40ecGsl7pba001m/iAdOBOY8xjHDsaYQBQ1cK0AULlwdGLLC+x1tZ6XnoREREREREREZFIo4JBRCLC3Lf/me3x+mww4K+uYACIBm6pvB2tAPgSWFl5W02oSFhvrfWHO6+IiIiIiIiIiEhzp4JBRCLCru2bEoMBv6eWmy8AlqFpjURERERERERERE6YCgYRiQhxCUl+j9frOMHg8UqGcuBUlQkiIiIiIiIiIiL1U9urfUVEmrR+w87Mo/r1Fw4IAnNVLoiIiIiIiIiIiNSfCgYRiQhnfef729Jaty/0eLw1lQcGuLexMomIHGAtKjblEI/+P4iIiIiISGRQwSAiEcHr89lfPvL6J3EJKeVer8856uEg4AA3W2vnuxBPWhjjpdCxxLmdQ5oGY4jDocjtHLUSbCY5mzMPDn5K3I4hIiIiIiISDioYRCRidOk9pOiPLy99b8TYS9fGJSSXV95dAXwIjLbWPuliPGlBbJCdXksbt3NIkxCPJRkfO9wOUku5WAJuh4hoDh5i2el2DBERERERkXBQwSAiESU9o33FT+9/cdFzc/e/8c+PC+6w1sZYa8+21n7idjZpObyGb6yHNl4fmW5nEXd5vfS1ljLi+NbtLLUS5BsVDA3IATyU8SSb3Y4iIiIiIiISDj63A4iINJTY+KSg2xmkZfq/C9hw70xWWIez8PISQY6etktaAK8hAcsIPLxz15jmcdLe/o31ZgqbsXTHYNzOE3EM5QR5Q2tyiIiIiEgkMsZ4gaeAtONs2vGoz683xpx6nH1KgSnW2vLjbCeNTAWDiIhIA/BV8I9ANPcYh/EBy7tegwqvFqSyXJiEZfduH++6nadOLFPxcB8Q73aUiOIQxEMuHma7HUVEREREpCFYa4PGmBXAn+u468DKW03uVbnQNGmKJBERkQZw+8Vst/BHD3SJ9nC1seR4PboiPNIFg0R5PZxk4DogWB7gj4+dR7P6IdhOIxfDI0AxjoqxsLCUA1uxPGSn4nc7joiIiIhIA3oKWBnmY24BHgrzMSVMNIJBRESkgdw1gVX3zOBOE8UVHi+TcSj2GfZgKHY7m4RXMIjH4yHe5yMTC1hmk8SM342hzO1sJ8L+hWXmB9wBXIelNxaLhxhQSVZrBgeHciwGw/vE8pp9rHmVTSIiIiIidWWt9Rtjboawjty9zVpbEsbjSRipYBARkXoxxrQFHgGWA6uBFcBae+mlruZqKu6aSB7w6D0zaQ30B9pgNfVMxPEAhv0By3YvLL7rApr9D792KtuBB8wPySBIfywZGBLdztUsOFg87MbLNmJZbP9IqduRREREREQai7V2jjFmJjAhDIf7FHgpDMeRBqKCQURE6sVau8MY0xe4AnAALxDMfOutsuHp6TF9UlKcfklJtldystMnOdmJ93rdDeySuyaQD3zgdg6RurJ/IQ+Y43YOEREREZFwMvcYH1G0wU8cHuLczhNxzuJJPuAcLNH1OIrDYB7lIvqY+zSYOqwcSvFTzi7y7FRbr2lcVTCIiEg4TCW0iNOBHxy8eeXlCW9t386snTu9DuB3HAakpjqfn3lmWYxHSwCJiIiIiIhI4zGXGS8DOYkAA7D0BtoSILRSnnU7XQQ6HdjJWpbR54SP0Z31fIfzsZwfvmAChCa/jQaycMw9Jg/DSixL2cbiuhYOKhhERCQcXiRUMByj3HHwGENKVJR9/ZRTylUuiIiIiIiISGMxD5skSjmXvowhQDL24LpiRsVCAxvPMtbSmbITGCHiw884lmDRSYSG5QHaYWmH4QzaU2LuMnPx8K69y+6rzQFUMIiISL1Za/cZY+YCZ8Gx3/wN8NrIkRU58fH68U1EREREREQanLnMeOnLuRguwhCLxRxWLpRg2EqQPXgpwVKuuqEBJAA9KGAJ36/zvl14jdbMDH8oqRSLJQ5DGpYsILHy/ZGAl/OwjDV3mZlkMdNOqXlEgwoGERE5YcaYdsC1wPVAz6q28RjDnwYMqDgrIyPYqOFERERERESkRTJ3mEz68mMMOZUnTQ1QisMSHJbjkIe3slAIuBo18p3HElYyEj99a72Ply2cycMEqGjAZHI4L22APkB/LIlANF4msp3h5n7zuL3Dbq1uVw0xERGROjHGeI0xY40xrwCbgAcJlQsrgMLDt432eJicnR34Sbdu+pFNREREREREGpz5relNNPdWlgseDH4MHxLkKSwfYNh5sFyQhheNQy8eoC4rXeTwMG1ULjSqILsIMpcgfwHmAOWV01NlEeAec58ZVN2uKhhERKRWjDG9jTEPAtuA2cAkoAh4Bhhsre0L/AkoB/AZ4/RNTnb+PmyYfigQERERERGRBmfuMQNx+AUQX3lydDMBphHgM9AJa9dcxCLieb9W20bzOVfyYQMnkuoFCPIl8AyWdYRG/8TgcIv5jRlZ1Q4qGEREpFrGmBRjzBRjzCeERijcBrQm1GZfBrS11v7AWvtN5S7/AKIBJ8Hn87996qla1FlEREREREQanPmt6Q7chCWK0EnRr4AXgAJXg0nIaH6PofQ4WzkM5MFGySM1C1KMn+k4zCf0fvLiZYq5z5x09KY66yMiIkcwxniMMacaY6YCW4GpwCjgW+AeoIu19mxr7XRr7RFXgFhrNwDzgeBLw4fPaxcbq2GnIiIiIiIi0qDMr0wKDj8BYgidDF1AkNkENRVSkzGUnaTybI3bJPMi57KmkRLJ8XixWD7G4b+E3lc+HH5sbjdtDt9MBYOIiABgjMk2xtwGrAHmAVMIfZ+YDpwN9LLW3m2t3XScQ00FbhzXtu2eBg0sIiIiIiIiAhDP94FUwOCwmCBz3Y4kVZjE3/CyrcrHDAWM5S+NnEhqw/IF8AVgMMQTy43GGHPgYRUMIiItmDEmxhgzyRjzFrCR0ILNXYCvgR8AGdbay6y1c6y1tb3y4wVr7d8bJrGIiIiIiIjIIeZ+MxQYULnmQh6WWW5nkmpkUk42f67ysQyeoC/7GjmR1FaQD4HcyvdZd+7mtAMPqWAQEWmBjDFDjDGPEpoC6RVgArATeAjoYa0daq19xlpbVNdjW2ud8KYVEREREREROZbBGAJcisUAFod3gYDbuaQG1/IO0Xx9xH1e1nEVr7iUSGrHIcjbGAKV77eLzQ9MFKhgEBFpMYwxaZULNn9DaLGrm4EkYCahBZs7WWt/Za3VfIciIiIiIiLS9N3HUAztCU3dsgxbzfQ70rScxEPAoYsTu/IgCSqGmoE9wDeE3m/ptGcUqGAQEYloxhivMWasMeYVYAeh9REGAiuAXwHZ1toLKhds1jdzERERERERaT4cRldeTQ0VfOpyGqmt8awgkTcAiOMDLte/XbNh+RyDg8VgGA3gczuTiIiEnzGm5/+zd+dxklXl4f8/p253D7MzG8uwjcAAw66giCIi4BoXFmckGJOQxWiMSYx7jKL+YtRvEjWLRk00MQaigwR3BUQUcJdVQdZhhoEBZoHZmK371vn9cW51VXdXVXf1dHf18nm/Xk1V3fWpaubervOccx7gt4FLgEOLxZuB/wK+GGO8qU2hSZIkSZK018L/C7OB44EArKXEpjaHpFacw8f5BmdxKh9rdyhqQc42OngAWAocET4cFplgkKRJIoQwBzgPeB1wDumPrDLwPeCLwFdijDvGMKS86drubu9BkjQRxNjZdH0IjoCTJEljbyfLCJSIQODudoejFp3IEzzJ7/B81rQ7FLXsblKCAfZwXEdPmbe1N56xNWNOtRp57OA9+Z6pM01UVmJr5XkH/O2e8tQZwVLeTbVQ7W7+kRk0/6I8mWynt0G5azef3N3JtHaGM5ZiiZ3tjmEshBBOAV4PXAzMKhavBS4HPh1jXN2m0J5qurazcyZUr8mSpHFrVtO1HR1bm66XJEkaHUf2To8UbaSekEwuTEyVf29pmqSlHRc9k7VtDqltlp/EI+2OoV0uOIVH2x1Du8T/5LF2x9Au5z+H9e2OQSMjhLCYNFLhj4Aji8W7gCuAzwLXxRhjm8JLQthKsxDyfDFM3euwJE0YIRzY9Hre3b1t7IKRJEkqlDmIEhDoIbKx3eFIU0aaJmkHkenA4inTg12SJroQwjTgRaTEwvlU6+jcTJoC6YsxxifaFN5AIaxp2iBVKh0F/GLM4pEkDU+MRzdZu4ssswODJEkaeyUWABDZQk57O9hJU03kSWA6kYUmGCRpnAshHEdKKvwBsKhY/BiwEvhcjPGOdsXWVJ7fTwiN14fwbOCyMYtHktS6FSsWEcLSJgnjVaxc2bzmjiRJ0ujYp3jc1dYopKlpF6n253QTDJI0DoUQ9gVWAH8CPKNYnJMKNn8W+GqMsbtN4Q1NZ+ev6GlS9zPGcwkh0O6pnCRJjcV4DtAkW8yvxioUSZKkPgJdxbgFOztIY61MTzFFWdeUKXAsSeNdCKEUQjg3hPDfwDrgM6Tkwm+AdwEHxRhfGGO8YtwnFwD+93/XAg822eJQXv3qM8YqHEnSsPx207Uh3DBGcUiSJDVipzVprJWq/+4cwSBJbRZCOJTUgPMGYEmxeAvVugo3tSm0kfAD4GkN14bwJuDGsQpGktSCiy5aBpzdcH0IkTy/fuwCkiRJkjTeOIJBktoghLBPCGF5COFaYDXwEeBQ4EekaZEOijH+yQRPLkAIVwyyxct59aufPiaxSJJak+fvofn3hZ9z5ZVrxiocSZIkSeOPCQZJGkMhhFNCCJ8B1pOKNJ8LPAJ8FFgaYzwjxvjZGONT7YxzxBx33I3A2obrYwyUSp9gxYps7IKSJA1q+fJzgPMH2epLYxGKJEmSpPHLBIMkjbIQwoEhhL8IIdwB/BJ4PdAFXAG8ElgSY3xXjHFVO+McFZdeWiaEf2u6TYynkmpMSJLGg4svXgh8epCtngQuH4NoJEmSJI1jJhgkaRSEELKiYPNKYA3wCeAE4GbgL0lTIK2IMX4jxpi3M9Yx8DngiaZbxPguli+/YGzCkSQ1dMkl+9DTczmweJAtP8XKldvHIiRJkiRJ45cJBkkaQSGEZSGEjwDrgGuB5cB24LPA02OMp8YY/ynGuKmdcY6plSu3E+NHBtmqRAj/wfLlLx+TmCRJA734xTPZvv1yYnzuIFs+xp49/zImMUmSJEka1zquuJ1ntTuIsZT1sPaCU3gU4IpbOIWMKTPvdxZYd8GJPAxwxS84mS662h3TWMnKPH7B01kDcNUdHN8TmdHumMZKVmLjBSewCiC8iWWUmd3umMZMzub4We4d7dOEEOYCrwF+F6g0ypSB75ESC1+LMe4Z7TjGtU2bPsPCha8jjeKoL8ZpwOWsWPHXXHHFJ4kxjll8kjTV/fZvH8KcOZcBpwy6ban0br72tW2jH5QkSZKk8a4j9PDWdgcxlvLIZcDXAULkzfQwvc0hjZly5CqKYnyxxB+XeljY5pDGTIxcDXweIO/m9wIc2uaQxkweuRH41/SCi4Bj2hrQWCpxC6l48ogLIZSA5wCvA14LzCxW3UP6d/b5GONDo3HuCen663u46KI/Ic+/D+zTZMuMGD/K8uVnsWLFX7FypZ+hJI2mEAKvfvXFpPvlvCFs/w1WrryCL3951EOTJEmSNP45RZIktSCEcHAI4Z3AfcCNpILNOfBF4IXAshjj+00u1PGlL90O/PWQto3xpcDNLF/+d1x44YGjGpckTUUf+ECJFStewvLl15NG2w0lubCG7u43OMJMkiRJUkVHuwOQpPEuhLAP8ArSFEgvhd6p1W4mNcpcHmO00OVQXHHFZ1i+/BhSYqa5GGcAf0GWvYnly68Dvk2W3cDjj6/i+ut7RjtUSZp0VqyYS5oC6WxivJBWfbzFewAAIABJREFURnSGsBl4NVddtXmUopMkSZI0AZlgkKQGQginkJIKrwUWFIvXkUYr/EeM8f52xTahHX/8W7nzzgVF49bgYuwAXgy8mDyHhQv3sGLFo8T4JCHYi1aSBhPjPsBCYNGw9g9hB3m+giuvvGtE45IkSZI04ZlgkKQaIYR5wHLgjcDJxeLdwDeB/wauijHae35vXHppmRUrLiGELcT4B8M4QhcxHgYchrN0SNLoqoxcuPLKn7Q7FEmSJEnjjwkGSZPPU+u6uO+Li1n/0wUhfLUbuD7GeGujzUMIGfAC0rQ95wGdxaq7SEmFz8cYN4x22FPKypU5Ifw5y5c/RIzvpTrtlCRpvAjhbkql3+ZLX7q33aFIkiRJGp9MMEiaXH76zqX86uMnEmMgkAPPBfYJIVwDXBxj3FTZNIRwNHAJ8HvAAcXizcBK4LMxxpvHOPqpJRUJ/XuWL/8p8DngoDZHJEmq+iJbtryVq69+qt2BSJIkSRq/TDBImjx+/FdH8+t/OpFYDgBESlRHI5wF3BhCeAFpPv/XAecAASgD3yPVVvhKjHHHGEc+tV1xxY2sWPEMyuX3EMIbqf7OJElj7x5Kpb/ky1++od2BSJIkqW1KRA6kRKDEo+Tkwz5SxjTKHEJGmcij5Owc0n6BxUUc64E9wz7/RJWxiDLTiGwGtrc7nGZMMEiaHDbfPZ1f//MJvcmFgbqAo4GHiucAa4HLgU/HGFePfpBqaOXK7cC7ufDCT1Mq/RlpZMn0NkclSVNHCKsolz/Opk3/zfXXW2tIkiRNNfuS8TlgZr/lt5PzznYE1FYlfp/AXxWvLgf+bhhHmUWJtxF4BSWmkUoo5mTcCryXnLUN9wy8klLvOa8h741laggcBfwfJQDWkPNb7Q2oORMMkiaHuz57KKEEsWlSvTKi4QvA54EbY7RK8Lhy5ZVrgLdz8cUfpbt7BSG8hhifAcVtVZI0clIB528SwmUce+xNXHppud0hSZIkjZBZZLwYeAawkNQD/GHgBnJuJc1kUJUxm9Qpsa/Y20Fxagl9Ei39ky6Dy5lGF58DjutNLKSfLiLPIND8787ArL06/8Q3q8HzcckEg6TJYcu9cyj3NBq90N8fxRjtnTmeXX75RuBTwKe44IIFlErPI4RTgCOBpYQwG5hFjPu2NU5JGv92kb5QbyOEdcD9lMt3AzcSwh2sXDn84e6SJEnjUcaFwDuo3zD9h5T4OWX+oM/SnLUEziMrEgqRl5JG1k9VTzR4PjRdvBo4jpTI+f/Yw9fJ2E0HhxOYTw+PNN0/sKnmVevnb7fAuZSYTs7VDGd6p4zNRHIgYwK8fxMMkiaHUld5CCMYAHro31NB49v//d8m4KvFjyRJkiRJ9QVeDnygeLUe+BZpquR9gWXAcwj8pu6+kfupdEXMePooRzq+5VxGJ18DSnSzteX9Iy8kdQH9ITlXkBXLe1gFrBrC+a8m4yZKZHSP7/oDA6TRMJ8AIPAIkVtaPkb6nE6jky5gx15UwBgTJhgkTQ77n76RNV9/GtBsFEMZ+GWM0QSDJEmSJEmTSQf7EXlP8eoGct4G7OizTUbGHsq9Dd5qbG8a9gNHABC5fdjHyHlqvDes15WT9f7/VSLbi/ewi252jUxQo8s5rSVNDse98WG65u4hlAarqfDhMYlHkiRJkiSNnTTt0Wwi68l5B/2TCwA5ORnWYhx9qW5A36mONEk5gkHS5NA5K+ecL97E1ReeRbm7RMz7J1DLwCdijN9oR3iSJEmSJGlUvQiAwFXQtml1SgQWU+IgysymRA48BtxL3kJf9lQk+QBgPwKBbp6kg4fJ2TnEI3QROICM/Yi98/ivI+eplt/RcESyYn6J9tW/TOMIDqfMAko8ReBxelg/hP3mAUsIzCWnixKbyLmH9v0/NTyBg4HDKbGbnPsYSi2HjE562I9O5pIzh0ggYw/wMDmPN9rNBIOkyeOQlz3Bq268hpv+/GQ2/OxAYrkyXdJq4D0xxsvbGJ0kSZIkSRoNqVF4v+LVL9sWRwffIXIQ0H/emA1k/Bs5K5vuX2YenbyVDl5KZBoAkUoLbqSDd9LDtxvun+b//yvglcC0AWM1Mi4l58oGey8k42oozlv17+T8U9O4OzieMv9DGNDW/GGyPjNJROCD5Fwx4Bg50+jkegJz+q25ipz3Nj1/rYy5wBuInE9gVu/vIQIZjxC5jsAn6yZbMt4O/F7v9qXe5XuAb5Dz9zRKNGT8O3B6v6X/2W86rjLwFnKuq3uMDi4jclK/pXeRs6Lu9vXjOAx4P/DMmmXdwNfJ+TA0mHYp4y+BP6KDvu+9un41gU/TwzcHhi1Jk8miU7dz/o9vYveWjA0/Xc23XvKPMcZH2x2WJEmSJEkaJT0srGnlfKhtcURyYA+wAdgCLCQlPhYB7yODhkmGjOlk/BdwRJEY2AmsK0YD7A/sQ87DTc7eBXyeVMwaYDeRRwiEIoaZ0GT/QIn6bcX71VnWV4pxKJUtQhHnQF01/231/BWpcf3fgcXFCIr0uwjMIDIPOIjA6yhzOdQdzdFDal7fCGwCpgOLi7guJGMxOX/c4Oz131dfJQKdDdfGAckdgP2HcNwksITAF4nMI1ImcD+ROQQOIMW/hBKX0E292qRHFI+7gI1E9hDYTfp/eCGwhMhHyJjeP0FkgkHS5DRtbs7BL95gckGSJEmSpEmug/m9z8dqGqB6cs4jNWpXBU4k8LcEDgfeRCdfqdvAG3lVURw5J/IhuvkaGbt713dSIq/bMFw5z0tIyYUI/L+iEbjaW72TUoOG5cr515NzGp1FQ3mZjwLPG/Q9A+TcDpxGZ9HWnHNTkbB4L6Wa3vplIjnbGhxjNxnPpVQ0spd5J/CqIZ0foJMOyvwrKSGwjTIfpoerez/DTuZQ5nRgFrFBomUPn6CLfyWnu3dZxgLg7cDLgdPJeCY5v6iz9x9QYiZl5gLfASDwZwRu6d0iff6Np1oqcREwA4CcCwi8bYjvnuJ87yMyj8AjBP6EHlaTE+jiYuDdwCnFaIgvNTnK1eS9xdIrx11MiX8ETgDeTMZX+4YtSZIkSZIkSRNVYHrv86ymcXjs7RmwJHIHkY8UrxbQw5F19wwcVjy7lzIr+yQXgKbJAYBS7/4PkfNF+k+FM9j+yS662Uo3W2m9fsKOmn0HLutma8PkQkXO7ppjDPwsm+nhZcDTgEiZNxH5ep/PMJ3/6iZTREFG7JNcSDFtAt5L4EkAIqc1iD0f8B4jT/V5/4PVceimp2bb+lMZNRI4isCzilfvpYfVNe/pMuCaYrvlLR0XILKOcu9UV/MJHF272gSDJEmSJEmSJI2WnN/0Pi81nPKm0qv+SLIBc/kPRdo/cggZLxjG/hNbiTMBiNxMrBk1MBJyuoncD1BMVzX+lHp/54/Qw88HrC/3jjo4ioxFLR8/ci8Uk3dFDux7akmSJEmSJEmaqPKa3u6lIdUCGFslttS8qj9Xf4mvAY8CncC/k/EFMt5M4AwyZg56jpzvAGuKqYn+hYzLyPgLAmfSyay9fxPj3lIAAreO0vHT7zA2qaHQXocWjw82WL+qeAzkLB7G8XcBO4rnM2pXWINBkiRJkiRJ0kT2RO+zyAzoM03PWJpPxkXAGcABRPYZ8p7dbCfwh5T4CHAicApwStE9fA8ZlwH/PGAKn6pdwB8BHyn2PQk4iRJQppuMlezhYwOmXpo85haPTw77CBkZZV5B4FwCRxFrEjORGUXh6PFqXvG4oe7aHjb0prY6mNfyBFhJGsFQLtJYvYeTJEmSJEmSpImrNsFwEPDYmEeQpjX6ONQ0SrfaIB15iJyLyTgOOJnICQSeQSpcfAmwL/DehvvnPAr8HoFjKPF0UqLiZOAQ4LV0soByi4WDJ46UeInDbu+eD3yGEst6l4zvhEJ/lfc/re7arpqRM3mL9S0GYYJBkiRJkiRJ0sQV2UhkK4E5pEb1m8f0/BlziXycwCxgHZH/InAnZZ4EykCJEt8e8vFy7gTuLI6dEXkLgd8HziPjs+Ssbbp/5G5y7gb+tzj3nxJ4A4GXEPhsMZ/+ZPMEcCBhWNP/QMb7gWVEeghcRuCHwMbexvgS7wWeO4TjVEeYlGuKj4+2yKYiIbJf3fVl9u8ddVDiSfKRO7U1GCRJkiRJkiRNZGXghuL5eWR7XYehDNB3IpimnlMkF8r0cAllLifndiIPEXmY2FvAuXU5OWU+XbwKwJEtHqFM4DNQNCmXiloFk88dxePzaLXNO2cakbMACPwjOX9PDz+nh1U1v7+dQzrWHnYQimmoMha2FMfeCEVCKnAMeZ1RDKWicHhgN3nDOg3DYoJBkiRJkiRJ0sQW+AKpEf0IIm8n34sJbspsK57NgxbqKESg1LBGwvAFTu59Xh5GjYEyJ0CRdBnO/hNBzjeLZweR8cZhHyfu5e8vIxL5TXGsF+7VsVrzfSI9wCw6eW2/mKYBFxUx/YxUr2PEOEWSJEmSJEmSpIkt5zeU+AyBPyXwO3SxDFhJmbVk7EtkGal3+7fI+d9Bjvbr4jGjxN8S+RKwhxKLKLOGyP39zv0TSmwnMIvAfwKfp8ydwHZgBhlzivK4jXVwAJEzCWwlsokyT1FiNpFnEvidYqs1dHBHgybwhWScTWA7aWqf7ZSYCZxK5HUARB6jh5vrju/IeBXU9LiPHFakaJaS8Ye9ywOP0MN3B3k3rQu8iBKH1Cw5png8qM/54QlyrqpzhNuBbwCvAN5IxgkEvk7Ow8XncChwOjCTnD+hMkoFIGM3gR8AZ1Pi7cBCIjcBT1AiI2cOqUbDUF1Jqn3xPEp8kDLfIaMHOA6AnP+q8/5PpcRJNa9PLP6fmd7v/e8C/o+834iKnCfJ+ALwhwT+goxOAteSsy/wJuAwIJLzmRbex5CYYJAkSZIkSZI08WV8mjLzSL21TwFOoQT9Gvc7YZAEQ2Q18B3gpUXdgpf0ritxHznn99tjM4G3Ah8jNeR+oM+8MbXnbzT3fZnfJ/A7vdtW9q+Ow9gA/BXdNQ3jtUpcDLx+wP7VYzxJ4C1kxfQ9fc0CPtRnSfW8xxY/SaRMztVkg6ZMWpPx4QYFipcAb+m37IfUFvau2MP76SIHzgPOIHJG3fl7IkcQuK/Pspz3k7GYyDFFvYo31MTWmpyvkfFc4CUELiDjgj5nj1xL4JE++2S8hViTYKh+ujPo//7LPAj8uM6ZP0VkKYEzgTcTeXPN+49E/omUiBlRJhgkSZIkSZIkTXyp8f1DBK6ixHmkXuQHFAWgHyZyA2WuGdKxSrybMr8GXgQcTCQnsA64q+72OT8CfouM1wCnkQoO70OZLQQ2E9kMbARurbt/4FZSL/vFROaQpmbaQWANkR+RsZJutjeJ9w4iPwQOAmYTmQ7sAB4m8CNyrgA2N4j9KTJWAnMH+1iAxwZNLgT+G1hEdSTI4CKXFbEPtt02OtlcdxRHxm5y/obAVwhcQOBEYAFpJMkG4BbgugHJheQJSlxEmVcAZwNPA+YR2F787tJP5KtDeDdl9vB2OriJwHkElgA5gVWU+QllNg5IWkS+Djw2hGN3F0mugXJ2k/Fm4ELg/OI97AF+A/wPZW5seNTAd4jsIXBtw20ilwMHA7cBT68sNsEgSZIkSZIkafKI3EXeIBEwVN30AF8ofoZqIzmfBD7Z8vlyrgaubri+/riFqh5+APyg5fNCqhuQ88Fh7VtPzj8MY5+PDXnbwT6LyG1Ebms5hvQ7v6r42TupFsNX6yYk6o2IyPky8OW9Pm9ODqwsfoauh28D3266TZl/rnnVm2CwyLMkSZIkSZIkSWqZCQZJkiRJkiRJktQyEwySJEmSJEmSJKllJhgkSZIkSZIkSVLLTDBIkiRJkiRJkqSWmWCQJEmSJEmSJEktM8EgSZIkSZIkSZJaZoJBkiRJkiRJkiS1zASDJEmSJEmSJElqmQkGSZIkSZIkSZLUMhMMkiRJkiRJkiSpZSYYJEmSJEmSJElSy0wwSJIkSZIkSZKklplgkCRJkiRJkiRJLetodwCSJEmSJEmSpBHQSYkenk3gOQQOArqIbCJwH2VuJLK63SFqcunYBy5pdxBjadZ29lSexz28cXoXoZ3xjKV1ge7K87CHt+0zRd/79gW8d+GmqTN6Z+cMenpfdPF37CZrYzhjaxp5u0OQJEmSJEkaExknUOaDlFjaZ3mlBbDEO8n5C+C6MY9Nk1aIMbY7BkmSJEmSJEkasvDB8Ckis4GHyLm83fG0XeBYSvwnMBPoBq4Gfg1sI3IQgWcBy4BXkvN4GyPVZJBxPnAMgbJTJEmSJEmSJEnSRFbiXaTkwhp6eD2BR/pt8ak2RKUpYMpMEyNJkiRJkiRJk07GbOAZAOR8rE5yQRo1jmCQJEmSJEmSpImqzP693cg7eLSmGufwddJBD4dTYi49bKaD9eRsGdK+OYEOjqDEgQQ20cP9UK2L22S/aXSwPxmzyJlBiYzANnp4DHiipfgz5gFLgK3AavKW63QuJONAckpkrGcPT5Cxu8VjTAkmGCRJkiRJkiRpoiqxASgDJco8C7hr2MfK2J/ImyjzEkrMAKotyBm/Aa4h53PF+QYKnEkXbweeBkAEMjYCnyTniibnvZyME3v3qSRM0v4ADwGfI+fKpvEHlhB4B/A8KuWtI+sp8WnKrGy6L5TI+F3gd4H9iriSLgCuIue9gxxjynGKJEmSJEmSJEmaqNLIgp8DEHgDHZw6zCOdROArBC4AZhDYTeQxYFexfhnwejI66+6d8VIC/0pKLuwE7gF2AAuBS8n44ybnXlY87gQeAdYA91MduXAo8AEyXtPwCBnLKPE/BM4EAoFHiGwlsB+B95Hx7qbvPuM9wNtIyYVy8d4fBLYDEHm06f5TlCMYJEmSJEmSJGli+yjwv8AsIp+ng+uBa4n8hJxNQ9h/XzL+hcg8IusJfIjIDZTpBtLIBjgD2E5ed6qg+cDfECgBN1DiHXSznYyZwIeBs4E/JXAtkdUNowhcSg/f7rMsYynwMVLi4veBLw/Yr5MOynwI2Be4nzJvI3J/sfxi4O3Aawn8iMgNdc57MLCiePUNynycyPqaGKZRdoqkehzBIEmSJEmSJEkTWc59lHl90eu+ROQcIh8BfkDGf5LxvKb7Z1xMShLsInAJOdeRF8mFdPzHybmSnKvr7l/ilcBcYHNvciHt9xQ57yKNROikxKuG9d4C/1a8OpicaQO26eEs4ChgD2X+ksj9AHTTQ85/QzE9UonXNjjLEipTKsE/9EkupBhMLjRggkGSJEmSJEmSJrrILQReQeRvgZuBnNRo/kzg38j4u4bTG8Hzi8fvkLOm5XMHzi5iuKY3uVC1A/hu8fyMlo+d3ELgPiJ3khHrnP/c4tkddUdIhCIxEjmNzjpt4hkP9z6PvIa8N9mgQThFkiRJkiRJkiRNBjk7gS8BXyJjNoHnE7kEOBp4JSnpUK9Q8VIAArcO88yHFPuvqrs2sqposj94WEfv4THg/IbrA4cXz06ixI/rnL9UbNdBDwuh3wiFHlaTcS3wQgJ/ShcvBn4M3ALcMsRppqYkRzBIkiRJkiRJ0mSTs40evkmJ1xD5ZrH0fDo4vt+W+wBdxfMnaFXq7T+/eLWh7jbl3uWzmoyiGL7IwuJZJ4E5A35gVu+2WcOj/DXwDSACRwCvAz5OmmbqX8hYNOJxTwKOYJAkSZIkSZKkyaqbHuD9ZJwFzKLMWcCve9dn5L3P82G0F6cpi3qAjHKd+ggAHXQSgUikXHO+kVMuHi8j8LUm2+2gp9/ohYo0+uPdBP6NEqcAJwMnkGo7vIBUp2E5sGukgp4MTDBIkiRJkiRJ0uS2i8ADRE4iMLvPmpxuSmwlMIcSBw6z+X8TsJgSi+ruH9kfgMBmqsmAkfQEcADQQQ937dWRIg+R8xBwFQAZ5wAfA55GiVdSLgpGC3CKJEmSJEmSJEma7LqIHFY8f2jA2sCvimdnDvP4lUb9kxqsf3bxeM8wj99c4I7i2Zl0jnCn+pzrqIz4CBw9oseeBEwwSJIkSZIkSdJk1UkHGe8D9gV2kHN1na0qNRpOp4OXDeMs1xaPzydwVJ81gSOB5xavfjiMYw+u3Bv/gZR5S1EXYmR0MofAocWrzSN23EnCKZIkSZIkSZIkaSIr8X4CC4AfU2YtkW3AQjKOpsyFwP5AJPARYGOdI3wbWAE8nchHyHgOcC2BDeTMocQRwLOJbKLM++vs/13gEuAYAp+mxIcpcycljiYVTy4RWU8oph0aaZHbgG8BvwX8Hl0cRyrYvIrUyX4JaRTFTHL+jP7TNGVklHkFGXuAjXTzJB3sAxxJziUE5hEpE/juqMQ/gZlgkCRJkiRJkqSJawZwAakh/QUN5qzZRODD9DRoIM/JKfPndPL3pIb484DziPSfA6dMxkeLgsh99+/gHZT5LIEDgI/3228HJd5FD08N4/0N1fuIRAIvB04tfgaKHEHgvj7LypxOib8lFq9rW83TWIgc+BB5v/1kgkGSJEmSJEmSJrAdBC4EziVyGoEDicwm8CSwmsgPKfNtYHvTo5R4kj38MRlnk/Fy4FgisymxjchjwM+JXEu5X3KhoodVwEVk/D7wQlLR5SeAn1HmP4jc3/DckcsI7E9e1DoYjpzdwLvo4CtELgROIDKfwE7gceAW4LoByYX03h8Fvg8cAswn0kkgAo8V+32JMg8MO7ZJLMQYB99KkiRJkiRJksaJ8MHwKSKzgYfIubzd8UhTSsb5pOmwyhZ5liRJkiRJkiRJLTPBIEmSJEmSJEmSWmaCQZIkSZIkSZIktcwEgyRJkiRJkiRJapkJBkmSJEmSJEmS1DITDJIkSZIkSZIkqWUmGCRJkiRJkiRJUstMMEiSJEmSJEmSpJaZYJAkSZIkSZIkSS0zwSBJkiRJkiRJklpmgkGSJEmSJEmSJLXMBIMkSZIkSZIkSWqZCQZJkiRJkiRJktQyEwySJEmSJEmSJKllJhgkSZIkSZIkSVLLTDBIkiRJkiRJmlhi8RgIbY1DmorK1X93JhgkSZIkSZIkTTS7AYh0tjkOaeop0VU822WCQZIkSZIkSdJEs7N4nN7WKKSpaToQiewwwSBJkiRJkiRpYgk8XjzOISNrczTSVDMfgBLrTTBIkiRJkiRJmlhy1hGIREr0cEC7w5GmjDILoJgiKfKICQZJkiRJkiRJE0sHd9c8X9K+QKQpprP495YSfPeYYJAkSZIkSZI0sWzgXiK7gQgc2+5wpCkk/XuLlJnOnSYYJEmSJEmSJE0o8Z/jbuDm4uVCAoe2Mx5pSgjsBxwMRAK/iu+I20wwSJIkSZIkSZqIvk8gAlDiuW2ORZr8Kv/OApHA99MiSZIkSZIkSZpg4qXxXuAe0jRJS4gc3eaQpMkr4zDgGNLkSGt5H7eBCQZJkiRJkiRJE1UXlxHIAejgJcCs9gYkTUIZ04CXAZXRC5fFGCOYYJAkSZIkSZI0QcV3xdVEvkegDMwg4zygo91xSZNIicirgH2Lf2c/ipfGu6orJUmSJEmSJGmiOpAvE1lTNH4eQgfnkZG1OyxpEijRwcsIHAFEIo8T+ULfDSRJkiRJkiRpgoqvj93s5hNEniA1gi4l8hoyprc7NmnCyphGBxcQOYFU52QbnXwsXhp31W4WiqmSJEmSJEmSJGnCCn8TDqKTdxLYl0iJwFbgW/Swut2xSRNK4GAyfovIfCAS2EbkH+KlcdWATU0wSJIkSZIkSZoMwl+HRUzjrQQWE3tnb7kbuImcDe2MTRr3yiwg47mUOA6AQJnIeuBj8dK4rt4uJhgkSZIkSZIkTRrhz8M05vG7ZDyPSABCsWoNcCc5DwJb2xehNK7MooMlRI4jsKRIzEUCEfgZs/l8fEvc2WhnEwySJEmSJEmSJp3wgXAs8DsEDu6XaADYRuQJSuwgsrtNIUrtEegkMpPAfCJzatakxELkMeDyeGm8bdBDmWCQJEmSJEmSNBmFEALv5xTgXGAZgVAkG6BvwkGailJyIFBJEtxDD98n42fx0lgeygFMMEiSJEmSJEma9MIHwnxKnECZZcDBwAEEutodl9QWkW7gMUo8Qpm72c2v4t/FluuUmGCQJEmSJEmSNCWFFSHjMPZpdxzSmJrF7nhp7BmJQ5lgkCRJkiRJkiRJLSu1OwBJkiRJkiRJkjTxmGCQJEmSJEmSJEktM8EgSZIkSZIkSZJaZoJBkiRJkiRJkiS1zASDJEmSJEmSJElqmQkGSZIkSZIkSZLUMhMMkiRJkiRJkiSpZSYYJEmSJEmSJElSy0wwSJIkSZIkSZKklplgkCRJkiRJkiRJLTPBIEmSJEmSJEmSWmaCQZIkSZIkSZIktcwEgyRJkiRJkiRJapkJBkmSJEmSJEmS1DITDJIkSZIkSZIkqWUmGCRJkiRJkiRJUstMMEiSJEmSJEmSpJaZYJAkSZIkSZIkSS0zwSBJkiRJkiRJklpmgkGSJEmSJEmSJLXMBIMkSZIkSZIkSWqZCQZJkiRJkiRJktQyEwySJEmSJEmSJKllJhgkSZIkSZIkSVLLTDBIkiRJkiRJkqSWmWCQJEmSJEmSJEktM8EgSZIkSZIkSZJaZoJBkiRJkiRJkiS1zASDJEmSJEmSJElqmQkGSZIkSZIkSZLUMhMMkiRJkiRJkiSpZSYYJEmSJEmSJElSy0wwSJIkSZIkSZKklplgkCRJkiRJkiRJLTPBIEmSJEmSJEmSWtbR7gAkSZL2WghnAQv6Le0BvkuMu0fg+NOBFwER+A4xdu/1MSVJqghhDnAO0A18mxjLI3z85wAH9luaA98jxu0jei5JVSEsA+YNY88HifHRkQ5nwguhBCwBDgbmAJ3ALmAr8AjwMDH2tC0+aYoywSBJ0mQRwgzgcCA02GIHMT4wSuc+hvQH/lCsJ8bHRziCBcDsOsunA3ufYEjHqXw53IfUADR6Qtif9OVpFpABO4ANwEPEuLPfdvsBjxLjxiEctwQcTRrFejcx5iMduqT4bvJLAAAgAElEQVR+UuNSq9+7Iqlx6alRiGjshZABxwBl0rUn1tlmEXAA6b3fM6KJ3BBOJd0b72r6mYYwt4hzBzH+asTOPzQzgbnF8w5gzwgffz7175MzARMM0ug5kfR3ZKsCMP4SDCF0AUcBe4jx3jE+90LgTFJiodZcYH9gKXArcPsox7EM/5aW+jDBIEnS5PE04NQm63uAkU8whDANeHYLe6wFRjrB8F1Swz+kRvkXjPDxx0YIs4CzgIV11h4JPJsQvk2MG4plh5MSBo8D3xnCGRYDpxXP7yf1XpU0WtLop9MG3a6+MnDXCEbTTnOBZxbP19K/QTuE2aRRYp3AFuA3I3bmEDqA44tXBxLCNxokODLghaR7SDcw1gmG0XYdMKN4Po30eUsafT+jfnLvWFLi4VFgXZ319ZaNB4uofN8I4YExa2BPCeAXAV2ka/R9pL9/d5E+xwNISYa1oxxH7X19PakDkDTlmWCQJGnymFY8bgUeq7N+yyidt3bExFpgZ6MNCw+NeASpR2rqlRrCxBwWHUIn8GLSl9AIrCH9HmOxbDGpp2nt7/FxUoJhESF0DGFI+AHF45MjMnWUpMHsIjVUT6uz7qjicR0De5CXgYdHMa526jvKLo2sOpOUXOgBfjDCDVZZzfP5wGHA6jrbHU1KLsBkrFWYRr+l+3MI+zTfWNKIiXF13eUhPI3UML6+DSOm9kajkdKj7emk5MJTpGnk+o9GWz1GcbTr/UvjmgkGSZImj0qDwTpi/GmbYriTGOslNzS4Z5ISCd2kObGHMsqj8lmXSFMlDdbbrZJg8HckjYXUU/7muutCqCQY7iHGNWMW0/hzMqlHLMDPiPHJET5+1u/1yYSwps8ohjR64fg++4QQ6o50kCS1Q6WGzJ2TZvpAaRIxwSBJ0uRR6SFrz/SJJk2NtLR49Ysh16iIcQchbCXNRXsAzRIMaYTE/OKVCQZJ7ZfqyJxQvHqAGO8bhbP0TzDsy8BRDEdTnT6oooTTyEkaj1JSdBFpZOtO4PGWRn6laX7mUB21tYc0Anprw8RqmhK1i771JGYTQu15e/rUChspaaRb5XvOyNSnSfUc5pA69mwgxl2DbN9J6sxVOwJsRjHFX0XZ5IemKhMMkiRNHpU/eJv/gTzepS8R80lfmnYBm4Yw9c9oxDCP9GWmm/SFajQdShpyvYfW62Q8RjXB0Mz+VKf9GOkaGJLGUiqyOZ90bdpOjE8Mcb9AGik1nWoB+S1t6amf3sOZpGvfFmC0Rt7VJhhWA0uAk3pHMaSGuhP6ra/sV7/BLt0jFpCSEruAjS1P65TOO4/qfOKtTVuUEtOVehGbx7TQaHr/M0j36crntI0Yd4xZDNJUFcKRwCn0bejfRQi3DFp0OYQZwOnAIQ22+CXw6zr7ZcAKBiZsz6uz7f8R49amcbQqxjIhbCdd8w4h1REbnhAWAc8hXX8rckK4G7iZGMsN9nwp1Y46FQNrvoXwg4bTYkmTmAkGSZImj4k/giGEJaTCcbNqlu4mhNuIceSKfjY+/z6k6TqOZGz/Tqp8YWm9kSolGI4CFg5Sh6G2/sLETkJJU1Vq2H0GcAy116gQngR+QozrG+y3D2katoMY2JC9ixDuAe4Y00bq1MAzk9Q4/QNi7B6l89Q2iN1JSrbOozqK4WhSQ90WUqPVkjr7VaV5008lxV6xq7hP3T1oNKmB7+nA4Q3P0Xz/+aQCo/vXLN1JCHeM+n0yJahOIBWnHZgQCWEVMd4wqjFIU1kIx5Gu5ZBGLjxJdSTCcwhhH2K8o8G+ncBLiu0hJZg3F89nkL5H1L+HpHpgO4ptAqlmDgwcTZAzeiO/VgEnAocRwkmke1ZryfEQDgBeSLr2dgObSEne+cBxpM/mugZ776D6/aTS8aib9NlUREZqhIU0wZhgkCRp8qgkGErFcN0eYHeTnjjjS+qRdUbxajdpqPYc0vs6jRC6iPH2UTz/XOBFVBuNdpEanALpC0X/6TNGUqUX2nB6fA21DoP1F6SJ70yqDeDbSdep+aQG8xcTwtUNkgwLgCNqXu8k3SNmkhqKTwIOIYRrxiQBGcJSqu9jNOou1Kot2Lyb1Dv3maRRDA9THb1wO+kzqRjY+J/qZjyn5liV+9Q+wLOL+1T9xr20/wJS41alcX4n6T6Tke4z0xvsWdl/v2L/TlIh8K2ke+R00n1yDjH+rOkx9s7pVIuTl4FtpMbE6aT3tGUUzy1Nbenv1FOKV/cAPyfGvEj8nUhKXJ5MCA8R4+Y6R1hCul5F4Ict9bJP3yWuLOI4GDi3WPPlMUxM/4o04ndf0ntdSgirgUdIhbKbx5FGYTyXdL1dD1zfO51TCIcAZ5Hug0vrTtcX4/eKbWeQRnMAXEOMG/bubUmTgwkGSZImj0qC4YyaZd2E8Djw63FdfDn9sX5a8WoNcCMx9hRfBp5HdUqL1cQ48g0Y6TznUJ2W6Sd9iq6mHqOvHPHzVlUam1rvwTuUOgx96y84PZI0EaWe80uKVz/t7S2fEsovIk19dDohfH2QXp2XE+OeYt8OUg/+Z5CuEc8Drh2N8GscRLWRbNWgU3rsvdpEQU5qmDuelJQ5m+rohQdJiZh6+0EIM6n2HH4QuKlo3OsgJX4OpXqfGpgsTtfhc0jX+x3Aj4jxkZr1BwIvbvgu0n3qDFJyYSNp1Mf2onHxGNI9dBkhPNznuCMlTclUSS7cB/ySGCfuiElp4llGSpg+QUrMpg5E6Xp/e1HTZjGpptcv6uxf6UDz1IScwifGbkL4Luk6eDApKXt88bOrmOLo101G8h5Kuk9WRs1Va0XEuJYQfkUaxXw06RonqQWlwTeRJEkTxMOk3pB7qDZUd5L+CH9JMax6tB1CCEc1+Tm8wX5HFrHuJDXapC8HqTfSTcXyUrHdaDicaq+u6/okF8ZGpdPHcEebVJJHBzZYvx/Vv/vGb6JJUjPHFo8P9pmKJ8ZtwI+KV/NIhT+bqV5nYuwhxjup1j84qJifejQ9m3S9fwr4ySifC/onGNL9pTLH+OLi8faikS5vsB+kRrtOUnLgpt7esul4N5KS0xl9R4rUOoo0Eq5M6vXaahJgCek+1UPqebu9OH8spka6p9huWYvHHao5Nc9vNbkgjblK3YRHgQ5C6OrzU+1Asn/93XunQ5pFCMcWycmJJcZdxUiCbwF3k0ZRQUrcngy8okiG1nNo8bgJ6Knz+VVG/y0sErqSWuAIBkmSJosYv9/ndQjTSL0xTykeTyWEx4lx4yhGMXgSI4RH+/QaSipfmlYPmIc7jWRYTWo0OQi4ee/DHODg4vHRCTrUuVKHYQEhdNaZy7ySeNhs/QVpAgphOrCweDWwuGWMj9UUwDyIxvNoN3I/acqJGaTr4WheBytxTidNdTHa19z+IxigOoqhdvRC7fr++0H1PvXggKk4Us/aNaSer4uBW+vEUbnPNJq+ZDCHFY+PE+NTddY/VJy/UaJ5b9WOyjiOEJoVQ5U0klKDd2UEwnE0/3u70ZSeD5Gut4uAZ5H+HT9OSkw8NiojhEdL+ls93TtCmEf6jnAUMBd4YTGSr/+USZUk6X7AxYOcYTrpXiVpiEwwSJI0WaXehesIYSNwHukLxzJST8uq9Id583mf+9ped/qH5DFSL85G9lC/CPXs4rHRcStfemY2WL+39i0e25VcqHwJGm5vsv51GPr3jN2/33aSJpZZVK8PjRqBthTbNeq92ViMkRA2kBqx5w1YH8JCqkUth2JLg0ZwgGtIUzEtAp5PCN8Y5d7wtYmCyui4HkL4Hqkxfm3NlFLNajAMdp+qLG/0+Vc+1+HeZ+YWjwcQQr3Gscr/HxkhTBvxzzRNx/QAaYTGcaRCq+tIjZOP946okDQaBhZVb6z+FEExlgnhWtJ0aoeT/qY+vPiBENaSRmdNrNFJqYbPj4t72HNJ18qlpBEOtYb6GZYZ/ohiacoywSBJ0mQX456iZ+UyUuNzVQgl4BW0Nm3iduArDdbd1nKthzREu1I/ov/IBvotn0YIpVHoNVk5f7u+VFWSMp3D2ntgHYbaeb07qc4rboJBmphqG0YGv04OT/39U42cl7d4rMeB7zRYlwqMpro2s0jzaV/X4vFbUUkUlPvUpohxE2mqjFr1RzCke2UlwdLo899RPDb6/Cv7D3cUWeX/gYx6BajHxo9JUzAeRfrdHUWlLkMIj5LqSphokEZe7d+919G8nlajGgQU9XduJIRfkJK8i0idUPYnjdI6m8bX7vEtxvsI4SjSe1rMwARD5fp+B9Vp8uopN6njIKkBEwySJE0NlZ6V/XvvRFJvykbDqesZ2SmWUs/ZMs0bTWobiEajV1HlS0e76lNVGpxa73lc9RjVBEMt6y9IE1//hu/+Uz9UlsPwe15W9u9/7G5SUdFWEqDN7xOpN/xPScWRDyGE44paEKOh8r6G0mDUaIqkSPpcSwzlPlVfZf/h3mcqx/011Smd6tk9aj2Q05QjPyWEO0j3moVUGykPJNV7+lqdafok7Z3dpOtTBkwvEgXDl6bLXFv8QAhLSb3/9yeE/Yix2TR7tdfJTurfj9plM+l6VO86vZP0d/LMvfz8+r9/SZhgkCRpqoj9HotXMTI+eirtIg3VbjRVUyUBMlojDCrn35sG/r1RmY97ISGEPr1sh65RHYZKwsH6C9LEVftvdwZpurn+ZtTZthWV61/fHvrpWvL1YR6zsRhXEcJBpCl3TiGE9aNUA6dR4qSe+gmGlAjfTbpHNUrID/b57yRNszTc+8yu4vylYvRF+8S4A1hV/EAIi4FzSe9tKXBX22KTJqM0vdFG0kiDw4F7R/gM9wOnkxKgc2hex6f//Wh8/G2ZRkRXRmrXq3OznvT5HdygXtlQ7aGa7BmtqVulCaddvfQkSdLYqvzBPV6nLqj0dt2/wfpK/ENpVKltnB9qTYNKo9bBxReUsba2eJxOtZBoq/rXYag4oN96SRPPFtJIAug/1R1UCoDOL1490fLRQ5hG6vUJIz1KrbmfkkbYlUj1GIY7vVMzQ08wpORuZbv+PWArn8si6qvcvxp9/pX7zHCv8ZX9Dy2mbBope3/Pi3Ed1fj2bbappGG7v3g8gBBOHOFjL6baPrij2Yak+1ElyX3ECMcxPCF0kBIkc0mjve6ps9X9pO8I04Azivtm69J9ovJ95PBhHUOahBzBIEnSZBfC/sCS4lX/4r/jxWpScdGDCWFBn96ZIcyn2iDz8BCOVdsjaRbw5BD2eQA4ptj+6cAtQ9hn5MT4JCE8DBwMPJsQNjcppN3oGAPrMFh/QZocYsyLApyHA8cRwqp+c0QfR5qqIVJNWA5NSqo+i/TdMCddj8dGjN2EcAPwMkavHkMrUyRBtWdq/0b81aR70aGEMK8oLJqkItiLi1eN7lMPkH5/84c5JdQDVGsfnAL8osX9a3WT/l8JjMTIvZQYqhShHh+9maXJp1Jk/QDgGYRwCOl6v510/Z5Hug49QYw/HLB3CLNJI4x21/x0kZLWRxZbbWWwvxfTaIp7geNJ96POYp8OUgJ2IzHWa+DfOyGcRvo7eR1phMJuUrJgLvA0qvVvfl73b+gYtxDCr4ATSd85zieEB4tjBdLfzweSPpOvDTIl692kz+1AQjgHeIiU2FgI5MT4y717s9LEY4JBkqTJIBXhPIrUq2gnqeFgFqlH5ZGkhpLdjN9pC1aTGsgWAucWxec2kRrHn0n6w387cN+gR4pxJyFsp9IIkxrPdpBGB5SJcWCSJcYNhFD54nZi0Vj0ALCN9IWlUY/VkfRj4FWk4eavIITfAI+SfpdzSF8aZwLfLebBrmddse0RRcLiENLvvowJBmmiu43UKDIXeCEh3EpqzF0CnFRscz8xPjXIcfYrrpGBNOrhWKrXuNuIsVER49ER48bivZzC6NRjaGWKJEiJiC4GjmB4kHSfmk/6/H9BGq1Qe5/aRrp3DBTjI0WS6BDgmYRwAGmKoadI95nFdfer7v84IayimmTarzjXFqrTmiwGMmK8ZpBj5YRQif0kQsiL2KcDgRgfGrBPGjVxFOlz3FX8lIrPYxmpxlOZ5vUhJA1Xati/jlQrYQnV+if99a+3VrGU1LjeyDbguiHWOruVdP04kNpi78mh1B9BsLcOIk0zd3SD9U+RkgtrmhzjVtJ16kTS94QT6mwTSffZxh2U0hR/+5E6Jx1C/5FpIfxmCPdiaVIxwSBJ0uRwJHByk/U7gB+MecPRUKX5rW8AXkT6g//MflvsAX7YpGG9v9tIPWH3Bc6uPRMhXFHMH93fj0lfOpaSGmkaNfaMTjG7NALhe8ALSEmGE6n/RXARjZMF/3979/pcV3ndcfy7JFmSLUs2wjdsI3yBhJsDtgkmgYQAgULbtJPpdHp50f5tnSal0yEMmU4zKU0o0NiYS4hjbAcbY/mG8Q18layrV1+svTlb5+x9bjq6HPn3mdHoXPbz7OccyftYaz3Ps/5EJElWAK9kHj+m+gsibc79Oma/I65t64GXy464TH2z2l/K6x34I+6fzG6QTTtEBKo2EonhC7i3aqumZlYwZNuFCO69A7xIfE49W9ZunNqfU+9SCg5WBqWSM1FcKHpv8vx2ioOLYDZQxyq4A8TnYz/lryUKNZcH1zYAT1Xp7zbwXk47Ecl3kAjSn6y7RdQNeBuztcSs/UFi9do48X/9cxSvojqTHNtDJBO7iX+3N5J2w3X/PzuSlG8SCc/7iAkwU0TStTJB2Rr/Sen/5yuJRMokseriHHCm5vhje6MDmB0n/nZam/QzRbx/F4HTdSUH3PcnSePtRIL3djKWs9TeZkpkybHmagiKiIjIohJ/aDxEBNTTmZcTxMzGc8DnsyhmVuvcBuwhJi58iHvzhZjNeonXMUT88TBGzOI/1PCWQVE89NvEkvEuYmbTTeD9qomWeC8foPRHxwSxKuQC8UfH3BbXjKXmW4k/2PqJn+UIsb/1adwv1Gi/hpjNPEiMfZh4/+qZkSYi88XsCSLI83FDMx1j27hHie0Zuonr2jDwp7Jtk7JtNhGBcYgASi9xXRwlrm1HG77GNiKua08SwfH9udcjs+XALiIANlxjFmoj515PfK6cxv1EHcc/RLy3B3OD5fE59TDxOdVHfD6kn1M36hzTBiIotYbS58wokTw+XTNIHzNns8Gx9HMqbV9fHY4Yx4OUgpSjxO/TRxWvxWyAmMjQm/lySkG5uf0dEhERkUVLCQYREREREZGlbGaC4WeFiQgRERERkQaVF64SERERERERERERERGpSQkGERERERERERERERFpmBIMIiIiIiIiIiIiIiLSMCUYRERERERERERERESkYUowiIiIiIiI3Dl8oQcgIiIiIktH10IPQERERERERObUReBDYAL36YUejIiIiIgsHeauCSwiIiIiIiIiIiIiItIYbZEkIiIiIiIiIiIiIiINU4JBREREREREREREREQapgSDiIiIiIiIiIiIiIg0TAkGERERERERERERERFpWNdCD0BERERERERERETmgVkXsCq5dx33yYUczrwz6wBWAwaM4D62wCMSaXtKMIiIiIiIiIi0ktlKSsGr2ws9HBFpYxEQ/ynQU/bMCO6/bKLHHcBjye0jwAezGF0wGwReAsZwf2PW/c2tzcDzye3zwK8XcCwiS4ISDCIiIiIiItI8s2XAy0B32TNf4/6/CzCihWX2ELAnufcZsHcBRyMizYrA/q7k3gHcp+posw7YDnyJ+8lWjQToo3Kb827MDHdvsL/OzO1WxQX7gF6gF7OORZ5Y7Sq4LSJN0j8kERERERGRpSKC/fcCG4AVyaO3gMvAWdxH5uCsXcDdOY+Xz7a9UywruC0i7aUDeDj5/hUwXEebXcT1twM42ZJRuE9j9h+Uricbge/NoscbwERy+/pshjbvIunzIHAb90+b7GUEGCN+RtdaNTSRO5kSDCIiIiIiIkuB2VbgSWB5zrMPAI7ZL3C/2dLzut/C7A1Ks2KHKG2/0V5ib/ItxDYfZ5vsZazgtoi0E/cpzC4D64ikQfUEg1knsDa5d77FYxkjvZ6Y3ZplX0eBo7Mf1IJYTXzOgdnZpj7P3C8A/97aYYnc2ZRgEBERERERaXdm24FniK00xoHjxMzM28AAMeO1m5i52XruVzNjGZyTc8yPe4j30YF/aaoH92OYDQOG+0TN40VkMTtPKcFQy1pKidYLczaiO5st9ABEpJISDCIiIiIiIu0stkX6LhF4+QJ4G/fJsqM+nvdxtSf75ntze5uHyvdfRNrTeeA7wCrMVuA+WuXYNAlxo+UrxUREFjElGERERERERNrbGqK4JsA+BbdFRFrmIrESrINIIJyocmyaYMjfHimSwauBfkrxuBHg0rysdjLrZWZdGAdGmkqkRl93EyvjJojXVKtNJ7AqOTat0TNGvP7q2z7Fe9dLqbYQQB9m2RUNU1X7MeumsjbQKO7TNcc+s58OYlVLPzAJXK6ZUIo2K5KvXuL3qROYIupgXG06oS2yCCjBICIiIiIi0t7SgIsTBZ1nJwI2A0QthxHcb8y6z8bHMEC8rlHcGy9CGoGs1UQw6RZwK9nDvD2Y3QWsJIJvV3CfqqNNF9BHBBC7k0cngWvaqkmkSZV1GPITDDPrLxRtj/RTZgbIU1OYHQYOzFmQ2awf+JucZw4BHzXQz13AE8CmJkbxfEE7x+xz4P0qCfK89+6VnPH9CveLBX38LTMTLADngDcLR1zZ/yaiwPbKzKOO2XFgf5VkxfPA5io9j2D2Me6f1z0WkUVECQYREREREZH2ltZVMCIAdq7pnsy2ATvJzkY1u0QEfi43P8S6z7+ZCF6tzjx2BfgQ99qvKxITO4lC051lz30NDOP+SdnjncDfUQrKp/6ZGZNjmQJez90iJWbG5gWvDuP+Yc1xl/rZRBQwXZV5dByzg7gfrtLuFWB9lefPAx/Ny89QZOmppw7DGkrXnKICzzcz3yeIJO5dRGzuMWAaODjbwRa4TSQcy69R5de9YnF9eo5SLPEKMfs+uzKhmhEi4TuafO8GBpP+7k++v13Qdpz85EzWNHGdLjLO7F//C8Tqg3HgEpFoWA08kIzvfwpap2O/Rfz800TKcuK96wN+gNlt3KsXExdZhJRgEBERERERaW+XiIDNCuB7mP0G92sN92K2A9id3JsGbhABo7XAK5i9hfsXrRly7vkfAL5PJEpuE4GrPiIA9yJmv6s6u9NsIxH8SgNIk8QKgHRbkEFgBWaHcmYJd1JbJ8UFRq2gj746+k16sPuAZ4ng1RRRpDvdSuS7mPXi/vuC1ncl38eJwGUaZOsjAmgbiJ/hf+H+dd1jEhEo1WEYqFKHIU0+3CzcLsf9VxWPma0k/t2vBXZgdrjhLXvq4T4C/Dxz3hdpZBWC2XLgR0Qc8Srw7oxridm9RPC92hj25fTbA+wBtgFbMFuV+/nl/svk+LuBnySPvtZQrQv31zLn3Q3sqLttrBD7PnF9Pg/89pvVFmaPkq7qMNtaI0FwDPc/lPW9Avgx8Rn1OKAEg7QdJRhERERERETamfs0Zu8TwfV+4CeYnQJOA1/WtT1OBG12JveOAR8kW4P0AE8TKwKewez1OanxEEG2PUSg/hSwF/eJZN/tJ4nZoU9hdj4JlJW37yOCX8uIxMR+4rV75vmNxFZJM5ML8f79G5Eg2Az8MHnmVWLbqdTtwq2K3Mcx+zmlJMPTwH0NvP4eYtuNDmIFytvJ6+9M+toGPIrZcI0EwUe4f1bW9ybgB0Si5XHgrbrHJSJQXx2G6vUXirjfxOwD4C8oJUIvNT3SufMQMb5J4M0axa7rF9fO94AtxPu7nkiuLjabiYTtNPB/Mz4H3Q8lq+82ECsxGksQuI9idoDYRqmeYuIii07HQg9AREREREREZsn9FPAuMXu9C9hOJBz+HrPnkwRCNY8Sfx9eBN77JpDuPg68Q2xtsRzYOifjh4eTcV8jZsZOJOefBPYBXxPBrfurtE+Ljf4a93MzEgnuI7h/hvvZ3Nbu08k5swmECdyzX9XrIKR9RD+3a73gMluJBMAE8E7m9U8De4n334hES2Ni1cmh5N49ZUVRRaSW+Lf/VXKvcpukSASuS+41lmAIVzO3lzfRfj6kqx2GWx78jut8uhJhsb7+e5PvF3KT3JCurtuQFHRuVPZ3oNZWUCKLjhIMIiIiIiIiS4H7CeB14GMiGObE33xDwF9i9lhuuwiOpcUnT+TO8Iczyb17Wj7ukM72P1mxPUiM51SN86fBn+NtOvNzKPl+JknqlMT7kc6YbqawKsRe6RBJmt4m+xC5k32ZfM+rw1BP/YVKZt1J/RajtFpqscbp0ro4rVtdYbYs8x6kCdx6tqtbCGlR5+sFz6ePd9LcNTZ73dduM9J29EsrIiIiIiKyVLiPEUVCDyZ7Zg8Re4f3ATsxm8T9SFmrPkp1C3Zjtiun587Msa0V2yCl/T6C2UM5R6VBt8qZnZEgGUjuXWz5+OZHWhy1aGuQ9PGVBc/XcitzW3EAkcZdSL4PYNZXNou9dv0FALN+4BEiUdjH4k0mzBTX2PQzYLzaoTX66SFWmw0R1+zFmkzI05N8v1XwfPbxHqIuksgdQ/+xEBERERERWYrcbwFHMRsGXiSKiO7C7HhZXYbsbMtlzL/s+Wv9jZq3vU935vbY7IezINL3oCgolT7egVl3XXU1RKSVyuswZAvO166/YLaFqO/SHkmFmW4TKyyKitnXZjYIvET7rqBKV1gUfUZmH299kW6RRU4JBhERERERkaUsigXvA/6a+BtwA1EA+psjMrd/S/WZl3l7T89W9vx7iXoLRfLGlq130I7BO4iA1DKKg3edZceKyHxyn8TsKyJRW0ow1FN/wawXeIa4Pl0F/khsNZRNFP4D+QnUhefumI0R9RH6ax1eIeq+PEskF8aAPxDvVXbW/8tEgetaste/+UyIp8nrovoI2cfbNdEt0jQlGERERERERJY69yuYTROB6u6yZ7NBnincv+ejAQMAAAVSSURBVGJ+jVGaHTvdxPnTosodNL+FUCqbrOgCJmfZX73GiOBbUfAq3UJqsqJGhYjMl/OUEgypeuov3ENcTxz472R12UzN117P1m+YSxeJWjlDxDZ8jRgAViW338X9XMURZvW+hmzwfgWl+jJz7SJRq2gdZlZRq6j0O3FdK8zkTtSusztERERERESkXmarKAXBZhapjD3D04DXfbROfREz9yliVm9z549Az+Xk3uZqh9YhG7xaPsu+GpEmVdYWPJ8+Xm11h4jMrTSB0I9ZmvSrp/5CmhQ0KhO8zcjWQRgoPKq1jiff12D2nQbbZpOiPYVH1SPqDN1I7m2bVV+NOUUkclYC35rxTPwuPJDcOzOPYxJZNLSCQUREREREZCmLwprPJPeuEFtzlDtBFB/9FmancP9yFmdMZ28ua6BewOfAE8AQZltxH27wnCeIbUqGMBvC/XStBgWuEsGwTiLZ8UmT/TTqJLAd2IjZWtxLPyOzAWBLcu/sPI1HRCpdJFY1LQN2J/Vt0mDzF1XanSOSuMuBlzH7lEgqjhEJh0aTDl8TNQG6gGcwO0Jcd1cAI7hXjsVsR9l50hUFazDbnXn8Ju5HK9q7n8HsNLGCYRdmG4FhYtu6XmKVRj73m5hdANYn411DJGvGKK2qa+Q9OEp8XmxPVn6cSfpZQ7z+QxUtzO6n9JqhlBjqK3v9U8AnuGdXs4H7NcyOAd8G9mC2gviZ9wM7k/FPAJXnFrkDKMEgIiIiIiLSzsw6gD8jAj1fEnUSpohtddYRgetlRGBsX87WDhCB9C1Jmxcx+5wIit0kgkeDwEbgNO6Ha4zoMqUtj55Ogl/TRHDtJu55W1ocBe4HVgPPYnYvEby5Tsx4XQ1sAi7j/vuc9p8RM0jvBp7D7DgRdBpJzrs6Gf813N8vHLn7VBI0vB94PNk7/Dzx/q1Lzl85Q9VsG6VtjKAUyFqVBPZSI7ifyDnzWSJ4uQ54AbMPiUTQauBJIngWRbtFZGFEHYbDwOPE7Pl0Bv0EcKRKuynMfgO8QCQBdrZgHAeBXURQ/YeZZycxe3VGgNxsObCbfIPMrH0wTfF15l1gD3Gt3cDMraJSU+Rv2fQO8OPkXI8kX3nq2SrpCGlCOT7ftmeem8bs02RlXNZu8lelLQd2lD12EriWc+xHxAqGTcBjyVdqitj+qXL7K5E7gOX/31JERERERETagtk64M9rHHUF2Iv75cIjYhul54igdpGruL9Rx5ieprRlRNZ13F8vaNMH/IjibYIAxnF/taD98qT9+irtp4GfFSRZ0n56gFfIfx9u4v5a2fEdwD9VOWe5f82to2C2EniJ/C1PpoC3cvcuj7b/SMyg3Yv7ZznPDwJ/ldz7Be43Ko4RkfrEbPjtRPLzCjHj/Wr1RoBZF5G8vIdINCwjtjtKv0aBI7iPF/Yxs7/NyTjShOYIkZT9fVmCwYikRm8dvV6rmUQ2u5tIrqyhNHN/lEjGnsF9tKCdEYnszcTM/7Rt+vrHgGO4X89tn9/XNiLon261dxb3UznHP0h9RaQngI8rVjCU+ukAthLve3reS8TPLS8pkbbbTiRFPi1IsoPZTuJ34oDqOEi7UYJBRERERESk3ZmtB+4lgvN9xGr1W8RWGmeAU1WD6qV+OojAyRBwV9LPGLGS4Yukn/zg0cx+DHiQCMT0E7NSR4AruO+r0W5L8jVIBKDGkrbnkvMXB8ej/VBy3kEioHaLCH6l7esJXi0DHia2Seojgl9XgZO5KxDM9lBfzYYx3PdXOW9Pct4tRPBqjAjaHSoMSkW7nURi4kBukCtez1NEEev9KhQtIiIirfL/fs4hc30fq5UAAAAASUVORK5CYII=" } }, "cell_type": "markdown", "id": "9721caaa", "metadata": {}, "source": [ "![CV.png](attachment:CV.png)" ] }, { "cell_type": "markdown", "id": "4bc21f7f-7c5c-49df-90c2-f4919b6a78aa", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "fb4aadbb", "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pathlib import Path\n", "\n", "from Hive_ML.utilities.feature_utils import data_shuffling\n", "import json \n", "from importlib.resources import as_file, files\n", "import Hive_ML.configs\n", "import os\n", "import plotly.express as px\n", "from sklearn.metrics import classification_report\n", "from sklearn.metrics import roc_auc_score" ] }, { "cell_type": "code", "execution_count": 2, "id": "5703db4b-9b28-44c4-ae1a-552f241cd655", "metadata": { "tags": [] }, "outputs": [], "source": [ "from Hive_ML.training.models import adab_tree, random_forest, knn, decicion_tree, lda, qda, naive, svm_kernel, logistic_regression, ridge, mlp\n", "from Hive_ML.training.model_trainer import model_fit_and_predict\n", "MODELS = {\n", " \"rf\": random_forest,\n", " \"adab\": adab_tree,\n", " \"lda\": lda,\n", " \"qda\": qda,\n", " \"logistic_regression\": logistic_regression,\n", " \"knn\": knn,\n", " \"naive\": naive,\n", " \"decision_tree\": decicion_tree,\n", " \"svm\": svm_kernel,\n", " \"ridge\": ridge,\n", " \"mlp\": mlp\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "id": "094509b8-5082-43b6-b424-6e4c7a3b709f", "metadata": { "tags": [] }, "outputs": [], "source": [ "from sklearn.model_selection import StratifiedKFold\n", "from sklearn import preprocessing\n", "from sklearn.decomposition import PCA\n", "from yellowbrick.classifier import ClassificationReport, ROCAUC, PrecisionRecallCurve, ClassPredictionError, DiscriminationThreshold\n", "from mlxtend.feature_selection import SequentialFeatureSelector as SFS" ] }, { "cell_type": "markdown", "id": "debb16f2-251a-4084-9b12-41ac4f60b5d3", "metadata": {}, "source": [ "## Load Configuration" ] }, { "cell_type": "code", "execution_count": 4, "id": "31370573", "metadata": { "tags": [] }, "outputs": [], "source": [ "config_file = \"Radiodynamics_config.json\"\n", "\n", "try:\n", " with open(config_file) as json_file:\n", " config_dict = json.load(json_file)\n", "except FileNotFoundError:\n", " with as_file(files(Hive_ML.configs).joinpath(config_file)) as json_path:\n", " with open(json_path) as json_file:\n", " config_dict = json.load(json_file)" ] }, { "cell_type": "markdown", "id": "9938e8ed-06d2-48cf-89e0-2dc790e8e2a4", "metadata": {}, "source": [ "## Features Loading and Aggregation\n", "\n", "Radiomics Features are aggregagated across the 3D Sequences:\n", "\n", "$F\\mu_{p,i}=\\frac{1}{N} \\sum_{n=0}^N F_{n, p, i}$\n", "\n", "$F\\sigma_{p,i}= \\sqrt{\\frac{\\sum_{n=0}^N (F_{n, p, i} - F\\mu_{p,i})^2}{N}}$\n", "\n", "$F\\Delta_{p,i}=\\frac{1}{N} \\sum_{n=0}^N |F_{n, p, i}- F\\mu_{p,i}|$\n", "\n", "with $ p = patient_p, i = feature_i$" ] }, { "cell_type": "code", "execution_count": 5, "id": "5773ea14-8d01-429b-8e61-04fa769da85a", "metadata": { "tags": [] }, "outputs": [], "source": [ "def get_feature_set_details(feature_set):\n", "\n", " n_sequences = feature_set[[\"Subject_ID\", \"Sequence_Number\"]].groupby(\"Subject_ID\").count()\n", " n_sequences = max(n_sequences[\"Sequence_Number\"])\n", " subjects_and_labels = feature_set[[\"Subject_ID\", \"Subject_Label\"]].drop_duplicates()\n", "\n", " subject_ids = subjects_and_labels[\"Subject_ID\"].values\n", " subject_labels = subjects_and_labels[\"Subject_Label\"].values\n", "\n", " feature_list = []\n", " for sequence in range(n_sequences):\n", " sequence_feature_list = []\n", " for subject in subject_ids:\n", " feature_set_subject = feature_set[\n", " (feature_set[\"Subject_ID\"] == subject) & (feature_set[\"Sequence_Number\"] == sequence)]\n", " feature_set_subject = feature_set_subject.copy(deep=True)\n", " feature_set_subject.drop(\"Subject_ID\", inplace=True, axis=1)\n", " feature_set_subject.drop(\"Subject_Label\", inplace=True, axis=1)\n", " feature_set_subject.drop(\"Sequence_Number\", inplace=True, axis=1)\n", " if feature_set_subject.values.shape[0] > 0:\n", " sequence_feature_list.append(feature_set_subject.values)\n", " else:\n", " nan_array = np.empty((1, feature_set_subject.values.shape[1]))\n", " nan_array[:] = np.nan\n", " sequence_feature_list.append(nan_array)\n", " feature_list.append(sequence_feature_list)\n", "\n", " feature_set_names = feature_set.copy(deep=True)\n", " feature_set_names.drop(\"Subject_ID\", inplace=True, axis=1)\n", " feature_set_names.drop(\"Subject_Label\", inplace=True, axis=1)\n", " feature_set_names.drop(\"Sequence_Number\", inplace=True, axis=1)\n", " feature_names = feature_set_names.columns\n", " return feature_list, subject_ids, subject_labels, feature_names\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "8dd15ead-7121-47e5-84ec-cbf383531a55", "metadata": { "tags": [] }, "outputs": [], "source": [ "def get_4D_feature_stats(feature_list):\n", "\n", " feature_arrays = np.array(feature_list).squeeze(axis=-2)\n", "\n", " mean_features = np.nanmean(feature_arrays, axis=0)\n", " sum_features = np.nansum(feature_arrays, axis=0)\n", " std_features = np.nanstd(feature_arrays, axis=0)\n", "\n", " delta_features = np.absolute(np.subtract(feature_arrays, mean_features))\n", " mean_delta_features = np.nanmean(delta_features, axis=0)\n", "\n", " return mean_features, sum_features, std_features, mean_delta_features" ] }, { "cell_type": "code", "execution_count": 7, "id": "b0a42801-7ef2-4378-a14c-f0354670b929", "metadata": { "tags": [] }, "outputs": [], "source": [ "data_folder = \"Data/DCE_MRI_dataset\"\n", "feature_set_filename = str(Path(data_folder).joinpath(\"Perfusion_Radiomics.xlsx\"))\n", "feature_set = pd.read_excel(feature_set_filename, index_col=0)\n", "\n", "feature_set = feature_set.sort_values(by=['Subject_Label','Subject_ID','Sequence_Number'])\n", "\n", "feature_list, subject_ids, subject_labels, feature_names = get_feature_set_details(feature_set)\n", "mean_features, sum_features, std_features, mean_delta_features = get_4D_feature_stats(feature_list)" ] }, { "cell_type": "markdown", "id": "09d68ea6-4101-48cb-8a5c-33d79ae8e825", "metadata": {}, "source": [ "## 3D Feature Array Flattening into 2D Array\n", "\n", "$ F_{n,p,i} $ to $ F_{p,i*n} $" ] }, { "cell_type": "code", "execution_count": 8, "id": "ba529b0f-d8ab-4234-9f95-267e4ff5cf40", "metadata": { "tags": [] }, "outputs": [], "source": [ "def flatten_4D_features(feature_list, feature_names):\n", " feature_arrays = np.array(feature_list).squeeze(axis=-2)\n", " flat_feature_array = np.zeros((feature_arrays.shape[1], 1))\n", "\n", " if feature_arrays.shape[0] > 1:\n", " T = feature_arrays.shape[0]\n", " n_features = feature_arrays.shape[2]\n", " for n in range(n_features):\n", " for t in range(T):\n", " flat_feature_array = np.hstack(\n", " [flat_feature_array, feature_arrays[t, :, n].reshape(feature_arrays.shape[1], 1)])\n", "\n", " flatten_feature_names = []\n", " for feature_name in feature_names:\n", " for t in range(T):\n", " flatten_feature_names.append(\"{}_{}\".format(t, feature_name))\n", " return flat_feature_array[:, 1:], flatten_feature_names\n", "\n", " else:\n", " return feature_list, feature_names" ] }, { "cell_type": "code", "execution_count": 9, "id": "83364fb3-b679-4391-93a2-074b68e03471", "metadata": { "tags": [] }, "outputs": [], "source": [ "feature_list_flatten, feature_names_flatten = flatten_4D_features(feature_list, feature_names)\n", "\n", "label_set = np.array(subject_labels)" ] }, { "cell_type": "code", "execution_count": 10, "id": "1a87e8d3-6ea3-4634-bec2-2ec7b5fc9619", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Features: (352,)\n", "#Labels: (80,)\n" ] } ], "source": [ "print(\"# Features: {}\".format(feature_list_flatten.shape[1:]))\n", "print(\"#Labels: {}\".format(label_set.shape))" ] }, { "cell_type": "markdown", "id": "bf1db891-464b-4205-849b-10e7bcc96f86", "metadata": {}, "source": [ "## Feature Filtering\n", "\n", "Features where > 50% of values are identical are removed." ] }, { "cell_type": "code", "execution_count": 11, "id": "fcffd4a3", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Excluding: 0_original_firstorder_10Percentile\n", "Excluding: 0_original_firstorder_90Percentile\n", "Excluding: 0_original_firstorder_Entropy\n", "Excluding: 3_original_firstorder_Entropy\n", "Excluding: 0_original_firstorder_Maximum\n", "Excluding: 0_original_firstorder_Minimum\n", "Excluding: 0_original_firstorder_Range\n", "Excluding: 0_original_firstorder_Uniformity\n", "Excluding: 3_original_firstorder_Uniformity\n", "Excluding: 0_original_glcm_Autocorrelation\n", "Excluding: 3_original_glcm_Autocorrelation\n", "Excluding: 0_original_glcm_ClusterProminence\n", "Excluding: 3_original_glcm_ClusterProminence\n", "Excluding: 0_original_glcm_ClusterShade\n", "Excluding: 3_original_glcm_ClusterShade\n", "Excluding: 0_original_glcm_ClusterTendency\n", "Excluding: 3_original_glcm_ClusterTendency\n", "Excluding: 0_original_glcm_Contrast\n", "Excluding: 3_original_glcm_Contrast\n", "Excluding: 0_original_glcm_Correlation\n", "Excluding: 3_original_glcm_Correlation\n", "Excluding: 0_original_glcm_DifferenceAverage\n", "Excluding: 3_original_glcm_DifferenceAverage\n", "Excluding: 0_original_glcm_DifferenceEntropy\n", "Excluding: 3_original_glcm_DifferenceEntropy\n", "Excluding: 0_original_glcm_DifferenceVariance\n", "Excluding: 3_original_glcm_DifferenceVariance\n", "Excluding: 0_original_glcm_Id\n", "Excluding: 3_original_glcm_Id\n", "Excluding: 0_original_glcm_Idm\n", "Excluding: 3_original_glcm_Idm\n", "Excluding: 0_original_glcm_Idmn\n", "Excluding: 3_original_glcm_Idmn\n", "Excluding: 0_original_glcm_Idn\n", "Excluding: 3_original_glcm_Idn\n", "Excluding: 0_original_glcm_Imc1\n", "Excluding: 3_original_glcm_Imc1\n", "Excluding: 0_original_glcm_Imc2\n", "Excluding: 3_original_glcm_Imc2\n", "Excluding: 0_original_glcm_InverseVariance\n", "Excluding: 3_original_glcm_InverseVariance\n", "Excluding: 0_original_glcm_JointAverage\n", "Excluding: 3_original_glcm_JointAverage\n", "Excluding: 0_original_glcm_JointEnergy\n", "Excluding: 3_original_glcm_JointEnergy\n", "Excluding: 0_original_glcm_JointEntropy\n", "Excluding: 3_original_glcm_JointEntropy\n", "Excluding: 0_original_glcm_MCC\n", "Excluding: 3_original_glcm_MCC\n", "Excluding: 0_original_glcm_MaximumProbability\n", "Excluding: 3_original_glcm_MaximumProbability\n", "Excluding: 0_original_glcm_SumAverage\n", "Excluding: 3_original_glcm_SumAverage\n", "Excluding: 0_original_glcm_SumEntropy\n", "Excluding: 3_original_glcm_SumEntropy\n", "Excluding: 0_original_glcm_SumSquares\n", "Excluding: 3_original_glcm_SumSquares\n", "Excluding: 0_original_glrlm_GrayLevelNonUniformityNormalized\n", "Excluding: 3_original_glrlm_GrayLevelNonUniformityNormalized\n", "Excluding: 0_original_glrlm_GrayLevelVariance\n", "Excluding: 3_original_glrlm_GrayLevelVariance\n", "Excluding: 0_original_glrlm_HighGrayLevelRunEmphasis\n", "Excluding: 3_original_glrlm_HighGrayLevelRunEmphasis\n", "Excluding: 0_original_glrlm_LowGrayLevelRunEmphasis\n", "Excluding: 3_original_glrlm_LowGrayLevelRunEmphasis\n", "Excluding: 0_original_glszm_GrayLevelNonUniformity\n", "Excluding: 3_original_glszm_GrayLevelNonUniformity\n", "Excluding: 0_original_glszm_GrayLevelNonUniformityNormalized\n", "Excluding: 3_original_glszm_GrayLevelNonUniformityNormalized\n", "Excluding: 0_original_glszm_GrayLevelVariance\n", "Excluding: 3_original_glszm_GrayLevelVariance\n", "Excluding: 0_original_glszm_HighGrayLevelZoneEmphasis\n", "Excluding: 3_original_glszm_HighGrayLevelZoneEmphasis\n", "Excluding: 0_original_glszm_LowGrayLevelZoneEmphasis\n", "Excluding: 3_original_glszm_LowGrayLevelZoneEmphasis\n", "Excluding: 0_original_glszm_SizeZoneNonUniformity\n", "Excluding: 3_original_glszm_SizeZoneNonUniformity\n", "Excluding: 0_original_glszm_SizeZoneNonUniformityNormalized\n", "Excluding: 0_original_glszm_ZoneEntropy\n", "Excluding: 0_original_glszm_ZoneVariance\n", "Excluding: 0_original_gldm_GrayLevelVariance\n", "Excluding: 3_original_gldm_GrayLevelVariance\n", "Excluding: 0_original_gldm_HighGrayLevelEmphasis\n", "Excluding: 3_original_gldm_HighGrayLevelEmphasis\n", "Excluding: 0_original_gldm_LowGrayLevelEmphasis\n", "Excluding: 3_original_gldm_LowGrayLevelEmphasis\n" ] } ], "source": [ "#features = mean_features\n", "features = feature_list_flatten\n", "feature_names = feature_names_flatten\n", "\n", "n_features = features.shape[1]\n", "n_subjects = features.shape[0]\n", "\n", "filtered_feature_set = []\n", "filtered_feature_names = []\n", "features = np.nan_to_num(features)\n", "for feature in range(n_features):\n", " exclude = False\n", " for feature_val in np.unique(features[:, feature]):\n", " if (np.count_nonzero(features[:, feature] == feature_val) / n_subjects) > 0.5:\n", " exclude = True\n", " print(\"Excluding:\", feature_names[feature])\n", " break\n", "\n", " if not exclude:\n", " filtered_feature_set.append(list(features[:, feature]))\n", " filtered_feature_names.append(feature_names[feature])\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "b9b0280c", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Features: 266\n", "#Labels: (80,)\n" ] } ], "source": [ "feature_set = np.vstack(filtered_feature_set).T\n", "feature_names = filtered_feature_names\n", "\n", "print(\"# Features: {}\".format(feature_set.shape[1]))\n", "print(\"#Labels: {}\".format(label_set.shape))" ] }, { "cell_type": "code", "execution_count": 13, "id": "837fd905", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(64, 266)\n", "(16, 266)\n" ] } ], "source": [ "train_feature_set, train_label_set,test_feature_set, test_label_set = data_shuffling(feature_set, label_set, config_dict[\"random_seed\"])\n", "\n", "print(train_feature_set.shape)\n", "print(test_feature_set.shape)" ] }, { "cell_type": "markdown", "id": "8b626f1f-11fb-4d7d-bdac-f3f3b886d33f", "metadata": {}, "source": [ "### 3D Features\n", "\n", "This section has to be executed ONLY for 3D feature set" ] }, { "cell_type": "code", "execution_count": 199, "id": "ca11d6d1-2c4a-42b3-9b17-2573eb4ec5bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4, 80, 88)\n", "(64, 4, 88)\n", "(16, 4, 88)\n" ] } ], "source": [ "feature_set_3D = np.array(feature_list).squeeze(-2)\n", "print(feature_set_3D.shape)\n", "\n", "train_feature_set, train_label_set,test_feature_set, test_label_set = data_shuffling(np.swapaxes(feature_set_3D, 0, 1), label_set, config_dict[\"random_seed\"])\n", "\n", "print(train_feature_set.shape)\n", "print(test_feature_set.shape)" ] }, { "cell_type": "markdown", "id": "481ea6d6-660f-41d6-b016-cb98ca679f69", "metadata": {}, "source": [ "## Experiment Folder Creation\n", "\n", "*experiment_name* and the type of *aggregation* are set here." ] }, { "cell_type": "code", "execution_count": 14, "id": "9d0a2624-b2cb-48cb-bded-9b4d13076b85", "metadata": { "tags": [] }, "outputs": [], "source": [ "os.environ[\"ROOT_FOLDER\"] = \"Experiments\"" ] }, { "cell_type": "code", "execution_count": 15, "id": "e15ff51b", "metadata": { "tags": [] }, "outputs": [], "source": [ "experiment_name = \"Perfusion_Radiomics\"\n", "\n", "aggregation = \"Flat\"\n", "\n", "experiment_dir = Path(os.environ[\"ROOT_FOLDER\"]).joinpath(\n", " experiment_name,config_dict[\"feature_selection\"],aggregation,\"FS\")\n", "experiment_dir.mkdir(parents=True, exist_ok=True)" ] }, { "cell_type": "markdown", "id": "5159abdb-2d0b-478e-9029-3a510a5fb16f", "metadata": {}, "source": [ "## CV Model Fitting\n", "\n", "5-Fold CV over the Train Set, to identify the best performing models." ] }, { "cell_type": "code", "execution_count": 16, "id": "d61eaa79-e94d-437d-993b-4e827913c775", "metadata": { "tags": [] }, "outputs": [], "source": [ "def prepare_features(feature_set, label_set, train_index, aggregation, val_index = None,test_feature_set=None,test_label_set=None):\n", " x_val = None\n", " y_val = None\n", "\n", " if test_feature_set is not None:\n", " x_train = feature_set\n", " x_val = test_feature_set\n", " else:\n", " x_train = feature_set[train_index, :]\n", " if val_index is not None:\n", " x_val = feature_set[val_index, :]\n", "\n", " if test_label_set is not None:\n", " y_train = label_set\n", " y_val = test_label_set\n", " else:\n", " y_train = label_set[train_index]\n", " if val_index is not None:\n", " y_val = label_set[val_index]\n", "\n", " if len(x_train.shape) > 2:\n", " for t in range(x_train.shape[1]):\n", " min_max_norm = preprocessing.MinMaxScaler(feature_range=(0, 1))\n", " x_train[:, t, :] = min_max_norm.fit_transform(x_train[:, t, :])\n", " if x_val is not None:\n", " x_val[:, t, :] = min_max_norm.transform(x_val[:, t, :])\n", "\n", " if aggregation == \"Mean_Norm\":\n", " x_train = np.nanmean(x_train, axis=1)\n", " if x_val is not None:\n", " x_val = np.nanmean(x_val, axis=1)\n", " elif aggregation == \"SD_Norm\":\n", " x_train = np.nanstd(x_train, axis=1)\n", " if x_val is not None:\n", " x_val = np.nanstd(x_val, axis=1)\n", "\n", " return x_train, y_train, x_val, y_val" ] }, { "cell_type": "code", "execution_count": 17, "id": "34b767b8-b9e3-4d49-9112-0fa37d6d6f5e", "metadata": { "tags": [] }, "outputs": [], "source": [ "def feature_normalization(x_train, x_val=None, x_test=None):\n", " min_max_norm = preprocessing.MinMaxScaler(feature_range=(0, 1))\n", " x_train = min_max_norm.fit_transform(x_train)\n", " if x_val is not None:\n", " x_val = min_max_norm.transform(x_val)\n", "\n", " if x_test is not None:\n", " x_test = min_max_norm.transform(x_test)\n", "\n", " return x_train, x_val, x_test" ] }, { "cell_type": "code", "execution_count": 18, "id": "460e96c0-a082-4c47-b4f1-b0272b420642", "metadata": { "tags": [] }, "outputs": [], "source": [ "COMPOSED_METRICS = {\n", " \"sensitivity\": lambda x: x[\"1\"][\"recall\"],\n", " \"specificity\": lambda x: x[\"0\"][\"recall\"]\n", "}\n", "\n", "models = config_dict[\"models\"]\n", "\n", "metrics = [\"accuracy\", \"roc_auc\", \"specificity\", \"sensitivity\"]\n", " \n", "classifier = \"qda\"\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "330519f5-6542-4de8-9520-ff8ca6c9faa0", "metadata": {}, "outputs": [], "source": [ "selected_features = {}\n", "df_summary = []\n", "\n", "selected_features[classifier] = {}\n", "\n", "n_features = config_dict[\"n_features\"]\n", "if n_features > train_feature_set.shape[-1]:\n", " n_features = train_feature_set.shape[-1]\n", "\n", "if n_features != \"All\":\n", " for n_feature in range(1, n_features + 1):\n", " kf = StratifiedKFold(n_splits=config_dict[\"n_folds\"], random_state=config_dict[\"random_seed\"], shuffle=True)\n", " for fold, (train_index, val_index) in enumerate(kf.split(train_feature_set, train_label_set)):\n", "\n", " x_train, y_train, x_val, y_val = prepare_features(train_feature_set, train_label_set,train_index, aggregation,val_index)\n", "\n", " if config_dict[\"feature_selection\"] == \"SFFS\":\n", "\n", " with open(Path(os.environ[\"ROOT_FOLDER\"]).joinpath(\n", " experiment_name,\n", " config_dict[\"feature_selection\"],\n", " aggregation, \"FS\",\n", " f\"FS_summary_{classifier}_fold_{fold}.json\"),\n", " 'rb') as fp:\n", " feature_selection = json.load(fp)\n", " selected_features = feature_selection[str(n_feature)][\"feature_names\"]\n", " train_feature_name_list = list(feature_names)\n", "\n", " feature_idx = []\n", "\n", " for selected_feature in selected_features:\n", " feature_idx.append(train_feature_name_list.index(selected_feature))\n", "\n", " x_train = x_train[:, feature_idx]\n", "\n", " x_val = x_val[:, feature_idx]\n", "\n", " elif config_dict[\"feature_selection\"] == \"PCA\":\n", " pca = PCA(n_components=n_features)\n", " x_train = pca.fit_transform(x_train)\n", " x_val = pca.transform(x_val)\n", "\n", " x_train, x_val, _ = feature_normalization(x_train, x_val)\n", " clf = MODELS[classifier](**models[classifier],random_state=config_dict[\"random_seed\"])\n", "\n", " y_val_pred = model_fit_and_predict(clf, x_train, y_train, x_val)\n", "\n", " roc_auc_val = roc_auc_score(y_val, y_val_pred[:, 1])\n", "\n", " report = classification_report(y_val,\n", " np.where(y_val_pred[:, 1] > 0.5, 1, 0), output_dict=True)\n", " report[\"roc_auc\"] = roc_auc_val\n", " \n", " for metric in metrics:\n", " if metric not in report:\n", " report[metric] = COMPOSED_METRICS[metric](report)\n", " df_summary.append(\n", " {\"Value\": report[metric], \"Classifier\": classifier, \"Metric\": metric,\n", " \"Fold\": str(fold),\n", " \"N_Features\": n_feature,\n", " \"Experiment\": experiment_name + \"_\" + config_dict[\"feature_selection\"] + \"_\" + aggregation\n", " })" ] }, { "cell_type": "code", "execution_count": 20, "id": "cb35de8c-6ea1-4f75-9130-6b1e3241a2c6", "metadata": { "tags": [] }, "outputs": [], "source": [ "df_summary = pd.DataFrame.from_records(df_summary)" ] }, { "cell_type": "markdown", "id": "befebe8b-6bf4-4b65-a940-0869a897fde5", "metadata": {}, "source": [ "### CV Summary" ] }, { "cell_type": "code", "execution_count": 21, "id": "fd8defdf-4a00-461b-a794-00389a298945", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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ValueClassifierMetricFoldN_FeaturesExperiment
00.615385qdaaccuracy01Perfusion_Radiomics_SFFS_Flat
10.619048qdaroc_auc01Perfusion_Radiomics_SFFS_Flat
20.714286qdaspecificity01Perfusion_Radiomics_SFFS_Flat
30.500000qdasensitivity01Perfusion_Radiomics_SFFS_Flat
40.615385qdaaccuracy11Perfusion_Radiomics_SFFS_Flat
.....................
5950.000000qdasensitivity330Perfusion_Radiomics_SFFS_Flat
5960.583333qdaaccuracy430Perfusion_Radiomics_SFFS_Flat
5970.583333qdaroc_auc430Perfusion_Radiomics_SFFS_Flat
5980.333333qdaspecificity430Perfusion_Radiomics_SFFS_Flat
5990.833333qdasensitivity430Perfusion_Radiomics_SFFS_Flat
\n", "

600 rows × 6 columns

\n", "
" ], "text/plain": [ " Value Classifier Metric Fold N_Features \\\n", "0 0.615385 qda accuracy 0 1 \n", "1 0.619048 qda roc_auc 0 1 \n", "2 0.714286 qda specificity 0 1 \n", "3 0.500000 qda sensitivity 0 1 \n", "4 0.615385 qda accuracy 1 1 \n", ".. ... ... ... ... ... \n", "595 0.000000 qda sensitivity 3 30 \n", "596 0.583333 qda accuracy 4 30 \n", "597 0.583333 qda roc_auc 4 30 \n", "598 0.333333 qda specificity 4 30 \n", "599 0.833333 qda sensitivity 4 30 \n", "\n", " Experiment \n", "0 Perfusion_Radiomics_SFFS_Flat \n", "1 Perfusion_Radiomics_SFFS_Flat \n", "2 Perfusion_Radiomics_SFFS_Flat \n", "3 Perfusion_Radiomics_SFFS_Flat \n", "4 Perfusion_Radiomics_SFFS_Flat \n", ".. ... \n", "595 Perfusion_Radiomics_SFFS_Flat \n", "596 Perfusion_Radiomics_SFFS_Flat \n", "597 Perfusion_Radiomics_SFFS_Flat \n", "598 Perfusion_Radiomics_SFFS_Flat \n", "599 Perfusion_Radiomics_SFFS_Flat \n", "\n", "[600 rows x 6 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_summary" ] }, { "cell_type": "markdown", "id": "2872f003-54fb-4a41-872f-ae3a21aeea46", "metadata": {}, "source": [ "## Best Classifier Selection" ] }, { "cell_type": "code", "execution_count": 22, "id": "2a0e0a2a-e76f-4e77-ac08-680f2ae286c5", "metadata": { "tags": [] }, "outputs": [], "source": [ "AGGR_NUMPY ={\n", "\"median\": np.median,\n", "\"mean\": np.mean\n", "}\n", "\n", "\n", "def select_best_classifiers(df_summary,metric,reduction, k = 1):\n", " aggr = df_summary[df_summary[\"Metric\"] == metric][[\"Value\", \"Classifier\"]].groupby([\"Classifier\"]).agg(reduction)\n", "\n", " aggr = aggr.loc[aggr[\"Value\"].nlargest(k).index]\n", " classifiers = aggr.index.values\n", "\n", " n_features = []\n", " best_val_scores = []\n", " for classifier in classifiers:\n", " aggr = df_summary[(df_summary[\"Metric\"] == metric) & (df_summary[\"Classifier\"] == classifier)][\n", " [\"Value\", \"N_Features\"]].groupby([\"N_Features\"]).agg(\n", " reduction)\n", " aggr = aggr.loc[aggr[\"Value\"].nlargest(1).index]\n", " n_features.append(aggr.index.values[0])\n", " best_val_scores.append(aggr.values[0][0])\n", "\n", " n_features_selected_classifier = [(n_features[i], classifiers[i]) for i in range(len(classifiers))]\n", "\n", " n_features, selected_classifier = n_features_selected_classifier[0]\n", " print(f\"Best Configuration: {selected_classifier}-{n_features}, {metric}: {best_val_scores[0]}\")\n", "\n", " return n_features_selected_classifier, best_val_scores" ] }, { "cell_type": "code", "execution_count": 23, "id": "2ce9684d-c657-4f03-afc4-133e953ec0f2", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Configuration: qda-14, roc_auc: 0.7603174603174603\n" ] } ], "source": [ "reduction = \"mean\"\n", "\n", "k = 1\n", "\n", "classifiers_features, scores = select_best_classifiers(df_summary,\"roc_auc\",reduction,k)" ] }, { "cell_type": "markdown", "id": "2f145017-739c-4549-8919-f2228bdfa08a", "metadata": {}, "source": [ "## Ensemble Configuration\n", "\n", "Display top-3 best models, to ensemble" ] }, { "cell_type": "code", "execution_count": 24, "id": "53239407-3dba-4d56-8909-aac6674b68b3", "metadata": { "tags": [] }, "outputs": [], "source": [ "classifiers = [classifier for n_features, classifier in classifiers_features]\n", "n_feature_list = [n_features for n_features, classifier in classifiers_features]\n", "\n", "classifier_kwargs_list = [models[classifier] for classifier in classifiers]\n", "\n", "ensemble_weights = scores\n", "\n", "ensemble_configuration_file = []\n", "\n", "for classifier,n_features, weight in zip(classifiers,n_feature_list, ensemble_weights):\n", " ensemble_configuration_file.append({\"Classifier\":classifier,\n", " \"N_Features\": n_features,\n", " \"weight\": weight})\n", " \n", "ensemble_configuration = pd.DataFrame.from_records(ensemble_configuration_file)" ] }, { "cell_type": "code", "execution_count": 25, "id": "c5367833-7852-43dc-8c3c-9760a8adf085", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ClassifierN_Featuresweight
0qda140.760317
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" ], "text/plain": [ " Classifier N_Features weight\n", "0 qda 14 0.760317" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ensemble_configuration" ] }, { "cell_type": "markdown", "id": "ffc32f04-1136-40e1-8b1d-60ea531c8e30", "metadata": {}, "source": [ "## Model Evaluation on test-set" ] }, { "cell_type": "markdown", "id": "ffbf9872-2c57-47d4-aaa0-687dcd48cca6", "metadata": {}, "source": [ "### YellowBrick modules to generate plot reports" ] }, { "cell_type": "code", "execution_count": 26, "id": "f43c8d18-dd6e-4721-9c61-a2d6f760906e", "metadata": { "tags": [] }, "outputs": [], "source": [ "visualizers = {\n", "\n", " \"Report\": {\"support\": True,\n", " \"classes\": [config_dict[\"label_dict\"][key] for key in config_dict[\"label_dict\"]]},\n", " \"ROCAUC\": {\"micro\": False, \"macro\":False, \"per_class\" : False,\n", " \"classes\": [config_dict[\"label_dict\"][key] for key in config_dict[\"label_dict\"]]},\n", " \"PR\": {},\n", " \"CPE\" :{\"classes\": [config_dict[\"label_dict\"][key] for key in config_dict[\"label_dict\"]]},\n", " \"DT\": {}\n", "}\n", "\n", "YB_VISUALIZERS = {\n", " \"Report\": ClassificationReport,\n", " \"ROCAUC\": ROCAUC,\n", " \"PR\": PrecisionRecallCurve,\n", " \"CPE\": ClassPredictionError,\n", " \"DT\": DiscriminationThreshold\n", "}\n", "\n", "\n", "def YB_Visualizer(clf,visualizer, x_train, y_train, x_test, y_test,kwargs):\n", " visualizer = YB_VISUALIZERS[visualizer](clf,\n", " **kwargs)\n", "\n", " \n", " \n", " if visualizer != \"DT\":\n", " visualizer.fit(x_train, y_train) # Fit the visualizer and the model\n", " visualizer.score(x_test, y_test) # Evaluate the model on the test data\n", " else:\n", " x = np.vstack((x_train,x_test))\n", " y = np.vstack((y_train,y_test))\n", " visualizer.fit(x, y)\n", " if visualizer == \"Report\":\n", " visualizer.draw()\n", " visualizer.finalize()\n", " \n", " return visualizer\n" ] }, { "cell_type": "markdown", "id": "1b8c241c-912b-44ad-9801-e905cb10a9a9", "metadata": {}, "source": [ "### Model Evaluation\n", "\n", "Given the ensemble configuration above, fit the Train Set and evaluate the ensemble model on the Test Set." ] }, { "cell_type": "code", "execution_count": 27, "id": "dd90998c-6fe8-497a-98a9-7273e9e1c5a9", "metadata": { "tags": [] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from yellowbrick.style import set_palette\n", "set_palette('sns_pastel')\n", "\n", "def evaluate_classifiers(ensemble_configuration, classifier_kwargs_list,\n", " train_feature_set,train_label_set,test_feature_set, test_label_set,\n", " feature_selection, aggregation,\n", " visualizers, output_file, plot_title):\n", " \n", " fig, axs = plt.subplots(int(len(visualizers)),int(ensemble_configuration.shape[0]),figsize=(int(ensemble_configuration.shape[0])*10*1.5,int(len(visualizers))*10*1),squeeze=False)\n", "\n", " visualgrid = []\n", " x_train, y_train, x_test, y_test = prepare_features(train_feature_set,train_label_set,None,aggregation,None,test_feature_set, test_label_set)\n", "\n", " x_train, x_test, _ = feature_normalization(x_train, x_test)\n", " \n", " ensemble_y_test_pred = np.zeros((x_test.shape[0], 2)) \n", " \n", " ensemble_weights = ensemble_configuration[\"weight\"].values\n", " \n", " weight_sum = np.sum(ensemble_weights)\n", " \n", " ensemble_weights = ensemble_weights / weight_sum\n", "\n", " for ensemble_idx, (classifier_configuration,classifier_kwargs, weight) in enumerate(zip(ensemble_configuration.iterrows(),classifier_kwargs_list, ensemble_weights)):\n", " classifier, n_features = classifier_configuration[1][\"Classifier\"], classifier_configuration[1][\"N_Features\"]\n", " \n", " clf = MODELS[classifier](**classifier_kwargs,random_state=config_dict[\"random_seed\"])\n", "\n", " x_train, y_train, x_test, y_test = prepare_features(train_feature_set, train_label_set, None, aggregation, None,\n", " test_feature_set, test_label_set)\n", "\n", " x_train, x_test, _ = feature_normalization(x_train, x_test)\n", "\n", " if n_features != \"All\" and feature_selection == \"SFFS\":\n", " sffs_model = SFS(clf,\n", " k_features=int(n_features),\n", " forward=True,\n", " floating=True,\n", " scoring='roc_auc',\n", " verbose=0,\n", " n_jobs=-1,\n", " cv=5)\n", " \n", " sffs = sffs_model.fit(x_train, y_train)\n", " sffs_features = sffs.subsets_\n", "\n", " feature_idx = sffs_features[n_features]['feature_idx']\n", "\n", " x_train = x_train[:, feature_idx]\n", " x_test = x_test[:, feature_idx]\n", "\n", " if n_features != \"All\" and feature_selection == \"PCA\":\n", " pca = PCA(n_components=n_features)\n", " x_train = pca.fit_transform(x_train)\n", " x_test = pca.transform(x_test)\n", "\n", " clf = MODELS[classifier](**classifier_kwargs)\n", "\n", " y_test_pred = model_fit_and_predict(clf, x_train, y_train, x_test)\n", "\n", " for idx_visualizer, visualizer in enumerate(visualizers):\n", " \n", " visualizers[visualizer][\"ax\"] = axs[idx_visualizer, ensemble_idx]\n", " visualizers[visualizer][\"title\"] = f\"{plot_title} {visualizer}, {classifier}-{n_features}\"\n", " visualgrid.append(YB_Visualizer(clf, visualizer,\n", " x_train, y_train, x_test, y_test,\n", " visualizers[visualizer]))\n", "\n", " ensemble_y_test_pred += y_test_pred * weight\n", "\n", " roc_auc_val = roc_auc_score(y_test, ensemble_y_test_pred[:, 1])\n", " report = classification_report(y_test, np.where(ensemble_y_test_pred[:, 1] > 0.5, 1, 0), output_dict=True)\n", " report[\"roc_auc\"] = roc_auc_val\n", "\n", " if output_file is not None:\n", " plt.savefig(output_file)\n", "\n", " return report\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "bd4e78b2-4051-4ac4-b1eb-20a564e2fd46", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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zg/dqq62WX/ziF3niiSfSt2/fbLbZZl95l+0Ffv7zn+fggw/O448/nn/961955plncs899+Tvf/97zjzzzBx44IE11v/iacVJqsPV511zzTW1rifZvHnzGo/XXXfdPPjggxkxYkSeeOKJDB8+PC+88EKeffbZ3HXXXRkyZEiNGYYLs6jjSeZ/vt18882pqqrKhx9+mPfeey/vvPNO/v3vf39lzP0qC45z7ty5ueOOO3LXXXfluOOOy9577129zqxZs/Liiy/m0EMPrfHLiRVXXDGdO3fOk08+WSNS7rbbbtV/r1evXrbddtsMHDgws2fPzvDhw9OhQ4fqQLnAzjvvnNtvvz0vvvhidaiui+8Hyfyvj7XXXrvGx7VPnz7p0KFDjXWS1Aj7JSUl+cEPflAjUi44PXzq1Kl555138t5771XPilzwb/BVM3C/zrXXXpupU6fmpJNO+iaHCADAd5BICVBHunfvnnPPPTfJ/BDRsGHDLL/88jUi3uTJk5Mk5557bvW6nzd27Ngaj+viBh0dO3bMH/7wh1x//fW5/fbbc/PNN6dFixbZd999c/zxx9e6TuCUKVPSunXrWrPeFgSqz4e3L95dfEF4WNh1Cb9Ohw4d0rNnzyTzZw22bt06Z5xxRkpLS2t8rGbPnp1f/vKXufvuu1NRUZGOHTumd+/eKSsrS1VVVfUxtGrVqtaxfdVNW77JcX9x9mmS6tOAF8WCz4Mvi2/16tXLkCFDcs011+Shhx7KXXfdlfr162ebbbbJueeeWyOQLkzLli2z4447Zscdd0ySvPbaaznllFNyySWXZKeddkrr1q2r113wMf86q6+++lfeOGeBkpKSrLfeellvvfWSzP+4XnHFFfnTn/6U22+/Pfvvv/9Xbr+o41ngb3/7Wy6//PJ88sknadWqVbp16/a1IfSrfP4411lnnVRUVKR///5p1qxZ9azAqVOnprKyMoMHD87gwYNr7eOLd6H/4iUG2rZtW30q+ZQpU77yrulTp06tXvZNPse+ypQpU2oE9QUW/JJhwTpJanyuJLW/ht5///30798///rXv1K/fv106tQpXbt2TZLqr8cv/vLm2GOPTb9+/b52nK+99lquvfbaDB48OA0aNEhFRUX195bKysrMmzfvG19WAgCAby+REqCONG3a9GsDy4KbwJx66qlZf/31az3/dfHpixZEuC9GwRkzZtR4/PlThUeMGJFbb7011157bbp27VrrZiotW7bMpEmTagWABQH1i9Ficdl9993zwAMP5M9//nO23Xbb9O3bN0lywQUX5IEHHsiVV16ZjTfeuDrcbLTRRtXbtm7deqHHsCAOLsySPO4FnwcTJ06scc2+t99+O5MnT06fPn3Svn37DBgwIOecc05GjhyZ+++/P4MHD07r1q1zzjnn1NrnmDFjsscee+TnP/959txzzxrPrbnmmjnhhBNyzDHH5IMPPlgs/4bHH398Jk+enN/97nc1lrds2TL9+/fPvffeW2MGXl147rnnctppp+WAAw7IoYceWh3ZLr744urZqv+rs88+O08++WQGDBiQDTbYIO3atUvTpk1Tr169HHzwwQs9nfmL8X7y5MnV0TGZf4Oj0tLStGrVKi1btsx7771Xax8LbkS0OP6tWrdunfHjx9daPnny5OrrnS543fHjx2eFFVaosc4ClZWVOfzww1O/fv3cfvvt6datW8rKyvLWW2/VuNTBF2fHftl1Yb/okUceydy5c3PwwQfXem7bbbfN+uuvn9///veLtC8AAL793N0bYAnq1KlT2rZtmw8//DA9e/as/tO+fftcdtllee21177R/hbM6Bs9enT1sgWha4Hf/e532XLLLTNnzpw0aNAgG220UfVNYD5/x/EF1l9//VRUVNS6A/Hf/va3JPNPC11SfvGLX6Rhw4Y5//zzq0/lHjFiRDbYYINss8021YHylVdeycSJE6tj7UYbbZSKioo8/PDD1fuaM2dOnnzyyS99rSV53Av2NWzYsBrLL7300lxwwQV5/vnns/HGG+ell15KvXr10q1bt5xwwglZffXVF/pvlsyfeVdWVpY//vGPC70D8zvvvJOGDRt+6U13/lcrr7xynn766bzwwgu1nhs7dmxmzpyZ1VdfvU5f8/nnn09lZWX69etXHSjnzZtXfUOpBZ8Pi3pq8cI0a9YsZ5xxRqZOnZrLLrusetmaa66Zd955p8bXcZcuXTJw4MBaN4H5/OdhVVVVHnzwwfTp0ycNGjTIeuutl48++ijPP/98jW3+9re/pX79+l96WYYk//Uswo022ijPP/98xowZU71swanaC2y44YZJUuvr4R//+Ef13ydNmpR33303P/7xj9OzZ8/qyzI89thjST77+H/+Y7Tg+92i2GuvvXL77bfX+HPssccmmX9a/sJmowMA8N1lJiXAElRaWpoTTjgh/fv3T2lpabbccstMnTo1V199dcaMGbPQm4Z8lQ022CCNGjXKr3/96/z85z/PjBkzctVVV9W4IcWGG26YSy+9NMccc0z233//lJaW5s9//nMaNGiQLbfcstY+F1yz8uyzz86YMWPStWvXDB8+PIMHD85uu+2W1VZb7X/9MCyyjh075tBDD83VV1+doUOH5rDDDkuvXr1y33335U9/+lM6d+6ckSNH5pprrkm9evWqr4W50UYbpW/fvjn77LMzYcKEdOjQITfffHMmTpz4padYL8nj7tq1a7bffvtccsklmT17drp165bHHnss//jHPzJo0KCsueaaadSoUU499dT069cv7dq1y1NPPZXXX3+91jUlFygtLc2AAQNyzDHHZI899sh+++2Xzp07Z9asWXnyySdzyy235Oc///k3nq27qH7605/m4YcfziGHHJJ99903G2ywQRo3bpw33ngjQ4YMSZcuXbL77rvX6WsuCHjnnXde9thjj0yZMiW33HJLRo4cmSTVN4Zq0aJFxo8fn0cffTTdunVb5Jl8C+ywww754x//mDvvvDM/+clP0qtXr5x44ok5/PDDc9JJJ2XnnXfOvHnzMmTIkLz44os1biKTzJ/ZWV5enlVXXTW33XZb3n777QwdOjTJ/BnDf/zjH3PMMcfkuOOOS8eOHTNs2LD89a9/zbHHHls963ZhFlwT9F//+lc6d+6ctdZaK9OnT89bb71VfcOghTnooINy11135ac//Wn69euXqqqqXHnllTVmZK+88srZe++9c8UVV6SioiLdunXL3XffnVGjRlWv07Zt23To0CG33HJLlltuubRo0SKPP/54br755iT/3bVpP699+/a1guabb76ZZNEvPwAAwHeHSAmwhO25555p2rRpbrjhhtx6661p0qRJ1llnnVx66aULvU7cV2nRokUGDhyYyy67LMccc0w6dOiQY489NnfddVf1Ol27ds21116b3/72tznxxBMzb9689OjRI0OGDEmnTp1q7bNevXq57rrrctVVV+V3v/tdJk6cmI4dO+bEE0/MIYcc8r8e/jd2+OGH56677srVV1+dnXfeOaeffnrmzp2bK6+8MnPmzEnHjh1z1FFH5a233sqwYcOqT9ceNGhQLr300lx11VUpLy/PDjvskL322iuPPPLIQl9nSR/3JZdckkGDBmXo0KGZNGlSOnfunKuuuirbbLNNkmTIkCG57LLLcsEFF2Tq1KlZZZVVct55531l6Ntiiy3yl7/8JTfeeGOuvfbaTJw4MQ0aNMiaa66ZK664otaNeupSy5Ytc+utt2bw4MEZNmxY/vSnP2Xu3Lnp0KFDdtxxxxx++OH/07UiF2aDDTZI//79c9NNN+X+++9Pu3btssEGG2TQoEE55phjMmLEiGy++ebZfffd8+ijj1aHwMMPP/wbv9bZZ5+d3XffPeedd15uu+226jubDxo0KMcdd1zq16+f7t2756abbqp1F/EBAwbkuuuuywcffJA111wzQ4YMqb4beePGjfP73/8+l112WX7zm99k+vTp6dSpUy644IL8+Mc//soxNWvWLIccckhuvfXWPProo3nyySfz6quv5sADD8yFF174pZ8rrVu3zi233JJf/epXOf3009OsWbMcdthh1XFxgXPOOSft2rXLH/7wh0yZMiWbbrppjjzyyFx55ZXV61x99dW54IILcvrpp6dBgwZZbbXVcs011+RXv/pVnnvuuRxwwAHf+GMNAMD3U72qBVc1BwCgztxxxx0544wz8sgjjyzRWX+/+c1vstpqqy30eplfZauttsr666+fX//614tpZAAA8OVckxKAxWLevHmpqKj4yj/z5s0repiwVBkzZkweeOCB9O7du+ihAADAN+J0bwAWi2233TYfffTRV67j7rxQt1q1apWBAwfWuCM3AAB8FzjdG4DFYtSoUZkzZ85XrtO0adOFXhcTAIClx5w5c7L77rvnF7/4RTbYYIOFrvPaa6/lnHPOyRtvvJHVVlst5557bnr06LGERwoUyUxKABaLNdZYo+ghAABQsPLy8px00kl58803v3SdmTNn5vDDD89OO+2UX//61/nTn/6UI444Ig899FCaNGmyBEcLFMk1KQEAAIA699Zbb2WvvfbK+++//5Xr3XvvvWnYsGFOPfXUdO7cOWeddVaaNm2a+++/fwmNFPg2ECkBAACAOjd8+PBssMEGufXWW79yvRdffDF9+vRJvXr1kiT16tXLOuuskxdeeGEJjBL4tnC692Lw/PPPp6qqKvXr1y96KAAAAN9pc+fOTb169dK7d++ih1KnFuX63d9GDRo0WOTL+uy7776LtN64ceOy2mqr1VjWtm3brzxFHFj6iJSLQVVVVdyPCAAA4H+3tP5sNWfOnMyeOSuzRk8oeiiLrPFybRfLfmfNmpUGDRrUWNagQYPvZMQF/nsi5WKwYAblmmfvU/BIYOlReverSZIPO6xY8Ehg6dHxow+SJPN26V7wSGDpseD9qvLyTQseCSw9Xt326qKHsNjMGj0h/9j1+KKHsci2vOvKNOrUsc7327Bhw1pBcs6cOWnUqFGdvxbw7eWalAAAAEBh2rdvn/Hjx9dYNn78+Cy77LIFjQgogkgJAAAAFGattdaqvrdDMv8U/3//+99Za621Ch4ZsCSJlAAAAMASNW7cuMyePTtJsv3222fq1Km54IIL8tZbb+WCCy7IrFmz8sMf/rDgUQJLkkgJAAAALFF9+/bNvffemyRp1qxZrrvuuowYMSK77757XnzxxVx//fVp0qRJwaMEliQ3zgEAAAAWq1GjRn3l4169euXOO+9ckkMCvmXMpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQZUUPAAAAAL6PGifZtOhBfAONix4AsFQzkxIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAANS58vLynHnmmVl33XXTt2/fDBky5EvXfeihh/LDH/4wvXv3zk9+8pO8+uqrS3CkwLeBSAkAAADUuYsvvjivvPJKhg4dmnPOOSeDBg3K/fffX2u9N998MyeddFKOOOKI3H333enWrVuOOOKIzJo1q4BRA0URKQEAAIA6NXPmzNx2220566yz0r1792y77bY57LDDcsstt9Ra98knn8xqq62WXXfdNSuttFJOPPHEjBs3Lm+99VYBIweKIlICAAAAdWrkyJGpqKhI7969q5f16dMnL774YiorK2us26pVq7z11lsZMWJEKisrc8cdd6RZs2ZZaaWVlvSwgQKVFT0AAAAAYOkybty4tG7dOg0aNKhe1q5du5SXl2fy5Mlp06ZN9fIddtghw4YNy7777pvS0tKUlJTkuuuuS8uWLYsYOlAQMykBAACAOjVr1qwagTJJ9eM5c+bUWD5p0qSMGzcu/fv3z1/+8pfssssuOeOMMzJhwoQlNl6geCIlAAAAUKcaNmxYK0YueNyoUaMayy+99NKsvvrq2W+//dKjR4/88pe/TOPGjfPXv/51iY0XKJ5ICQAAANSp9u3bZ9KkSamoqKheNm7cuDRq1CgtWrSose6rr76arl27Vj8uKSlJ165d8/HHHy+x8QLFEykBAACAOtWtW7eUlZXlhRdeqF42YsSI9OzZMyUlNVPEsssum7fffrvGsnfffTcdO3ZcEkMFviVESgAAAKBONW7cOLvuumsGDBiQl156KQ8//HCGDBmSAw88MMn8WZWzZ89Okuy11175y1/+krvuuivvvfdeLr300nz88cfZbbfdijwEYAlzd28AAACgzp1xxhkZMGBADjrooDRr1iz9+vXLdtttlyTp27dvLrzwwuy+++7ZYYcdMmPGjFx33XUZPXp0unXrlqFDh6Zt27YFHwGwJImUAAAAQJ1r3LhxLrroolx00UW1nhs1alSNx3vuuWf23HPPJTU04FvI6d4AAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUqqzoAQAAAMD3Uf2yyqy+wsyih7HIxpdVFj0EYClmJiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUKiyogcA/60Hx05P/5Fj8+q08rRvWJajVmmdEzu3Tb169b50m/8bMy2/HDUur0wtT9sGpdl9+RY5v9uyaVr2Wa9/bvKsnPrqmIyYPCstykpz4Eotc84ay6ZByWf7HTO7Iie/OjoPjJ2Riqqq/LB9s1zavX2Wb1R/sR4zLG4NN9ssLU87NWVrrJ7KceMy/Xc3Z/p11y103SZ77Zk2V1z+pfuaePwJmXnb7UmS5Z4bnrLll6+1zsc9eqVy0qQkSekqq6TVgP5puP76qZo3L7P+/n+ZcsGvUjV9eh0cGRRncb1fjZxWntNfG5NHJ8xMWb1ks7ZNc0n39unUtEGSpPNDb+a9WXMXuv9VmtTPW9t0qdsDhe+QwU+PzlWPfZz/TJqdlVo1zDGbLJ+jNln+K78uAYDFS6TkO+npiTOzyzPvZ68OLTOg67J5csLMnP7a2FRUJad1abfQbe4ZPS17DP8gB6zYMr/q1j6vTy/P2a+Pzbg5FflDn45JkndmzMkPnnovG7ZpnD+t2zEjp83JL0aOzaQ5lbl6rfmBpaKyKjs+836mzp2Xq9daPnMrq3Lm62Pyw3+9n2c375T6Jf5zy3dTg3V6p93QmzLznnsy5ZJL03D99dLy7DNTr6w00357da31Zz/8SMbutHOt5a0vuST1mjfL7EeGJUlKWrdO2fLLZ/J552fOs8NrrFs5dWqSpF6LFlnmL39O5dhxmXj8iSlt1zYtzzozZSuumPH7H7AYjhaWjMX1fvXBrLnZ7In/ZPVmDfKHPh0ya15V+r8+Nj/813t5YcvOaVxaktvX75jyeVU1xzNpVk5+dUwOX7n1Yj92+La64enROfK2t3Js3+Wzc/dOeeLdKTnurncyu6IyJ27RsejhAcD31lIbKceMGZOhQ4dm2LBh+eSTT9K8efP06NEjhx12WNZdd93q9QYOHJhBgwbV2r5x48Zp3759tt9++/Tr1y9lZUvth+o76dxR47J2y0YZuk6HJMn2yzbL3Kqq/PrN8TmuU5s0Lq19JYOTXxmdPVZokRt7z99mq2WaZl5VVQa9MzEzKyrTpKwkl7w1Ps3LSnLn+iulQUm97NA+aVJaL8e9PDqnd2mXlZrUz+0fT83zU2bnpS07Z83mDZMka7VslLX+8XZu+3hq9u3Ycsl9IKAOtTjppMx95dVMOu74JEn5P/+ZemVlad7v2Ey7cUgye3aN9SsnTsyciRNrLGv200NS1mW1jNtl11R++lz9Ht2TJLPuvz/z3ntvoa/d7MADUtK6dcb+4IfVMyvnffJJ2v3h92mw7rqZ89xzdXmosMQsrver80aNS8v6JXlwo5XT5NPZlas0qZ/dnvkgz02elU3bNk3vlo1r7Hfq3HnZ/98fZYf2zXLqlwRS+D743fAx6btqi/xmt85Jkq1Xb5VRY2flt09+IlICQIGWymtSjhgxIrvsskv+8Y9/5MADD8y1116bs846K7Nnz84BBxyQu+66q9Y2t956a40/V1xxRXr06JFrr702V1xxxZI/CL5U+bzKPDphZnZdvkWN5Xss3yLTKirzxISZtbZ5fsqsvD1zbo5ZtU2N5cd1aps3tulS/QPeg2NnZIf2zWuc2r3HCi1SmeTBcfNPOX1w3PSs0axBdaBMkjWbN0y35g1z35hpdXWYsGQ1aJCGG22YWfffX2PxzP+7NyXNm6fheut97S5K2rVLi1NPyYybf585z79Qvbz+mt1TOW3alwbKJGm0+eaZ88zw6kCZJLMffSyV06al0dZbffPjgW+BxfV+VVVVlTs+npqDV2pV/f6VJOu2apwPfrB6Nm3bdKHjueCN8RlbXpGBPZerg6OD767ZFZVp3qi0xrK2TetnwoyKgkYEACRL4UzKyZMn5/jjj88qq6ySm266KY0bfzaL4Ac/+EEOP/zw9O/fP3379k27dp/NIlh77bVr7WvLLbfMhx9+mDvuuCOnnHLKkhg+i+CdmXMzp7Iqq396za0FVvv08Rsz5mTbL2zz4pTyJEmj0nrZ+Zn3M2zcjDQurZcDVmyVC7stm4alJZk1rzLvzZqbLs1q7neZhmVpUVaSUdPn7+P1aeXp8oXXTpLOTevnjelz6ugoYckqW2ml1GvYMHPfeafG8or//Gf+8507pfzxx79yHy1OPjGprMyUiy+psbx+9zVTOXly2lx/XRpt2jcpLc3sR4Zl8jkDUjl27Pz9d1ktM/92T80dVlam4oMPUta50/92cFCQxfV+9Z+ZczOlojIrN66ffi99kj9/NCUz51Vlu2WbZmDP5dOxce3rI78/c24Gvjsxp63WNis3qf0eBt8n/TZdIT/7y5v5w4ix2WnNNnn6vWm5+bmxOaDPskUPDQC+175zkXKrrbbKTjvtlFmzZuXOO+9MSUlJNt9885x55plp1apV7rrrrowdOza//e1vawTKJCkpKcnJJ5+cu+++O9OnT68RKb9Ms2bNXED7W2bK3HlJkub1a04Ebv7pbJKpnz7/eePK5/9m/MfDP8g+HVvmhM5t89ykWTl31LiMLZ9/ja8pcyuTJC3Kak8wbl5WkmkV85+fWlGZLk1La69TWpqpFSIl300lLZonSa2b1Cx4XNK8+Vdv37Ztmv54z0y7/vpUfXqdyQUadO+e0uWWy9xb/pjpN9yY+l1WS4uTT8oyf70tY7fbPlWzZqWkefOF3iCnavr0lDT76teGb6vF9X41bs787c54bWzWa904t/TpmHHlFTnr9bHZ5qn3MmLzTjVusJMkv3lnQhqW1MtxndrW+XHCd81Pei+TR9+ekoP++Eb1su3WaJUrdl21wFEBAN+5SJkkf/zjH7PyyivnwgsvzMSJE3PZZZflvffey5///Oc8/vjjadeuXXr16rXQbbt27ZquXbvWWl5R8dnpHZWVlZk0aVL+9re/5cknn8whhxyy2I6Fb67ya54vWUhUnlM1/8YBuy7fIr9es32SZMt2TVOZ5KzXx6b/GuVptpA4WWO/mb/fyqqvWEfP5ruq5Guu/lH51V95Tff9SVJakuk33FjruUmnnpqqinmZ++KLSZI5w4dn7qg3suzdd6bJnj/OjJt//9Wv/zWvDd9Wi+v9as6nb0TtG5bl9vU6Vu+nc9MG6fvEf/LHD6fkZ6t8dmOc2fMqc9P7k3PISq3SukHtX7LB982uN72eJ9+dmot2XCXrrdQ8r3wyI+c+8H72GjoydxzSzQQFACjIdzJSlpSU5KabbkrzT2f2tGnTJsccc0wef/zxjB49Oh06dPjG++zevXutZSussEL69euXww8//H8eM3Wn5acxcXpFzR//pn76uOWXzIRMkh3aN6ux/AfLNstZr4/NC1NmZ4f28z+fplXU/rFyakVlWnw6E6Zl/ZJMm1d79svUinkLnYUJ3wWVU+dfT7Ve05pfI/U+/T5bOe2rr7fa+Ec7ZPZjj1XfLOfz5oz4d+1lzz2XyilTUn/NbtX7r9e09nX06jVrnnmjxyzaQcC3zOJ6v1qj2fxrIv+gfdMaoXPDNk3SsqwkL0ypeZOrh8bNyNSKSjd2gyRPvTs1D4yclOv2XC2HbTj/+qybd26ZVds2yk43vJb/e31SdlyzzdfsBQBYHL6TkXKrrbaqDpQLHpeVleXZZ59NaWlp5i0kIH2d22+/PUkya9as/O53v8szzzyTs88+O1tvvXWdjZu60blpg5TWS96aUfPU6gWPu37uhjYLLLj+V/kXpkHO/fRx49KSNCsrSYdGZXn7C/sdW16RaRWV6fbpD4WrN22YF6bW/AEwSd6eMSfrtW5cazl8F1S8916qKipStsrKNZaXrbLK/OfffPNLty1Zbrk06NkzExcyi7Je8+ZpvMMOmfPCC6kYNepzT9RLGjRI5YT5UbPi7bdTtuoqX9hxScpWWjGz77vvvzomKNrier/q3LRB6iUpn1d7an9FVVUaldacBfZ/Y6Zl1Sb1s24r71Hw3qT5133dZNWaN7TarNP8iP/a6JkiJQAU5Ds57at9+/Y1HpeUlKR169aZMmVKVlhhhXzyySdfuf3Cnu/Zs2d69uyZ9ddfP4MGDUr37t3z85//PM8991ydjp3/XaPSkmzatknu/GRaqqo++wHtjk+mpmVZSdZfyA9hm7Vtmqal9XLrR1NqLL9nzLSU1Us2/DQubrtMs/zfmOkpn/fZrJc7Pp6a0nrzT7dLkm2XbZrXp5XntWnl1eu8Nq08r0+fk22XqTnzBb4zystT/swzabzDD2ssbrLDD1M5ZUqNu3V/UYPea8/fxbO1v19WzZmTVhf8Mi2OPabG8kbbbZeSxo1T/tRTSebfybvhhhumpM1nPxg22nyzlDRrltmPPfZfHhQUa3G9XzUrm7/fu0ZPq/F+9ci46Zkxryqbtm1SY9tnJs7Kxm1qLoPvq67Lzv+6e/ydmtdPfvLd+Y9XbdtoiY8JAJjvOxkpJ02aVOPxvHnzMmnSpLRp0yabbrppJkyYkJdffnmh277++uvZYost8rvf/e5L919SUpILL7wwZWVlOf3001NeXv6l61KMM7ssk+GTZmWf5z7MfWOmpf/IsbnsrQk5vUu7NCkrydS58/L0xJnVNyBoVlaSAV2XzZ8/mpp+L32SR8ZNz/mjxuWSN8enX6c2Wabh/EnFJ6/WNmPLK/KjZ97P30dPyxVvT8hJr47Jz1ZunZWazL9b6l4rtMjqzRpkx6ffz58/nJI/fzglOz79fno2b5g9V2jxpWOGb7tpv7kqDXr3TpvrrkmjLbdIi1NOTrOjjszUgYNSNXt26jVrlgbr9K4REpOkfteuqZo9O/Pee6/2TsvLM23Qb9Nk993S8pz+abhp3zT72WFpc+XlmXX/Ayl/cn6knHHz71M1e3ba/fmPabT99mnyk33SZuDAzHpkWOY8N2IJHD0sHovr/eqCbsvm49kV2fGZ93PfmGkZ+v7kHDDio6zfunF2Wu6zs03mVVXl9enl6baQWZvwfdS7Y7Ps3qttTv7bO7l42If551uTc/UTH+fAP45Kn47NslsPN5cCgKJ8JyPlY489ljlzPjt16pFHHklFRUU22mij7LzzzllmmWVy4YUXZvbsmqfkzps3L5deemnq16+fH/7wh1/cbQ0dOnTI0UcfnQ8++CCDBw9eLMfBf2+rZZrmtvU65o3pc7LHsx/mTx9OyUVrts8pXebfsf3fU2an7xP/yb1jPrtb8Amd2+aGtVfIYxNmZqdnPsjvPpicc7oum4vW/GxmbtfmDXPfRitl5ryq7P3ch7ny7Qn5eac2uaLHctXrNCwtyf0brZx1WjbKkS9+kn4vf5INWzfOvRutlDJ3zuE7rPzJpzLhZ0ekrFPntL3xhjTZbddMOf+CTL/m2iRJg549suw9f0ujbWpeBqN0mWVS+YU7en/etN9clUlnnJlGm22WdjfdlGZHHJ4Zv/9DJhxzbPU6lRMnZtyee6dy4sS0GXRVWp52amb+398z8aijF8/BwhKyuN6vNmrTJA9vvHIqq5K9nvswp742Jjsu1zz3brhSSj93ncoJc+aloippXf87+V8+WCxu2W+NnLB5h1z3r0/yw+tfzW8e/zgHr9c+w47umbJS/5cDgKLUq/r8+UffAVtttVU+/vjjbLLJJjnwwAPzySef5PLLL0+vXr1yww03JEmeeOKJHHvssVlxxRWz//77Z5VVVsno0aNzyy235KWXXspll12WH/3oR0mSgQMHZtCgQRn1+WulfWrOnDnZcccdM2bMmNx7772LfEOeBbM41zx7nzo6aqD07leTJB92WLHgkcDSo+NHHyRJ5u1S++ZxwH9nwftV5eWbFjwSWHq8uu3VSeZfomtp8vLLL2fe+++n3eFHFj2URTb++mtTutJKS92/BfDt8J28cc6PfvSjtGjRIscff3yaNGmS3XbbLSeccEL183379s1tt92WIUOG5Lrrrsv48ePTqlWr9OjRI7feemvWWmutRXqdBg0a5Mwzz8wRRxyRiy66KFddddXiOiQAAAAA+N76TkbK+vXr55xzzsk555zzpet06dIlF1544dfuq1+/funXr9+XPr/FFlssdJYlAAAAAFA3XKAIAAAAACiUSAkAAAAAFOo7d7r3sGHDih4CAAAAAFCHzKQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUqqzoAQAAAMD3UWnDkiy/bouih7HIJjU0zwlYfHyHAQAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAqHPl5eU588wzs+6666Zv374ZMmTIl647atSo/OQnP0mvXr2y00475emnn16CIwW+DURKAAAAoM5dfPHFeeWVVzJ06NCcc845GTRoUO6///5a602bNi0//elPs9pqq+Wee+7Jtttum2OPPTYTJkwoYNRAUURKAAAAoE7NnDkzt912W84666x079492267bQ477LDccssttda9884706RJkwwYMCArr7xyjjvuuKy88sp55ZVXChg5UJSyogcAAAAALF1GjhyZioqK9O7du3pZnz59cu2116aysjIlJZ/NmRo+fHi23nrrlJaWVi/761//ukTHCxTPTEoAAACgTo0bNy6tW7dOgwYNqpe1a9cu5eXlmTx5co11P/jgg7Rp0ya/+MUvsskmm2SvvfbKiBEjlvCIgaKJlAAAAECdmjVrVo1AmaT68Zw5c2osnzlzZq6//voss8wyGTx4cNZbb70ceuih+eSTT5bYeIHiiZQAAABAnWrYsGGtGLngcaNGjWosLy0tTbdu3XLcccdlzTXXzCmnnJJVVlkld9999xIbL1A8kRIAAACoU+3bt8+kSZNSUVFRvWzcuHFp1KhRWrRoUWPdZZZZJp06daqxbJVVVjGTEr5nREoAAACgTnXr1i1lZWV54YUXqpeNGDEiPXv2rHHTnCRZe+21M2rUqBrL3nnnnXTo0GFJDBX4lhApAQAAgDrVuHHj7LrrrhkwYEBeeumlPPzwwxkyZEgOPPDAJPNnVc6ePTtJss8++2TUqFEZOHBg3nvvvfzmN7/JBx98kF122aXIQwCWMJESAAAAqHNnnHFGunfvnoMOOijnnntu+vXrl+222y5J0rdv39x7771Jkg4dOuSGG27IP/7xj+y44475xz/+keuvvz7t27cvcvjAElZW9AAAAACApU/jxo1z0UUX5aKLLqr13BdP7+7Tp0/uuOOOJTU04FvITEoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFBlRQ8AAAAAvpcalaTe5m2KHsWia2SeE7D4+A4DAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUKiyogewNCu9+9WihwBLnY4ffVD0EGCp4/0K6l7JiY8XPQRYerz8ctEjAGAJMJMSAAAAACiUmZSL0YcdVix6CLDUWDCDsvLyTQseCSw9Fsz0mrdL94JHAkuPBTOTvV9BHdr26qJHAMASYCYlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUGVFDwAAAAC+l+qXpl6XVkWPYtHVLy16BMBSzExKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAAAolEgJAAAAABRKpAQAAAAACiVSAgAAAACFEikBAAAAgEKJlAAAAABAoURKAAAAAKBQIiUAAAAAUCiREgAAAKhz5eXlOfPMM7Puuuumb9++GTJkyNdu8+GHH6Z379555plnlsAIgW+TsqIHAAAAACx9Lr744rzyyisZOnRoPv7445x22mlZYYUVsv3223/pNgMGDMjMmTOX4CiBbwuREgAAAKhTM2fOzG233ZbBgwene/fu6d69e958883ccsstXxop//a3v2XGjBlLeKTAt4XTvQEAAIA6NXLkyFRUVKR3797Vy/r06ZMXX3wxlZWVtdafNGlSLrnkkpx33nlLcpjAt4hICQAAANSpcePGpXXr1mnQoEH1snbt2qW8vDyTJ0+utf6vf/3r7LbbbunSpcsSHCXwbeJ0bwAAAKBOzZo1q0agTFL9eM6cOTWWP/XUUxkxYkT+/ve/L7HxAd8+ZlICAAAAdaphw4a1YuSCx40aNapeNnv27PTv3z/nnHNOjeXA94+ZlAAAAECdat++fSZNmpSKioqUlc1PD+PGjUujRo3SokWL6vVeeumlfPDBBznuuONqbP+zn/0su+66q2tUwveISAkAAADUqW7duqWsrCwvvPBC1l133STJiBEj0rNnz5SUfHZSZ69evfLggw/W2Ha77bbL+eefn0022WSJjhkolkgJAAAA1KnGjRtn1113zYABA/KrX/0qY8eOzZAhQ3LhhRcmmT+rsnnz5mnUqFFWXnnlWtu3b98+bdu2XdLDBgrkmpQAAABAnTvjjDPSvXv3HHTQQTn33HPTr1+/bLfddkmSvn375t577y14hMC3iZmUAAAAQJ1r3LhxLrroolx00UW1nhs1atSXbvdVzwFLLzMpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKJRICQAAAAAUSqQEAAAAAAolUgIAAAAAhRIpAQAAAIBCiZQAAAAAQKFESgAAAACgUCIlAAAAAFAokRIAAAAAKFRZ0QMAAACA76X69ZM11ix6FItuev2iRwAsxcykBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQomUAAAAAEChREoAAAAAoFAiJQAAAABQKJESAAAAACiUSAkAAAAAFEqkBAAAAAAKJVICAAAAAIUSKQEAAACAQpUVPQD4bzXcbLO0PO3UlK2xeirHjcv0392c6ddd95XbNNp6q7Q44YTU79o18yZPyqz/uzdTf31RqmbNql6nrHPntDz7rDTcaMOkYl7Kn346k8/7Zea9/36SZLmnn0rZiisudP8V77+f0RttUncHCUvYg6Mm5Rf3vZdXR89M++b1c/TGy+fELTqkXr16C13/rfGzssaFI2ot775ck7x0yjrfaL9vjZ+VE+9+J0+8MzVlJfXy47Xa5dc7rpIWjbxV8d324Njp6T9ybF6dVp72Dcty1Cqtc2Lntl/+dTV9TroOe6vW8u7NG+bFLTvnn+NnZJun3vvS1+u/xjLpv8YyNZZVVFZlsyf/kx8s0zTndF32fzsgWAoMfnp0rnrs4/xn0uys1Kphjtlk+Ry1yfJf+nUJACx+S+1PfuXl5fnTn/6Ue+65J//5z39SWlqaTp06ZZ999skuu+xS/R+QZ555JgceeGCt7evXr582bdpkww03zCmnnJJlllmm1joUp8E6vdNu6E2Zec89mXLJpWm4/nppefaZqVdWmmm/vXqh2zTadpu0vfGGzLz9r5ly4YUp69IlLU8/LaVt22bisf2SJKUrLJ9l7r4zFW+/nYnHHJt6jRunxamnZJk/3pLR22ybzJ6dCYf+LPUaNqg5nj590mrAOZnx+z8s9mOHxeXp96Zm5xtfy15rt8u526+cJ9+dmtP+7z+pqKzKaVsvPMy/8NGMJMlDR/ZIkwafTc5vUr/0G+138qyKbHPNK1muef3c9JPVM3b63Jz+93fz7sTZue/wHovxqGHxenrizOzyzPvZq0PLDOi6bJ6cMDOnvzY2FVXJaV3aLXSbF6fOTpI8uNHKaVL6WTBpUjr/a2ydlo3yRN9Vam3Xf+S4PDd5Vvbp0KLG8tnzKnPw8x9n+KRZ+cEyTevoyOC764anR+fI297KsX2Xz87dO+WJd6fkuLveyeyKypy4RceihwcA31tLZaQcP358DjvssHzyySc54IAD0qtXr1RWVuYf//hHTj/99Dz33HP55S9/WeM3pf3790/37t2rH8+YMSMjRozI9ddfn3fffTe33XZbEYfCl2hx0kmZ+8qrmXTc8UmS8n/+M/XKytK837GZduOQZPbsWtu0OueczPq/ezPpxJPmb/PkU6lXWppmP/1p6jVqlKrZs9PixBNTNXVqxu/9k1R9uo+K9z9Iu5uGpEGvXpkzfHjmvvpqjf3Wa9YsbX47KLMefjjTrr5m8R44LEYD7n8/vTs0zc37rpEk2b5r68ydV5kLH/kwx222Qhp/Ljwu8OLHM9KxZYNs1aXV/7Tfa5/6JBNmzs1zJ6ydds3qJ0k6tmyQHW94LU++OzWbrNriS/cP32bnjhqXtVs2ytB1OiRJtl+2WeZWVeXXb47PcZ3apHFp7SvvvDBldjo2KstWXxIUW9QvzYZtmtRYds/oaRk2fkZuXbdjVm/WsHr54xNm5Ocvj86Hsyrq8Kjgu+13w8ek76ot8pvdOidJtl69VUaNnZXfPvmJSAkABVoqr0l52mmnZfTo0bn11ltz7LHHZrPNNssWW2yRc889NyeccEJuu+22DBs2rMY2q622WtZee+3qP5tsskmOO+64HHzwwXnppZfy1lu1T7uiIA0apOFGG2bW/ffXWDzz/+5NSfPmabjeerU2qd+9e8pWXSXTh9xUY/n0G4dk9CZ9q4Nk4x1+mBm3/qX6cZLMfemlfNJn3cwZPnyhw2nx8+NS0q5dJp959v96ZFCY8orKPPr2lOzao22N5Xv0apdp5fPyxDtTF7rdCx9Nz1odvnxm1qLu98FRk9J31RbVgTJJtlujdZo3LM19r0/8bw8LClU+rzKPTpiZXZevGdn3WL5FplVU5okJMxe63YtTZ2etlo0W+XVmzavMz18enR3aN8seK9R8rd2Gf5AVG9fPs5uv+s0PAJZSsysq07xRzV+8tW1aPxNmiPkAUKRvFCm32mqrXHXVVbnooouy8cYbp1evXjn00EPzn//8p3qdJ598Mvvuu2/69OmTDTbYICeddFI++eST6ufvuOOOrLnmmnnxxRez9957p2fPntlyyy1z4403fu3rn3766TnggANy++23Z8stt0zv3r1z0EEHZeTIkdXrvP7663niiSdy6KGHZpVVVqm1j4MPPjj77bdfmjRpUuu5hWnRYv5/9l2f5tujbKWVUq9hw8x9550ayys+/Tws69yp1jb1P50lW1VenrZDb0qHt97MCq+8nJbnnpM0mH/qdumKK6akZcvM+/DDtLrg/Kzwysvp8PabaTvkxpQuv9xCx1K6wgppduhPM/2aazPvo4/q8ChhyXpnwuzMmVeVLss0rrF8tXbzH78xbtbCNsuLH8/I9PJ56XvVi2ly2pNZYcAzOePv/8nceZXfaL+vj5mV1b+wTmlJvazapmFGfclrw7fdOzPnZk5lVVZvWvMSIat9+viNGXMWut2LU2ZnWkVl+j7+bpr+/fV0eGBUznhtTOZWVi10/avemZiPZs/N5d1rv1f9Y5NVcvcGK2XlJg0WsiV8P/XbdIU8OGpS/jBibKbMqsgDIyfl5ufGZv8+rtcKAEX6xjMpb7755rzzzju58MILc/755+eVV17JaaedliS566678tOf/jTLL798Lr/88pxxxhl5/vnns/fee2fChAnV+6isrMzxxx+fHXbYIddff33WWWedXHzxxXn88ce/9vVff/31XHHFFTn22GNzySWXZNKkSdl///0zduzYJKnex1ZbbbXQ7Rs2bJj+/ftno402qrG8srIyFRUV1X8mT56cBx98MDfeeGN69eqVVVc1A+HboqRF8yRJ1fTpNZYveFzSvHntbdq2SZK0vXFwKka9kfEHHpipv/1tmu23f9pcftmn68yf6dXyzDNTutxymXD0MZl0yqmp37NH2t32l9Rr3LjWfpv97LBUzZkz/xRz+A6bMnv+7JEv3qSmecP5M02mzp5Xa5vx0+fmoylzMnLsrBy+8XK57/Ae+dmGy+XKxz7KIX968xvtd8rsioXeIKd5w7KFvjZ8F0yZO/9zt3n9mv/dal42//HUuQv5uiqvyEezKzJqenmOWKV17t1wpRy2cuv85p2J+enztX8ZNqeyKgPfmZi9V2iZ1ZrVDpE9Wyz6jEz4vvhJ72Wyf59lc9Af30ibs5/ODoNfzcarNM8Vu/r/PgAU6Rtfk7JFixa5+uqrU1o6/wfM999/PwMHDsykSZNy6aWXpm/fvrnsssuq119nnXWyww475MYbb8ypp56aJKmqqsrRRx+dPffcM0nSp0+fPPTQQ/nnP/+ZTTfd9Ctff9q0abn22muz7rrrJkl69eqVbbbZJjfffHNOPvnk6lmbHTt+s+vJHHzwwbWWtWzZMltvvXVOOeWUlJQslWfGfzd93b9FZWWtRfU+nS056777M+VXFyZJyp/6V+qVlKTlmWdk6uWXp179+aeZzhs/LhMO+1lSNX/GSsV//pNl7/lbmuy+W2bc8sfPdtqwYZrus3dm/PnWVE2ZUgcHBsVZyJdNDSULmUzetGFJ7j+8e7os0zirtJkfQjbv3DINy0ryi/vey1nbrrjI+/2SCWJf+trwXfA1n/4pWchZGk3LSnLfhiulS7MGWeXT2Y+bt2uahiX10n/kuJy5enm6Nf/smpN//XhqRpdX5KTV2tbaF7Bwu970ep58d2ou2nGVrLdS87zyyYyc+8D72WvoyNxxSDdnUAFAQb5xeevZs2d1oEyS5Zabf2rRm2++mXHjxmXHHXessf5KK62U3r17Z/gXrufXu3fv6r83aNAgbdq0ycyZ86/NNG/evBqzGufN+2ymQceOHasDZZIsu+yy6d27d5599tkkqR7b57dZFOeee25uv/32/OUvf8kRRxyR0tLSHHDAAbnwwgvTpk2bb7QvFq/KqdOSJPWaNquxvN6nMygrp02rtc2CWZazH36kxvLZ//xnkqR+jx6pmvHpOv/4Z3WgTJI5/34+lVOmpH6PmncYbrTZZilp0SIz77jzvz8Y+JZo2Xj+985p5TWvxzX108ctGtf+nVbj+qXZdo3W1YFygR26tU4y/1TwRd1vy0altdZZsF7LhcywhO+Clp/OmJxeUTNXTv308YLnP69xaUm2XbZZdaBcYIf289/jXppa88Zwf/14aro3b/iNrmEJ32dPvTs1D4yclMt2XjUnb9kxm3dumWP6rpDf7bt6/vbqxPzf65OKHiIAfG9940jZ+AunvC6YYbggDrZr167WNu3atcu0L4SjRo1q/me6pKQkVZ+GoYM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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "feature_selection_method = config_dict[\"feature_selection\"]\n", "\n", "output_file = str(Path(os.environ[\"ROOT_FOLDER\"]).joinpath(\n", " experiment_name,\n", " f\"{experiment_name} {feature_selection_method} {aggregation} {reduction}_{k}.png\"))\n", "\n", "plot_title = f\"{experiment_name} SFFS {aggregation}\"\n", "import warnings\n", "with warnings.catch_warnings():\n", " warnings.simplefilter('ignore')\n", " report = evaluate_classifiers(ensemble_configuration, classifier_kwargs_list,\n", " train_feature_set, train_label_set,test_feature_set, test_label_set,\n", " \"SFFS\", aggregation, visualizers,output_file, plot_title)" ] }, { "cell_type": "markdown", "id": "da74ce48-358f-4761-8f43-b6efc51f3dee", "metadata": {}, "source": [ "## Ensemble Model Test Set Report" ] }, { "cell_type": "code", "execution_count": 29, "id": "6ccaa647-ac4c-4466-85d8-fe4472b65c4a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'0': {'precision': 0.6666666666666666,\n", " 'recall': 0.5,\n", " 'f1-score': 0.5714285714285715,\n", " 'support': 8.0},\n", " '1': {'precision': 0.6,\n", " 'recall': 0.75,\n", " 'f1-score': 0.6666666666666665,\n", " 'support': 8.0},\n", " 'accuracy': 0.625,\n", " 'macro avg': {'precision': 0.6333333333333333,\n", " 'recall': 0.625,\n", " 'f1-score': 0.6190476190476191,\n", " 'support': 16.0},\n", " 'weighted avg': {'precision': 0.6333333333333333,\n", " 'recall': 0.625,\n", " 'f1-score': 0.6190476190476191,\n", " 'support': 16.0},\n", " 'roc_auc': 0.6328125}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "report" ] }, { "cell_type": "code", "execution_count": 30, "id": "e2663ac4-9a8b-42da-90d5-9eb0b883105d", "metadata": { "tags": [] }, "outputs": [], "source": [ "val_scores = []\n", "\n", "val_scores.append(\n", " {\"Metric\": metric,\n", " \"Experiment\": experiment_name,\n", " \"Score\": scores[0], \"Section\": f\"Validation Set [5-CV {reduction.capitalize()}]\"},\n", ")" ] }, { "cell_type": "code", "execution_count": 31, "id": "5921d1f7-8fb0-422d-8f70-7fbbdb062340", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "Experiment=Perfusion_Radiomics
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val_scores.append(\n", " {\"Metric\": metric,\n", " \"Experiment\": experiment_name,\n", " \"Score\": report['roc_auc'], \"Section\": f\"Test Set [k={k}]\"})\n", "\n", "val_scores = pd.DataFrame.from_records(val_scores)\n", "val_scores.to_excel(Path(os.environ[\"ROOT_FOLDER\"]).joinpath(experiment_name, f\"{plot_title}.xlsx\"))\n", "\n", "px.bar(val_scores, x='Section', y='Score', color=\"Experiment\", text_auto=True, title=plot_title,\n", " barmode='group')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 5 }