target_ensemble.ipynb

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        "# 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"
      ]
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            "\u001b[?25h"
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      "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",
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        "id": "VwChLrKexnNa"
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      "source": [
        "## 1. Auxilliary Targets\n",
        "\n",
        "Let's start by taking a look at the different targets in the training data."
      ]
    },
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              "\n",
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              "\n",
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            ],
            "text/plain": [
              "                   era  target_agnes_20  target_agnes_60  target_alpha_20  \\\n",
              "id                                                                          \n",
              "n0007b5abb0c3a25  0001             0.25             0.00             0.25   \n",
              "n003bba8a98662e4  0001             0.25             0.25             0.25   \n",
              "n003bee128c2fcfc  0001             1.00             1.00             1.00   \n",
              "n0048ac83aff7194  0001             0.25             0.25             0.25   \n",
              "n0055a2401ba6480  0001             0.25             0.50             0.25   \n",
              "...                ...              ...              ...              ...   \n",
              "nffc2d5e4b79a7ae  0573             0.00             0.25             0.00   \n",
              "nffc9844c1c7a6a9  0573             0.50             0.50             0.25   \n",
              "nffd79773f4109bb  0573             0.50             0.50             0.75   \n",
              "nfff6ab9d6dc0b32  0573             0.50             0.50             0.25   \n",
              "nfff87b21e4db902  0573             0.75             0.75             0.50   \n",
              "\n",
              "                  target_alpha_60  target_bravo_20  target_bravo_60  \\\n",
              "id                                                                    \n",
              "n0007b5abb0c3a25             0.25             0.00             0.00   \n",
              "n003bba8a98662e4             0.00             0.25             0.00   \n",
              "n003bee128c2fcfc             1.00             0.75             1.00   \n",
              "n0048ac83aff7194             0.25             0.50             0.25   \n",
              "n0055a2401ba6480             0.50             0.25             0.50   \n",
              "...                           ...              ...              ...   \n",
              "nffc2d5e4b79a7ae             0.25             0.00             0.25   \n",
              "nffc9844c1c7a6a9             0.50             0.50             0.50   \n",
              "nffd79773f4109bb             0.50             0.75             0.50   \n",
              "nfff6ab9d6dc0b32             0.50             0.50             0.50   \n",
              "nfff87b21e4db902             0.75             0.50             0.75   \n",
              "\n",
              "                  target_caroline_20  target_caroline_60  target_charlie_20  \\\n",
              "id                                                                            \n",
              "n0007b5abb0c3a25                0.25                0.00               0.25   \n",
              "n003bba8a98662e4                0.25                0.25               0.25   \n",
              "n003bee128c2fcfc                0.75                0.75               0.75   \n",
              "n0048ac83aff7194                0.50                0.25               0.50   \n",
              "n0055a2401ba6480                0.25                0.50               0.25   \n",
              "...                              ...                 ...                ...   \n",
              "nffc2d5e4b79a7ae                0.25                0.50               0.00   \n",
              "nffc9844c1c7a6a9                0.50                0.50               0.50   \n",
              "nffd79773f4109bb                0.50                0.50               0.75   \n",
              "nfff6ab9d6dc0b32                0.25                0.50               0.25   \n",
              "nfff87b21e4db902                0.50                0.50               0.50   \n",
              "\n",
              "                  ...  target_teager2b_60  target_tyler_20  target_tyler_60  \\\n",
              "id                ...                                                         \n",
              "n0007b5abb0c3a25  ...                0.50             0.25             0.25   \n",
              "n003bba8a98662e4  ...                0.50             0.25             0.25   \n",
              "n003bee128c2fcfc  ...                1.00             1.00             0.75   \n",
              "n0048ac83aff7194  ...                0.25             0.25             0.25   \n",
              "n0055a2401ba6480  ...                0.50             0.25             0.50   \n",
              "...               ...                 ...              ...              ...   \n",
              "nffc2d5e4b79a7ae  ...                0.50             0.25             0.50   \n",
              "nffc9844c1c7a6a9  ...                0.75             0.50             0.50   \n",
              "nffd79773f4109bb  ...                0.75             0.50             0.50   \n",
              "nfff6ab9d6dc0b32  ...                0.50             0.50             0.25   \n",
              "nfff87b21e4db902  ...                0.75             0.75             0.75   \n",
              "\n",
              "                  target_victor_20  target_victor_60  target_waldo_20  \\\n",
              "id                                                                      \n",
              "n0007b5abb0c3a25              0.25              0.25             0.25   \n",
              "n003bba8a98662e4              0.25              0.00             0.25   \n",
              "n003bee128c2fcfc              0.75              0.75             0.75   \n",
              "n0048ac83aff7194              0.50              0.25             0.25   \n",
              "n0055a2401ba6480              0.25              0.50             0.25   \n",
              "...                            ...               ...              ...   \n",
              "nffc2d5e4b79a7ae              0.25              0.50             0.00   \n",
              "nffc9844c1c7a6a9              0.50              0.50             0.50   \n",
              "nffd79773f4109bb              0.50              0.50             0.50   \n",
              "nfff6ab9d6dc0b32              0.25              0.50             0.50   \n",
              "nfff87b21e4db902              0.50              0.75             0.50   \n",
              "\n",
              "                  target_waldo_60  target_xerxes_20  target_xerxes_60  target  \n",
              "id                                                                             \n",
              "n0007b5abb0c3a25             0.00              0.25              0.00    0.25  \n",
              "n003bba8a98662e4             0.25              0.25              0.25    0.25  \n",
              "n003bee128c2fcfc             1.00              0.75              0.75    0.75  \n",
              "n0048ac83aff7194             0.25              0.25              0.25    0.25  \n",
              "n0055a2401ba6480             0.50              0.25              0.50    0.25  \n",
              "...                           ...               ...               ...     ...  \n",
              "nffc2d5e4b79a7ae             0.50              0.00              0.25    0.00  \n",
              "nffc9844c1c7a6a9             0.50              0.50              0.50    0.25  \n",
              "nffd79773f4109bb             0.75              0.50              0.50    0.50  \n",
              "nfff6ab9d6dc0b32             0.50              0.25              0.50    0.25  \n",
              "nfff87b21e4db902             0.50              0.50              0.50    0.50  \n",
              "\n",
              "[688184 rows x 38 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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 = \"v5.0\"\n",
        "MAIN_TARGET = \"target_cyrusd_20\"\n",
        "TARGET_CANDIDATES = [\n",
        "  MAIN_TARGET,\n",
        "  \"target_victor_20\",\n",
        "  \"target_xerxes_20\",\n",
        "  \"target_teager2b_20\"\n",
        "]\n",
        "FAVORITE_MODEL = \"v5_lgbm_ct_blend\"\n",
        "\n",
        "# Download data\n",
        "napi = NumerAPI()\n",
        "napi.download_dataset(f\"{DATA_VERSION}/train.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.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 the `cyrus` target, 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[MAIN_TARGET])\n",
        "targets_df = train[[\"era\"] + target_cols]"
      ]
    },
    {
      "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 `cyrus` as 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": 645
        },
        "id": "P7uAdarxxnNb",
        "outputId": "e0972e03-0e96-4b35-f304-0531c4592530"
      },
      "outputs": [
        {
          "data": {
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"agnes\",\n          \"alpha\",\n          \"echo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"20\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"target_agnes_20\",\n          \"target_alpha_20\",\n          \"target_echo_20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"60\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"target_agnes_60\",\n          \"target_alpha_60\",\n          \"target_echo_60\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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            "text/html": [
              "\n",
              "  <div id=\"df-7b900a44-7894-4c23-b3ac-98eab9be7c52\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        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>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>agnes</th>\n",
              "      <td>target_agnes_20</td>\n",
              "      <td>target_agnes_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alpha</th>\n",
              "      <td>target_alpha_20</td>\n",
              "      <td>target_alpha_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bravo</th>\n",
              "      <td>target_bravo_20</td>\n",
              "      <td>target_bravo_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caroline</th>\n",
              "      <td>target_caroline_20</td>\n",
              "      <td>target_caroline_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charlie</th>\n",
              "      <td>target_charlie_20</td>\n",
              "      <td>target_charlie_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>claudia</th>\n",
              "      <td>target_claudia_20</td>\n",
              "      <td>target_claudia_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrusd</th>\n",
              "      <td>target_cyrusd_20</td>\n",
              "      <td>target_cyrusd_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delta</th>\n",
              "      <td>target_delta_20</td>\n",
              "      <td>target_delta_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>echo</th>\n",
              "      <td>target_echo_20</td>\n",
              "      <td>target_echo_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jeremy</th>\n",
              "      <td>target_jeremy_20</td>\n",
              "      <td>target_jeremy_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ralph</th>\n",
              "      <td>target_ralph_20</td>\n",
              "      <td>target_ralph_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rowan</th>\n",
              "      <td>target_rowan_20</td>\n",
              "      <td>target_rowan_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sam</th>\n",
              "      <td>target_sam_20</td>\n",
              "      <td>target_sam_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>teager2b</th>\n",
              "      <td>target_teager2b_20</td>\n",
              "      <td>target_teager2b_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tyler</th>\n",
              "      <td>target_tyler_20</td>\n",
              "      <td>target_tyler_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>victor</th>\n",
              "      <td>target_victor_20</td>\n",
              "      <td>target_victor_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>waldo</th>\n",
              "      <td>target_waldo_20</td>\n",
              "      <td>target_waldo_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xerxes</th>\n",
              "      <td>target_xerxes_20</td>\n",
              "      <td>target_xerxes_60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      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-7b900a44-7894-4c23-b3ac-98eab9be7c52 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-7b900a44-7894-4c23-b3ac-98eab9be7c52');\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-3db0c9f0-5411-4284-9cad-52df8dbbd2ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3db0c9f0-5411-4284-9cad-52df8dbbd2ec')\"\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-3db0c9f0-5411-4284-9cad-52df8dbbd2ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                          20                  60\n",
              "name                                            \n",
              "agnes        target_agnes_20     target_agnes_60\n",
              "alpha        target_alpha_20     target_alpha_60\n",
              "bravo        target_bravo_20     target_bravo_60\n",
              "caroline  target_caroline_20  target_caroline_60\n",
              "charlie    target_charlie_20   target_charlie_60\n",
              "claudia    target_claudia_20   target_claudia_60\n",
              "cyrusd      target_cyrusd_20    target_cyrusd_60\n",
              "delta        target_delta_20     target_delta_60\n",
              "echo          target_echo_20      target_echo_60\n",
              "jeremy      target_jeremy_20    target_jeremy_60\n",
              "ralph        target_ralph_20     target_ralph_60\n",
              "rowan        target_rowan_20     target_rowan_60\n",
              "sam            target_sam_20       target_sam_60\n",
              "teager2b  target_teager2b_20  target_teager2b_60\n",
              "tyler        target_tyler_20     target_tyler_60\n",
              "victor      target_victor_20    target_victor_60\n",
              "waldo        target_waldo_20     target_waldo_60\n",
              "xerxes      target_xerxes_20    target_xerxes_60"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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_cols if t.endswith(\"_20\")]\n",
        "t60s = [t for t in target_cols if t.endswith(\"_60\")]\n",
        "names = [t.replace(\"target_\", \"\").replace(\"_20\", \"\") 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": 444
        },
        "id": "Uw_4oswnxnNb",
        "outputId": "3adace68-1a68-4fd6-8aa8-3b125832266b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: >, <Axes: >],\n",
              "       [<Axes: >, <Axes: >]], dtype=object)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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fB8jKynIEv+uuu85tddh8LuS7PiAgoJQqQVkUGHjpyO+xY8cUGRnJKWBcUzIzM5WWlqaoqCgFBQWV3I58CnjjTh+1tOLqo3zgwwNkX+NXog0JKAbZr1GuS8W1JisrS5Lk5+fn5kpgdcXRRwl/HoTrqFDW8RrFtY7XMNytOF6DhD8AAAALIfwBAABYCB/48HCxw78r1f3NHXxrqe4PrrvzzjvVuHFjvfnmm+4uBfAcL4WX8v5SSnd/1wCbzaavvvpK9957r7tLuWZw5A9udeedd+qpp55ydxkOZa0ed8jIyNCwYcPUoEEDBQcHKyoqSg8//LAOHz7sNO7kyZPq2bOnwsLCFBERoX79+uns2bNuqhpAbspaTyuJeo4cOaL27du7NPall15S48aNi3X/eRk7dqz++te/KjQ0VJGRkbr33nuVmJjoNObChQsaNGiQrrvuOoWEhOiBBx7Q0aNHS7w2wh+ueZ50t/2srKySvYGsC9LS0rRhwwaNGDFCGzZs0Jw5c5SYmKhOnTo5jevZs6e2bdumxYsXa968eVqxYoUeffRRN1UNoKSU9R5buXJl+fv7l+o+XXlOli9frkGDBumXX37R4sWLlZGRoXvuuUfnzp1zjBk6dKi+/fZbzZo1S8uXL9fhw4d1//33l2Tpkgh/cKM+ffpo+fLleuutt2Sz2WSz2bR7927169dPNWrUUGBgoOLi4vTWW2/leNy9996r0aNHKyoqSnFxcZKkVatWqXHjxgoICFCzZs309ddfy2azadOmTY7Hbt26Ve3bt1dISIgqVaqkXr166cSJE3nWs3fv3gLnsW3bNv39739XWFiYQkNDdfvtt2v37t1asWKFfH19lZyc7DT+qaee0u233y5Jmjp1qiIiIjR37lzVq1dP/v7+2r9/f67vju+991716dPH8fu7776r2rVrKyAgQJUqVVKXLl0c686dO6eHH35YISEhqlKliiZMmFDgPLKFh4dr8eLFio+PV1xcnG6++Wa98847Wr9+vfbvv/TtAzt27NCCBQs0efJkNW/eXLfddpsmTpyomTNn5jhCCMA9rvUea7fbFR0drffee89p+caNG+Xl5aV9+/ZJunTa9+uvv3asP3jwoLp3767y5csrODhYzZo105o1azR16lS9/PLL2rx5s2P/U6dOlSTt379fnTt3VkhIiMLCwhQfH+90BC77iOHkyZNVo0YNl+5bu2DBAvXp00f169dXo0aNNHXqVO3fv1/r16+XJKWkpOh///ufXn/9dbVq1UpNmzbVlClTtGrVKv3yyy8Fbv9qEP7gNm+99ZZatGih/v3768iRIzpy5Iiio6MVHR2tWbNmafv27XrxxRf1/PPP64svvnB67I8//qjExETHUafU1FR17NhRDRo00IYNGzRq1CgNGzbM6TGnT59Wq1at1KRJE61bt04LFizQ0aNHFR8fn2c9MTEx+c7h0KFDuuOOO+Tv768lS5Zo/fr1+sc//qHMzEzdcccdqlmzpj755BPH+IyMDCUkJOgf//iHY1laWppeffVVTZ48Wdu2bVNkZGSBz926des0ZMgQvfLKK0pMTNSCBQt0xx13ONY/99xzWr58ub755hstWrRIy5Yt04YNGwrcbl5SUlJks9kUEREhSVq9erUiIiLUrFkzx5jWrVvLy8tLa9asKfJ+ABSfa73Henl5qXv37poxY4bT8oSEBN16662qXr16jsecPXtWLVu21KFDhzR37lxt3rxZ//znP2W329WtWzc988wzql+/vmP/3bp1k91uV+fOnXXy5EktX75cixcv1p49e9StWzenbf/xxx+aPXu25syZ4xR4XZWScul6zfLly0uS1q9fr4yMDLVu3doxpk6dOqpWrZpWr15d6O0XBh/4gNuEh4fLz89PQUFBqly5smP5yy+/7PjvGjVqaPXq1friiy8cDUSSgoODNXnyZMcNV99//33ZbDZ99NFHCggIUL169XTo0CH179/f8Zh33nlHTZo00ZgxYxzLPv74Y8XExGjnzp264YYbcq0nP5MmTVJ4eLhmzpwpX19fSdINN9zgWN+vXz9NmTJFzz33nCTp22+/1YULF5zmkpGRoXfffVeNGjVyaZ/SpXepwcHB+vvf/67Q0FBVr15dTZo0kXSp+f3vf//Tp59+qrvvvluSNG3aNEVHR7u8/ctduHBBw4YNU/fu3RUWdukr+pKTk3OEVB8fH5UvXz7HkU4A7uEJPbZnz56aMGGC9u/fr2rVqslut2vmzJn697//nev4GTNm6Pjx41q7dq0jZF1//fWO9SEhIfLx8XHa/+LFi7VlyxYlJSU5wuj06dNVv359rV27Vn/9618lXTrVO336dFWsWNGl2i9nt9v11FNP6dZbb9Vf/vIXSZf6qJ+fn+NNdbZKlSqVeB/lyB/KnEmTJqlp06aqWLGiQkJC9OGHHzpON2Zr0KCB0532ExMT1bBhQ6dD8TfddJPTYzZv3qylS5cqJCTE8VOnTh1J0u7du4tU66ZNm3T77bc7gt+V+vTpoz/++MNxCH/q1KmKj49XcHCwY4yfn58aNmxYqP22adNG1atXV82aNdWrVy8lJCQoLS3NMZf09HQ1b97cMb58+fKOUzeFkZGRofj4eBljcpx6AXBtupZ6bOPGjVW3bl3H0b/ly5fr2LFj6tq1a67jN23apCZNmjiCnyt27NihmJgYp6OQ9erVU0REhHbs2OFYVr169SIFP0kaNGiQtm7dqpkzZxbp8cWN8IcyZebMmXr22WfVr18/LVq0SJs2bVLfvn1zXFx7eXhy1dmzZ9WxY0dt2rTJ6WfXrl1Op0wLI/t7FvMSGRmpjh07asqUKTp69Ki+//57p1O+2du48o7tXl5eMsY4Lbv8q3xCQ0O1YcMGffbZZ6pSpYpefPFFNWrUSKdPny7SPHKTHfz27dunxYsXO476SZcusM7+cvFsmZmZOnnypMvv6AGUvmutx0qXjv5lh78ZM2aoXbt2eX6PfUE9+WoU5TmRpMGDB2vevHlaunSp0xmYypUrKz09PUffPnr0aIn3UcIf3MrPz8/xnZmStHLlSt1yyy0aOHCgmjRpouuvv96ld4xxcXHasmWLLl686Fi2du1apzE33nijtm3bptjYWF1//fVOP9l/1FfWU5CGDRvqp59+yvc7Fh955BF9/vnn+vDDD1WrVi3demvB90KsWLGijhw54vg9KytLW7dudRrj4+Oj1q1b67XXXtNvv/2mvXv3asmSJapVq5Z8fX2drr07deqUdu7c6fK8soPfrl279MMPP+RotC1atNDp06cdFy5L0pIlS2S3252OOAJwr2u9x0pSjx49tHXrVq1fv15ffvmlevbsmefYhg0batOmTTp58mSu63Pbf926dXXgwAEdOHDAsWz79u06ffq06tWrV6haL2eM0eDBg/XVV19pyZIlqlGjhtP6pk2bytfXVz/++KNjWWJiovbv368WLVoUeb+uIPzBrWJjY7VmzRrt3btXJ06cUO3atbVu3TotXLhQO3fu1IgRI3I0mNz06NFDdrtdjz76qHbs2KGFCxdq/Pjxkv7vexAHDRqkkydPqnv37lq7dq12796thQsXqm/fvo5mcGU9Bd12ZfDgwUpNTdWDDz6odevWadeuXfrkk0+c7uXUtm1bhYWF6T//+Y/69u3r0vPSqlUrfffdd/ruu+/0+++/6/HHH3d6dzhv3jy9/fbb2rRpk/bt26fp06fLbrcrLi5OISEh6tevn5577jktWbJEW7duVZ8+feTl5dqfe0ZGhrp06aJ169YpISFBWVlZSk5OVnJysuPoQN26ddWuXTv1799fv/76q1auXKnBgwfrwQcfVFRUlEv7AVDyrvUem/2YW265Rf369VNWVlaO205drnv37qpcubLuvfderVy5Unv27NHs2bMdH6CIjY1VUlKSNm3apBMnTujixYtq3bq1GjRooJ49e2rDhg369ddf9fDDD6tly5ZOH2orrEGDBunTTz/VjBkzFBoa6uij58+fl3Tpmsx+/frp6aef1tKlS7V+/Xr17dtXLVq00M0331zk/brEuCAlJcVIMikpKa4MRyk7f/682b59uzl//rxb69h84FS+P7lJTEw0N998swkMDDSSzO+//2769OljwsPDTUREhHn88cfN8OHDTaNGjRyP6d27t+ncuXOOba1cudI0bNjQ+Pn5maZNm5oZM2Y4tplt586d5r777jMREREmMDDQ1KlTxzz11FPGbrfnWk9SUlLB89682dxzzz0mKCjIhIaGmttvv93s3r3bacyIESOMt7e3OXz4sNPyKVOmmPDw8BzbTE9PN48//rgpX768iYyMNGPHjjWdO3c2vXv3NsYY89NPP5mWLVuacuXKmcDAQNOwYUPz+eefOx5/5swZ89BDD5mgoCBTqVIl89prr5mWLVuaJ598ssD5JCUlGUm5/ixdutQx7s8//zTdu3c3ISEhJiwszPTt29ecOXMm323n91r19D7j6fPzdKXWZw9tyP+nkDyhxxpjzLvvvmskmYcffjjHOknmq6++cvy+d+9e88ADD5iwsDATFBRkmjVrZtasWWOMMebChQvmgQceMBEREUaSmTJlijHGmH379plOnTqZ4OBgExoaarp27WqSk5Md2xw5cqTTc+SKvPpo9j6NufS6GjhwoClXrpwJCgoy9913nzly5Ei+2y2OPmr7/wXmKzU1VeHh4UpJSXG67gdlw4ULF5SUlOTyvYdKym8HT+e7vmF0RKnUkS0hIUF9+/ZVSkpKiV4H4op+/frp+PHjmjt3rlvrcLf8Xque3mc8fX6ertT67OGN+a+PalJy+y6kstRjraQ4+ii3eoHHmD59umrWrKmqVatq8+bNGjZsmOLj493alFJSUrRlyxbNmDHD8sEPwLWtLPZYFA3X/MFjJCcn66GHHlLdunU1dOhQde3aVR9++OFVbXPAgAFOty24/GfAgAEFPr5z58665557NGDAALVp0+aqaikuP/30U55zCgkJcXd5AMqosthj3WX//v359tErb51T1nDa1wNw2rfkHDt2TKmpqbmuCwsLc+nbOMqa8+fP69ChQ3muv/yGqMWN076eOz9Px2nfknGt9tjMzMx8v5ouNjZWPj4lc3KV075ACYuMjCyzzaeoAgMDSzTgAYCrrtUe6+Pjc033UU77ehBXPjIPuBOvUVzrXDhZBpSo4uijHPnzAH5+fvLy8tLhw4dVsWJF+fn55fjGiNJgMtPzXX/hwoVSqgRljTFG6enpOn78uLy8vJy+Ngq4Fvj6+spms+n48eOqWLFiyfXYzALCJX3UsoqzjxL+PICXl5dq1KihI0eO6PDhw26r49ip8/mu9zvPJ8KsLigoSNWqVXP5htNAWeHt7a3o6GgdPHgw32u9rtrp4/mvP5dUcvvGNaE4+ijhz0P4+fmpWrVqyszMLPRX5xSXR+Ysy3f9j8/cWSp1oGzy9vaWj4+PW45KA8UhJCREtWvXzvfrHK/aO13zXz94XcntG2VecfVRwp8Hsdls8vX1la+vr1v2f+hM/qHTnZ9EBoDi4O3tLW9v75LbwdkD+a+nj6IYcO4FAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIX4uLsATxA7/Lt81+8d16GUKkFZkN/rgdcCkI+XwvNZl1J6dcD98nstSLwerhJH/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBCfktx47PDv8ly3d1yHktw1gFLA33gpeCk8n3UppVcHgJLhhr9xjvwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFuLSd/saYyRJqamphdq4/WJanusKu62yLL95Sp411/zwPFxilde9VLxzzR6f3W88TVH7qC7m83x42OvJUnPNS37PgcTzkM2TnodifN272kdtxoVOe/DgQcXExBSqAAAoigMHDig6OtrdZRQ7+iiA0lJQH3Up/Nntdh0+fFihoaGy2Wwu7Tg1NVUxMTE6cOCAwsLCXK+4DGIuZZcnzcfqczHG6MyZM4qKipKXl+ddkUIfZS5llSfNx+pzcbWPunTa18vLq8jvxMPCwq75f4BszKXs8qT5WHku4eHhJViNe9FHL2EuZZcnzcfKc3Glj3re22sAAADkifAHAABgISUW/vz9/TVy5Ej5+/uX1C5KDXMpuzxpPswFV/Kk55G5lF2eNB/m4hqXPvABAAAAz8BpXwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAW4uPKILvdrsOHDys0NFQ2m62kawJgQcYYnTlzRlFRUfLy8rz3pfRRACXN1T7qUvg7fPiwYmJiiq04AMjLgQMHFB0d7e4yih19FEBpKaiPuhT+QkNDHRsLCwsrnsoA4DKpqamKiYlx9BtPQx8FUNJc7aMuhb/sUxRhYWE0LQAlylNPidJHAZSWgvqo511YAwAAgDwR/gAAACyE8AcAAGAhLl3zh2uL3W5Xenq6u8sAnPj6+srb29vdZcDD0f/gyYqrjxL+PEx6erqSkpJkt9tLfd+Hzx7Od31USFQpVYKyKiIiQpUrV/bYD3XAvdzZ/4pLxuH8+6hvFH3U6oqjjxL+PIgxRkeOHJG3t7diYmJK/Ua5macy811fo1yNUqoEZY0xRmlpaTp27JgkqUqVKm6uCJ7G3f2vuFzIyMh3fUAN+qhVFWcfJfx5kMzMTKWlpSkqKkpBQUGlvn8v3/ybbUBAQClVgrIoMDBQknTs2DFFRkZyChjFyt39r7iYAkIrfdTaiquPXptvjZCrrKwsSZKfn5+bKwFyl/0/5YwCjm4AhUX/g1UURx8l/HkgrqdCWcVrEyWN1xg8XXG8xgl/AAAAFkL4AwAAsBA+8GEBDaY1KNX9zewws1T3BwB52VGnbqnur+7vO0p1fyg5d955pxo3bqw333zT3aUUO478we3uvPNOPfXUU+4uw6Gs1VPaTp48qSeeeEJxcXEKDAxUtWrVNGTIEKWkpDiN279/vzp06KCgoCBFRkbqueeeU2Zm/rf7AeCsrPWbwtazbNky2Ww2nT59usRqKglz5sxRmzZtVLFiRYWFhalFixZauHBhsW0/IyNDw4YNU4MGDRQcHKyoqCg9/PDDOnzFfRxPnjypnj17KiwsTBEREerXr5/Onj1bbHXkhfAHj+Bpd/R353wOHz6sw4cPa/z48dq6daumTp2qBQsWqF+/fo4xWVlZ6tChg9LT07Vq1SpNmzZNU6dO1Ysvvui2ugGr8rT+V5KMMcrMzNSKFSvUpk0bzZ8/X+vXr9ddd92ljh07auPGjcWyn7S0NG3YsEEjRozQhg0bNGfOHCUmJqpTp05O43r27Klt27Zp8eLFmjdvnlasWKFHH320WGrID+EPbtWnTx8tX75cb731lmw2m2w2m3bv3q1+/fqpRo0aCgwMVFxcnN56660cj7v33ns1evRoRUVFKS4uTpK0atUqNW7cWAEBAWrWrJm+/vpr2Ww2bdq0yfHYrVu3qn379goJCVGlSpXUq1cvnThxIs969u7dm+8cXnnlFUVFRenPP/90LOvQoYPuuusuxzcN/Pzzz7r99tsVGBiomJgYDRkyROfOnXOMj42N1ahRo/Twww8rLCxM/fv3V+vWrdW2bVsZYyRdeocYHR3tFLAmT56sunXrKiAgQHXq1NG7777rWJeenq7BgwerSpUqCggIUPXq1TV27NgC/03+8pe/aPbs2erYsaNq1aqlVq1aafTo0fr2228dR/YWLVqk7du369NPP1Xjxo3Vvn17jRo1SpMmTeJ/RICLcus3ew4c0IAXX1Tddu1UvlkzNerYUZM+/TTH48pC/9u7d6/uuusuSVK5cuVks9nUp08fSZe+Zm/s2LGOPt6oUSN9+eWXjsdmZWUV2OczMzM1ZMgQRURE6LrrrtOwYcPUu3dv3XvvvY4xBe0n+8jk999/r6ZNm8rf318///yz3nzzTf3zn//UX//6V9WuXVtjxoxR7dq19e233+aoYfDgwQoPD1eFChU0YsQIR0/OT3h4uBYvXqz4+HjFxcXp5ptv1jvvvKP169dr//79kqQdO3ZowYIFmjx5spo3b67bbrtNEydO1MyZM3McISxuhD+41VtvvaUWLVqof//+OnLkiI4cOaLo6GhFR0dr1qxZ2r59u1588UU9//zz+uKLL5we++OPPyoxMdHxjik1NVUdO3ZUgwYNtGHDBo0aNUrDhg1zeszp06fVqlUrNWnSROvWrdOCBQt09OhRxcfH51lPTExMvnN44YUXFBsbq0ceeUSSNGnSJMfRMC8vL+3evVvt2rXTAw88oN9++02ff/65fv75Zw0ePNhpO+PHj1ejRo20ceNGvfjii5o2bZrWrl2rt99+W5I0YMAAVa1a1RH+EhIS9OKLL2r06NHasWOHxowZoxEjRmjatGmSpLfffltz587VF198ocTERCUkJCg2NrZI/04pKSkKCwuTj8+ly4RXr16tBg0aqFKlSo4xbdu2VWpqqrZt21akfQBWk1u/qVqpkqpWqqRPJ0zQhq+/1r8ee0wj335bsxcscHpsWeh/MTExmj17tiQpMTFRR44ccQS4sWPHavr06Xr//fe1bds2DR06VA899JCWL18u6VJoK6jPv/rqq0pISNCUKVO0cuVKpaam6uuvv3aqoaD9ZBs+fLjGjRunHTt2qGHDhjnmYrfbdebMGZUvX95p+bRp0+Tj46Nff/1Vb731ll5//XVNnjw5z+ckPykpKbLZbIqIiJB0qY9GRESoWbNmjjGtW7eWl5eX1qxZU6R9uIoPfMCtwsPD5efnp6CgIFWuXNmx/OWXX3b8d40aNbR69Wp98cUXjiYlScHBwZo8ebLjpq7vv/++bDabPvroIwUEBKhevXo6dOiQ+vfv73jMO++8oyZNmmjMmDGOZR9//LFiYmK0c+dO3XDDDbnWkx9vb2/HEbDhw4fr7bff1uTJk1WtWjVJl5pTz549HdfR1K5dW2+//bZatmyp9957z3HH/latWumZZ55x2vYHH3yghx9+WMnJyZo/f742btzoCGAjR47UhAkTdP/99zuep+3bt+uDDz5Q7969tX//ftWuXVu33XabbDabqlev7tJ8rnTixAmNGjXK6VREcnKyU/CT5Pg9OTm5SPsBrCa3/nf+xAmNGDTIMSY2OlprNm/W7EWL9EC7do7lZaH/eXt7O8JSZGSkI9RcvHhRY8aM0Q8//KAWLVpIkmrWrKmff/5ZH3zwgVq2bClfX98C+/zEiRP1r3/9S/fdd5+j/vnz5zse48p+sr3yyitq06ZNnnMZP368zp496/T/GOlSwH3jjTdks9kUFxenLVu26I033nB6Xl1x4cIFDRs2TN27d1dYWJikS70yMjLSaZyPj4/Kly9f4n2U8IcyadKkSfr444+1f/9+nT9/Xunp6WrcuLHTmAYNGjjdzT8xMVENGzZ0+vqjm266yekxmzdv1tKlSxUSEpJjn7t379YNN9xQpHpr1qyp8ePH67HHHlO3bt3Uo0cPp33+9ttvSkhIcCwzxshutyspKUl16176NOLl7/6yde3aVV999ZXGjRun9957T7Vr15YknTt3znF6/PImlJmZqfDwcEmXTuG0adNGcXFxateunf7+97/rnnvuKdS8UlNT1aFDB9WrV08vvfRSoR4LoGje/+wzTf/qKx1MTtb5CxeUnpGhhnXqOI0pS/3vSn/88YfS0tJyhK309HQ1adLE8Xt+fT4lJUVHjx51moO3t7eaNm3quJzG1f1IuffXbDNmzNDLL7+sb775JkcYu/nmm51uqtyiRQtNmDBBWVlZLn+1WkZGhuLj42WM0XvvvefSY0oa4Q9lzsyZM/Xss89qwoQJatGihUJDQ/Xf//43x2Hw4ODgQm/77Nmz6tixo1599dUc667mS7IlacWKFfL29tbevXuVmZnpOEJ39uxZPfbYYxoyZEiOx2QfHZRyn09aWprWr18vb29v7dq1y2kekvTRRx+pefPmTo/Jbkg33nijkpKS9P333+uHH35QfHy8Wrdu7XQ9TH7OnDmjdu3aKTQ0VF999ZV8fX0d6ypXrqxff/3VafzRo0cd6wAUzazvv9fzEyZo7LPPqnmjRgoNDtYbU6Zo7ZYtTuPKWv+7cj+S9N1336lq1apO6/z9/SW53uevdj/Z8nq+Zs6cqUceeUSzZs1S69atXd63q7KD3759+7RkyRLHUT/pUq88duyY0/jMzEydPHmyxPso4Q9u5+fn5/heTklauXKlbrnlFg0cONCxbPfu3QVuJy4uTp9++qkuXrzo+MNfu3at05gbb7xRs2fPVmxsrCOcFVSPKz7//HPNmTNHy5YtU3x8vEaNGuU4pXHjjTdq+/btuv766wu1TUl65pln5OXlpe+//15/+9vf1KFDB7Vq1UqVKlVSVFSU9uzZo549e+b5+LCwMHXr1k3dunVTly5d1K5dO508eTLHdS1XSk1NVdu2beXv76+5c+fm+DL5Fi1aaPTo0Y4vF5ekxYsXKywsTPXq1Sv0PAGrurLfrN64UTc3bqzHHnzQsSzpwIECt+Ou/pd99PHyx9SrV0/+/v7av3+/06nXyxXU58PDw1WpUiWtXbtWd9xxh2MfGzZscBwddGU/+fnss8/0j3/8QzNnzlSHDh1yHXNlGP3ll19Uu3Ztl476ZQe/Xbt2aenSpbruuuuc1rdo0UKnT5/W+vXr1bRpU0nSkiVLZLfbc7ypL2584ANuFxsbqzVr1mjv3r06ceKEateurXXr1mnhwoXauXOnRowYkaOJ5aZHjx6y2+169NFHtWPHDi1cuFDjx4+X9H/fhTho0CCdPHlS3bt319q1a7V7924tXLhQffv2dTSvK+vJPsWQl4MHD+rxxx/Xq6++qttuu01TpkzRmDFj9Msvv0iShg0bplWrVmnw4MHatGmTdu3apW+++SbHBz6u9N133+njjz9WQkKC2rRpo+eee069e/fWqVOnJF26LnLs2LF6++23tXPnTm3ZskVTpkzR66+/Lkl6/fXX9dlnn+n333/Xzp07NWvWLFWuXNlxXU5eUlNTdc899+jcuXP63//+p9TUVCUnJys5OdnxHN1zzz2qV6+eevXqpc2bN2vhwoX697//rUGDBuV4xw0gb1f2m+urV9eGbdu0eOVK7dq7Vy9PnKj1LnyIyl39r3r16rLZbJo3b56OHz+us2fPKjQ0VM8++6yGDh2qadOmaffu3dqwYYMmTpzo+ECaK33+iSee0NixY/XNN98oMTFRTz75pE6dOuWYjyv7ycuMGTP08MMPa8KECWrevLmjx+V2P9Onn35aiYmJ+uyzzzRx4kQ9+eSTBf57ZGRkqEuXLlq3bp0SEhKUlZXl2Ef2HRHq1q2rdu3aqX///vr111+1cuVKDR48WA8++KCioqIK3MdVMS5ISUkxkkxKSoorw+Em58+fN9u3bzfnz593y/63Ht+a709eEhMTzc0332wCAwONJPP777+bPn36mPDwcBMREWEef/xxM3z4cNOoUSPHY3r37m06d+6cY1srV640DRs2NH5+fqZp06ZmxowZjm1m27lzp7nvvvtMRESECQwMNHXq1DFPPfWUsdvtudaTlJSUZ+12u93cfffdpm3bto7HG2PME088YWrVqmXOnDljjDHm119/NW3atDEhISEmODjYNGzY0IwePdoxvnr16uaNN95w/H7s2DFTqVIlM2bMGMey9PR007RpUxMfH+9YlpCQYBo3bmz8/PxMuXLlzB133GHmzJljjDHmww8/NI0bNzbBwcEmLCzM3H333WbDhg15ziXb0qVLjaRcfy5/Lvbu3Wvat29vAgMDTYUKFcwzzzxjMjIy8t12fq9RT+8znj4/d3N3/yuqK/vNprlzzUOdO5vw0FATERpq+nfrZp7t1880iIszaVu2GGPKTv/L9sorr5jKlSsbm81mevfubYy51BvffPNNExcXZ3x9fU3FihVN27ZtzfLly40xxly4cKHAPp+RkWEGDx5swsLCTLly5cywYcNM165dzYMPPugYU9B+svvZqVOnnGpu2bJlrj0uu/7sMQMHDjQDBgxw1PD888879fq8JCUl5dlHly5d6hj3559/mu7du5uQkBATFhZm+vbt6/j/Rl6Ko4/ajCn4hjWpqakKDw933O4BZdOFCxeUlJSkGjVq5DhNVxq2ncj/3Wn9CvVLqZL/k5CQoL59+yolJUWBgYGlvn84y+816ul9xtPn527u7n/F5fzWrfmuD/zLX1zelqf1P7vdrrp16zourbGq4uijXPMHjzJ9+nTVrFlTVatW1ebNmzVs2DDFx8d7ROMDgPx4Wv/bt2+fFi1apJYtW+rixYt65513lJSU5HQ3BRQN1/zBoyQnJ+uhhx5S3bp1NXToUHXt2lUffvjhVW1zwIABCgkJyfVnwIABxVR56UlISMhzPvXrl/7RWQDFw9P6n5eXl6ZOnaq//vWvuvXWW7Vlyxb98MMPjttjudNPP/2U5/OS2610yhpO+3oQd5/2KIunfYvDsWPHlJqamuu6sLCwHPeFKuvOnDnjuC3LlXx9fYt8M2hXcNrXc+fnbu7uf8WlOE/7FgdP63/F5fz58zp06FCe64tydwdXcdoXKAWRkZEe1eBCQ0MVGhrq7jIAXAM8rf8Vl8DAwBINeCWN074eyIWDuYBbFHTbCOBq0f/g6Yqjj3Lkz4P4+vrKZrPp+PHjqlixotNX0pQGe0b+L8gLFy6UUiUoa4wxSk9P1/Hjx+Xl5eX0tVRAcXB3/ysuFwv4H7uNPmpZxdlHCX8exNvbW9HR0Tp48KD27t1b6vs/dvZYvut9TvNys7qgoCBVq1ZNXl6cdEDxcnf/Ky4Zx4/nu/7yr1mENRVHH+X/xh4mJCREtWvXVkZGRqnv+8mv8r/r+dz75pZSJSiLvL295ePjc80ekUHZ587+V1x2DxyU7/oa388vpUpQFhVXHyX8eSBvb2+XvnewuB1JP5Lv+mv5E3gArg3u6n/FxesIfRQlj3MvAAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACzEx90FeIIG0xrku35L7y2lVAnKgvxeD7wWgLztqFM3z3V1f99RipXA3fJ7LUi8Hq4WR/4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQn5LceINpDfJct6X3lpLcNYBSwN94ydtRp26e6+r+vqMUKwFQEtzxN86RPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCXPpuX2OMJCk1NbVQG886n5XnusJuqyzLb56SZ801PzwPl1jldS8V71yzx2f3G09T1D56Nss6rycrzTUv+T0HEs9DNk96Horzde9qH7UZFzrtwYMHFRMTU6gCAKAoDhw4oOjoaHeXUezoowBKS0F91KXwZ7fbdfjwYYWGhspms7m049TUVMXExOjAgQMKCwtzveIyiLmUXZ40H6vPxRijM2fOKCoqSl5enndFCn2UuZRVnjQfq8/F1T7q0mlfLy+vIr8TDwsLu+b/AbIxl7LLk+Zj5bmEh4eXYDXuRR+9hLmUXZ40HyvPxZU+6nlvrwEAAJAnwh8AAICFlFj48/f318iRI+Xv719Suyg1zKXs8qT5MBdcyZOeR+ZSdnnSfJiLa1z6wAcAAAA8A6d9AQAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCrir8TZo0SbGxsQoICFDz5s3166+/5jt+1qxZqlOnjgICAtSgQQPNnz//anZfrAozl48++ki33367ypUrp3Llyql169YFzr00FfbfJdvMmTNls9l07733lmyBhVDYuZw+fVqDBg1SlSpV5O/vrxtuuOGafZ1J0ptvvqm4uDgFBgYqJiZGQ4cO1YULF0qp2rytWLFCHTt2VFRUlGw2m77++usCH7Ns2TLdeOON8vf31/XXX6+pU6eWeJ3XAvoofbQ0eFIvpY8WQx81RTRz5kzj5+dnPv74Y7Nt2zbTv39/ExERYY4ePZrr+JUrVxpvb2/z2muvme3bt5t///vfxtfX12zZsqWoJRSbws6lR48eZtKkSWbjxo1mx44dpk+fPiY8PNwcPHiwlCvPqbBzyZaUlGSqVq1qbr/9dtO5c+fSKbYAhZ3LxYsXTbNmzczf/vY38/PPP5ukpCSzbNkys2nTplKuPHeFnU9CQoLx9/c3CQkJJikpySxcuNBUqVLFDB06tJQrz2n+/PnmhRdeMHPmzDGSzFdffZXv+D179pigoCDz9NNPm+3bt5uJEycab29vs2DBgtIpuIyij9JHS4Mn9VL6aPH00SKHv5tuuskMGjTI8XtWVpaJiooyY8eOzXV8fHy86dChg9Oy5s2bm8cee6yoJRSbws7lSpmZmSY0NNRMmzatpEp0WVHmkpmZaW655RYzefJk07t37zLTtAo7l/fee8/UrFnTpKenl1aJhVLY+QwaNMi0atXKadnTTz9tbr311hKts7BcaVr//Oc/Tf369Z2WdevWzbRt27YEKyv76KP/hz5acjypl9JHi6ePFum0b3p6utavX6/WrVs7lnl5eal169ZavXp1ro9ZvXq103hJatu2bZ7jS0tR5nKltLQ0ZWRkqHz58iVVpkuKOpdXXnlFkZGR6tevX2mU6ZKizGXu3Llq0aKFBg0apEqVKukvf/mLxowZo6ysrNIqO09Fmc8tt9yi9evXO05p7NmzR/Pnz9ff/va3Uqm5OJXVv393oo86o4+WDE/qpfTR4vv79ylKASdOnFBWVpYqVarktLxSpUr6/fffc31McnJyruOTk5OLUkKxKcpcrjRs2DBFRUXl+EcpbUWZy88//6z//e9/2rRpUylU6LqizGXPnj1asmSJevbsqfnz5+uPP/7QwIEDlZGRoZEjR5ZG2Xkqynx69OihEydO6LbbbpMxRpmZmRowYICef/750ii5WOX195+amqrz588rMDDQTZW5D33UGX20ZHhSL6WPFl8f5dO+V2ncuHGaOXOmvvrqKwUEBLi7nEI5c+aMevXqpY8++kgVKlRwdzlXzW63KzIyUh9++KGaNm2qbt266YUXXtD777/v7tKKZNmyZRozZozeffddbdiwQXPmzNF3332nUaNGubs0oFjRR8sWT+ql9NHcFenIX4UKFeTt7a2jR486LT969KgqV66c62MqV65cqPGlpShzyTZ+/HiNGzdOP/zwgxo2bFiSZbqksHPZvXu39u7dq44dOzqW2e12SZKPj48SExNVq1atki06D0X5d6lSpYp8fX3l7e3tWFa3bl0lJycrPT1dfn5+JVpzfooynxEjRqhXr1565JFHJEkNGjTQuXPn9Oijj+qFF16Ql9e1894tr7//sLAwSx71k+ij2eijJcuTeil9tPj6aJFm7efnp6ZNm+rHH390LLPb7frxxx/VokWLXB/TokULp/GStHjx4jzHl5aizEWSXnvtNY0aNUoLFixQs2bNSqPUAhV2LnXq1NGWLVu0adMmx0+nTp101113adOmTYqJiSnN8p0U5d/l1ltv1R9//OFovJK0c+dOValSxa3BTyrafNLS0nI0puxmfOn64GtHWf37dyf6KH20NHhSL6WPFuPff6E/IvL/zZw50/j7+5upU6ea7du3m0cffdRERESY5ORkY4wxvXr1MsOHD3eMX7lypfHx8THjx483O3bsMCNHjixTtygozFzGjRtn/Pz8zJdffmmOHDni+Dlz5oy7puBQ2LlcqSx9Sq2wc9m/f78JDQ01gwcPNomJiWbevHkmMjLS/Oc//3HXFJwUdj4jR440oaGh5rPPPjN79uwxixYtMrVq1TLx8fHumoLDmTNnzMaNG83GjRuNJPP666+bjRs3mn379hljjBk+fLjp1auXY3z2LQqee+45s2PHDjNp0iRu9WLoo/TR0uFJvZQ+Wjx9tMjhzxhjJk6caKpVq2b8/PzMTTfdZH755RfHupYtW5revXs7jf/iiy/MDTfcYPz8/Ez9+vXNd999dzW7L1aFmUv16tWNpBw/I0eOLP3Cc1HYf5fLlbWmVdi5rFq1yjRv3tz4+/ubmjVrmtGjR5vMzMxSrjpvhZlPRkaGeemll0ytWrVMQECAiYmJMQMHDjSnTp0q/cKvsHTp0lz/BrLr7927t2nZsmWOxzRu3Nj4+fmZmjVrmilTppR63WURfZQ+Who8qZfSR6++j9qMucaOewIAAKDIrp0rHQEAAHDVCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwkP8HkJTtP61bT5cAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 800x400 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot target distributions\n",
        "targets_df[TARGET_CANDIDATES].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": 428
        },
        "id": "FT3YCXrYxnNc",
        "outputId": "76284215-aba5-4218-ed4d-54f54ccec80a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Number of NaNs per Era'}, xlabel='era'>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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_cols].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": 430
        },
        "id": "jAvpw-XvxnNc",
        "outputId": "5bc9a30d-bf7c-4f1f-cfcd-81cdb5f6cb13"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot correlation matrix of targets\n",
        "import seaborn as sns\n",
        "sns.heatmap(\n",
        "  targets_df[target_cols].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, 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": "70e6fae9-7b9b-44ed-eed5-b8327b7762c2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \")\",\n  \"rows\": 37,\n  \"fields\": [\n    {\n      \"column\": \"corr_with_cyrus_v4_20\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20790557337103682,\n        \"min\": 0.3917396776329279,\n        \"max\": 1.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.3917396776329279,\n          0.7451322228078574,\n          0.4417977189305002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-7a952b35-b89b-4ec4-9385-ed0884902428\" 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>corr_with_cyrus_v4_20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_cyrusd_20</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_xerxes_20</th>\n",
              "      <td>0.942907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_caroline_20</th>\n",
              "      <td>0.922111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_sam_20</th>\n",
              "      <td>0.911981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ralph_20</th>\n",
              "      <td>0.894999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_echo_20</th>\n",
              "      <td>0.851763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_victor_20</th>\n",
              "      <td>0.838408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_waldo_20</th>\n",
              "      <td>0.833260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_delta_20</th>\n",
              "      <td>0.806752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_bravo_20</th>\n",
              "      <td>0.801685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jeremy_20</th>\n",
              "      <td>0.790352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_charlie_20</th>\n",
              "      <td>0.767458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_alpha_20</th>\n",
              "      <td>0.765305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_claudia_20</th>\n",
              "      <td>0.745132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_teager2b_20</th>\n",
              "      <td>0.717080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_agnes_20</th>\n",
              "      <td>0.710171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_tyler_20</th>\n",
              "      <td>0.707080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_rowan_20</th>\n",
              "      <td>0.704642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_cyrusd_60</th>\n",
              "      <td>0.489257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_xerxes_60</th>\n",
              "      <td>0.485867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_caroline_60</th>\n",
              "      <td>0.482128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_sam_60</th>\n",
              "      <td>0.479950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ralph_60</th>\n",
              "      <td>0.477175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_echo_60</th>\n",
              "      <td>0.461583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_victor_60</th>\n",
              "      <td>0.459265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_waldo_60</th>\n",
              "      <td>0.455948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_delta_60</th>\n",
              "      <td>0.441798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_bravo_60</th>\n",
              "      <td>0.441597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jeremy_60</th>\n",
              "      <td>0.437988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_charlie_60</th>\n",
              "      <td>0.424056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_alpha_60</th>\n",
              "      <td>0.423646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_claudia_60</th>\n",
              "      <td>0.410047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_teager2b_60</th>\n",
              "      <td>0.398701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_agnes_60</th>\n",
              "      <td>0.396759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_tyler_60</th>\n",
              "      <td>0.396696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_rowan_60</th>\n",
              "      <td>0.391740</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                    corr_with_cyrus_v4_20\n",
              "target                           1.000000\n",
              "target_cyrusd_20                 1.000000\n",
              "target_xerxes_20                 0.942907\n",
              "target_caroline_20               0.922111\n",
              "target_sam_20                    0.911981\n",
              "target_ralph_20                  0.894999\n",
              "target_echo_20                   0.851763\n",
              "target_victor_20                 0.838408\n",
              "target_waldo_20                  0.833260\n",
              "target_delta_20                  0.806752\n",
              "target_bravo_20                  0.801685\n",
              "target_jeremy_20                 0.790352\n",
              "target_charlie_20                0.767458\n",
              "target_alpha_20                  0.765305\n",
              "target_claudia_20                0.745132\n",
              "target_teager2b_20               0.717080\n",
              "target_agnes_20                  0.710171\n",
              "target_tyler_20                  0.707080\n",
              "target_rowan_20                  0.704642\n",
              "target_cyrusd_60                 0.489257\n",
              "target_xerxes_60                 0.485867\n",
              "target_caroline_60               0.482128\n",
              "target_sam_60                    0.479950\n",
              "target_ralph_60                  0.477175\n",
              "target_echo_60                   0.461583\n",
              "target_victor_60                 0.459265\n",
              "target_waldo_60                  0.455948\n",
              "target_delta_60                  0.441798\n",
              "target_bravo_60                  0.441597\n",
              "target_jeremy_60                 0.437988\n",
              "target_charlie_60                0.424056\n",
              "target_alpha_60                  0.423646\n",
              "target_claudia_60                0.410047\n",
              "target_teager2b_60               0.398701\n",
              "target_agnes_60                  0.396759\n",
              "target_tyler_60                  0.396696\n",
              "target_rowan_60                  0.391740"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(\n",
        "    targets_df[target_cols]\n",
        "    .corrwith(targets_df[MAIN_TARGET])\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": "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": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VqBaYwmqxnNd",
        "outputId": "5da7bec6-e470-46a5-a553-b26e05a9084f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
            "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
            "\n",
            "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
            "This will raise in a future version.\n",
            "\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.170857 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: 688184, 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 0.170923 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: 688184, number of used features: 705\n",
            "[LightGBM] [Info] Start training from score 0.500003\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.172196 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: 688184, number of used features: 705\n",
            "[LightGBM] [Info] Start training from score 0.500031\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.315183 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: 688184, number of used features: 705\n",
            "[LightGBM] [Info] Start training from score 0.499948\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",
        "    # We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
        "    # model = lgb.LGBMRegressor(\n",
        "    #     n_estimators=2 30_000,\n",
        "    #     learning_rate=0.001,\n",
        "    #     max_depth=10,\n",
        "    #     num_leaves=2**10,\n",
        "    #     colsample_bytree=0.1\n",
        "    #     min_data_in_leaf=10000,\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": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "Ic9eGKxSxnNe",
        "outputId": "165e1b5d-1c72-4651-b159-471d26a09563"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v5.0/validation.parquet: 3.20GB [01:50, 29.0MB/s]                            \n"
          ]
        },
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              "      <th></th>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <th>prediction_target_xerxes_20</th>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n000c290e4364875</th>\n",
              "      <td>0.490942</td>\n",
              "      <td>0.496484</td>\n",
              "      <td>0.490848</td>\n",
              "      <td>0.497285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002a15bc5575bbb</th>\n",
              "      <td>0.504698</td>\n",
              "      <td>0.500795</td>\n",
              "      <td>0.504960</td>\n",
              "      <td>0.498469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n00309caaa0f955e</th>\n",
              "      <td>0.512116</td>\n",
              "      <td>0.514978</td>\n",
              "      <td>0.508068</td>\n",
              "      <td>0.510910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0039cbdcf835708</th>\n",
              "      <td>0.500933</td>\n",
              "      <td>0.505761</td>\n",
              "      <td>0.497882</td>\n",
              "      <td>0.502858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n004143458984f89</th>\n",
              "      <td>0.494765</td>\n",
              "      <td>0.500962</td>\n",
              "      <td>0.492008</td>\n",
              "      <td>0.498323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffa8c414845a031</th>\n",
              "      <td>0.506696</td>\n",
              "      <td>0.506523</td>\n",
              "      <td>0.506469</td>\n",
              "      <td>0.501209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb0047d0286c64</th>\n",
              "      <td>0.498714</td>\n",
              "      <td>0.500570</td>\n",
              "      <td>0.500940</td>\n",
              "      <td>0.501488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb29b13d911aaa</th>\n",
              "      <td>0.509094</td>\n",
              "      <td>0.517021</td>\n",
              "      <td>0.508252</td>\n",
              "      <td>0.506371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb37244ff34f68</th>\n",
              "      <td>0.501890</td>\n",
              "      <td>0.507994</td>\n",
              "      <td>0.501873</td>\n",
              "      <td>0.504459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb9ed28a77e554</th>\n",
              "      <td>0.511638</td>\n",
              "      <td>0.515364</td>\n",
              "      <td>0.508191</td>\n",
              "      <td>0.501787</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>853017 rows × 4 columns</p>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    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",
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              "    60% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        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-22729fe7-a0a2-46d0-ac07-2d00cf429abb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
              "id                                                                           \n",
              "n000c290e4364875                     0.490942                     0.496484   \n",
              "n002a15bc5575bbb                     0.504698                     0.500795   \n",
              "n00309caaa0f955e                     0.512116                     0.514978   \n",
              "n0039cbdcf835708                     0.500933                     0.505761   \n",
              "n004143458984f89                     0.494765                     0.500962   \n",
              "...                                       ...                          ...   \n",
              "nffa8c414845a031                     0.506696                     0.506523   \n",
              "nffb0047d0286c64                     0.498714                     0.500570   \n",
              "nffb29b13d911aaa                     0.509094                     0.517021   \n",
              "nffb37244ff34f68                     0.501890                     0.507994   \n",
              "nffb9ed28a77e554                     0.511638                     0.515364   \n",
              "\n",
              "                  prediction_target_xerxes_20  prediction_target_teager2b_20  \n",
              "id                                                                            \n",
              "n000c290e4364875                     0.490848                       0.497285  \n",
              "n002a15bc5575bbb                     0.504960                       0.498469  \n",
              "n00309caaa0f955e                     0.508068                       0.510910  \n",
              "n0039cbdcf835708                     0.497882                       0.502858  \n",
              "n004143458984f89                     0.492008                       0.498323  \n",
              "...                                       ...                            ...  \n",
              "nffa8c414845a031                     0.506469                       0.501209  \n",
              "nffb0047d0286c64                     0.500940                       0.501488  \n",
              "nffb29b13d911aaa                     0.508252                       0.506371  \n",
              "nffb37244ff34f68                     0.501873                       0.504459  \n",
              "nffb9ed28a77e554                     0.508191                       0.501787  \n",
              "\n",
              "[853017 rows x 4 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Download validation data\n",
        "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
        "\n",
        "# Load the validation data, filtering for data_type == \"validation\"\n",
        "validation = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/validation.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": 11,
      "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": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "WUvsFi-VxnNf",
        "outputId": "16111ca8-3c75-4ace-ba9e-4d2e04eb354c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "smz_GLLAxnNf",
        "outputId": "513b1dab-2863-430d-dfa5-1c2ec0b2fee2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      <td>0.027888</td>\n",
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            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown  \\\n",
              "prediction_target_cyrusd_20   0.027203 0.022530 1.207444      0.041989   \n",
              "prediction_target_victor_20   0.027888 0.021271 1.311068      0.031970   \n",
              "prediction_target_xerxes_20   0.027263 0.022812 1.195147      0.041950   \n",
              "prediction_target_teager2b_20 0.023608 0.018413 1.282164      0.034677   \n",
              "\n",
              "                               mean_corr_with_cryus  \n",
              "prediction_target_cyrusd_20                0.027196  \n",
              "prediction_target_victor_20                0.027780  \n",
              "prediction_target_xerxes_20                0.027220  \n",
              "prediction_target_teager2b_20              0.024270  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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[MAIN_TARGET])\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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "y27jxEUwxnNg",
        "outputId": "88aa87b5-5671-4074-a05f-bc43424cef9a"
      },
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              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <th>ensemble_cyrus_teager</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n000c290e4364875</th>\n",
              "      <td>0.490942</td>\n",
              "      <td>0.496484</td>\n",
              "      <td>0.497285</td>\n",
              "      <td>0.229635</td>\n",
              "      <td>0.202198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002a15bc5575bbb</th>\n",
              "      <td>0.504698</td>\n",
              "      <td>0.500795</td>\n",
              "      <td>0.498469</td>\n",
              "      <td>0.635651</td>\n",
              "      <td>0.534424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n00309caaa0f955e</th>\n",
              "      <td>0.512116</td>\n",
              "      <td>0.514978</td>\n",
              "      <td>0.510910</td>\n",
              "      <td>0.934305</td>\n",
              "      <td>0.943337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0039cbdcf835708</th>\n",
              "      <td>0.500933</td>\n",
              "      <td>0.505761</td>\n",
              "      <td>0.502858</td>\n",
              "      <td>0.658998</td>\n",
              "      <td>0.629005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n004143458984f89</th>\n",
              "      <td>0.494765</td>\n",
              "      <td>0.500962</td>\n",
              "      <td>0.498323</td>\n",
              "      <td>0.411810</td>\n",
              "      <td>0.300613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffa8c414845a031</th>\n",
              "      <td>0.506696</td>\n",
              "      <td>0.506523</td>\n",
              "      <td>0.501209</td>\n",
              "      <td>0.808514</td>\n",
              "      <td>0.720311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb0047d0286c64</th>\n",
              "      <td>0.498714</td>\n",
              "      <td>0.500570</td>\n",
              "      <td>0.501488</td>\n",
              "      <td>0.523013</td>\n",
              "      <td>0.564876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb29b13d911aaa</th>\n",
              "      <td>0.509094</td>\n",
              "      <td>0.517021</td>\n",
              "      <td>0.506371</td>\n",
              "      <td>0.919887</td>\n",
              "      <td>0.891062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb37244ff34f68</th>\n",
              "      <td>0.501890</td>\n",
              "      <td>0.507994</td>\n",
              "      <td>0.504459</td>\n",
              "      <td>0.733192</td>\n",
              "      <td>0.727066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffb9ed28a77e554</th>\n",
              "      <td>0.511638</td>\n",
              "      <td>0.515364</td>\n",
              "      <td>0.501787</td>\n",
              "      <td>0.937873</td>\n",
              "      <td>0.799246</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>853017 rows × 5 columns</p>\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    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",
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              "    }\n",
              "  }\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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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
              "id                                                                           \n",
              "n000c290e4364875                     0.490942                     0.496484   \n",
              "n002a15bc5575bbb                     0.504698                     0.500795   \n",
              "n00309caaa0f955e                     0.512116                     0.514978   \n",
              "n0039cbdcf835708                     0.500933                     0.505761   \n",
              "n004143458984f89                     0.494765                     0.500962   \n",
              "...                                       ...                          ...   \n",
              "nffa8c414845a031                     0.506696                     0.506523   \n",
              "nffb0047d0286c64                     0.498714                     0.500570   \n",
              "nffb29b13d911aaa                     0.509094                     0.517021   \n",
              "nffb37244ff34f68                     0.501890                     0.507994   \n",
              "nffb9ed28a77e554                     0.511638                     0.515364   \n",
              "\n",
              "                  prediction_target_teager2b_20  ensemble_cyrus_victor  \\\n",
              "id                                                                       \n",
              "n000c290e4364875                       0.497285               0.229635   \n",
              "n002a15bc5575bbb                       0.498469               0.635651   \n",
              "n00309caaa0f955e                       0.510910               0.934305   \n",
              "n0039cbdcf835708                       0.502858               0.658998   \n",
              "n004143458984f89                       0.498323               0.411810   \n",
              "...                                         ...                    ...   \n",
              "nffa8c414845a031                       0.501209               0.808514   \n",
              "nffb0047d0286c64                       0.501488               0.523013   \n",
              "nffb29b13d911aaa                       0.506371               0.919887   \n",
              "nffb37244ff34f68                       0.504459               0.733192   \n",
              "nffb9ed28a77e554                       0.501787               0.937873   \n",
              "\n",
              "                  ensemble_cyrus_teager  \n",
              "id                                       \n",
              "n000c290e4364875               0.202198  \n",
              "n002a15bc5575bbb               0.534424  \n",
              "n00309caaa0f955e               0.943337  \n",
              "n0039cbdcf835708               0.629005  \n",
              "n004143458984f89               0.300613  \n",
              "...                                 ...  \n",
              "nffa8c414845a031               0.720311  \n",
              "nffb0047d0286c64               0.564876  \n",
              "nffb29b13d911aaa               0.891062  \n",
              "nffb37244ff34f68               0.727066  \n",
              "nffb9ed28a77e554               0.799246  \n",
              "\n",
              "[853017 rows x 5 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Ensemble predictions together with a simple average\n",
        "validation[\"ensemble_cyrus_victor\"] = (\n",
        "    validation\n",
        "    .groupby(\"era\")[[\n",
        "      f\"prediction_{MAIN_TARGET}\",\n",
        "      \"prediction_target_victor_20\",\n",
        "    ]]\n",
        "    .rank(pct=True)\n",
        "    .mean(axis=1)\n",
        ")\n",
        "validation[\"ensemble_cyrus_teager\"] = (\n",
        "    validation\n",
        "    .groupby(\"era\")[[\n",
        "      f\"prediction_{MAIN_TARGET}\",\n",
        "      \"prediction_target_teager2b_20\",\n",
        "    ]]\n",
        "    .rank(pct=True)\n",
        "    .mean(axis=1)\n",
        ")\n",
        "\n",
        "# Print the ensemble predictions\n",
        "prediction_cols = [\n",
        "  \"prediction_target_cyrusd_20\",\n",
        "  \"prediction_target_victor_20\",\n",
        "  \"prediction_target_teager2b_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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "jCRbKxZmxnNg",
        "outputId": "9f4c981e-e8c0-4a40-a54c-996cbdf4cb64"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_79JgZULxnNh",
        "outputId": "e9ff8217-56db-474f-9f4c-3356753c4b5f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0020508609868983003,\n        \"min\": 0.023608082519458163,\n        \"max\": 0.029022787029295862,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.027887756110600202,\n          0.027652622617961028,\n          0.023608082519458163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.001632974209470622,\n        \"min\": 0.018412690655779843,\n        \"max\": 0.022529812835260364,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.02127102579361958,\n          0.021055134174702374,\n          0.018412690655779843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.044230335890586996,\n        \"min\": 1.2074444589134816,\n        \"max\": 1.3133434528850212,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.3110677586111228,\n          1.3133434528850212,\n          1.2821636425009648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003779599885100666,\n        \"min\": 0.03196979652293441,\n        \"max\": 0.041989209807464345,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.03196979652293441,\n          0.03478589743665417,\n          0.034677088514795784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "summary"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-555020a7-d937-4cd2-ab45-504d57ec129e\" 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",
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              "      <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_cyrusd_20</th>\n",
              "      <td>0.027203</td>\n",
              "      <td>0.022530</td>\n",
              "      <td>1.207444</td>\n",
              "      <td>0.041989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <td>0.027888</td>\n",
              "      <td>0.021271</td>\n",
              "      <td>1.311068</td>\n",
              "      <td>0.031970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <td>0.023608</td>\n",
              "      <td>0.018413</td>\n",
              "      <td>1.282164</td>\n",
              "      <td>0.034677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <td>0.029023</td>\n",
              "      <td>0.022270</td>\n",
              "      <td>1.303209</td>\n",
              "      <td>0.037392</td>\n",
              "    </tr>\n",
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              "      <th>ensemble_cyrus_teager</th>\n",
              "      <td>0.027653</td>\n",
              "      <td>0.021055</td>\n",
              "      <td>1.313343</td>\n",
              "      <td>0.034786</td>\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20   0.027203 0.022530 1.207444      0.041989\n",
              "prediction_target_victor_20   0.027888 0.021271 1.311068      0.031970\n",
              "prediction_target_teager2b_20 0.023608 0.018413 1.282164      0.034677\n",
              "ensemble_cyrus_victor         0.029023 0.022270 1.303209      0.037392\n",
              "ensemble_cyrus_teager         0.027653 0.021055 1.313343      0.034786"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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 ensembling also improves MMC performance significantly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "k9jKnsnuL1se",
        "outputId": "a3c2288f-ffad-4f3b-c20d-7979d46e4679"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v4.3/meta_model.parquet: 29.0MB [00:00, 29.3MB/s]                            \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "    }\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-dbf6f8f4-c648-44b5-994e-3d275e7e789b 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-dbf6f8f4-c648-44b5-994e-3d275e7e789b');\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-2daef20f-3347-4db1-8bf2-65a51c3af3ad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2daef20f-3347-4db1-8bf2-65a51c3af3ad')\"\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-2daef20f-3347-4db1-8bf2-65a51c3af3ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9ce60230-2de7-4af7-b4ba-07640671b7e8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9ce60230-2de7-4af7-b4ba-07640671b7e8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20   0.005621 0.014596 0.385090      0.053169\n",
              "prediction_target_victor_20   0.007130 0.017015 0.419028      0.069184\n",
              "prediction_target_teager2b_20 0.006287 0.015018 0.418651      0.063949\n",
              "ensemble_cyrus_victor         0.006832 0.015630 0.437112      0.060396\n",
              "ensemble_cyrus_teager         0.006460 0.014058 0.459535      0.054560"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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\"v4.3/meta_model.parquet\", round_num=842)\n",
        "validation[\"meta_model\"] = pd.read_parquet(\n",
        "    f\"v4.3/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` nicely ensembles with `cyrus`. We have seen in our research that models trained or ensembled using `teager` perform 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": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "QNSLiLDWOx6D",
        "outputId": "78e2a934-a2d1-465a-b29f-ddd65e5c187b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v5.0/validation_benchmark_models.parquet: 134MB [00:03, 37.3MB/s]                           \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "benchmark_models"
            },
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                   era  v5_lgbm_cyrusd20  v5_lgbm_teager2b20  v5_lgbm_ct_blend\n",
              "id                                                                            \n",
              "n000101811a8a843  0575          0.323319            0.506974          0.404506\n",
              "n001e1318d5072ac  0575          0.860873            0.823677          0.851931\n",
              "n002a9c5ab785cbb  0575          0.749285            0.950823          0.883941\n",
              "n002ccf6d0e8c5ad  0575          0.981402            0.977289          0.981760\n",
              "n0041544c345c91d  0575          0.862482            0.831903          0.857117\n",
              "...                ...               ...                 ...               ...\n",
              "nff72554d2d664cf  1134          0.642077            0.742044          0.699454\n",
              "nff7be4df2d6a60c  1134          0.866442            0.954034          0.927676\n",
              "nff7edf1cfda1e76  1134          0.256348            0.280296          0.261331\n",
              "nff9988e5ff68ab2  1134          0.182899            0.407586          0.277081\n",
              "nffe8ab63ce99cbe  1134          0.636451            0.349727          0.493571\n",
              "\n",
              "[3458685 rows x 4 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "OcUNnnkUWnwg",
        "outputId": "be9e503c-1c38-48d2-fba4-3b33148bbe2c"
      },
      "outputs": [
        {
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              "</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-fe25a490-ead5-4f97-bc09-e02a516aade6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1c72b555-09e0-453e-8db0-6796e676480c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1c72b555-09e0-453e-8db0-6796e676480c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                   mean      std    sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20    0.001858 0.018428  0.100840      0.131340\n",
              "prediction_target_victor_20    0.001276 0.017539  0.072760      0.133161\n",
              "prediction_target_teager2b_20 -0.001304 0.015186 -0.085892      0.162892\n",
              "ensemble_cyrus_victor          0.001800 0.017888  0.100621      0.136361\n",
              "ensemble_cyrus_teager          0.000221 0.016377  0.013521      0.145697"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "validation[FAVORITE_MODEL] = benchmark_models[FAVORITE_MODEL]\n",
        "\n",
        "\n",
        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, FAVORITE_MODEL)\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": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "39UfnEmifTMh",
        "outputId": "11293a92-389f-48b7-9180-0eaeaf4c67a3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0013216515976393576,\n        \"min\": -0.0012331771487687378,\n        \"max\": 0.0019003027266499188,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0013227448448252734,\n          0.0002713018738297983,\n          -0.0012331771487687378\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0013062115206924371,\n        \"min\": 0.015253513559403895,\n        \"max\": 0.018515270582733535,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.017612143800656406,\n          0.016467562981259593,\n          0.015253513559403895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07775457672452596,\n        \"min\": -0.08084544875291869,\n        \"max\": 0.10263434812678629,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07510413608909862,\n          0.016474925533216122,\n          -0.08084544875291869\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012526947808221448,\n        \"min\": 0.13136692118762627,\n        \"max\": 0.16160106837514787,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.13272315502394502,\n          0.14486512503359503,\n          0.16160106837514787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "summary"
            },
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              "      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-46529106-c5e5-4a01-9c6a-51f28d5f5999 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-46529106-c5e5-4a01-9c6a-51f28d5f5999');\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-e34fc178-c8f9-4121-b08b-54c8003f5df7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e34fc178-c8f9-4121-b08b-54c8003f5df7')\"\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-e34fc178-c8f9-4121-b08b-54c8003f5df7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3f0b8274-fef4-441d-81a0-52ba86a6b8b6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3f0b8274-fef4-441d-81a0-52ba86a6b8b6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                   mean      std    sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20    0.001900 0.018515  0.102634      0.131367\n",
              "prediction_target_victor_20    0.001323 0.017612  0.075104      0.132723\n",
              "prediction_target_teager2b_20 -0.001233 0.015254 -0.080845      0.161601\n",
              "ensemble_cyrus_victor          0.001845 0.017978  0.102625      0.135765\n",
              "ensemble_cyrus_teager          0.000271 0.016468  0.016475      0.144865"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": 21,
      "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_cyrusd_20',\n",
        "        'target_teager2b_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": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "kPq_ATf0xnNr",
        "outputId": "dc1b5ed0-e3d6-4d19-ba95-58d3a7c5f44b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v5.0/live.parquet: 7.93MB [00:00, 18.7MB/s]                            \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      <th>id</th>\n",
              "      <th></th>\n",
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              "      <th>n0010b524b4f54c5</th>\n",
              "      <td>0.060377</td>\n",
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              "      <th>n0010c45e0ffdfc3</th>\n",
              "      <td>0.825000</td>\n",
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              "      <td>0.335220</td>\n",
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              "      <th>n002989248b0f148</th>\n",
              "      <td>0.790252</td>\n",
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              "      <td>0.107390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>nffc7d5c3bb883aa</th>\n",
              "      <td>0.928459</td>\n",
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              "        document.querySelector('#df-09fe9923-956c-4f4e-813e-0bfde1c2e56c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-09fe9923-956c-4f4e-813e-0bfde1c2e56c');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @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",
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              "      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",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                  prediction\n",
              "id                          \n",
              "n0010b524b4f54c5    0.060377\n",
              "n0010c45e0ffdfc3    0.825000\n",
              "n0021b7acb16c138    0.335220\n",
              "n002989248b0f148    0.790252\n",
              "n00308342652732d    0.107390\n",
              "...                      ...\n",
              "nffc7d5c3bb883aa    0.928459\n",
              "nffd2ddf669b3c4c    0.168239\n",
              "nffd838736b2cb68    0.602201\n",
              "nffea35973af0581    0.249214\n",
              "nfff17061c841a61    0.041195\n",
              "\n",
              "[6360 rows x 1 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Quick test\n",
        "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
        "live_features = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\", columns=feature_cols)\n",
        "predict_ensemble(live_features, benchmark_models)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "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(\"target_ensemble.pkl\", \"wb\") as f:\n",
        "    f.write(p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "RiJjBD-zxnNr",
        "outputId": "f8b3f091-0117-4112-8685-5d6844f6061a"
      },
      "outputs": [
        {
          "data": {
            "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  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_01079083-d931-4b27-be5e-93f3d9dd4384\", \"predict_ensemble.pkl\", 13383662)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Download file if running in Google Colab\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download('target_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": "3.10.14"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}