Training_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Training_model.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJbYp0QeyBl1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import gc\n",
        "from scipy import sparse\n",
        "from sklearn.metrics import roc_auc_score\n",
        "import sklearn.metrics as metrics\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas import read_csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxuY9fB3Cbul",
        "colab_type": "code",
        "outputId": "96cb8742-61db-42b2-9ed5-806c6d948afa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from __future__ import print_function\n",
        "import numpy as np\n",
        "\n",
        "from keras.preprocessing import sequence\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, LSTM, Bidirectional, Activation, Conv1D, MaxPooling1D\n",
        "from keras import losses\n",
        "from sklearn.metrics import r2_score\n",
        "from tensorflow.keras import activations\n",
        "from keras import backend as K\n",
        "\n",
        "def root_mean_squared_error(y_true, y_pred):\n",
        "  return K.sqrt(K.mean(K.square(y_pred - y_true)))\n",
        "\n",
        "# Define a model\n",
        "losses.custom_loss = root_mean_squared_error\n",
        "batch_size = 128\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv1D(16, 5, input_shape=(1000, 5)))\n",
        "model.add(Activation(activations.relu))\n",
        "model.add(MaxPooling1D(5))\n",
        "\n",
        "model.add(Conv1D(16, 5))\n",
        "model.add(Activation(activations.relu))\n",
        "model.add(MaxPooling1D(5))\n",
        "\n",
        "model.add(Bidirectional(LSTM(64)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1000))\n",
        "\n",
        "model.compile('adam', losses.custom_loss, metrics=['accuracy', 'mean_squared_error', 'logcosh'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eAi_lOblmDU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Applies model for all 4/5 folds, trains ans saves result\n",
        "for i in range(5):\n",
        "  X_train = np.array([])\n",
        "  y_train = np.array([])\n",
        "  for j in range(5):\n",
        "    if i != j:\n",
        "      data_feat = np.array(sparse.csr_matrix.todense(sparse.load_npz(\"folds/folds_feat_{j}.npz\".format(j=j)))).reshape(-1, 1000, 5)\n",
        "      if ((X_train).shape[0] == 0):\n",
        "        X_train = data_feat\n",
        "      else:\n",
        "        X_train = np.concatenate((X_train, data_feat), axis=0)\n",
        "      \n",
        "      data_target = np.array(sparse.csr_matrix.todense(sparse.load_npz(\"folds/folds_target_{j}.npz\".format(j=j))))\n",
        "      if ((y_train).shape[0] == 0):\n",
        "        y_train = data_target\n",
        "      else:\n",
        "        y_train = np.concatenate((y_train, data_target), axis=0)\n",
        "\n",
        "  model.fit(X_train, y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=30, verbose=1)\n",
        "  model.save('folds/model_{i}'.format(i=i))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFY3ddP8Qix-",
        "colab_type": "code",
        "outputId": "9d7dc5b0-d37d-4907-e983-a041496308c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# Plots ROC function\n",
        "X_test = np.array(sparse.csr_matrix.todense(sparse.load_npz(\"folds/folds_feat_{j}.npz\".format(j=0)))).reshape(-1, 1000, 5)\n",
        "y_test = np.array(sparse.csr_matrix.todense(sparse.load_npz(\"folds/folds_target_{j}.npz\".format(j=0))))\n",
        "y_test_binary = y_test >= 0.35\n",
        "probs = model.predict_proba(X_test)\n",
        "fpr, tpr, threshold = metrics.roc_curve(y_test_binary.flatten(), probs.flatten())\n",
        "roc_auc = metrics.auc(fpr, tpr)\n",
        "\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:248: UserWarning: Network returning invalid probability values. The last layer might not normalize predictions into probabilities (like softmax or sigmoid would).\n",
            "  warnings.warn('Network returning invalid probability values. '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8E0aOtBYEWCLo1Qs6d7bl4IOjjdE5F7GcHHj2WWs5ZGdHNlAdhieKUvLTT/DSS/DKK3ZmkgiccAI88ICd7ty0qbcYnHOBb7+1sYhx4+D0061GU8OGUUdVKE8UJaBqA9BPPmktiF27oG1beOwxuOACu4rZOef2MHeuncnywgt2tkqS/4r0RLEXcnLg7bdh4EC7or56dbjuOvjd72zswTnn9jBjhvVFX3459Oxp5RJq1ow6qlC8Ik8xqNqV88cdB717W3XUJ5+0K+sffdSThHOuADt2wF//amcz/fWvNqMVpEySAE8UoU2aZGVWevSwGc9efdVKZvzhD1bK2jnn9vD11/bL8r77rH7OtGllUsSvtHnXUxHWr4e77rKTEw46CJ5+2oo3VqgQdWTOuaS2ciV06mSnOH7wAZx1VtQR7TVPFHG8846NO6xZAzfcAAMGlHktLudcqpk3z/qh69SBN96wIn4pXn7Zu54KsHmztRrOPdfOXJo82cYgPEk45wq1fj1ceaWdC//FF/bcueemfJIAb1HsYcEC+7dduNC6nO65J2muonfOJau337ZTH9esgT//OfIifqXNE0WMcePsrLUKFeDTT20WN+eci+vKK+E//7HKne+/b5U804wnisBHH1lLomFDq8102GFRR+ScS1qxRfzat4fGjeG229L2LBdPFNgJCeedB82awf/+l9A5yp1zqW7pUjvL5eKLbWKY/v2jjijhMn4we+xYK9bXvLl1N3mScM4VKCcHBg2yg8X48VazJ0NkdIti1iwbk2jYED7+2GeJc84VYsECK+I3fjyccYZdWJVB/dMZmyjWrYNzzoGqVS1J1K4ddUTOuaS1YAHMmQNDh1p3U5IX8SttGZkocnJsfpCVK22WuXr1oo7IOZd0pk2zIn5XXGG/KhcvzthpJzNyjOKJJ2wA++GH0+50Z+dcSW3fbhdRHX883HtvXhG/DE0SkIGJYtEiux7m7LOtoJ9zzv3qyy/teoj777cupunTU7KIX2nLqK4nVZuOtkIFG4vKsG5G51w8K1faVbZ16tjA5RlnRB1R0sioRDFiBHz2mc0hUadO1NE455LC3LlWn6lOHXjrLUsWPnfAbjKm62n7drtw8thjrVXhnMtw69bZNKTNmln9HrAJZzxJ7CFjWhT//jcsW2YlWcqXjzoa51yk3nrLBinXroW777bJ7l2hMiJRbNwIDz5oM9R17hx1NM65SPXrBy++aMX7PvrIBq9dXBmRKO66C7Ztg4ceijoS51wkYov4nXCCTSx0662wT0YcAkssoWMUItJVRBaIyCIRubOA1+uLyBgRmSYiM0WkW2nHsHkzDBtmLUtvXTqXgZYssTOYXnrJHvfvD3fc4UmiGBKWKESkPDAIOAtoClwkIk3zrfYX4A1VPQ7oCzxV2nEMGWJjVo8+Wtpbds4ltexsePxxK+I3YUJeq8IVWyJbFG2BRaq6WFV3AsOBnvnWUSB3gtEawA+lGcCuXdbt1LEjdOhQmlt2ziW1efNsUPLGG6FTJ6vT1K9f1FGlrES2veoAy2MerwDa5VvnXuATEfkjUBU4vaANiUh/oD9A/fr1QwcwZIidFutXYDuXYRYtskJ+L78Mv/2tX11bQlFfR3ERMFRV6wLdgJdFZI+YVHWwqrZR1TYHHnhg6I0/9hjsuy/07l16ATvnktSUKfDCC3a/Rw8bm7jkEk8SpSCRiWIlEFuXtW7wXKyrgDcAVPVroDJQKgW/Z8yAhQuthHy5qNOhcy5xtm2DO++Edu3gH//IK+JXvXr897nQEnkInQQ0FpGGIlIRG6welW+dZcBpACLSBEsUa0rjw19/3W7v3ONcK+dc2hg3zsotPPCAjUFMm+ZF/BIgYWMUqpolItcDHwPlgRdUdY6IDAAmq+oo4FZgiIjcjA1s91Mt+akJOTnWNXnWWXDIISXdmnMuKa1cCaedZhPKfPqp3XcJkdATiVX1A+CDfM/9Leb+XODE0v7c8eNhxQq7Gts5l2ZmzYJjjrEifm+/bUX8qlaNOqq0lpa990OGWOuzR4+oI3HOlZqff7apKVu0yCvi1727J4kykHaXJubkWCnxE07wIpDOpQVVePNNuP56WL8e7rnHBq5dmUm7RPHNN7BqFQwcGHUkzrlScfnlNujYpo39CjzmmKgjyjhplyheftnKiHcr9apRzrkyE1vEr1Mn62666SavzxSRtBuj+PprKwxZs2bUkTjn9srixXD66TB0qD2+6iqbdcyTRGTSKlGsXm1zoV98cdSROOeKLTvbqncecwxMmuRXyiaRtErRn31mt2eeGW0czrlimjsXrrzSBhnPPhueeQbq1o06KhdIq0QxerR1OfmEVc6lmCVL4Lvv4LXXoG9fr8+UZNIqUYwaZZWFvcXqXAqYNMn6iq+5xloRixdDtWpRR+UKkDaH1NWr7XqcFi2ijsQ5F9fWrTY43b493H9/XhE/TxJJK20SxVdf2a2fFutcEhs71n7N/d//WUvCi/ilhLTpevr6a6hYEVq1ijoS51yBVqyALl2gQQMbUDz11KgjciGlTYti4kQ47jioVCnqSJxzu5kxw27r1oV33oGZMz1JpJi0SBTZ2TB1Khx/fNSROOd+tWaNXdTUsiV8/rk9162bTTvpUkpadD19+y1s3gytW0cdiXMOVRg+HG64AX75Bf7+d+jQIeqoXAmkRaKYOtVufXzCuSRw6aXw6qtW4fX556FZs6gjciUUOlGIyL6qujWRweyt6dNtILtJk6gjcS5D5eTYRXIiNv7QurW1KMqXjzoyVwqKHKMQkRNEZC4wP3h8rIg8lfDIimHmTEsSFSpEHYlzGWjRIpuG9D//scdXXQU33+xJIo2EGcx+BDgTWAugqjOAjokMqrhmz/YS9c6VuawseOgh++ObNs2a9S4thep6UtXlsnvtlezEhFN8GzbYHOvNm0cdiXMZZPZsuOIKmDwZevaEp56CQw+NOiqXIGESxXIROQFQEakA3AjMS2xY4c0LImnaNNo4nMsoy5bB0qV2dlOfPl7EL82FSRTXAo8BdYCVwCfAdYkMqjjmz7dbH8h2LsG++cYunuvf366HWLzYJ6bPEGHGKI5S1d+q6m9U9SBVvQRImsPyggU2iH3YYVFH4lya2rIFbrnFroV48EHYscOe9ySRMcIkiidCPheJRYvg8MN9lkTnEmL0aCvi98gjcO21dtGS18nJOIUeXkWkA3ACcKCI3BLzUnUgac57W7QIGjWKOgrn0tCKFTZdZMOGVoKjY1Kd7OjKULwWRUVgPyyZVItZNgK9Ex9a0VRtUqzDD486EufSyLRpdlu3Lrz7ro1LeJLIaIW2KFT1c+BzERmqqkvLMKbQfv7Zajx5onCuFPz4o11N/cYbNm9Ep07QtWvUUbkkEKZnf6uIDASaAb/OMKKqnRMWVUhLltitJwrnSkDVajPdeKP98rrvPjjhhKijckkkzGD2q1j5jobA34HvgUkJjCm03ETRsGG0cTiX0i6+2Ar5HXWUFU67+26vh+N2E6ZFUUtVnxeRG2O6o5IiUXz/vd36qbHOFVNsEb8zzrBTX//wB6/P5AoUpkWxK7hdJSJni8hxwAEJjCm0H36A6tV9TnbnimXhQqvw+sIL9viKK7zSq4srTIviPhGpAdyKXT9RHbgpoVGFtHo1HHxw1FE4lyKysuDhh+Gee6ByZahSJeqIXIooMlGo6nvB3V+AUwFE5MREBhWWJwrnQpo5E668EqZMgfPOg0GD4JBDoo7KpYh4F9yVB/pgNZ4+UtXZItIduAuoAhxXNiEWbtUqn9XOuVBWrIDly+HNN6FXLy/i54ol3hjF88DVQC3gcRF5BXgIeFBVQyUJEekqIgtEZJGI3FnIOn1EZK6IzBGR14oTvLconIvjq6/gmWfsfm4Rv969PUm4YovX9dQGaKGqOSJSGVgNHKGqa8NsOGiRDAK6ACuASSIySlXnxqzTGPgzcKKqrheRg8IGvmULbNrkicK5PWzebKe4PvEEHHGEDVZXqgRVq0YdmUtR8VoUO1U1B0BVtwOLwyaJQFtgkaouVtWdwHCgZ751rgEGqer64HN+CrvxH3+0W08UzsX45BObxeuJJ+x0Vy/i50pBvBbF0SIyM7gvwBHBYwFUVVsUse06wPKYxyuAdvnWORJARL7ECg3eq6of5d+QiPQH+gPUr18fsG4n8PE45361fDmcfba1IsaNg5NOijoilybiJYqymHNiH6AxcApQFxgnIseo6obYlVR1MDAYoE2bNgp5icJbFC7jTZkCrVtDvXrwwQdw8sl2+qtzpaTQridVXRpvCbHtlUC9mMd1g+dirQBGqeouVV0CLMQSR5FWrbJbTxQuY61eDRdcAG3aWBlwgC5dPEm4Uhfmyuy9NQloLCINRaQi0BcYlW+dkVhrAhGpjXVFLQ6z8dWroVw5qF279AJ2LiWowosv2kTx774L//qXF/FzCZWweeFUNUtErgc+xsYfXlDVOSIyAJisqqOC184QkblANnB72AHz1avhoIO86oDLQH37WinwE0+E556Do4+OOiKX5kIlChGpAtRX1QXF2biqfgB8kO+5v8XcV+CWYCmW1at9INtlkNgift262TjEdddZs9q5BCvyf5mI9ACmAx8Fj1uKSP4upDLnF9u5jDF/vs0w9/zz9vjyy+H66z1JuDIT5n/avdg1ERsAVHU6NjdFpDxRuLS3a5eNPxx7LMydC/vtF3VELkOF6Xrapaq/yO6X/WuC4glF1S64Oyj0ddzOpZjp0+2K6unTrezGE0/4LyMXmTCJYo6IXAyUD0pu3AB8ldiw4tuyxX5s1aoVZRTOJdDq1ba89Racf37U0bgMF6br6Y/YfNk7gNewcuORzkexbp3dHpAU0yc5V0rGj4ennrL7XbvCd995knBJIUyiOFpV71bV44PlL0Htp8jkJoqaNaOMwrlSsmmTDU6ffDI8+ijs2GHP77tvtHE5FwiTKP5PROaJyD9EpHnCIwph/Xq79UThUt7HH1sRv6eeghtv9CJ+LikVmShU9VRsZrs1wLMiMktE/pLwyOLYEFSC2n//KKNwroSWL4fu3a3lMH68tSb8zCaXhEKdiK2qq1X1ceBa7JqKvxXxloTyROFSlipMnGj369WDDz+EadO8BIdLamEuuGsiIveKyCzgCeyMp7oJjyyOX36x2xo1oozCuWJatcqmIW3XLq+I3+mnexE/l/TCnB77AvA6cKaq/pDgeELZuNFuq1ePNg7nQlGFoUPhlltg+3Z44AGr0+RciigyUahqh7IIpDg2brRu3X0SVtLQuVLUpw+MGGFnNT33HBx5ZNQROVcshR5qReQNVe0TdDnFXokddoa7hNm40VsTLsllZ1sBv3LloEcP6NwZfvc7r8/kUlK83+Q3BrfdyyKQ4vBE4ZLavHlw1VVWguOaa+Cyy6KOyLkSiTfDXTCHHNcVMLvddWUTXsHmz/drkVwS2rUL7rsPWraEBQv8bAuXNsK0g7sU8NxZpR1IcdSoAVu3RhmBc/lMm2ZTkv71r3Deedaq6NMn6qicKxXxxih+j7UcDheRmTEvVQO+THRg8Wza5OOBLsn8+CP8/DOMHAk9e0YdjXOlKt4YxWvAh8D9wJ0xz29S1XUJjaoIW7ZA1apRRuAcMG4czJoFf/iDFfFbtAiqVIk6KudKXbyuJ1XV74E/AJtiFkQk0rqtmzdDtWpRRuAy2saNNg1pp07w+ON5Rfw8Sbg0VVSLojswBTs9NnbmIgUOT2BccW3e7C0KF5EPPrDTXH/4wS6gGzDAi/i5tFdoolDV7sFt5NOe5ueJwkVi+XIbfzjqKLuArl27qCNyrkyEqfV0oohUDe5fIiIPi0j9xIdWMFXIyfEim66MqMKECXa/Xj345BMrBe5JwmWQMKfHPg1sFZFjgVuB74CXExpVHNnZduvXUbiE++EHOPdc6NAhr4jfqadCxYrRxuVcGQuTKLJUVYGewJOqOgg7RTYSOTl2611PLmFUrSZT06bWgnjoIS/i5zJamLJ6m0Tkz8ClwMkiUg6okNiwCpebKLxF4RKmd2/473/trKbnnoNGjaKOyLlIhWlRXAjsAK5U1dXYXBQDExpVHLmJws9EdKUqOzvvP9e558Izz8Do0a2/U+QAABnbSURBVJ4knCPcVKirgVeBGiLSHdiuqi8lPLJCeNeTK3WzZ1vX0vPP2+NLL/VKr87FCHPWUx9gInAB0Af4RkR6JzqwwnjXkys1O3fC3/8OrVrBd99BzZpRR+RcUgozRnE3cLyq/gQgIgcCnwIjEhlYYbzryZWKKVOgXz9rTVx8MTz6KBx4YNRROZeUwiSKcrlJIrCWcGMbCaHBFEqeKFyJrF0LGzbAu+9C96SbcsW5pBImUXwkIh8Dw4LHFwIfJC6k+LxF4fbamDFWxO+GG+CMM+Dbb6Fy5aijci7phRnMvh14FmgRLINV9Y5EB1aY3EThf98utF9+scHpzp3h6afzivj5fyLnQok3H0Vj4CHgCGAWcJuqriyrwArjLQpXLO++C9deC6tXw2232eC1F/FzrljitSheAN4DemEVZJ8ok4iKkDtG4T8GXZGWL4devaBWLavXNHCgny7n3F6IN0ZRTVWHBPcXiMjUsgioKLktCv9R6AqkCl9/DSeckFfE74QTvD6TcyUQr0VRWUSOE5FWItIKqJLvcZFEpKuILBCRRSJyZ5z1eomIikiboraZk2N/8yJFrekyzooVcM45dvFcbhG/U07xJOFcCcVrUawCHo55vDrmsQKd421YRMoDg4AuwApgkoiMUtW5+darBtwIfBMmYFUfn3D55OTAkCFw++2QlQUPPwwnnRR1VM6ljXgTF51awm23BRap6mIAERmOVaCdm2+9fwAPALeH2WhOjnc7uXx69YKRI+2spiFD4PDIJl90Li0l8sK5OsDymMcrgud+FXRh1VPV9+NtSET6i8hkEZm8bdt2H8h21nLIHbDq1csSxKefepJwLgEiu8I6KFf+MDYZUlyqOlhV26hqm4oVK3uLItPNnGmTCQ0JzrW45BK4+mofuHIuQRKZKFYC9WIe1w2ey1UNaA6MFZHvgfbAqKIGtFW96ylj7dgB99wDrVvD0qVem8m5MhKmeqwEc2X/LXhcX0Tahtj2JKCxiDQUkYpAX2BU7ouq+ouq1lbVw1T1MGACcI6qTo63UU8UGWrSJKvyOmAAXHQRzJsH558fdVTOZYQwLYqngA7ARcHjTdjZTHGpahZwPfAxMA94Q1XniMgAETlnL+P1wexMtX49bN4MH3wAL71kF9E558pEmKKA7VS1lYhMA1DV9UELoUiq+gH5Cgiq6t8KWfeUcNv00+IzxujRVsTvxhutiN/Chf4rwbkIhGlR7AquiVD4dT6KnIRGFYd3PWWADRvgmmvgtNPg2Wfzivj5P7xzkQiTKB4H3gYOEpF/AuOBfyU0qjhyr8x2aeqdd6BpU3jhBfjTn2yCIU8QzkWqyK4nVX1VRKYApwECnKuq8xIeWaHxeKJIW8uWwQUXQJMmMGoUtCmyootzrgwUmShEpD6wFXg39jlVXZbIwArjiSLNqML48XDyyVC/vl001769/yM7l0TCDGa/j41PCFAZaAgsAJolMK5CqUKFClF8sit1y5bZXBEffghjx0KnTtCxY9RROefyCdP1dEzs46DsxnUJi6gI3qJIAzk58MwzcMcd9g/6+ONexM+5JBamRbEbVZ0qIu0SEUwYPpidBs4/3watu3SBwYPhsMOijsg5F0eYMYpbYh6WA1oBPyQsoiJ411OKysqCcuVsufBC6NkT+vXz+kzOpYAwp8dWi1kqYWMWPRMZVDze9ZSCZsyAdu2s9QBWguOKKzxJOJci4rYoggvtqqnqbWUUT5G8RZFCtm+H++6DBx6AAw6Agw+OOiLn3F4oNFGIyD6qmiUiJ5ZlQGF4okgBEyfC5ZfD/Pl2+/DDliyccyknXotiIjYeMV1ERgFvAltyX1TV/yY4tkJ5okgBGzfCtm3w0Udw5plRR+OcK4EwZz1VBtZic2TnXk+hQGSJYp9in6vlysQnn8CcOXDzzXD66bBggZffcC4NxDvkHhSc8TSbvASRSxMaVRG8RZFk1q+HW26BoUOhWTO47jpLEJ4knEsL8c56Kg/sFyzVYu7nLpHxFkUS+e9/rYjfyy/Dn/8Mkyd7gnAuzcQ75K5S1QFlFkkxeKJIEsuWQd++0Ly5TSh03HFRR+ScS4B4LYqkPcndu54ipAqff27369e3yYW++caThHNpLF6iOK3Moiim8uWjjiBDLV0KZ50Fp5ySlyxOOskzt3NprtBEoarryjKQ4vDjUhnLyYEnn7SB6vHj4YknrCy4cy4jpGRvv49RlLFzz4V337XrIZ59Fho0iDoi51wZSslDrieKMrBrl/XxlStntZl694ZLL/X6TM5loDBFAZOOJ4oEmzoV2ra1OSPAEsVll3mScC5DeaJwebZts2sh2raF1auhXr2oI3LOJYGUPOT6WU8JMGGCFe9buBCuvBIeeghq1ow6KudcEvBE4cyWLTYu8b//WZ0m55wLpGSi8K6nUvLRR1bE79Zb4bTTrCS4zwrlnMsnJccovEVRQmvXWjfTWWfBiy/Czp32vCcJ51wBPFFkElUYMcKK+L32GvzlLzBpkicI51xcKdmJ44liLy1bBhdfDC1a2NwRxx4bdUTOuRTgLYp0p2qF+8CuqB471s5w8iThnAvJE0U6W7IEzjjDBqpzi/idcIKfDeCcK5aUTBR+nCtCdjY89pjNE/HNN/D0017Ezzm311LykOstiiL07Anvvw/dulkZDr/C2jlXAp4o0kVsEb9LL7X6TBdf7PWZnHMlltCuJxHpKiILRGSRiNxZwOu3iMhcEZkpIp+JSKj61Z4o8pk8Gdq0sS4mgAsvhN/+1pOEc65UJCxRiEh5YBBwFtAUuEhEmuZbbRrQRlVbACOAB8Nsu1xKjqwkwLZtcMcd0K4drFnj80Q45xIikYfctsAiVV2sqjuB4UDP2BVUdYyqbg0eTgDqhtmwtyiAr7+2U1wffNCK+M2dC927Rx2Vcy4NJXKMog6wPObxCqBdnPWvAj4s6AUR6Q/0t0etvUUB1prIyYFPP7XTX51zLkGSYjBbRC4B2gCdCnpdVQcDg23dNpqxieKDD6yI3+23Q+fOMG+eTyDunEu4RB5yVwKx52XWDZ7bjYicDtwNnKOqO8JsOOO6nn7+GS65BM4+G159Na+InycJ51wZSGSimAQ0FpGGIlIR6AuMil1BRI4DnsWSxE9hN5wxLQpVGD4cmjSBN96Ae+6BiRO9iJ9zrkwlrOtJVbNE5HrgY6A88IKqzhGRAcBkVR0FDAT2A94UO5VzmaqeU9S2M6ZFsWyZlQM/9lh4/nk45pioI3LOZSBR1ahjKBaRNrpmzWRq1446kgRRhc8+y5tlbsIEOP74DMqOzrlEEJEpqtpmb96bkp04aXvM/O47O4OpS5e8In7t26fxF3bOpYKUTBRpN0aRnQ0PP2xdS1OmwLPPehE/51zSSIrTY4sr7X5g9+gBH35oF8w9/TTUDXXdoXPOlQlPFFHZudPqpZcrB/36WSG/vn29PpNzLumkZCdOync9TZwIrVvDU0/Z4z59rNqrJwnnXBJKyUNuyiaKrVvh1luhQwdYvx6OOCLqiJxzrkgp2fWUkoli/Hi7JmLxYvjd7+CBB6BGjaijcs65InmiKCu5EwuNGQOnnBJ1NM45F1pKJoqU6cp/910r3PenP8Gpp1opcJ/w2zmXYlLxt3nyW7PGpiE95xwYNiyviJ8nCedcCvJEUZpU4bXXrIjfiBEwYAB8840X8XPOpbSU+4mb1N1Oy5bBFVfAccdZEb9mzaKOyDnnSsxbFCWVkwMff2z3GzSAL76AL7/0JOGcSxsplyiSqkXx7bc201zXrjBunD3Xtm2aXDrunHMm5RJFUsjKgoEDoUULmD7dupm8iJ9zLk2l3BhFUuje3bqbeva0MhyHHhp1RM4lpV27drFixQq2b98edSgZo3LlytStW5cKpThVcspNXLTPPm00K2ty2X/wjh02R3W5cnZGU04OXHBBkvWFOZdclixZQrVq1ahVqxbifysJp6qsXbuWTZs20bBhw91ey7iJi8rchAnQqhUMGmSPe/e2Qn7+H9+5uLZv3+5JogyJCLVq1Sr1Fpwnini2bIGbb4YTToBNm6Bx46gjci7leJIoW4nY3z5GUZgvvrAifkuWwHXXwf33Q/XqUUflnHNlLuVaFGX24yQry8YkPv/cupw8STiXskaOHImIMH/+/F+fGzt2LN27d99tvX79+jFixAjABuLvvPNOGjduTKtWrejQoQMffvhhiWO5//77adSoEUcddRQf516Dlc/o0aNp1aoVzZs35/LLLycrK2u3uFu2bEmzZs3o1KlTieMJI+USRUKNHGktB7AifnPmQMeO0cbknCuxYcOGcdJJJzFs2LDQ7/nrX//KqlWrmD17NlOnTmXkyJFs2rSpRHHMnTuX4cOHM2fOHD766COuu+46srOzd1snJyeHyy+/nOHDhzN79mwaNGjAiy++CMCGDRu47rrrGDVqFHPmzOHNN98sUTxhedcTwI8/wh//CG++aYPWt95q9Zm8iJ9zpeamm+yyo9LUsiU8+mj8dTZv3sz48eMZM2YMPXr04O9//3uR2926dStDhgxhyZIlVKpUCYDf/OY39OnTp0TxvvPOO/Tt25dKlSrRsGFDGjVqxMSJE+nQocOv66xdu5aKFSty5JFHAtClSxfuv/9+rrrqKl577TXOP/986tevD8BBBx1UonjCSrkWRal2PanCyy9D06bwzjvwz3/aGU5exM+5tPHOO+/QtWtXjjzySGrVqsWUKVOKfM+iRYuoX78+1UN0Od988820bNlyj+Xf//73HuuuXLmSevXq/fq4bt26rFy5crd1ateuTVZWFpMn22UAI0aMYPny5QAsXLiQ9evXc8opp9C6dWteeumlIuMrDZn9k3nZMrj6amjTxq6uPvroqCNyLm0V9cs/UYYNG8aNN94IQN++fRk2bBitW7cu9Oyg4p419Mgjj5Q4xvyfP3z4cG6++WZ27NjBGWecQfmgLFBWVhZTpkzhs88+Y9u2bXTo0IH27dv/2vpIlMxLFLlF/M46y4r4ffmlVXv1+kzOpZ1169YxevRoZs2ahYiQnZ2NiDBw4EBq1arF+vXr91i/du3aNGrUiGXLlrFx48YiWxU333wzY8aM2eP5vn37cuedd+72XJ06dX5tHQCsWLGCOnXq7PHeDh068MUXXwDwySefsHDhQsBaILVq1aJq1apUrVqVjh07MmPGjIQnClQ1pZYKFVrrXluwQPXkk1VBdezYvd+Ocy6UuXPnRvr5zz77rPbv33+35zp27Kiff/65bt++XQ877LBfY/z++++1fv36umHDBlVVvf3227Vfv366Y8cOVVX96aef9I033ihRPLNnz9YWLVro9u3bdfHixdqwYUPNysraY70ff/xRVVW3b9+unTt31s8++0xVbX927txZd+3apVu2bNFmzZrprFmz9nh/QfsdmKx7edzNjDGKrCx44AEr4jdrFvznP342k3MZYNiwYZx33nm7PderVy+GDRtGpUqVeOWVV7jiiito2bIlvXv35rnnnqNGjRoA3HfffRx44IE0bdqU5s2b071791BjFvE0a9aMPn360LRpU7p27cqgQYN+7Vbq1q0bP/zwAwADBw6kSZMmtGjRgh49etC5c2cAmjRpQteuXWnRogVt27bl6quvpnnz5iWKKYyUq/VUqVIb3bGjmLWezjwTPvkEzj/frok4+ODEBOec2828efNo0qRJ1GFknIL2e0lqPaXvGMX27XbBXPny0L+/Lb16RR2Vc86lnJTregrlyy/tBOvcIn69enmScM65vZRyiSLuGMXmzXDDDTaJ0Pbt4E1e5yKXat3bqS4R+zvlEkWhPv8cmjeHJ5+E66+H2bOhS5eoo3Iuo1WuXJm1a9d6sigjGsxHUbly5VLdbnqNUey7r1V9PfHEqCNxzmHn/a9YsYI1a9ZEHUrGyJ3hrjSl3FlPlSu30e3bg7Oe/vtfmD8f7rrLHmdn+4VzzjlXgKSd4U5EuorIAhFZJCJ3FvB6JRF5PXj9GxE5rOhtAqtX2yxzvXrB22/Dzp32oicJ55wrdQlLFCJSHhgEnAU0BS4Skab5VrsKWK+qjYBHgAeK2u7+2WttkPq996wk+FdfeRE/55xLoES2KNoCi1R1saruBIYDPfOt0xN4Mbg/AjhNiqjIdeiupTZoPWMG3HmnXSvhnHMuYRI5mF0HWB7zeAXQrrB1VDVLRH4BagE/x64kIv2B/sHDHTJ+/Gyv9ApAbfLtqwzm+yKP74s8vi/yHLW3b0yJs55UdTAwGEBEJu/tgEy68X2Rx/dFHt8XeXxf5BGRYtY+ypPIrqeVQL2Yx3WD5wpcR0T2AWoAaxMYk3POuWJKZKKYBDQWkYYiUhHoC4zKt84o4PLgfm9gtKba+brOOZfmEtb1FIw5XA98DJQHXlDVOSIyAKuLPgp4HnhZRBYB67BkUpTBiYo5Bfm+yOP7Io/vizy+L/Ls9b5IuQvunHPOla30qfXknHMuITxROOeciytpE0Uiyn+kqhD74hYRmSsiM0XkMxFpEEWcZaGofRGzXi8RURFJ21Mjw+wLEekT/N+YIyKvlXWMZSXE30h9ERkjItOCv5NuUcSZaCLygoj8JCKzC3ldROTxYD/NFJFWoTa8t5NtJ3LBBr+/Aw4HKgIzgKb51rkOeCa43xd4Peq4I9wXpwL7Bvd/n8n7IlivGjAOmAC0iTruCP9fNAamATWDxwdFHXeE+2Iw8PvgflPg+6jjTtC+6Ai0AmYX8no34ENAgPbAN2G2m6wtioSU/0hRRe4LVR2jqluDhxOwa1bSUZj/FwD/wOqGbS/L4MpYmH1xDTBIVdcDqOpPZRxjWQmzLxSoHtyvAfxQhvGVGVUdh51BWpiewEtqJgD7i8ghRW03WRNFQeU/6hS2jqpmAbnlP9JNmH0R6yrsF0M6KnJfBE3peqr6flkGFoEw/y+OBI4UkS9FZIKIdC2z6MpWmH1xL3CJiKwAPgD+WDahJZ3iHk+AFCnh4cIRkUuANkCnqGOJgoiUAx4G+kUcSrLYB+t+OgVrZY4TkWNUdUOkUUXjImCoqv6fiHTArt9qrqo5UQeWCpK1ReHlP/KE2ReIyOnA3cA5qrqjjGIra0Xti2pAc2CsiHyP9cGOStMB7TD/L1YAo1R1l6ouARZiiSPdhNkXVwFvAKjq10BlrGBgpgl1PMkvWROFl//IU+S+EJHjgGexJJGu/dBQxL5Q1V9UtbaqHqaqh2HjNeeo6l4XQ0tiYf5GRmKtCUSkNtYVtbgsgywjYfbFMuA0ABFpgiWKTJyfdRRwWXD2U3vgF1VdVdSbkrLrSRNX/iPlhNwXA4H9gDeD8fxlqnpOZEEnSMh9kRFC7ouPgTNEZC6QDdyuqmnX6g65L24FhojIzdjAdr90/GEpIsOwHwe1g/GYe4AKAKr6DDY+0w1YBGwFrgi13TTcV84550pRsnY9OeecSxKeKJxzzsXlicI551xcniicc87F5YnCOedcXJ4oXFISkWwRmR6zHBZn3c2l8HlDRWRJ8FlTg6t3i7uN50SkaXD/rnyvfVXSGIPt5O6X2SLyrojsX8T6LdO1UqorO356rEtKIrJZVfcr7XXjbGMo8J6qjhCRM4CHVLVFCbZX4piK2q6IvAgsVNV/xlm/H1ZB9/rSjsVlDm9RuJQgIvsFc21MFZFZIrJH1VgROURExsX84j45eP4MEfk6eO+bIlLUAXwc0Ch47y3BtmaLyE3Bc1VF5H0RmRE8f2Hw/FgRaSMi/waqBHG8Gry2ObgdLiJnx8Q8VER6i0h5ERkoIpOCeQJ+F2K3fE1Q0E1E2gbfcZqIfCUiRwVXKQ8ALgxiuTCI/QURmRisW1D1Xed2F3X9dF98KWjBriSeHixvY1UEqgev1cauLM1tEW8Obm8F7g7ul8dqP9XGDvxVg+fvAP5WwOcNBXoH9y8AvgFaA7OAqtiV73OA44BewJCY99YIbscSzH+RG1PMOrkxnge8GNyviFXyrAL0B/4SPF8JmAw0LCDOzTHf702ga/C4OrBPcP904K3gfj/gyZj3/wu4JLi/P1b/qWrU/96+JPeSlCU8nAO2qWrL3AciUgH4l4h0BHKwX9K/AVbHvGcS8EKw7khVnS4inbCJar4MyptUxH6JF2SgiPwFqwF0FVYb6G1V3RLE8F/gZOAj4P9E5AGsu+qLYnyvD4HHRKQS0BUYp6rbgu6uFiLSO1ivBlbAb0m+91cRkenB958H/C9m/RdFpDFWoqJCIZ9/BnCOiNwWPK4M1A+25VyBPFG4VPFb4ECgtaruEqsOWzl2BVUdFySSs4GhIvIwsB74n6peFOIzblfVEbkPROS0glZS1YVi8150A+4Tkc9UdUCYL6Gq20VkLHAmcCE2yQ7YjGN/VNWPi9jENlVtKSL7YrWN/gA8jk3WNEZVzwsG/scW8n4BeqnqgjDxOgc+RuFSRw3gpyBJnArsMS+42FzhP6rqEOA5bErICcCJIpI75lBVRI4M+ZlfAOeKyL4iUhXrNvpCRA4FtqrqK1hBxoLmHd4VtGwK8jpWjC23dQJ20P997ntE5MjgMwukNqPhDcCtkldmP7dcdL+YVTdhXXC5Pgb+KEHzSqzysHNxeaJwqeJVoI2IzAIuA+YXsM4pwAwRmYb9Wn9MVddgB85hIjIT63Y6OswHqupUbOxiIjZm8ZyqTgOOASYGXUD3APcV8PbBwMzcwex8PsEml/pUbepOsMQ2F5gqIrOxsvFxW/xBLDOxSXkeBO4Pvnvs+8YATXMHs7GWR4UgtjnBY+fi8tNjnXPOxeUtCuecc3F5onDOOReXJwrnnHNxeaJwzjkXlycK55xzcXmicM45F5cnCuecc3H9P/V4wdslFrmFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}