diff --git a/NeuralNetwork.ipynb b/NeuralNetwork.ipynb new file mode 100644 index 0000000..dcc6de8 --- /dev/null +++ b/NeuralNetwork.ipynb @@ -0,0 +1,3125 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b49e1760-bb2f-49c7-9b65-44960f9b3571", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9c0269c0-ebb6-4e08-875b-d65b3d80f7d7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-12 10:01:17.237220: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error\n", + "from sklearn.metrics import roc_auc_score, RocCurveDisplay" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6cecf819-5aac-4c5e-8099-2af286e34abd", + "metadata": {}, + "outputs": [], + "source": [ + "#Load the data\n", + "folder='/home/unipi/v.vichi3/Desktop/'\n", + "X_train, X_val, X_test, y_train, y_val, y_test=np.load(folder+'X_train.npy'), np.load(folder+'X_val.npy'), np.load(folder+'X_test.npy'), np.load(folder+'y_train.npy'), np.load(folder+'y_val.npy'), np.load(folder+'y_test.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "49dac575-43c3-44ba-90a3-ba2869eb58eb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-12 10:01:54.035011: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2024-05-12 10:01:54.035943: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n", + "2024-05-12 10:01:54.047761: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n", + "2024-05-12 10:01:54.047780: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (a4-lab19): /proc/driver/nvidia/version does not exist\n", + "2024-05-12 10:01:54.048081: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-05-12 10:01:54.048625: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n" + ] + } + ], + "source": [ + "#Define the model\n", + "model=keras.models.Sequential()\n", + "model.add(keras.layers.Dense(units=32,\n", + " activation=\"relu\",\n", + " input_dim=X_train.shape[1],\n", + " kernel_initializer=keras.initializers.RandomNormal(mean=0.0,stddev=0.1)))\n", + "model.add(keras.layers.Dense(units=32,\n", + " activation=\"sigmoid\",\n", + " kernel_initializer=keras.initializers.RandomNormal(mean=0.0,stddev=0.1)))\n", + "model.add(keras.layers.Dense(units=64,\n", + " activation=\"sigmoid\",\n", + " kernel_initializer=keras.initializers.RandomNormal(mean=0.0,stddev=0.1)))\n", + "model.add(keras.layers.Dense(units=1,\n", + " activation=\"relu\",\n", + " kernel_initializer=keras.initializers.RandomNormal(mean=0.0,stddev=0.1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "abbaf178-ecf4-4d52-bc24-52d49de137f3", + "metadata": {}, + "outputs": [], + "source": [ + "def r2_score(y_true, y_pred):\n", + " SS_res = keras.backend.sum(keras.backend.square( y_true-y_pred ))\n", + " SS_tot = keras.backend.sum(keras.backend.square( y_true - keras.backend.mean(y_true) ) )\n", + " return ( 1 - SS_res/(SS_tot + keras.backend.epsilon()) )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "886b8252-ffc4-4de1-b1d1-966bdd3ba15b", + "metadata": {}, + "outputs": [], + "source": [ + "model.load_weights(folder+'nn_model1_1000epochs.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6c116269-1c09-469d-b107-5f5c525df0a3", + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(\n", + " optimizer=\"adam\",\n", + " loss=\"mean_squared_error\",\n", + " metrics=[\"mean_absolute_error\",r2_score]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5244e4cd-0a17-45a1-8a53-2e82deaf3f58", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense (Dense) (None, 32) 192 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 32) 1056 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 64) 2112 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 1) 65 \n", + "=================================================================\n", + "Total params: 3,425\n", + "Trainable params: 3,425\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "#Display the model's architecture\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "90ae96fc-3764-407e-8fcd-0ac3d3d07e24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1/1637 [..............................] - ETA: 4:16 - loss: 8.4898e-05 - mean_absolute_error: 0.0074 - r2_score: 0.9951" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-12 10:02:04.694166: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n", + "2024-05-12 10:02:04.694441: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3892690000 Hz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1637/1637 [==============================] - 1s 253us/step - loss: 1.0265e-04 - mean_absolute_error: 0.0073 - r2_score: 0.9932\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.00010416494478704408, 0.007332309614866972, 0.9931228756904602]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(X_test,y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f13eb2f1-8118-4808-bdd7-420f25e5248a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/500\n", + "18750/18750 [==============================] - 8s 393us/step - loss: 2.4147e-04 - mean_absolute_error: 0.0111 - r2_score: 0.9840 - val_loss: 2.1745e-04 - val_mean_absolute_error: 0.0103 - val_r2_score: 0.9856\n", + "Epoch 2/500\n", + "18750/18750 [==============================] - 7s 384us/step - loss: 2.4598e-04 - mean_absolute_error: 0.0112 - r2_score: 0.9837 - val_loss: 2.0596e-04 - val_mean_absolute_error: 0.0106 - val_r2_score: 0.9864\n", + "Epoch 3/500\n", + "18750/18750 [==============================] - 7s 389us/step - loss: 2.3998e-04 - mean_absolute_error: 0.0111 - r2_score: 0.9841 - val_loss: 2.4425e-04 - val_mean_absolute_error: 0.0114 - val_r2_score: 0.9839\n", + "Epoch 4/500\n", + "18750/18750 [==============================] - 7s 387us/step - loss: 2.3892e-04 - mean_absolute_error: 0.0111 - r2_score: 0.9842 - val_loss: 1.8591e-04 - val_mean_absolute_error: 0.0099 - val_r2_score: 0.9878\n", + "Epoch 5/500\n", + "18750/18750 [==============================] - 7s 382us/step - loss: 2.3697e-04 - mean_absolute_error: 0.0111 - r2_score: 0.9844 - val_loss: 2.7226e-04 - val_mean_absolute_error: 0.0116 - val_r2_score: 0.9820\n", + "Epoch 6/500\n", + "18750/18750 [==============================] - 7s 382us/step - loss: 2.4067e-04 - mean_absolute_error: 0.0111 - r2_score: 0.9841 - val_loss: 2.6640e-04 - val_mean_absolute_error: 0.0120 - val_r2_score: 0.9824\n", + "Epoch 7/500\n", + "18750/18750 [==============================] - 7s 383us/step - loss: 2.4342e-04 - mean_absolute_error: 0.0111 - r2_score: 0.9839 - val_loss: 1.9361e-04 - val_mean_absolute_error: 0.0100 - val_r2_score: 0.9873\n", + "Epoch 8/500\n", + "18750/18750 [==============================] - 8s 406us/step - loss: 2.3618e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9844 - val_loss: 1.9148e-04 - val_mean_absolute_error: 0.0097 - val_r2_score: 0.9874\n", + "Epoch 9/500\n", + "18750/18750 [==============================] - 7s 387us/step - loss: 2.3705e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9843 - val_loss: 1.8227e-04 - val_mean_absolute_error: 0.0096 - val_r2_score: 0.9880\n", + "Epoch 10/500\n", + "18750/18750 [==============================] - 7s 397us/step - loss: 2.3720e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9843 - val_loss: 2.8300e-04 - val_mean_absolute_error: 0.0123 - val_r2_score: 0.9812\n", + "Epoch 11/500\n", + "18750/18750 [==============================] - 8s 401us/step - loss: 2.3581e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9844 - val_loss: 1.5858e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9896\n", + "Epoch 12/500\n", + "18750/18750 [==============================] - 8s 400us/step - loss: 2.3420e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9846 - val_loss: 1.8181e-04 - val_mean_absolute_error: 0.0095 - val_r2_score: 0.9880\n", + "Epoch 13/500\n", + "18750/18750 [==============================] - 7s 392us/step - loss: 2.3380e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9845 - val_loss: 2.3095e-04 - val_mean_absolute_error: 0.0108 - val_r2_score: 0.9849\n", + "Epoch 14/500\n", + "18750/18750 [==============================] - 7s 393us/step - loss: 2.3374e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9846 - val_loss: 2.3746e-04 - val_mean_absolute_error: 0.0111 - val_r2_score: 0.9846\n", + "Epoch 15/500\n", + "18750/18750 [==============================] - 7s 388us/step - loss: 2.3588e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9844 - val_loss: 1.8413e-04 - val_mean_absolute_error: 0.0096 - val_r2_score: 0.9879\n", + "Epoch 16/500\n", + "18750/18750 [==============================] - 7s 392us/step - loss: 2.3772e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9842 - val_loss: 2.5248e-04 - val_mean_absolute_error: 0.0111 - val_r2_score: 0.9834\n", + "Epoch 17/500\n", + "18750/18750 [==============================] - 7s 386us/step - loss: 2.3437e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9845 - val_loss: 1.6079e-04 - val_mean_absolute_error: 0.0089 - val_r2_score: 0.9895\n", + "Epoch 18/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 2.4294e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9840 - val_loss: 1.6717e-04 - val_mean_absolute_error: 0.0091 - val_r2_score: 0.9890\n", + "Epoch 19/500\n", + "18750/18750 [==============================] - 7s 387us/step - loss: 2.3357e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9846 - val_loss: 1.6934e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9889\n", + "Epoch 20/500\n", + "18750/18750 [==============================] - 8s 402us/step - loss: 2.3630e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9844 - val_loss: 2.2015e-04 - val_mean_absolute_error: 0.0112 - val_r2_score: 0.9854\n", + "Epoch 21/500\n", + "18750/18750 [==============================] - 8s 410us/step - loss: 2.3163e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9847 - val_loss: 2.0513e-04 - val_mean_absolute_error: 0.0103 - val_r2_score: 0.9865\n", + "Epoch 22/500\n", + "18750/18750 [==============================] - 8s 405us/step - loss: 2.3465e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9844 - val_loss: 2.9082e-04 - val_mean_absolute_error: 0.0131 - val_r2_score: 0.9808\n", + "Epoch 23/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 2.3996e-04 - mean_absolute_error: 0.0110 - r2_score: 0.9841 - val_loss: 3.1719e-04 - val_mean_absolute_error: 0.0132 - val_r2_score: 0.9790\n", + "Epoch 24/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.3319e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9846 - val_loss: 2.7639e-04 - val_mean_absolute_error: 0.0125 - val_r2_score: 0.9817\n", + "Epoch 25/500\n", + "18750/18750 [==============================] - 8s 417us/step - loss: 2.2851e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9849 - val_loss: 1.8782e-04 - val_mean_absolute_error: 0.0097 - val_r2_score: 0.9877\n", + "Epoch 26/500\n", + "18750/18750 [==============================] - 8s 400us/step - loss: 2.3122e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9847 - val_loss: 1.7076e-04 - val_mean_absolute_error: 0.0093 - val_r2_score: 0.9888\n", + "Epoch 27/500\n", + "18750/18750 [==============================] - 8s 400us/step - loss: 2.3099e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9847 - val_loss: 2.7527e-04 - val_mean_absolute_error: 0.0120 - val_r2_score: 0.9818\n", + "Epoch 28/500\n", + "18750/18750 [==============================] - 7s 393us/step - loss: 2.2881e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9849 - val_loss: 4.2009e-04 - val_mean_absolute_error: 0.0154 - val_r2_score: 0.9722\n", + "Epoch 29/500\n", + "18750/18750 [==============================] - 7s 390us/step - loss: 2.3413e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9845 - val_loss: 3.7911e-04 - val_mean_absolute_error: 0.0138 - val_r2_score: 0.9749\n", + "Epoch 30/500\n", + "18750/18750 [==============================] - 7s 391us/step - loss: 2.2946e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9848 - val_loss: 1.9619e-04 - val_mean_absolute_error: 0.0103 - val_r2_score: 0.9871\n", + "Epoch 31/500\n", + "18750/18750 [==============================] - 7s 398us/step - loss: 2.3257e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9846 - val_loss: 1.5597e-04 - val_mean_absolute_error: 0.0089 - val_r2_score: 0.9898\n", + "Epoch 32/500\n", + "18750/18750 [==============================] - 7s 396us/step - loss: 2.2773e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9849 - val_loss: 1.4662e-04 - val_mean_absolute_error: 0.0085 - val_r2_score: 0.9904\n", + "Epoch 33/500\n", + "18750/18750 [==============================] - 7s 400us/step - loss: 2.3321e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9846 - val_loss: 3.3411e-04 - val_mean_absolute_error: 0.0126 - val_r2_score: 0.9778\n", + "Epoch 34/500\n", + "18750/18750 [==============================] - 7s 390us/step - loss: 2.2646e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9850 - val_loss: 2.0536e-04 - val_mean_absolute_error: 0.0104 - val_r2_score: 0.9865\n", + "Epoch 35/500\n", + "18750/18750 [==============================] - 7s 396us/step - loss: 2.2997e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9848 - val_loss: 1.8161e-04 - val_mean_absolute_error: 0.0096 - val_r2_score: 0.9880\n", + "Epoch 36/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.3087e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9848 - val_loss: 3.7053e-04 - val_mean_absolute_error: 0.0146 - val_r2_score: 0.9757\n", + "Epoch 37/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 2.3304e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9846 - val_loss: 2.5159e-04 - val_mean_absolute_error: 0.0114 - val_r2_score: 0.9836\n", + "Epoch 38/500\n", + "18750/18750 [==============================] - 8s 415us/step - loss: 2.3301e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9845 - val_loss: 3.9798e-04 - val_mean_absolute_error: 0.0136 - val_r2_score: 0.9738\n", + "Epoch 39/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.2696e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9850 - val_loss: 1.6641e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9890\n", + "Epoch 40/500\n", + "18750/18750 [==============================] - 8s 419us/step - loss: 2.3131e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9847 - val_loss: 3.5746e-04 - val_mean_absolute_error: 0.0136 - val_r2_score: 0.9762\n", + "Epoch 41/500\n", + "18750/18750 [==============================] - 8s 417us/step - loss: 2.2500e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9852 - val_loss: 6.0576e-04 - val_mean_absolute_error: 0.0167 - val_r2_score: 0.9602\n", + "Epoch 42/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 2.2902e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9849 - val_loss: 1.7194e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9887\n", + "Epoch 43/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.2706e-04 - mean_absolute_error: 0.0108 - r2_score: 0.9849 - val_loss: 2.3367e-04 - val_mean_absolute_error: 0.0113 - val_r2_score: 0.9845\n", + "Epoch 44/500\n", + "18750/18750 [==============================] - 8s 417us/step - loss: 2.3242e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9846 - val_loss: 2.0060e-04 - val_mean_absolute_error: 0.0103 - val_r2_score: 0.9868\n", + "Epoch 45/500\n", + "18750/18750 [==============================] - 8s 420us/step - loss: 2.2993e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9848 - val_loss: 1.9677e-04 - val_mean_absolute_error: 0.0099 - val_r2_score: 0.9870\n", + "Epoch 46/500\n", + "18750/18750 [==============================] - 8s 424us/step - loss: 2.3383e-04 - mean_absolute_error: 0.0109 - r2_score: 0.9845 - val_loss: 1.9046e-04 - 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loss: 2.1067e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9861 - val_loss: 1.6689e-04 - val_mean_absolute_error: 0.0092 - val_r2_score: 0.9890\n", + "Epoch 161/500\n", + "18750/18750 [==============================] - 8s 415us/step - loss: 2.0665e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9864 - val_loss: 1.7574e-04 - val_mean_absolute_error: 0.0093 - val_r2_score: 0.9885\n", + "Epoch 162/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 2.0581e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9864 - val_loss: 1.9030e-04 - val_mean_absolute_error: 0.0098 - val_r2_score: 0.9875\n", + "Epoch 163/500\n", + "18750/18750 [==============================] - 8s 418us/step - loss: 2.0797e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9863 - val_loss: 1.8410e-04 - val_mean_absolute_error: 0.0097 - val_r2_score: 0.9879\n", + "Epoch 164/500\n", + "18750/18750 [==============================] - 8s 405us/step - loss: 2.0690e-04 - mean_absolute_error: 0.0102 - 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val_mean_absolute_error: 0.0089 - val_r2_score: 0.9900\n", + "Epoch 169/500\n", + "18750/18750 [==============================] - 8s 422us/step - loss: 2.0755e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9863 - val_loss: 1.8963e-04 - val_mean_absolute_error: 0.0093 - val_r2_score: 0.9875\n", + "Epoch 170/500\n", + "18750/18750 [==============================] - 8s 413us/step - loss: 2.0652e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9863 - val_loss: 1.6038e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9894\n", + "Epoch 171/500\n", + "18750/18750 [==============================] - 8s 419us/step - loss: 2.0488e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 1.4526e-04 - val_mean_absolute_error: 0.0086 - val_r2_score: 0.9905\n", + "Epoch 172/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.0631e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9864 - val_loss: 1.8382e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9879\n", + "Epoch 173/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.0643e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9864 - val_loss: 1.7316e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9887\n", + "Epoch 174/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 2.0698e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9863 - val_loss: 2.2814e-04 - val_mean_absolute_error: 0.0114 - val_r2_score: 0.9848\n", + "Epoch 175/500\n", + "18750/18750 [==============================] - 8s 420us/step - loss: 2.0505e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 2.5427e-04 - val_mean_absolute_error: 0.0119 - val_r2_score: 0.9832\n", + "Epoch 176/500\n", + "18750/18750 [==============================] - 8s 411us/step - loss: 2.1037e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9861 - val_loss: 2.2106e-04 - val_mean_absolute_error: 0.0107 - val_r2_score: 0.9854\n", + "Epoch 177/500\n", + "18750/18750 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loss: 2.0812e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9863 - val_loss: 1.6460e-04 - val_mean_absolute_error: 0.0092 - val_r2_score: 0.9892\n", + "Epoch 182/500\n", + "18750/18750 [==============================] - 8s 421us/step - loss: 2.0670e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9864 - val_loss: 2.5581e-04 - val_mean_absolute_error: 0.0123 - val_r2_score: 0.9830\n", + "Epoch 183/500\n", + "18750/18750 [==============================] - 8s 415us/step - loss: 2.0118e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9866 - val_loss: 2.3395e-04 - val_mean_absolute_error: 0.0109 - val_r2_score: 0.9845\n", + "Epoch 184/500\n", + "18750/18750 [==============================] - 8s 417us/step - loss: 2.0434e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 1.8383e-04 - val_mean_absolute_error: 0.0096 - val_r2_score: 0.9879\n", + "Epoch 185/500\n", + "18750/18750 [==============================] - 8s 410us/step - loss: 2.0421e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 1.6686e-04 - val_mean_absolute_error: 0.0093 - val_r2_score: 0.9890\n", + "Epoch 186/500\n", + "18750/18750 [==============================] - 8s 409us/step - loss: 2.0335e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 2.1468e-04 - val_mean_absolute_error: 0.0104 - val_r2_score: 0.9859\n", + "Epoch 187/500\n", + "18750/18750 [==============================] - 8s 412us/step - loss: 2.0973e-04 - mean_absolute_error: 0.0103 - r2_score: 0.9861 - val_loss: 2.0780e-04 - val_mean_absolute_error: 0.0102 - val_r2_score: 0.9863\n", + "Epoch 188/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 2.0483e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 2.3762e-04 - val_mean_absolute_error: 0.0109 - val_r2_score: 0.9843\n", + "Epoch 189/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.0251e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9866 - val_loss: 3.2795e-04 - 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loss: 2.0418e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9865 - val_loss: 1.7855e-04 - val_mean_absolute_error: 0.0096 - val_r2_score: 0.9883\n", + "Epoch 224/500\n", + "18750/18750 [==============================] - 8s 412us/step - loss: 1.9885e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 2.1109e-04 - val_mean_absolute_error: 0.0109 - val_r2_score: 0.9860\n", + "Epoch 225/500\n", + "18750/18750 [==============================] - 8s 411us/step - loss: 2.0104e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9867 - val_loss: 1.4623e-04 - val_mean_absolute_error: 0.0087 - val_r2_score: 0.9904\n", + "Epoch 226/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 2.0123e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9867 - val_loss: 2.0507e-04 - val_mean_absolute_error: 0.0106 - val_r2_score: 0.9866\n", + "Epoch 227/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 1.9973e-04 - mean_absolute_error: 0.0101 - 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loss: 1.9817e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9868 - val_loss: 1.4602e-04 - val_mean_absolute_error: 0.0088 - val_r2_score: 0.9904\n", + "Epoch 245/500\n", + "18750/18750 [==============================] - 8s 407us/step - loss: 1.9809e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.4179e-04 - val_mean_absolute_error: 0.0084 - val_r2_score: 0.9907\n", + "Epoch 246/500\n", + "18750/18750 [==============================] - 8s 409us/step - loss: 1.9766e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.4565e-04 - val_mean_absolute_error: 0.0085 - val_r2_score: 0.9905\n", + "Epoch 247/500\n", + "18750/18750 [==============================] - 8s 413us/step - loss: 1.9986e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9868 - val_loss: 1.5946e-04 - val_mean_absolute_error: 0.0092 - val_r2_score: 0.9895\n", + "Epoch 248/500\n", + "18750/18750 [==============================] - 8s 417us/step - loss: 2.0258e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9866 - val_loss: 1.8918e-04 - val_mean_absolute_error: 0.0102 - val_r2_score: 0.9876\n", + "Epoch 249/500\n", + "18750/18750 [==============================] - 8s 420us/step - loss: 1.9960e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9868 - val_loss: 1.6536e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9892\n", + "Epoch 250/500\n", + "18750/18750 [==============================] - 8s 417us/step - loss: 1.9832e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.5631e-04 - val_mean_absolute_error: 0.0087 - val_r2_score: 0.9897\n", + "Epoch 251/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 2.0156e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9867 - val_loss: 1.5764e-04 - val_mean_absolute_error: 0.0087 - val_r2_score: 0.9896\n", + "Epoch 252/500\n", + "18750/18750 [==============================] - 8s 415us/step - loss: 1.9708e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 1.6328e-04 - val_mean_absolute_error: 0.0092 - val_r2_score: 0.9892\n", + "Epoch 253/500\n", + "18750/18750 [==============================] - 8s 409us/step - loss: 1.9931e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9868 - val_loss: 1.6282e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9893\n", + "Epoch 254/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 1.9726e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 2.7173e-04 - val_mean_absolute_error: 0.0120 - val_r2_score: 0.9823\n", + "Epoch 255/500\n", + "18750/18750 [==============================] - 8s 418us/step - loss: 1.9906e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.9092e-04 - val_mean_absolute_error: 0.0100 - val_r2_score: 0.9874\n", + "Epoch 256/500\n", + "18750/18750 [==============================] - 8s 418us/step - loss: 1.9731e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 1.8191e-04 - val_mean_absolute_error: 0.0102 - val_r2_score: 0.9880\n", + "Epoch 257/500\n", + "18750/18750 [==============================] - 8s 413us/step - loss: 1.9925e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9868 - val_loss: 1.8220e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9881\n", + "Epoch 258/500\n", + "18750/18750 [==============================] - 8s 430us/step - loss: 1.9533e-04 - mean_absolute_error: 0.0099 - r2_score: 0.9871 - val_loss: 2.0540e-04 - val_mean_absolute_error: 0.0107 - val_r2_score: 0.9864\n", + "Epoch 259/500\n", + "18750/18750 [==============================] - 8s 414us/step - loss: 1.9825e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.5711e-04 - val_mean_absolute_error: 0.0087 - val_r2_score: 0.9897\n", + "Epoch 260/500\n", + "18750/18750 [==============================] - 8s 416us/step - loss: 1.9677e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 1.7581e-04 - val_mean_absolute_error: 0.0096 - val_r2_score: 0.9884\n", + "Epoch 261/500\n", + "18750/18750 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loss: 2.0428e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 2.0118e-04 - val_mean_absolute_error: 0.0104 - val_r2_score: 0.9867\n", + "Epoch 392/500\n", + "18750/18750 [==============================] - 7s 396us/step - loss: 2.0301e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 2.0198e-04 - val_mean_absolute_error: 0.0106 - val_r2_score: 0.9867\n", + "Epoch 393/500\n", + "18750/18750 [==============================] - 7s 397us/step - loss: 2.0281e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 1.6845e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9890\n", + "Epoch 394/500\n", + "18750/18750 [==============================] - 7s 397us/step - loss: 2.0317e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 2.5192e-04 - val_mean_absolute_error: 0.0122 - val_r2_score: 0.9833\n", + "Epoch 395/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 2.0252e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 2.2945e-04 - val_mean_absolute_error: 0.0106 - val_r2_score: 0.9850\n", + "Epoch 396/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 2.0009e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9867 - val_loss: 2.2315e-04 - val_mean_absolute_error: 0.0111 - val_r2_score: 0.9853\n", + "Epoch 397/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 2.0284e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 1.6890e-04 - val_mean_absolute_error: 0.0092 - val_r2_score: 0.9889\n", + "Epoch 398/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 2.0450e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 1.7637e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9883\n", + "Epoch 399/500\n", + "18750/18750 [==============================] - 7s 397us/step - loss: 2.0227e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 1.9290e-04 - val_mean_absolute_error: 0.0100 - val_r2_score: 0.9874\n", + "Epoch 400/500\n", + "18750/18750 [==============================] - 7s 397us/step - loss: 2.0070e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9868 - val_loss: 1.6930e-04 - val_mean_absolute_error: 0.0092 - val_r2_score: 0.9888\n", + "Epoch 401/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 2.0087e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9867 - val_loss: 1.4508e-04 - val_mean_absolute_error: 0.0087 - val_r2_score: 0.9905\n", + "Epoch 402/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 2.0244e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9866 - val_loss: 3.4619e-04 - val_mean_absolute_error: 0.0141 - val_r2_score: 0.9768\n", + "Epoch 403/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 2.0364e-04 - mean_absolute_error: 0.0102 - r2_score: 0.9865 - val_loss: 3.2165e-04 - val_mean_absolute_error: 0.0133 - val_r2_score: 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loss: 1.9878e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9868 - val_loss: 1.9979e-04 - val_mean_absolute_error: 0.0099 - val_r2_score: 0.9868\n", + "Epoch 476/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9543e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.8494e-04 - val_mean_absolute_error: 0.0095 - val_r2_score: 0.9879\n", + "Epoch 477/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9673e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 1.5122e-04 - val_mean_absolute_error: 0.0087 - val_r2_score: 0.9901\n", + "Epoch 478/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9727e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.9779e-04 - val_mean_absolute_error: 0.0100 - val_r2_score: 0.9872\n", + "Epoch 479/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9497e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.8822e-04 - val_mean_absolute_error: 0.0100 - val_r2_score: 0.9877\n", + "Epoch 480/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9586e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.3897e-04 - val_mean_absolute_error: 0.0084 - val_r2_score: 0.9909\n", + "Epoch 481/500\n", + "18750/18750 [==============================] - 7s 396us/step - loss: 1.9679e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 1.9391e-04 - val_mean_absolute_error: 0.0104 - val_r2_score: 0.9872\n", + "Epoch 482/500\n", + "18750/18750 [==============================] - 7s 397us/step - loss: 1.9807e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 2.3741e-04 - val_mean_absolute_error: 0.0112 - val_r2_score: 0.9843\n", + "Epoch 483/500\n", + "18750/18750 [==============================] - 7s 396us/step - loss: 1.9648e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.7585e-04 - val_mean_absolute_error: 0.0094 - val_r2_score: 0.9885\n", + "Epoch 484/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9794e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.6669e-04 - val_mean_absolute_error: 0.0093 - val_r2_score: 0.9891\n", + "Epoch 485/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9685e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 1.9442e-04 - val_mean_absolute_error: 0.0098 - val_r2_score: 0.9873\n", + "Epoch 486/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9356e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9872 - val_loss: 2.6054e-04 - val_mean_absolute_error: 0.0109 - val_r2_score: 0.9832\n", + "Epoch 487/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9709e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 2.4525e-04 - val_mean_absolute_error: 0.0116 - val_r2_score: 0.9838\n", + "Epoch 488/500\n", + "18750/18750 [==============================] - 7s 396us/step - loss: 1.9573e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.7302e-04 - val_mean_absolute_error: 0.0096 - val_r2_score: 0.9886\n", + "Epoch 489/500\n", + "18750/18750 [==============================] - 7s 393us/step - loss: 1.9571e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.4382e-04 - val_mean_absolute_error: 0.0084 - val_r2_score: 0.9906\n", + "Epoch 490/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9539e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.5850e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9896\n", + "Epoch 491/500\n", + "18750/18750 [==============================] - 7s 393us/step - loss: 1.9440e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9872 - val_loss: 1.6654e-04 - val_mean_absolute_error: 0.0092 - val_r2_score: 0.9890\n", + "Epoch 492/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9648e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 2.3267e-04 - val_mean_absolute_error: 0.0111 - val_r2_score: 0.9846\n", + "Epoch 493/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9689e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9869 - val_loss: 1.7944e-04 - val_mean_absolute_error: 0.0098 - val_r2_score: 0.9881\n", + "Epoch 494/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9231e-04 - mean_absolute_error: 0.0099 - r2_score: 0.9873 - val_loss: 1.3785e-04 - val_mean_absolute_error: 0.0084 - val_r2_score: 0.9909\n", + "Epoch 495/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9615e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 2.4649e-04 - val_mean_absolute_error: 0.0117 - val_r2_score: 0.9839\n", + "Epoch 496/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9358e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9872 - val_loss: 1.7657e-04 - val_mean_absolute_error: 0.0098 - val_r2_score: 0.9884\n", + "Epoch 497/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9900e-04 - mean_absolute_error: 0.0101 - r2_score: 0.9868 - val_loss: 1.6527e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9891\n", + "Epoch 498/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9345e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9872 - val_loss: 1.5912e-04 - val_mean_absolute_error: 0.0090 - val_r2_score: 0.9895\n", + "Epoch 499/500\n", + "18750/18750 [==============================] - 7s 395us/step - loss: 1.9531e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9871 - val_loss: 1.9499e-04 - val_mean_absolute_error: 0.0100 - val_r2_score: 0.9871\n", + "Epoch 500/500\n", + "18750/18750 [==============================] - 7s 394us/step - loss: 1.9684e-04 - mean_absolute_error: 0.0100 - r2_score: 0.9870 - val_loss: 2.4936e-04 - val_mean_absolute_error: 0.0117 - val_r2_score: 0.9835\n", + "Model training time: 3793.7955465316772\n" + ] + } + ], + "source": [ + "#Train the model\n", + "start=time.time()\n", + "history=model.fit(X_train,y_train,\n", + " validation_data=(X_val,y_val),\n", + " batch_size=32,\n", + " epochs=500)\n", + "end=time.time()-start\n", + "print(\"Model training time:\",end)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4d34ec8b-2c98-433e-a8e6-1621f5f525ea", + "metadata": {}, + "outputs": [], + "source": [ + "#Save the trained model for future reference (if you want to reload the weights, you have to define a model with \n", + "#the same architecture and then write new_model.load_weights(folder+'nn_model1.h5'))\n", + "model.save(folder+'nn_model1_1000epochs.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f178b0ac-85c0-4a59-95d4-dfa74fdc089a", + "metadata": {}, + "outputs": [], + "source": [ + "#We want to understand which are the most important features, i.e. what the NN bases its prediction on\n", + "w0 = model.weights[0].numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "16450bb0-a1c0-4f16-b958-8b0511c9251c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31.204111\n", + "83.64697\n", + "0.9327214\n", + "0.5049178\n", + "0.76805365\n" + ] + } + ], + "source": [ + "for i in range(5):\n", + " print(np.linalg.norm(w0[i,:])) #we can see that the most important features are e, a; the angular variables are less important" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2a0b3591-760e-464b-ae42-8140e88f0ec5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'][:250])\n", + "plt.plot(history.history['val_loss'][:250])\n", + "plt.yscale('log')\n", + "plt.title('Model MSE')\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('mean squared error')\n", + "plt.legend(['training','validation'],loc='upper right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fb7e90ff-51a0-4a63-9550-39e25dfc98d7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['mean_absolute_error'][:250])\n", + "plt.plot(history.history['val_mean_absolute_error'][:250])\n", + "plt.yscale('log')\n", + "plt.title('Model MAE')\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('mean absolute error')\n", + "plt.legend(['training','validation'],loc='upper right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "259fc031-7912-40f9-b28a-4cf410586b1c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['r2_score'][:250])\n", + "plt.plot(history.history['val_r2_score'][:250])\n", + "plt.title('Model R2 Score')\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('R2 score')\n", + "plt.legend(['training','validation'],loc='lower right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "72382f07-3613-4c00-bdf5-77855c9eb501", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-11 17:31:21.244068: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n", + "2024-05-11 17:31:21.270182: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3892575000 Hz\n", + "2024-05-11 17:31:21.571777: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1637/1637 [==============================] - 3s 632us/step - loss: 1.0265e-04 - mean_absolute_error: 0.0073 - r2_score: 0.9932\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.00010416490113129839, 0.007332313805818558, 0.9931228160858154]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Evaluate the model\n", + "model.evaluate(X_test,y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "de69f7a1-b8f9-42d9-acfe-bc012f5f3925", + "metadata": {}, + "outputs": [], + "source": [ + "z=model.predict(X_test)[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b4d9830f-6986-491c-b703-6fea0a0b60ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "true: 0.037 , predicted: 0.03611117601394653 \n", + "\n", + "true: 0.1491 , predicted: 0.15110592544078827 \n", + "\n", + "true: 0.2769 , predicted: 0.2777317762374878 \n", + "\n", + "true: 0.2442 , predicted: 0.24263232946395874 \n", + "\n", + "true: 0.4245 , predicted: 0.42558854818344116 \n", + "\n", + "true: 0.2792 , predicted: 0.2945525646209717 \n", + "\n", + "true: 0.4637 , predicted: 0.44423896074295044 \n", + "\n", + "true: 0.1727 , predicted: 0.17932043969631195 \n", + "\n", + "true: 0.1143 , predicted: 0.12211602181196213 \n", + "\n", + "true: 0.2929 , predicted: 0.29719555377960205 \n", + "\n" + ] + } + ], + "source": [ + "n=10\n", + "for el in range(n):\n", + " print(f\"true: {y_test[el]} , predicted: {z[el]} \\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f9e8e120-4993-4ece-a639-a2a488b4d3d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(z[:100],'bo',markersize=4)\n", + "plt.plot(y_test[:100],'r*',markersize=4)\n", + "plt.legend(['predicted','true'],loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a2fccb9f-b114-4f81-a94d-08dc5bdf4547", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Distribution of the MOID and predicted MOID\n", + "num_bins=math.ceil(math.log2(len(y_test))+1) #Sturges rule\n", + "plt.hist(y_test,bins=num_bins,color='orange')\n", + "plt.hist(z,bins=num_bins,color='blue',histtype='step')\n", + "plt.xlabel('MOID')\n", + "plt.ylabel('count')\n", + "plt.legend(['true','predicted'],loc='upper right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "157d380e-95e7-4972-b17a-cf9d150a5a6d", + "metadata": {}, + "outputs": [], + "source": [ + "#Convert the problem into a classification problem: we are going to create an array containing 1 if the MOID is greater than 0.05, and 0 otherwise\n", + "#so the positive class is made of the non-hazardous objects\n", + "y_test_binary, z_binary = np.zeros_like(y_test),np.zeros_like(z)\n", + "for el in range(len(y_test)):\n", + " if (y_test[el]>0.05):\n", + " y_test_binary[el]=1\n", + " if (z[el]>0.05):\n", + " z_binary[el]=1" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e3011655-b0b1-4250-9274-44a6aa109657", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True positives: 42143\n", + "True negatives: 8949\n", + "False positives: 541\n", + "False negatives: 743\n" + ] + } + ], + "source": [ + "tp=np.count_nonzero((y_test_binary==1)&(z_binary==1))\n", + "tn=np.count_nonzero((y_test_binary==0)&(z_binary==0))\n", + "fp=np.count_nonzero((y_test_binary==0)&(z_binary==1))\n", + "fn=np.count_nonzero((y_test_binary==1)&(z_binary==0))\n", + "print(\"True positives:\", tp)\n", + "print(\"True negatives:\", tn)\n", + "print(\"False positives:\", fp)\n", + "print(\"False negatives:\", fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7b018ea6-6ae3-437b-bc27-4ef39f2852c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.9754849549411945\n", + "Precision: 0.9873254615312529\n", + "Recall: 0.9826749988341184\n", + "False positive rate: 0.057007376185458376\n", + "F1-score: 0.9849947411475984\n" + ] + } + ], + "source": [ + "acc=(tp+tn)/len(y_test) #accuracy\n", + "p=tp/(tp+fp) #precision\n", + "r=tp/(tp+fn) #recall\n", + "fpr=fp/(tn+fp) #false positive rate\n", + "f1=2*p*r/(p+r) #F1-score\n", + "print(\"Accuracy:\", acc)\n", + "print(\"Precision:\", p)\n", + "print(\"Recall:\", r)\n", + "print(\"False positive rate:\", fpr)\n", + "print(\"F1-score:\", f1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a08bbcaa-f926-476c-9505-41ec1b21c933", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9962262045277288" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roc_auc_score(y_test_binary,z)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4d4e75c1-457b-4ff3-8c37-1fef5310f948", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Unnamed: 0spkidphaHepoch_mjdeaiomwmamoid
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" + ], + "text/plain": [ + " Unnamed: 0 spkid pha H epoch_mjd e a i om \\\n", + "0 0 20000433 N 10.41 60400 0.2227 1.458 10.83 304.28 \n", + "1 1 20000719 N 15.59 60400 0.5469 2.636 11.58 183.85 \n", + "2 2 20000887 N 13.88 60400 0.5710 2.472 9.40 110.42 \n", + "3 3 20001036 N 9.26 60400 0.5328 2.665 26.69 215.50 \n", + "4 4 20001221 N 17.38 60400 0.4352 1.920 11.88 171.31 \n", + "\n", + " w ma moid \n", + "0 178.90 334.73 0.1500 \n", + "1 156.22 102.37 0.2010 \n", + "2 350.48 289.48 0.0803 \n", + "3 132.48 321.69 0.3450 \n", + "4 26.68 197.64 0.1080 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Now test the model on the real NEOs dataset\n", + "import pandas as pd\n", + "df=pd.read_csv(folder+'neos_dataframe.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7dc77a36-e409-4de6-bc5c-092f3ec471be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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spkidphaHepoch_mjdaeiomwmamoid
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" + ], + "text/plain": [ + " spkid pha H epoch_mjd a e i om w \\\n", + "0 20000433 N 10.41 60400 1.458 0.2227 10.83 304.28 178.90 \n", + "1 20000719 N 15.59 60400 2.636 0.5469 11.58 183.85 156.22 \n", + "2 20000887 N 13.88 60400 2.472 0.5710 9.40 110.42 350.48 \n", + "3 20001036 N 9.26 60400 2.665 0.5328 26.69 215.50 132.48 \n", + "4 20001221 N 17.38 60400 1.920 0.4352 11.88 171.31 26.68 \n", + "\n", + " ma moid \n", + "0 334.73 0.1500 \n", + "1 102.37 0.2010 \n", + "2 289.48 0.0803 \n", + "3 321.69 0.3450 \n", + "4 197.64 0.1080 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "desired_order=['spkid','pha','H','epoch_mjd','a','e','i','om','w','ma','moid']\n", + "df=df[desired_order]\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "772a4d3d-3134-4f2f-a8fe-a3cc0098f7f3", + "metadata": {}, + "outputs": [], + "source": [ + "X=df.iloc[:,4:9].to_numpy()\n", + "target=df.iloc[:,-1].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "0c6f6118-48fa-4260-8b97-e29918511d53", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1090/1090 [==============================] - 0s 256us/step - loss: 8.2916e-05 - mean_absolute_error: 0.0067 - r2_score: 0.9863\n" + ] + }, + { + "data": { + "text/plain": [ + "[8.291556150652468e-05, 0.00671815313398838, 0.9862860441207886]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(X,target)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "57c7cef4-dfb8-4887-ae2f-03b416b76f34", + "metadata": {}, + "outputs": [], + "source": [ + "predictions=model.predict(X)[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e4ef3370-09d4-4108-a002-49aea3ba1d00", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "true: 0.15 , predicted: 0.14063376188278198 \n", + "\n", + "true: 0.201 , predicted: 0.20540137588977814 \n", + "\n", + "true: 0.0803 , predicted: 0.06878837943077087 \n", + "\n", + "true: 0.345 , predicted: 0.35209476947784424 \n", + "\n", + "true: 0.108 , predicted: 0.1077912226319313 \n", + "\n", + "true: 0.0339 , predicted: 0.02879190444946289 \n", + "\n", + "true: 0.135 , predicted: 0.12797904014587402 \n", + "\n", + "true: 0.0302 , predicted: 0.017508333548903465 \n", + "\n", + "true: 0.112 , predicted: 0.11151407659053802 \n", + "\n", + "true: 0.0506 , predicted: 0.05622444674372673 \n", + "\n" + ] + } + ], + "source": [ + "n=10\n", + "for el in range(n):\n", + " print(f\"true: {target[el]} , predicted: {predictions[el]} \\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0dcbf78b-b9af-46e4-bf4e-f1a68560aba1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(predictions[:100],'bo',markersize=4)\n", + "plt.plot(target[:100],'r*',markersize=4)\n", + "plt.legend(['predicted','true'],loc='upper left')\n", + "plt.title(\"Actual and predicted values for the real NEOs DataFrame\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "bdf99fb4-6104-44e2-9fff-8818b91d0e37", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Distribution of the MOID and predicted MOID\n", + "num_bins=math.ceil(math.log2(len(target))+1) #Sturges rule\n", + "plt.hist(target,bins=num_bins,color='orange')\n", + "plt.hist(predictions,bins=num_bins,color='blue',histtype='step')\n", + "plt.xlabel('MOID')\n", + "plt.ylabel('count')\n", + "plt.legend(['true','predicted'],loc='upper right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "28ac08eb-15bd-4fb1-9b35-8eca4e867004", + "metadata": {}, + "outputs": [], + "source": [ + "target_binary, predictions_binary = np.zeros_like(target),np.zeros_like(predictions)\n", + "for el in range(len(target)):\n", + " if (target[el]>0.05):\n", + " target_binary[el]=1\n", + " if (predictions[el]>0.05):\n", + " predictions_binary[el]=1" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "fd3e671c-150f-472f-9296-ef83cdce7a60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True positives: 15859\n", + "True negatives: 17822\n", + "False positives: 469\n", + "False negatives: 714\n" + ] + } + ], + "source": [ + "tp=np.count_nonzero((target_binary==1)&(predictions_binary==1))\n", + "tn=np.count_nonzero((target_binary==0)&(predictions_binary==0))\n", + "fp=np.count_nonzero((target_binary==0)&(predictions_binary==1))\n", + "fn=np.count_nonzero((target_binary==1)&(predictions_binary==0))\n", + "print(\"True positives:\", tp)\n", + "print(\"True negatives:\", tn)\n", + "print(\"False positives:\", fp)\n", + "print(\"False negatives:\", fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "72508c9d-4168-42a8-9090-a46490711dec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.9660681505277651\n", + "Precision: 0.9712763351298384\n", + "Recall: 0.956917878477041\n", + "False positive rate: 0.02564102564102564\n", + "F1-score: 0.9640436460897847\n" + ] + } + ], + "source": [ + "acc=(tp+tn)/len(target) #accuracy\n", + "p=tp/(tp+fp) #precision\n", + "r=tp/(tp+fn) #recall\n", + "fpr=fp/(tn+fp) #false positive rate\n", + "f1=2*p*r/(p+r) #F1-score\n", + "print(\"Accuracy:\", acc)\n", + "print(\"Precision:\", p)\n", + "print(\"Recall:\", r)\n", + "print(\"False positive rate:\", fpr)\n", + "print(\"F1-score:\", f1)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "2435b2e3-7a2d-4be5-8361-84c709d3b0d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9951386031748715" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roc_auc_score(target_binary,predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3d77f238-af62-475a-8822-685a12b321e2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9fX0cOnRIJgFKvyvZpaWloW/fvnjy5AmCg4NRqVIlmeX79u1DQkICAgMD4ejoKJTfvHlT4Wsr+mwtLS1x7do1ufKcByTpD4Hc9quOjo7Qo9fQ0FDmupOUou9cr1690KtXL6SkpODKlStYtGgRvL294eTkBDc3N4XvA1Du+whkXb/z8fFBQkICzp8/Dz8/P3z++ed4+PChzD4rjNjYWBw6dAh+fn6YPn26UJ6SkoIPHz7I1Q8KCsLw4cMxbNgwzJ07V27533//jSFDhmDhwoUy5TExMShXrpxc/Zyfq/SzUpRUFH2uV69eBRHJbCc6Ohrp6enC30r240h2ipKglZUV/P394e/vj8jISBw4cADTp09HdHR0rscGdeKWXAEsWbIE5cuXx+zZs4XTI7a2thg+fDiOHz+usLffw4cPsXjxYtStW1foJGJrayv80sutp9OTJ09w+/btXGNp0qQJunXrho0bN+Z6gT4kJASRkZEAIPRyy7nN/O7Fy0lXVxeDBw/Gvn37EBwcjJCQELlecJ9//jnev3+PjIwMhb+Ac14wL4z58+cjNTVV4UGiY8eOOH36tJDUpP78808YGxujZcuWKr2WpaWl3HspKGn37uwdWpKSkvDXX3/J1R0xYgTOnj2LwMBAhT1upQel7D9eiAh//PGH0vF06NAB8fHxOHDggEz5tm3bZJ7XqlULDg4O2LZtm0wv0ISEBOzZs0focQlkfeeio6NleiOnpqbi+PHjucZhYGCAdu3aCacCw8LC8oxbme9jdiYmJujWrRtmzpyJ1NRU3Lt3L8/tq0IkEoGIZD4HANiwYQMyMjJkym7evIm+ffvis88+w++//57r9nJu6/Dhw0qfzq1Vqxbs7Oywfft2mc/q+fPnuHTpkkzdjh074tOnT3I/sqTHp44dOwIAbGxsYGhoKHcc2b9/f56xVKlSBePGjYOHhwdu3LihVPyFprGrgaWAtDPG9evX5ZYtWbKEANBff/0llH369InatWtHenp6NHbsWDp69CidPn2aFi5cSBUqVKBKlSrJdRBJSkqiLl26kEgkIm9vb9q9ezedP3+eAgMDacyYMWRoaEj79u3LM853795R06ZNSSwWk6+vL+3fv5/Onz9PO3fupK+++op0dXXp5s2bREQUGxtLFSpUoPr169PevXvp4MGD1LdvX3J2dlbY8aR79+65vu6DBw8IAFWqVImMjIzkOgykp6dTt27dqEKFCjR37lw6evQonTx5kgICAmjo0KEUGBgo1C1ox5PsJkyYIHQCyN7x5N9//yUzMzOqWbMm/f3333TkyBEaNGgQAaAlS5YI9aQX7nfv3i2z3bwusuck7Uzx7t07hcvr1q0r0/Hk1KlTBID69etHJ06coO3bt1PTpk2pRo0aMp+H9Pv27bff0uXLl2Ue9+7dIyKi+/fvk1gspvbt29ORI0coMDCQPDw8hG1l7xiR2z5MSEigmjVrkoWFBf366690/PhxmjBhAlWpUkVuH2zdulXoqLN//37atWsXNWvWjMRiMQUHBwv1nj59Svr6+tS+fXs6fPgw7dmzh9q1ayd856RmzZpFPj4+9Pfff9PZs2dp37591KFDB9LX16e7d+/mu+/z+z6OHDmSvv32W9qxYwedO3eOdu7cSY0aNSILCwuKjo7Oc9sA6JtvvlG4bPfu3XL7t23btlShQgX6448/KCgoiH788Ueys7OjcuXK0dChQ4ko62/Rzs6OrK2tKSgoSO5zjY2NJSKiIUOGkIGBAa1cuZJOnTpFS5YsoYoVK1KlSpVkvku5fX+JiDZs2EAAqFevXnTo0CH6+++/qXr16lS5cmWZjidJSUnUoEEDMjMzoxUrVlBQUBD5+fmRvr6+XIeskSNHkqGhIS1fvpxOnjxJCxcupHr16sl0PJFIJNS4cWNaunQpHTx4kM6ePUtLly4lQ0ND8vb2znOfqwsnuTzkleSSkpKoSpUqVKNGDUpPTxfKU1NTac2aNdSiRQsyNTUlAwMDqlWrFk2bNo1iYmIUvk56ejpt2bKFPvvsM6pQoQLp6elRxYoVqVu3brRt2zaZ3mu5SUpKol9++YXc3NzI3Nyc9PT0yN7enr744gs6fPiwTN1r166Ru7s7mZiYkIODA/n5+Ql/BKokOSIid3d3AkCDBg1SuDwtLY2WLVtGDRs2JENDQzI1NaXatWvT6NGj6dGjR0I96b5WJpHkdoB+9+4dmZubyyU5IqI7d+5Qjx49yMLCgsRiMTVs2FDutTSR5IiINm3aRLVq1SIDAwOqWrUqLVq0iDZu3CjzeQwdOlSp3poHDx4U9rWDgwNNnTqVjh49qnSSIyJ6+fIl9e3bl0xNTcnMzIz69u1Lly5dUrgP9u3bRy1atCBDQ0MyMTGhjh070sWLF+W2eeTIEWrUqBEZGRlR1apV6ddff5XrXXno0CHq1q0bOTg4kFgsJmtra/L09JRJmPnJ6/u4ZcsW6tChA9nY2JBYLCZ7e3vq378/3b59O9/tqprkpPuwfPnyZGZmRl27dqW7d++So6OjkOSk36vcHtLtffz4kUaMGEHW1tZkbGxMrVu3puDgYGrXrp3SSY4oK9HVqFGDxGIx1axZkzZt2qSw5+v79+/J19eX7OzsSE9PjxwdHWnGjBmUnJwsUy82NpZGjhxJNjY2ZGJiQj169KBnz57JJLnk5GTy9fWlBg0akLm5ORkZGVGtWrXIz8+PEhIS8t3v6iAiytZ+ZYwxxrQIX5NjjDGmtTjJMcYY01qc5BhjjGktTnKMMca0Fic5JggLC0O7du1gYWEBkUgEf3//InutxMREzJkzR+FEk/lNPFoaKZrlIa960sGJc5LOHpBdXiPZK5qg9cqVK/jyyy9hZ2cHsVgMW1tb9OvXT254s8LIOWuCoaEhbG1t0aFDByxatEjhcGXKCg8Px5w5c/Ldl6rEl/3x3XffFXi7rOThEU+YYPjw4UhISMCOHTtQvnz5Ip0xODExUbhxuzTOlF3UUlJS8OOPPyq8KVyRVq1aKRzWzNzcXOb56tWrMXHiRDRv3hxLliyBo6MjIiMjsWbNGrRu3RqrVq3KdQbsgti8eTNq166NtLQ0REdH48KFC1i8eDGWLVuGnTt3olOnTipvMzw8HHPnzkX79u0L/R2VxpeddLg3ph04yTHB3bt3MWrUKJnBYQujNE7YWFImDu3atSu2bduG7777Dg0bNsy3frly5fIdueXixYuYOHEiPD09sXfvXpnPZcCAAejTpw8mTJiAxo0bo1WrVoV+D4DshMNA1uDBkyZNQuvWrfHFF1/g0aNHCsfKLC4548tLafw+Mz5dyfDfqZv09HSsW7dOOG0jpcqEo7lN2Jjds2fPULFiRQBZE4ZKXy/nPHJv377FwIEDYWFhARsbGwwfPhyxsbEydYgIa9euRaNGjWBkZITy5cujX79+ePr0ab7vW3pa9MaNG+jXrx/Kly8vzBKg7HaDgoLQq1cvVKpUCYaGhqhevTpGjx5d6FOt06ZNg6WlJb7//vtCbSe7RYsWQSQSYd26dXIHaj09PaxduxYikQg///yzUF4UE15WqVIFy5cvR3x8PH777TehXJmJRgMCAvDll18CyBqCTPrdkU7eqa7PI6/v87t37zB27Fi4uLjA1NQU1tbW+OyzzxAcHCyzDenEokuXLsXixYuF99W+fXs8fPgQaWlpmD59Ouzt7WFhYYE+ffooPI27c+dOuLm5wcTEBKampujSpUu+w5yx/3CSYzITckqvzUifSyccvXfvHn755RcEBgbCxcUFw4YNw5IlS+S2NWPGDERGRmL9+vU4ePAgrK2t5erY2dkJA7OOGDFCeL1Zs2bJ1Ovbty9q1qyJPXv2YPr06di2bRsmTZokU2f06NGYOHEiOnXqhH379mHt2rW4d+8e3N3dZcZKzMsXX3yB6tWrY/fu3Vi/fr1K233y5Anc3Nywbt06nDhxArNnz8bVq1fRunXrQk0pYmZmhh9//BHHjx/Pd9JQQPH0P+np6cJYhRkZGThz5gxcXV3lBnaWqly5Mpo2bYrTp08LYyxKx4OcPXs2Tpw4gQ0bNqBTp06FnvDS09MTurq6OH/+vFAmnWjU398fx48fx+LFixEVFYVmzZoJSap79+7CQMVr1qwRvjvdu3cHoPrnIZ3cNrdpkxR9n6WDLPv5+eHw4cPYvHkzqlativbt2yu8xrxmzRpcvHgRa9aswYYNG/Dvv/+iR48eGDFiBN69e4dNmzZhyZIlOHnyJEaOHCmz7sKFCzFw4EC4uLhg165d+OuvvxAfH482bdooPdlrmVcs46qwUgEKhi5SdcLRnBM25ubdu3dykytKSYd7yj6uJBHR2LFjydDQUJj48fLlywSAli9fLlPvxYsXZGRkRNOmTcszBunrzJ49W6a8oNvNzMyktLQ0ev78udzEo4ompVUk+1ByKSkpVLVqVXJ1dRXes6LhuBwdHXMdGuqnn34iIqI3b94QABowYECer+/l5UUA6O3bt0RU8Akv8xoST8rGxobq1KmT6/LcJhpVNIyWIsp8HooeaWlpKn2f09PTKS0tjTp27Eh9+vQRyqXDdjVs2FBmaD5/f38CQD179pTZzsSJEwmAMGZlZGQk6enp0bfffitTLz4+nmxtbal///75xsZ40lSWD1UnHM05YWNhKJq8NDk5WTilc+jQIYhEInz11Vcyv8RtbW3RsGFDhb+qFckZsyrbjY6Ohq+vLypXrgw9PT3o6+sLU7bkNjmmssRiMebPn4+QkBDs2rUrz7qtW7dWOKFnzrnJ8kP/b/lJT1cX5YSXlGNEwYJMNJqTqp+HdK7I7I/sp3Jz+z6vX78eTZo0gaGhofA6p06dUvganp6eMhPL1qlTBwCE1mfOcumMIcePHxfm1sv+PTQ0NES7du2U/n6XdXwFleVJ1QlHc5tktSDym7z07du3IKJcOy4omsxSkZwxK7vdzMxMdO7cGa9fv8asWbNQv359mJiYIDMzEy1btlQ4oaWqBgwYgGXLlmHmzJn44osvcq1nYWGRZwcKKysrGBsb5zu57rNnz2BsbIwKFSoAyLoeNH/+fGzYsAGzZs2Cqakp+vTpgyVLliichFNZCQkJeP/+PerXry+UeXt749SpU5g1axaaNWsGc3NziEQieHp6KrUvC/J5ZJ8rUhFF3+cVK1ZgypQp8PX1xU8//QQrKyvo6upi1qxZCpOcdF9KSWdyz608OTkZAITT4tIZ03PKnjhZ7jjJsTypOuFobpOsFgUrKyuIRCIEBwfLzbcFQGGZIjljVna7d+/exa1btxAQEIChQ4cKyxV1tikokUiExYsXw8PDI9f5xpShq6uLDh064NixY3j58qXC63IvX75EaGgounXrJsxvV1QTXh4+fBgZGRnC7SOqTjSqSFF8Hoq+z3///Tfat2+PdevWyZTHx8cX+HUUkf5t/fPPP2qb0LUs4iTH8tSxY0fs3bsXr1+/lrl/qKATjkrlbJUVxOeff46ff/4Zr169Qv/+/Qu8nYJuV9EkpQBkegyqQ6dOneDh4YF58+bJnTZWxYwZM3D06FGMHTsWe/fulZmoNSMjA2PGjAERYcaMGQrXl054eerUKVy8eLHAcURGRuK7776DhYUFRo8eDUC1iUZz++4U1+ehaBLT27dv4/Lly4X6fHLq0qUL9PT08OTJE7VeBihrOMmxPPn5+eHQoUPo0KEDZs+ejQoVKmDr1q04fPgwlixZAgsLiwJt18zMDI6Ojti/fz86duyIChUqwMrKSqWbe1u1aoWvv/4aPj4+CAkJQdu2bWFiYoKoqChcuHAB9evXx5gxY1SOTdnt1q5dG9WqVcP06dNBRKhQoQIOHjyIoKAglV8zP4sXL0bTpk0RHR2NunXryi2XSCS4cuWKXLmBgQEaN24svC9/f39MnDgRrVu3xrhx41ClShXhZvCrV6/C398f7u7uALJaVx06dIC3tzdq164NMzMzXL9+HceOHcvz1Gl2d+/eFa4lRUdHIzg4GJs3b4auri727t0r3Epibm6Otm3bYunSpcL34Ny5c9i4cSPKlSsns03piC+///47zMzMYGhoCGdn52L7PD7//HP89NNP8PPzQ7t27fDgwQPMmzcPzs7Ocr0zC8PJyQnz5s3DzJkz8fTpU3Tt2hXly5fH27dvce3aNZiYmAgDKrA8aK7PCytpkMvEkIWZcDQvJ0+epMaNG5OBgQEBECaTzG3i0dx6KG7atIlatGhBJiYmZGRkRNWqVaMhQ4ZQSEhInq+f3wSnymw3PDycPDw8yMzMjMqXL09ffvklRUZGyvUcLUjvypy8vb0JgEq9Kx0cHOS2c/nyZerXrx/Z2NiQnp4eWVtb0xdffEGXLl2SqVeYCS9z9l6UToLarl07WrhwocKZuJWZaFTK39+fnJ2dSVdXV2YyV1U/j9x6f+b1fU5JSaHvvvuOHBwcyNDQkJo0aUL79u2Tm4BU2rsy5wS+uW07t5ikM6Sbm5uTgYEBOTo6Ur9+/ejkyZMKY2eyeNJUxhhjWou75zDGGNNanOQYY4xpLU5yjDHGtBYnOcYYY1qLkxxjjDGtxUmOMcaY1ipzN4NnZmbi9evXMDMzK9YhqBhjjKkHESE+Ph729vb5juFZ5pLc69ev1Tr0DmOMMc148eJFrvMjSpW5JGdmZgYga+eYm5trOBrGGGOqiouLQ+XKlYXjeV7KXJKTnqI0NzfnJMcYY6WYMpecuOMJY4wxrcVJjjHGmNbiJMcYY0xrcZJjjDGmtTjJMcYY01qc5BhjjGktTnKMMca0Fic5xhhjWouTHGOMMa3FSY4xxpjW0miSO3/+PHr06AF7e3uIRCLs27cv33XOnTuHpk2bwtDQEFWrVsX69euLPlDGGGOlkkaTXEJCAho2bIhff/1VqfoRERHw9PREmzZtEBYWhh9++AHjx4/Hnj17ijhSxhhjpZFGB2ju1q0bunXrpnT99evXo0qVKvD39wcA1KlTByEhIVi2bBn69u1bRFEy9h8iAhFAADKJkJaRqeR6StRRIQal6im9PWUrKltNyfi0YJ8o+17VWU3dsSm/PSXrKblBK1MDGOrrKrnVgitVsxBcvnwZnTt3linr0qULNm7ciLS0NOjr68utk5KSgpSUFOF5XFxckcdZ1hER4pLSkZKegbRMQlxSGlLSM5GanomktAx8TEiFvq4O0jMzkZZBSM/IxLP3ibA0ESMtMxPpGYTktAw8jv4E+3JGyMgkpGcSMjIzkZ5JuPsqFo6WJiCCUJZJhPQMwuPoTzA11IOxWA+ZmYQMov/+JUJmJvBKkoRyxvpZySpH0sr6PyGTAND/y7ItY4wVTsK/F2BUzRW7v2mPFlUti/z1SlWSe/PmDWxsbGTKbGxskJ6ejpiYGNjZ2cmts2jRIsydO7e4QtQKyWkZ+JCQiuj4FHxISEF8cjqevkuAob4ubr2QwMRAD/ej4mBlZoArT9+jqpUJUtIzERGTABOxLhJSM4o8xodvP+W67H1Car7rSxLT1BkOK8OUmO0lq57S28u/pvLbUrKesltUQzX9Wi0Qd/cMRKIOym2skEpVkgPkvwDSpnFuX4wZM2Zg8uTJwnPpZHtlSWp6Jj4kpOLuq1hEx6dgV8gLlDPWR2p6JtIyMvH0XQIyifBR1QN/VNY//76JF4qyJziRCNDX1YGejgiJqRmoamUCsZ4OElLTkZ5BqFrRBHo6OtDXFQEQ4bUkCXXtzaGnqwOxbtbn+T4hFdUqmkJPRwRdXVHWvzo6eBefAidLY+jqiGQe0tcqbyKGvo4OdHQAXVHWMh0dkfB/IsBIrAORKOvPW0ckgkiU9ccuEmXFLi3T+X8dkVDnv2XS11WGMgcSZQ9KylL3QU4rDujq3sksX0lJSTA0NMy273sX22uXqiRna2uLN2/eyJRFR0dDT08PlpaKm70GBgYwMDAojvA0Iik1A2/ikvH8fQIiYhJwI1KC0GcfYGygh0/J6Yj5lIL0zIKdZxPr6iCDCFamYjiUM0JKeiaaOVWAJDEVLvbmyMgEHC2NkUkEG3NDGOjpwEBPF+WN9WFupA8DPR0+oDBWxkkkEnh4eMDDwwMLFiwo9mNCqUpybm5uOHjwoEzZiRMn4OrqqvB6nDZ68u4T9oW9QkRMAg7djirQNgY2r4LMTEIz5wowEetCrKeDTAKsTMUwM9RDRVNDmBvpcYJijBWKNMGFhIQgIiIC3377rcLLSkVJo0nu06dPePz4sfA8IiICN2/eRIUKFVClShXMmDEDr169wp9//gkA8PX1xa+//orJkydj1KhRuHz5MjZu3Ijt27dr6i0UqY8JqTj9bzT+vPIc8clpePouIde6zlYmqFTeCE6WJrC1MISJWBf1HCxgY24Ic0N9GBvoQl+X7/1njBWP7AnO0tISp0+fLvYEB2g4yYWEhKBDh/8uPkqvnQ0dOhQBAQGIiopCZGSksNzZ2RlHjhzBpEmTsGbNGtjb2+OXX37RmtsHiAj3o+Jx7N4bhDz7gEtP3ius16RKOdhZGKF7AzvUs7dAFUvjYo6UMcZypyjBNWjQQCOxiEjZmxq0RFxcHCwsLBAbGwtzc3NNhwMAiI5Pxt4br7Dv5mvcj5K9xcHMQA99m1ZCg0oWqFbRFE6WJrAwLhunZhljpU9xJDhVjuOl6pqcNnktScL2a5EIff5RrsXWpoYVWlW3QpsaVnCxM+drY4yxUuPMmTMlogUnxUmumN15GYvZB+4iLFIiUy7W04GXa2WM71gDFc20tzcoY0y79enTB1u2bEGjRo00nuAATnLFJjOT8MvpR/A/+UimvLOLDcZ9Vh31HSy4xcYYK5UkEglSU1NhbW0NABgyZIiGI/oPJ7licPrft/h+zx28i88aXszR0hhzetZFuxoVoaPkjcSMMVYSSSQSdO7cGYmJiTh9+rSQ6EoKTnJFiIgwedct7A17BSBrRIZZ3V3g08qJW22MsVJPmuCuX78OS0tLREdHc5IrK9IyMjFySwjOPXwHAKjnYI7Nw5rz9TbGmFbImeBOnz6NevXqaTosOZzkikBKegYG/H5F6Fzi264apnerrdmgGGNMTRQluJLQyUQRTnJqlp6RiS/WXsK911n3u/n1cIFPK2cNR8UYY+pRmhIcwElO7bZfixQS3M9f1MeA5lU0HBFjjKlPbGwsoqOjS0WCAzjJqd3q01ljcbapYcUJjjGmdRwdHXH27FnExcWV+AQHADxirxpdfByD6P/fJjCxU00NR8MYY+ohkUhw8uRJ4bmTk1OpSHAAJzm1ISLMOXAPQFZPyqaO5TUcEWOMFZ70Gly3bt2wf/9+TYejMk5yarLnxis8iv4EAPhjiKuGo2GMscLL3snEwsICzs6lrxMdJzk1SMvIxHe7bwHImpDUzsJIwxExxljhlLZelLnhJKcGp+6/BZA1osmP3etoOBrGGCscbUlwACc5tdhy6TkAoFdDe5gYcIdVxljp9enTJ61JcAAnuUKL+ZSCy0+z5oPjm74ZY6WdsbExGjVqpBUJDuAkV2hn/o0GANhZGKJh5XKaDYYxxgpJR0cH69evR2hoaKlPcAAnuUKT3vzdrZ6dhiNhjLGCkUgkmD17NtLT0wFkJTpHR0cNR6UefAGpkBJTMwAA1a1NNRwJY4ypLnsnk+joaKxfv17TIakVt+QKIeZTCmI+ZY1w0rWerYajYYwx1eTsRTl27FhNh6R2nOQK4eCt1wCyrsdVMBFrOBrGGFOeNt0mkBdOcoUQl/T/89c8yzdjrBQpKwkO4CRXKLdfSgAAQ9y04wItY0z7ERF69+5dJhIcwEmuUKTzxrnYm2s4EsYYU45IJMKsWbNQuXJlrU9wAPeuLLC45DS8iUsGANS1t9BwNIwxpryOHTvi0aNHMDAw0HQoRY5bcgUU+T4RAGAs1uVOJ4yxEk0ikaBHjx64f/++UFYWEhzALbkCe/7/JCe9T44xxkoiiUQCDw8PhISE4NmzZ7h16xZ0dMpO+6bsvFM1S0nPSm5WpmXj1xBjrPTJnuAsLS2xdevWMpXgAE5yBRYdn3UTeAvnChqOhDHG5OVMcGWhk4kinOQK6ENCKgDAxtxQw5EwxpgsTnD/4SRXQK8+JgEALE250wljrGT5/vvvOcH9Hye5AoqISQAAOFuZaDgSxhiTtWTJEnTv3r3MJziAe1cWWHxKGgDAxpw7njDGNC81NRVicdaZJQsLCxw6dEjDEZUM3JIroChJ1o3ghvq6Go6EMVbWSSQStG7dGsuWLdN0KCUOJ7kCkg7KzEmOMaZJ2QdbXrx4MWJiYjQdUonCSa4AMjIJqRmZAIDyxtzxhDGmGTlnEzh16hSsrKw0HVaJwkmuAJLS/hvlxIhbcowxDShL0+UUBie5AohPThP+b6jPu5AxVrw4wSmPj9AFIB2v0khfFyKeMJUxVswOHDjACU5JfAtBAUhHO+FWHGNME4YMGYLY2Fi0a9eOE1w+OMkVQNL/W3IfE9PyqckYY+ohkUggEolgYZE1f+W3336r4YhKB26KFMDL/w/p5WLHM4Izxoqe9Bpc165dERsbq+lwShVuyRWAgV7WbwPpaUvGGCsqOTuZvHr1SmjNsfxxS64A0v5/j1w9B27JMcaKjqJelC4uLpoOq1ThJFcAaZkEANDX5d3HGCsafJuAevBRugBi/j9hKic5xlhR4ASnPnyULoDk9KzelXHJ3LuSMaZ+b9++RWRkJCc4NeCOJwVgKs7abbp8IzhjrAjUqlULp0+fRnp6Oie4QuIkVwD/vyQHGwtDzQbCGNMaEokE9+/fh5ubGwBwBxM14dOVBZBJWVlOhxtyjDE1kF6D69ixI86cOaPpcLQKJ7kCkCY5Pl3JGCus7J1MjI2NYWlpqemQtAonuQKQJjkenJkxVhjci7LocZIrAOk1OR1OcoyxAuIEVzw4yRWAcLqS9x5jrADi4uI4wRUTPkwXQGamtOMJt+QYY6ozMjKCo6MjJ7hioPEkt3btWjg7O8PQ0BBNmzZFcHBwnvW3bt2Khg0bwtjYGHZ2dvDx8cH79++LKdos0tOVfE2OMVYQ+vr62LZtG65evcoJrohpNMnt3LkTEydOxMyZMxEWFoY2bdqgW7duiIyMVFj/woULGDJkCEaMGIF79+5h9+7duH79OkaOHFmscfPpSsaYqiQSCZYsWYLMzKwB3vX19VGtWjUNR6X9NHqYXrFiBUaMGIGRI0eiTp068Pf3R+XKlbFu3TqF9a9cuQInJyeMHz8ezs7OaN26NUaPHo2QkJBijZu44wljTAUSiQQeHh74/vvvMX36dE2HU6ZoLMmlpqYiNDQUnTt3linv3LkzLl26pHAdd3d3vHz5EkeOHAER4e3bt/jnn3/QvXv3XF8nJSUFcXFxMo/CCn+dtQ1OcYyx/EgTXEhICCwtLfHVV19pOqQyRWNJLiYmBhkZGbCxsZEpt7GxwZs3bxSu4+7ujq1bt8LLywtisRi2trYoV64cVq9enevrLFq0CBYWFsKjcuXKhY7d2coEAPA6NrnQ22KMaa+cCY47mRQ/jV9Vytl5g4hy7dARHh6O8ePHY/bs2QgNDcWxY8cQEREBX1/fXLc/Y8YMxMbGCo8XL14UOmbpNTlpsmOMsZw4wZUMGhug2crKCrq6unKttujoaLnWndSiRYvQqlUrTJ06FQDQoEEDmJiYoE2bNpg/fz7s7Ozk1jEwMICBgYFaY///JTm+JscYUygzMxOenp6c4EoAjbXkxGIxmjZtiqCgIJnyoKAguLu7K1wnMTEROjqyIevq6gLIagEWFx6gmTGWFx0dHUyZMgW2trac4DRMo6crJ0+ejA0bNmDTpk24f/8+Jk2ahMjISOH044wZMzBkyBChfo8ePRAYGIh169bh6dOnuHjxIsaPH4/mzZvD3t6+2OLm3pWMsfz07dsXjx8/5gSnYRqdT87Lywvv37/HvHnzEBUVhXr16uHIkSNwdHQEAERFRcncMzds2DDEx8fj119/xZQpU1CuXDl89tlnWLx4cbHG/d8AzcX6soyxEkwikeDrr7/G0qVLhWOYiQlft9c0ERXneb4SIC4uDhYWFoiNjYW5uXmBtvHt9jAcvPUasz93wfDWzmqOkDFW2mQfbLlZs2a4evUqj4hUhFQ5jmu8d2VpxNfkGGNSOWcT2LBhAye4EoSTXEFIr8lxlmOsTOPpcko+TnIFwJOmMsY4wZUOnOQKgE9XMsbGjx/PCa4U4CRXADwzOGNs6dKlaNeuHSe4Ek6jtxCUVsQtOcbKpPT0dOjpZR02bWxscObMGb5sUcJxS64AhElTeR4CxsoMiUSC1q1bY9OmTUIZJ7iSj1tyBXDrhQQA3wzOWFmRvZPJ06dP0bdvX1hYWGg6LKYEbskVQG07MwDAh4RUDUfCGCtqOXtRnjx5khNcKcJJrhBsLQw1HQJjrAjxbQKlHyc5xhhTgBOcduAkxxhjCmzbto0TnBbgjieMMabAmDFj8OHDB/Ts2ZMTXCnGSY4xxv4vNjYWYrEYRkZGEIlE+PHHHzUdEiskPl3JGGPIugbn4eGB3r17IykpSdPhMDXhJMcYK/OydzIJDQ2VmayZlW6c5BhjZZqiXpS1atXSdFhMTTjJFUDZmkudMe3FtwloP05yjLEyiRNc2cBJjjFWJj179gwPHz7kBKfl+BYCxliZ1KhRI5w4cQKGhoac4LQYJznGWJkhkUjw/PlzNGzYEADQvHlzDUfEihqfrmSMlQnSa3Dt2rVDSEiIpsNhxYSTHGNM62XvZKKnpwexWKzpkFgx4STHGNNq3IuybOMkxxjTWpzgGCe5ApDeDC4SiTQbCGMsV7GxsZzgGCc5xph2EovFsLCw4ARXxvEtBIwxrWRkZIQDBw4gMjKSx6Isw7glxxjTGhKJBGvXrgX9/5qCkZERJ7gyjltyjDGtkL2TyYcPH3jCUwaAW3KMMS2Qsxdlz549NR0SKyE4yTHGSjW+TYDlhZMcY6zU4gTH8lOgJJeeno6TJ0/it99+Q3x8PADg9evX+PTpk1qDY4yx3KSnp6Nr166c4FieVO548vz5c3Tt2hWRkZFISUmBh4cHzMzMsGTJEiQnJ2P9+vVFEWeJQsjqucW3gjOmOXp6ehg1ahSePn2KkydPcoJjCqnckpswYQJcXV3x8eNHGBkZCeV9+vTBqVOn1BocY4zlZcSIEXj06BEnOJYrlZPchQsX8OOPP8qN4u3o6IhXr16pLTDGGMtJIpFgyJAhiI6OFsosLCw0GBEr6VQ+XZmZmYmMjAy58pcvX8LMzEwtQTHGWE4SiQQeHh4ICQnBixcvcPr0aR4/luVL5Zach4cH/P39hecikQifPn2Cn58fPD091RkbY4wBkE1wlpaWWLVqFSc4phSVW3IrV65Ehw4d4OLiguTkZHh7e+PRo0ewsrLC9u3biyJGxlgZljPBcS9KpgqVk5y9vT1u3ryJHTt2IDQ0FJmZmRgxYgQGDRok0xGFMcYKixMcKyyVk9z58+fh7u4OHx8f+Pj4COXp6ek4f/482rZtq9YAGWNl16hRozjBsUJR+Zpchw4d8OHDB7ny2NhYdOjQQS1BMcYYACxduhSurq6c4FiBqdySIyKFF3zfv38PExMTtQRV0v03M7hm42BMG2VmZkJHJ+v3t5OTE65du8adTFiBKZ3kvvjiCwBZvSmHDRsGAwMDYVlGRgZu374Nd3d39UfIGCszJBIJunfvju+++w59+vQBAE5wrFCUTnLSGy6JCGZmZjKdTMRiMVq2bIlRo0apP0LGWJmQfbDlb775Bl26dIGxsbGmw2KlnNJJbvPmzQCyTh989913ZebUJGOs6OWcTeDYsWOc4JhaqHxNzs/PryjiYIyVUTxdDitKKic5APjnn3+wa9cuREZGIjU1VWbZjRs31BIYY0z7cYJjRU3lWwh++eUX+Pj4wNraGmFhYWjevDksLS3x9OlTdOvWrShiZIxpqd9++40THCtSKrfk1q5di99//x0DBw7Eli1bMG3aNFStWhWzZ89WeP8cY4zlZurUqYiJicHgwYM5wbEioXJLLjIyUrhVwMjISJgZfPDgwTx2JWMsX3FxcUhPTwcA6OjoYOnSpZzgWJFROcnZ2tri/fv3ALLmkLty5QoAICIiAiS9S1rLSd+liOcGZ0wlEokEnTp1gre3t5DoGCtKKie5zz77DAcPHgSQNSvvpEmT4OHhAS8vL+HmTcYYyyl7J5PTp0/j+fPnmg6JlQEqX5P7/fffkZmZCQDw9fVFhQoVcOHCBfTo0QO+vr5qD5AxVvop6kVZrVo1TYfFygCVW3I6OjrQ0/svN/bv3x+//PILxo8fj3fv3qkcwNq1a+Hs7AxDQ0M0bdoUwcHBedZPSUnBzJkz4ejoCAMDA1SrVg2bNm1S+XUZY8WDbxNgmqRyklPkzZs3+Pbbb1G9enWV1tu5cycmTpyImTNnIiwsDG3atEG3bt0QGRmZ6zr9+/fHqVOnsHHjRjx48ADbt29H7dq1C/sWGGNFgBMc0zSlk5xEIsGgQYNQsWJF2Nvb45dffkFmZiZmz56NqlWr4sqVKyq3qFasWIERI0Zg5MiRqFOnDvz9/VG5cmWsW7dOYf1jx47h3LlzOHLkCDp16gQnJyc0b96cB4ZmrIQKDw/HnTt3OMExjVE6yf3www84f/48hg4digoVKmDSpEn4/PPPceHCBRw9ehTXr1/HwIEDlX7h1NRUhIaGonPnzjLlnTt3xqVLlxSuc+DAAbi6umLJkiVwcHBAzZo18d133yEpKSnX10lJSUFcXJzMgzFWPNzd3XH48GFOcExjlO54cvjwYWzevBmdOnXC2LFjUb16ddSsWRP+/v4FeuGYmBhkZGTAxsZGptzGxgZv3rxRuM7Tp09x4cIFGBoaYu/evYiJicHYsWPx4cOHXFuRixYtwty5cwsUI2NMdRKJBG/fvkWtWrUAZPXIZkxTlG7JvX79Gi4uLgCAqlWrwtDQECNHjix0ADnnisptUlYgazJFkUiErVu3onnz5vD09MSKFSsQEBCQa2tuxowZiI2NFR4vXrwodMz/xa62TTGmFaTX4Nq1a4fw8HBNh8OY8kkuMzMT+vr6wnNdXd1CTbdjZWUFXV1duVZbdHS0XOtOys7ODg4ODsLcdgBQp04dEBFevnypcB0DAwOYm5vLPAqtbNzzzphKsncySU9P55u9WYmg9OlKIpKZETw5ORm+vr5yiS4wMFCp7YnFYjRt2hRBQUEyN5EHBQWhV69eCtdp1aoVdu/ejU+fPsHU1BQA8PDhQ+jo6KBSpUrKvhXGmJpxL0pWUimd5IYOHSrz/Kuvvir0i0+ePBmDBw+Gq6sr3Nzc8PvvvyMyMlK4qXzGjBl49eoV/vzzTwCAt7c3fvrpJ/j4+GDu3LmIiYnB1KlTMXz4cJmZyhljxYcTHCvJVJ4ZXJ28vLzw/v17zJs3D1FRUahXrx6OHDkCR0dHAEBUVJTMPXOmpqYICgrCt99+C1dXV1haWqJ///6YP3++2mNjjOWPExwr6Qo0aao6jR07FmPHjlW4LCAgQK6sdu3aCAoKKuKoGGPKEIlEEIlEnOBYiaXxJMcYK70sLCxw4sQJvHr1Suh9zVhJopZhvRhjZYdEIsFff/0lPLewsOAEx0osbskxxpQmkUjg4eGBkJAQxMbGYty4cZoOibE8cUuuEPhecFaWZE9wlpaWaNu2raZDYixfBUpyf/31F1q1agV7e3th4kN/f3/s379frcGVVMR3g7MyJmeC404mrLRQOcmtW7cOkydPhqenJyQSCTIyMgAA5cqVK/A4loyxkosTHCvNVE5yq1evxh9//IGZM2dCV1dXKHd1dcWdO3fUGhxjTLNSU1PRuXNnTnCs1FI5yUVERKBx48Zy5QYGBkhISFBLUIyxkkEsFuPLL7+ElZUVJzhWKqmc5JydnXHz5k258qNHj3I3Ysa00NSpU3H//n1OcKxUUjnJTZ06Fd988w127twJIsK1a9ewYMEC/PDDD5g6dWpRxMgYK0YSiQRjxoxBbGysUGZlZaXBiBgrOJXvk/Px8UF6ejqmTZuGxMREeHt7w8HBAatWrcKAAQOKIkbGWDHJPhbly5cvcfDgQU2HxFihFOhm8FGjRmHUqFGIiYlBZmYmrK2t1R0XY6yY5RxsecGCBZoOibFCU/l05dy5c/HkyRMAWacwynKC45nBmbbg2QSYtlI5ye3Zswc1a9ZEy5Yt8euvv+Ldu3dFEVeJRnwvONMinOCYNlM5yd2+fRu3b9/GZ599hhUrVsDBwQGenp7Ytm0bEhMTiyJGxlgRGjJkCCc4prUKNKxX3bp1sXDhQjx9+hRnzpyBs7MzJk6cCFtbW3XHxxgrYj///DPq1q3LCY5ppULPQmBiYgIjIyOIxWLEx8erIybGWBEjIoj+f1HZxcUFt2/fho4Oj9fOtE+BvtURERFYsGABXFxc4Orqihs3bmDOnDl48+aNuuNjjKmZRCJBhw4dcPr0aaGMExzTViq35Nzc3HDt2jXUr18fPj4+wn1yjLGSL3snEx8fHzx8+BAGBgaaDouxIqNykuvQoQM2bNiAunXrFkU8jLEikrMX5cGDBznBMa2ncpJbuHBhUcTBGCtCfJsAK6uUSnKTJ0/GTz/9BBMTE0yePDnPuitWrFBLYKUD3w3OSj5OcKwsUyrJhYWFIS0tTfg/Y6z0WL58OSc4VmYpleTOnDmj8P9lFQ94wkqT2bNn4+3btxg3bhwnOFbmqNxvePjw4Qrvh0tISMDw4cPVEhRjrHA+ffqEzMxMAIC+vj5+//13TnCsTFI5yW3ZsgVJSUly5UlJSfjzzz/VEhRjrOAkEgk+++wz+Pr6ComOsbJK6SQXFxeH2NhYEBHi4+MRFxcnPD5+/IgjR46U6RkJGCsJsncyCQwMxIsXLzQdEmMapfQtBOXKlYNIJIJIJELNmjXllotEIsydO1etwTHGlKeoF6Wjo6Omw2JMo5ROcmfOnAER4bPPPsOePXtQoUIFYZlYLIajoyPs7e2LJEjGWN74NgHGFFM6ybVr1w5A1riVVapUEQZ3Lct4F7CSgBMcY7lTKsndvn0b9erVg46ODmJjY3Hnzp1c6/IfF2PF6/r16wgLC+MEx5gCSiW5Ro0a4c2bN7C2tkajRo0gEolACqbHFolEyMjIUHuQjLHceXh4YPfu3ahatSonOMZyUCrJRUREoGLFisL/yzpFCZ6x4iSRSBAXF4cqVaoAAHr37q3ZgBgroZRKctl7aHFvLcY0SyKRwMPDAzExMTh79iz/TTKWhwLdDH748GHh+bRp01CuXDm4u7vj+fPnag2OMSZLmuBCQkKE+1UZY7lTOcktXLgQRkZGAIDLly/j119/xZIlS2BlZYVJkyapPUDGWJbsCU7ayaR+/fqaDouxEk3l+eRevHiB6tWrAwD27duHfv364euvv0arVq3Qvn17dcfHGIPiBMedTBjLn8otOVNTU7x//x4AcOLECXTq1AkAYGhoqHBMS8ZY4XCCY6zgVG7JeXh4YOTIkWjcuDEePnyI7t27AwDu3bsHJycndcdXovG94Kw4pKamIjExkRMcYwWgcktuzZo1cHNzw7t377Bnzx5YWloCAEJDQzFw4EC1B8hYWWdtbY0zZ87g7NmznOAYU5HKLbly5crh119/lSvnwZkZUx+JRIKzZ88K979ZW1vzLB+MFYDKSQ7I+gPcuHEj7t+/D5FIhDp16mDEiBGwsLBQd3wlEt8KzoqSdCzKkJAQbNmyBYMHD9Z0SIyVWiqfrgwJCUG1atWwcuVKfPjwATExMVi5ciWqVauGGzduFEWMjJUZ2QdbrlChAho2bKjpkBgr1VRuyU2aNAk9e/bEH3/8AT29rNXT09MxcuRITJw4EefPn1d7kIyVBTybAGPqp3KSCwkJkUlwAKCnp4dp06bB1dVVrcExVlZwgmOsaKh8utLc3ByRkZFy5S9evICZmZlagmKsLElKSuIEx1gRUTnJeXl5YcSIEdi5cydevHiBly9fYseOHRg5ciTfQsBYARgaGqJjx46c4BgrAiqfrly2bBlEIhGGDBmC9PR0AIC+vj7GjBmDn3/+We0BlmQ8OzpTB5FIhIULF2L8+PGws7PTdDiMaRWVW3JisRirVq3Cx48fcfPmTYSFheHDhw9YuXIlDAwMiiJGxrSORCLBlClThKHwRCIRJzjGioDSSS4xMRHffPMNHBwcYG1tjZEjR8LOzg4NGjSAsbFxUcbImFaRdjJZsWIFRowYoelwGNNqSic5Pz8/BAQEoHv37hgwYACCgoIwZsyYooytxOKJwVlB5exFOX36dE2HxJhWU/qaXGBgIDZu3IgBAwYAAL766iu0atUKGRkZ0NXVLbIAGdMWfJsAY8VP6Zbcixcv0KZNG+F58+bNoaenh9evXxdJYIxpE05wjGmG0kkuIyMDYrFYpkxPT0/oYckYy13//v05wTGmAUqfriQiDBs2TKYHZXJyMnx9fWFiYiKUBQYGqjdCxrTA/Pnz8fTpUwQGBnKCY6wYKZ3khg4dKlf21VdfqTUYxrRV8+bN8e+//8oMh8cYK3pK/8Vt3ry5KOMolfhWcJYbiUSC/v37Y/78+WjevDkAcIJjTANUvhlc3dauXQtnZ2cYGhqiadOmCA4OVmq9ixcvQk9PD40aNSraABlTkbSTSVBQELy9vfm6NWMapNEkt3PnTkycOBEzZ85EWFgY2rRpg27duikcADq72NhYDBkyBB07diymSBlTTs5elIGBgdyCY0yDNJrkpCM+jBw5EnXq1IG/vz8qV66MdevW5bne6NGj4e3tDTc3t2KKVBbfC84U4dsEGCt5NJbkUlNTERoais6dO8uUd+7cGZcuXcp1vc2bN+PJkyfw8/NT6nVSUlIQFxcn82BM3TjBMVYyaSzJxcTEICMjAzY2NjLlNjY2ePPmjcJ1Hj16hOnTp2Pr1q1KnwJatGgRLCwshEflypULHTtjOc2bN48THGMlUIGS3F9//YVWrVrB3t4ez58/BwD4+/tj//79Km8r53Q1RKRwCpuMjAx4e3tj7ty5qFmzptLbnzFjBmJjY4XHixcvVI6RsfwsWLAAAwYM4ATHWAmjcpJbt24dJk+eDE9PT0gkEmRkZAAAypUrB39/f6W3Y2VlBV1dXblWW3R0tFzrDgDi4+MREhKCcePGQU9PD3p6epg3bx5u3boFPT09nD59WuHrGBgYwNzcXObBmDokJSWB/j9at5GREbZv384JjrESRuUkt3r1avzxxx+YOXOmzMDMrq6uuHPnjtLbEYvFaNq0KYKCgmTKg4KC4O7uLlff3Nwcd+7cwc2bN4WHr68vatWqhZs3b6JFixaqvhXGCkwikaBt27b44YcfhETHGCt5VO7bHBERgcaNG8uVGxgYICEhQaVtTZ48GYMHD4arqyvc3Nzw+++/IzIyEr6+vgCyTjW+evUKf/75J3R0dFCvXj2Z9a2trWFoaChXXlx4YvCySSKRwMPDAyEhIYiIiOAZvRkrwVROcs7Ozrh58yYcHR1lyo8ePQoXFxeVtuXl5YX3799j3rx5iIqKQr169XDkyBFh21FRUfneM8dYccqe4KSdTDjBMVZyqZzkpk6dim+++QbJyckgIly7dg3bt2/HokWLsGHDBpUDGDt2LMaOHatwWUBAQJ7rzpkzB3PmzFH5NRkrCEUJjq/BMVayqZzkfHx8kJ6ejmnTpiExMRHe3t5wcHDAqlWrhAlVtR5fgylzOMExVjoVaLyhUaNGYdSoUYiJiUFmZiasra3VHRdjJcqZM2cQGhrKCY6xUqZQg+pZWVmpKw7GSrQ+ffogICAAjRo14gTHWClSoI4nim7Wlnr69GmhAmKspJBIJEhNTRXOVAwZMkTDETHGVKVykps4caLM87S0NISFheHYsWOYOnWquuJiTKOkY1EmJibi9OnTfEqesVJK5SQ3YcIEheVr1qxBSEhIoQMqTfg+Oe2Uc7Dl6OhoTnKMlVJqG6C5W7du2LNnj7o2x5hGKJpNQFODDTDGCk9tSe6ff/5BhQoV1LU5xoodT5fDmPZR+XRl48aNZTqeEBHevHmDd+/eYe3atWoNjrHiwgmOMe2kcpLr3bu3zHMdHR1UrFgR7du3R+3atdUVV4nGt4Jrn7i4OLx7944THGNaRqUkl56eDicnJ3Tp0gW2trZFFRNjxa5KlSo4e/Ys4uLiUL9+fU2HwxhTE5Wuyenp6WHMmDFISUkpqngYKzYSiQQnT54Unjs6OnKCY0zLqNzxpEWLFggLCyuKWBgrNtJrcN26dSvQjPaMsdJB5WtyY8eOxZQpU/Dy5Us0bdoUJiYmMsv5WgYr6XJ2MnF2dtZ0SIyxIqJ0khs+fDj8/f3h5eUFABg/frywTCQSgYggEomQkZGh/ihLKBH4bvDShntRMla2KJ3ktmzZgp9//hkRERFFGQ9jRYYTHGNlj9JJjv4/h1rOGcEZKw0+ffrECY6xMkiljid5zT7AWElmbGyMRo0acYJjrIxRqeNJzZo18010Hz58KFRApQFPDF766OjoYP369Zg5cyafjWCsDFEpyc2dOxcWFhZFFQtjaiWRSLB8+XLMnj0b+vr60NHR4QTHWBmjUpIbMGAATznCSoXsnUzevn2L33//XdMhMcY0QOlrcnw9jpUWOXtRjhs3TtMhMcY0ROkkR3whipUCfJsAYyw7pU9XZmZmFmUcpRM3bksUTnCMsZzUNmkqY5pEROjduzcnOMaYDE5yTCuIRCLMmjULlStX5gTHGBOoPEAzYyVVx44d8ejRIxgYGGg6FMZYCcEtuQIgnhu8RJBIJOjRowfCw8OFMk5wjLHsuCXHSiWJRAIPDw+EhIQgIiICt2/fho4O/2ZjjMniowIrdbInOEtLS2zbto0THGNMIT4ysFIlZ4LjTiaMsbxwkmOlBic4xpiqOMkVAt8LXrymT5/OCY4xphJOcqzUWLx4Mbp3784JjjGmNO5dyUq01NRUiMViAICFhQUOHTqk4YgYY6UJt+RYiSWRSNC6dWssW7ZM06EwxkopTnIFwBMyFL3sgy0vXrwYMTExmg6JMVYKcZJjJU7O2QROnToFKysrTYfFGCuFOMmxEoWny2GMqRMnOVZicIJjjKkbJzlWYhw8eJATHGNMrfgWgkIQifh2cHUaPHgwYmNj0bZtW05wjDG14CTHNEoikUAkEsHCwgIAMG7cOA1HxBjTJny6kmmM9Bpc165dERsbq+lwGGNaiFtyTCNydjJ59eqV0JpjjDF14ZYcK3aKelG6uLhoOizGmBbiJFcAPOJJwfFtAoyx4sRJjhUbTnCMseLGSY4Vm7dv3yIyMpITHGOs2HDHk0Lgu+RUU6tWLZw+fRrp6emc4BhjxYKTHCtSEokE4eHhcHd3BwDuYMIYK1Z8upIVGek1uI4dO+L06dOaDocxVgZxkmNFInsnExMTE54qhzGmEZzkmNpxL0rGWEnBSY6pFSc4xlhJwkmuAPhecMXi4uI4wTHGShSNJ7m1a9fC2dkZhoaGaNq0KYKDg3OtGxgYCA8PD1SsWBHm5uZwc3PD8ePHizFalhcjIyM4OjpygmOMlRgavYVg586dmDhxItauXYtWrVrht99+Q7du3RAeHo4qVarI1T9//jw8PDywcOFClCtXDps3b0aPHj1w9epVNG7cWAPvgGWnr6+Pbdu2ITIyEtWqVdN0OGqXkZGBtLQ0TYfBmNbT19eHrq6uWrYlItLcSIwtWrRAkyZNsG7dOqGsTp066N27NxYtWqTUNurWrQsvLy/Mnj1bqfpxcXGwsLBAbGwszM3NCxR3t1XBuB8Vh79GNEebGhULtA1tIZFI8Ntvv2Hq1KnQ0dH4iYEiQUR48+YNJBKJpkNhrMwoV64cbG1tFU5OrcpxXGMtudTUVISGhmL69Oky5Z07d8alS5eU2kZmZibi4+NRoUKFXOukpKQgJSVFeB4XF1ewgJkciUQCDw8PhISEICYmBkuXLtV0SEVCmuCsra1hbGzMM8IzVoSICImJiYiOjgYA2NnZFWp7GktyMTExyMjIgI2NjUy5jY0N3rx5o9Q2li9fjoSEBPTv3z/XOosWLcLcuXMLFSuTlz3BWVpaYvDgwZoOqUhkZGQICc7S0lLT4TBWJhgZGQEAoqOjYW1tXahTlxo/v5TzVzERKfVLefv27ZgzZw527twJa2vrXOvNmDEDsbGxwuPFixeFjrmsy5ngtLmTifQanLGxsYYjYaxskf7NFfY6uMZaclZWVtDV1ZVrtUVHR8u17nLauXMnRowYgd27d6NTp0551jUwMICBgUGh42VZylKCy45PUTJWvNT1N6exlpxYLEbTpk0RFBQkUx4UFCQM5qvI9u3bMWzYMGzbtg3du3cv6jBZNpmZmejevXuZS3CMsdJLo6crJ0+ejA0bNmDTpk24f/8+Jk2ahMjISPj6+gLIOtU4ZMgQof727dsxZMgQLF++HC1btsSbN2/w5s0bxMbGFmvcGuyQqlE6OjqYMmUK7OzsOMGxfJ09exYikajU9EoNCAhAuXLlNB0GUzONJjkvLy/4+/tj3rx5aNSoEc6fP48jR47A0dERABAVFYXIyEih/m+//Yb09HR88803sLOzEx4TJkzQ1Fsoc7744gs8evSIE1wpMGzYMIhEIvz8888y5fv27ePTr6zM0HjHk7Fjx+LZs2dISUlBaGgo2rZtKywLCAjA2bNnhednz54FEck9AgICij/wMkIikaB///54/vy5UGZiYqLBiJgqDA0NsXjxYnz8+FHToZQYqampmg6BFSONJ7nSTKTlc4NLB1vevXs3vvzyyzJ7mrY069SpE2xtbfMdXGHPnj2oW7cuDAwM4OTkhOXLl8ssd3JywsKFCzF8+HCYmZmhSpUq+P3335WKITQ0FK6urjA2Noa7uzsePHggLHvy5Al69eoFGxsbmJqaolmzZjh58qSwXHrKM+dj2LBhSq0vjX3+/PkYNmwYLCwsMGrUKABZP6KrVKkCY2Nj9OnTB+/fv5eLfd26dahWrRrEYjFq1aqFv/76S1j27NkziEQi3Lx5UyiTSCQQiUTCj/OPHz9i0KBBqFixIoyMjFCjRg1s3rxZqf3G1IOTHFMo52wCGzZs4FNcpZCuri4WLlyI1atX4+XLlwrrhIaGon///hgwYADu3LmDOXPmYNasWXJnSJYvXw5XV1eEhYVh7NixGDNmDP799998Y5g5cyaWL1+OkJAQ6OnpYfjw4cKyT58+wdPTEydPnkRYWBi6dOmCHj16CJcp3N3dERUVJTxOnz4NQ0ND4YxPfutLLV26FPXq1UNoaChmzZqFq1evYvjw4Rg7dixu3ryJDh06YP78+TLr7N27FxMmTMCUKVNw9+5djB49Gj4+Pjhz5ky+71lq1qxZCA8Px9GjR3H//n2sW7eO51YsblTGxMbGEgCKjY0t8Da6rDxHjt8fouCH79QYWcnx8eNHatasGQEgS0tLunXrlqZD0pikpCQKDw+npKQkTYeisqFDh1KvXr2IiKhly5Y0fPhwIiLau3cvZf/T9/b2Jg8PD5l1p06dSi4uLsJzR0dH+uqrr4TnmZmZZG1tTevWrcv19c+cOUMA6OTJk0LZ4cOHCUCe+9PFxYVWr14tVx4TE0PVqlWjsWPH5rquovUdHR2pd+/eMnUGDhxIXbt2lSnz8vIiCwsL4bm7uzuNGjVKps6XX35Jnp6eREQUERFBACgsLExY/vHjRwJAZ86cISKiHj16kI+PT57xMsXy+ttT5TjOLTkmg+eD006LFy/Gli1bEB4eLrfs/v37aNWqlUxZq1at8OjRI2RkZAhl2b8HIpEItra2wtBL3bp1g6mpKUxNTVG3bl2ZbWVfTzpEk3S9hIQETJs2DS4uLihXrhxMTU3x77//yrXE0tLS0LdvX1SpUgWrVq0SypVd39XVVe49u7m5yZTlfJ7bfrl//z6UNWbMGOzYsQONGjXCtGnTlB6ykKmPRmchYCXPhAkTOMFpobZt26JLly744YcfhOtZUqRglCFScP1VX19f5rlIJEJmZiYAYMOGDUhKSlJYL/tz6etI15s6dSqOHz+OZcuWoXr16jAyMkK/fv3kOoeMGTMGkZGRuH79OvT0/jtsKbt+zs5Sit6fInmNyCQdkDz7tnKOztGtWzc8f/4chw8fxsmTJ9GxY0d88803WLZsmVKvzwqPkxyTsXTpUkRGRmLVqlWc4LTMzz//jEaNGqFmzZoy5S4uLrhw4YJM2aVLl1CzZk2lxwx0cHAoUEzBwcEYNmwY+vTpAyDrGtuzZ89k6qxYsQI7d+7E5cuX5cYPVWZ9RVxcXHDlyhWZspzP69SpgwsXLsjcq3vp0iXUqVMHAFCxYtYMJFFRUcJUX9k7oUhVrFgRw4YNw7Bhw9CmTRtMnTqVk1wx4iTHkJ6eLvw6tra2xunTp7mTiRaqX78+Bg0ahNWrV8uUT5kyBc2aNcNPP/0ELy8vXL58Gb/++ivWrl1b5DFVr14dgYGB6NGjB0QiEWbNmiW08gDg5MmTmDZtGtasWQMrKythGEAjIyNYWFjku35uxo8fD3d3dyxZsgS9e/fGiRMncOzYMZk6U6dORf/+/dGkSRN07NgRBw8eRGBgoNB708jICC1btsTPP/8MJycnxMTE4Mcff5TZxuzZs9G0aVPUrVsXKSkpOHTokJAkWfHga3JlnEQiQevWrbFp0yahjBOc9vrpp5/kTtU1adIEu3btwo4dO1CvXj3Mnj0b8+bNkzutWRRWrlyJ8uXLw93dHT169ECXLl3QpEkTYfmFCxeQkZEBX19fhQNA5Ld+blq2bIkNGzZg9erVaNSoEU6cOCGXoHr37o1Vq1Zh6dKlqFu3Ln777Tds3rwZ7du3F+ps2rQJaWlpcHV1xYQJE+R6aIrFYsyYMQMNGjRA27Ztoaurix07dhRijzFVaXTSVE1Qx6SpXf3P49838fh7RAu0rlF6uwNn72RSsWJFPHr0CBYWFpoOq0RJTk5GREQEnJ2dYWhoqOlwGCsz8vrbU+U4zi25QijNDZ6cvShPnjzJCY4xpnU4yZVBfJsAY6ys4CRXxnCCY4yVJZzkypht27ZxgmOMlRl8C0EZM2bMGHz48AE9e/bkBMcY03qc5MoAiUQCAwMDGBkZQSQSyXWVZowxbcWnKwugNN10IZFI4OHhgV69egnDLjHGWFnBSU6LSRNcSEgIbty4ITdoLWOMaTtOcloqe4KTdjKpVauWpsNiGhIbGwsjIyO5oasCAwNhYmKCT58+ITU1FePGjYOdnR0MDQ3h5OSU72SrjJV0nOQKoaTeC64owXEnk7LNwsIC3bt3x9atW2XKt23bhl69esHU1BS//PILDhw4gF27duHBgwf4+++/4eTkpJmAGVMT7niiZTjBFT0iQlJaRv4Vi4CRvm6BxxYdNGgQhgwZgsTERBgbGyMuLg6HDx/Gnj17AACRkZGoUaMGWrduDZFIBEdHR3WGzphGcJLTMs+ePcOjR484wRWhpLQMuMw+rpHXDp/XBcbigv3Zdu/eHXp6ejhw4AAGDBiAPXv2wMzMDJ07dwYADBs2DB4eHqhVqxa6du2Kzz//XFjGWGnFpyu1jHREdU5wLCexWIx+/fph27ZtALJOVXp5eQnTLDVp0gQRERH46aefkJSUhP79+6Nfv36aDJmxQuOWnBaQSCR49uwZGjVqBABo3ry5ZgPSckb6ugif10Vjr10YgwYNQufOnXHv3j2cOXMGP/30k8xyc3NzeHl5wcvLC/369UPXrl3x4cMHVKhQoVCvy5imcJIr5aRjUT58+BAnTpzgBFcMRCJRgU8Zalq7du1gY2ODQYMGwcnJCS1bthSWrVy5EnZ2dmjUqBF0dHSwe/du2Nraoly5cpoLmLFC4tOVBUAoGXeDZx9sWU9Pj+c7Y/kSiUQYOHAgbt26hUGDBsksMzU1xeLFi+Hq6opmzZrh2bNnOHLkCHR0+DDBSq/S+XOU8WwCrMCWLFmCJUuWyJWPGjUKo0aN0kBEjBUd/olWGBq6UY4THGOMKYeTXCkTGxvLCY4xxpTESa6UEYvFsLCw4ATHGGNK4GtypYyRkREOHDiAyMhIHouSMcbywS25UkAikWDt2rWg/8/xY2RkxAmOqSwgIKBU3Q4wZ84c4d5PTXrw4AFsbW0RHx+v6VC0xqFDh9C4cWNkZmYW+WtxkivhpJ1MvvnmGyxYsEDT4TBW5sycORPffPMNzMzM5JbVqlULYrEYr169klvm5OQEf39/uXJ/f3+5ga/j4uIwc+ZM1K5dG4aGhrC1tUWnTp0QGBgo/LhVt+TkZAwbNgz169eHnp4eevfurdR6Hz9+xODBg2FhYQELCwsMHjwYEolEpk5kZCR69OgBExMTWFlZYfz48UhNTRWWf/755xCJRMLoO0WJk1wJlrMXZc+ePTUdEmNql5aWpukQcvXy5UscOHAAPj4+cssuXLiA5ORkfPnllwgICCjwa0gkEri7u+PPP//EjBkzcOPGDZw/fx5eXl6YNm0aYmNjC/EOcpeRkQEjIyOMHz8enTp1Uno9b29v3Lx5E8eOHcOxY8dw8+ZNDB48WGa73bt3R0JCAi5cuIAdO3Zgz549mDJlisx2fHx8sHr1arW9n1xRGRMbG0sAKDY2tsDb6LT8LDl+f4guPn6nxshkffz4kZo1a0YAyNLSkm7dulVkr8Vyl5SUROHh4ZSUlKTpUApt8+bNZGFhQceOHaPatWuTiYkJdenShV6/fi3UuXbtGnXq1IksLS3J3Nyc2rZtS6GhoTLbACD38PPzU2p9IiIAtG7dOurZsycZGxvT7NmziYho0aJFZG1tTaampjR8+HD6/vvvqWHDhsJ6GRkZNHfuXHJwcCCxWEwNGzako0ePCsvPnDlDAOjjx49CWVhYGAGgiIgIIiJ69uwZff7551SuXDkyNjYmFxcXOnz4cK77bPny5eTq6qpw2bBhw2j69Ol09OhRqlq1KmVmZsosd3R0pJUrV8qtt3LlSnJ0dBSejxkzhkxMTOjVq1dydePj4yktLS3X+NRl6NCh1KtXr3zrhYeHEwC6cuWKUHb58mUCQP/++y8RER05coR0dHRk3s/27dvJwMBA5rj77NkzAkBPnjxR+Fp5/e2pchznllwJxPfBsaKSmJiIZcuW4a+//sL58+cRGRmJ7777TlgeHx+PoUOHIjg4GFeuXEGNGjXg6ekpXI/y8vJCVFSU8Ni+fTv09PTQqlUrpdaX8vPzQ69evXDnzh0MHz4cu3btgp+fHxYsWICQkBDY2dlh7dq1MuusWrUKy5cvx7Jly3D79m106dIFPXv2xKNHj5R+/9988w1SUlJw/vx53LlzB4sXL4apqWmu9c+fPw9XV1e58vj4eOzevRtfffUVPDw8kJCQgLNnzyodh1RmZiZ27NiBQYMGwd7eXm65qampMIB2TsHBwTA1Nc3zsXDhQpVjysvly5dhYWGBFi1aCGUtW7aEhYUFLl26JNSpV6+ezPvp0qULUlJSEBoaKpQ5OjrC2toawcHBao0xJ+5dWQiiIrgbPD09HV27duUEx4pEWloa1q9fj2rVqgEAxo0bh3nz5gnLP/vsM5n6v/32G8qXL49z587h888/h5GREYyMjAAAT548wbhx47Bw4UJ4eHgotb6Ut7c3hg8fLjwfOHAghg8fjpEjRwIA5s+fj5MnTyI5OVmos2zZMnz//fcYMGAAAGDx4sU4c+YM/P39sWbNGqXef2RkJPr27Yv69esDAKpWrZpn/WfPnqFp06Zy5Tt27ECNGjVQt25dAMCAAQOwceNGdOjQQak4pGJiYvDx40fUrl1bpfUAwNXVFTdv3syzjroH1n7z5g2sra3lyq2trfHmzRuhjo2Njczy8uXLQywWC3WkHBwc8OzZM7XGmBMnuRJGT08PX3/9NZ4+fYqTJ09ygmNqZWxsLCQ4ALCzs0N0dLTwPDo6GrNnz8bp06fx9u1bZGRkIDExEZGRkTLbiY2Nxeeff45u3bph6tSpKq+fs3V0//59+Pr6ypS5ubnhzJkzALI6Zrx+/VpoMUq1atUKt27dUvr9jx8/HmPGjMGJEyfQqVMn9O3bN8+/saSkJIVjwm7cuBFfffWV8Pyrr75C27ZtIZFIVOrBSv/vVFKQiXCNjIxQvXp1ldcrLEWxEpFMuTJ1gKz3kJiYqP4gs+HTlSXQ8OHD8ejRI05wTO309fVlnotEIpnee8OGDUNoaCj8/f1x6dIl3Lx5E5aWljI94zIyMuDl5QVzc3P88ccfMttTZn0AMDExKVD8OQ+S2Q+c0oGks7+fnJ1aRo4ciadPn2Lw4MG4c+cOXF1d8+z8YGVlhY8fP8qUhYeH4+rVq5g2bRr09PSgp6eHli1bIikpCdu3bxfqmZubK+w0IpFIYGFhAQCoWLEiypcvj/v37yvz9mVo4nSlra0t3r59K1f+7t07ofVma2sr12L7+PEj0tLS5Fp4Hz58QMWKFdUaY06c5EoAiUSCoUOHyvyilv4RMFacgoODMX78eHh6eqJu3bowMDBATEyMTJ1Jkybhzp072Lt3r1wrR5n1FalTpw6uXLkiU5b9ubm5Oezt7XHhwgWZOpcuXUKdOnUAQDhYRkVFCcsVnc6rXLkyfH19ERgYiClTpsgl6uwaN26M8PBwmbKNGzeibdu2uHXrFm7evCk8pk2bho0bNwr1ateujevXr8tt8/r168J9rjo6OvDy8sLWrVvx+vVruboJCQlIT09XGJv0dGVej5yt48Jyc3NDbGwsrl27JpRdvXoVsbGxcHd3F+rcvXtX5nM4ceIEDAwMZE79Jicn48mTJ2jcuLFaY5STb9cULaPO3pWXHscUOp7svSjbt28v10OLaZY29q7Mbu/evZT9MNCoUSPy8PCg8PBwunLlCrVp04aMjIyEXoKbNm0iXV1dOnDgAEVFRQmP+Ph4pdYnyupduXfvXpk4duzYQQYGBrRx40Z68OABzZ49m8zMzGR6V65cuZLMzc1px44d9O+//9L3339P+vr69PDhQyIiSk1NpcqVK9OXX35JDx48oEOHDlGtWrVkeldOmDCBjh07Rk+fPqXQ0FBq3rw59e/fP9d9duDAAbK2tqb09HThNSpWrEjr1q2Tq/vw4UMCQDdv3iSirF6HOjo6NHfuXLp37x7du3eP5s2bRzo6OjK9Ez98+EC1a9emSpUq0ZYtW+jevXv08OFD2rhxI1WvXl2mt6i63bt3j8LCwqhHjx7Uvn17CgsLo7CwMGH51atXqVatWvTy5UuhrGvXrtSgQQO6fPkyXb58merXr0+ff/65sDw9PZ3q1atHHTt2pBs3btDJkyepUqVKNG7cOJnXPnPmDJmamlJCQoLC2NTVu5KTXAGoK8nxbQIlX1lLcjdu3CBXV1cyMDCgGjVq0O7du2W6wg8dOjTPWwjyW59IcZIjIlqwYAFZWVmRqakpDR06lKZNm5brLQT6+vpytxAQEV24cIHq169PhoaG1KZNG9q9e7dMkhs3bhxVq1aNDAwMqGLFijR48GCKicn97zg9PZ0cHBzo2LFjRET0zz//kI6ODr1580Zh/fr169O3334rPA8KCqI2bdpQ+fLlqXz58tS6dWsKCgqSW08ikdD06dOpRo0aJBaLycbGhjp16kR79+4t0h++jo6OCj9PKeltGdL9R0T0/v17GjRoEJmZmZGZmRkNGjRILhE/f/6cunfvTkZGRlShQgUaN24cJScny9T5+uuvafTo0bnGpq4kJyIqotvpS6i4uDhYWFggNjYW5ubmBdqGx4pzeBT9CdtHtYRbNcsCbYNvEygdkpOTERERAWdnZ56Utoxau3Yt9u/fj+PHj2s6FK3x7t071K5dGyEhIXB2dlZYJ6+/PVWO49y7sgAK+6uAExxjpcfXX3+Njx8/Ij4+XuHQXkx1ERERWLt2ba4JTp04yWnA119/zQmOsVJCT08PM2fO1HQYWqV58+Zo3rx5sbwW964shALc2gIAWLp0KZo1a8YJjjHGihi35IpJZmamcB+Po6Mjrl69WqAbQBljjCmPW3LFQCKRoE2bNggMDBTKOMExxljR45ZcEZNIJPDw8EBISAgiIiLQpUuXAo/2wBhjTDXckitC2ROcpaUljh07xgmOMcaKESe5IpIzwXEnE8YYK36c5IoAJzjGGCsZOMkVQH6DxPz222+c4FiJ9uzZM4hEonznIyuJ5syZg0aNGhXrawYEBCg1hc7GjRvRuXPnog+oFLhz5w4qVaqEhIQEjcbBSa4ITJ06Fd999x0nOMbKkJSUFMyePRuzZs2SW/by5UuIxWKFk6Pm9YOjd+/eGDZsmEzZ48eP4ePjg0qVKsHAwADOzs4YOHAgQkJCChz7nTt30K5dOxgZGcHBwQHz5s3L98e8k5MTRCKRzGP69OnC8vr166N58+ZYuXJlgeNSB05yhZD9JoC4uDhh7iodHR0sXbqUExxjZciePXtgamqKNm3ayC0LCAhA//79kZiYiIsXLxb4NUJCQtC0aVM8fPgQv/32G8LDw7F3717Url0bU6ZMKdA24+Li4OHhAXt7e1y/fh2rV6/GsmXLsGLFinzXnTdvHqKiooTHjz/+KLPcx8cH69atQ0ZGRoFiUwdOcmogkUjQqVMneHt7y03SyLRXQkJCro/k5GSl6yYlJSlVV1WZmZlYvHgxqlevDgMDA1SpUgULFiyQqfP06VN06NABxsbGaNiwIS5fviwse//+PQYOHIhKlSrB2NgY9evXl5kUFADat2+P8ePHY9q0aahQoQJsbW0xZ84cmToSiQRff/01bGxsYGhoiHr16uHQoUPC8kuXLqFt27YwMjJC5cqVMX78eJXf7+bNm1GnTh0YGhqidu3aWLt2rbDMzc1NpoUBZA0QrK+vL8w8npqaimnTpsHBwQEmJiZo0aIFzp49q1IMO3bsQM+ePeXKiQibN2/G4MGD4e3tLTPnnCqICMOGDUONGjUQHByM7t27o1q1amjUqBH8/Pywf//+Am1369atSE5ORkBAAOrVq4cvvvgCP/zwA1asWJFva87MzAy2trbCw9TUVGZ5ly5d8P79e5w7d65AsalFvvMUaBl1TLXz2bIz5Pj9IbryJEZuupzHjx+rMVqmaXlN9wEFU5RIH56enjJ1jY2Nc63brl07mbpWVlZ5Tn+irGnTplH58uUpICCAHj9+TMHBwfTHH38QEVFERAQBoNq1a9OhQ4fowYMH1K9fP3J0dKS0tDQiInr58iUtXbqUwsLC6MmTJ/TLL7+Qrq6uzFxo7dq1I3Nzc5ozZw49fPiQtmzZQiKRiE6cOEFEWdPjtGzZkurWrUsnTpygJ0+e0MGDB+nIkSNERHT79m0yNTWllStX0sOHD+nixYvUuHFjGjZsWK7vy8/PT2YKnt9//53s7Oxoz5499PTpU9qzZw9VqFCBAgICiIho9erVVKVKFZkpa1avXk0ODg6UkZFBRETe3t7k7u5O58+fp8ePH9PSpUvJwMBAmKtO0TRFOZUrV4527NghV37q1CmytbWl9PR0unv3LpmYmFBcXJywXPpZZJ/HTapXr140dOhQIsqaxggAbdu2Lc84iIhcXFzIxMQk14eLi4tQd/DgwdSzZ0+Z9aWv9fTp01xfw9HRkWxtbalChQrUsGFDmj9/PqWkpMjVa968Oc2ZMyffmHPSmvnk1qxZQ05OTmRgYEBNmjSh8+fP51n/7Nmz1KRJEzIwMCBnZ2eFkxfmRZ1JLijsCc8Hp+VKa5KLi4sjAwMDIanlJD2wbtiwQSi7d+8eAaD79+/nul1PT0+aMmWK8Lxdu3bUunVrmTrNmjWj77//noiIjh8/Tjo6OvTgwQOF2xs8eDB9/fXXMmXBwcGko6OT6xx+OZNc5cqV5Q78P/30E7m5uRERUXR0NOnp6ckcW9zc3Gjq1KlERPT48WMSiUT06tUrmW107NiRZsyYQUT5J7mPHz8SAIXHL29vb5o4caLwvGHDhjKfi7JJbufOnQSAbty4kWscUs+ePaNHjx7l+nj27JlQ18PDg0aNGiWz/qtXrwgAXbp0KdfXWLFiBZ09e5Zu3bpFf/zxB1lZWdGIESPk6vXp0yfPHy25UVeS0+iIJzt37sTEiROxdu1atGrVCr/99hu6deuG8PBwVKlSRa5+REQEPD09MWrUKPz999+4ePEixo4di4oVK6Jv377FGntm8idMGNoP4bfDuBdlGfXp06dcl+nq6so8j46OzrWudExTqWfPnhUqLgC4f/8+UlJS0LFjxzzrZf/O2tnZAciKtXbt2sjIyMDPP/+MnTt34tWrV0hJSUFKSorcgAY5v/d2dnbC+7158yYqVaqEmjVrKnz90NBQPH78GFu3bhXKiAiZmZmIiIhAnTp18oz/3bt3ePHiBUaMGIFRo0YJ5enp6bCwsAAAVKxYER4eHti6dSvatGmDiIgIXL58GevWrQMA3LhxA0QkF2NKSgosLZWbL1J6yjnnvGcSiQSBgYG4cOGCUPbVV19h06ZNGDlypFLblqL/nzpUZkhAR0dHlbadc5vKvNakSZOE/zdo0ADly5dHv379sHjxYpn9ZmRkhMTERJXiUSeNJrkVK1ZgxIgRwoft7++P48ePY926dVi0aJFc/fXr16NKlSrw9/cHANSpUwchISFYtmxZsSa5tKRPeLtrFl5EPeIEV4apMnpNUdXNjZGRkVL19PX1hf9LD2iZmZkAgOXLl2PlypXw9/dH/fr1YWJigokTJyI1NTXXbUi3I91GfnFkZmZi9OjRGD9+vNwyRT90Fa0PAH/88QdatGghsyz7D41BgwZhwoQJWL16NbZt24a6deuiYcOGwjZ0dXURGhoq9+Mk5zWm3FhaWkIkEuHjx48y5du2bUNycrJMbNIkHh4eDhcXFyEZx8bGym1XIpEICUuahO/fv5/vLRR169bF8+fPc13u6OiIe/fuAQBsbW3x5s0bmeXSHyk2NjZ5vk52LVu2BJDV+zN7kvvw4QOqVaum9HbUTWNJLjU1FaGhoXIXhDt37oxLly4pXOfy5cty96B06dIFGzduRFpamtwfGwDh16dUXFxcoWNPfPsMae+ew6J8BU5wrESqUaMGjIyMcOrUKZVbDFLBwcHo1asXvvrqKwBZyeDRo0f5tq6ya9CgAV6+fImHDx8qbM01adIE9+7dQ/Xq1QsUo42NDRwcHPD06VMMGjQo13q9e/fG6NGjcezYMWzbtg2DBw8WljVu3BgZGRmIjo5W2DNSGWKxGC4uLggPD5c5Rm3cuBFTpkyRuw1g/Pjx2LRpE5YtW4by5cujYsWKuH79Otq1ayfUSUpKwr1799C/f38AQKNGjeDi4oLly5fDy8tL7gyARCIR7uU7cuRInp3gsh8r3dzc8MMPPyA1NRVisRgAcOLECdjb28PJyUnpfRAWFgbgvzMCUnfv3kW/fv2U3o7aqXyiVE2k53wvXrwoU75gwQKqWbOmwnVq1KhBCxYskCm7ePEiAaDXr18rXMfPz0/h9Y3CXJPr6n+eKg1aSFuP5H39kJV+eV0XKOnmzJlD5cuXpy1bttDjx4/p8uXLwjU4RdeBpNeVzpw5Q0REEydOpMqVK9PFixcpPDycRo4cSebm5tSrVy9hnXbt2tGECRNkXjf7dSQiovbt21O9evXoxIkT9PTpUzpy5AgdPXqUiIhu3bpFRkZGNHbsWAoLC6OHDx/S/v37ady4cbm+r5zX5P744w8yMjIif39/evDgAd2+fZs2bdpEy5cvl1nP29ubGjZsSCKRiJ4/fy6zbNCgQeTk5CR0Xrl27Rr9/PPPdPjwYSJSruPJ5MmTqW/fvsLzsLCwXK9x/v7771SxYkVKTU0lIqLFixdT+fLl6c8//6THjx/T9evXqV+/fmRraytzrLp69SqZmZlRq1at6PDhw/TkyRO6desWzZ8/n9q2bZtnfLmRSCRkY2NDAwcOpDt37lBgYCCZm5vTsmXLZF63Vq1a9PLlSyIiunTpEq1YsYLCwsLo6dOntHPnTrK3t5frwBIREUEikUjmGqCySn3Hk9wubM6fP59q1aqlcJ0aNWrQwoULZcouXLhAACgqKkrhOsnJyRQbGys8Xrx4Uegkx8qO0pzkMjIyaP78+eTo6Ej6+vpUpUoV4e9HmST3/v176tWrF5mampK1tTX9+OOPNGTIEJWT3Pv378nHx4csLS3J0NCQ6tWrR4cOHRKWX7t2jTw8PMjU1JRMTEyoQYMGcj9ms8uZ5IiItm7dSo0aNSKxWEzly5entm3bUmBgoEydw4cPEwCFySA1NZVmz55NTk5OpK+vT7a2ttSnTx+6ffs2ESmX5O7fv09GRkYkkUiIiGjcuHEyvRizi46OJl1dXdqzZw8RZX1Wa9asoQYNGpCJiQk5ODhQ37596dGjR3LrPnjwgIYMGUL29vYkFovJ0dGRBg4cqFSHlNzcvn2b2rRpQwYGBmRra0tz5syR6Y165swZAkARERFERBQaGkotWrQgCwsLMjQ0pFq1apGfnx8lJCTIbHfhwoXUpUuXAsWkriQnIsrnRogikpqaCmNjY+zevRt9+vQRyidMmICbN28qvK+ibdu2aNy4MVatWiWU7d27V7jJUtHpypzi4uJgYWGB2NhYmJubq+fNMK2VnJyMiIgIODs7y3UqYCyn/v37o3HjxpgxY4amQ9G4lJQU1KhRA9u3b0erVq1UXj+vvz1VjuMauxlcLBajadOmCAoKkikPCgqCu7u7wnXc3Nzk6p84cQKurq5KJTjGGCtKS5cuVbqzirZ7/vw5Zs6cWaAEp04a7V05efJkDB48GK6urnBzc8Pvv/+OyMhI+Pr6AgBmzJiBV69e4c8//wQA+Pr64tdff8XkyZMxatQoXL58GRs3bpQbhYExxjTB0dER3377rabDKBFq1qyZ660jxUmjSc7Lywvv378Xxj+rV68ejhw5InSZjYqKQmRkpFDf2dkZR44cwaRJk7BmzRrY29vjl19+KfZ75BhjjJUOGrsmpyl8TY6pgq/JMaYZpf6aHGOlSRn7LciYxqnrb46THGN5kHZo0uSwRIyVRdK/ucJ2KtToNTnGSjpdXV2UK1dOGObI2NhYqbEDGWMFQ0RITExEdHQ0ypUrJzfUmqo4yTGWD1tbWwB5D7LMGFOvcuXKCX97hcFJjrF8iEQi2NnZwdramifFZawY6OvrF7oFJ8VJjjEl6erqqu0PjzFWPLjjCWOMMa3FSY4xxpjW4iTHGGNMa5W5a3LSGwzVMXkqY4yx4ic9fitzw3iZS3Lx8fEAgMqVK2s4EsYYY4URHx8PCwuLPOuUubErMzMz8fr1a5iZmRX4pt64uDhUrlwZL168KPPjX/K+kMX74z+8L/7D++I/6tgXRIT4+HjY29tDRyfvq25lriWno6ODSpUqqWVb5ubmZf4LK8X7Qhbvj//wvvgP74v/FHZf5NeCk+KOJ4wxxrQWJznGGGNai5NcARgYGMDPzw8GBgaaDkXjeF/I4v3xH94X/+F98Z/i3hdlruMJY4yxsoNbcowxxrQWJznGGGNai5McY4wxrcVJjjHGmNbiJJeLtWvXwtnZGYaGhmjatCmCg4PzrH/u3Dk0bdoUhoaGqFq1KtavX19MkRY9VfZFYGAgPDw8ULFiRZibm8PNzQ3Hjx8vxmiLlqrfC6mLFy9CT08PjRo1KtoAi5mq+yMlJQUzZ86Eo6MjDAwMUK1aNWzatKmYoi1aqu6LrVu3omHDhjA2NoadnR18fHzw/v37Yoq26Jw/fx49evSAvb09RCIR9u3bl+86RXr8JCZnx44dpK+vT3/88QeFh4fThAkTyMTEhJ4/f66w/tOnT8nY2JgmTJhA4eHh9Mcff5C+vj79888/xRy5+qm6LyZMmECLFy+ma9eu0cOHD2nGjBmkr69PN27cKObI1U/VfSElkUioatWq1LlzZ2rYsGHxBFsMCrI/evbsSS1atKCgoCCKiIigq1ev0sWLF4sx6qKh6r4IDg4mHR0dWrVqFT19+pSCg4Opbt261Lt372KOXP2OHDlCM2fOpD179hAA2rt3b571i/r4yUlOgebNm5Ovr69MWe3atWn69OkK60+bNo1q164tUzZ69Ghq2bJlkcVYXFTdF4q4uLjQ3Llz1R1asSvovvDy8qIff/yR/Pz8tCrJqbo/jh49ShYWFvT+/fviCK9Yqbovli5dSlWrVpUp++WXX6hSpUpFFqMmKJPkivr4yacrc0hNTUVoaCg6d+4sU965c2dcunRJ4TqXL1+Wq9+lSxeEhIQgLS2tyGItagXZFzllZmYiPj4eFSpUKIoQi01B98XmzZvx5MkT+Pn5FXWIxaog++PAgQNwdXXFkiVL4ODggJo1a+K7775DUlJScYRcZAqyL9zd3fHy5UscOXIERIS3b9/in3/+Qffu3Ysj5BKlqI+fZW6A5vzExMQgIyMDNjY2MuU2NjZ48+aNwnXevHmjsH56ejpiYmJgZ2dXZPEWpYLsi5yWL1+OhIQE9O/fvyhCLDYF2RePHj3C9OnTERwcDD097fpTK8j+ePr0KS5cuABDQ0Ps3bsXMTExGDt2LD58+FCqr8sVZF+4u7tj69at8PLyQnJyMtLT09GzZ0+sXr26OEIuUYr6+MktuVzknIaHiPKcmkdRfUXlpZGq+0Jq+/btmDNnDnbu3Alra+uiCq9YKbsvMjIy4O3tjblz56JmzZrFFV6xU+W7kZmZCZFIhK1bt6J58+bw9PTEihUrEBAQUOpbc4Bq+yI8PBzjx4/H7NmzERoaimPHjiEiIgK+vr7FEWqJU5THT+36eakGVlZW0NXVlfsFFh0dLfdrQ8rW1lZhfT09PVhaWhZZrEWtIPtCaufOnRgxYgR2796NTp06FWWYxULVfREfH4+QkBCEhYVh3LhxALIO8kQEPT09nDhxAp999lmxxF4UCvLdsLOzg4ODg8wUKXXq1AER4eXLl6hRo0aRxlxUCrIvFi1ahFatWmHq1KkAgAYNGsDExARt2rTB/PnzS+3Zn4Io6uMnt+RyEIvFaNq0KYKCgmTKg4KC4O7urnAdNzc3ufonTpyAq6sr9PX1iyzWolaQfQFkteCGDRuGbdu2ac01BlX3hbm5Oe7cuYObN28KD19fX9SqVQs3b95EixYtiiv0IlGQ70arVq3w+vVrfPr0SSh7+PChWud41ISC7IvExES5yT51dXUB/NeKKSuK/Piplu4rWkbaHXjjxo0UHh5OEydOJBMTE3r27BkREU2fPp0GDx4s1Jd2gZ00aRKFh4fTxo0bte4WAmX3xbZt20hPT4/WrFlDUVFRwkMikWjqLaiNqvsiJ23rXanq/oiPj6dKlSpRv3796N69e3Tu3DmqUaMGjRw5UlNvQW1U3RebN28mPT09Wrt2LT158oQuXLhArq6u1Lx5c029BbWJj4+nsLAwCgsLIwC0YsUKCgsLE26nKO7jJye5XKxZs4YcHR1JLBZTkyZN6Ny5c8KyoUOHUrt27WTqnz17lho3bkxisZicnJxo3bp1xRxx0VFlX7Rr144AyD2GDh1a/IEXAVW/F9lpW5IjUn1/3L9/nzp16kRGRkZUqVIlmjx5MiUmJhZz1EVD1X3xyy+/kIuLCxkZGZGdnR0NGjSIXr58WcxRq9+ZM2fyPAYU9/GTp9phjDGmtfiaHGOMMa3FSY4xxpjW4iTHGGNMa3GSY4wxprU4yTHGGNNanOQYY4xpLU5yjDHGtBYnOcYYY1qLkxxjCgQEBKBcuXKaDqPAnJyc4O/vn2edOXPmoFGjRsUSD2OawkmOaa1hw4ZBJBLJPR4/fqzp0BAQECATk52dHfr374+IiAi1bP/69ev4+uuvhecikQj79u2TqfPdd9/h1KlTanm93OR8nzY2NujRowfu3bun8nZK848Opjmc5JhW69q1K6KiomQezs7Omg4LQNZMBVFRUXj9+jW2bduGmzdvomfPnsjIyCj0titWrAhjY+M865iamhbLVFDZ3+fhw4eRkJCA7t27IzU1tchfmzFOckyrGRgYwNbWVuahq6uLFStWoH79+jAxMUHlypUxduxYmSlgcrp16xY6dOgAMzMzmJubo2nTpggJCRGWX7p0CW3btoWRkREqV66M8ePHIyEhIc/YRCIRbG1tYWdnhw4dOsDPzw93794VWprr1q1DtWrVIBaLUatWLfz1118y68+ZMwdVqlSBgYEB7O3tMX78eGFZ9tOVTk5OAIA+ffpAJBIJz7Ofrjx+/DgMDQ0hkUhkXmP8+PFo166d2t6nq6srJk2ahOfPn+PBgwdCnbw+j7Nnz8LHxwexsbFCi3DOnDkAgNTUVEybNg0ODg4wMTFBixYtcPbs2TzjYWULJzlWJuno6OCXX37B3bt3sWXLFpw+fRrTpk3Ltf6gQYNQqVIlXL9+HaGhoZg+fbow19WdO3fQpUsXfPHFF7h9+zZ27tyJCxcuCJOlKsvIyAgAkJaWhr1792LChAmYMmUK7t69i9GjR8PHxwdnzpwBAPzzzz9YuXIlfvvtNzx69Aj79u1D/fr1FW73+vXrAIDNmzcjKipKeJ5dp06dUK5cOezZs0coy8jIwK5duzBo0CC1vU+JRIJt27YBgMxcYXl9Hu7u7vD39xdahFFRUfjuu+8AAD4+Prh48SJ27NiB27dv48svv0TXrl3x6NEjpWNiWk5t8xkwVsIMHTqUdHV1ycTERHj069dPYd1du3aRpaWl8Hzz5s1kYWEhPDczM6OAgACF6w4ePJi+/vprmbLg4GDS0dGhpKQkhevk3P6LFy+oZcuWVKlSJUpJSSF3d3caNWqUzDpffvkleXp6EhHR8uXLqWbNmpSamqpw+46OjrRy5UrhOQDau3evTJ2cU/+MHz+ePvvsM+H58ePHSSwW04cPHwr1PgGQiYkJGRsbC9Ou9OzZU2F9qfw+DyKix48fk0gkolevXsmUd+zYkWbMmJHn9lnZoafZFMtY0erQoQPWrVsnPDcxMQEAnDlzBgsXLkR4eDji4uKQnp6O5ORkJCQkCHWymzx5MkaOHIm//voLnTp1wpdffolq1aoBAEJDQ/H48WNs3bpVqE9EyMzMREREBOrUqaMwttjYWJiamoKIkJiYiCZNmiAwMBBisRj379+X6TgCZM2svWrVKgDAl19+CX9/f1StWhVdu3aFp6cnevToAT29gv9JDxo0CG5ubnj9+jXs7e2xdetWeHp6onz58oV6n2ZmZrhx4wbS09Nx7tw5LF26FOvXr5epo+rnAQA3btwAEaFmzZoy5SkpKcVyrZGVDpzkmFYzMTFB9erVZcqeP38OT09P+Pr64qeffkKFChVw4cIFjBgxAmlpaQq3M2fOHHh7e+Pw4cM4evQo/Pz8sGPHDvTp0weZmZkYPXq0zDUxqSpVquQam/Tgr6OjAxsbG7mDuUgkknlOREJZ5cqV8eDBAwQFBeHkyZMYO3Ysli5dinPnzsmcBlRF8+bNUa1aNezYsQNjxozB3r17sXnzZmF5Qd+njo6O8BnUrl0bb968gZeXF86fPw+gYJ+HNB5dXV2EhoZCV1dXZpmpqalK751pL05yrMwJCQlBeno6li9fDh2drMvSu3btyne9mjVrombNmpg0aRIGDhyIzZs3o0+fPmjSpAnu3bsnl0zzk/3gn1OdOnVw4cIFDBkyRCi7dOmSTGvJyMgIPXv2RM+ePfHNN9+gdu3auHPnDpo0aSK3PX19faV6bXp7e2Pr1q2oVKkSdHR00L17d2FZQd9nTpMmTcKKFSuwd+9e9OnTR6nPQywWy8XfuHFjZGRkIDo6Gm3atClUTEx7cccTVuZUq1YN6enpWL16NZ4+fYq//vpL7vRZdklJSRg3bhzOnj2L58+f4+LFi7h+/bqQcL7//ntcvnwZ33zzDW7evIlHjx7hwIED+Pbbbwsc49SpUxEQEID169fj0aNHWLFiBQIDA4UOFwEBAdi4cSPu3r0rvAcjIyM4Ojoq3J6TkxNOnTqFN2/e4OPHj7m+7qBBg3Djxg0sWLAA/fr1g6GhobBMXe/T3NwcI0eOhJ+fH4hIqc/DyckJnz59wqlTpxATE4PExETUrFkTgwYNwpAhQxAYGIiIiAhcv34dixcvxpEjR1SKiWkxTV4QZKw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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "RocCurveDisplay.from_predictions(target_binary, predictions, name=\"Non-hazardous \\n vs \\n hazardous\")\n", + "plt.plot([0,1],[0,1],\"k--\",label=\"chance level (AUC=0.5)\")\n", + "plt.axis(\"square\")\n", + "plt.xlabel(\"False Positive Rate\")\n", + "plt.ylabel(\"True Positive Rate\")\n", + "plt.title(\"ROC Curve: Non-Hazardous vs Hazardous \\n for the real NEOs DataFrame\")\n", + "plt.legend(loc='lower right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a800c44d-4d34-4284-b023-33b5fa177f63", + "metadata": {}, + "outputs": [], + "source": [ + "#See the asteroids with the highest absolute error:\n", + "abs_errors=np.zeros_like(target)\n", + "for el in range(len(target)):\n", + " abs_errors[el]=np.absolute(target[el]-predictions[el])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2cd2924e-9aa3-4c54-ac6e-8c32a26c2f91", + "metadata": {}, + "outputs": [], + "source": [ + "ind=np.argpartition(abs_errors,-10)[-10:] #indices of the 10 largest values in abs_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b9d3dfb8-881d-4fcf-96e6-52aaa7c8fa90", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_319274/3456925052.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " top10_df['predicted_moid']=predictions[ind]\n", + "/tmp/ipykernel_319274/3456925052.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " top10_df['abs_error']=abs_errors[ind]\n" + ] + } + ], + "source": [ + "top10_df=df.iloc[ind,:] #extract the rows corresponding to the indices in ind\n", + "top10_df['predicted_moid']=predictions[ind]\n", + "top10_df['abs_error']=abs_errors[ind]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "027dce2c-0ef3-490e-9513-cc7932bd29e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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spkidphaHepoch_mjdaeiomwmamoidpredicted_moidabs_error
3368254414533Y19.51604004.63400.799913.9167.26136.58335.880.0249000.0911750.066275
2432154087802N18.58604002.12900.933255.90238.6918.8879.020.2820000.3449300.062930
243920495021N22.70604001.27100.03314.050.8996.7559.200.2580000.1982070.059793
101663640993N17.16604004.44700.833829.38129.67357.3159.300.2450000.1860450.058955
82063557637N23.51556054.81600.770117.30333.33213.55356.060.1620000.2207250.058725
3385154416579N21.50604004.64400.761025.48269.26160.4612.080.1480000.2024400.054440
164093778084N24.50604000.89450.137620.03298.28186.15141.050.0006530.0539180.053265
181193803895N22.14604000.81020.588322.0614.38354.84277.100.1810000.1291200.051880
78920164294N20.45604000.61750.45462.95211.165.4288.230.0941000.0428840.051216
194463837721N25.50604000.74520.35180.32182.1898.90239.070.0048900.0550660.050176
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" + ], + "text/plain": [ + " spkid pha H epoch_mjd a e i om w \\\n", + "33682 54414533 Y 19.51 60400 4.6340 0.7999 13.91 67.26 136.58 \n", + "24321 54087802 N 18.58 60400 2.1290 0.9332 55.90 238.69 18.88 \n", + "2439 20495021 N 22.70 60400 1.2710 0.0331 4.05 0.89 96.75 \n", + "10166 3640993 N 17.16 60400 4.4470 0.8338 29.38 129.67 357.31 \n", + "8206 3557637 N 23.51 55605 4.8160 0.7701 17.30 333.33 213.55 \n", + "33851 54416579 N 21.50 60400 4.6440 0.7610 25.48 269.26 160.46 \n", + "16409 3778084 N 24.50 60400 0.8945 0.1376 20.03 298.28 186.15 \n", + "18119 3803895 N 22.14 60400 0.8102 0.5883 22.06 14.38 354.84 \n", + "789 20164294 N 20.45 60400 0.6175 0.4546 2.95 211.16 5.42 \n", + "19446 3837721 N 25.50 60400 0.7452 0.3518 0.32 182.18 98.90 \n", + "\n", + " ma moid predicted_moid abs_error \n", + "33682 335.88 0.024900 0.091175 0.066275 \n", + "24321 79.02 0.282000 0.344930 0.062930 \n", + "2439 59.20 0.258000 0.198207 0.059793 \n", + "10166 59.30 0.245000 0.186045 0.058955 \n", + "8206 356.06 0.162000 0.220725 0.058725 \n", + "33851 12.08 0.148000 0.202440 0.054440 \n", + "16409 141.05 0.000653 0.053918 0.053265 \n", + "18119 277.10 0.181000 0.129120 0.051880 \n", + "789 88.23 0.094100 0.042884 0.051216 \n", + "19446 239.07 0.004890 0.055066 0.050176 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top10_df.sort_values(by=['abs_error'],ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "d818261d-dc84-4475-a002-0a133077d973", + "metadata": {}, + "outputs": [], + "source": [ + "#Now the idea is to change the threshold 0.05, setting it for example to threshold=0.06, and see how the true positives etc. change\n", + "threshold=0.06\n", + "pred_binary_new=np.zeros_like(target)\n", + "for el in range(len(target)):\n", + " if (predictions[el]>threshold): #I will monitor with classical methods all the asteroids with predicted MOID <=0.06\n", + " pred_binary_new[el]=1" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f9144c1d-8e7c-4dd4-b158-e1613310bcb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of asteroids to be tested with classical methods, for that value of the threshols: 20023\n", + "Number of asteroids with MOID<=0.05: 18291\n" + ] + } + ], + "source": [ + "print(\"Number of asteroids to be tested with classical methods, for that value of the threshols:\", np.count_nonzero(predictions<=threshold))\n", + "print(\"Number of asteroids with MOID<=0.05:\",np.count_nonzero(target<=0.05))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "83b77a9d-302b-4d43-9e17-08bf893c9342", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True positives: 14765\n", + "True negatives: 18215\n", + "False positives: 76\n", + "False negatives: 1808\n" + ] + } + ], + "source": [ + "tp=np.count_nonzero((target_binary==1)&(pred_binary_new==1))\n", + "tn=np.count_nonzero((target_binary==0)&(pred_binary_new==0))\n", + "fp=np.count_nonzero((target_binary==0)&(pred_binary_new==1))\n", + "fn=np.count_nonzero((target_binary==1)&(pred_binary_new==0))\n", + "print(\"True positives:\", tp)\n", + "print(\"True negatives:\", tn)\n", + "print(\"False positives:\", fp)\n", + "print(\"False negatives:\", fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "886f4902-9dc2-47a7-95ad-7033a4f96445", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.9459614502065168\n", + "Precision: 0.9948790512768682\n", + "Recall: 0.8909068967597901\n", + "False positive rate: 0.004155048931168334\n", + "F1-score: 0.9400267396702107\n" + ] + } + ], + "source": [ + "acc=(tp+tn)/len(target) #accuracy\n", + "p=tp/(tp+fp) #precision\n", + "r=tp/(tp+fn) #recall\n", + "fpr=fp/(tn+fp) #false positive rate\n", + "f1=2*p*r/(p+r) #F1-score\n", + "print(\"Accuracy:\", acc)\n", + "print(\"Precision:\", p)\n", + "print(\"Recall:\", r)\n", + "print(\"False positive rate:\", fpr) #this modification reduces drastically the FPR\n", + "print(\"F1-score:\", f1)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e9982d71-957c-4667-9a64-c766e56f3e5f", + "metadata": {}, + "outputs": [], + "source": [ + "ind_new=(target_binary==0)&(pred_binary_new==1)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "6f820801-fe0e-423c-a9ee-7273afbb1265", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_319274/4044024848.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_new['predicted_moid']=predictions[ind_new]\n", + "/tmp/ipykernel_319274/4044024848.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_new['abs_error']=abs_errors[ind_new]\n" + ] + }, + { + "data": { + "text/html": [ + "
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spkidphaHepoch_mjdaeiomwmamoidpredicted_moidabs_error
67720162116Y19.54604001.91600.55847.11114.18331.43193.350.04670.0600360.013336
89020202683Y19.78604000.63710.56243.44193.2656.42229.730.04390.0797520.035852
203420452376Y19.97604001.76000.493716.5582.37331.71206.050.04720.0625370.015337
240720488789Y19.13604001.45100.688638.2394.05104.03336.110.04140.0627150.021315
265820516454N18.65604002.66300.763929.33190.47105.69114.580.05000.0654250.015425
..........................................
3232854374822N23.96604000.94130.284621.07283.04134.07181.900.04960.0622160.012616
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3482254439420N25.16604000.91800.15449.6650.30355.84137.570.04830.0650980.016798
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76 rows × 13 columns

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spkidphaHepoch_mjdaeiomwmamoidpredicted_moidabs_error
3368254414533Y19.51604004.63400.799913.9167.26136.58335.880.02490.0911750.066275
66473444376N25.10604001.02100.13991.0224.73195.03179.960.01630.0635100.047210
2893654278256N23.31597062.80800.957642.86226.93213.048.650.04140.0881650.046765
2345254051248N24.20604001.04300.23371.16216.702.99223.880.01980.0652170.045417
3119854340154N23.67604000.97750.20611.54187.80173.43183.230.02390.0683030.044403
44073182169N23.74604001.08500.17561.50143.811.97275.990.02320.0670330.043833
195423837918N24.10604000.97770.17672.5496.51163.8310.430.03780.0815070.043707
2598354147140N28.28604000.88960.15645.82234.62184.32328.810.01710.0600610.042961
141653748472N26.90604001.00600.16552.12223.95198.7992.530.03360.0759800.042380
2326054049796N26.90604000.90060.185320.03303.50165.03311.170.04330.0843990.041099
\n", + "
" + ], + "text/plain": [ + " spkid pha H epoch_mjd a e i om w \\\n", + "33682 54414533 Y 19.51 60400 4.6340 0.7999 13.91 67.26 136.58 \n", + "6647 3444376 N 25.10 60400 1.0210 0.1399 1.02 24.73 195.03 \n", + "28936 54278256 N 23.31 59706 2.8080 0.9576 42.86 226.93 213.04 \n", + "23452 54051248 N 24.20 60400 1.0430 0.2337 1.16 216.70 2.99 \n", + "31198 54340154 N 23.67 60400 0.9775 0.2061 1.54 187.80 173.43 \n", + "4407 3182169 N 23.74 60400 1.0850 0.1756 1.50 143.81 1.97 \n", + "19542 3837918 N 24.10 60400 0.9777 0.1767 2.54 96.51 163.83 \n", + "25983 54147140 N 28.28 60400 0.8896 0.1564 5.82 234.62 184.32 \n", + "14165 3748472 N 26.90 60400 1.0060 0.1655 2.12 223.95 198.79 \n", + "23260 54049796 N 26.90 60400 0.9006 0.1853 20.03 303.50 165.03 \n", + "\n", + " ma moid predicted_moid abs_error \n", + "33682 335.88 0.0249 0.091175 0.066275 \n", + "6647 179.96 0.0163 0.063510 0.047210 \n", + "28936 8.65 0.0414 0.088165 0.046765 \n", + "23452 223.88 0.0198 0.065217 0.045417 \n", + "31198 183.23 0.0239 0.068303 0.044403 \n", + "4407 275.99 0.0232 0.067033 0.043833 \n", + "19542 10.43 0.0378 0.081507 0.043707 \n", + "25983 328.81 0.0171 0.060061 0.042961 \n", + "14165 92.53 0.0336 0.075980 0.042380 \n", + "23260 311.17 0.0433 0.084399 0.041099 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_new.sort_values(by=['abs_error'],ascending=False).head(10)" + ] + } + ], + "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": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}