Hackathon_Data_Loading_V2.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lisahqwang/ML-DL-CV/blob/main/Hackathon_Data_Loading_V2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kE8nx1qx7R11",
        "outputId": "ebc21fb6-b15f-46e5-ee27-9578aa0c8149"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "from datetime import datetime\n",
        "from itertools import chain\n",
        "from pprint import pprint\n",
        "\n",
        "from google.colab import drive\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "drive.mount('/content/gdrive', force_remount=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If this cell crashes due to OOM, just retry.\n",
        "\n",
        "binary_questions = pd.read_json(\n",
        "    \"/content/gdrive/MyDrive/Million Predictions Hackathon/questions-binary-hackathon.json\",\n",
        "    orient=\"records\",\n",
        "    convert_dates=False, # This is necessary, otherwise Pandas messes up date conversion.\n",
        ")\n",
        "continuous_questions = pd.read_json(\n",
        "    \"/content/gdrive/MyDrive/Million Predictions Hackathon/questions-continuous-hackathon.json\",\n",
        "    orient=\"records\",\n",
        "    convert_dates=False, # This is necessary, otherwise Pandas messes up date conversion.\n",
        ")\n",
        "binary_predictions = pd.read_json(\n",
        "    \"/content/gdrive/MyDrive/Million Predictions Hackathon/predictions-binary-hackathon.json\",\n",
        "    orient=\"records\",\n",
        ")\n",
        "continuous_predictions = pd.read_parquet( # We use parquet here for memory reasons. Locally, use JSON\n",
        "    \"/content/gdrive/MyDrive/Million Predictions Hackathon/predictions-continuous-hackathon.parquet\",\n",
        ")"
      ],
      "metadata": {
        "id": "x0immuY19gG1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "db42fb80-9554-4a0c-a52d-ed1a8470100b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-92bc85936856>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# But it usually works. If this cell crashes due to OOM, just retry.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m binary_questions = pd.read_json(\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;34m\"/content/gdrive/MyDrive/Million Predictions Hackathon/questions-binary-hackathon.json\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0morient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"records\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mread_json\u001b[0;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, encoding_errors, lines, chunksize, compression, nrows, storage_options)\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    746\u001b[0m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_lines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    747\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 748\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    749\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[0;34m(self, json)\u001b[0m\n\u001b[1;32m    768\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"frame\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 770\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    771\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    772\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"series\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1157\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m             self.obj = DataFrame(\n\u001b[0;32m-> 1159\u001b[0;31m                 \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprecise_float\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m             )\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Expected object or value"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert prediction binary prediction timestamps to datetimes\n",
        "binary_predictions[\"t\"] = binary_predictions[\"t\"].apply(datetime.fromtimestamp)"
      ],
      "metadata": {
        "id": "Fgr-eEnUeaUO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the history of the community and metaculus predictions for a binary question\n",
        "binary_predictions = binary_predictions.set_index(\"t\", drop=False)\n",
        "binary_predictions[binary_predictions[\"question_id\"] == 6635].mp.plot(label=\"mp\")\n",
        "binary_predictions[binary_predictions[\"question_id\"] == 6635].cp.plot(label=\"cp\")\n",
        "plt.legend()\n",
        "plt.title(binary_questions[binary_questions[\"question_id\"] == 6635].title.item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "4SCylT3tJogF",
        "outputId": "6fb4f1c8-5698-4334-9118-2934c9439443"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'On 2021-12-31, will the FiveThirtyEight average of polls indicate that Joe Biden has a higher approval than disapproval rating?')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the cdf of a user's prediction for a continuous prediction\n",
        "row = continuous_predictions.iloc[1000]\n",
        "question = continuous_questions[continuous_questions[\"question_id\"] == row.question_id]\n",
        "plt.plot(question.x_grid.item(), row.cdf)\n",
        "plt.title(f\"CDF for user {row.user_id} on question \\n \\\"{question.title.item()}\\\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "KA7lXdmnKUlQ",
        "outputId": "68c06877-b071-433d-d36d-48c98924d75a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'CDF for user 124280 on question \\n \"Drake\\'s Equation 3rd parameter n_e: What is the average number of habitable planets per star?\"')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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PRb86p+0AbiS9uqaH9LT2pcBz6h3DwLeBz+b87AX8DnhtnnZ63tY35XKfVOe82EI6TjuB/wKuG2HfjJTPpwFrhpn/vcB38+cX5vL8x6pxS6vOsb+RArRJefi/C9NHKv8PjLA9A8c26V2MT8+fZwCH15nndOBXVeMups5xnvfPMuCVedqT8nGxoM7yTwAeRfpC8gxS4HR4nnYO8LWqtLeO5rqTy+4e4NCcj+4R1nUs6aXbh5KugZdUldfHSE/Y3yPvg+8B/zVMmfWR3pDQCbye9L7Myi/m7yJfj6qPd7afe58hnUfPJh233yEd97NJbyZ4RtWx/+95G08mvRFhj3E6b84jfanYi1SL/xvS+0aLee2qUw6V6Z8nHc9PIL1L9pAxXIuvzmU+l3TfeU2t45Kh1+3R3M9WAIflsvm/Gvuga6QyrHOd6SOd292kt03cCXRX7/tRbv/3gel5+3uBY/O0k0j3okPy/GcDvxmmPP6LdEx157+nk4/HOteJeuU+mvX+JM9b63h6LencmUw6N44Adi/sk9dUpX9p3pddpMqkVcDEqrIeuEcW5tsr5/NNw10T614rd2SmOgdDrWDsdcAd+fMxpABg4jDLeSKwuurg/Wg+mE4tjH8n8NWqea8CXlFjmdcA7wNmjrANx5De3bam6m8K21/EPKWQ/uuMMhirsa5/A749zEE8sLx8AK8COgrTLwXOzZ8vBr5QmHY8cNso9tlE0itpXlEYdy5wTWF42O2uU4bV6S8H/qNO+o8DH8uf5+VyOGiYPE/PaaYVtr0YTL6JfOPMw48jBy6k18TcU7W8dwFfqnUMk77dbGXwyXYq24O706uXV+e8+GlheAGweYR5hs3nKPbrMaQAQaTq8n8hfQm5rzDuS4X0vwDOLgy/AfjRGMp/LMHYPaQL4+4jzHM6tYOxmsc5KRi4tir9Z4H3jrLMvgO8JX8+mPSFY3Ie/hpwTv487HUnl+V5Y1jXFykEV3ndkf8X6YvMowrTnwzcOUyZLSkMT87L2icP38XIwdjswvQHgZMLw/9HvmnndQ0Eennc70ivtRqP8+ZvwPGF4ecAd1XldaRgbE5V3k6pk77WtfjYqvPhZ7WOS6qu2zWWXet+Vvyis4B0T+wsbtdIZVhjPedS+JJHChKKX3wG7ftRbP/TCsOXA2flzz8EXl21nk3AAbXKgxRUf3e4MhpluY9mvX8/zLJfRQroH19j2i+oCsZqpFkNPKFQ1tfUSNNDelfuJ0ba1np/je7TMJv0zrWK3ojYUhlQesnyZyXdnav8rgGmV1XHnkb6NnFFYdwBwItyleMapaaMpwG1+qe9mvSt/7ZcPfncGmkq7o2I6VV/G4H9SCfVxkLau0fe/IHtfHSurl2Vt/M/Gaa5r8p+wLJITYvFdc8uDK8qfN5EuvkOK1KT5aXAWVVV2suq1j3W7a6Vfj8ApRdMX52r8deSgvXqchhYv6ROSf8t6W+53O7Kk4rz3Ff4vLnGcKUsDgD2qzpm3k39KuUDSN/mVhbSf5b07WdIXodRvW8mavh+DWPNZ7XrSNt8GKkm89qI2JDzWhlX3V+s5vEzyvIfixeQgqi7lZq+x/rjmHrH+QHAUVVldhqp3+oQSi91vy43nazJeZoJEBFLSLUFz5M0GTiR9AWksp6RrjuDjonh1kU+t+vMO4sUUN1YWNeP8vgRyyciNuWPI14LCkZ7LgGsiHwXyirn+XicN/sx+DozcA0Zg3rH9GiuxcX8jXrdo7yfVS+7u8b6R1OG1QaWm+8Vy2vle5TbP9x59olCnh4ifWmYTW0fJtUU/Tg3P541TP4HbQODy3006x3umPoq6UvTZUrN3h9S+iFITZLelptE1+b1TWNwGdVa1zGk2tB/HyYfw2pYMJbbe58HXFsYHVXJzgQeAxwVEbuT+7WQCrriXFKTw9cLB/Uy0jfUYtA0JSL+uzofEXFHRJxKOpA/CFwhacoYN2clqb9Kcb65hc8bSRfOlPmUz+JF89PAbcD8vJ3vrtrG4dwL7F/V1j6XFKCOh25SU1hFcR+NtN211EpfeTn210nNLvtHxDRSFXZ1ORTX/xJSFfWzSCfEvDx+tGVXtIxUq1A8ZqZGxPE11ltJv5VUo1pJv3tEHFpIUz3PeBgpn8PKX3ZuIJ17+0bEbXnStXnc4xl95/2Ryn9M2x8RN0TESaRz8Tukb901k45luaQy+2VVme0WEa+vTihpAqmW5yPA3hExHbiSwcfUpaSaiJOAW3KAVlnPSNedgbyPYl0rgWKfov0Lnx8gBUCHFtY1LSLGElwVDbpGUSdQHYPZkoplVjnPx+O8uZd0A65e9ngYzbW4uB/Gsu7R3M+ql91H2tdFoynDagPLzfeKOXXy/XDuRctITaXF439SRPymVuKIWB8RZ0bEQaQvNW9VoW/hcNvA4HIfzXrrHlOR+rG/LyIWAE8BnkvqyjJkvtw/7B3Ai4EZ+Zxdy+AyqrWufYFVVZUmYzLuwZhSh9dDSBe0fUjNjPVMJV1w1ih1ZHxvjTR9wItIzYVfyQfaJaRvrs/J394nKnUeH9L5X9JLJc3KhVTpDDymAouIu0m/VHufpB5JTyPd2Cr+SqrxOCFH3GeT+tgUt3MdsEHSY0n9OYruY3BAVHQ96dvJOyR1K3UYfh5w2Vi2AQY6Hz8tb8MkSe8k1bhcXyv9KLa7nkr6p5MO/G/m8VOBhyJii6QjSTf74UwlXZQeJN1I/nMU667nd8B6pR+QTMrHzWGS/i5Pvw+YVwl6I2Il8GPgfyTtLqlD0qMkPeNh5GE88jka1wBvIVXNV/wqj1sZEX8b5XJGKv/hjttB8vFwmqRpkX5osI765+F9wBwN7Vhdz/eBR0t6WT5HupU6nx9SI20P6dzsBfqVOrk/uyrNZXnc69leKwZjuO6Mcl2XA69U+oHOZOA/KhPy9erzwMeUfnSBpNmSnjOaAqnhj8ApuWwWkvoXPRx7AW/Oy3sRqT/PleN03lwKnC1pltKPSc4hlf14GOlaDPB2pc74+5POmW/UWVb18T+a+9lLJS3I+/s84IqIKBUT7GAZHiHpn5Vq3f+NdN5eVyPdaLa/ns8A79L2H/FMy/u+YlB5SHqupINz0L6W9IOx4e699cp9pPUOS9IzJT0uV5KsI8UUlXzU2of9pHO2S9I5wO6jWM3lpIBzh41nMHaypA2kQl9EuoAfERHDfav4OKmT5QOkA+dHtRJFxDbST0f3JvWzWEH61vpuUqEtA95O7e05Frg55+0TpL4Dm+vkZz8Nfc7YC/K0l5D68zxEOsm+UsjfWlIb9xdy3jaSqokr3pbnX0+6wFaf3OcCX1aqhh30K7C87c8DjiOV06eAlxdqPMZiAnAhad+sIDWZnDDCPqq73XWsIrWx30vqb/O6Ql7fAJwnaT3pAluvZqTiK6Tq6hXALdS+uIxKvuA9l9SP405SWX6BVOMD2wPGByX9Pn9+OemGekvepiuo3RQ+bkbKp9IDgTeMsJhfkm6WxWd1/SqPu7bmHLWNVP4XAQvycfudUSzvZcBdSs0jryM1Jdbyc9KjC1ZJqq41GCIi1pOCnFNIx90qtv9QqFbaN5OOvdWk43tRVZqVpB+uPIXCuRoRyxj9dWfEdUXED4FPkjouL2F7+W7N/7+zMj6X2U9JNS874j9IPyRYTepD+/Xhk4/oemA+6fg8H3hhbH/m18M9bz5A+hJ4E/Bn4Pd53HgY6VoMqZ/TjaQA9gek47yWcxl83R7N/eyrpP6Pq0j9duv9UnqsZfhdUt/J1aTz7J/zl55qo9n+miLi26Tz6rJ8PP6FdF+qOJfB5TGfdMxuIJ1Pn4qIq0fYhiHlPor1jmQfUvmtI3VB+CVpP0CKCV6o9CvNT5KaM39EqmC5m/RDltF0R/lndqCCpMjvptxBSo9lODgiXtrqvOwscq3dJRFRr6bAzOrINXl/ASZE+pm82Yja4V6k9DD5+YUuAbscP5TQzKxFJP2T0jO0Bh7C7EDMbNfjYMysySTNrdEcXvkb6QcS1l5eS3qG199IfWrG0ofHzNqEmynNzMzMWsg1Y2ZmZmYttDO83NgeAWbOnBnz5s1rdTbMzB5RbrzxxgciYriH9Zo5GLPRmTdvHosXL251NszMHlEkjfptLbbrcjOlmZmZWQs5GDMzMzNrIQdjZmZmZi3kYKwNSfqipPsl/aXOdEn6pKQlkm6SdHiz82hmZmaJg7H2dDHpnZz1HEd6b9h84Azg003Ik5mZmdXgYKwNRcQ1pBd713MS8JVIrgOmS2roC7DNzMysNgdju6bZDH4T/fI8bhBJZ0haLGlxb29v0zJnZma2K/FzxqyuiPgc8DmAhQsX+r1Z1jYigggoRxAw8BnyuIAofKaQdvv0/H/+XI7tyx0ybtA6t887sPzCcot5iqr8VZabNoKBaWkwCp+3z7s9beRtZ2D7qEqX5i+kG2H5FOYbzfKH5GOY5VOVr8r6gqHLr87H4HwOXf5AvoZJF4WFzp4xiZP/zq+NtcZxMLZrWgHsXxiek8fZI1BE0FcKtpXKbOsv01dKf6VyGl8qB/3lMv2loL+ch0tl+gvjS+WgrxyUCun6c7pS4XNl/rTccmH5QbkclCIoR/pcDihFEJHSlIM8Pijlm2t5mGlDx28PVkrl9LmUp6W/wnDV+quDHLOxOOrAPRyMWUM5GNs1LQLeKOky4ChgbUSsbHGe2kp/qcyGrf2s39LPhq39bO4rsWVbiU3bSmzuS39b+vLwtvR5c9/26Vv7SmztT8FVJcga8rm/zNYceDUzwOgQdHV00NUpOjtEd2cHHRJdHaJD0NEhOpSmSdCpNNyRp6fx+fPANOjq6hiY1ilqztOZ5xt2WtX6O/J4kYYlIdJ4ifS5QwAD6YvTKXwuzqOBz3n5hWVXtk9V6y3mI61Sg9fZkaZTGFecTmH+PDiQj+3DlT21Pa+wPW+V7Swuh8J8g5Zfc30Pc/nDpNPAP4VyG2b5DMrnjm1n3fmKM5k1mIOxNiTpUuAYYKak5cB7gW6AiPgMcCVwPLAE2AS8sjU53flt6Svx0MZtPLhhGw9u3Lr9/43bWLe5n/Vb+li/pfh/+rxxW2lM65nQ1cGknk4md3cysaeTiV2d9HR10NPVwW4TuuiZ3DEw3NNZ+NzVwYTODrqrxnUXgqVK4NTVIbo6O+jqqARRorOjI4/P0ztSQJSGi9Py+A4NBC5mZjY+HIy1oYg4dYTpAfxrk7Kz09rSV+LeNZtZvjr9rVizKf2/ejMPbEiB1/qt/TXn7ensYPdJ3ew+sYupE7uYOrGbvXefOPB56sQudpvQxe4Tu9ltYheTejqZ1N3J5Pz/xO7OFHzlwMsBjpnZrsvBmLW9LX0l7rhvA7euWsetK9dx28r1LOndQO/6rYPSdXaI/aZPZPb0STx+znT2mNLDzN162HO3Cew5Jf1fGZ7S0+lmDDMzGxcOxqytRARLH9jI7+58iBvufIibVqxlae8GyrlP1cTuDh6zz+4c8+hZ7L/HZObMmMScGZOZPWMSe0+dQFenn/ZiZmbN5WDMHvH6SmV+dccD/PTW+/jF7b2sWLMZgD2n9PCkuTM4/rB9eOy+u3PIvrszd4/JdLpJ0MzMdiIOxuwR65Z71/G16+/mB39eyZpNfUzp6eSpB8/kDc98FEcftCcHzZzipkQzM9vpORizR5SI4Jo7HuCCn9/BDXetZkJXB885dB9OfMJ+PP3RM5nQ1dnqLJqZmY2JgzF7xPjLirWcu+hmFt+9mtnTJ3H2CYfwoiP2Z9rk7lZnzczMbIc5GLOd3tb+Eh/+0e1c9Os72XNKD+f/02G86Ij96elyZ3szM3vkczBmO7VlD23itV+9kVtWruO0o+byjmMfy7RJrgkzM7P24WDMdlp/Xr6WV158A1v7S3zh5Qt51oK9W50lMzOzcedgzHZKf1mxltO+cB1TJ3Zz2RlHcfBeU1udJTMzs4ZwMGY7nXvXbOb0L92QA7Gj2X+Pya3OkpmZWcM4GLOdyrb+Mq+75Ea29JW47IyjHIiZmVnbczBmO5VP/uwOblq+ls+89Ag3TZqZ2S7BzwawncZtq9bxqV8s4YVHzOHYw/ZpdXbMzMyawsGY7RQigvd//xamTuzm7BMOaXV2zMzMmsbBmO0UriE9gDgAABNtSURBVFv6EL9e8iBv+Yf5TJ/c0+rsmJmZNY2DMdspXHD1HcyaOoGXHDW31VkxMzNrKgdj1nJ33LeeXy95kFc99UAmdvtF32ZmtmtxMGYt97Xr76Gns4MXL5zT6qyYmZk1nYMxa6lyOfjBn1fyrAV7seduE1qdHTMzs6ZzMGYt9Ydlq+ldv5XnHOpHWZiZ2a7JwZi11I/+soruTvHMx+7V6qyYmZm1hIMxa5mI4Kqb7+Mpj5rJ7hO7W50dMzOzlnAwZi3zt96N3PPQJp596N6tzoqZmVnLOBizlrnx7ocAOPqgPVucEzMzs9ZxMGYts/iu1cyY3M1BM6e0OitmZmYt42DMWubGe1ZzxAEzkNTqrJiZmbWMgzFriYc2bmNp70YOP2BGq7NiZmbWUg7GrCX+tGwNAIfPdTBmZma7Ngdj1hK3rVoPwCH77t7inJiZmbWWgzFriTvuW88+u09k2iQ/X8zMzHZtDsbakKRjJd0uaYmks2pMnyvpakl/kHSTpOObncc77t/A/L13a/ZqzczMdjoOxtqMpE7gQuA4YAFwqqQFVcnOBi6PiCcBpwCfamYey+Vgyf0bmL/X1Gau1szMbKfkYKz9HAksiYilEbENuAw4qSpNAJXOWtOAe5uYP5av3szmvpJrxszMzICuVmfAxt1sYFlheDlwVFWac4EfS3oTMAV4VnOylvytdwMAB+/lYMzMzMw1Y7umU4GLI2IOcDzwVUlDjgVJZ0haLGlxb2/vuK18+epNAMzdY/K4LdPMzOyRysFY+1kB7F8YnpPHFb0auBwgIn4LTARmVi8oIj4XEQsjYuGsWbPGLYPL12ymp7ODWbtNGLdlmpmZPVI5GGs/NwDzJR0oqYfUQX9RVZp7gH8AkHQIKRgbv6qvESxfvZn9pk+ko8OvQTIzM3Mw1mYioh94I3AVcCvpV5M3SzpP0ok52ZnAv0j6E3ApcHpERLPyuGL1ZmbPmNSs1ZmZme3U3IG/DUXElcCVVePOKXy+BXhqs/NVsWLNZp75mPFr9jQzM3skc82YNdWWvhK967cyZ4Y775uZmYGDMWuy+9dtBWCfaRNbnBMzM7Odg4Mxa6reDSkYmzXVv6Q0MzMDB2PWZL3rczDmx1qYmZkBDsasyR5wzZiZmdkgDsasqR7YsBUJ9pjS0+qsmJmZ7RQcjFlT9a7fyozJPXR3+tAzMzMDB2PWZA9s2MrM3VwrZmZmVuFgzJrqwQ3bmOnO+2ZmZgMcjFlTrd60jRmTXTNmZmZW4WDMmmrt5n6mTe5udTbMzMx2Gg7GrGkigrWbtzF9koMxMzOzCgdj1jSbtpXoKwXTHIyZmZkNcDBmTbNmcx8A091MaWZmNsDBmDXN2k0pGHPNmJmZ2XYOxqxp1mzeBsC0Sf41pZmZWYWDMWuadZtdM2ZmZlbNwZg1zfot/QBMndjV4pyYmZntPByMWdNs3JqCsSkTHIyZmZlVOBizptm4rQTAlAmdLc6JmZnZzsPBmDXNhq39dHeKCV0OxszMzCocjFnTbNza7yZKMzOzKg7GrGk2bO1nSo+DMTMzsyIHY9Y0m7aW3F/MzMysioMxa5qN29xMaWZmVs3BmDXNhq397OZgzMzMbBAHY9Y0G91nzMzMbAgHY9Y0G7eWmOw+Y2ZmZoM4GLOmcTOlmZnZUA7GrCkiws8ZMzMzq8HBmDVFXynoLweTu91MaWZmVuRgzJpiS396L+VEB2NmZmaDOBhrQ5KOlXS7pCWSzqqT5sWSbpF0s6SvNzpPW/oqwZgPOTMzsyJ34GkzkjqBC4F/BJYDN0haFBG3FNLMB94FPDUiVkvaq9H52tpXBmCCa8bMzMwGcTVF+zkSWBIRSyNiG3AZcFJVmn8BLoyI1QARcX+jM1WpGZvkYMzMzGwQB2PtZzawrDC8PI8rejTwaEm/lnSdpGNrLUjSGZIWS1rc29v7sDK1JdeMuc+YmZnZYA7Gdk1dwHzgGOBU4POSplcniojPRcTCiFg4a9ash7XC7R34fciZmZkV+c7YflYA+xeG5+RxRcuBRRHRFxF3An8lBWcNs70Dv2vGzMzMihyMtZ8bgPmSDpTUA5wCLKpK8x1SrRiSZpKaLZc2MlMDzZRdDsbMzMyKHIy1mYjoB94IXAXcClweETdLOk/SiTnZVcCDkm4BrgbeHhEPNjJffrSFmZlZbX60RRuKiCuBK6vGnVP4HMBb819TuJnSzMysNldTWFNs6a88Z8yHnJmZWZHvjNYUW10zZmZmVpODMWuKgWZKd+A3MzMbxMGYNcXW/jIdgu5OtTorZmZmOxUHY9YU2/rL9HR1IDkYMzMzK3IwZk2xrVSmu9OHm5mZWTXfHa0p+kplJnT5cDMzM6vmu6M1xbZ+14yZmZnV4rujNUVfKRyMmZmZ1eC7ozXFtlLqwG9mZmaD+e5oTeFmSjMzs9p8d7Sm6CuV6fEzxszMzIZwMGZNUXnOmJmZmQ3mu6M1RZ+fM2ZmZlaT747WFNv8a0ozM7OafHe0pnAHfjMzs9p8d7Sm8BP4zczMavPd0Zoi9RnzrynNzMyqORizpnAzpZmZWW2+O1pT9PkJ/GZmZjX57mhN4ZoxMzOz2nx3tKbwuynNzMxq893RmqKvFPS4ZszMzGwI3x2t4crloFQOuvxrSjMzsyEcjFnD9ZcDwH3GzMzMavDd0Rquv1wGoLPDNWNmZmbVHIxZw1VqxrocjJmZmQ3hYMwarlRyMGZmZlaPgzFruL5KM6X7jJmZmQ3hu6M1XMnNlGZmZnU5GLOG63czpZmZWV0OxtqQpGMl3S5piaSzhkn3AkkhaWEj8zPQgd/PGTMzMxvCwVibkdQJXAgcBywATpW0oEa6qcBbgOsbnadS7jPW1eHDzczMrJrvju3nSGBJRCyNiG3AZcBJNdK9H/ggsKXRGfKjLczMzOpzMNZ+ZgPLCsPL87gBkg4H9o+IHwy3IElnSFosaXFvb+8OZ6jSZ8wPfTUzMxvKwdguRlIH8FHgzJHSRsTnImJhRCycNWvWDq/Tr0MyMzOrz3fH9rMC2L8wPCePq5gKHAb8QtJdwNHAokZ24i/5dUhmZmZ1ORhrPzcA8yUdKKkHOAVYVJkYEWsjYmZEzIuIecB1wIkRsbhRGerzoy3MzMzqcjDWZiKiH3gjcBVwK3B5RNws6TxJJ7YiTwMPfXUzpZmZ2RBdrc6Ajb+IuBK4smrcOXXSHtPo/FT6jLmZ0szMbChXVVjD9ZcqzxlzMGZmZlbNwZg1nJ/Ab2ZmVp+DMWu47S8K9+FmZmZWzXdHa7i+kh9tYWZmVo+DMWu40sBDXx2MmZmZVXMwZg3nX1OamZnV52DMGq6/5D5jZmZm9fjuaA1XeR2Sf01pZmY2lIMxa7iBR1u4mdLMzGwIB2PWcJVmSvcZMzMzG8rBmDVc/8CvKX24mZmZVfPd0Rqu0mesQ64ZMzMzq+ZgzBouP/PVzZRmZmY1OBizhitHaqZ0LGZmZjaUgzFruHIEEsjNlGZmZkM4GLOGK5WDTgdiZmZmNTkYs4YrB3S4jdLMzKwmB2PWcOUI9xczMzOrw8GYNZybKc3MzOpzMGYNVyqHmynNzMzqcDBmDRcRfuCrmZlZHQ7GrOFKEX7gq5mZWR0OxqzhSmW/CsnMzKweB2PWcBGB3xFuZmZWm2+R1nClsvuMmZmZ1eNgzBqu5A78ZmZmdTkYs4aLwB34zczM6nAwZg2XmilbnQszM7Odk4Mxa7hS+KGvZmZm9TgYs4Yr+3VIZmZmdTkYs4Yr+6GvZmZmdTkYa0OSjpV0u6Qlks6qMf2tkm6RdJOkn0k6oJH5KZVBrhkzMzOrycFYm5HUCVwIHAcsAE6VtKAq2R+AhRHxeOAK4EONzFPZD301MzOry7fI9nMksCQilkbENuAy4KRigoi4OiI25cHrgDmNzFA53GfMzMysHgdj7Wc2sKwwvDyPq+fVwA8bmaFSOdxMaWZmVkdXqzNgrSPppcBC4Bl1pp8BnAEwd+7cHV6PO/CbmZnV55qx9rMC2L8wPCePG0TSs4D3ACdGxNZaC4qIz0XEwohYOGvWrB3OULmMmynNzMzqcDDWfm4A5ks6UFIPcAqwqJhA0pOAz5ICsfsbnaFSBI7FzMzManMw1mYioh94I3AVcCtweUTcLOk8SSfmZB8GdgO+KemPkhbVWdy4KJfdTGlmZlaP+4y1oYi4Eriyatw5hc/PamZ+Su4zZmZmVpdrxqzhygEdbqc0MzOrycGYNVy5HLhizMzMrDYHY9ZwJfcZMzMzq8vBmDVcOcLNlGZmZnU4GLOGczBmZmZWn4Mxazg3U5qZmdXnYMwaLgI6HIyZmZnV5GDMGq4U/jWlmZlZPQ7GrOFK5fC7Kc3MzOpwMGYNVy6HmynNzMzqcDBmDVcOXDNmZmZWh4Mxa7hSBB0+0szMzGryLdIaLr0OyTVjZmZmtTgYs4Yrh58zZmZmVo+DMWu4kmvGzMzM6nIwZg1XDhyMmZmZ1eFgzBouvQ6p1bkwMzPbOfkWaQ3nF4WbmZnV52DMGq4cfuirmZlZPQ7GrOH8OiQzM7P6HIxZw5UD14yZmZnV4WDMGqpcDgAci5mZmdXmYMwaqhQpGHMzpZmZWW0OxqyhyjkYczOlmZlZbQ7GrKFyLOZHW5iZmdXhYMwaaqBmzLGYmZlZTQ7GrKFKAx34HY2ZmZnV4mDMGirHYjgWMzMzq83BmDVUVH5N6XZKMzOzmhyMWUOV3YHfzMxsWA7GrKFKfuirmZnZsByMWUNVminlmjEzM7OaHIy1IUnHSrpd0hJJZ9WYPkHSN/L06yXNa1ReKs2U7jNmZmZWm4OxNiOpE7gQOA5YAJwqaUFVslcDqyPiYOBjwAcblZ8JXR2c8Lh9mbvH5EatwszM7BGtq9UZsHF3JLAkIpYCSLoMOAm4pZDmJODc/PkK4AJJikqb4jiaMaWHC087fLwXa2Zm1jZcM9Z+ZgPLCsPL87iaaSKiH1gL7Fm9IElnSFosaXFvb2+DsmtmZrZrczBmdUXE5yJiYUQsnDVrVquzY2Zm1pYcjLWfFcD+heE5eVzNNJK6gGnAg03JnZmZmQ3iYKz93ADMl3SgpB7gFGBRVZpFwCvy5xcCP29EfzEzMzMbmTvwt5mI6Jf0RuAqoBP4YkTcLOk8YHFELAIuAr4qaQnwEClgMzMzsxZwMNaGIuJK4MqqcecUPm8BXtTsfJmZmdlQbqY0MzMzayEHY2ZmZmYtJPfbttGQ1Avc/TAWMRN4YJyy80ixq23zrra94G3eVTycbT4gIvxsIBuWgzFrCkmLI2Jhq/PRTLvaNu9q2wve5l3FrrjN1lxupjQzMzNrIQdjZmZmZi3kYMya5XOtzkAL7GrbvKttL3ibdxW74jZbE7nPmJmZmVkLuWbMzMzMrIUcjJmZmZm1kIMxayhJx0q6XdISSWe1Oj+NIGl/SVdLukXSzZLeksfvIeknku7I/89odV7Hm6ROSX+Q9P08fKCk6/P+/kZ+WX3bkDRd0hWSbpN0q6Qnt/t+lvTv+bj+i6RLJU1st/0s6YuS7pf0l8K4mvtVySfztt8k6fDW5dzahYMxaxhJncCFwHHAAuBUSQtam6uG6AfOjIgFwNHAv+btPAv4WUTMB36Wh9vNW4BbC8MfBD4WEQcDq4FXtyRXjfMJ4EcR8VjgCaRtb9v9LGk28GZgYUQcBnQCp9B++/li4NiqcfX263HA/Px3BvDpJuXR2piDMWukI4ElEbE0IrYBlwEntThP4y4iVkbE7/Pn9aQb9GzStn45J/sy8PzW5LAxJM0BTgC+kIcF/D1wRU7SVtssaRrw/4CLACJiW0Ssoc33M9AFTJLUBUwGVtJm+zkirgEeqhpdb7+eBHwlkuuA6ZL2bU5OrV05GLNGmg0sKwwvz+PalqR5wJOA64G9I2JlnrQK2LtF2WqUjwPvAMp5eE9gTUT05+F2298HAr3Al3LT7BckTaGN93NErAA+AtxDCsLWAjfS3vu5ot5+3eWua9Z4DsbMxomk3YD/A/4tItYVp0V6hkzbPEdG0nOB+yPixlbnpYm6gMOBT0fEk4CNVDVJtuF+nkGqCToQ2A+YwtDmvLbXbvvVdj4OxqyRVgD7F4bn5HFtR1I3KRD7WkR8K4++r9J8kf+/v1X5a4CnAidKuovU/Pz3pP5U03NzFrTf/l4OLI+I6/PwFaTgrJ3387OAOyOiNyL6gG+R9n077+eKevt1l7muWfM4GLNGugGYn3951UPq+LuoxXkad7mv1EXArRHx0cKkRcAr8udXAN9tdt4aJSLeFRFzImIeab/+PCJOA64GXpiTtds2rwKWSXpMHvUPwC208X4mNU8eLWlyPs4r29y2+7mg3n5dBLw8/6ryaGBtoTnTbIf4CfzWUJKOJ/Ut6gS+GBHntzhL407S04BrgT+zvf/Uu0n9xi4H5gJ3Ay+OiOpOwo94ko4B3hYRz5V0EKmmbA/gD8BLI2JrK/M3niQ9kfSDhR5gKfBK0pfatt3Pkt4HnEz61fAfgNeQ+ki1zX6WdClwDDATuA94L/AdauzXHJReQGqu3QS8MiIWtyLf1j4cjJmZmZm1kJspzczMzFrIwZiZmZlZCzkYMzMzM2shB2NmZmZmLeRgzMzMzKyFHIyZmZmZtZCDMTMzM7MW+v/48dhXeJ76SQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the most common question categories\n",
        "continuous_categoreis = [category or [] for category in continuous_questions.categories.to_list()]\n",
        "binary_categories = [category or [] for category in binary_questions.categories.to_list()]\n",
        "all_categories = chain.from_iterable(binary_categories + continuous_categoreis)\n",
        "pprint(Counter(all_categories).most_common(30))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jBOgE7vMfqR",
        "outputId": "b7081e9e-b618-4acc-8e51-0f916a1ede29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('Novel Coronavirus (Covid-19)', 479),\n",
            " ('Effective Altruism', 478),\n",
            " ('Politics &ndash; US', 409),\n",
            " ('Geopolitics &ndash; Armed Conflict', 359),\n",
            " ('Geopolitics', 297),\n",
            " ('Computer Science &ndash; AI and Machine Learning', 226),\n",
            " ('Economy &ndash; US', 180),\n",
            " ('Social issues', 166),\n",
            " ('Computing &ndash; Artificial Intelligence', 161),\n",
            " ('Politics &ndash; European', 161),\n",
            " ('Politics', 157),\n",
            " ('Technology &ndash; Space', 155),\n",
            " ('Biological Sciences &ndash; Medicine', 151),\n",
            " ('Industry &ndash; Space', 151),\n",
            " ('Environment &ndash; Climate', 150),\n",
            " ('Economy &ndash; US &ndash; Economic Indicators', 150),\n",
            " ('Series &ndash; Nuclear Threats', 136),\n",
            " ('Ukraine', 133),\n",
            " ('Biology &ndash; Infectious Disease', 125),\n",
            " ('Business', 124),\n",
            " ('Economy', 122),\n",
            " ('Technological Advances', 115),\n",
            " ('Physical Sciences &ndash; Astrophysics and Cosmology ', 107),\n",
            " ('Series &mdash; Forecasting AI Progress', 100),\n",
            " ('Finance &mdash; Cryptocurrencies', 99),\n",
            " ('Elections &ndash; U.S. &ndash; President', 96),\n",
            " ('Finance &ndash; markets', 94),\n",
            " ('Industry &ndash; Energy', 90),\n",
            " ('Global Epidemiology Cases', 83),\n",
            " ('Industry &ndash; Transportation', 78)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add data from questions table to predictions, and add useful time features\n",
        "def augment_prediction_data(questions, predictions):\n",
        "  questions = questions[questions.resolution_comment == \"resolved\"]\n",
        "  questions[\"duration\"] = questions.close_time - questions.publish_time\n",
        "  question_data = questions[\n",
        "      [\n",
        "       \"question_id\",\n",
        "       \"resolution\",\n",
        "       \"created_time\",\n",
        "       \"publish_time\",\n",
        "       \"close_time\",\n",
        "       \"resolve_time\",\n",
        "       \"duration\",\n",
        "      ]\n",
        "  ]\n",
        "  predictions = predictions.merge(question_data, on=\"question_id\")\n",
        "  predictions[\"t\"] = predictions[\"t\"].apply(lambda x: x.timestamp())\n",
        "  predictions[\"time_to_resolution\"] = predictions.resolve_time - predictions.t\n",
        "  predictions[\"time_since_publish\"] = predictions.t - predictions.publish_time\n",
        "  predictions[\"relative_t\"] = predictions.time_since_publish / predictions.duration\n",
        "  return predictions"
      ],
      "metadata": {
        "id": "rDGY83kYV1o3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Quick-and-dirty calibration plot\n",
        "predictions = augment_prediction_data(binary_questions, binary_predictions)\n",
        "predictions[\"mp_decile\"] = (predictions.mp * 100) // 10\n",
        "predictions[\"user_decile\"] = (predictions.prediction * 100) // 10\n",
        "predictions.groupby(by=[\"mp_decile\"]).apply(lambda df: df.resolution.mean()).plot()\n",
        "predictions.groupby(by=[\"user_decile\"]).apply(lambda df: df.resolution.mean()).plot()\n",
        "plt.plot([0, 9], [.05, .95])\n",
        "plt.legend([\"mp\", \"user\", \"perfect calibration\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "i5OXBVARiT9L",
        "outputId": "b11a219f-4e85-4f08-b2ce-a82fc59f40bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-c60fb4bb706b>:4: 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",
            "  questions[\"duration\"] = questions.close_time - questions.publish_time\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# More sophisticated calibration plot\n",
        "def bootstrap_outcome_quantiles(predictions, alpha=.1):\n",
        "  \"\"\"\n",
        "  Treats `predictions` as real probabilities for independent events and\n",
        "  samples them 10000 times to bootstrap alpha/2, 50%, and 1-alpha/2 quantiles \n",
        "  of the average outcome.\n",
        "  \"\"\"\n",
        "  P = np.array(predictions)\n",
        "  bootstrapped_outcomes = [np.mean((np.random.random(size=len(P)) < P).astype(float)) for i in range(10000)]\n",
        "  return np.quantile(bootstrapped_outcomes, alpha)\n",
        "\n",
        "alpha = .5\n",
        "gdf = predictions.groupby(\"question_id\").apply(\n",
        "    lambda df: df.sort_values(by=['relative_t']).iloc[-1,:]\n",
        "  ).groupby(by=[\"mp_decile\"]) # get only the last prediction on each question and group by 'mp_decile'\n",
        "decile_predictions = gdf.apply(lambda df: df.mp.mean())\n",
        "decile_resolutions = gdf.apply(lambda df: df.resolution.mean())\n",
        "decile_bootstrap_lower_bounds = gdf.apply(lambda df: bootstrap_outcome_quantiles(list(df.mp), alpha / 2))\n",
        "decile_bootstrap_upper_bounds = gdf.apply(lambda df: bootstrap_outcome_quantiles(list(df.mp), 1 - alpha / 2))\n",
        "\n",
        "plt.plot(decile_predictions, decile_resolutions)\n",
        "plt.fill_between(decile_predictions, decile_bootstrap_lower_bounds, decile_bootstrap_upper_bounds, alpha=0.2)\n",
        "plt.legend([\"fraction of questions resolved positively\",f\"{int(100*(1-alpha))}% centered CI\"])\n",
        "plt.title(\"calibration plot of final Metaculus predictions\")\n",
        "plt.xlabel(\"prediction\")\n",
        "plt.ylabel(\"fraction resolved yes\")\n",
        "plt.show()\n",
        "gdf.mp.hist()\n",
        "plt.title(\"density of final Metaculus predictions\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "FhJJwQDnirZ7",
        "outputId": "23c7ac8a-b3d2-477f-e361-89cf9aaa30c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAc1ElEQVR4nO3df5xcdX3v8dfbBEFZBDW6QvixIsgViKJs1d6q3RWsiF5Rqwj+gBQkYtXbXrGKP25Ni1zT1mj1omJUBPQhCWrRFFFBZMRYoyQa+Sk2gaVJCEQSCExUhPDpH+fschhmsvN7dr7zfj4e+8j5Nd/z/Zw5ec+Z75ydVURgZmZpeUyvO2BmZu3ncDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DDvQcknS/pox3exw2Sxjq5j3w/kvRlSXdL+nmNbT4q6S5Jd0jaX1JZ0qw27Lvjx7FbJM2XtKLX/WiEpBFJIWl2Pv9dSSc30U7bzgl7mMM9URFxWESUACQtlPTVDu3qRcDLgH0j4vmVKyXtD5wBHBoRT4uI/4qIoYjY0aH+TO53fh48n6xYfly+/Pw62ylJeltHOpmYiHhFRFww3XaSJiQdXXhcV86JQeNwt1YdAExExPYa6/cHtkTE5i72adI64PjJK8vcycBvetCXGa/iOFmfc7h3gaTnSvqFpPskLQN2q1j/KklrJN0j6T8kPbuwbkLSeyVdK2mbpGWSdsvXzZF0af64rZJ+LOkxhccdLekY4IPAG/O3vr+S9AZJqyv68B5J367R/30kLc/3sVbSafnyU4EvAn+at/0PFY87GrgC2Cdff36Vt/IlSWdJ+kl+fC6XNKfQxtfz4Zxtkq6WdFgDh/4O4Drg5XlbTwL+J7C8op8vzI/7PfnxGcuXnw28GDgn7/85+fJPSVov6V5JqyW9uNDWLEkflLQur2e1pP0q6y7U/qh3BdNtK+kgST/Kj8ld+Tn1KIV2Fki6XdImSe8trF8o6RuSvirpXmC+pD0lfSnfdqOyIbVZhdo+nu/zFuCVFft7RD2STpN0U34cbpT0PElfIXvB//f8mL6vyjlR9Xwr9PliSRfm7d4gabSw/v15v++TdLOko6odm4EQEf7p4A/wWOA24P8AuwCvBx4APpqvfy6wGXgBMIvsynIC2DVfPwH8HNgHeBJwE3B6vu5jwLl5u7uQBZEKjzs6n14IfLXQp12BrcCzCst+CfxljRquBj5L9qJ0BPBb4KX5uvnAip3UPwZsKMyPAAHMzudLZFfYzwQel88vKmx/CrBH3ud/BdYU1p0/eRyr7Hc+sAJ4E7AsX/bXwOeBjwLn58vmAluAY8kudl6Wzz+l0L+3VbT9FuDJwGyyIac7gN3ydX9H9oJyCCDgOfm2j6i7su3icaxj24uAD+X93Q14UY1jMNnORcDuwLz8uSueFw8Ar8nbehxwSX6MdgeeSnbuvT3f/nTg18B+ZOfiVVWey8k+vgHYCPxJfhwOAg6oPDdrnBM7O98WAn/In69ZZP8HVubrDgHWA/sU2n1GrzOgVz++cu+8F5IF779GxAMR8Q3gmsL6BcDnI+JnEbEjsjHL+/PHTfp0RNweEVuBfyc74SH7j7k32X+aByLix5Gf1TsTEfcDy8hCivxqeAS4tHJbSfsBfwa8PyL+EBFryK7WT6r/EEzryxHxm4j4PXAxD9dHRJwXEfflfV4IPEfSng20fQkwlj/mJODCivVvAS6LiMsi4qGIuAJYRRYeVUXEVyNiS0Q8GBGLyV54DslXvw34cETcHJlfRcSWBvpbjwfIhsP2yZ+T6T6I/YeI2B4R1wFfBk4srPtpRHwrIh4CnkBW99/m228GPgmckG97PNl5vD4/Fz+2k32+DfjniLgmPw5rI+K26Qqr83xbkT9fO4CvkL2AAuwgey4OlbRLRExExLrp9pkqh3vn7QNsrAjd4kl+AHBGPiRwj6R7yK6M9ilsc0dh+nfAUD79L8Ba4HJJt0g6s4F+XQC8SZKAtwIX5wFarf9bI+K+iv7PbWBf06laXz4MsCgf4riX7IoPYA51yl8wvgN8GHhyRPykYpMDgDdUHP8Xkb1oVqVsmOymfFjkHmDPQp/2I3sn0knvI7sa/nk+LHHKNNuvL0zfxiPPreK6A8guRDYVjsXnya7gyR9X2VYtzR6Hes63yvNlN0mzI2It8LdkFwGbJS2VVKx1oDjcO28TMDcP0Un7F6bXA2dHxF6Fn8dHxEXTNZxf0Z4REQcCrwbeU2OM8VFX8xGxEvgj2VDOm8iugKq5HXiSpD0q+r9xuv61wZuA44CjyQJ0JF+uWg+o4UKy4ZNqdwytB75Scfx3j4hF+fpHHLt8fP19ZFexT4yIvYBthT6tB55RZT+THzg/vrDsaTX6u9NtI+KOiDgtIvYB3g58VtJBNdqCLGgn7U/2nE41V5heT/aucU7hWDwhIiY/59hUpa1aah2Hyn1Waul8i4ivRcSLyF6oAvineh6XIod75/0UeBD435J2kfQ6oHjL4BeA0yW9QJndJb2y4uSuStkHsQflLxzbyN6WPlRl0zuBEeUfthZcCJwDPFDrrX1ErAf+A/iYpN2Ufdh7KtWDst32IAubLWRB9/+abOdHZGPp/7/Kuq8C/0vSy/N3CrtJGpO0b77+TuDAij49SDYOPFvS35MNZ0z6InCWpIPz5/PZkp4cEb8lC6i35Ps5hRrhN922yj4Qn+zf3WQhVu15n/R/JT0+H377K7IhuWr73QRcDiyW9ARJj5H0DEl/nm9yMdl5vK+kJwI7e6f4ReC9ko7Mj8NBkg7I11Ue02Ifmj7fJB0i6aWSdiUbl/89Oz8uSXO4d1hE/BF4HdkHZluBNwL/Vli/CjiNLGTvJhtmmV9n8wcDPwDKZC8in42Iq6ps9/X83y2SflFY/hXgcKb/j3Mi2VXz7WRj2B+JiB/U2cdWXEj2lnwjcCOwsplG8jHfK/Nx4sp168neHXyQLLDXk30oOvl/41PA65X9ktange8D3yO7nfI2shApDlV8giwELwfuBb5E9kElZM/z35G9WB1GFmK17GzbPwF+JqlMdufP30TELTtp60dk59WVwMcj4vKdbHsS2U0AN5Kdj9/g4SGqL5DV/yvgFxTO40oR8XXgbOBrwH3At8g+hIVsrP7D+dDPe6s8vNnzbVdgEXAX2dDNU4EP1PG4JE3eWWEDSNLjyO7UeV5E/Gev+2PtJWkEuBXYJSIe7G1vrNt85T7Y3gFc42A3S49/I21ASZog+xDwNT3uipl1gIdlzMwS5GEZM7MEzYhhmTlz5sTIyEhTj92+fTu77757ezvUBwax7kGsGQaz7kGsGRqve/Xq1XdFxFOqrZsR4T4yMsKqVauaemypVGJsbKy9HeoDg1j3INYMg1n3INYMjdctqeZvCXtYxswsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQTPiN1RbNXLmd6amJxa9soc9MTObGXzlbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJWjacJd0nqTNkq4vLFsmaU3+MyFpTb58RNLvC+vO7WTnzcysunq+FfJ84BzgwskFEfHGyWlJi4Fthe3XRcQR7eqgmZk1btpwj4irJY1UWydJwPHAS9vbLTMza4UiYvqNsnC/NCIOr1j+EuATETFa2O4G4DfAvcCHI+LHNdpcACwAGB4ePnLp0qVNFVAul7l1246p+Xlz92yqnX5TLpcZGhrqdTe6ahBrhsGsexBrhsbrHh8fXz2Zv5Va/WMdJwIXFeY3AftHxBZJRwLfknRYRNxb+cCIWAIsARgdHY2xsbGmOlAqlVi8YvvU/MSbm2un35RKJZo9Zv1qEGuGwax7EGuG9tbd9N0ykmYDrwOWTS6LiPsjYks+vRpYBzyz1U6amVljWrkV8mjg1xGxYXKBpKdImpVPHwgcDNzSWhfNzKxR9dwKeRHwU+AQSRsknZqvOoFHDskAvAS4Nr818hvA6RGxtZ0dNjOz6dVzt8yJNZbPr7Lsm8A3W++WmZm1wr+hamaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmC6vkbqudJ2izp+sKyhZI2SlqT/xxbWPcBSWsl3Szp5Z3quJmZ1VbPlfv5wDFVln8yIo7Ify4DkHQo2R/OPix/zGclzWpXZ83MrD7ThntEXA1srbO944ClEXF/RNwKrAWe30L/zMysCbNbeOy7JJ0ErALOiIi7gbnAysI2G/JljyJpAbAAYHh4mFKp1FQnyuUyZ8zbMTXfbDv9plwuD0ytkwaxZhjMugexZmhv3c2G++eAs4DI/10MnNJIAxGxBFgCMDo6GmNjY011pFQqsXjF9qn5iTc3106/KZVKNHvM+tUg1gyDWfcg1gztrbupu2Ui4s6I2BERDwFf4OGhl43AfoVN982XmZlZFzUV7pL2Lsy+Fpi8k2Y5cIKkXSU9HTgY+HlrXTQzs0ZNOywj6SJgDJgjaQPwEWBM0hFkwzITwNsBIuIGSRcDNwIPAu+MiB3V2jUzs86ZNtwj4sQqi7+0k+3PBs5upVNmZtYa/4aqmVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZgmaNtwlnSdps6TrC8v+RdKvJV0r6RJJe+XLRyT9XtKa/OfcTnbezMyqq+fK/XzgmIplVwCHR8Szgd8AHyisWxcRR+Q/p7enm2Zm1ohpwz0irga2Viy7PCIezGdXAvt2oG9mZtakdoy5nwJ8tzD/dEm/lPQjSS9uQ/tmZtYgRcT0G0kjwKURcXjF8g8Bo8DrIiIk7QoMRcQWSUcC3wIOi4h7q7S5AFgAMDw8fOTSpUubKqBcLnPrth1T8/Pm7tlUO/2mXC4zNDTU62501SDWDINZ9yDWDI3XPT4+vjoiRqutm91sJyTNB14FHBX5K0RE3A/cn0+vlrQOeCawqvLxEbEEWAIwOjoaY2NjTfWjVCqxeMX2qfmJNzfXTr8plUo0e8z61SDWDINZ9yDWDO2tu6lhGUnHAO8DXh0Rvyssf4qkWfn0gcDBwC3t6KiZmdVv2it3SRcBY8AcSRuAj5DdHbMrcIUkgJX5nTEvAf5R0gPAQ8DpEbG1asNmZtYx04Z7RJxYZfGXamz7TeCbrXbKzMxa499QNTNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswTVFe6SzpO0WdL1hWVPknSFpP/M/31ivlySPi1praRrJT2vU503M7Pq6r1yPx84pmLZmcCVEXEwcGU+D/AK4OD8ZwHwuda7aWZmjagr3CPiamBrxeLjgAvy6QuA1xSWXxiZlcBekvZuR2fNzKw+ioj6NpRGgEsj4vB8/p6I2CufFnB3ROwl6VJgUUSsyNddCbw/IlZVtLeA7Mqe4eHhI5cuXdpUAeVymVu37Zianzd3z6ba6TflcpmhoaFed6OrBrFmGMy6B7FmaLzu8fHx1RExWm3d7HZ0KCJCUn2vEg8/ZgmwBGB0dDTGxsaa2nepVGLxiu1T8xNvbq6dflMqlWj2mPWrQawZBrPuQawZ2lt3K3fL3Dk53JL/uzlfvhHYr7DdvvkyMzPrklbCfTlwcj59MvDtwvKT8rtmXghsi4hNLezHzMwaVNewjKSLgDFgjqQNwEeARcDFkk4FbgOOzze/DDgWWAv8DvirNvfZzMymUVe4R8SJNVYdVWXbAN7ZSqfMzKw1/g1VM7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBJU15/Zq0bSIcCywqIDgb8H9gJOA36bL/9gRFzWdA/NzKxhTYd7RNwMHAEgaRawEbiE7A9ifzIiPt6WHpqZWcPaNSxzFLAuIm5rU3tmZtYCRUTrjUjnAb+IiHMkLQTmA/cCq4AzIuLuKo9ZACwAGB4ePnLp0qVN7btcLnPrth1T8/Pm7tlUO/2mXC4zNDTU62501SDWDINZ9yDWDI3XPT4+vjoiRqutazncJT0WuB04LCLulDQM3AUEcBawd0ScsrM2RkdHY9WqVU3tv1QqMf9726fmJxa9sql2+k2pVGJsbKzX3eiqQawZBrPuQawZGq9bUs1wb3rMveAVZFftdwJM/pvv+AvApW3YR91GzvzO1PSgBL2ZWaV2jLmfCFw0OSNp78K61wLXt2EfZmbWgJau3CXtDrwMeHth8T9LOoJsWGaiYp2ZmXVBS+EeEduBJ1cse2tLPTIzs5b5N1TNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS1BLf2YPQNIEcB+wA3gwIkYlPQlYBoyQ/R3V4yPi7lb3ZWZm9WnXlft4RBwREaP5/JnAlRFxMHBlPm9mZl3SqWGZ44AL8ukLgNd0aD9mZlaFIqK1BqRbgbuBAD4fEUsk3RMRe+XrBdw9OV943AJgAcDw8PCRS5cubWr/5XKZW7ftqLpu3tw9m2qzH5TLZYaGhnrdja4axJphMOsexJqh8brHx8dXF0ZMHqHlMXfgRRGxUdJTgSsk/bq4MiJC0qNeQSJiCbAEYHR0NMbGxpraealUYvGK7VXXTby5uTb7QalUotlj1q8GsWYYzLoHsWZob90tD8tExMb8383AJcDzgTsl7Q2Q/7u51f2YmVn9Wgp3SbtL2mNyGvgL4HpgOXByvtnJwLdb2Y+ZmTWm1WGZYeCSbFid2cDXIuJ7kq4BLpZ0KnAbcHyL+zEzswa0FO4RcQvwnCrLtwBHtdK2mZk1rx0fqM5YI2d+Z2p6YtEre9gTM7Pu8tcPmJklyOFuZpYgh7uZWYIc7mZmCXK4m5klKOm7ZerhO2rMLEW+cjczS9DAXLn7Ct3MBomv3M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEtR0uEvaT9JVkm6UdIOkv8mXL5S0UdKa/OfY9nXXzMzq0crXDzwInBERv5C0B7Ba0hX5uk9GxMdb715nFL+KoNHt/dUFZtYPmg73iNgEbMqn75N0EzC3XR0zM0vV065aMzV9x/gRHdlHW8bcJY0AzwV+li96l6RrJZ0n6Ynt2IeZmdVPEdFaA9IQ8CPg7Ij4N0nDwF1AAGcBe0fEKVUetwBYADA8PHzk0qVLm9p/uVzm1m07mu3+I8ybu2fV5ddt3DbtNt1WLpcZGhrqdTe6ahBrhsGsO/War73v91PTz97jcVPTjdY9Pj6+OiJGq61rKdwl7QJcCnw/Ij5RZf0IcGlEHL6zdkZHR2PVqlVN9aFUKjH/e9ubeuzOFMfWa43R93L8vVQqMTY21rP998Ig1gyDWXfqNdcalmm0bkk1w72Vu2UEfAm4qRjskvYubPZa4Ppm92FmZs1p5W6ZPwPeClwnafJl6IPAiZKOIBuWmQDe3lIPe6TRO2rMzHameLXeDa3cLbMCUJVVlzXfnT6zsDD+vnBb7e3MzLpsYP7MnplZt3TjVsfpONzNzJo0E0K8Foe7mVkHdXusfZLDvdM6NC5/45YbefcF7wbgupOva1u7ZpYGfyukmVmCfOXeCQub+C1W33ljZm3kK3czswT5yn0m6sBV/LwL5k1Ne4zeUnHlD58xNX3US9f1sCczj8O9myqHazz8YlUsfuOrpqbPWHZpD3ti/czh3kv1jM3X2mbex9rbFzNrSa9ueazF4d4uzXyI2mHFoZh6tikO19R6rId0ZpYNZ/54anrfRS/uYU/6Q61hnEaHd2ZakFfjcDdrk8nhlJSGUhYuXFh1OmUz+bdOG+FwT0w9V+sz0U3/41lT08/69U1Vl3Pu5zrah8+c/sOp6Xee+9Ka23lMvD61Xhg69YLhD1cfyeGegH4NdKtft4ZfmtlPPWHdTIjPhLDu56t4h/sAqvVi0KkXiUdcfRcUr9AHSfHKv6ib7wI6FeLdUAz9buqHcfYih7v1vXqHU6Z7bK/VCv16FQO7qBjetbaZCYovGK3+ib16XgB69SLRLQ53a1it8fFa29TTTr1aCfJBNZMDvV6pB3EnONytJc0EdCfVE/71XK134kWk1SvzTkkh/O3R/N0yZmYJ6tiVu6RjgE8Bs4AvRsSiTu3L0jLT3g00ahBulWzXB6qDcu98L3Qk3CXNAj4DvAzYAFwjaXlE3NiJ/ZlNp1cfnM7UoRhLX6eu3J8PrI2IWwAkLQWOAxzu1jbTBfZMuhOmnWbyGLmvxGcORUT7G5VeDxwTEW/L598KvCAi3lXYZgGwIJ89BLi5yd3NAe5qobv9ahDrHsSaYTDrHsSaofG6D4iIp1Rb0bO7ZSJiCbCk1XYkrYqI0TZ0qa8MYt2DWDMMZt2DWDO0t+5O3S2zEdivML9vvszMzLqgU+F+DXCwpKdLeixwArC8Q/syM7MKHRmWiYgHJb0L+D7ZrZDnRcQNndgXbRja6VODWPcg1gyDWfcg1gxtrLsjH6iamVlv+TdUzcwS5HA3M0tQ34S7pGMk3SxpraQzq6zfVdKyfP3PJI10v5ftV0fd75F0o6RrJV0p6YBe9LOdpqu5sN1fSgpJSdwyV0/dko7Pn+8bJH2t231stzrO7/0lXSXpl/k5fmwv+tlOks6TtFnS9TXWS9Kn82NyraTnNbWjiJjxP2Qfyq4DDgQeC/wKOLRim78Gzs2nTwCW9brfXap7HHh8Pv2Ofq+7nprz7fYArgZWAqO97neXnuuDgV8CT8znn9rrfneh5iXAO/LpQ4GJXve7DXW/BHgecH2N9ccC3wUEvBD4WTP76Zcr96mvM4iIPwKTX2dQdBxwQT79DeAoSepiHzth2roj4qqI+F0+u5Lsdwr6WT3PNcBZwD8Bf+hm5zqonrpPAz4TEXcDRMTmLvex3eqpOYAn5NN7Ard3sX8dERFXA1t3sslxwIWRWQnsJWnvRvfTL+E+F1hfmN+QL6u6TUQ8CGwDntyV3nVOPXUXnUr2it/Ppq05f5u6X0R8p5sd67B6nutnAs+U9BNJK/NvXu1n9dS8EHiLpA3AZcC7u9O1nmr0/31V/mMdiZD0FmAU+PNe96WTJD0G+AQwv8dd6YXZZEMzY2Tv0K6WNC8i7ulprzrrROD8iFgs6U+Br0g6PCIe6nXHZrp+uXKv5+sMpraRNJvsLdyWrvSuc+r6GgdJRwMfAl4dEfd3qW+dMl3NewCHAyVJE2RjkssT+FC1nud6A7A8Ih6IiFuB35CFfb+qp+ZTgYsBIuKnwG5kX66VsrZ8fUu/hHs9X2ewHDg5n3498MPIP53oY9PWLem5wOfJgr3fx2BhmpojYltEzImIkYgYIfuc4dURsao33W2bes7xb5FdtSNpDtkwzS3d7GSb1VPzfwFHAUh6Flm4/7arvey+5cBJ+V0zLwS2RcSmhlvp9SfHDXzCfCzZlco64EP5sn8k+48N2ZP+dWAt8HPgwF73uUt1/wC4E1iT/yzvdZ87XXPFtiUSuFumzudaZENSNwLXASf0us9dqPlQ4Cdkd9KsAf6i131uQ80XAZuAB8jejZ0KnA6cXnieP5Mfk+uaPb/99QNmZgnql2EZMzNrgMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswT9N0oqsuVtY9tgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Demo of aggregating binary forecasts\n",
        "# NOTE: This will take forever to run in colab and will likely go OOM!!\n",
        "\n",
        "def get_idxs_of_users_latest_forecasts(user_ids):\n",
        "  latest_idxs = {}\n",
        "  idxs_of_users_latest_forecasts = [0,]*len(user_ids)\n",
        "  for i in range(len(user_ids)):\n",
        "    latest_idxs[user_ids[i]] = i\n",
        "    idxs_of_users_latest_forecasts[i] = [v for k, v in latest_idxs.items()]\n",
        "  return idxs_of_users_latest_forecasts\n",
        "\n",
        "def add_features_of_latest_forecasts(question_df):\n",
        "  question_df = question_df.sort_values(by=['t'])\n",
        "  idxs_of_users_latest_forecasts = get_idxs_of_users_latest_forecasts(list(question_df[\"user_id\"]))\n",
        "  features_of_latest_forecasts = [0,]*len(question_df.index)\n",
        "  for i, idxs in enumerate(idxs_of_users_latest_forecasts):\n",
        "    relevant_df = question_df.iloc[idxs]\n",
        "    features_of_latest_forecasts[i] = {\n",
        "      \"predictions\": list(relevant_df[\"prediction\"]),\n",
        "      \"question_lifetime_portion_elapsed\": list(relevant_df[\"relative_t\"]),\n",
        "      \"reputations\": list(relevant_df[\"reputation_at_t\"]),\n",
        "      \"user_ids\": list(relevant_df[\"user_id\"])\n",
        "    }\n",
        "  question_df[\"features_of_latest_forecasts\"] = features_of_latest_forecasts\n",
        "  return question_df\n",
        "\n",
        "predictions = predictions.groupby(\"question_id\").apply(add_features_of_latest_forecasts).reset_index(drop=True)\n",
        "\n",
        "\n",
        "def get_weighted_forecast(features_of_latest_forecasts, aggressiveness, relative_importance_of_reputation_vs_recency):\n",
        "  predictions = np.array(features_of_latest_forecasts[\"predictions\"])\n",
        "  reputations = np.array(features_of_latest_forecasts[\"reputations\"])\n",
        "  relative_ts = np.array(features_of_latest_forecasts[\"question_lifetime_portion_elapsed\"])\n",
        "  s = relative_importance_of_reputation_vs_recency\n",
        "  weight_logits = aggressiveness * (s * reputations + (1-s) * relative_ts)\n",
        "  weight_logits -= max(weight_logits)\n",
        "  weights = np.exp(weight_logits)\n",
        "  weights = weights / np.sum(weights)\n",
        "  return np.inner(weights, predictions)\n",
        "\n",
        "def add_weighted_forecasts(df, aggressiveness, relative_importance_of_reputation_vs_recency):\n",
        "  df[\"weighted_forecast\"] = df.apply(\n",
        "    lambda row:\n",
        "      get_weighted_forecast(\n",
        "          row.features_of_latest_forecasts, \n",
        "          aggressiveness, \n",
        "          relative_importance_of_reputation_vs_recency\n",
        "      ), \n",
        "  axis=1)\n",
        "  return df\n",
        "\n",
        "add_weighted_forecasts(predictions, 1, .5)"
      ],
      "metadata": {
        "id": "FmMV7VMWksQ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ylDtVyawmDR2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}