Constrained Evaluation on Hetionet.ipynb

constrained-evaluation-on-hetionet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Constrained Evaluation on Hetionet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOTzu//BKYj18jmkRcTzkXY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/cthoyt/190233fd98a11306ceb13f2ee0e95a9e/constrained-evaluation-on-hetionet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFJlGzGGUixD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "cda143f5-ba0b-4b2b-dc10-cef7b69de818"
      },
      "source": [
        "!pip install --quiet --upgrade pip\n",
        "!pip install --quiet wheel\n",
        "!pip install --quiet git+https://github.com/pykeen/pykeen.git@evaluate-on-subset-of-entities "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25h  Building wheel for pykeen (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYMzPewlU9o4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import getpass\n",
        "import sys\n",
        "import time\n",
        "\n",
        "import pykeen\n",
        "from pykeen.pipeline import pipeline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDkPsBmyVCUn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "acb873e5-34eb-41d9-ade9-74786e53c266"
      },
      "source": [
        "print(sys.version)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.6.9 (default, Jul 17 2020, 12:50:27) \n",
            "[GCC 8.4.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9unQGZtVEG-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8296654d-c20f-45c3-fab2-8503ca661306"
      },
      "source": [
        "print(time.asctime())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Aug 25 01:57:57 2020\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvPK1QUmVESl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "905b73b3-e3cf-438b-c739-bc6b5a93bd1e"
      },
      "source": [
        "print(getpass.getuser())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4zrEyS6VEbn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "8c79a2b7-32c0-4414-cb6c-0028bb3d6198"
      },
      "source": [
        "pykeen.get_version()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.0.4-dev'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-xHhVhFVKjy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "restrict_evaluation_to_relations = {'CtD', 'CpD'}"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHgB1NU5VUpu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0447c5d4-bc74-4e30-8331-24c77ef7b444"
      },
      "source": [
        "results = pipeline(\n",
        "    dataset='Hetionet',\n",
        "    restrict_evaluation_to_relations=restrict_evaluation_to_relations,\n",
        "    model='RotatE',\n",
        ")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No random seed is specified. Setting to 1534516120.\n",
            "Requested ratio[0]=0.800 (equal to size 1800157), but got 0.800 (equal to size 1800806) to ensure that all entities/relations occur in train.\n",
            "Requested ratio[1]=0.100 (equal to size 225019), but got 0.100 (equal to size 224647) to ensure that all entities/relations occur in train.\n",
            "Training epochs on cuda:   0%|          | 0/5 [00:00<?, ?epoch/s]INFO:pykeen.training.training_loop:using stopper: <pykeen.stoppers.stopper.NopStopper object at 0x7fa4841787b8>\n",
            "\n",
            "Training batches on cuda:   0%|          | 0/7035 [00:00<?, ?batch/s]\u001b[A\n",
            "Training batches on cuda:   0%|          | 1/7035 [00:00<14:51,  7.89batch/s]\u001b[A\n",
            "Training batches on cuda:   0%|          | 11/7035 [00:00<10:46, 10.87batch/s]\u001b[A\n",
            "Training batches on cuda:   0%|          | 20/7035 [00:00<07:55, 14.74batch/s]\u001b[A\n",
            "Training batches on cuda:   0%|          | 30/7035 [00:00<05:55, 19.68batch/s]\u001b[A\n",
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            "INFO:pykeen.evaluation.evaluator:Starting batch_size search for evaluation now...\n",
            "/usr/local/lib/python3.6/dist-packages/pykeen/evaluation/evaluator.py:379: UserWarning: This overload of nonzero is deprecated:\n",
            "\tnonzero()\n",
            "Consider using one of the following signatures instead:\n",
            "\tnonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)\n",
            "  filter_batch = (entity_filter_test & relation_filter).nonzero()\n",
            "INFO:pykeen.evaluation.evaluator:Concluded batch_size search with batch_size=61.\n",
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            "INFO:pykeen.evaluation.evaluator:Evaluation took 0.52s seconds\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnM8jztkbV_o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3268483e-1c27-483c-c48e-eaa3f76383b7"
      },
      "source": [
        "results.metric_results.to_df()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Side</th>\n",
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              "      <th>Value</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>hits_at_10</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
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              "    <tr>\n",
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              "      <td>worst</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>head</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>head</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>head</td>\n",
              "      <td>best</td>\n",
              "      <td>mean_rank</td>\n",
              "      <td>8491.754098</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>head</td>\n",
              "      <td>best</td>\n",
              "      <td>mean_reciprocal_rank</td>\n",
              "      <td>0.000654</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>head</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>head</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>head</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>head</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>tail</td>\n",
              "      <td>avg</td>\n",
              "      <td>mean_rank</td>\n",
              "      <td>5218.696721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>tail</td>\n",
              "      <td>avg</td>\n",
              "      <td>mean_reciprocal_rank</td>\n",
              "      <td>0.001418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>tail</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>tail</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>tail</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>tail</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>tail</td>\n",
              "      <td>worst</td>\n",
              "      <td>mean_rank</td>\n",
              "      <td>5218.696721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>tail</td>\n",
              "      <td>worst</td>\n",
              "      <td>mean_reciprocal_rank</td>\n",
              "      <td>0.001418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>tail</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>tail</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>tail</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>tail</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>tail</td>\n",
              "      <td>best</td>\n",
              "      <td>mean_rank</td>\n",
              "      <td>5218.696721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>tail</td>\n",
              "      <td>best</td>\n",
              "      <td>mean_reciprocal_rank</td>\n",
              "      <td>0.001418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>tail</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>tail</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>tail</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>tail</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>both</td>\n",
              "      <td>avg</td>\n",
              "      <td>mean_rank</td>\n",
              "      <td>6855.227459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>both</td>\n",
              "      <td>avg</td>\n",
              "      <td>mean_reciprocal_rank</td>\n",
              "      <td>0.001036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>both</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>both</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>both</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>both</td>\n",
              "      <td>avg</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>both</td>\n",
              "      <td>worst</td>\n",
              "      <td>mean_rank</td>\n",
              "      <td>6855.229508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>both</td>\n",
              "      <td>worst</td>\n",
              "      <td>mean_reciprocal_rank</td>\n",
              "      <td>0.001036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>both</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>both</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>both</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>both</td>\n",
              "      <td>worst</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>both</td>\n",
              "      <td>best</td>\n",
              "      <td>mean_rank</td>\n",
              "      <td>6855.225410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>both</td>\n",
              "      <td>best</td>\n",
              "      <td>mean_reciprocal_rank</td>\n",
              "      <td>0.001036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>both</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>both</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>both</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>both</td>\n",
              "      <td>best</td>\n",
              "      <td>hits_at_10</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Side   Type                Metric        Value\n",
              "0   head    avg    adjusted_mean_rank     0.376230\n",
              "1   tail    avg    adjusted_mean_rank     0.231138\n",
              "2   both    avg    adjusted_mean_rank     0.303684\n",
              "3   head    avg             mean_rank  8491.758197\n",
              "4   head    avg  mean_reciprocal_rank     0.000654\n",
              "5   head    avg             hits_at_1     0.000000\n",
              "6   head    avg             hits_at_3     0.000000\n",
              "7   head    avg             hits_at_5     0.000000\n",
              "8   head    avg            hits_at_10     0.000000\n",
              "9   head  worst             mean_rank  8491.762295\n",
              "10  head  worst  mean_reciprocal_rank     0.000654\n",
              "11  head  worst             hits_at_1     0.000000\n",
              "12  head  worst             hits_at_3     0.000000\n",
              "13  head  worst             hits_at_5     0.000000\n",
              "14  head  worst            hits_at_10     0.000000\n",
              "15  head   best             mean_rank  8491.754098\n",
              "16  head   best  mean_reciprocal_rank     0.000654\n",
              "17  head   best             hits_at_1     0.000000\n",
              "18  head   best             hits_at_3     0.000000\n",
              "19  head   best             hits_at_5     0.000000\n",
              "20  head   best            hits_at_10     0.000000\n",
              "21  tail    avg             mean_rank  5218.696721\n",
              "22  tail    avg  mean_reciprocal_rank     0.001418\n",
              "23  tail    avg             hits_at_1     0.000000\n",
              "24  tail    avg             hits_at_3     0.000000\n",
              "25  tail    avg             hits_at_5     0.000000\n",
              "26  tail    avg            hits_at_10     0.000000\n",
              "27  tail  worst             mean_rank  5218.696721\n",
              "28  tail  worst  mean_reciprocal_rank     0.001418\n",
              "29  tail  worst             hits_at_1     0.000000\n",
              "30  tail  worst             hits_at_3     0.000000\n",
              "31  tail  worst             hits_at_5     0.000000\n",
              "32  tail  worst            hits_at_10     0.000000\n",
              "33  tail   best             mean_rank  5218.696721\n",
              "34  tail   best  mean_reciprocal_rank     0.001418\n",
              "35  tail   best             hits_at_1     0.000000\n",
              "36  tail   best             hits_at_3     0.000000\n",
              "37  tail   best             hits_at_5     0.000000\n",
              "38  tail   best            hits_at_10     0.000000\n",
              "39  both    avg             mean_rank  6855.227459\n",
              "40  both    avg  mean_reciprocal_rank     0.001036\n",
              "41  both    avg             hits_at_1     0.000000\n",
              "42  both    avg             hits_at_3     0.000000\n",
              "43  both    avg             hits_at_5     0.000000\n",
              "44  both    avg            hits_at_10     0.000000\n",
              "45  both  worst             mean_rank  6855.229508\n",
              "46  both  worst  mean_reciprocal_rank     0.001036\n",
              "47  both  worst             hits_at_1     0.000000\n",
              "48  both  worst             hits_at_3     0.000000\n",
              "49  both  worst             hits_at_5     0.000000\n",
              "50  both  worst            hits_at_10     0.000000\n",
              "51  both   best             mean_rank  6855.225410\n",
              "52  both   best  mean_reciprocal_rank     0.001036\n",
              "53  both   best             hits_at_1     0.000000\n",
              "54  both   best             hits_at_3     0.000000\n",
              "55  both   best             hits_at_5     0.000000\n",
              "56  both   best            hits_at_10     0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    }
  ]
}