target_ensemble.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOUvgVp3xnNW"
      },
      "source": [
        "# Target Ensemble\n",
        "\n",
        "Apart from the main target, there are actually many auxilliary targets in the dataset.\n",
        "\n",
        "These targets are fundamentally related to the main target which make them potentially helpful to model. And because these targets have a wide range of correlations to the main targets, it means that we could potentially build some nice ensembles to boost our performance.\n",
        "\n",
        "In this notebook, we will\n",
        "1. Explore the auxilliary targets\n",
        "2. Select our favorite targets to include in the ensemble\n",
        "3. Create an ensemble of models trained on different targets\n",
        "4. Pickle and upload our ensemble model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KD826S8uxnNY",
        "outputId": "a0c8da1d-65dd-45c3-be31-3a6595acf64b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.4/34.4 MB\u001b[0m \u001b[31m39.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# Install dependencies\n",
        "!pip install -q numerapi pandas pyarrow matplotlib lightgbm scikit-learn cloudpickle seaborn scipy==1.10.1\n",
        "\n",
        "# Inline plots\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwChLrKexnNa"
      },
      "source": [
        "## 1. Auxilliary Targets\n",
        "\n",
        "Let's start by taking a look at the different targets in the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "R1I_xkY4xnNa",
        "outputId": "97a2d407-5f3a-46b1-bac5-8bbc2811a818"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "v4.3/train_int8.parquet: 2.09GB [03:18, 10.6MB/s]                            \n",
            "v4.3/features.json: 1.12MB [00:00, 5.42MB/s]                           \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   era  target  target_tyler_v4_20  target_tyler_v4_60  \\\n",
              "id                                                                       \n",
              "n003bba8a98662e4  0001    0.25                0.50                0.25   \n",
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              "n00b8720a2fdc4f2  0001    0.50                0.75                0.75   \n",
              "...                ...     ...                 ...                 ...   \n",
              "nffc2d5e4b79a7ae  0573    0.25                0.25                0.50   \n",
              "nffc7d24176548a4  0573    0.50                0.50                0.25   \n",
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              "nfff87b21e4db902  0573    0.50                1.00                0.50   \n",
              "\n",
              "                  target_victor_v4_20  target_victor_v4_60  \\\n",
              "id                                                           \n",
              "n003bba8a98662e4                 0.25                 0.00   \n",
              "n003bee128c2fcfc                 0.75                 0.75   \n",
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              "n00691bec80d3e02                 0.75                 0.50   \n",
              "n00b8720a2fdc4f2                 0.75                 0.50   \n",
              "...                               ...                  ...   \n",
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              "nffd79773f4109bb                 0.50                 0.50   \n",
              "nfff87b21e4db902                 0.50                 0.75   \n",
              "\n",
              "                  target_ralph_v4_20  target_ralph_v4_60  target_waldo_v4_20  \\\n",
              "id                                                                             \n",
              "n003bba8a98662e4                0.25                0.25                0.50   \n",
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              "n00b8720a2fdc4f2                0.50                0.50                0.50   \n",
              "...                              ...                 ...                 ...   \n",
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              "nfff87b21e4db902                0.50                0.50                0.50   \n",
              "\n",
              "                  target_waldo_v4_60  ...  target_jeremy_v4_20  \\\n",
              "id                                    ...                        \n",
              "n003bba8a98662e4                0.25  ...                 0.25   \n",
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              "nfff87b21e4db902                0.50  ...                 0.50   \n",
              "\n",
              "                  target_jeremy_v4_60  target_teager_v4_20  \\\n",
              "id                                                           \n",
              "n003bba8a98662e4                 0.25                 0.50   \n",
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              "...                               ...                  ...   \n",
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              "\n",
              "                  target_teager_v4_60  target_agnes_v4_20  target_agnes_v4_60  \\\n",
              "id                                                                              \n",
              "n003bba8a98662e4                 0.75                0.25                0.00   \n",
              "n003bee128c2fcfc                 0.75                1.00                1.00   \n",
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              "...                               ...                 ...                 ...   \n",
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              "nfff87b21e4db902                 0.75                0.75                0.75   \n",
              "\n",
              "                  target_claudia_v4_20  target_claudia_v4_60  \\\n",
              "id                                                             \n",
              "n003bba8a98662e4                  0.50                  0.50   \n",
              "n003bee128c2fcfc                  1.00                  0.75   \n",
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              "...                                ...                   ...   \n",
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              "\n",
              "                  target_rowan_v4_20  target_rowan_v4_60  \n",
              "id                                                        \n",
              "n003bba8a98662e4                0.50                0.75  \n",
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              "\n",
              "[606176 rows x 42 columns]"
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              "      <th>target_tyler_v4_20</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "from numerapi import NumerAPI\n",
        "\n",
        "# Set the data version to one of the most recent versions\n",
        "DATA_VERSION = \"v4.3\"\n",
        "\n",
        "# Download data\n",
        "napi = NumerAPI()\n",
        "napi.download_dataset(f\"{DATA_VERSION}/train_int8.parquet\");\n",
        "napi.download_dataset(f\"{DATA_VERSION}/features.json\");\n",
        "\n",
        "# Load data\n",
        "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
        "feature_cols = feature_metadata[\"feature_sets\"][\"medium\"]\n",
        "target_cols = feature_metadata[\"targets\"]\n",
        "train = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/train_int8.parquet\",\n",
        "    columns=[\"era\"] + feature_cols + target_cols\n",
        ")\n",
        "\n",
        "# Downsample to every 4th era to reduce memory usage and speedup model training (suggested for Colab free tier)\n",
        "# Comment out the line below to use all the data (higher memory usage, slower model training, potentially better performance)\n",
        "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n",
        "\n",
        "# Print target columns\n",
        "train[[\"era\"] + target_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YzbRO5uxnNa"
      },
      "source": [
        "### The main target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1o6PJcbxnNa"
      },
      "source": [
        "First thing to note is that `target` is just an alias for `target_cyrus_v4_20`, so we can drop this column for the rest of the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "pP6LnWcExnNa"
      },
      "outputs": [],
      "source": [
        "# Drop `target` column\n",
        "assert train[\"target\"].equals(train[\"target_cyrus_v4_20\"])\n",
        "target_names = target_cols[1:]\n",
        "targets_df = train[[\"era\"] + target_names]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d46TQDtrxnNb"
      },
      "source": [
        "### Target names\n",
        "\n",
        "At a high level, each target represents a different kind of stock market return\n",
        "- the `name` represents the type of stock market return (eg. residual to market/country/sector vs market/country/style)\n",
        "- the `_20` or `_60` suffix denotes the time horizon of the target (ie. 20 vs 60 market days)\n",
        "\n",
        "The reason why `target_cyrus_v4_20` is our main target is because it most closely matches the type of returns we want for our hedge fund. Just like how we are always in search for better features to include in the dataset, we are also always in search for better targets to make our main target. During our research, we often come up with targets we like but not as much as the main target, and these are instead released as auxilliary targets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "P7uAdarxxnNb",
        "outputId": "e0915284-2237-492d-97f0-dff19ff2635b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             20                     60\n",
              "name                                                  \n",
              "tyler        target_tyler_v4_20     target_tyler_v4_60\n",
              "victor      target_victor_v4_20    target_victor_v4_60\n",
              "ralph        target_ralph_v4_20     target_ralph_v4_60\n",
              "waldo        target_waldo_v4_20     target_waldo_v4_60\n",
              "cyrus        target_cyrus_v4_20     target_cyrus_v4_60\n",
              "caroline  target_caroline_v4_20  target_caroline_v4_60\n",
              "sam            target_sam_v4_20       target_sam_v4_60\n",
              "xerxes      target_xerxes_v4_20    target_xerxes_v4_60\n",
              "alpha        target_alpha_v4_20     target_alpha_v4_60\n",
              "bravo        target_bravo_v4_20     target_bravo_v4_60\n",
              "charlie    target_charlie_v4_20   target_charlie_v4_60\n",
              "delta        target_delta_v4_20     target_delta_v4_60\n",
              "echo          target_echo_v4_20      target_echo_v4_60\n",
              "jerome      target_jerome_v4_20    target_jerome_v4_60\n",
              "ben            target_ben_v4_20       target_ben_v4_60\n",
              "jeremy      target_jeremy_v4_20    target_jeremy_v4_60\n",
              "teager      target_teager_v4_20    target_teager_v4_60\n",
              "agnes        target_agnes_v4_20     target_agnes_v4_60\n",
              "claudia    target_claudia_v4_20   target_claudia_v4_60\n",
              "rowan        target_rowan_v4_20     target_rowan_v4_60"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0639ced-dd38-4a0d-a1e2-b544be214469\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>20</th>\n",
              "      <th>60</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>tyler</th>\n",
              "      <td>target_tyler_v4_20</td>\n",
              "      <td>target_tyler_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>victor</th>\n",
              "      <td>target_victor_v4_20</td>\n",
              "      <td>target_victor_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ralph</th>\n",
              "      <td>target_ralph_v4_20</td>\n",
              "      <td>target_ralph_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>waldo</th>\n",
              "      <td>target_waldo_v4_20</td>\n",
              "      <td>target_waldo_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrus</th>\n",
              "      <td>target_cyrus_v4_20</td>\n",
              "      <td>target_cyrus_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caroline</th>\n",
              "      <td>target_caroline_v4_20</td>\n",
              "      <td>target_caroline_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sam</th>\n",
              "      <td>target_sam_v4_20</td>\n",
              "      <td>target_sam_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xerxes</th>\n",
              "      <td>target_xerxes_v4_20</td>\n",
              "      <td>target_xerxes_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alpha</th>\n",
              "      <td>target_alpha_v4_20</td>\n",
              "      <td>target_alpha_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bravo</th>\n",
              "      <td>target_bravo_v4_20</td>\n",
              "      <td>target_bravo_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charlie</th>\n",
              "      <td>target_charlie_v4_20</td>\n",
              "      <td>target_charlie_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delta</th>\n",
              "      <td>target_delta_v4_20</td>\n",
              "      <td>target_delta_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>echo</th>\n",
              "      <td>target_echo_v4_20</td>\n",
              "      <td>target_echo_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jerome</th>\n",
              "      <td>target_jerome_v4_20</td>\n",
              "      <td>target_jerome_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ben</th>\n",
              "      <td>target_ben_v4_20</td>\n",
              "      <td>target_ben_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jeremy</th>\n",
              "      <td>target_jeremy_v4_20</td>\n",
              "      <td>target_jeremy_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>teager</th>\n",
              "      <td>target_teager_v4_20</td>\n",
              "      <td>target_teager_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agnes</th>\n",
              "      <td>target_agnes_v4_20</td>\n",
              "      <td>target_agnes_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>claudia</th>\n",
              "      <td>target_claudia_v4_20</td>\n",
              "      <td>target_claudia_v4_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rowan</th>\n",
              "      <td>target_rowan_v4_20</td>\n",
              "      <td>target_rowan_v4_60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0639ced-dd38-4a0d-a1e2-b544be214469')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c0639ced-dd38-4a0d-a1e2-b544be214469 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c0639ced-dd38-4a0d-a1e2-b544be214469');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d6012448-6c75-434d-a976-f5eb67e1c8b2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6012448-6c75-434d-a976-f5eb67e1c8b2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d6012448-6c75-434d-a976-f5eb67e1c8b2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Print target names grouped by name and time horizon\n",
        "pd.set_option('display.max_rows', 100)\n",
        "t20s = [t for t in target_names if t.endswith(\"_20\")]\n",
        "t60s = [t for t in target_names if t.endswith(\"_60\")]\n",
        "names = [t[7:-6] for t in t20s]\n",
        "pd.DataFrame({\"name\": names,\"20\": t20s,\"60\": t60s}).set_index(\"name\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDkTNQMrxnNb"
      },
      "source": [
        "### Target values\n",
        "\n",
        "Note that some targets are binned into 5 bins while others are binned into 7 bins.\n",
        "\n",
        "Unlike feature values which are integers ranging from 0-4, target values are floats which range from 0-1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "Uw_4oswnxnNb",
        "outputId": "51609c3f-df3c-41c3-eed3-9b79f1bd8d37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: ylabel='Frequency'>, <Axes: ylabel='Frequency'>],\n",
              "       [<Axes: ylabel='Frequency'>, <Axes: ylabel='Frequency'>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot target distributions\n",
        "targets_df[[\n",
        "  \"target_cyrus_v4_20\",\n",
        "  \"target_cyrus_v4_60\",\n",
        "  \"target_teager_v4_20\",\n",
        "  \"target_teager_v4_60\"\n",
        "]].plot(\n",
        "  title=\"Target Distributions\",\n",
        "  kind=\"hist\",\n",
        "  bins=35,\n",
        "  density=True,\n",
        "  figsize=(8, 4),\n",
        "  subplots=True,\n",
        "  layout=(2, 2),\n",
        "  ylabel=\"\",\n",
        "  yticks=[]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DM-mHG_xnNc"
      },
      "source": [
        "It is also important to note that the auxilary targets can be `NaN`, but the primary target will never be `NaN`. Since we are using tree-based models here we won't need to do any special pre-processing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "FT3YCXrYxnNc",
        "outputId": "9ffc0490-f194-42d3-98af-caf525695786"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# print number of NaNs per era\n",
        "nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n",
        "nans_per_era[target_names].plot(figsize=(8, 4), title=\"Number of NaNs per Era\", legend=False);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdPdFvXFxnNc"
      },
      "source": [
        "### Target correlations\n",
        "\n",
        "The targets have a wide range of correlations with each other even though they are all fundamentally related, which should allow the construction of diverse models that ensemble together nicely."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "jAvpw-XvxnNc",
        "outputId": "863f6d7b-0326-443f-d2fc-6329c3c31332"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot correlation matrix of targets\n",
        "import seaborn as sns\n",
        "sns.heatmap(\n",
        "  targets_df[target_names].corr(),\n",
        "  cmap=\"coolwarm\",\n",
        "  xticklabels=False,\n",
        "  yticklabels=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4DcPlrixnNd"
      },
      "source": [
        "Since we are ultimately trying to predict the main target (`target_cyrus_v4_20`), it is perhaps most important to consider each auxilliary target's correlation to it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sFN8_azMxnNd",
        "outputId": "42a6d562-921e-44a6-f4c6-8e8905c5b577"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       corr_with_cyrus_v4_20\n",
              "target_cyrus_v4_20                  1.000000\n",
              "target_xerxes_v4_20                 0.951474\n",
              "target_caroline_v4_20               0.938870\n",
              "target_sam_v4_20                    0.923488\n",
              "target_ralph_v4_20                  0.898095\n",
              "target_echo_v4_20                   0.853768\n",
              "target_victor_v4_20                 0.841411\n",
              "target_waldo_v4_20                  0.833776\n",
              "target_delta_v4_20                  0.807416\n",
              "target_bravo_v4_20                  0.803201\n",
              "target_jeremy_v4_20                 0.786834\n",
              "target_jerome_v4_20                 0.783397\n",
              "target_charlie_v4_20                0.765212\n",
              "target_ben_v4_20                    0.763667\n",
              "target_alpha_v4_20                  0.761541\n",
              "target_claudia_v4_20                0.733342\n",
              "target_agnes_v4_20                  0.710428\n",
              "target_teager_v4_20                 0.701812\n",
              "target_tyler_v4_20                  0.694839\n",
              "target_rowan_v4_20                  0.689129\n",
              "target_cyrus_v4_60                  0.486983\n",
              "target_xerxes_v4_60                 0.484650\n",
              "target_caroline_v4_60               0.482795\n",
              "target_sam_v4_60                    0.481005\n",
              "target_ralph_v4_60                  0.477027\n",
              "target_echo_v4_60                   0.460963\n",
              "target_victor_v4_60                 0.459944\n",
              "target_waldo_v4_60                  0.455399\n",
              "target_delta_v4_60                  0.441717\n",
              "target_bravo_v4_60                  0.441140\n",
              "target_jeremy_v4_60                 0.436126\n",
              "target_jerome_v4_60                 0.434988\n",
              "target_ben_v4_60                    0.424300\n",
              "target_charlie_v4_60                0.421626\n",
              "target_alpha_v4_60                  0.420699\n",
              "target_claudia_v4_60                0.402794\n",
              "target_agnes_v4_60                  0.396517\n",
              "target_tyler_v4_60                  0.390309\n",
              "target_teager_v4_60                 0.388601\n",
              "target_rowan_v4_60                  0.380916"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>corr_with_cyrus_v4_20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>target_cyrus_v4_20</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_xerxes_v4_20</th>\n",
              "      <td>0.951474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_caroline_v4_20</th>\n",
              "      <td>0.938870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_sam_v4_20</th>\n",
              "      <td>0.923488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ralph_v4_20</th>\n",
              "      <td>0.898095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_echo_v4_20</th>\n",
              "      <td>0.853768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_victor_v4_20</th>\n",
              "      <td>0.841411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_waldo_v4_20</th>\n",
              "      <td>0.833776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_delta_v4_20</th>\n",
              "      <td>0.807416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_bravo_v4_20</th>\n",
              "      <td>0.803201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jeremy_v4_20</th>\n",
              "      <td>0.786834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jerome_v4_20</th>\n",
              "      <td>0.783397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_charlie_v4_20</th>\n",
              "      <td>0.765212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ben_v4_20</th>\n",
              "      <td>0.763667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_alpha_v4_20</th>\n",
              "      <td>0.761541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_claudia_v4_20</th>\n",
              "      <td>0.733342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_agnes_v4_20</th>\n",
              "      <td>0.710428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_teager_v4_20</th>\n",
              "      <td>0.701812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_tyler_v4_20</th>\n",
              "      <td>0.694839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_rowan_v4_20</th>\n",
              "      <td>0.689129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_cyrus_v4_60</th>\n",
              "      <td>0.486983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_xerxes_v4_60</th>\n",
              "      <td>0.484650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_caroline_v4_60</th>\n",
              "      <td>0.482795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_sam_v4_60</th>\n",
              "      <td>0.481005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ralph_v4_60</th>\n",
              "      <td>0.477027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_echo_v4_60</th>\n",
              "      <td>0.460963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_victor_v4_60</th>\n",
              "      <td>0.459944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_waldo_v4_60</th>\n",
              "      <td>0.455399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_delta_v4_60</th>\n",
              "      <td>0.441717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_bravo_v4_60</th>\n",
              "      <td>0.441140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jeremy_v4_60</th>\n",
              "      <td>0.436126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jerome_v4_60</th>\n",
              "      <td>0.434988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ben_v4_60</th>\n",
              "      <td>0.424300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_charlie_v4_60</th>\n",
              "      <td>0.421626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_alpha_v4_60</th>\n",
              "      <td>0.420699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_claudia_v4_60</th>\n",
              "      <td>0.402794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_agnes_v4_60</th>\n",
              "      <td>0.396517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_tyler_v4_60</th>\n",
              "      <td>0.390309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_teager_v4_60</th>\n",
              "      <td>0.388601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_rowan_v4_60</th>\n",
              "      <td>0.380916</td>\n",
              "    </tr>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "(\n",
        "    targets_df[target_names]\n",
        "    .corrwith(targets_df[\"target_cyrus_v4_20\"])\n",
        "    .sort_values(ascending=False)\n",
        "    .to_frame(\"corr_with_cyrus_v4_20\")\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jl7_XIKfxnNd"
      },
      "source": [
        "## 2. Target Selection\n",
        "\n",
        "Our goal is to create an ensemble of models trained on different targets. But which targets should we use?\n",
        "\n",
        "When deciding which model to ensemble, we should consider a few things:\n",
        "\n",
        "- The performance of the predictions of the model trained on the target vs the main target\n",
        "\n",
        "- The correlation between the target and the main target\n",
        "\n",
        "To keep things simple and fast, let's just arbitrarily pick a few 20-day targets to evaluate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "vDpOPHgZxnNd"
      },
      "outputs": [],
      "source": [
        "# pick a few 20-day target candidates\n",
        "target_candidates = [\n",
        "  \"target_cyrus_v4_20\",\n",
        "  \"target_victor_v4_20\",\n",
        "  \"target_xerxes_v4_20\",\n",
        "  \"target_teager_v4_20\"\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YDRiSoPxnNd"
      },
      "source": [
        "### Model training and generating validation predictions\n",
        "\n",
        "Like usual we train on the training dataset, but this time we do it for each target.\n",
        "\n",
        "Sit back and relax, this will take a while\n",
        "# ☕"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VqBaYwmqxnNd",
        "outputId": "31747bef-95e4-41ad-c1e0-050aefb6cdaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.267848 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 3525\n",
            "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 705\n",
            "[LightGBM] [Info] Start training from score 0.499979\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.152033 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 3525\n",
            "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 705\n",
            "[LightGBM] [Info] Start training from score 0.500008\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.150920 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 3525\n",
            "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 705\n",
            "[LightGBM] [Info] Start training from score 0.499991\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.162540 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 3525\n",
            "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 705\n",
            "[LightGBM] [Info] Start training from score 0.499941\n"
          ]
        }
      ],
      "source": [
        "import lightgbm as lgb\n",
        "\n",
        "models = {}\n",
        "for target in target_candidates:\n",
        "    model = lgb.LGBMRegressor(\n",
        "        n_estimators=2000,\n",
        "        learning_rate=0.01,\n",
        "        max_depth=5,\n",
        "        num_leaves=2**4-1,\n",
        "        colsample_bytree=0.1\n",
        "    )\n",
        "    model.fit(\n",
        "        train[feature_cols],\n",
        "        train[target]\n",
        "    );\n",
        "    models[target] = model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jD6j1JBxnNe"
      },
      "source": [
        "Then we will generate predictions on the validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Ic9eGKxSxnNe",
        "outputId": "673e6f85-ebc3-4306-bc2e-4bd2da85b833"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "v4.3/validation_int8.parquet: 2.49GB [02:23, 17.3MB/s]                            \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "                  prediction_target_cyrus_v4_20  \\\n",
              "id                                                \n",
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              "\n",
              "[661951 rows x 4 columns]"
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              "<p>661951 rows × 4 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-e70a8869-10e9-4982-b993-4706221b927b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e70a8869-10e9-4982-b993-4706221b927b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        element.innerHTML = '';\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Download validation data\n",
        "napi.download_dataset(f\"{DATA_VERSION}/validation_int8.parquet\");\n",
        "\n",
        "# Load the validation data, filtering for data_type == \"validation\"\n",
        "validation = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/validation_int8.parquet\",\n",
        "    columns=[\"era\", \"data_type\"] + feature_cols + target_cols\n",
        ")\n",
        "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
        "del validation[\"data_type\"]\n",
        "\n",
        "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n",
        "# Comment out the line below to use all the data\n",
        "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
        "\n",
        "# Embargo overlapping eras from training data\n",
        "last_train_era = int(train[\"era\"].unique()[-1])\n",
        "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
        "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n",
        "\n",
        "# Generate validation predictions for each model\n",
        "for target in target_candidates:\n",
        "    validation[f\"prediction_{target}\"] = models[target].predict(validation[feature_cols])\n",
        "\n",
        "pred_cols = [f\"prediction_{target}\" for target in target_candidates]\n",
        "validation[pred_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ea2Z98CIxnNe"
      },
      "source": [
        "### Evaluating the performance of each model\n",
        "\n",
        "Now we can evaluate the performance of our models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "NjuAERHhxnNe"
      },
      "outputs": [],
      "source": [
        "# install Numerai's open-source scoring tools\n",
        "!pip install -q --no-deps numerai-tools\n",
        "\n",
        "# import the 2 scoring functions\n",
        "from numerai_tools.scoring import numerai_corr, correlation_contribution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8aLpCC3xnNf"
      },
      "source": [
        "As you can see in the performance chart below, models trained on the auxiliary target are able to predict the main target pretty well, but the model trained on the main target performs the best."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "WUvsFi-VxnNf",
        "outputId": "0b197a4b-bc56-4cff-99db-c6d1d2ec4551"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "prediction_cols = [\n",
        "    f\"prediction_{target}\"\n",
        "    for target in target_candidates\n",
        "]\n",
        "correlations = validation.groupby(\"era\").apply(\n",
        "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
        ")\n",
        "cumsum_corrs = correlations.cumsum()\n",
        "cumsum_corrs.plot(\n",
        "  title=\"Cumulative Correlation of validation Predictions\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDUjJ3guxnNf"
      },
      "source": [
        "Looking at the summary metrics below:\n",
        "- the models trained on `victor` and `xerxes` have the highest means, but `victor` is less correlated with `cyrus` than `xerxes` is, which means `victor` could be better in ensembling\n",
        "- the model trained on `teager` has the lowest mean, but `teager` is significantly less correlated with `cyrus` than any other target shown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "smz_GLLAxnNf",
        "outputId": "3cebec2a-8c18-4d1c-c30b-6f3fd7170781"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   mean      std   sharpe  max_drawdown  \\\n",
              "prediction_target_cyrus_v4_20  0.023527 0.022559 1.042921      0.072247   \n",
              "prediction_target_victor_v4_20 0.022967 0.021090 1.089043      0.104255   \n",
              "prediction_target_xerxes_v4_20 0.022827 0.022882 0.997568      0.076184   \n",
              "prediction_target_teager_v4_20 0.019972 0.019316 1.033969      0.043845   \n",
              "\n",
              "                                mean_corr_with_cryus  \n",
              "prediction_target_cyrus_v4_20               0.023754  \n",
              "prediction_target_victor_v4_20              0.022921  \n",
              "prediction_target_xerxes_v4_20              0.023171  \n",
              "prediction_target_teager_v4_20              0.020489  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53422d66-6597-4c51-ad02-654d29d1d12b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
              "      <th>mean_corr_with_cryus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>prediction_target_cyrus_v4_20</th>\n",
              "      <td>0.023527</td>\n",
              "      <td>0.022559</td>\n",
              "      <td>1.042921</td>\n",
              "      <td>0.072247</td>\n",
              "      <td>0.023754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_v4_20</th>\n",
              "      <td>0.022967</td>\n",
              "      <td>0.021090</td>\n",
              "      <td>1.089043</td>\n",
              "      <td>0.104255</td>\n",
              "      <td>0.022921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_xerxes_v4_20</th>\n",
              "      <td>0.022827</td>\n",
              "      <td>0.022882</td>\n",
              "      <td>0.997568</td>\n",
              "      <td>0.076184</td>\n",
              "      <td>0.023171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager_v4_20</th>\n",
              "      <td>0.019972</td>\n",
              "      <td>0.019316</td>\n",
              "      <td>1.033969</td>\n",
              "      <td>0.043845</td>\n",
              "      <td>0.020489</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "def get_summary_metrics(scores, cumsum_scores):\n",
        "    summary_metrics = {}\n",
        "    # per era correlation between predictions of the model trained on this target and cyrus\n",
        "    mean = scores.mean()\n",
        "    std = scores.std()\n",
        "    sharpe = mean / std\n",
        "    rolling_max = cumsum_scores.expanding(min_periods=1).max()\n",
        "    max_drawdown = (rolling_max - cumsum_scores).max()\n",
        "    return {\n",
        "        \"mean\": mean,\n",
        "        \"std\": std,\n",
        "        \"sharpe\": sharpe,\n",
        "        \"max_drawdown\": max_drawdown,\n",
        "    }\n",
        "\n",
        "target_summary_metrics = {}\n",
        "for pred_col in prediction_cols:\n",
        "  target_summary_metrics[pred_col] = get_summary_metrics(\n",
        "      correlations[pred_col], cumsum_corrs[pred_col]\n",
        "  )\n",
        "  # per era correlation between this target and cyrus\n",
        "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
        "      lambda d: d[pred_col].corr(d[\"target_cyrus_v4_20\"])\n",
        "  ).mean()\n",
        "  target_summary_metrics[pred_col].update({\n",
        "      \"mean_corr_with_cryus\": mean_corr_with_cryus\n",
        "  })\n",
        "\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary = pd.DataFrame(target_summary_metrics).T\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yju_5cHJxnNf"
      },
      "source": [
        "### Selecting our favorite target\n",
        "Based on our observations above, it seems like target `victor` is the best candidate target for our ensemble since it has great performance and it is not too correlated with `cyrus`. However, it's interesting to look at how models that are very uncorrelated ensemble together; we are going to also look at how `teager` ensembles with `cyrus`.\n",
        "\n",
        "What do you think?\n",
        "\n",
        "Note that this target selection heuristic is extremely basic. In your own research, you will most likely want to consider all targets instead of just our favorites, and may want to experiment with different ways of selecting your ensemble targets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeqgd58hxnNg"
      },
      "source": [
        "## 3. Ensembling\n",
        "\n",
        "Now that we have reviewed and selected our favorite targets, let's ensemble our predictions and re-evaluate performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rap7tHjGxnNg"
      },
      "source": [
        "### Creating the ensemble\n",
        "\n",
        "For simplicity, we will equal weight the predictions from target `victor` and `cyrus`. Note that this is an extremely basic and arbitrary way of selecting ensemble weights. In your research, you may want to experiment with different ways of setting ensemble weights.\n",
        "\n",
        "Tip: remember to always normalize (percentile rank) your predictions before averaging so that they are comparable!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "y27jxEUwxnNg",
        "outputId": "5c2de63b-b7c6-4e77-a0ac-0ed56f2ee15d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "<p>661951 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3802ae96-b2ec-47f2-9128-e99eae461799')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3802ae96-b2ec-47f2-9128-e99eae461799 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3802ae96-b2ec-47f2-9128-e99eae461799');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b7863a6c-5a6b-4dd0-967d-c142a00f8a99\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b7863a6c-5a6b-4dd0-967d-c142a00f8a99')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b7863a6c-5a6b-4dd0-967d-c142a00f8a99 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Ensemble predictions together with a simple average\n",
        "validation[\"ensemble_cyrus_victor\"] = (\n",
        "    validation\n",
        "    .groupby(\"era\")[[\n",
        "      \"prediction_target_cyrus_v4_20\",\n",
        "      \"prediction_target_victor_v4_20\",\n",
        "    ]]\n",
        "    .rank(pct=True)\n",
        "    .mean(axis=1)\n",
        ")\n",
        "validation[\"ensemble_cyrus_teager\"] = (\n",
        "    validation\n",
        "    .groupby(\"era\")[[\n",
        "      \"prediction_target_cyrus_v4_20\",\n",
        "      \"prediction_target_teager_v4_20\",\n",
        "    ]]\n",
        "    .rank(pct=True)\n",
        "    .mean(axis=1)\n",
        ")\n",
        "\n",
        "# Print the ensemble predictions\n",
        "prediction_cols = [\n",
        "  \"prediction_target_cyrus_v4_20\",\n",
        "  \"prediction_target_victor_v4_20\",\n",
        "  \"prediction_target_teager_v4_20\",\n",
        "  \"ensemble_cyrus_victor\",\n",
        "  \"ensemble_cyrus_teager\"\n",
        "]\n",
        "validation[prediction_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMLgtnXTxnNg"
      },
      "source": [
        "### Evaluating performance of the ensemble\n",
        "Looking at the performance chart below, we can see that the peformance of our ensembles are better than that of the models trained on individual targets. Is this a result you would have expected or does it surprise you?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "jCRbKxZmxnNg",
        "outputId": "7ade0fdd-6949-437f-d83b-24069914295c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlations = validation.groupby(\"era\").apply(\n",
        "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
        ")\n",
        "cumsum_corrs = correlations.cumsum()\n",
        "\n",
        "cumsum_corrs = pd.DataFrame(cumsum_corrs)\n",
        "cumsum_corrs.plot(\n",
        "    title=\"Cumulative Correlation of validation Predictions\",\n",
        "    figsize=(10, 6),\n",
        "    xticks=[]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FjFZq-hxnNh"
      },
      "source": [
        "Looking at the summary metrics below, we can see that our ensemble seems to have better `mean`, `sharpe`, and `max_drawdown` than our original model. Much more interestingly, however, is that our ensemble with `teager` has even higher sharpe than the ensemble with `victor`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_79JgZULxnNh",
        "outputId": "7176ffd5-ce54-4fa0-ba08-690711396a15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrus_v4_20  0.023527 0.022559 1.042921      0.072247\n",
              "prediction_target_victor_v4_20 0.022967 0.021090 1.089043      0.104255\n",
              "prediction_target_teager_v4_20 0.019972 0.019316 1.033969      0.043845\n",
              "ensemble_cyrus_victor          0.024685 0.022000 1.122062      0.084085\n",
              "ensemble_cyrus_teager          0.024334 0.021118 1.152323      0.043172"
            ],
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              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>prediction_target_cyrus_v4_20</th>\n",
              "      <td>0.023527</td>\n",
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              "      <td>1.042921</td>\n",
              "      <td>0.072247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_v4_20</th>\n",
              "      <td>0.022967</td>\n",
              "      <td>0.021090</td>\n",
              "      <td>1.089043</td>\n",
              "      <td>0.104255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager_v4_20</th>\n",
              "      <td>0.019972</td>\n",
              "      <td>0.019316</td>\n",
              "      <td>1.033969</td>\n",
              "      <td>0.043845</td>\n",
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              "      <th>ensemble_cyrus_victor</th>\n",
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              "      <td>0.084085</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "summary_metrics = get_summary_metrics(correlations, cumsum_corrs)\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary = pd.DataFrame(summary_metrics)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kns8BQ1kL2mE"
      },
      "source": [
        "You can see below that `teager` also improves MMC performance significantly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "k9jKnsnuL1se",
        "outputId": "283f90ad-1226-44a0-b07a-79194adca1cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "v4.3/meta_model.parquet: 25.2MB [00:00, 26.6MB/s]                            \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrus_v4_20  0.004393 0.012995 0.338057      0.052187\n",
              "prediction_target_victor_v4_20 0.005118 0.016711 0.306277      0.088543\n",
              "prediction_target_teager_v4_20 0.006744 0.016389 0.411500      0.082615\n",
              "ensemble_cyrus_victor          0.005118 0.014612 0.350272      0.067359\n",
              "ensemble_cyrus_teager          0.006637 0.014658 0.452803      0.045460"
            ],
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              "          + ' to learn more about interactive tables.';\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-39bc9c62-ea22-41a5-8f79-2fc0d1fd4c3d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-39bc9c62-ea22-41a5-8f79-2fc0d1fd4c3d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from numerai_tools.scoring import correlation_contribution\n",
        "\n",
        "# Download and join in the meta_model for the validation eras\n",
        "napi.download_dataset(f\"{DATA_VERSION}/meta_model.parquet\")\n",
        "validation[\"meta_model\"] = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/meta_model.parquet\"\n",
        ")[\"numerai_meta_model\"]\n",
        "\n",
        "def get_mmc(validation, meta_model_col):\n",
        "    # Compute the per-era mmc between our predictions, the meta model, and the target values\n",
        "    per_era_mmc = validation.dropna().groupby(\"era\").apply(\n",
        "        lambda x: correlation_contribution(\n",
        "            x[prediction_cols], x[meta_model_col], x[\"target\"]\n",
        "        )\n",
        "    )\n",
        "\n",
        "    cumsum_mmc = per_era_mmc.cumsum()\n",
        "\n",
        "    # compute summary metrics\n",
        "    summary_metrics = get_summary_metrics(per_era_mmc, cumsum_mmc)\n",
        "    summary = pd.DataFrame(summary_metrics)\n",
        "\n",
        "    return per_era_mmc, cumsum_mmc, summary\n",
        "\n",
        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"meta_model\")\n",
        "# plot the cumsum mmc performance\n",
        "cumsum_mmc.plot(\n",
        "  title=\"Cumulative MMC of Neutralized Predictions\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bijsOnqqOqpf"
      },
      "source": [
        "#### Benchmark Models\n",
        "\n",
        "It's no accident that a model trained on `teager` ensembles so well. We have seen in our research that models trained or ensembled using `teager` perform extremely well. We even released a benchmark for a [teager ensemble](https://numer.ai/v42_teager_ensemble). We submit predictions for all internally known models [here](https://numer.ai/~benchmark_models) and release files with their predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "QNSLiLDWOx6D",
        "outputId": "18df7735-c6af-4a6d-a5a8-8806c6c2e3af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "v4.3/validation_benchmark_models.parquet: 816MB [00:35, 23.1MB/s]                           \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   era  v43_lgbm_teager60  v43_lgbm_teager20  \\\n",
              "id                                                             \n",
              "n000101811a8a843  0575           0.331251           0.204456   \n",
              "n001e1318d5072ac  0575           0.382886           0.584010   \n",
              "n002a9c5ab785cbb  0575           0.820737           0.912138   \n",
              "n002ccf6d0e8c5ad  0575           0.965022           0.987716   \n",
              "n0051ab821295c29  0575           0.135332           0.106184   \n",
              "...                ...                ...                ...   \n",
              "nffc93463f094655  1101           0.841633           0.739184   \n",
              "nffdaf3cd2c727c9  1101           0.531020           0.520000   \n",
              "nffe0f7cdc53c812  1101           0.519184           0.638163   \n",
              "nfff0d7e8380b4ac  1101           0.285510           0.351020   \n",
              "nfffa7149dc9ad49  1101           0.319796           0.296531   \n",
              "\n",
              "                  v43_lgbm_cyrus60  v43_lgbm_cyrus20  v42_example_preds  \\\n",
              "id                                                                        \n",
              "n000101811a8a843          0.335415          0.201333           0.122215   \n",
              "n001e1318d5072ac          0.617739          0.772642           0.624193   \n",
              "n002a9c5ab785cbb          0.435145          0.527587           0.533417   \n",
              "n002ccf6d0e8c5ad          0.966479          0.973766           0.955445   \n",
              "n0051ab821295c29          0.122007          0.095773           0.075161   \n",
              "...                            ...               ...                ...   \n",
              "nffc93463f094655          0.480204          0.251020           0.265306   \n",
              "nffdaf3cd2c727c9          0.808980          0.722449           0.669388   \n",
              "nffe0f7cdc53c812          0.486327          0.402245           0.452653   \n",
              "nfff0d7e8380b4ac          0.448980          0.491633           0.475714   \n",
              "nfffa7149dc9ad49          0.377755          0.785918           0.864898   \n",
              "\n",
              "                  v42_lgbm_teager60  v42_lgbm_teager20  v42_lgbm_agnes20  \\\n",
              "id                                                                         \n",
              "n000101811a8a843           0.226733           0.127629          0.290235   \n",
              "n001e1318d5072ac           0.318551           0.493650          0.960025   \n",
              "n002a9c5ab785cbb           0.895274           0.897564          0.422236   \n",
              "n002ccf6d0e8c5ad           0.978139           0.989798          0.987716   \n",
              "n0051ab821295c29           0.156777           0.252967          0.120341   \n",
              "...                             ...                ...               ...   \n",
              "nffc93463f094655           0.768980           0.734898          0.127959   \n",
              "nffdaf3cd2c727c9           0.424082           0.505918          0.637347   \n",
              "nffe0f7cdc53c812           0.603061           0.702449          0.664082   \n",
              "nfff0d7e8380b4ac           0.147755           0.385714          0.453265   \n",
              "nfffa7149dc9ad49           0.533878           0.446122          0.677755   \n",
              "\n",
              "                  v42_lgbm_claudia20  ...  v4_lgbm_jerome20  v3_example_preds  \\\n",
              "id                                    ...                                       \n",
              "n000101811a8a843            0.054966  ...          0.496148          0.231938   \n",
              "n001e1318d5072ac            0.479492  ...          0.499479          0.549240   \n",
              "n002a9c5ab785cbb            0.905892  ...          0.413908          0.343743   \n",
              "n002ccf6d0e8c5ad            0.985009  ...          0.890069          0.953362   \n",
              "n0051ab821295c29            0.280242  ...          0.079325          0.462211   \n",
              "...                              ...  ...               ...               ...   \n",
              "nffc93463f094655            0.582449  ...          0.262653          0.295918   \n",
              "nffdaf3cd2c727c9            0.403878  ...          0.657755          0.847347   \n",
              "nffe0f7cdc53c812            0.618980  ...          0.524490          0.673673   \n",
              "nfff0d7e8380b4ac            0.468163  ...          0.683673          0.628367   \n",
              "nfffa7149dc9ad49            0.551837  ...          0.529592          0.453469   \n",
              "\n",
              "                  v2_example_preds  v41_example_preds  v42_rain_ensemble  \\\n",
              "id                                                                         \n",
              "n000101811a8a843          0.616698           0.353321           0.131168   \n",
              "n001e1318d5072ac          0.632313           0.443056           0.569436   \n",
              "n002a9c5ab785cbb          0.521549           0.450552           0.634395   \n",
              "n002ccf6d0e8c5ad          0.884447           0.969602           0.977306   \n",
              "n0051ab821295c29          0.078493           0.239017           0.064335   \n",
              "...                            ...                ...                ...   \n",
              "nffc93463f094655          0.002857           0.026327           0.297959   \n",
              "nffdaf3cd2c727c9          0.866531           0.540000           0.515306   \n",
              "nffe0f7cdc53c812          0.651224           0.536531           0.567959   \n",
              "nfff0d7e8380b4ac          0.649592           0.428571           0.441429   \n",
              "nfffa7149dc9ad49          0.233673           0.792449           0.714082   \n",
              "\n",
              "                  v42_rain_ensemble2  v42_teager_plus_cyrus  \\\n",
              "id                                                            \n",
              "n000101811a8a843            0.062045               0.105143   \n",
              "n001e1318d5072ac            0.605663               0.572975   \n",
              "n002a9c5ab785cbb            0.484072               0.769727   \n",
              "n002ccf6d0e8c5ad            0.966271               0.981262   \n",
              "n0051ab821295c29            0.164689               0.126588   \n",
              "...                              ...                    ...   \n",
              "nffc93463f094655            0.189184               0.504898   \n",
              "nffdaf3cd2c727c9            0.498367               0.597755   \n",
              "nffe0f7cdc53c812            0.549388               0.588980   \n",
              "nfff0d7e8380b4ac            0.455102               0.429592   \n",
              "nfffa7149dc9ad49            0.818163               0.699184   \n",
              "\n",
              "                  v42_teager_ensemble  v42_lgbm_ct_blend  v43_lgbm_ct_blend  \n",
              "id                                                                           \n",
              "n000101811a8a843             0.121382           0.105143           0.181970  \n",
              "n001e1318d5072ac             0.515095           0.572975           0.702061  \n",
              "n002a9c5ab785cbb             0.792421           0.769727           0.780554  \n",
              "n002ccf6d0e8c5ad             0.987716           0.981262           0.986467  \n",
              "n0051ab821295c29             0.100979           0.126588           0.083281  \n",
              "...                               ...                ...                ...  \n",
              "nffc93463f094655             0.514286           0.504898           0.495714  \n",
              "nffdaf3cd2c727c9             0.492653           0.597755           0.640816  \n",
              "nffe0f7cdc53c812             0.640408           0.588980           0.526939  \n",
              "nfff0d7e8380b4ac             0.355918           0.429592           0.415510  \n",
              "nfffa7149dc9ad49             0.607959           0.699184           0.559184  \n",
              "\n",
              "[2682459 rows x 40 columns]"
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              "      <th></th>\n",
              "      <th>era</th>\n",
              "      <th>v43_lgbm_teager60</th>\n",
              "      <th>v43_lgbm_teager20</th>\n",
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              "      <td>0.714082</td>\n",
              "      <td>0.818163</td>\n",
              "      <td>0.699184</td>\n",
              "      <td>0.607959</td>\n",
              "      <td>0.699184</td>\n",
              "      <td>0.559184</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2682459 rows × 40 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-947eab0c-85db-4a03-92dc-e22b833ee595')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-947eab0c-85db-4a03-92dc-e22b833ee595 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-947eab0c-85db-4a03-92dc-e22b833ee595');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-afeaba61-66d6-4ad1-bb4a-7cd33a32f017\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-afeaba61-66d6-4ad1-bb4a-7cd33a32f017')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-afeaba61-66d6-4ad1-bb4a-7cd33a32f017 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# download Numerai's benchmark models\n",
        "napi.download_dataset(f\"{DATA_VERSION}/validation_benchmark_models.parquet\")\n",
        "benchmark_models = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/validation_benchmark_models.parquet\"\n",
        ")\n",
        "benchmark_models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZNr_xbQWJsf"
      },
      "source": [
        "Because models trained on newer targets perform so well and we release their predictions, it's likely many users will begin to shift their models to include newer data and targets. By extension, the Meta Model will begin to include information from from these new targets.\n",
        "\n",
        "This means that MMC over the validation period may not be truly indicative of out-of-sample performance. The Meta Model over the early validation period did not have access to newer data/targets and MMC over the validation period may be misleading.\n",
        "\n",
        "So if the Meta Model was much closer to our teager ensemble, what would your MMC look like?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "OcUNnnkUWnwg",
        "outputId": "d89c8493-20e3-4f66-b37c-31549a8fb844"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrus_v4_20  0.001517 0.014545 0.104283      0.093143\n",
              "prediction_target_victor_v4_20 0.001748 0.016670 0.104872      0.136661\n",
              "prediction_target_teager_v4_20 0.000940 0.014938 0.062910      0.136988\n",
              "ensemble_cyrus_victor          0.001851 0.015301 0.120991      0.118687\n",
              "ensemble_cyrus_teager          0.001746 0.013703 0.127424      0.075935"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab3f7115-a3d0-4484-9c35-61971426ae5a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>prediction_target_cyrus_v4_20</th>\n",
              "      <td>0.001517</td>\n",
              "      <td>0.014545</td>\n",
              "      <td>0.104283</td>\n",
              "      <td>0.093143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_v4_20</th>\n",
              "      <td>0.001748</td>\n",
              "      <td>0.016670</td>\n",
              "      <td>0.104872</td>\n",
              "      <td>0.136661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager_v4_20</th>\n",
              "      <td>0.000940</td>\n",
              "      <td>0.014938</td>\n",
              "      <td>0.062910</td>\n",
              "      <td>0.136988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <td>0.001851</td>\n",
              "      <td>0.015301</td>\n",
              "      <td>0.120991</td>\n",
              "      <td>0.118687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_teager</th>\n",
              "      <td>0.001746</td>\n",
              "      <td>0.013703</td>\n",
              "      <td>0.127424</td>\n",
              "      <td>0.075935</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab3f7115-a3d0-4484-9c35-61971426ae5a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ab3f7115-a3d0-4484-9c35-61971426ae5a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ab3f7115-a3d0-4484-9c35-61971426ae5a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d9b37f49-83fb-45a8-b80b-0b1c7d5843de\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9b37f49-83fb-45a8-b80b-0b1c7d5843de')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d9b37f49-83fb-45a8-b80b-0b1c7d5843de button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "validation[\"v42_teager_plus_cyrus\"] = benchmark_models[\"v42_teager_plus_cyrus\"]\n",
        "\n",
        "\n",
        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"v42_teager_plus_cyrus\")\n",
        "# plot the cumsum mmc performance\n",
        "cumsum_mmc.plot(\n",
        "  title=\"Contribution of Neutralized Predictions to Numerai's Teager Ensemble\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KSqVvJBxnNh"
      },
      "source": [
        "Ouch. Our teager models actually perform the worst. This means we aren't adding very useful signal to a model that Numerai already created, but this should not be surprising since we are training basically the same model. The model trained with `xerxes`, however, still does well against Numerai's model. What do you think this means?\n",
        "\n",
        "It's also helpful to if we measured the contribution of your models to all of Numerai's benchmark models. We call this Benchmark Model Contribution or `BMC`.  On the website, `BMC` measures your model's contribution to a weighted ensemble of all of our Benchmark Models.\n",
        "\n",
        "This is an important metric to track because it tells you how additive your model is to Numerai's known models and, by extension, how additive you might be to the Meta Model in the future.\n",
        "\n",
        "To keep things simple, we will use an unweighted ensemble of Numerai's Benchmarks to measure your models' BMC, let's take a look:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "id": "39UfnEmifTMh",
        "outputId": "2a01fa31-a1fb-41a5-836f-a439630d3098"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-a474e4cb0ab1>:4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  .apply(lambda x: x.mean(axis=1))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    mean      std    sharpe  max_drawdown\n",
              "prediction_target_cyrus_v4_20  -0.001121 0.012304 -0.091107      0.138525\n",
              "prediction_target_victor_v4_20 -0.000508 0.015500 -0.032759      0.150758\n",
              "prediction_target_teager_v4_20  0.000992 0.015920  0.062283      0.172569\n",
              "ensemble_cyrus_victor          -0.000727 0.013352 -0.054470      0.144182\n",
              "ensemble_cyrus_teager           0.000255 0.013384  0.019080      0.140402"
            ],
            "text/html": [
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8a599df4-49f4-4ec5-8c11-b778c26ff127 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8a599df4-49f4-4ec5-8c11-b778c26ff127');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-343e7f90-aa42-49d3-ad70-5c14ebce7286\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-343e7f90-aa42-49d3-ad70-5c14ebce7286')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-343e7f90-aa42-49d3-ad70-5c14ebce7286 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "validation[\"numerai_benchmark\"] = (\n",
        "    benchmark_models\n",
        "    .groupby(\"era\")\n",
        "    .apply(lambda x: x.mean(axis=1))\n",
        "    .reset_index()\n",
        "    .set_index(\"id\")[0]\n",
        ")\n",
        "\n",
        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"numerai_benchmark\")\n",
        "# plot the cumsum mmc performance\n",
        "cumsum_mmc.plot(\n",
        "  title=\"Cumulative BMC of Neutralized Predictions\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-FSdGrhqWkD"
      },
      "source": [
        "Looking at the results above, none of these models seem very additive to models that Numerai can already create. It will take some research and experimentation to find something additive to Numerai's benchmarks.\n",
        "\n",
        "Ensembling models trained on different targets can be a very fruitful avenue of research. However, it is completely up to you whether or not to create an ensemble - there are many great performing models that don't make use of the auxilliary targets at all.\n",
        "\n",
        "If you are interested in learning more about targets, we highly encourage you to read up on these forum posts\n",
        "- https://forum.numer.ai/t/how-to-ensemble-models/4034\n",
        "- https://forum.numer.ai/t/target-jerome-is-dominating-and-thats-weird/6513"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihVoBcnhxnNq"
      },
      "source": [
        "## 4. Model Upload\n",
        "To wrap up this notebook, let's pickle and upload our ensemble.\n",
        "\n",
        "As usual, we will be wrapping our submission pipeline into a function. Since we already have our favorite targets and trained models in memory, we can simply reference them in our function.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "WWdOTGy4xnNq"
      },
      "outputs": [],
      "source": [
        "# we now give you access to the live_benchmark_models if you want to use them in your ensemble\n",
        "def predict_ensemble(\n",
        "    live_features: pd.DataFrame,\n",
        "    live_benchmark_models: pd.DataFrame\n",
        ") -> pd.DataFrame:\n",
        "    favorite_targets = [\n",
        "        'target_cyrus_v4_20',\n",
        "        'target_teager_v4_20'\n",
        "    ]\n",
        "    # generate predictions from each model\n",
        "    predictions = pd.DataFrame(index=live_features.index)\n",
        "    for target in favorite_targets:\n",
        "        predictions[target] = models[target].predict(live_features[feature_cols])\n",
        "    # ensemble predictions\n",
        "    ensemble = predictions.rank(pct=True).mean(axis=1)\n",
        "    # format submission\n",
        "    submission = ensemble.rank(pct=True, method=\"first\")\n",
        "    return submission.to_frame(\"prediction\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kPq_ATf0xnNr",
        "outputId": "4e932231-5260-4a72-dfa9-b534417865b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "v4.3/live_int8.parquet: 6.61MB [00:00, 17.8MB/s]                            \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "                  prediction\n",
              "id                          \n",
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              "n001760719081fe2    0.210483\n",
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              "n0029da0af4a3d33    0.637977\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c1f75a2d-7113-46b0-b2fc-26d6adeb5376 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c1f75a2d-7113-46b0-b2fc-26d6adeb5376');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1342ea0d-ae66-4cf0-acd4-13412c60369e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1342ea0d-ae66-4cf0-acd4-13412c60369e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1342ea0d-ae66-4cf0-acd4-13412c60369e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Quick test\n",
        "napi.download_dataset(f\"{DATA_VERSION}/live_int8.parquet\")\n",
        "live_features = pd.read_parquet(f\"{DATA_VERSION}/live_int8.parquet\", columns=feature_cols)\n",
        "predict_ensemble(live_features, benchmark_models)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "5VTrZ1Q6xnNr"
      },
      "outputs": [],
      "source": [
        "# Use the cloudpickle library to serialize your function and its dependencies\n",
        "import cloudpickle\n",
        "p = cloudpickle.dumps(predict_ensemble)\n",
        "with open(\"predict_ensemble.pkl\", \"wb\") as f:\n",
        "    f.write(p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "RiJjBD-zxnNr",
        "outputId": "a6b0bd38-896b-4529-8ec0-8862ac258993"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e3e48bd2-7a90-4a33-a1cb-4e99f316d6b9\", \"predict_ensemble.pkl\", 13355725)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Download file if running in Google Colab\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download('predict_ensemble.pkl')\n",
        "except:\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxO2YzmIxnNr"
      },
      "source": [
        "That's it! Now head back to [numer.ai](numer.ai) to upload your model!"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "venv",
      "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": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}