parameter_optimization_with_era_subsets.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "parameter_optimization_with_era_subsets.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPlWjeAstr3eWs07qL0KiK8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/numerai/example-scripts/blob/master/parameter_optimization_with_era_subsets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uaiJthdeQWH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "017f1637-88e1-4b71-dbf3-668388169297"
      },
      "source": [
        "!pip install --upgrade numerox\n",
        "import numerox as nx\n",
        "import pandas as pd\n",
        "from sklearn import model_selection\n",
        "from sklearn import linear_model\n",
        "\n",
        "#download the training data using numerox\n",
        "\n",
        "data = nx.download('numerai_dataset.zip')\n",
        "\n",
        "#define your train and test data by selecting which eras you want to evaluate on\n",
        "\n",
        "X1_train = data['era1':'era40'].x\n",
        "X1_test = data['validation'].x\n",
        "y1_train = data['era1':'era40'].y['target']\n",
        "y1_test = data['validation'].y['target']\n",
        "era_train = data['era1':'era40'].era_float\n",
        "\n",
        "#define your cross-validation iterator\n",
        "\n",
        "CV = model_selection.GroupKFold(n_splits = 3)\n",
        "\n",
        "#store the train/test set as a list which will be passed to grid search\n",
        "\n",
        "grp = list(CV.split(X = X1_train, y = y1_train,  groups = era_train))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: numerox in /usr/local/lib/python3.6/dist-packages (4.1.6)\n",
            "Requirement already satisfied, skipping upgrade: tables in /usr/local/lib/python3.6/dist-packages (from numerox) (3.4.4)\n",
            "Requirement already satisfied, skipping upgrade: scikit-learn in /usr/local/lib/python3.6/dist-packages (from numerox) (0.22.2.post1)\n",
            "Requirement already satisfied, skipping upgrade: numerapi>=2.2.4 in /usr/local/lib/python3.6/dist-packages (from numerox) (2.2.4)\n",
            "Requirement already satisfied, skipping upgrade: nose in /usr/local/lib/python3.6/dist-packages (from numerox) (1.3.7)\n",
            "Requirement already satisfied, skipping upgrade: pandas in /usr/local/lib/python3.6/dist-packages (from numerox) (1.0.3)\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from numerox) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from numerox) (1.18.4)\n",
            "Requirement already satisfied, skipping upgrade: numexpr>=2.5.2 in /usr/local/lib/python3.6/dist-packages (from tables->numerox) (2.7.1)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from tables->numerox) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->numerox) (0.14.1)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil in /usr/local/lib/python3.6/dist-packages (from numerapi>=2.2.4->numerox) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pytz in /usr/local/lib/python3.6/dist-packages (from numerapi>=2.2.4->numerox) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from numerapi>=2.2.4->numerox) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: tqdm>=4.29.1 in /usr/local/lib/python3.6/dist-packages (from numerapi>=2.2.4->numerox) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: click>=7.0 in /usr/local/lib/python3.6/dist-packages (from numerapi>=2.2.4->numerox) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->numerapi>=2.2.4->numerox) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->numerapi>=2.2.4->numerox) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->numerapi>=2.2.4->numerox) (2020.4.5.1)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->numerapi>=2.2.4->numerox) (2.9)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "numerai_dataset.zip: 372MB [00:39, 16.9MB/s]                           "
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nTJb-NA5fChB",
        "colab_type": "text"
      },
      "source": [
        "Let's optimize [Ridge Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge) on 'alpha' and 'tol':"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_B4EmzZe2V-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "93527cb7-882d-4f9e-8918-28c987e7d2f0"
      },
      "source": [
        "R = linear_model.Ridge(copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto') #make sure you omit the keyword arguments for the parameter(s) you wish to optimize\n",
        "params1 = {'alpha': [0.001, 0.01, 0.1, 1.0, 10.0], 'tol': [.00001, .0001, .001, .01, 0.1]}\n",
        "GS = model_selection.GridSearchCV(estimator = R, param_grid = params1, cv = grp, return_train_score = True)\n",
        "GS.fit(X1_train, y1_train)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=[(array([  2408,   2409,   2410, ..., 137657, 137658, 137659]),\n",
              "                  array([     0,      1,      2, ..., 142094, 142095, 142096])),\n",
              "                 (array([     0,      1,      2, ..., 142094, 142095, 142096]),\n",
              "                  array([  2408,   2409,   2410, ..., 137657, 137658, 137659])),\n",
              "                 (array([     0,      1,      2, ..., 142094, 142095, 142096]),\n",
              "                  array([  7203,   7204,   7205, ..., 133291, 133292, 133293]))],\n",
              "             error_score=nan,\n",
              "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=None, normalize=False, random_state=None,\n",
              "                             solver='auto', tol=0.001),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'alpha': [0.001, 0.01, 0.1, 1.0, 10.0],\n",
              "                         'tol': [1e-05, 0.0001, 0.001, 0.01, 0.1]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pm8I-xJ_jgy2",
        "colab_type": "text"
      },
      "source": [
        "What was the best score?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgiNAtbtjJrN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9fcc0f1b-db05-4c6e-e8c1-d96fa79f8828"
      },
      "source": [
        "GS.best_score_"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00023175979651816428"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5Is3IrFgE7O",
        "colab_type": "text"
      },
      "source": [
        "Which parameter value(s) give the best score?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jANzkryhgMrm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "371d0b39-480c-4f33-c659-e1a3dae53534"
      },
      "source": [
        "print(GS.best_estimator_)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ridge(alpha=10.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
            "      normalize=False, random_state=None, solver='auto', tol=1e-05)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ysLZ9jekgH-",
        "colab_type": "text"
      },
      "source": [
        "Want to see more data? No problem!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNZsrLBqkjfo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c48f1cfa-b760-44a6-84fb-68ff490846e8"
      },
      "source": [
        "scores = pd.DataFrame(GS.cv_results_); scores\n",
        "\n",
        "#if we type GS.cv_results_ then we are given a dictionary. We can use Pandas to convert a dictionary to a dataframe using method chaining.\n",
        "\n",
        "#the code above defines a new variable called scores, and we use the DataFrame() function from Pandas to interpret the GS1.cv_results_ method's output as a dataframe.\n",
        "#we then used the shortcut method: ; scores to print the table below."
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_alpha</th>\n",
              "      <th>param_tol</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.553780</td>\n",
              "      <td>0.006401</td>\n",
              "      <td>0.033214</td>\n",
              "      <td>0.002122</td>\n",
              "      <td>0.001</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'alpha': 0.001, 'tol': 1e-05}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>21</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.542435</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>0.032584</td>\n",
              "      <td>0.002471</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'alpha': 0.001, 'tol': 0.0001}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>21</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.550618</td>\n",
              "      <td>0.012439</td>\n",
              "      <td>0.031429</td>\n",
              "      <td>0.000543</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'alpha': 0.001, 'tol': 0.001}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>21</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.547604</td>\n",
              "      <td>0.010559</td>\n",
              "      <td>0.031290</td>\n",
              "      <td>0.000683</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'alpha': 0.001, 'tol': 0.01}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>21</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.547448</td>\n",
              "      <td>0.008178</td>\n",
              "      <td>0.031289</td>\n",
              "      <td>0.000520</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'alpha': 0.001, 'tol': 0.1}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>21</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.547646</td>\n",
              "      <td>0.010054</td>\n",
              "      <td>0.031569</td>\n",
              "      <td>0.000851</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'alpha': 0.01, 'tol': 1e-05}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>16</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.553102</td>\n",
              "      <td>0.014821</td>\n",
              "      <td>0.031070</td>\n",
              "      <td>0.000995</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'alpha': 0.01, 'tol': 0.0001}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>16</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.546482</td>\n",
              "      <td>0.006615</td>\n",
              "      <td>0.031441</td>\n",
              "      <td>0.000723</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'alpha': 0.01, 'tol': 0.001}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>16</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.549666</td>\n",
              "      <td>0.006136</td>\n",
              "      <td>0.031407</td>\n",
              "      <td>0.000579</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'alpha': 0.01, 'tol': 0.01}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>16</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.550602</td>\n",
              "      <td>0.005078</td>\n",
              "      <td>0.034892</td>\n",
              "      <td>0.004736</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'alpha': 0.01, 'tol': 0.1}</td>\n",
              "      <td>-0.001754</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001041</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>16</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.547111</td>\n",
              "      <td>0.001477</td>\n",
              "      <td>0.031839</td>\n",
              "      <td>0.000798</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'alpha': 0.1, 'tol': 1e-05}</td>\n",
              "      <td>-0.001753</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001042</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>11</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.546423</td>\n",
              "      <td>0.001896</td>\n",
              "      <td>0.033585</td>\n",
              "      <td>0.002807</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'alpha': 0.1, 'tol': 0.0001}</td>\n",
              "      <td>-0.001753</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001042</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>11</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.547192</td>\n",
              "      <td>0.000751</td>\n",
              "      <td>0.031353</td>\n",
              "      <td>0.000583</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'alpha': 0.1, 'tol': 0.001}</td>\n",
              "      <td>-0.001753</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001042</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>11</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.547492</td>\n",
              "      <td>0.001905</td>\n",
              "      <td>0.031434</td>\n",
              "      <td>0.000824</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'alpha': 0.1, 'tol': 0.01}</td>\n",
              "      <td>-0.001753</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001042</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>11</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.545482</td>\n",
              "      <td>0.007879</td>\n",
              "      <td>0.031247</td>\n",
              "      <td>0.000563</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'alpha': 0.1, 'tol': 0.1}</td>\n",
              "      <td>-0.001753</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.001042</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>11</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.552635</td>\n",
              "      <td>0.011738</td>\n",
              "      <td>0.031519</td>\n",
              "      <td>0.000866</td>\n",
              "      <td>1</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'alpha': 1.0, 'tol': 1e-05}</td>\n",
              "      <td>-0.001746</td>\n",
              "      <td>0.001215</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000172</td>\n",
              "      <td>0.001358</td>\n",
              "      <td>6</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.555447</td>\n",
              "      <td>0.003138</td>\n",
              "      <td>0.031380</td>\n",
              "      <td>0.000962</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'alpha': 1.0, 'tol': 0.0001}</td>\n",
              "      <td>-0.001746</td>\n",
              "      <td>0.001215</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000172</td>\n",
              "      <td>0.001358</td>\n",
              "      <td>6</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.547908</td>\n",
              "      <td>0.007798</td>\n",
              "      <td>0.031361</td>\n",
              "      <td>0.000930</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'alpha': 1.0, 'tol': 0.001}</td>\n",
              "      <td>-0.001746</td>\n",
              "      <td>0.001215</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000172</td>\n",
              "      <td>0.001358</td>\n",
              "      <td>6</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.546633</td>\n",
              "      <td>0.009493</td>\n",
              "      <td>0.031367</td>\n",
              "      <td>0.000753</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'alpha': 1.0, 'tol': 0.01}</td>\n",
              "      <td>-0.001746</td>\n",
              "      <td>0.001215</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000172</td>\n",
              "      <td>0.001358</td>\n",
              "      <td>6</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.544727</td>\n",
              "      <td>0.009274</td>\n",
              "      <td>0.031152</td>\n",
              "      <td>0.000932</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'alpha': 1.0, 'tol': 0.1}</td>\n",
              "      <td>-0.001746</td>\n",
              "      <td>0.001215</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000172</td>\n",
              "      <td>0.001358</td>\n",
              "      <td>6</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007766</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.008309</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.548687</td>\n",
              "      <td>0.005057</td>\n",
              "      <td>0.031519</td>\n",
              "      <td>0.000849</td>\n",
              "      <td>10</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'alpha': 10.0, 'tol': 1e-05}</td>\n",
              "      <td>-0.001678</td>\n",
              "      <td>0.001271</td>\n",
              "      <td>0.001102</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.001352</td>\n",
              "      <td>1</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007765</td>\n",
              "      <td>0.008056</td>\n",
              "      <td>0.008308</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.563012</td>\n",
              "      <td>0.028410</td>\n",
              "      <td>0.031144</td>\n",
              "      <td>0.000737</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'alpha': 10.0, 'tol': 0.0001}</td>\n",
              "      <td>-0.001678</td>\n",
              "      <td>0.001271</td>\n",
              "      <td>0.001102</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.001352</td>\n",
              "      <td>1</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007765</td>\n",
              "      <td>0.008056</td>\n",
              "      <td>0.008308</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.545159</td>\n",
              "      <td>0.002444</td>\n",
              "      <td>0.034565</td>\n",
              "      <td>0.004332</td>\n",
              "      <td>10</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'alpha': 10.0, 'tol': 0.001}</td>\n",
              "      <td>-0.001678</td>\n",
              "      <td>0.001271</td>\n",
              "      <td>0.001102</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.001352</td>\n",
              "      <td>1</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007765</td>\n",
              "      <td>0.008056</td>\n",
              "      <td>0.008308</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.544601</td>\n",
              "      <td>0.004210</td>\n",
              "      <td>0.031576</td>\n",
              "      <td>0.000532</td>\n",
              "      <td>10</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'alpha': 10.0, 'tol': 0.01}</td>\n",
              "      <td>-0.001678</td>\n",
              "      <td>0.001271</td>\n",
              "      <td>0.001102</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.001352</td>\n",
              "      <td>1</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007765</td>\n",
              "      <td>0.008056</td>\n",
              "      <td>0.008308</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.549863</td>\n",
              "      <td>0.005190</td>\n",
              "      <td>0.031565</td>\n",
              "      <td>0.000565</td>\n",
              "      <td>10</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'alpha': 10.0, 'tol': 0.1}</td>\n",
              "      <td>-0.001678</td>\n",
              "      <td>0.001271</td>\n",
              "      <td>0.001102</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.001352</td>\n",
              "      <td>1</td>\n",
              "      <td>0.009104</td>\n",
              "      <td>0.007765</td>\n",
              "      <td>0.008056</td>\n",
              "      <td>0.008308</td>\n",
              "      <td>0.000575</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0        0.553780      0.006401  ...          0.008309         0.000575\n",
              "1        0.542435      0.005866  ...          0.008309         0.000575\n",
              "2        0.550618      0.012439  ...          0.008309         0.000575\n",
              "3        0.547604      0.010559  ...          0.008309         0.000575\n",
              "4        0.547448      0.008178  ...          0.008309         0.000575\n",
              "5        0.547646      0.010054  ...          0.008309         0.000575\n",
              "6        0.553102      0.014821  ...          0.008309         0.000575\n",
              "7        0.546482      0.006615  ...          0.008309         0.000575\n",
              "8        0.549666      0.006136  ...          0.008309         0.000575\n",
              "9        0.550602      0.005078  ...          0.008309         0.000575\n",
              "10       0.547111      0.001477  ...          0.008309         0.000575\n",
              "11       0.546423      0.001896  ...          0.008309         0.000575\n",
              "12       0.547192      0.000751  ...          0.008309         0.000575\n",
              "13       0.547492      0.001905  ...          0.008309         0.000575\n",
              "14       0.545482      0.007879  ...          0.008309         0.000575\n",
              "15       0.552635      0.011738  ...          0.008309         0.000575\n",
              "16       0.555447      0.003138  ...          0.008309         0.000575\n",
              "17       0.547908      0.007798  ...          0.008309         0.000575\n",
              "18       0.546633      0.009493  ...          0.008309         0.000575\n",
              "19       0.544727      0.009274  ...          0.008309         0.000575\n",
              "20       0.548687      0.005057  ...          0.008308         0.000575\n",
              "21       0.563012      0.028410  ...          0.008308         0.000575\n",
              "22       0.545159      0.002444  ...          0.008308         0.000575\n",
              "23       0.544601      0.004210  ...          0.008308         0.000575\n",
              "24       0.549863      0.005190  ...          0.008308         0.000575\n",
              "\n",
              "[25 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    }
  ]
}