{
  "id": 395826,
  "title": "what does cmAP stands for ?",
  "url": "/competitions/birdclef-2023/discussion/395826",
  "author_name": "",
  "post_date": "2023-03-19T03:27:59.081712900Z",
  "votes": 3,
  "comment_count": 3,
  "views": 0,
  "content": "<p>It is provided that </p>\n<blockquote>\n  <p>The evaluation metric for this contest is padded cmAP, a derivative of the macro-averaged average precision score as implemented by scikit-learn. </p>\n</blockquote>\n<p>with the code</p>\n<pre><code> pandas  pd\n sklearn.metrics\n\n ():\n    solution = solution.drop([], axis=, errors=)\n    submission = submission.drop([], axis=, errors=)\n    new_rows = []\n     i  (padding_factor):\n        new_rows.append([  i  ((solution.columns))])\n    new_rows = pd.DataFrame(new_rows)\n    new_rows.columns = solution.columns\n    padded_solution = pd.concat([solution, new_rows]).reset_index(drop=).copy()\n    padded_submission = pd.concat([submission, new_rows]).reset_index(drop=).copy()\n    score = sklearn.metrics.average_precision_score(\n        padded_solution.values,\n        padded_submission.values,\n        average=,\n    )\n     score\n</code></pre>\n<p>It seems that it is the average_precision_score with padding so i am confused on why it is said to be a derivative of the macro-averaged average precision score as implemented by scikit-learn. ?</p>",
  "messages": [
    {
      "id": "2187758",
      "postDate": "03/19/2023 03:27:59",
      "content": "<p>It is provided that </p>\n<blockquote>\n  <p>The evaluation metric for this contest is padded cmAP, a derivative of the macro-averaged average precision score as implemented by scikit-learn. </p>\n</blockquote>\n<p>with the code</p>\n<pre><code> pandas  pd\n sklearn.metrics\n\n ():\n    solution = solution.drop([], axis=, errors=)\n    submission = submission.drop([], axis=, errors=)\n    new_rows = []\n     i  (padding_factor):\n        new_rows.append([  i  ((solution.columns))])\n    new_rows = pd.DataFrame(new_rows)\n    new_rows.columns = solution.columns\n    padded_solution = pd.concat([solution, new_rows]).reset_index(drop=).copy()\n    padded_submission = pd.concat([submission, new_rows]).reset_index(drop=).copy()\n    score = sklearn.metrics.average_precision_score(\n        padded_solution.values,\n        padded_submission.values,\n        average=,\n    )\n     score\n</code></pre>\n<p>It seems that it is the average_precision_score with padding so i am confused on why it is said to be a derivative of the macro-averaged average precision score as implemented by scikit-learn. ?</p>",
      "rawMarkdown": "It is provided that \n\n>The evaluation metric for this contest is padded cmAP, a derivative of the macro-averaged average precision score as implemented by scikit-learn. \n\nwith the code\n```python\n\nimport pandas as pd\nimport sklearn.metrics\n\ndef padded_cmap(solution, submission, padding_factor=5):\n    solution = solution.drop(['row_id'], axis=1, errors='ignore')\n    submission = submission.drop(['row_id'], axis=1, errors='ignore')\n    new_rows = []\n    for i in range(padding_factor):\n        new_rows.append([1 for i in range(len(solution.columns))])\n    new_rows = pd.DataFrame(new_rows)\n    new_rows.columns = solution.columns\n    padded_solution = pd.concat([solution, new_rows]).reset_index(drop=True).copy()\n    padded_submission = pd.concat([submission, new_rows]).reset_index(drop=True).copy()\n    score = sklearn.metrics.average_precision_score(\n        padded_solution.values,\n        padded_submission.values,\n        average='macro',\n    )\n    return score\n```\n\nIt seems that it is the average_precision_score with padding so i am confused on why it is said to be a derivative of the macro-averaged average precision score as implemented by scikit-learn. ?",
      "votes": null
    },
    {
      "id": "2187855",
      "postDate": "03/19/2023 05:07:10",
      "content": "<p>The term \"cmAP\" stands for \"Class-wise Macro Average Precision\". It is a metric used in classification tasks that evaluates the quality of predicted probabilities for multiple classes.</p>\n<p>In the context of the competition you mentioned, \"padded cmAP\" is a modified version of the traditional cmAP metric that takes into account the presence of unobserved classes in the predictions. The padding factor in the code you provided adds extra rows to both the solution and submission data frames to account for the possibility of unobserved classes.</p>\n<p>Regarding the confusion with the term \"derivative of the macro-averaged average precision score\", it is likely that the term \"derivative\" is being used in the sense of \"modified\" or \"adapted\". So, padded cmAP is a modified version of the macro-averaged average precision score implemented in scikit-learn. The modification comes from the use of padding to handle unobserved classes.</p>\n<p>I hope this helps clarify the use of the term \"cmAP\" and the relationship between padded cmAP and the macro-averaged average precision score.</p>",
      "rawMarkdown": "The term \"cmAP\" stands for \"Class-wise Macro Average Precision\". It is a metric used in classification tasks that evaluates the quality of predicted probabilities for multiple classes.\n\nIn the context of the competition you mentioned, \"padded cmAP\" is a modified version of the traditional cmAP metric that takes into account the presence of unobserved classes in the predictions. The padding factor in the code you provided adds extra rows to both the solution and submission data frames to account for the possibility of unobserved classes.\n\nRegarding the confusion with the term \"derivative of the macro-averaged average precision score\", it is likely that the term \"derivative\" is being used in the sense of \"modified\" or \"adapted\". So, padded cmAP is a modified version of the macro-averaged average precision score implemented in scikit-learn. The modification comes from the use of padding to handle unobserved classes.\n\nI hope this helps clarify the use of the term \"cmAP\" and the relationship between padded cmAP and the macro-averaged average precision score.",
      "votes": null
    },
    {
      "id": "2232746",
      "postDate": "04/24/2023 15:25:53",
      "content": "<p>Thank you very much for the answer.</p>",
      "rawMarkdown": "Thank you very much for the answer.",
      "votes": null
    },
    {
      "id": "2243623",
      "postDate": "05/03/2023 03:46:19",
      "content": "<p>Say that to the author 😉 <a href=\"https://www.kaggle.com/rojinabarati\" target=\"_blank\">@rojinabarati</a> <br>\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F12870466%2Fc9c490df2f924ab2b6ad050e8f646b63%2FScreenshot_93.jpg?generation=1683085574019523&amp;alt=media\" alt=\"\"></p>",
      "rawMarkdown": "Say that to the author 😉 @rojinabarati \n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F12870466%2Fc9c490df2f924ab2b6ad050e8f646b63%2FScreenshot_93.jpg?generation=1683085574019523&alt=media)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2187855,
      "author_name": "siddharthkumarsah",
      "author_url": "",
      "post_date": "03/19/2023 05:07:10",
      "content": "<p>The term \"cmAP\" stands for \"Class-wise Macro Average Precision\". It is a metric used in classification tasks that evaluates the quality of predicted probabilities for multiple classes.</p>\n<p>In the context of the competition you mentioned, \"padded cmAP\" is a modified version of the traditional cmAP metric that takes into account the presence of unobserved classes in the predictions. The padding factor in the code you provided adds extra rows to both the solution and submission data frames to account for the possibility of unobserved classes.</p>\n<p>Regarding the confusion with the term \"derivative of the macro-averaged average precision score\", it is likely that the term \"derivative\" is being used in the sense of \"modified\" or \"adapted\". So, padded cmAP is a modified version of the macro-averaged average precision score implemented in scikit-learn. The modification comes from the use of padding to handle unobserved classes.</p>\n<p>I hope this helps clarify the use of the term \"cmAP\" and the relationship between padded cmAP and the macro-averaged average precision score.</p>",
      "votes": null,
      "replies": [
        {
          "id": 2232746,
          "author_name": "rojina",
          "author_url": "",
          "post_date": "04/24/2023 15:25:53",
          "content": "<p>Thank you very much for the answer.</p>",
          "votes": null,
          "replies": [
            {
              "id": 2243623,
              "author_name": "janmpia",
              "author_url": "",
              "post_date": "05/03/2023 03:46:19",
              "content": "<p>Say that to the author 😉 <a href=\"https://www.kaggle.com/rojinabarati\" target=\"_blank\">@rojinabarati</a> <br>\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F12870466%2Fc9c490df2f924ab2b6ad050e8f646b63%2FScreenshot_93.jpg?generation=1683085574019523&amp;alt=media\" alt=\"\"></p>",
              "votes": null,
              "replies": []
            }
          ]
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "2187758": "It is provided that \n\n>The evaluation metric for this contest is padded cmAP, a derivative of the macro-averaged average precision score as implemented by scikit-learn. \n\nwith the code\n```python\n\nimport pandas as pd\nimport sklearn.metrics\n\ndef padded_cmap(solution, submission, padding_factor=5):\n    solution = solution.drop(['row_id'], axis=1, errors='ignore')\n    submission = submission.drop(['row_id'], axis=1, errors='ignore')\n    new_rows = []\n    for i in range(padding_factor):\n        new_rows.append([1 for i in range(len(solution.columns))])\n    new_rows = pd.DataFrame(new_rows)\n    new_rows.columns = solution.columns\n    padded_solution = pd.concat([solution, new_rows]).reset_index(drop=True).copy()\n    padded_submission = pd.concat([submission, new_rows]).reset_index(drop=True).copy()\n    score = sklearn.metrics.average_precision_score(\n        padded_solution.values,\n        padded_submission.values,\n        average='macro',\n    )\n    return score\n```\n\nIt seems that it is the average_precision_score with padding so i am confused on why it is said to be a derivative of the macro-averaged average precision score as implemented by scikit-learn. ?",
    "2187855": "The term \"cmAP\" stands for \"Class-wise Macro Average Precision\". It is a metric used in classification tasks that evaluates the quality of predicted probabilities for multiple classes.\n\nIn the context of the competition you mentioned, \"padded cmAP\" is a modified version of the traditional cmAP metric that takes into account the presence of unobserved classes in the predictions. The padding factor in the code you provided adds extra rows to both the solution and submission data frames to account for the possibility of unobserved classes.\n\nRegarding the confusion with the term \"derivative of the macro-averaged average precision score\", it is likely that the term \"derivative\" is being used in the sense of \"modified\" or \"adapted\". So, padded cmAP is a modified version of the macro-averaged average precision score implemented in scikit-learn. The modification comes from the use of padding to handle unobserved classes.\n\nI hope this helps clarify the use of the term \"cmAP\" and the relationship between padded cmAP and the macro-averaged average precision score.",
    "2232746": "Thank you very much for the answer.",
    "2243623": "Say that to the author 😉 @rojinabarati \n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F12870466%2Fc9c490df2f924ab2b6ad050e8f646b63%2FScreenshot_93.jpg?generation=1683085574019523&alt=media)"
  },
  "source": "meta"
}