{
  "id": 402807,
  "title": "Please help with Submission Scoring Error",
  "url": "/competitions/predict-student-performance-from-game-play/discussion/402807",
  "author_name": "",
  "post_date": "2023-04-19T19:17:10.214400100Z",
  "votes": 2,
  "comment_count": 5,
  "views": 0,
  "content": "<h2>Submission code</h2>\n<h3>Already 25 attempts to send (</h3>\n<pre><code> jo_wilder\nenv = jo_wilder.make_env()\niter_test = env.iter_test()\n\n\n (test, sample_submission)  iter_test:\n\n    test = optimize_memory_usage(test)\n    \n    test.drop(DROP_l_v, axis = , inplace=)\n    \n    test =  add_question( test )\n\n    test = future_engineering( test )\n    test = join_s_q(test)\n    \n    session_id = test.session_id_q\n    question_number = test.q\n    \n    test[NUMERIC] = scaler.transform( test[NUMERIC])\n    \n    score_fa = pd.DataFrame(fa.transform(test[NUMERIC]), columns=[,,])\n    test.drop([*DROP_F, , ], axis = , inplace =  )\n    test = pd.concat([test, score_fa ], axis=)\n\n\n\n    answer = predict(data_for_predict = test, model = trained_model,  \n                  percent =percentage_for_pred_prob, q_num= question_number.to_list() )\n\n    :\n         i, v  ( answer ):\n            sample_submission.loc[sample_submission.session_id == session_id[i], ] = (v)\n    :\n        sample_submission.loc[:, ] = \n     :\n        env.predict(sample_submission)\n</code></pre>\n<h3>There are no errors in notepad itself.</h3>\n<pre><code>df = pd.read_csv()\n( df.shape )\ndisplay(df.info())\ndf.tail()\n</code></pre>\n<h3>At the output I get</h3>\n<blockquote>\n  <p>(18, 2)<br>\n  <br>\n  RangeIndex: 18 entries, 0 to 17<br>\n  Data columns (total 2 columns):<br>\n   #   Column      Non-Null Count  Dtype <br>\n  ---  ------      --------------  ----- <br>\n   0   session_id  18 non-null     object<br>\n   1   correct     18 non-null     int64 <br>\n  dtypes: int64(1), object(1)<br>\n  memory usage: 416.0+ bytes<br>\n  None</p>\n</blockquote>",
  "messages": [
    {
      "id": "2227468",
      "postDate": "04/19/2023 19:17:10",
      "content": "<h2>Submission code</h2>\n<h3>Already 25 attempts to send (</h3>\n<pre><code> jo_wilder\nenv = jo_wilder.make_env()\niter_test = env.iter_test()\n\n\n (test, sample_submission)  iter_test:\n\n    test = optimize_memory_usage(test)\n    \n    test.drop(DROP_l_v, axis = , inplace=)\n    \n    test =  add_question( test )\n\n    test = future_engineering( test )\n    test = join_s_q(test)\n    \n    session_id = test.session_id_q\n    question_number = test.q\n    \n    test[NUMERIC] = scaler.transform( test[NUMERIC])\n    \n    score_fa = pd.DataFrame(fa.transform(test[NUMERIC]), columns=[,,])\n    test.drop([*DROP_F, , ], axis = , inplace =  )\n    test = pd.concat([test, score_fa ], axis=)\n\n\n\n    answer = predict(data_for_predict = test, model = trained_model,  \n                  percent =percentage_for_pred_prob, q_num= question_number.to_list() )\n\n    :\n         i, v  ( answer ):\n            sample_submission.loc[sample_submission.session_id == session_id[i], ] = (v)\n    :\n        sample_submission.loc[:, ] = \n     :\n        env.predict(sample_submission)\n</code></pre>\n<h3>There are no errors in notepad itself.</h3>\n<pre><code>df = pd.read_csv()\n( df.shape )\ndisplay(df.info())\ndf.tail()\n</code></pre>\n<h3>At the output I get</h3>\n<blockquote>\n  <p>(18, 2)<br>\n  <br>\n  RangeIndex: 18 entries, 0 to 17<br>\n  Data columns (total 2 columns):<br>\n   #   Column      Non-Null Count  Dtype <br>\n  ---  ------      --------------  ----- <br>\n   0   session_id  18 non-null     object<br>\n   1   correct     18 non-null     int64 <br>\n  dtypes: int64(1), object(1)<br>\n  memory usage: 416.0+ bytes<br>\n  None</p>\n</blockquote>",
      "rawMarkdown": "## Submission code \n### Already 25 attempts to send (\n\n```python\nimport jo_wilder\nenv = jo_wilder.make_env()\niter_test = env.iter_test()\n\n\nfor (test, sample_submission) in iter_test:\n\n    test = optimize_memory_usage(test)\n    ################################################\n    test.drop(DROP_l_v, axis = 1, inplace=True)\n    ################################################\n    test =  add_question( test )\n    \n    test = future_engineering( test )\n    test = join_s_q(test)\n    ################################################\n    session_id = test.session_id_q\n    question_number = test.q\n    ################################################\n    test[NUMERIC] = scaler.transform( test[NUMERIC])\n    ################################################    \n    score_fa = pd.DataFrame(fa.transform(test[NUMERIC]), columns=[\"f_1\",\"f_2\",\"f_1\"])\n    test.drop([*DROP_F, \"q\", \"session_id_q\"], axis = 1, inplace = True )\n    test = pd.concat([test, score_fa ], axis=1)\n    \n    \n    \n    answer = predict(data_for_predict = test, model = trained_model,  \n                  percent =percentage_for_pred_prob, q_num= question_number.to_list() )\n    \n    try:\n        for i, v in enumerate( answer ):\n            sample_submission.loc[sample_submission.session_id == session_id[i], \"correct\"] = int(v)\n    except:\n        sample_submission.loc[:, \"correct\"] = 999\n    finally :\n        env.predict(sample_submission)\n```\n\n\n\n### There are no errors in notepad itself.\n\n```python\ndf = pd.read_csv('submission.csv')\nprint( df.shape )\ndisplay(df.info())\ndf.tail(54)\n```\n\n### At the output I get\n\n>(18, 2)\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 18 entries, 0 to 17\nData columns (total 2 columns):\n #   Column      Non-Null Count  Dtype \n---  ------      --------------  ----- \n 0   session_id  18 non-null     object\n 1   correct     18 non-null     int64 \ndtypes: int64(1), object(1)\nmemory usage: 416.0+ bytes\nNone",
      "votes": null
    },
    {
      "id": "2312561",
      "postDate": "06/22/2023 02:37:28",
      "content": "<p>I am on my 24th erroneous attempt myself, have you found a fix ? </p>",
      "rawMarkdown": "I am on my 24th erroneous attempt myself, have you found a fix ?",
      "votes": null
    },
    {
      "id": "2312690",
      "postDate": "06/22/2023 06:05:10",
      "content": "<p>Unfortunately, I didn't find an answer.</p>",
      "rawMarkdown": "Unfortunately, I didn't find an answer.",
      "votes": null
    },
    {
      "id": "2312702",
      "postDate": "06/22/2023 06:16:46",
      "content": "<p>I sent a message to the competition hosts earlier, you could try that as well. We can let each other know if someone finds a fix.</p>",
      "rawMarkdown": "I sent a message to the competition hosts earlier, you could try that as well. We can let each other know if someone finds a fix.",
      "votes": null
    },
    {
      "id": "2312740",
      "postDate": "06/22/2023 06:51:57",
      "content": "<p>Yes, of course, if I have half a problem, I will definitely let you know.)</p>",
      "rawMarkdown": "Yes, of course, if I have half a problem, I will definitely let you know.)",
      "votes": null
    },
    {
      "id": "2313981",
      "postDate": "06/23/2023 05:14:37",
      "content": "<p>I found a way to submit properly even if I don't understand yet what was wrong with my original submit method. </p>\n<p>The idea is that I took a public notebook such as :<br>\n<a href=\"https://www.kaggle.com/code/gusthema/student-performance-w-tensorflow-decision-forests\" target=\"_blank\">https://www.kaggle.com/code/gusthema/student-performance-w-tensorflow-decision-forests</a></p>\n<p>And I changed the code to my models and it worked.</p>",
      "rawMarkdown": "I found a way to submit properly even if I don't understand yet what was wrong with my original submit method. \n\nThe idea is that I took a public notebook such as :\nhttps://www.kaggle.com/code/gusthema/student-performance-w-tensorflow-decision-forests\n\nAnd I changed the code to my models and it worked.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2312561,
      "author_name": "qurious",
      "author_url": "",
      "post_date": "06/22/2023 02:37:28",
      "content": "<p>I am on my 24th erroneous attempt myself, have you found a fix ? </p>",
      "votes": null,
      "replies": [
        {
          "id": 2312690,
          "author_name": "oleksandrkanalosh",
          "author_url": "",
          "post_date": "06/22/2023 06:05:10",
          "content": "<p>Unfortunately, I didn't find an answer.</p>",
          "votes": null,
          "replies": [
            {
              "id": 2312702,
              "author_name": "qurious",
              "author_url": "",
              "post_date": "06/22/2023 06:16:46",
              "content": "<p>I sent a message to the competition hosts earlier, you could try that as well. We can let each other know if someone finds a fix.</p>",
              "votes": null,
              "replies": [
                {
                  "id": 2312740,
                  "author_name": "oleksandrkanalosh",
                  "author_url": "",
                  "post_date": "06/22/2023 06:51:57",
                  "content": "<p>Yes, of course, if I have half a problem, I will definitely let you know.)</p>",
                  "votes": null,
                  "replies": [
                    {
                      "id": 2313981,
                      "author_name": "qurious",
                      "author_url": "",
                      "post_date": "06/23/2023 05:14:37",
                      "content": "<p>I found a way to submit properly even if I don't understand yet what was wrong with my original submit method. </p>\n<p>The idea is that I took a public notebook such as :<br>\n<a href=\"https://www.kaggle.com/code/gusthema/student-performance-w-tensorflow-decision-forests\" target=\"_blank\">https://www.kaggle.com/code/gusthema/student-performance-w-tensorflow-decision-forests</a></p>\n<p>And I changed the code to my models and it worked.</p>",
                      "votes": null,
                      "replies": []
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "2227468": "## Submission code \n### Already 25 attempts to send (\n\n```python\nimport jo_wilder\nenv = jo_wilder.make_env()\niter_test = env.iter_test()\n\n\nfor (test, sample_submission) in iter_test:\n\n    test = optimize_memory_usage(test)\n    ################################################\n    test.drop(DROP_l_v, axis = 1, inplace=True)\n    ################################################\n    test =  add_question( test )\n    \n    test = future_engineering( test )\n    test = join_s_q(test)\n    ################################################\n    session_id = test.session_id_q\n    question_number = test.q\n    ################################################\n    test[NUMERIC] = scaler.transform( test[NUMERIC])\n    ################################################    \n    score_fa = pd.DataFrame(fa.transform(test[NUMERIC]), columns=[\"f_1\",\"f_2\",\"f_1\"])\n    test.drop([*DROP_F, \"q\", \"session_id_q\"], axis = 1, inplace = True )\n    test = pd.concat([test, score_fa ], axis=1)\n    \n    \n    \n    answer = predict(data_for_predict = test, model = trained_model,  \n                  percent =percentage_for_pred_prob, q_num= question_number.to_list() )\n    \n    try:\n        for i, v in enumerate( answer ):\n            sample_submission.loc[sample_submission.session_id == session_id[i], \"correct\"] = int(v)\n    except:\n        sample_submission.loc[:, \"correct\"] = 999\n    finally :\n        env.predict(sample_submission)\n```\n\n\n\n### There are no errors in notepad itself.\n\n```python\ndf = pd.read_csv('submission.csv')\nprint( df.shape )\ndisplay(df.info())\ndf.tail(54)\n```\n\n### At the output I get\n\n>(18, 2)\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 18 entries, 0 to 17\nData columns (total 2 columns):\n #   Column      Non-Null Count  Dtype \n---  ------      --------------  ----- \n 0   session_id  18 non-null     object\n 1   correct     18 non-null     int64 \ndtypes: int64(1), object(1)\nmemory usage: 416.0+ bytes\nNone",
    "2312561": "I am on my 24th erroneous attempt myself, have you found a fix ?",
    "2312690": "Unfortunately, I didn't find an answer.",
    "2312702": "I sent a message to the competition hosts earlier, you could try that as well. We can let each other know if someone finds a fix.",
    "2312740": "Yes, of course, if I have half a problem, I will definitely let you know.)",
    "2313981": "I found a way to submit properly even if I don't understand yet what was wrong with my original submit method. \n\nThe idea is that I took a public notebook such as :\nhttps://www.kaggle.com/code/gusthema/student-performance-w-tensorflow-decision-forests\n\nAnd I changed the code to my models and it worked."
  },
  "source": "meta"
}