Experiments/kaggle/NBME/src/nbme-meta-pseudo-labels.ipynb

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   "source": [
    "# Introduction\n",
    "\n",
    "* This notebook demonstrates a way to apply meta-pseudo-labels for NBME token classification task\n",
    "* It uses code snippets from: \n",
    "    * https://www.kaggle.com/code/hengck23/playground-for-meta-pseudo-label\n",
    "    * https://www.kaggle.com/code/theoviel/evaluation-metric-folds-baseline\n",
    "\n",
    "\n",
    "* Meta Pseudo Labels paper:\n",
    "    * https://arxiv.org/abs/2003.10580\n",
    "    \n",
    "    \n",
    "* Thanks @hengck23, @theoviel for sharing these great notebooks!\n",
    "* Hope that you find this approach useful. Please let me know if there are mistakes. \n",
    "* Looking forward to your comments / feedback / queries. Thanks!"
   ]
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    "tags": []
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import ast\n",
    "import json\n",
    "import itertools\n",
    "import os\n",
    "import re\n",
    "import shutil\n",
    "import sys\n",
    "import traceback\n",
    "from dataclasses import dataclass\n",
    "from functools import partial\n",
    "from itertools import chain\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.checkpoint\n",
    "from IPython.core.debugger import set_trace\n",
    "from IPython.display import display\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import GroupKFold\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "from transformers import (AutoModel, AutoTokenizer,\n",
    "                          DataCollatorForTokenClassification,\n",
    "                          get_cosine_schedule_with_warmup, get_scheduler)\n",
    "from transformers.trainer_pt_utils import get_parameter_names\n",
    "\n",
    "pd.options.display.max_colwidth = None"
   ]
  },
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "cudatoolkit               11.0.221             h6bb024c_0    nvidia\r\n"
     ]
    }
   ],
   "source": [
    "!conda list | grep cudatoolkit"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "17ddbf3c",
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     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qq bitsandbytes-cuda110 # optimizer\n",
    "%pip install -qq pynvml\n",
    "%pip install -qq accelerate\n",
    "%pip install -qq datasets"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "import bitsandbytes as bnb\n",
    "from accelerate import Accelerator\n",
    "from pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\n",
    "from datasets import Dataset, load_from_disk"
   ]
  },
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   "cell_type": "markdown",
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     "start_time": "2022-05-04T12:49:05.615339",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Config"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "source": [
    "config = {\n",
    "    \"debug\": False,\n",
    "    \n",
    "    \"num_layers_in_head\": 6, # Number of layer hidden states to be used in token classification head\n",
    "    \"mtl_tok_num_labels\": 3, # Predict Inside, Begin, End of answer spans\n",
    "    \"gradient_checkpointing\": True,\n",
    "    \"mixed_precision\": True,\n",
    "    \"n_freeze\": 2,\n",
    "    \n",
    "    \"batch_size\": 8,\n",
    "    \"train_folds\": [0, 1, 2, 3],\n",
    "    \"valid_folds\": [4],\n",
    "    \n",
    "    \"num_unlabelled\": 10000, # number of unlabelled data points to be used for Meta Pseudo Labels\n",
    "\n",
    "    \"base_model_path\": \"microsoft/deberta-large\", # backbone\n",
    "    \"student_model_dir\": \"./trained_student\", # save dir for student model\n",
    "    \"teacher_model_dir\": \"./trained_teacher\", # save dir for teacher model\n",
    "    \"teacher_save_name\": \"teacher\", # teacher checkpoint \n",
    "    \"student_save_name\": \"student\", # student checkpoint\n",
    "    \n",
    "    # data path\n",
    "    \"data_dir\": \"../input/nbme-score-clinical-patient-notes\",\n",
    "    \"train_path\": \"train.csv\",\n",
    "    \"features_path\": \"features.csv\",\n",
    "    \"notes_path\": \"patient_notes.csv\",\n",
    "    \n",
    "    # save tokenized datasets here\n",
    "    \"output_dir\": \"./\",\n",
    "    \"train_dataset_path\": \"train_dataset\",\n",
    "    \"valid_dataset_path\": \"valid_dataset\",\n",
    "    \n",
    "    # column names\n",
    "    \"text_col\": \"pn_history\",\n",
    "    \"feature_col\": \"feature_text\",\n",
    "    \"label_col\": \"label_spans\",\n",
    "    \"annotation_col\": \"annotation\",\n",
    "    \"text_sequence_identifier\": 1,\n",
    "    \"max_length\": 480,\n",
    "    \n",
    "    # optimizer & scheduler params\n",
    "    \"weight_decay\": 1e-3,\n",
    "    \"lr\": 2e-5,\n",
    "    \"eps\": 1e-6,\n",
    "    \"beta1\": 0.9,\n",
    "    \"beta2\": 0.99,\n",
    "    \"num_epochs\": 1,\n",
    "    \"grad_accumulation\": 1,\n",
    "    \"warmup_pct\": 0.02,\n",
    "    \"validation_interval\": 200\n",
    "}"
   ]
  },
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   "cell_type": "markdown",
   "id": "b7c94129",
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     "exception": false,
     "start_time": "2022-05-04T12:49:05.777257",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "675d58b4",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-05-04T12:49:05.852808",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU memory occupied: 0 MB.\n"
     ]
    }
   ],
   "source": [
    "def print_gpu_utilization():\n",
    "    nvmlInit()\n",
    "    handle = nvmlDeviceGetHandleByIndex(0)\n",
    "    info = nvmlDeviceGetMemoryInfo(handle)\n",
    "    print(f\"GPU memory occupied: {info.used//1024**2} MB.\")\n",
    "print_gpu_utilization()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7348d010",
   "metadata": {
    "papermill": {
     "duration": 0.037654,
     "end_time": "2022-05-04T12:49:05.985720",
     "exception": false,
     "start_time": "2022-05-04T12:49:05.948066",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37b09217",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:49:06.063711Z",
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     "end_time": "2022-05-04T12:49:06.830722",
     "exception": false,
     "start_time": "2022-05-04T12:49:06.023557",
     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "data_dir = config[\"data_dir\"]\n",
    "train_df = pd.read_csv(os.path.join(data_dir, config[\"train_path\"]))\n",
    "features_df = pd.read_csv(os.path.join(data_dir, config[\"features_path\"]))\n",
    "notes_df = pd.read_csv(os.path.join(data_dir, config[\"notes_path\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f222605",
   "metadata": {
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     "duration": 0.039458,
     "end_time": "2022-05-04T12:49:06.908943",
     "exception": false,
     "start_time": "2022-05-04T12:49:06.869485",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bcb79d64",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_feature_text(text):\n",
    "    return re.sub('-', ' ', text)\n",
    "\n",
    "def apply_ast(df):\n",
    "    columns = [\n",
    "        \"annotation\",\n",
    "        \"location\",\n",
    "    ]\n",
    "\n",
    "    for col in columns:\n",
    "        try:\n",
    "            if type(df[col].values[0]) != list:\n",
    "                df[col] = df[col].apply(ast.literal_eval)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            traceback.print_exc()\n",
    "\n",
    "    return df\n",
    "\n",
    "def location2spans(location):\n",
    "    \"\"\"\n",
    "    a helper function to compute the label spans from the input location list\n",
    "    \"\"\"\n",
    "    spans = [loc.split(\";\") for loc in location]\n",
    "    spans = [list(map(int, s.split())) for s in chain(*spans)]\n",
    "    return spans\n",
    "\n",
    "features_df[\"feature_text\"] = features_df[\"feature_text\"].apply(process_feature_text)\n",
    "train_df = apply_ast(train_df)\n",
    "train_df = pd.merge(train_df, features_df, on=['feature_num', 'case_num'], how='left')\n",
    "train_df = pd.merge(train_df, notes_df, on=['pn_num', 'case_num'], how='left')\n",
    "train_df['label_spans'] = train_df['location'].apply(location2spans)"
   ]
  },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <th>case_num</th>\n",
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       "      <th>annotation</th>\n",
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       "      <th>feature_text</th>\n",
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       "      <th>12281</th>\n",
       "      <td>83757_805</td>\n",
       "      <td>8</td>\n",
       "      <td>83757</td>\n",
       "      <td>805</td>\n",
       "      <td>[67-year-old]</td>\n",
       "      <td>[17 28]</td>\n",
       "      <td>67 year</td>\n",
       "      <td>Loraine Wicks, a 67-year-old female, presents because of trouble sleeping. She has had trouble falling and staying asleep for the last 3 weeks, and recently experienced the death of her son on August 29th. Her mood is \"devastated\" and she feels \"drained\" and uninterested in her normal activites and has been eating more than usual. 3 days ago she also experienced seeing her son at the kitchen table and heard what she described as a party later than evening, but she knows that both of these things did not actually happen. She has no previous episodes like this and her FH is negative. She denies thoughts of hurting herself or others, denies psychomotor changes, denies difficulty concentrating. She reports a good support  system at home with her family. She has good sleep hygiene.\\r\\n\\r\\nROS:negative\\r\\nFH:non-contributory\\r\\nSH: normally 1-2 glasses of wine, none last few weeks, no tobacco, no recreational drugs\\r\\nSurgH: negative\\r\\nPMH: none</td>\n",
       "      <td>[[17, 28]]</td>\n",
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       "              id  case_num  pn_num  feature_num     annotation location  \\\n",
       "12281  83757_805         8   83757          805  [67-year-old]  [17 28]   \n",
       "\n",
       "      feature_text  \\\n",
       "12281      67 year   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       pn_history  \\\n",
       "12281  Loraine Wicks, a 67-year-old female, presents because of trouble sleeping. She has had trouble falling and staying asleep for the last 3 weeks, and recently experienced the death of her son on August 29th. Her mood is \"devastated\" and she feels \"drained\" and uninterested in her normal activites and has been eating more than usual. 3 days ago she also experienced seeing her son at the kitchen table and heard what she described as a party later than evening, but she knows that both of these things did not actually happen. She has no previous episodes like this and her FH is negative. She denies thoughts of hurting herself or others, denies psychomotor changes, denies difficulty concentrating. She reports a good support  system at home with her family. She has good sleep hygiene.\\r\\n\\r\\nROS:negative\\r\\nFH:non-contributory\\r\\nSH: normally 1-2 glasses of wine, none last few weeks, no tobacco, no recreational drugs\\r\\nSurgH: negative\\r\\nPMH: none   \n",
       "\n",
       "      label_spans  \n",
       "12281  [[17, 28]]  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be178ec2",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Unlabelled Data"
   ]
  },
  {
   "cell_type": "code",
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   "id": "5b0fd246",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# unlabelled examples = 612602\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pn_num</th>\n",
       "      <th>case_num</th>\n",
       "      <th>pn_history</th>\n",
       "      <th>feature_num</th>\n",
       "      <th>feature_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209519</th>\n",
       "      <td>39041</td>\n",
       "      <td>3</td>\n",
       "      <td>35 year old M presents with epigastric pain, nausea and dark stools.\\r\\n- Pain described as a gnawing/burning, starting 2 months ago and now worsening up to a 5/10 on the pain scale. No prior episodes of pain\\r\\n- stools are dark, soft and once per day. No vomiting.\\r\\n- Ibuprophen used 1x per week for back pain that patient attributes to his job.\\r\\n- Tried tums with no relief\\r\\n- Eating makes him feel bloates\\r\\nPMH- none. meds: none.\\r\\nFam history of ulcer in uncle.\\r\\nSocial history: works construction, smokes ~.5-1 ppd for 15 years, does not want to quit given his other stressors. recently cut acloh</td>\n",
       "      <td>301</td>\n",
       "      <td>Epigastric discomfort</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pn_num  case_num  \\\n",
       "209519   39041         3   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   pn_history  \\\n",
       "209519  35 year old M presents with epigastric pain, nausea and dark stools.\\r\\n- Pain described as a gnawing/burning, starting 2 months ago and now worsening up to a 5/10 on the pain scale. No prior episodes of pain\\r\\n- stools are dark, soft and once per day. No vomiting.\\r\\n- Ibuprophen used 1x per week for back pain that patient attributes to his job.\\r\\n- Tried tums with no relief\\r\\n- Eating makes him feel bloates\\r\\nPMH- none. meds: none.\\r\\nFam history of ulcer in uncle.\\r\\nSocial history: works construction, smokes ~.5-1 ppd for 15 years, does not want to quit given his other stressors. recently cut acloh   \n",
       "\n",
       "        feature_num           feature_text  \n",
       "209519          301  Epigastric discomfort  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_patients = set(train_df[\"pn_num\"].unique())\n",
    "unlabelled_notes_df = notes_df[~notes_df[\"pn_num\"].isin(train_patients)].copy()\n",
    "unlabelled_df = pd.merge(unlabelled_notes_df, features_df, on=['case_num'], how='left')\n",
    "print(f\"# unlabelled examples = {len(unlabelled_df)}\\n\")\n",
    "display(unlabelled_df.sample())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8185823a",
   "metadata": {
    "papermill": {
     "duration": 0.040961,
     "end_time": "2022-05-04T12:49:07.950806",
     "exception": false,
     "start_time": "2022-05-04T12:49:07.909845",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train-Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2afddd7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:49:08.033671Z",
     "iopub.status.busy": "2022-05-04T12:49:08.033393Z",
     "iopub.status.idle": "2022-05-04T12:49:08.050561Z",
     "shell.execute_reply": "2022-05-04T12:49:08.049839Z"
    },
    "papermill": {
     "duration": 0.060939,
     "end_time": "2022-05-04T12:49:08.052560",
     "exception": false,
     "start_time": "2022-05-04T12:49:07.991621",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# the code for train-validation split is taken from: \n",
    "# https://www.kaggle.com/competitions/nbme-score-clinical-patient-notes/discussion/315707\n",
    "\n",
    "def make_fold(df):\n",
    "    df.loc[:,'fold']=-1\n",
    "    gkf = GroupKFold(n_splits=5)\n",
    "    for n, (train_index, valid_index) in enumerate(gkf.split(df['id'], df['location'], df['pn_num'])):\n",
    "        df.loc[valid_index, 'fold'] = n\n",
    "    return df\n",
    "\n",
    "train_df = make_fold(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1e68d0e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:49:08.133540Z",
     "iopub.status.busy": "2022-05-04T12:49:08.133301Z",
     "iopub.status.idle": "2022-05-04T12:49:08.146849Z",
     "shell.execute_reply": "2022-05-04T12:49:08.146056Z"
    },
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     "duration": 0.056392,
     "end_time": "2022-05-04T12:49:08.148722",
     "exception": false,
     "start_time": "2022-05-04T12:49:08.092330",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>case_num</th>\n",
       "      <th>pn_num</th>\n",
       "      <th>feature_num</th>\n",
       "      <th>annotation</th>\n",
       "      <th>location</th>\n",
       "      <th>feature_text</th>\n",
       "      <th>pn_history</th>\n",
       "      <th>label_spans</th>\n",
       "      <th>fold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>10537_105</td>\n",
       "      <td>1</td>\n",
       "      <td>10537</td>\n",
       "      <td>105</td>\n",
       "      <td>[diarrhea no blood]</td>\n",
       "      <td>[311 319;380 388]</td>\n",
       "      <td>No bloody bowel movements</td>\n",
       "      <td>20 year old female comes to the ED for worsening RLQ abdominal pain.  The pain began 8-10 hours ago while she was sleeping and she describes it as a constant 5/10 dull ache in the RLQ.  It does not radiate.  The pain is worse while walking and is dimished by ibuprofen.  She also notes having a few episodes of diarrhea over the past 2 days which is described as loose and brown, no blood.  She has had no nausea, no vomiting, no fever/chills, and no changes in urinary habits.  She was last sexually active 9 months ago with a condom.  Her LMP was 2 weeks ago.  She has never had an STI and STD and denies vaginal bleeding and discharge.\\r\\n\\r\\nROS:  negative except as above\\r\\nAllergies:  None\\r\\nPMH, Meds, PSH, FH: None\\r\\nSH:  Student, no alcohol, recreational drugs, or tobacco.</td>\n",
       "      <td>[[311, 319], [380, 388]]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  case_num  pn_num  feature_num           annotation  \\\n",
       "2033  10537_105         1   10537          105  [diarrhea no blood]   \n",
       "\n",
       "               location               feature_text  \\\n",
       "2033  [311 319;380 388]  No bloody bowel movements   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             pn_history  \\\n",
       "2033  20 year old female comes to the ED for worsening RLQ abdominal pain.  The pain began 8-10 hours ago while she was sleeping and she describes it as a constant 5/10 dull ache in the RLQ.  It does not radiate.  The pain is worse while walking and is dimished by ibuprofen.  She also notes having a few episodes of diarrhea over the past 2 days which is described as loose and brown, no blood.  She has had no nausea, no vomiting, no fever/chills, and no changes in urinary habits.  She was last sexually active 9 months ago with a condom.  Her LMP was 2 weeks ago.  She has never had an STI and STD and denies vaginal bleeding and discharge.\\r\\n\\r\\nROS:  negative except as above\\r\\nAllergies:  None\\r\\nPMH, Meds, PSH, FH: None\\r\\nSH:  Student, no alcohol, recreational drugs, or tobacco.   \n",
       "\n",
       "                   label_spans  fold  \n",
       "2033  [[311, 319], [380, 388]]     3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763e5b00",
   "metadata": {
    "papermill": {
     "duration": 0.040397,
     "end_time": "2022-05-04T12:49:08.238819",
     "exception": false,
     "start_time": "2022-05-04T12:49:08.198422",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train Valid Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf21fcce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:49:08.323187Z",
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     "duration": 0.0566,
     "end_time": "2022-05-04T12:49:08.336323",
     "exception": false,
     "start_time": "2022-05-04T12:49:08.279723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = train_df.copy()\n",
    "\n",
    "if config[\"debug\"]:\n",
    "    print(\"DEBUG Mode: sampling 1024 examples from train data\")\n",
    "    df = df.sample(min(1024, len(df)))\n",
    "    \n",
    "train_df = df[df['fold'].isin(config['train_folds'])].copy()\n",
    "valid_df = df[df['fold'].isin(config['valid_folds'])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "584558fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:49:08.419549Z",
     "iopub.status.busy": "2022-05-04T12:49:08.419290Z",
     "iopub.status.idle": "2022-05-04T12:49:08.451849Z",
     "shell.execute_reply": "2022-05-04T12:49:08.451126Z"
    },
    "papermill": {
     "duration": 0.076724,
     "end_time": "2022-05-04T12:49:08.453558",
     "exception": false,
     "start_time": "2022-05-04T12:49:08.376834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def strip_offset_mapping(text, offset_mapping):\n",
    "    \"\"\"process offset mapping produced by huggingface tokenizers\n",
    "    by stripping spaces from the tokens\n",
    "\n",
    "    :param text: input text that is tokenized\n",
    "    :type text: str\n",
    "    :param offset_mapping: offsets returned from huggingface tokenizers\n",
    "    :type offset_mapping: list\n",
    "    :return: processed offset mapping\n",
    "    :rtype: list\n",
    "    \"\"\"\n",
    "    to_return = []\n",
    "    for start, end in offset_mapping:\n",
    "        match = list(re.finditer('\\S+', text[start:end]))\n",
    "        if len(match) == 0:\n",
    "            to_return.append((start, end))\n",
    "        else:\n",
    "            span_start, span_end = match[0].span()\n",
    "            to_return.append((start + span_start, start + span_end))\n",
    "    return to_return\n",
    "\n",
    "\n",
    "def get_sequence_ids(input_ids, tokenizer):\n",
    "    \"\"\"if a pair of texts are given to HF tokenizers, the first text\n",
    "    has sequence id of 0 and second text has sequence id 1. This function\n",
    "    derives sequence ids for a given tokenizer based on token input ids\n",
    "\n",
    "    :param input_ids: token input id sequence\n",
    "    :type input_ids: List[int]\n",
    "    :param tokenizer: HF tokenizer\n",
    "    :type tokenizer: PreTrainedTokenizer\n",
    "    :return: sequence ids\n",
    "    :rtype: List\n",
    "    \"\"\"\n",
    "    sequence_ids = [0]*len(input_ids)\n",
    "\n",
    "    switch = False\n",
    "    special_token_ids = set(\n",
    "        tokenizer.convert_tokens_to_ids(\n",
    "            tokenizer.special_tokens_map.values()\n",
    "        )\n",
    "    )\n",
    "    for i, input_id in enumerate(input_ids):\n",
    "        if input_id == tokenizer.sep_token_id:\n",
    "            switch = True\n",
    "        if switch:\n",
    "            sequence_ids[i] = 1\n",
    "        if input_id in special_token_ids:\n",
    "            sequence_ids[i] = None\n",
    "    return sequence_ids\n",
    "\n",
    "\n",
    "class NbmeMTLDataset:\n",
    "    \"\"\"Dataset class for NBME token classification task\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, config):\n",
    "        self.config = config\n",
    "\n",
    "        # column names\n",
    "        self.text_col = self.config[\"text_col\"]\n",
    "        self.feature_col = self.config[\"feature_col\"]\n",
    "        self.label_col = self.config[\"label_col\"]\n",
    "        self.annotation_col = self.config[\"annotation_col\"]\n",
    "\n",
    "        # sequence number for patient history texts\n",
    "        self.focus_seq = self.config[\"text_sequence_identifier\"]\n",
    "\n",
    "        # load tokenizer\n",
    "        self.load_tokenizer()\n",
    "\n",
    "    def load_tokenizer(self):\n",
    "        \"\"\"load tokenizer as per config \n",
    "        \"\"\"\n",
    "        print(\"using auto tokenizer\")\n",
    "        self.tokenizer = AutoTokenizer.from_pretrained(self.config[\"base_model_path\"], trim_offsets=False)\n",
    "\n",
    "    def tokenize_function(self, examples):\n",
    "        if self.focus_seq == 0:\n",
    "            tz = self.tokenizer(\n",
    "                examples[self.text_col],\n",
    "                examples[self.feature_col],\n",
    "                padding=False,\n",
    "                truncation='only_first',\n",
    "                max_length=self.config[\"max_length\"],\n",
    "                add_special_tokens=True,\n",
    "                return_offsets_mapping=True,\n",
    "                return_token_type_ids=True,\n",
    "            )\n",
    "\n",
    "        elif self.focus_seq == 1:\n",
    "            tz = self.tokenizer(\n",
    "                examples[self.feature_col],\n",
    "                examples[self.text_col],\n",
    "                padding=False,\n",
    "                truncation='only_second',\n",
    "                max_length=self.config[\"max_length\"],\n",
    "                add_special_tokens=True,\n",
    "                return_offsets_mapping=True,\n",
    "                return_token_type_ids=True,\n",
    "            )\n",
    "\n",
    "        else:\n",
    "            raise ValueError(\"bad text_sequence_identifier in config\")\n",
    "        return tz\n",
    "\n",
    "    def add_sequence_ids(self, examples):\n",
    "        sequence_ids = []\n",
    "        input_ids = examples[\"input_ids\"]\n",
    "\n",
    "        for tok_ids in input_ids:\n",
    "            sequence_ids.append(get_sequence_ids(tok_ids, self.tokenizer))\n",
    "        return {\"sequence_ids\": sequence_ids}\n",
    "\n",
    "    def process_token_offsets(self, examples):\n",
    "        stripped_offsets, unstripped_offsets = [], []\n",
    "        prev_offset = None\n",
    "\n",
    "        for offsets, seq_ids, feature_text, pn_history in zip(\n",
    "            examples[\"offset_mapping\"],\n",
    "            examples['sequence_ids'],\n",
    "            examples[self.feature_col],\n",
    "            examples[self.text_col]\n",
    "        ):\n",
    "            current_stripped, current_unstripped = [],  []\n",
    "\n",
    "            for pos, offset in enumerate(offsets):\n",
    "                start, end = offset\n",
    "                seq_id = seq_ids[pos]\n",
    "\n",
    "                if seq_id is None:\n",
    "                    current_stripped.append(offset)\n",
    "                    current_unstripped.append(offset)\n",
    "                    prev_offset = offset\n",
    "                    continue\n",
    "\n",
    "                elif seq_id == self.focus_seq:\n",
    "                    focus_text = pn_history[start:end]\n",
    "                else:\n",
    "                    focus_text = feature_text[start:end]\n",
    "\n",
    "                # strip offsets\n",
    "                match = list(re.finditer('\\S+', focus_text))\n",
    "                if len(match) == 0:\n",
    "                    current_stripped.append((start, end))\n",
    "                else:\n",
    "                    span_start, span_end = match[0].span()\n",
    "                    current_stripped.append((start + span_start, start + span_end))\n",
    "\n",
    "                # upstrip offsets\n",
    "                if prev_offset[-1] != offset[0]:\n",
    "                    offset[0] = prev_offset[-1]\n",
    "                current_unstripped.append(offset)\n",
    "                prev_offset = offset\n",
    "\n",
    "            stripped_offsets.append(np.array(current_stripped))\n",
    "            unstripped_offsets.append(np.array(current_unstripped))\n",
    "\n",
    "        return {\n",
    "            'offset_mapping_stripped': stripped_offsets,\n",
    "            \"offset_mapping_unstripped\": unstripped_offsets\n",
    "        }\n",
    "\n",
    "    def generate_labels(self, examples):\n",
    "        labels = []\n",
    "        for offsets, inputs, seq_ids, locations in zip(\n",
    "            examples[\"offset_mapping_stripped\"],\n",
    "            examples[\"input_ids\"],\n",
    "            examples[\"sequence_ids\"],\n",
    "            examples[self.label_col]\n",
    "        ):\n",
    "            this_label = np.zeros(shape=(3, len(inputs)))\n",
    "            for idx, (seq_id, offset) in enumerate(zip(seq_ids, offsets)):\n",
    "                if seq_id != self.focus_seq:  # ignore this token\n",
    "                    this_label[:, idx] = -1.0\n",
    "                    continue\n",
    "\n",
    "                token_start_char_idx, token_end_char_idx = offset\n",
    "                for label_start_char_idx, label_end_char_idx in locations:\n",
    "                    # case 1: location char start is inside token\n",
    "                    if token_start_char_idx <= label_start_char_idx < token_end_char_idx:\n",
    "                        this_label[0, idx] = 1.0\n",
    "                        this_label[1, idx] = 1.0  # detection\n",
    "\n",
    "                    # case 2: location char end is inside token\n",
    "                    if token_start_char_idx < label_end_char_idx <= token_end_char_idx:\n",
    "                        this_label[0, idx] = 1.0\n",
    "                        this_label[2, idx] = 1.0  # termination\n",
    "\n",
    "                    # case 3: token in between location\n",
    "                    if label_start_char_idx < token_start_char_idx < label_end_char_idx:\n",
    "                        this_label[0, idx] = 1.0\n",
    "\n",
    "                    # break the loop if token is already detected positive\n",
    "                    if this_label[0, idx] > 0:\n",
    "                        break\n",
    "\n",
    "            labels.append(this_label)\n",
    "        return {\"labels\": labels}\n",
    "\n",
    "    def get_dataset(self, df, mode='train'):\n",
    "        \"\"\"main api for creating the NBME dataset\n",
    "\n",
    "        :param df: input dataframe\n",
    "        :type df: pd.DataFrame\n",
    "        :param mode: check if required for train or infer, defaults to 'train'\n",
    "        :type mode: str, optional\n",
    "        :return: the created dataset\n",
    "        :rtype: Dataset\n",
    "        \"\"\"\n",
    "\n",
    "        # create the dataset\n",
    "        nbme_dataset = Dataset.from_pandas(df)\n",
    "        nbme_dataset = nbme_dataset.map(self.tokenize_function, batched=True)\n",
    "        nbme_dataset = nbme_dataset.map(self.add_sequence_ids, batched=True)\n",
    "        nbme_dataset = nbme_dataset.map(self.process_token_offsets, batched=True)\n",
    "\n",
    "        if mode == \"train\":\n",
    "            nbme_dataset = nbme_dataset.map(self.generate_labels, batched=True)\n",
    "        try:\n",
    "            nbme_dataset = nbme_dataset.remove_columns(column_names=[\"__index_level_0__\"])\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "        return nbme_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "524c4e80",
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     "duration": 221.234842,
     "end_time": "2022-05-04T12:52:49.728386",
     "exception": false,
     "start_time": "2022-05-04T12:49:08.493544",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using auto tokenizer\n"
     ]
    },
    {
     "data": {
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       "model_id": "274b501135524820a6bd16a43489f271",
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     },
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     "metadata": {},
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    },
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     },
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    },
    {
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    },
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     "metadata": {},
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    },
    {
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    },
    {
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     },
     "metadata": {},
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    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "36cd619b7eac485d9f7df056792d7b26",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_creator = NbmeMTLDataset(config)\n",
    "train_ds = dataset_creator.get_dataset(train_df, mode='train')\n",
    "valid_ds = dataset_creator.get_dataset(valid_df, mode='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a74f49dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:52:49.881697Z",
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     "end_time": "2022-05-04T12:52:49.889563",
     "exception": false,
     "start_time": "2022-05-04T12:52:49.804380",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['id', 'case_num', 'pn_num', 'feature_num', 'annotation', 'location', 'feature_text', 'pn_history', 'label_spans', 'fold', 'input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'sequence_ids', 'offset_mapping_stripped', 'offset_mapping_unstripped', 'labels'],\n",
       "    num_rows: 11440\n",
       "})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d59b9e6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:52:49.984524Z",
     "iopub.status.busy": "2022-05-04T12:52:49.984249Z",
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     "shell.execute_reply": "2022-05-04T12:52:50.242271Z"
    },
    "papermill": {
     "duration": 0.307976,
     "end_time": "2022-05-04T12:52:50.245261",
     "exception": false,
     "start_time": "2022-05-04T12:52:49.937285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save train dataset\n",
    "train_dataset_path = os.path.join(\n",
    "    config[\"output_dir\"], config[\"train_dataset_path\"]\n",
    ")\n",
    "train_ds.save_to_disk(train_dataset_path)\n",
    "\n",
    "# save valid dataset\n",
    "valid_dataset_path = os.path.join(\n",
    "    config[\"output_dir\"], config[\"valid_dataset_path\"]\n",
    ")\n",
    "valid_ds.save_to_disk(valid_dataset_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f26ed51",
   "metadata": {
    "papermill": {
     "duration": 0.046143,
     "end_time": "2022-05-04T12:52:50.337831",
     "exception": false,
     "start_time": "2022-05-04T12:52:50.291688",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Unlabelled Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39af02fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:52:50.432560Z",
     "iopub.status.busy": "2022-05-04T12:52:50.431943Z",
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    },
    "papermill": {
     "duration": 106.64458,
     "end_time": "2022-05-04T12:54:37.028639",
     "exception": false,
     "start_time": "2022-05-04T12:52:50.384059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading unlabelled data...\n",
      "unlabelled data loaded and sampled ...\n",
      "shape of sampled unlabelled data: (10000, 3)\n",
      "using auto tokenizer\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0a4391d709844ccb820621eef67c912",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2dfd16f51a134db09fca6c8780347899",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83d6a9614f514b889d87ea8d36f28162",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"loading unlabelled data...\")\n",
    "required_examples = config[\"num_unlabelled\"]\n",
    "df = unlabelled_df.sample(required_examples)\n",
    "keep_cols = [\"pn_history\", \"feature_text\", \"feature_num\"]\n",
    "df = df[keep_cols].copy()\n",
    "print(\"unlabelled data loaded and sampled ...\")\n",
    "print(f\"shape of sampled unlabelled data: {df.shape}\")\n",
    "\n",
    "########### Create Unlabelled Dataset ##############\n",
    "dataset_creator = NbmeMTLDataset(config)\n",
    "unlabelled_ds = dataset_creator.get_dataset(df, mode='infer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d91172",
   "metadata": {
    "papermill": {
     "duration": 0.047509,
     "end_time": "2022-05-04T12:54:37.125240",
     "exception": false,
     "start_time": "2022-05-04T12:54:37.077731",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0de27d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:37.222947Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class DataCollatorForMTLNeo(DataCollatorForTokenClassification):\n",
    "    \"\"\"\n",
    "    Data collator that will dynamically pad the inputs received\n",
    "    Multitask learning targets will be added to batch and padded\n",
    "    \"\"\"\n",
    "    tokenizer = None\n",
    "    padding = True\n",
    "    max_length = None\n",
    "    pad_to_multiple_of = None\n",
    "    label_pad_token_id = -1\n",
    "    return_tensors = \"pt\"\n",
    "\n",
    "    def torch_call(self, features):\n",
    "        label_name = \"labels\"\n",
    "        labels = None\n",
    "\n",
    "        if label_name in features[0].keys():\n",
    "            labels = [feature[label_name] for feature in features]\n",
    "\n",
    "        batch = self.tokenizer.pad(\n",
    "            features,\n",
    "            padding=self.padding,\n",
    "            max_length=self.max_length,\n",
    "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
    "            return_tensors=\"pt\" if labels is None else None,\n",
    "        )\n",
    "\n",
    "        if labels is None:  # e.g. in eval mode\n",
    "            return batch\n",
    "\n",
    "        sequence_length = torch.tensor(batch[\"input_ids\"]).shape[1]\n",
    "\n",
    "        # main labels\n",
    "        num_labels, padded_labels = len(labels[0]), []\n",
    "\n",
    "        for this_example in labels:\n",
    "            padding_length = sequence_length - len(this_example[0])\n",
    "            padding_matrix = (self.label_pad_token_id)*np.ones(shape=(num_labels, padding_length))\n",
    "            this_example = (np.concatenate([np.array(this_example), padding_matrix], axis=1).T).tolist()\n",
    "            padded_labels.append(this_example)\n",
    "        batch[label_name] = padded_labels\n",
    "\n",
    "        batch = {k: torch.tensor(v, dtype=torch.int64) for k, v in batch.items()}\n",
    "\n",
    "        # cast lables to float for bce loss\n",
    "        batch[label_name] = batch[label_name].to(torch.float32)\n",
    "        return batch\n",
    "    \n",
    "@dataclass\n",
    "class DataCollatorForMPL(DataCollatorForTokenClassification):\n",
    "    \"\"\"\n",
    "    Data collator for unlabelled data\n",
    "    \"\"\"\n",
    "    tokenizer = None\n",
    "    padding = True\n",
    "    max_length = None\n",
    "    pad_to_multiple_of = None\n",
    "    label_pad_token_id = -1\n",
    "    return_tensors = \"pt\"\n",
    "\n",
    "    def torch_call(self, features):\n",
    "        buffer = [feature[\"sequence_ids\"] for feature in features]\n",
    "\n",
    "        batch = self.tokenizer.pad(\n",
    "            features,\n",
    "            padding=self.padding,\n",
    "            max_length=self.max_length,\n",
    "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
    "            return_tensors=None,\n",
    "        )\n",
    "\n",
    "        # create masks\n",
    "        sequence_length = torch.tensor(batch[\"input_ids\"]).shape[1]\n",
    "\n",
    "        # main labels\n",
    "        masks = []  # (batch, seq_len)\n",
    "\n",
    "        for seq in buffer:\n",
    "            padding_length = sequence_length - len(seq)\n",
    "            mask = [seq_id == 1 for seq_id in seq] + [False]*padding_length\n",
    "            masks.append(mask)\n",
    "            \n",
    "        batch['label_mask'] = masks\n",
    "        batch.pop('sequence_ids', None)\n",
    "\n",
    "        batch = {k: torch.tensor(v, dtype=torch.int64) for k, v in batch.items()}\n",
    "        return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed0f0697",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:37.341718Z",
     "iopub.status.busy": "2022-05-04T12:54:37.341500Z",
     "iopub.status.idle": "2022-05-04T12:54:41.264505Z",
     "shell.execute_reply": "2022-05-04T12:54:41.263775Z"
    },
    "papermill": {
     "duration": 3.977711,
     "end_time": "2022-05-04T12:54:41.266631",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(config[\"base_model_path\"])\n",
    "data_collator = DataCollatorForMTLNeo(tokenizer=tokenizer, label_pad_token_id=-1)\n",
    "\n",
    "train_ds.set_format(\n",
    "    type=None,\n",
    "    columns=['input_ids', 'attention_mask', 'token_type_ids', 'labels']\n",
    ")\n",
    "\n",
    "train_dl = DataLoader(\n",
    "    train_ds,\n",
    "    batch_size=config[\"batch_size\"],\n",
    "    collate_fn=data_collator,\n",
    "    pin_memory=True,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "valid_ds.set_format(\n",
    "    type=None,\n",
    "    columns=['input_ids', 'attention_mask', 'token_type_ids', 'labels']\n",
    ")\n",
    "\n",
    "valid_dl = DataLoader(\n",
    "    valid_ds,\n",
    "    batch_size=config[\"batch_size\"],\n",
    "    collate_fn=data_collator,\n",
    "    pin_memory=True,\n",
    "    shuffle=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd3ffd70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:41.364906Z",
     "iopub.status.busy": "2022-05-04T12:54:41.364661Z",
     "iopub.status.idle": "2022-05-04T12:54:41.370175Z",
     "shell.execute_reply": "2022-05-04T12:54:41.369494Z"
    },
    "papermill": {
     "duration": 0.056989,
     "end_time": "2022-05-04T12:54:41.371966",
     "exception": false,
     "start_time": "2022-05-04T12:54:41.314977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorForMPL(tokenizer=tokenizer)\n",
    "\n",
    "unlabelled_ds.set_format(\n",
    "    type=None,\n",
    "    columns=['input_ids', 'attention_mask', 'token_type_ids', 'sequence_ids']\n",
    ")\n",
    "\n",
    "unlabelled_dl = DataLoader(\n",
    "    unlabelled_ds,\n",
    "    batch_size=config[\"batch_size\"],\n",
    "    collate_fn=data_collator,\n",
    "    pin_memory=True,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a59650",
   "metadata": {
    "papermill": {
     "duration": 0.048869,
     "end_time": "2022-05-04T12:54:41.470289",
     "exception": false,
     "start_time": "2022-05-04T12:54:41.421420",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "693119c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:41.569760Z",
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    },
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     "end_time": "2022-05-04T12:54:41.583710",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class NbmeMPL(nn.Module):\n",
    "    \"\"\"The Multi-task NBME model class for Meta Pseudo Labels\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, config):\n",
    "        super(NbmeMPL, self).__init__()\n",
    "\n",
    "        self.config = config\n",
    "\n",
    "        # base transformer\n",
    "        self.base_model = AutoModel.from_pretrained(\n",
    "            self.config[\"base_model_path\"],\n",
    "        )\n",
    "        self.base_model.gradient_checkpointing_enable()\n",
    "\n",
    "        n_freeze = config[\"n_freeze\"]\n",
    "        if n_freeze > 0:\n",
    "            print(f\"setting requires grad to false for last {n_freeze} layers\")\n",
    "            self.base_model.embeddings.requires_grad_(False)\n",
    "            self.base_model.encoder.layer[:n_freeze].requires_grad_(False)\n",
    "\n",
    "        hidden_size = self.base_model.config.hidden_size\n",
    "        num_layers_in_head = self.config[\"num_layers_in_head\"]\n",
    "\n",
    "        # token classification head\n",
    "        self.tok_classifier = nn.Linear(\n",
    "            in_features=hidden_size * num_layers_in_head,\n",
    "            out_features=self.config['mtl_tok_num_labels'],\n",
    "        )\n",
    "\n",
    "        self.dropout1 = nn.Dropout(0.1)\n",
    "        self.dropout2 = nn.Dropout(0.2)\n",
    "        self.dropout3 = nn.Dropout(0.3)\n",
    "        self.dropout4 = nn.Dropout(0.4)\n",
    "        self.dropout5 = nn.Dropout(0.5)\n",
    "\n",
    "    def get_logits(self, batch):\n",
    "        input_ids = batch[\"input_ids\"]\n",
    "        attention_mask = batch[\"attention_mask\"]\n",
    "        token_type_ids = batch[\"token_type_ids\"]\n",
    "\n",
    "        out = self.base_model(\n",
    "            input_ids=input_ids,\n",
    "            attention_mask=attention_mask,\n",
    "            token_type_ids=token_type_ids,\n",
    "            output_hidden_states=True\n",
    "        )\n",
    "\n",
    "        all_hidden_states = out[\"hidden_states\"]\n",
    "        # token classification logits\n",
    "        n = self.config[\"num_layers_in_head\"]\n",
    "        tok_output = torch.cat(all_hidden_states[-n:], dim=-1)\n",
    "\n",
    "        # pass through 5 dropout layers and take average\n",
    "        tok_output1 = self.dropout1(tok_output)\n",
    "        tok_output2 = self.dropout2(tok_output)\n",
    "        tok_output3 = self.dropout3(tok_output)\n",
    "        tok_output4 = self.dropout4(tok_output)\n",
    "        tok_output5 = self.dropout5(tok_output)\n",
    "        tok_output = (tok_output1 + tok_output2 + tok_output3 + tok_output4 + tok_output5)/5\n",
    "\n",
    "        tok_logits = self.tok_classifier(tok_output)\n",
    "\n",
    "        return tok_logits\n",
    "\n",
    "    def compute_loss(self, logits, labels, masks):\n",
    "        loss = F.binary_cross_entropy_with_logits(\n",
    "            logits, labels,\n",
    "            reduction='none'\n",
    "        )\n",
    "        loss = torch.masked_select(loss, masks).mean()\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64359fc4",
   "metadata": {
    "papermill": {
     "duration": 0.048738,
     "end_time": "2022-05-04T12:54:41.680812",
     "exception": false,
     "start_time": "2022-05-04T12:54:41.632074",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Optimizer & Scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7673567d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:41.779266Z",
     "iopub.status.busy": "2022-05-04T12:54:41.778724Z",
     "iopub.status.idle": "2022-05-04T12:54:41.787188Z",
     "shell.execute_reply": "2022-05-04T12:54:41.786506Z"
    },
    "papermill": {
     "duration": 0.059851,
     "end_time": "2022-05-04T12:54:41.788811",
     "exception": false,
     "start_time": "2022-05-04T12:54:41.728960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_optimizer(model, config):\n",
    "    decay_parameters = get_parameter_names(model, [nn.LayerNorm])\n",
    "    # print(decay_parameters)\n",
    "    decay_parameters = [name for name in decay_parameters if \"bias\" not in name]\n",
    "    # print(decay_parameters)\n",
    "\n",
    "    optimizer_grouped_parameters = [\n",
    "        {\n",
    "            \"params\": [p for n, p in model.named_parameters() if n in decay_parameters],\n",
    "            \"weight_decay\": config[\"weight_decay\"],\n",
    "        },\n",
    "        {\n",
    "            \"params\": [p for n, p in model.named_parameters() if n not in decay_parameters],\n",
    "            \"weight_decay\": 0.0,\n",
    "        },\n",
    "    ]\n",
    "\n",
    "    optimizer_kwargs = {\n",
    "        \"betas\": (config[\"beta1\"], config[\"beta2\"]),\n",
    "        \"eps\": config['eps'],\n",
    "    }\n",
    "\n",
    "    optimizer_kwargs[\"lr\"] = config[\"lr\"]\n",
    "\n",
    "    adam_bnb_optim = bnb.optim.Adam8bit(\n",
    "        optimizer_grouped_parameters,\n",
    "        betas=(config['beta1'], config['beta2']),\n",
    "        eps=config['eps'],\n",
    "        lr=config['lr'],\n",
    "    )\n",
    "\n",
    "    return adam_bnb_optim\n",
    "\n",
    "\n",
    "def get_scheduler(optimizer, warmup_steps, total_steps):\n",
    "    scheduler = get_cosine_schedule_with_warmup(\n",
    "        optimizer=optimizer,\n",
    "        num_warmup_steps=warmup_steps,\n",
    "        num_training_steps=total_steps\n",
    "    )\n",
    "    return scheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe56696e",
   "metadata": {
    "papermill": {
     "duration": 0.047704,
     "end_time": "2022-05-04T12:54:41.885094",
     "exception": false,
     "start_time": "2022-05-04T12:54:41.837390",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed83b471",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:41.982382Z",
     "iopub.status.busy": "2022-05-04T12:54:41.982132Z",
     "iopub.status.idle": "2022-05-04T12:54:41.991292Z",
     "shell.execute_reply": "2022-05-04T12:54:41.990639Z"
    },
    "papermill": {
     "duration": 0.060085,
     "end_time": "2022-05-04T12:54:41.992960",
     "exception": false,
     "start_time": "2022-05-04T12:54:41.932875",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_lr(optimizer):\n",
    "    return optimizer.param_groups[0]['lr']*1e6\n",
    "\n",
    "\n",
    "class AverageMeter(object):\n",
    "    \"\"\"Computes and stores the average and current value\n",
    "       Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.count = 0\n",
    "\n",
    "    def update(self, val, n=1):\n",
    "        self.val = val\n",
    "        self.sum += val * n\n",
    "        self.count += n\n",
    "        self.avg = self.sum / self.count\n",
    "        \n",
    "def save_checkpoint(config, state, is_teacher, is_best):\n",
    "    if is_teacher:\n",
    "        os.makedirs(config[\"teacher_model_dir\"], exist_ok=True)\n",
    "        name = config[\"teacher_save_name\"]\n",
    "        filename = f'{config[\"teacher_model_dir\"]}/{name}_last.pth.tar'\n",
    "    else:\n",
    "        os.makedirs(config[\"student_model_dir\"], exist_ok=True)\n",
    "        name = config[\"student_save_name\"]\n",
    "        filename = f'{config[\"student_model_dir\"]}/{name}_last.pth.tar'\n",
    "    torch.save(state, filename, _use_new_zipfile_serialization=False)\n",
    "    if is_best:\n",
    "        if is_teacher:\n",
    "            shutil.copyfile(filename, f'{config[\"teacher_model_dir\"]}/{name}_best.pth.tar')\n",
    "        else:\n",
    "            shutil.copyfile(filename, f'{config[\"student_model_dir\"]}/{name}_best.pth.tar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9151416",
   "metadata": {
    "papermill": {
     "duration": 0.049378,
     "end_time": "2022-05-04T12:54:42.089595",
     "exception": false,
     "start_time": "2022-05-04T12:54:42.040217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "00b0028a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:42.186991Z",
     "iopub.status.busy": "2022-05-04T12:54:42.186795Z",
     "iopub.status.idle": "2022-05-04T12:54:42.206549Z",
     "shell.execute_reply": "2022-05-04T12:54:42.205867Z"
    },
    "papermill": {
     "duration": 0.069834,
     "end_time": "2022-05-04T12:54:42.208215",
     "exception": false,
     "start_time": "2022-05-04T12:54:42.138381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def perform_localization(char_probs, threshold=0.5):\n",
    "    \"\"\"convert character wise prediction to location spans\n",
    "\n",
    "    :param char_probs: character wise predictions\n",
    "    :type char_prob: list\n",
    "    :param threshold: threshold for label decision, defaults to 0.5\n",
    "    :type threshold: float, optional\n",
    "    :return: locations\n",
    "    :rtype: list\n",
    "    \"\"\"\n",
    "    results = np.where(char_probs >= threshold)[0]\n",
    "    results = [list(g) for _, g in itertools.groupby(\n",
    "        results, key=lambda n, c=itertools.count(): n - next(c))]\n",
    "    results = [[min(r), max(r)+1] for r in results]\n",
    "    return results\n",
    "\n",
    "\n",
    "def postprocess_localization(text, span_offsets):\n",
    "    \"\"\"remove spaces at the beginning of label span prediction\n",
    "\n",
    "    :param span: input text (patient history)\n",
    "    :type text: str\n",
    "    :param span_offset: prediction span offsets\n",
    "    :type offset_mapping: list\n",
    "    :return: updated span offsets \n",
    "    :rtype: list\n",
    "    \"\"\"\n",
    "    to_return = []\n",
    "    for start, end in span_offsets:\n",
    "        match = list(re.finditer('\\S+', text[start:end]))\n",
    "        if len(match) == 0:\n",
    "            to_return.append((start, end))\n",
    "        else:\n",
    "            span_start, _ = match[0].span()\n",
    "            to_return.append((start + span_start, end))\n",
    "    return to_return\n",
    "\n",
    "\n",
    "def token2char(text, quantities, offsets, seq_ids, focus_seq=1):\n",
    "    \"\"\"convert token prediction/truths to character wise predictions/truths\n",
    "\n",
    "    :param text: patient notes text\n",
    "    :type text: str\n",
    "    :param quantities: token level variable values\n",
    "    :type quantities: list\n",
    "    :param offsets: token offsets without stripping\n",
    "    :type offsets: list\n",
    "    :param seq_ids: sequence id of the tokens\n",
    "    :type seq_ids: list\n",
    "    :param focus_seq: which sequence to focus on\n",
    "    :type focus_seq: int\n",
    "    :return: character probabilities\n",
    "    :rtype: list\n",
    "    \"\"\"\n",
    "    results = np.zeros(len(text))\n",
    "    for q, offset, seq_id in zip(quantities, offsets, seq_ids):\n",
    "        if seq_id != focus_seq:\n",
    "            continue\n",
    "        char_start_idx, char_end_idx = offset[0], offset[1]\n",
    "        results[char_start_idx:char_end_idx] = q\n",
    "    return results\n",
    "\n",
    "def micro_f1(preds, truths):\n",
    "    \"\"\"\n",
    "    Micro f1 on binary arrays.\n",
    "\n",
    "    Args:\n",
    "        preds (list of lists of ints): Predictions.\n",
    "        truths (list of lists of ints): Ground truths.\n",
    "\n",
    "    Returns:\n",
    "        float: f1 score.\n",
    "    \"\"\"\n",
    "    # Micro : aggregating over all instances\n",
    "    preds = np.concatenate(preds)\n",
    "    truths = np.concatenate(truths)\n",
    "\n",
    "    return f1_score(truths, preds)\n",
    "\n",
    "\n",
    "def spans_to_binary(spans, length=None):\n",
    "    \"\"\"\n",
    "    Converts spans to a binary array indicating whether each character is in the span.\n",
    "\n",
    "    Args:\n",
    "        spans (list of lists of two ints): Spans.\n",
    "\n",
    "    Returns:\n",
    "        np array [length]: Binarized spans.\n",
    "    \"\"\"\n",
    "    length = np.max(spans) if length is None else length\n",
    "    binary = np.zeros(length)\n",
    "    for start, end in spans:\n",
    "        binary[start:end] = 1\n",
    "\n",
    "    return binary\n",
    "\n",
    "\n",
    "def span_micro_f1(preds, truths):\n",
    "    \"\"\"\n",
    "    Micro f1 on spans.\n",
    "\n",
    "    Args:\n",
    "        preds (list of lists of two ints): Prediction spans.\n",
    "        truths (list of lists of two ints): Ground truth spans.\n",
    "\n",
    "    Returns:\n",
    "        float: f1 score.\n",
    "    \"\"\"\n",
    "\n",
    "    bin_preds = []\n",
    "    bin_truths = []\n",
    "\n",
    "    for pred, truth in zip(preds, truths):\n",
    "        if not len(pred) and not len(truth):\n",
    "            continue\n",
    "\n",
    "        length = max(np.max(pred) if len(pred) else 0, np.max(truth) if len(truth) else 0)\n",
    "        bin_preds.append(spans_to_binary(pred, length))\n",
    "        bin_truths.append(spans_to_binary(truth, length))\n",
    "\n",
    "    return micro_f1(bin_preds, bin_truths)\n",
    "\n",
    "def scorer(preds, valid_dataset, threshold=0.5, focus_seq=1):\n",
    "    \"\"\"scorer for evaluation during training of models\n",
    "\n",
    "    :param preds: model preds on valid dataset [char probs]\n",
    "    :type preds: list\n",
    "    :param valid_dataset: validation dataset\n",
    "    :type valid_dataset: dataset\n",
    "    :param threshold: threshold at which model is evaluated, defaults to 0.5\n",
    "    :type threshold: float, optional\n",
    "    :param focus_seq: patient notes sequence, defaults to 1\n",
    "    :type focus_seq: int, optional\n",
    "    :return: Leaderboard metric\n",
    "    :rtype: float\n",
    "    \"\"\"\n",
    "    info_df = pd.DataFrame()\n",
    "    required_cols = [\n",
    "        \"pn_history\",\n",
    "        \"sequence_ids\",\n",
    "        \"offset_mapping_unstripped\",\n",
    "        \"label_spans\",\n",
    "    ]\n",
    "\n",
    "    for col in required_cols:\n",
    "        info_df[col] = valid_dataset[col]\n",
    "\n",
    "    info_df[\"token_preds\"] = preds\n",
    "\n",
    "    # convert token preds to char preds\n",
    "    input_cols = [\"pn_history\", \"token_preds\", \"offset_mapping_unstripped\", \"sequence_ids\"]\n",
    "    info_df[\"char_probs\"] = info_df[input_cols].apply(\n",
    "        lambda x: token2char(x[0], x[1], x[2], x[3], focus_seq), axis=1\n",
    "    )\n",
    "\n",
    "    #  location\n",
    "    info_df[\"location_preds\"] = info_df[\"char_probs\"].apply(\n",
    "        lambda x: perform_localization(x, threshold)\n",
    "    )\n",
    "    info_df['location_preds'] = info_df[[\"pn_history\", \"location_preds\"]].apply(\n",
    "        lambda x: postprocess_localization(x[0], x[1]), axis=1\n",
    "    )\n",
    "    lb = span_micro_f1(info_df[\"label_spans\"].values, info_df[\"location_preds\"].values)\n",
    "    return lb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0333fb33",
   "metadata": {
    "papermill": {
     "duration": 0.048462,
     "end_time": "2022-05-04T12:54:42.304711",
     "exception": false,
     "start_time": "2022-05-04T12:54:42.256249",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13ab5133",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-04T12:54:42.402642Z",
     "iopub.status.busy": "2022-05-04T12:54:42.402374Z",
     "iopub.status.idle": "2022-05-04T12:55:47.477027Z",
     "shell.execute_reply": "2022-05-04T12:55:47.473053Z"
    },
    "papermill": {
     "duration": 65.174019,
     "end_time": "2022-05-04T12:55:47.527116",
     "exception": false,
     "start_time": "2022-05-04T12:54:42.353097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "GPU utilization at the very start:\n",
      "GPU memory occupied: 0 MB.\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75ae1a8aaf344eeeb65a2b49b328212d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-large were not used when initializing DebertaModel: ['lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.bias']\n",
      "- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting requires grad to false for last 2 layers\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-large were not used when initializing DebertaModel: ['lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.bias']\n",
      "- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting requires grad to false for last 2 layers\n",
      "================================================================================\n",
      "GPU utilization after accelerator preparation:\n",
      "GPU memory occupied: 3911 MB.\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"==\"*40)\n",
    "print(\"GPU utilization at the very start:\")\n",
    "print_gpu_utilization()\n",
    "print(\"==\"*40)\n",
    "\n",
    "#------- load student and teacher into memory --------------------#\n",
    "student = NbmeMPL(config)\n",
    "teacher = NbmeMPL(config)\n",
    "\n",
    "#------- get optimizers for student and teacher ------------------#\n",
    "s_optimizer = get_optimizer(student, config)\n",
    "t_optimizer = get_optimizer(teacher, config)\n",
    "\n",
    "\n",
    "#------- prepare accelerator  ------------------------------------#\n",
    "accelerator = Accelerator(fp16=True)\n",
    "student, teacher, s_optimizer, t_optimizer, train_dl, valid_dl, unlabelled_dl = accelerator.prepare(\n",
    "    student, teacher, s_optimizer, t_optimizer, train_dl, valid_dl, unlabelled_dl\n",
    ")\n",
    "print(\"==\"*40)\n",
    "print(\"GPU utilization after accelerator preparation:\")\n",
    "print_gpu_utilization()\n",
    "print(\"==\"*40)\n",
    "\n",
    "#------- setup schedulers  ---------------------------------------#\n",
    "num_epochs = config[\"num_epochs\"]\n",
    "grad_accumulation_steps = config[\"grad_accumulation\"]\n",
    "warmup_pct = config[\"warmup_pct\"]\n",
    "\n",
    "n_train_steps_per_epoch = len(train_dl)//grad_accumulation_steps\n",
    "n_mpl_steps_per_epoch = len(unlabelled_dl)//grad_accumulation_steps\n",
    "n_steps_per_epoch = max(n_train_steps_per_epoch, n_mpl_steps_per_epoch)\n",
    "num_steps = num_epochs * n_steps_per_epoch\n",
    "num_warmup_steps = int(warmup_pct*num_steps)\n",
    "\n",
    "s_scheduler = get_scheduler(s_optimizer, num_warmup_steps, num_steps)\n",
    "t_scheduler = get_scheduler(t_optimizer, num_warmup_steps, num_steps)\n",
    "\n",
    "#------- Scorer & Trackers ----------------------------------------#\n",
    "best_teacher_score = 0\n",
    "best_student_score = 0\n",
    "\n",
    "valid_ds_path = os.path.join(config[\"output_dir\"], config[\"valid_dataset_path\"])\n",
    "valid_ds = load_from_disk(valid_ds_path)\n",
    "\n",
    "scorer_fn = partial(\n",
    "    scorer,\n",
    "    valid_dataset=valid_ds,\n",
    "    threshold=0.5,\n",
    "    focus_seq=config[\"text_sequence_identifier\"]\n",
    ")\n",
    "\n",
    "#------------- Data Iterators -------------------------------------#\n",
    "train_iter = iter(train_dl)\n",
    "unlabelled_iter = iter(unlabelled_dl)"
   ]
  },
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   "cell_type": "code",
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    {
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       "  0%|          | 0/200 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After step 200 Teache LB: 0.6896623967436849\n",
      "After step 200 Student LB: 0.0\n",
      "================================================================================\n",
      "GPU utilization after eval:\n",
      "GPU memory occupied: 12313 MB.\n",
      "clearning the cache\n",
      "GPU memory occupied: 9515 MB.\n",
      "================================================================================\n"
     ]
    },
    {
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       "  0%|          | 0/200 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After step 400 Teache LB: 0.7777596342259961\n",
      "After step 400 Student LB: 0.43751595500263796\n",
      "================================================================================\n",
      "GPU utilization after eval:\n",
      "GPU memory occupied: 12337 MB.\n",
      "clearning the cache\n",
      "GPU memory occupied: 9437 MB.\n",
      "================================================================================\n"
     ]
    },
    {
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       "  0%|          | 0/200 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After step 600 Teache LB: 0.8224815560026827\n",
      "After step 600 Student LB: 0.7334233913970849\n",
      "================================================================================\n",
      "GPU utilization after eval:\n",
      "GPU memory occupied: 12425 MB.\n",
      "clearning the cache\n",
      "GPU memory occupied: 9405 MB.\n",
      "================================================================================\n"
     ]
    },
    {
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       "  0%|          | 0/200 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After step 800 Teache LB: 0.8278633987371852\n",
      "After step 800 Student LB: 0.7036973290982774\n",
      "================================================================================\n",
      "GPU utilization after eval:\n",
      "GPU memory occupied: 12371 MB.\n",
      "clearning the cache\n",
      "GPU memory occupied: 9619 MB.\n",
      "================================================================================\n"
     ]
    },
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       "  0%|          | 0/200 [00:00<?, ?it/s]"
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     "output_type": "stream",
     "text": [
      "After step 1000 Teache LB: 0.8565476342629278\n",
      "After step 1000 Student LB: 0.8127251536342446\n",
      "================================================================================\n",
      "GPU utilization after eval:\n",
      "GPU memory occupied: 12297 MB.\n",
      "clearning the cache\n",
      "GPU memory occupied: 9347 MB.\n",
      "================================================================================\n"
     ]
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       "  0%|          | 0/200 [00:00<?, ?it/s]"
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     "output_type": "stream",
     "text": [
      "After step 1200 Teache LB: 0.8625815530254574\n",
      "After step 1200 Student LB: 0.8267061932074499\n",
      "================================================================================\n",
      "GPU utilization after eval:\n",
      "GPU memory occupied: 13157 MB.\n",
      "clearning the cache\n",
      "GPU memory occupied: 9741 MB.\n",
      "================================================================================\n"
     ]
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      "After step 1400 Teache LB: 0.8641767199627641\n",
      "After step 1400 Student LB: 0.8247616894615878\n",
      "================================================================================\n",
      "GPU utilization after eval:\n",
      "GPU memory occupied: 12573 MB.\n",
      "clearning the cache\n",
      "GPU memory occupied: 9287 MB.\n",
      "================================================================================\n"
     ]
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   "source": [
    "# ------- Training Loop  ------------------------------------------#\n",
    "for step in range(num_steps):\n",
    "\n",
    "    #------ Reset buffers After Validation ------------------------#\n",
    "    if step % config[\"validation_interval\"] == 0:\n",
    "        progress_bar = tqdm(range(min(config[\"validation_interval\"], num_steps)))\n",
    "        s_loss_meter = AverageMeter()\n",
    "        t_loss_meter = AverageMeter()\n",
    "\n",
    "    teacher.train()\n",
    "    student.train()\n",
    "\n",
    "    t_optimizer.zero_grad()\n",
    "    s_optimizer.zero_grad()\n",
    "\n",
    "    #------ Get Train & Unlabelled Batch -------------------------#\n",
    "    try:\n",
    "        train_b = train_iter.next()\n",
    "    except Exception as e:  # TODO: change to stop iteration error\n",
    "        train_b = next(train_dl.__iter__())\n",
    "\n",
    "    try:\n",
    "        unlabelled_b = unlabelled_iter.next()\n",
    "    except:\n",
    "        unlabelled_b = next(unlabelled_dl.__iter__())\n",
    "\n",
    "    #------- Meta Training Steps ---------------------------------#\n",
    "    # get loss of current student on labelled train data\n",
    "    s_logits_train_b = student.get_logits(train_b)\n",
    "\n",
    "    # get loss of current student on labelled train data\n",
    "    train_b_labels = train_b[\"labels\"]\n",
    "    train_b_masks = train_b_labels.gt(-0.5)\n",
    "    s_loss_train_b = student.compute_loss(\n",
    "        logits=s_logits_train_b.detach(),\n",
    "        labels=train_b_labels,\n",
    "        masks=train_b_masks,\n",
    "    )\n",
    "\n",
    "    # get teacher generated pseudo labels for unlabelled data\n",
    "    unlabelled_b_masks = unlabelled_b[\"label_mask\"].eq(1).unsqueeze(-1)\n",
    "    t_logits_unlabelled_b = teacher.get_logits(unlabelled_b)\n",
    "    pseudo_y_unlabelled_b = (t_logits_unlabelled_b.detach() > 0).float()  # hard pseudo label\n",
    "\n",
    "    #------ Train Student: With Pesudo Label Data ------------------#\n",
    "    s_logits_unlabelled_b = student.get_logits(unlabelled_b)\n",
    "    s_loss_unlabelled_b = student.compute_loss(\n",
    "        logits=s_logits_unlabelled_b,\n",
    "        labels=pseudo_y_unlabelled_b,\n",
    "        masks=unlabelled_b_masks\n",
    "    )\n",
    "\n",
    "    # backpropagation of student loss on unlabelled data\n",
    "    accelerator.backward(s_loss_unlabelled_b)\n",
    "    s_optimizer.step()  # update student params\n",
    "    s_scheduler.step()\n",
    "\n",
    "    #------ Train Teacher ------------------------------------------#\n",
    "    s_logits_train_b_new = student.get_logits(train_b)\n",
    "    s_loss_train_b_new = student.compute_loss(\n",
    "        logits=s_logits_train_b_new.detach(),\n",
    "        labels=train_b_labels,\n",
    "        masks=train_b_masks,\n",
    "    )\n",
    "    change = s_loss_train_b_new - s_loss_train_b  # performance improvement from student\n",
    "\n",
    "    t_logits_train_b = teacher.get_logits(train_b)\n",
    "    t_loss_train_b = teacher.compute_loss(\n",
    "        logits=t_logits_train_b,\n",
    "        labels=train_b_labels,\n",
    "        masks=train_b_masks\n",
    "    )\n",
    "\n",
    "    t_loss_mpl = change * F.binary_cross_entropy_with_logits(\n",
    "        t_logits_unlabelled_b, pseudo_y_unlabelled_b, reduction='none')  # mpl loss\n",
    "    t_loss_mpl = torch.masked_select(t_loss_mpl, unlabelled_b_masks).mean()\n",
    "    t_loss = t_loss_train_b + t_loss_mpl\n",
    "\n",
    "    # backpropagation of teacher's loss\n",
    "    accelerator.backward(t_loss)\n",
    "    t_optimizer.step()\n",
    "    t_scheduler.step()\n",
    "\n",
    "    #------ Progress Bar Updates ----------------------------------#\n",
    "    s_loss_meter.update(s_loss_train_b_new.item())\n",
    "    t_loss_meter.update(t_loss.item())\n",
    "\n",
    "    progress_bar.set_description(\n",
    "        f\"STEP: {step+1:5}/{num_steps:5}. \"\n",
    "        f\"LR: {get_lr(s_optimizer):.4f}. \"\n",
    "        f\"TL: {t_loss_meter.avg:.4f}. \"\n",
    "        f\"SL: {s_loss_meter.avg:.4f}. \"\n",
    "    )\n",
    "    progress_bar.update()\n",
    "\n",
    "    #------ Evaluation & Checkpointing -----------------------------#\n",
    "    if (step + 1) % config[\"validation_interval\"] == 0:\n",
    "        progress_bar.close()\n",
    "\n",
    "        #----- Teacher Evaluation  ---------------------------------#\n",
    "        teacher.eval()\n",
    "        teacher_preds = []\n",
    "        with torch.no_grad():\n",
    "            for batch in valid_dl:\n",
    "                p = teacher.get_logits(batch)\n",
    "                teacher_preds.append(p)\n",
    "        teacher_preds = [torch.sigmoid(p).detach().cpu().numpy()[:, :, 0] for p in teacher_preds]\n",
    "        teacher_preds = list(chain(*teacher_preds))\n",
    "        teacher_lb = scorer_fn(teacher_preds)\n",
    "        print(f\"After step {step+1} Teache LB: {teacher_lb}\")\n",
    "\n",
    "        # save teacher\n",
    "        accelerator.wait_for_everyone()\n",
    "        teacher = accelerator.unwrap_model(teacher)\n",
    "        teacher_state = {\n",
    "            'step': step + 1,\n",
    "            'state_dict': teacher.state_dict(),\n",
    "            'optimizer': t_optimizer.state_dict(),\n",
    "            'lb': teacher_lb\n",
    "        }\n",
    "        is_best = False\n",
    "        if teacher_lb > best_teacher_score:\n",
    "            best_teacher_score = teacher_lb\n",
    "            is_best = True\n",
    "        # save_checkpoint(config, teacher_state, is_teacher=True, is_best=is_best)\n",
    "\n",
    "        #----- Student Evaluation  ---------------------------------#\n",
    "        student.eval()\n",
    "        student_preds = []\n",
    "        with torch.no_grad():\n",
    "            for batch in valid_dl:\n",
    "                p = student.get_logits(batch)\n",
    "                student_preds.append(p)\n",
    "        student_preds = [torch.sigmoid(p).detach().cpu().numpy()[:, :, 0] for p in student_preds]\n",
    "        student_preds = list(chain(*student_preds))\n",
    "        student_lb = scorer_fn(student_preds)\n",
    "        print(f\"After step {step+1} Student LB: {student_lb}\")\n",
    "\n",
    "        # save student\n",
    "        accelerator.wait_for_everyone()\n",
    "        student = accelerator.unwrap_model(student)\n",
    "        student_state = {\n",
    "            'step': step + 1,\n",
    "            'state_dict': student.state_dict(),\n",
    "            'optimizer': s_optimizer.state_dict(),\n",
    "            'lb': student_lb\n",
    "        }\n",
    "        is_best = False\n",
    "        if student_lb > best_student_score:\n",
    "            best_student_score = student_lb\n",
    "            is_best = True\n",
    "        save_checkpoint(config, student_state, is_teacher=False, is_best=is_best)\n",
    "\n",
    "        print(\"==\"*40)\n",
    "        print(\"GPU utilization after eval:\")\n",
    "        print_gpu_utilization()\n",
    "        print(\"clearning the cache\")\n",
    "        torch.cuda.empty_cache()\n",
    "        print_gpu_utilization()\n",
    "        print(\"==\"*40)"
   ]
  },
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   "source": [
    "# Next Steps\n",
    "* Run MPL with more unlabelled examples\n",
    "* Experiment with other hyper-parameters\n",
    "* Experiment with soft pseudo labels\n",
    "* During MPL, the student model is trained only on unlabelled data using pseudo labels from teacher\n",
    "* So the student can be further fine-tuned on actual training data for additional performance boost"
   ]
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