{
  "id": 475819,
  "title": "LOFO IMPORTANCE",
  "url": "/competitions/hms-harmful-brain-activity-classification/discussion/475819",
  "author_name": "Peter",
  "post_date": "2024-02-10T01:30:29.803000",
  "votes": 1,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hi, kagglers! If you want to use <strong>LOFO  importance</strong></p>\n<p>check out this code!</p>\n<h2>CV</h2>\n<pre><code> = []   \ngkf = GroupKFold(n_splits=)\n i, (train_idx, valid_idx)  enumerate(gkf.(train, train.target, train.patient_id)):\n\n      # lofo importance \n      .((train_idx, valid_idx))\n</code></pre>\n<h2>featuers &amp; target</h2>\n<pre><code> train_lofo = train.()\n features = train_lofo.().\n # TARS = {: , : , :, :, :, :}\n train_lofo[] = train_lofo[].map(TARS)\n target = \n</code></pre>\n<h2>LOFO Dataset</h2>\n<pre><code> import lofo\n from lofo.lofo_importance import LOFOImportance\n from kaggle_kl_div import score\n\n ds = lofo.\n\n lofo_imp = lofo. \n imp_df = lofo_imp.get\n\n # LOFO Importance Plot\n lofo.plot)\n</code></pre>",
  "messages": [
    {
      "id": 2645129,
      "postDate": "2024-02-10T01:30:29.803Z",
      "content": "<p>Hi, kagglers! If you want to use <strong>LOFO  importance</strong></p>\n<p>check out this code!</p>\n<h2>CV</h2>\n<pre><code> = []   \ngkf = GroupKFold(n_splits=)\n i, (train_idx, valid_idx)  enumerate(gkf.(train, train.target, train.patient_id)):\n\n      # lofo importance \n      .((train_idx, valid_idx))\n</code></pre>\n<h2>featuers &amp; target</h2>\n<pre><code> train_lofo = train.()\n features = train_lofo.().\n # TARS = {: , : , :, :, :, :}\n train_lofo[] = train_lofo[].map(TARS)\n target = \n</code></pre>\n<h2>LOFO Dataset</h2>\n<pre><code> import lofo\n from lofo.lofo_importance import LOFOImportance\n from kaggle_kl_div import score\n\n ds = lofo.\n\n lofo_imp = lofo. \n imp_df = lofo_imp.get\n\n # LOFO Importance Plot\n lofo.plot)\n</code></pre>",
      "rawMarkdown": "Hi, kagglers! If you want to use **LOFO  importance**\n\ncheck out this code!\n\n##  CV \n    cv = []   \n    gkf = GroupKFold(n_splits=5)\n    for i, (train_idx, valid_idx) in enumerate(gkf.split(train, train.target, train.patient_id)):\n    \n          # lofo importance \n          cv.append((train_idx, valid_idx))\n\n## featuers & target\n     train_lofo = train.copy()\n     features = train_lofo.drop('target').columns\n     # TARS = {'Seizure': 0, 'LPD': 1, 'GPD':2, 'LRDA':3, 'GRDA':4, 'Other':5}\n     train_lofo['target'] = train_lofo['target'].map(TARS)\n     target = 'target'\n\n## LOFO Dataset\n     import lofo\n     from lofo.lofo_importance import LOFOImportance\n     from kaggle_kl_div import score\n\n     ds = lofo.Dataset(train_lofo, target=target, features=features)\n\n     lofo_imp = lofo.LOFOImportance(ds, cv=cv, scoring=score) \n     imp_df = lofo_imp.get_importance()\n\n     # LOFO Importance Plot\n     lofo.plot_importance(imp_df, figsize=(12, 6))\n\n\n\n\n",
      "votes": 1
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2645129": "Hi, kagglers! If you want to use **LOFO  importance**\n\ncheck out this code!\n\n##  CV \n    cv = []   \n    gkf = GroupKFold(n_splits=5)\n    for i, (train_idx, valid_idx) in enumerate(gkf.split(train, train.target, train.patient_id)):\n    \n          # lofo importance \n          cv.append((train_idx, valid_idx))\n\n## featuers & target\n     train_lofo = train.copy()\n     features = train_lofo.drop('target').columns\n     # TARS = {'Seizure': 0, 'LPD': 1, 'GPD':2, 'LRDA':3, 'GRDA':4, 'Other':5}\n     train_lofo['target'] = train_lofo['target'].map(TARS)\n     target = 'target'\n\n## LOFO Dataset\n     import lofo\n     from lofo.lofo_importance import LOFOImportance\n     from kaggle_kl_div import score\n\n     ds = lofo.Dataset(train_lofo, target=target, features=features)\n\n     lofo_imp = lofo.LOFOImportance(ds, cv=cv, scoring=score) \n     imp_df = lofo_imp.get_importance()\n\n     # LOFO Importance Plot\n     lofo.plot_importance(imp_df, figsize=(12, 6))\n\n\n\n\n"
  }
}