Paper_Peek.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PAPER PEEK📜👀\n",
    "\n",
    "The purpose of the project is to build an NLP model to make reading medical abstracts easier.\n",
    "\n",
    "The paper we're replicating (the source of the dataset that we'll be using) is available here: https://arxiv.org/abs/1710.06071\n",
    "\n",
    "Reading through the paper above, we see that the model architecture that they use to achieve their best results is available here: https://arxiv.org/abs/1612.05251"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confirm access to a GPU "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU 0: NVIDIA GeForce MX330 (UUID: GPU-77eebd61-350b-2f9c-b351-0acab8f44c51)\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi -L"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get data\n",
    "\n",
    "Since we'll be replicating the paper above (PubMed 200k RCT), let's download the dataset they used.\n",
    "\n",
    "We can do so from the authors GitHub: https://github.com/Franck-Dernoncourt/pubmed-rct "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I have used the zip file but you can run below command which will clone the github repository and import the dataset.\n",
    "# !git clone https://github.com/Franck-Dernoncourt/pubmed-rct\n",
    "# !ls pubmed-rct\n",
    "\n",
    "#OUTPUT:\n",
    "# Cloning into 'pubmed-rct'...\n",
    "# remote: Enumerating objects: 33, done.\n",
    "# remote: Counting objects: 100% (8/8), done.\n",
    "# remote: Compressing objects: 100% (3/3), done.\n",
    "# remote: Total 33 (delta 5), reused 5 (delta 5), pack-reused 25\n",
    "# Unpacking objects: 100% (33/33), 177.08 MiB | 14.49 MiB/s, done.\n",
    "# PubMed_200k_RCT\n",
    "# PubMed_200k_RCT_numbers_replaced_with_at_sign\n",
    "# PubMed_20k_RCT\n",
    "# PubMed_20k_RCT_numbers_replaced_with_at_sign\n",
    "# README.md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check what files are in the PubMed_20K dataset\n",
    "# !ls pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/\n",
    "\n",
    "#OUTPUT:\n",
    "# dev.txt  test.txt  train.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start our experiments using the 20k dataset with numbers replaced by \"@\" sign\n",
    "data_dir = \"pubmed-rct-master/PubMed_20k_RCT_numbers_replaced_with_at_sign/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pubmed-rct-master/PubMed_20k_RCT_numbers_replaced_with_at_sign/dev.txt',\n",
       " 'pubmed-rct-master/PubMed_20k_RCT_numbers_replaced_with_at_sign/test.txt',\n",
       " 'pubmed-rct-master/PubMed_20k_RCT_numbers_replaced_with_at_sign/train.txt']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check all of the filenames in the target directory\n",
    "import os\n",
    "filenames = [data_dir + filename for filename in os.listdir(data_dir)]\n",
    "filenames"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess data\n",
    "\n",
    "Writing a function to read in all of the lines of a target text file and Visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function to read the lines of a document\n",
    "def get_lines(filename):\n",
    "  \"\"\"\n",
    "  Reads filename (a text filename) and returns the lines of text as a list.\n",
    "\n",
    "  Args:\n",
    "    filename: a string containing the target filepath.\n",
    "\n",
    "  Returns:\n",
    "    A list of strings with one string per line from the target filename.\n",
    "  \"\"\"\n",
    "  with open(filename, \"r\") as f:\n",
    "    return f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['###24293578\\n',\n",
       " 'OBJECTIVE\\tTo investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( OA ) .\\n',\n",
       " 'METHODS\\tA total of @ patients with primary knee OA were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .\\n',\n",
       " 'METHODS\\tOutcome measures included pain reduction and improvement in function scores and systemic inflammation markers .\\n',\n",
       " 'METHODS\\tPain was assessed using the visual analog pain scale ( @-@ mm ) .\\n',\n",
       " 'METHODS\\tSecondary outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index scores , patient global assessment ( PGA ) of the severity of knee OA , and @-min walk distance ( @MWD ) .\\n',\n",
       " 'METHODS\\tSerum levels of interleukin @ ( IL-@ ) , IL-@ , tumor necrosis factor ( TNF ) - , and high-sensitivity C-reactive protein ( hsCRP ) were measured .\\n',\n",
       " 'RESULTS\\tThere was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , PGA , and @MWD at @ weeks .\\n',\n",
       " 'RESULTS\\tThe mean difference between treatment arms ( @ % CI ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .\\n',\n",
       " 'RESULTS\\tFurther , there was a clinically relevant reduction in the serum levels of IL-@ , IL-@ , TNF - , and hsCRP at @ weeks in the intervention group when compared to the placebo group .\\n',\n",
       " 'RESULTS\\tThese differences remained significant at @ weeks .\\n',\n",
       " 'RESULTS\\tThe Outcome Measures in Rheumatology Clinical Trials-Osteoarthritis Research Society International responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ) .\\n',\n",
       " 'CONCLUSIONS\\tLow-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee OA ( ClinicalTrials.gov identifier NCT@ ) .\\n',\n",
       " '\\n',\n",
       " '###24854809\\n',\n",
       " 'BACKGROUND\\tEmotional eating is associated with overeating and the development of obesity .\\n',\n",
       " 'BACKGROUND\\tYet , empirical evidence for individual ( trait ) differences in emotional eating and cognitive mechanisms that contribute to eating during sad mood remain equivocal .\\n',\n",
       " 'OBJECTIVE\\tThe aim of this study was to test if attention bias for food moderates the effect of self-reported emotional eating during sad mood ( vs neutral mood ) on actual food intake .\\n',\n",
       " 'OBJECTIVE\\tIt was expected that emotional eating is predictive of elevated attention for food and higher food intake after an experimentally induced sad mood and that attentional maintenance on food predicts food intake during a sad versus a neutral mood .\\n',\n",
       " 'METHODS\\tParticipants ( N = @ ) were randomly assigned to one of the two experimental mood induction conditions ( sad/neutral ) .\\n',\n",
       " 'METHODS\\tAttentional biases for high caloric foods were measured by eye tracking during a visual probe task with pictorial food and neutral stimuli .\\n',\n",
       " 'METHODS\\tSelf-reported emotional eating was assessed with the Dutch Eating Behavior Questionnaire ( DEBQ ) and ad libitum food intake was tested by a disguised food offer .\\n',\n",
       " 'RESULTS\\tHierarchical multivariate regression modeling showed that self-reported emotional eating did not account for changes in attention allocation for food or food intake in either condition .\\n',\n",
       " 'RESULTS\\tYet , attention maintenance on food cues was significantly related to increased intake specifically in the neutral condition , but not in the sad mood condition .\\n',\n",
       " 'CONCLUSIONS\\tThe current findings show that self-reported emotional eating ( based on the DEBQ ) might not validly predict who overeats when sad , at least not in a laboratory setting with healthy women .\\n',\n",
       " 'CONCLUSIONS\\tResults further suggest that attention maintenance on food relates to eating motivation when in a neutral affective state , and might therefore be a cognitive mechanism contributing to increased food intake in general , but maybe not during sad mood .\\n',\n",
       " '\\n']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the training lines\n",
    "train_lines = get_lines(data_dir+\"train.txt\") # read the lines with the training file\n",
    "train_lines[:27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210040"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_lines)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data would be represented as below (Converting data into dictionary):\n",
    "\n",
    "```\n",
    "[{'line_number': 0,\n",
    "   'target': 'BACKGROUND',\n",
    "   'text': \"Emotional eating is associated with overeating and the development of obesity .\\n\"\n",
    "   'total_lines': 11},\n",
    "   ...]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text_with_line_numbers(filename):\n",
    "  \"\"\"\n",
    "  Returns a list of dictionaries of abstract line data.\n",
    "\n",
    "  Takes in filename, reads it contents and sorts through each line,\n",
    "  extracting things like the target label, the text of the sentnece,\n",
    "  how many sentences are in the current abstract and what sentence\n",
    "  number the target line is.\n",
    "  \"\"\"\n",
    "  input_lines = get_lines(filename) # get all lines from filename\n",
    "  abstract_lines = \"\" # create an empty abstract\n",
    "  abstract_samples = [] # create an empty list of abstracts\n",
    "\n",
    "  # Loop through each line in the target file\n",
    "  for line in input_lines:\n",
    "    if line.startswith(\"###\"): # check to see if the is an ID line\n",
    "      abstract_id = line\n",
    "      abstract_lines = \"\" # reset the abstract string if the line is an ID line\n",
    "\n",
    "    elif line.isspace(): # check to see if line is a new line\n",
    "      abstract_line_split = abstract_lines.splitlines() # split abstract into separate lines\n",
    "\n",
    "      # Iterate through each line in a single abstract and count them at the same time\n",
    "      for abstract_line_number, abstract_line in enumerate(abstract_line_split):\n",
    "        line_data = {} # create an empty dictionary for each line\n",
    "        target_text_split = abstract_line.split(\"\\t\") # split target label from text \n",
    "        line_data[\"target\"] = target_text_split[0] # get target label\n",
    "        line_data[\"text\"] = target_text_split[1].lower() # get target text and lower it\n",
    "        line_data[\"line_number\"] = abstract_line_number # what number line does the line appear in the abstract?\n",
    "        line_data[\"total_lines\"] = len(abstract_line_split) - 1 # how many total lines are there in the target abstract? (start from 0)\n",
    "        abstract_samples.append(line_data) # add line data to abstract samples list\n",
    "\n",
    "    else: # if the above conditions aren't fulfilled, the line contains a labelled sentence\n",
    "      abstract_lines += line\n",
    "  \n",
    "  return abstract_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180040 30212 30135\n",
      "CPU times: total: 2.25 s\n",
      "Wall time: 4.26 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Get data from file and preprocess it\n",
    "train_samples = preprocess_text_with_line_numbers(data_dir + \"train.txt\")\n",
    "val_samples = preprocess_text_with_line_numbers(data_dir + \"dev.txt\") # dev is another name for validation dataset\n",
    "test_samples = preprocess_text_with_line_numbers(data_dir + \"test.txt\")\n",
    "print(len(train_samples), len(val_samples), len(test_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'target': 'OBJECTIVE',\n",
       "  'text': 'to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
       "  'line_number': 0,\n",
       "  'total_lines': 11},\n",
       " {'target': 'METHODS',\n",
       "  'text': 'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
       "  'line_number': 1,\n",
       "  'total_lines': 11},\n",
       " {'target': 'METHODS',\n",
       "  'text': 'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
       "  'line_number': 2,\n",
       "  'total_lines': 11},\n",
       " {'target': 'METHODS',\n",
       "  'text': 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
       "  'line_number': 3,\n",
       "  'total_lines': 11},\n",
       " {'target': 'METHODS',\n",
       "  'text': 'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .',\n",
       "  'line_number': 4,\n",
       "  'total_lines': 11},\n",
       " {'target': 'METHODS',\n",
       "  'text': 'serum levels of interleukin @ ( il-@ ) , il-@ , tumor necrosis factor ( tnf ) - , and high-sensitivity c-reactive protein ( hscrp ) were measured .',\n",
       "  'line_number': 5,\n",
       "  'total_lines': 11},\n",
       " {'target': 'RESULTS',\n",
       "  'text': 'there was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , pga , and @mwd at @ weeks .',\n",
       "  'line_number': 6,\n",
       "  'total_lines': 11},\n",
       " {'target': 'RESULTS',\n",
       "  'text': 'the mean difference between treatment arms ( @ % ci ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .',\n",
       "  'line_number': 7,\n",
       "  'total_lines': 11},\n",
       " {'target': 'RESULTS',\n",
       "  'text': 'further , there was a clinically relevant reduction in the serum levels of il-@ , il-@ , tnf - , and hscrp at @ weeks in the intervention group when compared to the placebo group .',\n",
       "  'line_number': 8,\n",
       "  'total_lines': 11},\n",
       " {'target': 'RESULTS',\n",
       "  'text': 'these differences remained significant at @ weeks .',\n",
       "  'line_number': 9,\n",
       "  'total_lines': 11},\n",
       " {'target': 'RESULTS',\n",
       "  'text': 'the outcome measures in rheumatology clinical trials-osteoarthritis research society international responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ) .',\n",
       "  'line_number': 10,\n",
       "  'total_lines': 11},\n",
       " {'target': 'CONCLUSIONS',\n",
       "  'text': 'low-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee oa ( clinicaltrials.gov identifier nct@ ) .',\n",
       "  'line_number': 11,\n",
       "  'total_lines': 11},\n",
       " {'target': 'BACKGROUND',\n",
       "  'text': 'emotional eating is associated with overeating and the development of obesity .',\n",
       "  'line_number': 0,\n",
       "  'total_lines': 10},\n",
       " {'target': 'BACKGROUND',\n",
       "  'text': 'yet , empirical evidence for individual ( trait ) differences in emotional eating and cognitive mechanisms that contribute to eating during sad mood remain equivocal .',\n",
       "  'line_number': 1,\n",
       "  'total_lines': 10},\n",
       " {'target': 'OBJECTIVE',\n",
       "  'text': 'the aim of this study was to test if attention bias for food moderates the effect of self-reported emotional eating during sad mood ( vs neutral mood ) on actual food intake .',\n",
       "  'line_number': 2,\n",
       "  'total_lines': 10}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first abstract of our training data\n",
    "train_samples[:15]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is in the format of a list of dictionaries, So we turn it into a DataFrame to visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>line_number</th>\n",
       "      <th>total_lines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OBJECTIVE</td>\n",
       "      <td>to investigate the efficacy of @ weeks of dail...</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>a total of @ patients with primary knee oa wer...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>outcome measures included pain reduction and i...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>pain was assessed using the visual analog pain...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>secondary outcome measures included the wester...</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>serum levels of interleukin @ ( il-@ ) , il-@ ...</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>there was a clinically relevant reduction in t...</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>the mean difference between treatment arms ( @...</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>further , there was a clinically relevant redu...</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>these differences remained significant at @ we...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>the outcome measures in rheumatology clinical ...</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>low-dose oral prednisolone had both a short-te...</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BACKGROUND</td>\n",
       "      <td>emotional eating is associated with overeating...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BACKGROUND</td>\n",
       "      <td>yet , empirical evidence for individual ( trai...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OBJECTIVE</td>\n",
       "      <td>the aim of this study was to test if attention...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         target                                               text  \\\n",
       "0     OBJECTIVE  to investigate the efficacy of @ weeks of dail...   \n",
       "1       METHODS  a total of @ patients with primary knee oa wer...   \n",
       "2       METHODS  outcome measures included pain reduction and i...   \n",
       "3       METHODS  pain was assessed using the visual analog pain...   \n",
       "4       METHODS  secondary outcome measures included the wester...   \n",
       "5       METHODS  serum levels of interleukin @ ( il-@ ) , il-@ ...   \n",
       "6       RESULTS  there was a clinically relevant reduction in t...   \n",
       "7       RESULTS  the mean difference between treatment arms ( @...   \n",
       "8       RESULTS  further , there was a clinically relevant redu...   \n",
       "9       RESULTS  these differences remained significant at @ we...   \n",
       "10      RESULTS  the outcome measures in rheumatology clinical ...   \n",
       "11  CONCLUSIONS  low-dose oral prednisolone had both a short-te...   \n",
       "12   BACKGROUND  emotional eating is associated with overeating...   \n",
       "13   BACKGROUND  yet , empirical evidence for individual ( trai...   \n",
       "14    OBJECTIVE  the aim of this study was to test if attention...   \n",
       "\n",
       "    line_number  total_lines  \n",
       "0             0           11  \n",
       "1             1           11  \n",
       "2             2           11  \n",
       "3             3           11  \n",
       "4             4           11  \n",
       "5             5           11  \n",
       "6             6           11  \n",
       "7             7           11  \n",
       "8             8           11  \n",
       "9             9           11  \n",
       "10           10           11  \n",
       "11           11           11  \n",
       "12            0           10  \n",
       "13            1           10  \n",
       "14            2           10  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "train_df = pd.DataFrame(train_samples)\n",
    "val_df = pd.DataFrame(val_samples)\n",
    "test_df = pd.DataFrame(test_samples)\n",
    "train_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "METHODS        59353\n",
       "RESULTS        57953\n",
       "CONCLUSIONS    27168\n",
       "BACKGROUND     21727\n",
       "OBJECTIVE      13839\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribution of labels in training data\n",
    "train_df.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's check the length of different lines\n",
    "train_df.total_lines.plot.hist(); "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get lists of sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180040, 30212, 30135)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert abstract text lines into lists\n",
    "train_sentences = train_df[\"text\"].tolist()\n",
    "val_sentences = val_df[\"text\"].tolist()\n",
    "test_sentences = test_df[\"text\"].tolist()\n",
    "len(train_sentences), len(val_sentences), len(test_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
       " 'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
       " 'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
       " 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
       " 'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .',\n",
       " 'serum levels of interleukin @ ( il-@ ) , il-@ , tumor necrosis factor ( tnf ) - , and high-sensitivity c-reactive protein ( hscrp ) were measured .',\n",
       " 'there was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , pga , and @mwd at @ weeks .',\n",
       " 'the mean difference between treatment arms ( @ % ci ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .',\n",
       " 'further , there was a clinically relevant reduction in the serum levels of il-@ , il-@ , tnf - , and hscrp at @ weeks in the intervention group when compared to the placebo group .',\n",
       " 'these differences remained significant at @ weeks .']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the 10 lines of training sentences\n",
    "train_sentences[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ige sensitization to aspergillus fumigatus and a positive sputum fungal culture result are common in patients with refractory asthma .',\n",
       " 'it is not clear whether these patients would benefit from antifungal treatment .',\n",
       " 'we sought to determine whether a @-month course of voriconazole improved asthma-related outcomes in patients with asthma who are ige sensitized to a fumigatus .',\n",
       " 'asthmatic patients who were ige sensitized to a fumigatus with a history of at least @ severe exacerbations in the previous @ months were treated for @ months with @ mg of voriconazole twice daily , followed by observation for @ months , in a double-blind , placebo-controlled , randomized design .',\n",
       " 'primary outcomes were improvement in quality of life at the end of the treatment period and a reduction in the number of severe exacerbations over the @ months of the study .',\n",
       " 'sixty-five patients were randomized .',\n",
       " 'fifty-nine patients started treatment ( @ receiving voriconazole and @ receiving placebo ) and were included in an intention-to-treat analysis .',\n",
       " 'fifty-six patients took the full @ months of medication .',\n",
       " 'between the voriconazole and placebo groups , there were no significant differences in the number of severe exacerbations ( @ vs @ per patient per year , respectively ; mean difference , @ ; @ % ci , @-@ @ ) , quality of life ( change in asthma quality of life questionnaire score , @ vs @ ; mean difference between groups , @ ; @ % ci , -@ to -@ ) , or any of our secondary outcome measures .',\n",
       " 'we were unable to show a beneficial effect of @ months of treatment with voriconazole in patients with moderate-to-severe asthma who were ige sensitized to a fumigatus on either the rate of severe exacerbations , quality of life , or other markers of asthma control .']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_sentences[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['this study analyzed liver function abnormalities in heart failure patients admitted with severe acute decompensated heart failure ( adhf ) .',\n",
       " 'a post hoc analysis was conducted with the use of data from the evaluation study of congestive heart failure and pulmonary artery catheterization effectiveness ( escape ) .',\n",
       " 'liver function tests ( lfts ) were measured at @ time points from baseline , at discharge , and up to @ months follow-up .',\n",
       " 'survival analyses were used to assess the association between admission model of end-stage liver disease excluding international normalized ratio ( meld-xi ) scores and patient outcome.there was a high prevalence of abnormal baseline ( admission ) lfts ( albumin @ % , aspartate transaminase @ % , alanine transaminase @ % , and total bilirubin @ % ) .',\n",
       " \"the percentage of patients with abnormal lfts decreased significantly from baseline to @-months ' follow-up .\",\n",
       " 'when mean hemodynamic profiles were compared in patients with abnormal versus normal lfts , elevated total bilirubin was associated with a significantly lower cardiac index ( @ vs @ ; p < @ ) and higher central venous pressure ( @ vs @ ; p = @ ) .',\n",
       " 'multivariable analyses revealed that patients with elevated meld-xi scores ( @ ) had a @-fold ( hazard ratio@ @ , @ % confidence interval @-@ @ ) increased risk of death , rehospitalization , or transplantation after adjusting for baseline lfts , age , sex , race , body mass index , diabetes , and systolic blood pressure .',\n",
       " 'abnormal lfts are common in the adhf population and are a dynamic marker of an impaired hemodynamic state .',\n",
       " 'elevated meld-xi scores are associated with poor outcomes among patients admitted with adhf .',\n",
       " 'minimally invasive endovascular aneurysm repair ( evar ) could be a surgical technique that improves outcome of patients with ruptured abdominal aortic aneurysm ( raaa ) .']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sentences[:10]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make numeric labels (ML models require numeric labels)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LABEL ENCODE LABELS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 2, ..., 4, 1, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LABEL ENCODE LABELS: Required by Baseline Model\n",
    "# Extract labels (\"target\" columns) and encode them into integers\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "label_encoder = LabelEncoder()\n",
    "train_labels_encoded = label_encoder.fit_transform(train_df[\"target\"].to_numpy())\n",
    "val_labels_encoded = label_encoder.transform(val_df[\"target\"].to_numpy())\n",
    "test_label_encoded = label_encoder.transform(test_df[\"target\"].to_numpy())\n",
    "\n",
    "# check what training labels look like\n",
    "train_labels_encoded"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ONE HOT ENCODE LABELS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ONE HOT ENCODE LABELS: Required by CategoricalCrossEntropy Loss function\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "one_hot_encoder = OneHotEncoder(sparse_output=False) # we want non-sparse matrix\n",
    "train_labels_one_hot = one_hot_encoder.fit_transform(train_df[\"target\"].to_numpy().reshape(-1, 1))\n",
    "val_labels_one_hot = one_hot_encoder.transform(val_df[\"target\"].to_numpy().reshape(-1, 1))\n",
    "test_labels_one_hot = one_hot_encoder.transform(test_df[\"target\"].to_numpy().reshape(-1, 1))\n",
    "\n",
    "# check what one hot encoded labels look like\n",
    "train_labels_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,\n",
       " array(['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get class names and number of classes from LabelEncoder instance\n",
    "num_classes = len(label_encoder.classes_)\n",
    "class_names = label_encoder.classes_\n",
    "num_classes, class_names"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series of modelling experiments\n",
    "\n",
    "Building different models and seeing which one works best.\n",
    "\n",
    "We will initially begin with a baseline model (TF-IDF Multinomial Naive Bayes classifier)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 0: Building a baseline model (Naive Bayes with TF-IDF encoder)\n",
    "\n",
    "* A baseline model is an important first step in any series of modelling experiments.\n",
    "\n",
    "* It sets the pace for the subsequent experiments.\n",
    "\n",
    "* It should start with a simple model and later models (with increasing complexity) will try to beat it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tf-idf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tf-idf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tf-idf', TfidfVectorizer()), ('clf', MultinomialNB())])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Create a pipeline\n",
    "model_0 = Pipeline([\n",
    "  (\"tf-idf\", TfidfVectorizer()),\n",
    "  (\"clf\", MultinomialNB())\n",
    "])\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "model_0.fit(X=train_sentences,\n",
    "            y=train_labels_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7218323844829869"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate baseline model on validation dataset\n",
    "model_0.score(X=val_sentences,\n",
    "              y=val_labels_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 3, ..., 4, 4, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions using our baseline model\n",
    "baseline_preds = model_0.predict(val_sentences)\n",
    "baseline_preds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download helper function script\n",
    "\n",
    "We had wrote a function to compare predictions across different metrics (accuracy, precision, recall and f1) and rather than rewriting it here we will just import it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helper_functions import calculate_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 72.1832384482987,\n",
       " 'precision': 0.7186466952323352,\n",
       " 'recall': 0.7218323844829869,\n",
       " 'f1': 0.6989250353450294}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate baseline results\n",
    "baseline_results = calculate_results(y_true=val_labels_encoded,\n",
    "                                     y_pred=baseline_preds)\n",
    "baseline_results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing data (the text) for deep sequence models\n",
    "\n",
    "Before we start building deeper models, we've to create vectorization and embedding layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
       " 'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
       " 'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
       " 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
       " 'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .',\n",
       " 'serum levels of interleukin @ ( il-@ ) , il-@ , tumor necrosis factor ( tnf ) - , and high-sensitivity c-reactive protein ( hscrp ) were measured .',\n",
       " 'there was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , pga , and @mwd at @ weeks .',\n",
       " 'the mean difference between treatment arms ( @ % ci ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .',\n",
       " 'further , there was a clinically relevant reduction in the serum levels of il-@ , il-@ , tnf - , and hscrp at @ weeks in the intervention group when compared to the placebo group .',\n",
       " 'these differences remained significant at @ weeks .']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sentences[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.338269273494777"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How long is each sentence on average?\n",
    "sent_lens = [len(sentence.split()) for sentence in train_sentences]\n",
    "avg_sent_len = np.mean(sent_lens)\n",
    "avg_sent_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How does the distribution look?\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(sent_lens, bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How long of a sentence length covers 95% of examples?\n",
    "output_seq_len = int(np.percentile(sent_lens, 95))\n",
    "output_seq_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "296"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum sequence length in the training set\n",
    "max(sent_lens)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create text vectorizer layer\n",
    "\n",
    "Make a layer which maps our tokens(texts) from words to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many words are in our vocab? i.e. size of vocabulary \n",
    "# SOURCE: (taken from table 2 in: https://arxiv.org/pdf/1710.06071.pdf)\n",
    "max_tokens = 68000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create text vectorizer\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "\n",
    "text_vectorizer = TextVectorization(max_tokens=max_tokens, # number of words in vocabulary\n",
    "                                    output_sequence_length=output_seq_len) # desired output length of vectorized sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adapt text vectorizer to training sentences\n",
    "text_vectorizer.adapt(train_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text:\n",
      "we projected feasible reductions in tb incidence and mortality in the general population and among plhiv specifically at the end of @ years after implementing an ipt program .\n",
      "\n",
      "Length of text: 29\n",
      "\n",
      "Vectorized text: [[   43  9877  1099   626     5  2436   214     3   259     5     2   351\n",
      "    278     3   116 22136  1850    15     2   199     4    64    21  3263\n",
      "     26  4703   256     0     0     0     0     0     0     0     0     0\n",
      "      0     0     0     0     0     0     0     0     0     0     0     0\n",
      "      0     0     0     0     0     0     0]]\n"
     ]
    }
   ],
   "source": [
    "# Test out text vectorizer on random sentences\n",
    "import random\n",
    "target_sentence = random.choice(train_sentences)\n",
    "print(f\"Text:\\n{target_sentence}\")\n",
    "print(f\"\\nLength of text: {len(target_sentence.split())}\")\n",
    "print(f\"\\nVectorized text: {text_vectorizer([target_sentence])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of words in vocab: 64841\n",
      "Most common words in the vocabulary: ['', '[UNK]', 'the', 'and', 'of']\n",
      "Least common words in the vocabulary: ['aainduced', 'aaigroup', 'aachener', 'aachen', 'aaacp']\n"
     ]
    }
   ],
   "source": [
    "# How many words in our training vocabulary\n",
    "rct_20k_text_vocab = text_vectorizer.get_vocabulary()\n",
    "print(f\"Number of words in vocab: {len(rct_20k_text_vocab)}\")\n",
    "print(f\"Most common words in the vocabulary: {rct_20k_text_vocab[:5]}\")\n",
    "print(f\"Least common words in the vocabulary: {rct_20k_text_vocab[-5:]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'text_vectorization',\n",
       " 'trainable': True,\n",
       " 'dtype': 'string',\n",
       " 'batch_input_shape': (None,),\n",
       " 'max_tokens': 68000,\n",
       " 'standardize': 'lower_and_strip_punctuation',\n",
       " 'split': 'whitespace',\n",
       " 'ngrams': None,\n",
       " 'output_mode': 'int',\n",
       " 'output_sequence_length': 55,\n",
       " 'pad_to_max_tokens': False,\n",
       " 'sparse': False,\n",
       " 'ragged': False,\n",
       " 'vocabulary': None,\n",
       " 'idf_weights': None,\n",
       " 'encoding': 'utf-8',\n",
       " 'vocabulary_size': 64841}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the config of our text vectorizer\n",
    "text_vectorizer.get_config()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create custom text embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create token embedding layer\n",
    "token_embed = layers.Embedding(input_dim=len(rct_20k_text_vocab), # length of vocabulary\n",
    "                               output_dim=128, # Note: different embedding sizes result in drastically differnt numbers of parameters to train\n",
    "                               mask_zero=True, # use masking to handle variable sequence lengths (save space),\n",
    "                               name=\"token_embedding\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence before vectorization:\n",
      " we projected feasible reductions in tb incidence and mortality in the general population and among plhiv specifically at the end of @ years after implementing an ipt program .\n",
      "\n",
      "Sentence after vectorization (before embedding):\n",
      " [[   43  9877  1099   626     5  2436   214     3   259     5     2   351\n",
      "    278     3   116 22136  1850    15     2   199     4    64    21  3263\n",
      "     26  4703   256     0     0     0     0     0     0     0     0     0\n",
      "      0     0     0     0     0     0     0     0     0     0     0     0\n",
      "      0     0     0     0     0     0     0]]\n",
      "\n",
      "Sentence after embedding:\n",
      " [[[ 0.01805509  0.04085538 -0.04613863 ...  0.00118493  0.04094953\n",
      "   -0.00905318]\n",
      "  [ 0.02489264  0.03495001  0.00305319 ... -0.01757627 -0.00626468\n",
      "    0.00420761]\n",
      "  [-0.00700867  0.00440992 -0.00031871 ...  0.03252386  0.01992958\n",
      "   -0.01793767]\n",
      "  ...\n",
      "  [ 0.00547037 -0.00327476  0.01179194 ... -0.02815155  0.0291023\n",
      "   -0.01924567]\n",
      "  [ 0.00547037 -0.00327476  0.01179194 ... -0.02815155  0.0291023\n",
      "   -0.01924567]\n",
      "  [ 0.00547037 -0.00327476  0.01179194 ... -0.02815155  0.0291023\n",
      "   -0.01924567]]]\n",
      "\n",
      "Embedded sentence shape: (1, 55, 128)\n"
     ]
    }
   ],
   "source": [
    "# Show example embedding\n",
    "print(f\"Sentence before vectorization:\\n {target_sentence}\\n\")\n",
    "vectorized_sentence = text_vectorizer([target_sentence])\n",
    "print(f\"Sentence after vectorization (before embedding):\\n {vectorized_sentence}\\n\")\n",
    "embedded_sentence = token_embed(vectorized_sentence)\n",
    "print(f\"Sentence after embedding:\\n {embedded_sentence}\\n\")\n",
    "print(f\"Embedded sentence shape: {embedded_sentence.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating datasets (To load data as fast as possible)\n",
    "\n",
    "Setup data to run as fast as possible with the TensorFlow `tf.data API`.\n",
    "\n",
    "**Resources:** Many of the steps here are discussed at length in these two resources\n",
    "* https://www.tensorflow.org/guide/data_performance\n",
    "* https://www.tensorflow.org/guide/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_TensorSliceDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(5,), dtype=tf.float64, name=None))>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn our data into TensorFlow Datasets\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_sentences, train_labels_one_hot))\n",
    "valid_dataset = tf.data.Dataset.from_tensor_slices((val_sentences, val_labels_one_hot))\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((test_sentences, test_labels_one_hot))\n",
    "\n",
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the TensorSliceDataset's and turn them into prefetched datasets\n",
    "train_dataset = train_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
    "valid_dataset = valid_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
    "test_dataset = test_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
    "\n",
    "train_dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1: Conv1D with token embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 1D conv model to process sequences\n",
    "inputs = layers.Input(shape=(1,), dtype=tf.string)\n",
    "text_vectors = text_vectorizer(inputs) # vectorize text inputs\n",
    "token_embeddings = token_embed(text_vectors) # create embedding\n",
    "x = layers.Conv1D(64, kernel_size=5, padding=\"same\", activation=\"relu\")(token_embeddings)\n",
    "x = layers.GlobalAveragePooling1D()(x) # condense the output of our feature vector from conv layer\n",
    "outputs = layers.Dense(num_classes, activation=\"softmax\")(x)\n",
    "model_1 = tf.keras.Model(inputs, outputs)\n",
    "\n",
    "# Compile the model\n",
    "model_1.compile(loss=\"categorical_crossentropy\",\n",
    "                optimizer=tf.keras.optimizers.Adam(),\n",
    "                metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 1)]               0         \n",
      "                                                                 \n",
      " text_vectorization (TextVec  (None, 55)               0         \n",
      " torization)                                                     \n",
      "                                                                 \n",
      " token_embedding (Embedding)  (None, 55, 128)          8299648   \n",
      "                                                                 \n",
      " conv1d (Conv1D)             (None, 55, 64)            41024     \n",
      "                                                                 \n",
      " global_average_pooling1d (G  (None, 64)               0         \n",
      " lobalAveragePooling1D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 5)                 325       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,340,997\n",
      "Trainable params: 8,340,997\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5627"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NO. OF BATCHES OF TRAINING DATASET\n",
    "len(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180064"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NO. OF BATCHES OF TRAINING DATASET x BATCH SIZE = TOTAL TRAINING DATA\n",
    "5627*32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "562/562 [==============================] - 160s 281ms/step - loss: 0.9210 - accuracy: 0.6356 - val_loss: 0.6845 - val_accuracy: 0.7400\n",
      "Epoch 2/3\n",
      "562/562 [==============================] - 143s 255ms/step - loss: 0.6555 - accuracy: 0.7590 - val_loss: 0.6308 - val_accuracy: 0.7723\n",
      "Epoch 3/3\n",
      "562/562 [==============================] - 144s 256ms/step - loss: 0.6147 - accuracy: 0.7758 - val_loss: 0.5950 - val_accuracy: 0.7852\n"
     ]
    }
   ],
   "source": [
    "# Fit the model (Training on 10% of batch every epoch)\n",
    "history_model_1 = model_1.fit(train_dataset,\n",
    "                              steps_per_epoch=int(0.1*len(train_dataset)),\n",
    "                              epochs=3,\n",
    "                              validation_data=valid_dataset,\n",
    "                              validation_steps=int(0.1 * len(valid_dataset))) # only validate on 10% of batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 15s 16ms/step - loss: 0.5958 - accuracy: 0.7874\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5957702398300171, 0.787402331829071]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evalaute on whole validation dataset\n",
    "model_1.evaluate(valid_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 19s 20ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([[4.6097702e-01, 1.3752647e-01, 5.7644691e-02, 3.1610054e-01,\n",
       "         2.7751246e-02],\n",
       "        [4.1314289e-01, 3.3882919e-01, 1.2666935e-02, 2.2287488e-01,\n",
       "         1.2486029e-02],\n",
       "        [1.6740589e-01, 5.6855823e-03, 2.4008309e-03, 8.2447821e-01,\n",
       "         2.9445820e-05],\n",
       "        ...,\n",
       "        [8.6765549e-06, 6.8700133e-04, 1.0829525e-03, 6.8306258e-06,\n",
       "         9.9821460e-01],\n",
       "        [4.5166261e-02, 4.5261934e-01, 8.1862710e-02, 6.3288316e-02,\n",
       "         3.5706332e-01],\n",
       "        [1.7953430e-01, 6.8866545e-01, 3.9604239e-02, 5.2708190e-02,\n",
       "         3.9487883e-02]], dtype=float32),\n",
       " (30212, 5))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions (our model predicts prediction probabilities for each class)\n",
    "model_1_pred_probs = model_1.predict(valid_dataset)\n",
    "model_1_pred_probs, model_1_pred_probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 0, 3, ..., 4, 1, 1], dtype=int64)>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert pred probs to classes\n",
    "model_1_preds = tf.argmax(model_1_pred_probs, axis=1)\n",
    "model_1_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 78.74023566794651,\n",
       " 'precision': 0.7841459843482087,\n",
       " 'recall': 0.7874023566794651,\n",
       " 'f1': 0.7847392767407068}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate model_1 results\n",
    "model_1_results = calculate_results(y_true=val_labels_encoded,\n",
    "                                    y_pred=model_1_preds)\n",
    "model_1_results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2: Feature extraction with pretrained token embeddings\n",
    "\n",
    "***The paper originally used GloVe embeddings.***\n",
    "\n",
    "However, we will be using pretrained word embeddings from TensorFlow Hub, more specifically the universal sentence encoder (USE): https://tfhub.dev/google/universal-sentence-encoder/4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download pretrained TensorFlow Hub USE\n",
    "import tensorflow_hub as hub\n",
    "tf_hub_embedding_layer = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder/4\",\n",
    "                                        trainable=False,\n",
    "                                        name=\"universal_sentence_encoder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random sentence:\n",
      " the majority of patients had long-standing disease ; were older , male and white .\n",
      "Sentence after embedding:\n",
      "[-0.04641222  0.05291244  0.03199166  0.06344666 -0.08438665  0.0319013\n",
      "  0.05883302  0.03167362 -0.0473523  -0.02395477  0.09963678  0.03936958\n",
      "  0.014139    0.07592112  0.03464148  0.01824743 -0.10207924 -0.05733482\n",
      " -0.07457951 -0.07248326  0.00526465  0.04947443 -0.01523794  0.03316908\n",
      "  0.03893929  0.02814494  0.06209082 -0.00913386  0.0187242   0.07579207]\n",
      "\n",
      "Length of sentence embedding: 512\n"
     ]
    }
   ],
   "source": [
    "# Test out the pretrained embedding on a random sentence \n",
    "random_train_sentence = random.choice(train_sentences)\n",
    "print(f\"Random sentence:\\n {random_train_sentence}\")\n",
    "use_embedded_sentence = tf_hub_embedding_layer([random_train_sentence])\n",
    "print(f\"Sentence after embedding:\\n{use_embedded_sentence[0][:30]}\\n\")\n",
    "print(f\"Length of sentence embedding: {len(use_embedded_sentence[0])}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building and fitting an NLP feature extraction model using pretrained embeddings TensorFlow Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define feature extraction model using TF Hub layer\n",
    "inputs = layers.Input(shape=[], dtype=tf.string)\n",
    "pretrained_embedding = tf_hub_embedding_layer(inputs) # tokenize text and create embedding of each sequence (512 long vector)\n",
    "x = layers.Dense(128, activation=\"relu\")(pretrained_embedding)\n",
    "# Note: you could add more layers here...\n",
    "outputs = layers.Dense(num_classes, activation=\"softmax\")(x) # create the output layer\n",
    "model_2 = tf.keras.Model(inputs=inputs,\n",
    "                         outputs=outputs,\n",
    "                         name=\"model_2_USE_feature_extractor\")\n",
    "\n",
    "# Compile the model\n",
    "model_2.compile(loss=\"categorical_crossentropy\",\n",
    "                optimizer=tf.keras.optimizers.Adam(),\n",
    "                metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2_USE_feature_extractor\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None,)]                 0         \n",
      "                                                                 \n",
      " universal_sentence_encoder   (None, 512)              256797824 \n",
      " (KerasLayer)                                                    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               65664     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 5)                 645       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 256,864,133\n",
      "Trainable params: 66,309\n",
      "Non-trainable params: 256,797,824\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "562/562 [==============================] - 35s 37ms/step - loss: 0.9181 - accuracy: 0.6472 - val_loss: 0.7982 - val_accuracy: 0.6918\n",
      "Epoch 2/3\n",
      "562/562 [==============================] - 19s 33ms/step - loss: 0.7688 - accuracy: 0.7016 - val_loss: 0.7556 - val_accuracy: 0.7064\n",
      "Epoch 3/3\n",
      "562/562 [==============================] - 20s 35ms/step - loss: 0.7519 - accuracy: 0.7119 - val_loss: 0.7381 - val_accuracy: 0.7144\n"
     ]
    }
   ],
   "source": [
    "# Fit model_2 to the data\n",
    "history_model_2 = model_2.fit(train_dataset,\n",
    "                              epochs=3,\n",
    "                              steps_per_epoch=int(0.1 * len(train_dataset)),\n",
    "                              validation_data=valid_dataset,\n",
    "                              validation_steps=int(0.1 * len(valid_dataset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  5/945 [..............................] - ETA: 32s - loss: 0.8230 - accuracy: 0.6625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 26s 28ms/step - loss: 0.7397 - accuracy: 0.7135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.7397141456604004, 0.7134913206100464]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate on the whole validation dataset\n",
    "model_2.evaluate(valid_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 28s 28ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.4398787 , 0.37129903, 0.00156954, 0.1794733 , 0.00777941],\n",
       "       [0.32227963, 0.5301669 , 0.00259148, 0.14172454, 0.00323741],\n",
       "       [0.23333113, 0.1403095 , 0.01561137, 0.575426  , 0.03532192],\n",
       "       ...,\n",
       "       [0.00187462, 0.0048889 , 0.04799303, 0.00117084, 0.9440726 ],\n",
       "       [0.00404886, 0.05095106, 0.20947237, 0.00166163, 0.7338661 ],\n",
       "       [0.19572282, 0.2750672 , 0.45809418, 0.00807271, 0.06304313]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with feature extraction model\n",
    "model_2_pred_probs = model_2.predict(valid_dataset)\n",
    "model_2_pred_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 1, 3, ..., 4, 4, 2], dtype=int64)>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the prediction probabilities found with feature extraction model to labels\n",
    "model_2_preds = tf.argmax(model_2_pred_probs, axis=1)\n",
    "model_2_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 71.34913279491593,\n",
       " 'precision': 0.713568824981841,\n",
       " 'recall': 0.7134913279491593,\n",
       " 'f1': 0.7103639464735759}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate results from TF Hub pretrained embeddings results on val set\n",
    "model_2_results = calculate_results(y_true=val_labels_encoded,\n",
    "                                   y_pred=model_2_preds)\n",
    "model_2_results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3: Conv1D with character embeddings\n",
    "\n",
    "The paper which we're replicating states that it has been build using a combination of token and character-level embeddings.\n",
    "\n",
    "We've done token-level embeddings but we'll need to do similar steps for characters if we want to use char-level embeddings. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a character-level tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
       " 'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
       " 'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
       " 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
       " 'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sentences[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'t h e   m a j o r i t y   o f   p a t i e n t s   h a d   l o n g - s t a n d i n g   d i s e a s e   ;   w e r e   o l d e r   ,   m a l e   a n d   w h i t e   .'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make function to split sentences into characters\n",
    "def split_chars(text):\n",
    "  return \" \".join(list(text))\n",
    "\n",
    "# Text splitting non-character-level sequence into characters\n",
    "split_chars(random_train_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split sequence-level data splits into character-level data splits\n",
    "train_chars = [split_chars(sentence) for sentence in train_sentences]\n",
    "val_chars = [split_chars(sentence) for sentence in val_sentences]\n",
    "test_chars = [split_chars(sentence) for sentence in test_sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['t o   i n v e s t i g a t e   t h e   e f f i c a c y   o f   @   w e e k s   o f   d a i l y   l o w - d o s e   o r a l   p r e d n i s o l o n e   i n   i m p r o v i n g   p a i n   ,   m o b i l i t y   ,   a n d   s y s t e m i c   l o w - g r a d e   i n f l a m m a t i o n   i n   t h e   s h o r t   t e r m   a n d   w h e t h e r   t h e   e f f e c t   w o u l d   b e   s u s t a i n e d   a t   @   w e e k s   i n   o l d e r   a d u l t s   w i t h   m o d e r a t e   t o   s e v e r e   k n e e   o s t e o a r t h r i t i s   (   o a   )   .',\n",
       " 'a   t o t a l   o f   @   p a t i e n t s   w i t h   p r i m a r y   k n e e   o a   w e r e   r a n d o m i z e d   @ : @   ;   @   r e c e i v e d   @   m g / d a y   o f   p r e d n i s o l o n e   a n d   @   r e c e i v e d   p l a c e b o   f o r   @   w e e k s   .',\n",
       " 'o u t c o m e   m e a s u r e s   i n c l u d e d   p a i n   r e d u c t i o n   a n d   i m p r o v e m e n t   i n   f u n c t i o n   s c o r e s   a n d   s y s t e m i c   i n f l a m m a t i o n   m a r k e r s   .',\n",
       " 'p a i n   w a s   a s s e s s e d   u s i n g   t h e   v i s u a l   a n a l o g   p a i n   s c a l e   (   @ - @   m m   )   .',\n",
       " 's e c o n d a r y   o u t c o m e   m e a s u r e s   i n c l u d e d   t h e   w e s t e r n   o n t a r i o   a n d   m c m a s t e r   u n i v e r s i t i e s   o s t e o a r t h r i t i s   i n d e x   s c o r e s   ,   p a t i e n t   g l o b a l   a s s e s s m e n t   (   p g a   )   o f   t h e   s e v e r i t y   o f   k n e e   o a   ,   a n d   @ - m i n   w a l k   d i s t a n c e   (   @ m w d   )   .']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_chars[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149.3662574983337"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What's the average character length?\n",
    "char_lens = [len(sentence) for sentence in train_sentences]\n",
    "mean_char_len = np.mean(char_lens)\n",
    "mean_char_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of our sequences at a character-level\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(char_lens, bins=7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find what character length covers 95% of sequences\n",
    "output_seq_char_len = int(np.percentile(char_lens, 95))\n",
    "output_seq_char_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdefghijklmnopqrstuvwxyz0123456789!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all keyboard characters\n",
    "import string\n",
    "alphabet = string.ascii_lowercase + string.digits + string.punctuation\n",
    "alphabet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create char-level token vectorizer instance\n",
    "NUM_CHAR_TOKENS = len(alphabet) + 2 # add 2 for space and OOV token (OOV = out of vocabulary, '[UNK]')\n",
    "char_vectorizer = TextVectorization(max_tokens=NUM_CHAR_TOKENS,\n",
    "                                    output_sequence_length=output_seq_char_len,\n",
    "                                    # standardize=None, # set standardization to \"None\", which will then consider punctuations too.\n",
    "                                    name=\"char_vectorizer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adapt character vectorizer to training character\n",
    "char_vectorizer.adapt(train_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of different characters in character vocab: 28\n",
      "5 most common characters: ['', '[UNK]', 'e', 't', 'i']\n",
      "5 least common characters: ['k', 'x', 'z', 'q', 'j']\n"
     ]
    }
   ],
   "source": [
    "# Check character vocabulary stats\n",
    "char_vocab = char_vectorizer.get_vocabulary()\n",
    "print(f\"Number of different characters in character vocab: {len(char_vocab)}\")\n",
    "print(f\"5 most common characters: {char_vocab[:5]}\")\n",
    "print(f\"5 least common characters: {char_vocab[-5:]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Characterized text:\n",
      " n i t r o g l y c e r i n   (   a r m   a   ,   n   =   @   )   o r   p l a c e b o   p a t c h e s   (   a r m   b   ,   n   =   @   )   w e r e   a d m i n i s t e r e d   o n c e   d a i l y   f r o m   d a y   - @   t o   d a y   @   o f   e a c h   c y c l e   a n d   w e r e   r e m o v e d   a b o u t   @ h   a f t e r   a d m i n i s t r a t i o n   .\n",
      "\n",
      "Length of random_train_chars: 141\n",
      "\n",
      "Vectorized chars:\n",
      " [[ 6  4  3  8  7 18 12 19 11  2  8  4  6  5  8 15  5  6  7  8 14 12  5 11\n",
      "   2 22  7 14  5  3 11 13  2  9  5  8 15 22  6 20  2  8  2  5 10 15  4  6\n",
      "   4  9  3  2  8  2 10  7  6 11  2 10  5  4 12 19 17  8  7 15 10  5 19  3\n",
      "   7 10  5 19  7 17  2  5 11 13 11 19 11 12  2  5  6 10 20  2  8  2  8  2\n",
      "  15  7 21  2 10  5 22  7 16  3 13  5 17  3  2  8  5 10 15  4  6  4  9  3\n",
      "   8  5  3  4  7  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0]]\n",
      "\n",
      "Length of vectorized chars: 290\n"
     ]
    }
   ],
   "source": [
    "# Test out character vectorizer\n",
    "random_train_chars = random.choice(train_chars)\n",
    "print(f\"Characterized text:\\n {random_train_chars}\")\n",
    "print(f\"\\nLength of random_train_chars: {len(random_train_chars.split())}\")\n",
    "vectorized_chars = char_vectorizer([random_train_chars])\n",
    "print(f\"\\nVectorized chars:\\n {vectorized_chars}\")\n",
    "print(f\"\\nLength of vectorized chars: {len(vectorized_chars[0])}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a character-level embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create char embedding layer\n",
    "char_embed = layers.Embedding(input_dim=len(char_vocab), # number of different characters\n",
    "                              output_dim=25, # Size of the char embedding in the paper: https://arxiv.org/pdf/1612.05251.pdf (Figure 1)\n",
    "                              mask_zero=True,\n",
    "                              name=\"char_embed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Characterized text:\n",
      " n i t r o g l y c e r i n   (   a r m   a   ,   n   =   @   )   o r   p l a c e b o   p a t c h e s   (   a r m   b   ,   n   =   @   )   w e r e   a d m i n i s t e r e d   o n c e   d a i l y   f r o m   d a y   - @   t o   d a y   @   o f   e a c h   c y c l e   a n d   w e r e   r e m o v e d   a b o u t   @ h   a f t e r   a d m i n i s t r a t i o n   .\n",
      "\n",
      "Embedded chars (after vectorization and embedding):\n",
      " [[[-0.00192921  0.04983452 -0.03335271 ...  0.00291107  0.00177381\n",
      "    0.00019157]\n",
      "  [ 0.02936722  0.0289104  -0.03012349 ... -0.048692   -0.00263015\n",
      "    0.036999  ]\n",
      "  [-0.00384073 -0.03506893 -0.04689621 ... -0.04432083  0.03063968\n",
      "    0.00661234]\n",
      "  ...\n",
      "  [-0.04842533  0.01026727 -0.04399216 ... -0.00670341 -0.02752235\n",
      "    0.03544762]\n",
      "  [-0.04842533  0.01026727 -0.04399216 ... -0.00670341 -0.02752235\n",
      "    0.03544762]\n",
      "  [-0.04842533  0.01026727 -0.04399216 ... -0.00670341 -0.02752235\n",
      "    0.03544762]]]\n",
      "\n",
      "Character embedding shape: (1, 290, 25)\n"
     ]
    }
   ],
   "source": [
    "# Test our character embedding layer\n",
    "print(f\"Characterized text:\\n {random_train_chars}\\n\")\n",
    "char_embed_example = char_embed(char_vectorizer([random_train_chars]))\n",
    "print(f\"Embedded chars (after vectorization and embedding):\\n {char_embed_example}\\n\")\n",
    "print(f\"Character embedding shape: {char_embed_example.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a Conv1D model to fit on character embeddings\n",
    "**NOTE:** The research paper has used Bi-LSTM model while we will be using Conv1D model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Conv1D on chars only\n",
    "inputs = layers.Input(shape=(1,), dtype=\"string\")\n",
    "char_vectors = char_vectorizer(inputs)\n",
    "char_embeddings = char_embed(char_vectors)\n",
    "x = layers.Conv1D(64, kernel_size=5, padding=\"same\", activation=\"relu\")(char_embeddings)\n",
    "x = layers.GlobalMaxPool1D()(x)\n",
    "outputs = layers.Dense(num_classes, activation=\"softmax\")(x)\n",
    "model_3 = tf.keras.Model(inputs=inputs,\n",
    "                         outputs=outputs,\n",
    "                         name=\"model_3_conv1d_char_embeddings\")\n",
    "\n",
    "# Compile\n",
    "model_3.compile(loss=\"categorical_crossentropy\",\n",
    "                optimizer=tf.keras.optimizers.Adam(),\n",
    "                metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3_conv1d_char_embeddings\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 1)]               0         \n",
      "                                                                 \n",
      " char_vectorizer (TextVector  (None, 290)              0         \n",
      " ization)                                                        \n",
      "                                                                 \n",
      " char_embed (Embedding)      (None, 290, 25)           700       \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 290, 64)           8064      \n",
      "                                                                 \n",
      " global_max_pooling1d (Globa  (None, 64)               0         \n",
      " lMaxPooling1D)                                                  \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 5)                 325       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 9,089\n",
      "Trainable params: 9,089\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summary of model_3\n",
    "model_3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create char level datasets\n",
    "train_char_dataset = tf.data.Dataset.from_tensor_slices((train_chars, train_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n",
    "val_char_dataset = tf.data.Dataset.from_tensor_slices((val_chars, val_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n",
    "test_char_dataset = tf.data.Dataset.from_tensor_slices((test_chars, test_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_char_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "562/562 [==============================] - 25s 42ms/step - loss: 1.2989 - accuracy: 0.4738 - val_loss: 1.0924 - val_accuracy: 0.5658\n",
      "Epoch 2/3\n",
      "562/562 [==============================] - 26s 47ms/step - loss: 1.0354 - accuracy: 0.5876 - val_loss: 0.9631 - val_accuracy: 0.6273\n",
      "Epoch 3/3\n",
      "562/562 [==============================] - 28s 50ms/step - loss: 0.9483 - accuracy: 0.6306 - val_loss: 0.8887 - val_accuracy: 0.6599\n"
     ]
    }
   ],
   "source": [
    "# Fit the model on chars only\n",
    "model_3_history = model_3.fit(train_char_dataset,\n",
    "                              steps_per_epoch=int(0.1*len(train_char_dataset)),\n",
    "                              epochs=3,\n",
    "                              validation_data=val_char_dataset,\n",
    "                              validation_steps=int(0.1*len(val_char_dataset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 19s 19ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.10846337, 0.32191765, 0.29305914, 0.18400002, 0.09255979],\n",
       "       [0.14122458, 0.41675508, 0.09861554, 0.20125008, 0.1421547 ],\n",
       "       [0.1315112 , 0.1860536 , 0.47080803, 0.12334735, 0.08827983],\n",
       "       ...,\n",
       "       [0.08418404, 0.09399771, 0.21414782, 0.10465451, 0.5030158 ],\n",
       "       [0.06042651, 0.12937668, 0.34476465, 0.04952929, 0.41590282],\n",
       "       [0.33991405, 0.5094571 , 0.04489843, 0.09396446, 0.01176596]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with character model only\n",
    "model_3_pred_probs = model_3.predict(val_char_dataset)\n",
    "model_3_pred_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([1, 1, 2, ..., 4, 4, 1], dtype=int64)>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert prediction probabilities to class labels\n",
    "model_3_preds = tf.argmax(model_3_pred_probs, axis=1)\n",
    "model_3_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 64.90798358268238,\n",
       " 'precision': 0.6447104203479036,\n",
       " 'recall': 0.6490798358268238,\n",
       " 'f1': 0.6372319435137308}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate results for Conv1D model chars only\n",
    "model_3_results = calculate_results(y_true=val_labels_encoded,\n",
    "                                    y_pred=model_3_preds)\n",
    "model_3_results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 4: Combining pretrained token embeddings + characters embeddings (hybrid embedding layer)\n",
    "\n",
    "1. Create a token-level embedding model (similar `model_1`)\n",
    "2. Create a character-level model (similar to `model_3` with a slight modification)\n",
    "3. Combine 1 & 2 with a concatenate (`layers.Concatenate`) \n",
    "4. Build a series of output layers on top of 3 similar to Figure 1 and section 4.2 of https://arxiv.org/pdf/1612.05251.pdf\n",
    "5. Construct a model which takes token and character-level sequences as input and produces sequence label probabilities as output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Setup token inputs/model (similiar to Model_1)\n",
    "token_inputs = layers.Input(shape=[], dtype=tf.string, name=\"token_input\")\n",
    "token_embeddings = tf_hub_embedding_layer(token_inputs)\n",
    "token_outputs = layers.Dense(128, activation=\"relu\")(token_embeddings)\n",
    "token_model = tf.keras.Model(inputs=token_inputs,\n",
    "                             outputs=token_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 2. Setup char inputs/model (similiar to Model_3 but with Bi-LSTM model)\n",
    "char_inputs = layers.Input(shape=(1,), dtype=tf.string, name=\"char_input\")\n",
    "char_vectors = char_vectorizer(char_inputs)\n",
    "char_embeddings = char_embed(char_vectors)\n",
    "char_bi_lstm = layers.Bidirectional(layers.LSTM(24))(char_embeddings) # bi-LSTM shown in Figure 1 of https://arxiv.org/pdf/1612.05251.pdf\n",
    "char_model = tf.keras.Model(inputs=char_inputs,\n",
    "                            outputs=char_bi_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 3. Concatenate token and char inputs (create hybrid token embedding)\n",
    "token_char_concat = layers.Concatenate(name=\"token_char_hybrid\")([token_model.output,\n",
    "                                                                  char_model.output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 4. Create output layers - adding in Dropout, discussed in section 4.2 of https://arxiv.org/pdf/1612.05251.pdf\n",
    "# NOTE: The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting.\n",
    "combined_dropout = layers.Dropout(0.5)(token_char_concat)\n",
    "combined_dense = layers.Dense(128, activation=\"relu\")(combined_dropout)\n",
    "final_dropout = layers.Dropout(0.5)(combined_dense)\n",
    "output_layer = layers.Dense(num_classes, activation=\"softmax\")(final_dropout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 5. Construct model with char and token inputs\n",
    "model_4 = tf.keras.Model(inputs=[token_model.input, char_model.input],\n",
    "                         outputs=output_layer,\n",
    "                         name=\"model_4_token_and_char_embeddings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4_token_and_char_embeddings\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " char_input (InputLayer)        [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " token_input (InputLayer)       [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " char_vectorizer (TextVectoriza  (None, 290)         0           ['char_input[0][0]']             \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " universal_sentence_encoder (Ke  (None, 512)         256797824   ['token_input[0][0]']            \n",
      " rasLayer)                                                                                        \n",
      "                                                                                                  \n",
      " char_embed (Embedding)         (None, 290, 25)      700         ['char_vectorizer[1][0]']        \n",
      "                                                                                                  \n",
      " dense_4 (Dense)                (None, 128)          65664       ['universal_sentence_encoder[1][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " bidirectional (Bidirectional)  (None, 48)           9600        ['char_embed[1][0]']             \n",
      "                                                                                                  \n",
      " token_char_hybrid (Concatenate  (None, 176)         0           ['dense_4[0][0]',                \n",
      " )                                                                'bidirectional[0][0]']          \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 176)          0           ['token_char_hybrid[0][0]']      \n",
      "                                                                                                  \n",
      " dense_5 (Dense)                (None, 128)          22656       ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 128)          0           ['dense_5[0][0]']                \n",
      "                                                                                                  \n",
      " dense_6 (Dense)                (None, 5)            645         ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 256,897,089\n",
      "Trainable params: 99,265\n",
      "Non-trainable params: 256,797,824\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Get a summary of our model\n",
    "model_4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot hybrid token and character model\n",
    "from keras.utils import plot_model\n",
    "plot_model(model_4, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile token char model\n",
    "model_4.compile(loss=\"categorical_crossentropy\",\n",
    "                optimizer=tf.keras.optimizers.Adam(), # section 4.2 of the paper uses SGD for optimization\n",
    "                metrics=[\"accuracy\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining token and character data into a tf.data Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine chars and tokens into a dataset\n",
    "train_char_token_data = tf.data.Dataset.from_tensor_slices((train_sentences, train_chars)) # make data\n",
    "train_char_token_labels = tf.data.Dataset.from_tensor_slices(train_labels_one_hot) # make labels\n",
    "train_char_token_dataset = tf.data.Dataset.zip((train_char_token_data, train_char_token_labels)) # combine data and labels\n",
    "\n",
    "# Prefetch and batch train data\n",
    "train_char_token_dataset = train_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repeat the above steps for our validation data\n",
    "val_char_token_data = tf.data.Dataset.from_tensor_slices((val_sentences, val_chars)) # make data\n",
    "val_char_token_labels = tf.data.Dataset.from_tensor_slices(val_labels_one_hot) # make labels\n",
    "val_char_token_dataset = tf.data.Dataset.zip((val_char_token_data, val_char_token_labels)) # combine data and labels\n",
    "\n",
    "# Prefetch and batch train data\n",
    "val_char_token_dataset = val_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<_PrefetchDataset element_spec=((TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>,\n",
       " <_PrefetchDataset element_spec=((TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out our training char and token embedding dataset\n",
    "train_char_token_dataset, val_char_token_dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a model on token and character-level sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "562/562 [==============================] - 244s 362ms/step - loss: 0.9884 - accuracy: 0.6085 - val_loss: 0.7889 - val_accuracy: 0.6998\n",
      "Epoch 2/3\n",
      "562/562 [==============================] - 168s 299ms/step - loss: 0.8053 - accuracy: 0.6916 - val_loss: 0.7218 - val_accuracy: 0.7277\n",
      "Epoch 3/3\n",
      "562/562 [==============================] - 118s 210ms/step - loss: 0.7751 - accuracy: 0.7043 - val_loss: 0.6910 - val_accuracy: 0.7370\n"
     ]
    }
   ],
   "source": [
    "# Fit the model on tokens and chars\n",
    "history_model_4 = model_4.fit(train_char_token_dataset,\n",
    "                              steps_per_epoch=int(0.1 * len(train_char_token_dataset)),\n",
    "                              epochs=3,\n",
    "                              validation_data=val_char_token_dataset,\n",
    "                              validation_steps=int(0.1 * len(val_char_token_dataset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 28s 30ms/step - loss: 0.6942 - accuracy: 0.7363\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6941604018211365, 0.7362968325614929]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate one the whole validation dataset\n",
    "model_4.evaluate(val_char_token_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 33s 32ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3.9385906e-01, 4.4228050e-01, 5.7938523e-03, 1.4885210e-01,\n",
       "        9.2144338e-03],\n",
       "       [3.6813557e-01, 4.4470021e-01, 5.0936383e-03, 1.7900482e-01,\n",
       "        3.0656757e-03],\n",
       "       [2.9843700e-01, 1.7119420e-01, 7.4066445e-02, 4.0346575e-01,\n",
       "        5.2836649e-02],\n",
       "       ...,\n",
       "       [4.1703996e-04, 5.5917082e-03, 7.2827578e-02, 2.0155024e-04,\n",
       "        9.2096210e-01],\n",
       "       [1.2324390e-02, 9.7220853e-02, 1.9658142e-01, 5.1058047e-03,\n",
       "        6.8876749e-01],\n",
       "       [2.8228682e-01, 3.0204123e-01, 2.7768183e-01, 4.8876014e-02,\n",
       "        8.9114107e-02]], dtype=float32)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions using the token-character model hybrid\n",
    "model_4_pred_probs = model_4.predict(val_char_token_dataset)\n",
    "model_4_pred_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([1, 1, 3, ..., 4, 4, 1], dtype=int64)>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format pred probs into pred labels\n",
    "model_4_preds = tf.argmax(model_4_pred_probs, axis=1)\n",
    "model_4_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 73.6296835694426,\n",
       " 'precision': 0.7351228110505333,\n",
       " 'recall': 0.736296835694426,\n",
       " 'f1': 0.7323180629040671}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get results of token-char-hybrid model\n",
    "model_4_results = calculate_results(y_true=val_labels_encoded,\n",
    "                                    y_pred=model_4_preds)\n",
    "model_4_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 5: Transfer learning with pretrained token embeddings + character embeddings + positional embeddings"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** We will add positional embeddings using feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>line_number</th>\n",
       "      <th>total_lines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OBJECTIVE</td>\n",
       "      <td>to investigate the efficacy of @ weeks of dail...</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>a total of @ patients with primary knee oa wer...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>outcome measures included pain reduction and i...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>pain was assessed using the visual analog pain...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>secondary outcome measures included the wester...</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      target                                               text  line_number  \\\n",
       "0  OBJECTIVE  to investigate the efficacy of @ weeks of dail...            0   \n",
       "1    METHODS  a total of @ patients with primary knee oa wer...            1   \n",
       "2    METHODS  outcome measures included pain reduction and i...            2   \n",
       "3    METHODS  pain was assessed using the visual analog pain...            3   \n",
       "4    METHODS  secondary outcome measures included the wester...            4   \n",
       "\n",
       "   total_lines  \n",
       "0           11  \n",
       "1           11  \n",
       "2           11  \n",
       "3           11  \n",
       "4           11  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **IMPORTANT NOTE:** Any engineered features used to train a model need to be available at test time. In our case, line numbers and total lines are available."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create positional embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     15000\n",
       "1     15000\n",
       "2     15000\n",
       "3     15000\n",
       "4     14992\n",
       "5     14949\n",
       "6     14758\n",
       "7     14279\n",
       "8     13346\n",
       "9     11981\n",
       "10    10041\n",
       "11     7892\n",
       "12     5853\n",
       "13     4152\n",
       "14     2835\n",
       "15     1861\n",
       "16     1188\n",
       "17      751\n",
       "18      462\n",
       "19      286\n",
       "20      162\n",
       "21      101\n",
       "22       66\n",
       "23       33\n",
       "24       22\n",
       "25       14\n",
       "26        7\n",
       "27        4\n",
       "28        3\n",
       "29        1\n",
       "30        1\n",
       "Name: line_number, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many different line numbers are there?\n",
    "train_df[\"line_number\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of \"line_number\" column\n",
    "train_df.line_number.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(14, 15), dtype=float32, numpy=\n",
       " array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],\n",
       "       dtype=float32)>,\n",
       " TensorShape([180040, 15]))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use TensorFlow to create one-hot-encoded tensors of our \"line_number\" column\n",
    "train_line_numbers_one_hot = tf.one_hot(train_df[\"line_number\"].to_numpy(), depth=15)\n",
    "val_line_numbers_one_hot = tf.one_hot(val_df[\"line_number\"].to_numpy(), depth=15)\n",
    "test_line_numbers_one_hot = tf.one_hot(test_df[\"line_number\"].to_numpy(), depth=15)\n",
    "train_line_numbers_one_hot[:14], train_line_numbers_one_hot.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've encoded our line numbers feature, Similarly, for total lines feature..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    24468\n",
       "10    23639\n",
       "12    22113\n",
       "9     19400\n",
       "13    18438\n",
       "14    14610\n",
       "8     12285\n",
       "15    10768\n",
       "7      7464\n",
       "16     7429\n",
       "17     5202\n",
       "6      3353\n",
       "18     3344\n",
       "19     2480\n",
       "20     1281\n",
       "5      1146\n",
       "21      770\n",
       "22      759\n",
       "23      264\n",
       "4       215\n",
       "24      200\n",
       "25      182\n",
       "26       81\n",
       "28       58\n",
       "3        32\n",
       "30       31\n",
       "27       28\n",
       "Name: total_lines, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many different numbers of lines are there?\n",
    "train_df[\"total_lines\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distributions of total lines\n",
    "train_df.total_lines.plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the coverage of a \"total_lines\" value of 20\n",
    "np.percentile(train_df.total_lines, 98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([180040, 20]),\n",
       " <tf.Tensor: shape=(10, 20), dtype=float32, numpy=\n",
       " array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0.]], dtype=float32)>)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use TensorFlow to create one-hot-encoded tensors of our \"total_lines\" feature\n",
    "train_total_lines_one_hot = tf.one_hot(train_df[\"total_lines\"].to_numpy(), depth=20)\n",
    "val_total_lines_one_hot = tf.one_hot(val_df[\"total_lines\"].to_numpy(), depth=20)\n",
    "test_total_lines_one_hot = tf.one_hot(test_df[\"total_lines\"].to_numpy(), depth=20)\n",
    "train_total_lines_one_hot.shape, train_total_lines_one_hot[:10]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a tribrid embedding model\n",
    "\n",
    "1. Create a token-level model\n",
    "2. Create a character-level model\n",
    "3. Create a model for the \"line_number\" feature\n",
    "4. Create a model for the \"total_lines\" feature\n",
    "5. Combine the outputs of 1 & 2 using tf.keras.layers.Concatenate\n",
    "6. Combine the outputs of 3, 4, 5 using tf.keras.layers.Concatenate\n",
    "7. Create an output layer to accept the tribrid embedding and output label probabilities \n",
    "8. Combine the inputs of 1, 2, 3, 4 and outputs of into a tf.keras.Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Token inputs\n",
    "token_inputs = layers.Input(shape=[], dtype=\"string\", name=\"token_inputs\")\n",
    "token_embeddings = tf_hub_embedding_layer(token_inputs)\n",
    "token_outputs = layers.Dense(128, activation=\"relu\")(token_embeddings)\n",
    "token_model = tf.keras.Model(inputs=token_inputs, outputs=token_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Char inputs\n",
    "char_inputs = layers.Input(shape=(1, ), dtype=\"string\", name=\"char_inputs\")\n",
    "char_vectors = char_vectorizer(char_inputs)\n",
    "char_embeddings = char_embed(char_vectors)\n",
    "char_bi_lstm = layers.Bidirectional(layers.LSTM(24))(char_embeddings)\n",
    "char_model = tf.keras.Model(inputs=char_inputs, outputs=char_bi_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Line numbers model\n",
    "line_number_inputs = layers.Input(shape=(15,), dtype=tf.float32, name=\"line_number_input\")\n",
    "line_number_outputs = layers.Dense(32, activation=\"relu\")(line_number_inputs)\n",
    "line_number_model = tf.keras.Model(inputs=line_number_inputs, outputs=line_number_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Total lines model\n",
    "total_lines_inputs = layers.Input(shape=(20,), dtype=tf.float32, name=\"total_lines_input\")\n",
    "total_lines_outputs = layers.Dense(32, activation=\"relu\")(total_lines_inputs)\n",
    "total_lines_model = tf.keras.Model(inputs=total_lines_inputs, outputs=total_lines_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Combine token and char embeddings into a hybrid embedding\n",
    "combined_embeddings = layers.Concatenate(name=\"char_token_hybrid_embedding\")([token_model.output, char_model.output])\n",
    "z = layers.Dense(256, activation=\"relu\")(combined_embeddings)\n",
    "combined_embeddings_outputs = layers.Dropout(0.5)(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Combine positional embedding with combined token and char embeddings\n",
    "tribrid_embeddings = layers.Concatenate(name=\"char_token_positional_embedding\")([line_number_model.output,\n",
    "                                                                                 total_lines_model.output,\n",
    "                                                                                 combined_embeddings_outputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. Create output layer\n",
    "output_layer = layers.Dense(num_classes, activation=\"softmax\", name=\"output_layer\")(tribrid_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. Put together model with all kinds of inputs\n",
    "model_5 = tf.keras.Model(inputs=[line_number_model.input,\n",
    "                                 total_lines_model.input,\n",
    "                                 token_model.input,\n",
    "                                 char_model.input],\n",
    "                         outputs=output_layer,\n",
    "                         name=\"model_5_tribrid_embedding_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_5_tribrid_embedding_model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " char_inputs (InputLayer)       [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " token_inputs (InputLayer)      [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " char_vectorizer (TextVectoriza  (None, 290)         0           ['char_inputs[0][0]']            \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " universal_sentence_encoder (Ke  (None, 512)         256797824   ['token_inputs[0][0]']           \n",
      " rasLayer)                                                                                        \n",
      "                                                                                                  \n",
      " char_embed (Embedding)         (None, 290, 25)      700         ['char_vectorizer[2][0]']        \n",
      "                                                                                                  \n",
      " dense_7 (Dense)                (None, 128)          65664       ['universal_sentence_encoder[2][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " bidirectional_1 (Bidirectional  (None, 48)          9600        ['char_embed[2][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " char_token_hybrid_embedding (C  (None, 176)         0           ['dense_7[0][0]',                \n",
      " oncatenate)                                                      'bidirectional_1[0][0]']        \n",
      "                                                                                                  \n",
      " line_number_input (InputLayer)  [(None, 15)]        0           []                               \n",
      "                                                                                                  \n",
      " total_lines_input (InputLayer)  [(None, 20)]        0           []                               \n",
      "                                                                                                  \n",
      " dense_10 (Dense)               (None, 256)          45312       ['char_token_hybrid_embedding[0][\n",
      "                                                                 0]']                             \n",
      "                                                                                                  \n",
      " dense_8 (Dense)                (None, 32)           512         ['line_number_input[0][0]']      \n",
      "                                                                                                  \n",
      " dense_9 (Dense)                (None, 32)           672         ['total_lines_input[0][0]']      \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 256)          0           ['dense_10[0][0]']               \n",
      "                                                                                                  \n",
      " char_token_positional_embeddin  (None, 320)         0           ['dense_8[0][0]',                \n",
      " g (Concatenate)                                                  'dense_9[0][0]',                \n",
      "                                                                  'dropout_2[0][0]']              \n",
      "                                                                                                  \n",
      " output_layer (Dense)           (None, 5)            1605        ['char_token_positional_embedding\n",
      "                                                                 [0][0]']                         \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 256,921,889\n",
      "Trainable params: 124,065\n",
      "Non-trainable params: 256,797,824\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summary of tribrid embedding model\n",
    "model_5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot model_5 to explore it visually...\n",
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model_5, show_shapes=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is label smoothing?\n",
    "\n",
    "For example, if our model gets too confident on a single class (e.g. its prediction probability is really high), it may get stuck on that class and not consider other classes...\n",
    "\n",
    "Really confident: `[0.0, 0.0, 1.0, 0.0, 0.0]` \n",
    "\n",
    "What label smoothing does is it assigns some of the value from the highest pred prob to other classes, in turn, hopefully improving generalization: `[0.01, 0.01, 0.96, 0.01, 0.01]` \n",
    "\n",
    "> **RESOURCE:** For more information on label smoothing, see this blog post from PyImageSearch: https://www.pyimagesearch.com/2019/12/30/label-smoothing-with-keras-tensorflow-and-deep-learning/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile token, char, and positional embedding model\n",
    "model_5.compile(loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.2), # helps to prevent overfitting\n",
    "                optimizer=tf.keras.optimizers.Adam(),\n",
    "                metrics=[\"accuracy\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create tribrid embedding datasets using tf.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training and validation TensorFlow datasets (with all four different types of input data)\n",
    "train_char_token_pos_data = tf.data.Dataset.from_tensor_slices((train_line_numbers_one_hot,\n",
    "                                                                train_total_lines_one_hot,\n",
    "                                                                train_sentences,\n",
    "                                                                train_chars))\n",
    "train_char_token_pos_labels = tf.data.Dataset.from_tensor_slices(train_labels_one_hot)\n",
    "train_char_token_pos_dataset = tf.data.Dataset.zip((train_char_token_pos_data, train_char_token_pos_labels))\n",
    "train_char_token_pos_dataset = train_char_token_pos_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
    "\n",
    "# For validation dataset\n",
    "val_char_token_pos_data = tf.data.Dataset.from_tensor_slices((val_line_numbers_one_hot,\n",
    "                                                              val_total_lines_one_hot,\n",
    "                                                              val_sentences,\n",
    "                                                              val_chars))\n",
    "val_char_token_pos_labels = tf.data.Dataset.from_tensor_slices(val_labels_one_hot)\n",
    "val_char_token_pos_dataset = tf.data.Dataset.zip((val_char_token_pos_data, val_char_token_pos_labels))\n",
    "val_char_token_pos_dataset = val_char_token_pos_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<_PrefetchDataset element_spec=((TensorSpec(shape=(None, 15), dtype=tf.float32, name=None), TensorSpec(shape=(None, 20), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>,\n",
       " <_PrefetchDataset element_spec=((TensorSpec(shape=(None, 15), dtype=tf.float32, name=None), TensorSpec(shape=(None, 20), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check input shapes\n",
    "train_char_token_pos_dataset, val_char_token_pos_dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting, evaluating and making predicitions with our tribrid model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "562/562 [==============================] - 120s 180ms/step - loss: 1.0954 - accuracy: 0.7263 - val_loss: 0.9801 - val_accuracy: 0.8115\n",
      "Epoch 2/3\n",
      "562/562 [==============================] - 172s 306ms/step - loss: 0.9664 - accuracy: 0.8128 - val_loss: 0.9511 - val_accuracy: 0.8191\n",
      "Epoch 3/3\n",
      "562/562 [==============================] - 165s 294ms/step - loss: 0.9500 - accuracy: 0.8247 - val_loss: 0.9391 - val_accuracy: 0.8314\n"
     ]
    }
   ],
   "source": [
    "# Fit our tribrid embedding model\n",
    "history_model_5 = model_5.fit(train_char_token_pos_dataset,\n",
    "                              steps_per_epoch=int(0.1 * len(train_char_token_pos_dataset)),\n",
    "                              epochs=3,\n",
    "                              validation_data=val_char_token_pos_dataset,\n",
    "                              validation_steps=int(0.1 * len(val_char_token_pos_dataset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 79s 83ms/step - loss: 0.9369 - accuracy: 0.8312\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9368525147438049, 0.8311929106712341]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate one the whole validation dataset\n",
    "model_5.evaluate(val_char_token_pos_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 73s 70ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.50525045, 0.11979675, 0.01636513, 0.33940354, 0.0191841 ],\n",
       "       [0.5496148 , 0.0895056 , 0.06042027, 0.2901021 , 0.01035729],\n",
       "       [0.29560685, 0.11489639, 0.14568748, 0.37579617, 0.06801306],\n",
       "       ...,\n",
       "       [0.03876924, 0.11454909, 0.03976453, 0.03628057, 0.7706365 ],\n",
       "       [0.02943186, 0.32509103, 0.07706442, 0.02550894, 0.5429037 ],\n",
       "       [0.15289108, 0.66214764, 0.09623531, 0.03741942, 0.05130653]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with the char token pos model\n",
    "model_5_pred_probs = model_5.predict(val_char_token_pos_dataset, verbose=1)\n",
    "model_5_pred_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 0, 3, ..., 4, 4, 1], dtype=int64)>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert pred probs to pred labels\n",
    "model_5_preds = tf.argmax(model_5_pred_probs, axis=1)\n",
    "model_5_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 83.11929034820601,\n",
       " 'precision': 0.8300064360312509,\n",
       " 'recall': 0.8311929034820601,\n",
       " 'f1': 0.8300789995917315}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate results of char token pos model\n",
    "model_5_results = calculate_results(y_true=val_labels_encoded,\n",
    "                                    y_pred=model_5_preds)\n",
    "model_5_results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare all model results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_0_baseline</th>\n",
       "      <td>72.183238</td>\n",
       "      <td>0.718647</td>\n",
       "      <td>0.721832</td>\n",
       "      <td>0.698925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_1_custom_token_embedding</th>\n",
       "      <td>78.740236</td>\n",
       "      <td>0.784146</td>\n",
       "      <td>0.787402</td>\n",
       "      <td>0.784739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_2_pretrained_token_embedding</th>\n",
       "      <td>71.349133</td>\n",
       "      <td>0.713569</td>\n",
       "      <td>0.713491</td>\n",
       "      <td>0.710364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_3_custom_char_embedding</th>\n",
       "      <td>64.907984</td>\n",
       "      <td>0.644710</td>\n",
       "      <td>0.649080</td>\n",
       "      <td>0.637232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_4_hybrid_char_token_embedding</th>\n",
       "      <td>73.629684</td>\n",
       "      <td>0.735123</td>\n",
       "      <td>0.736297</td>\n",
       "      <td>0.732318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_5_pos_char_token_embedding</th>\n",
       "      <td>83.119290</td>\n",
       "      <td>0.830006</td>\n",
       "      <td>0.831193</td>\n",
       "      <td>0.830079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      accuracy  precision    recall        f1\n",
       "model_0_baseline                     72.183238   0.718647  0.721832  0.698925\n",
       "model_1_custom_token_embedding       78.740236   0.784146  0.787402  0.784739\n",
       "model_2_pretrained_token_embedding   71.349133   0.713569  0.713491  0.710364\n",
       "model_3_custom_char_embedding        64.907984   0.644710  0.649080  0.637232\n",
       "model_4_hybrid_char_token_embedding  73.629684   0.735123  0.736297  0.732318\n",
       "model_5_pos_char_token_embedding     83.119290   0.830006  0.831193  0.830079"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine model results into a dataframe\n",
    "all_model_results = pd.DataFrame({\"model_0_baseline\": baseline_results,\n",
    "                                  \"model_1_custom_token_embedding\": model_1_results,\n",
    "                                  \"model_2_pretrained_token_embedding\": model_2_results,\n",
    "                                  \"model_3_custom_char_embedding\": model_3_results,\n",
    "                                  \"model_4_hybrid_char_token_embedding\": model_4_results,\n",
    "                                  \"model_5_pos_char_token_embedding\": model_5_results})\n",
    "all_model_results = all_model_results.transpose()\n",
    "all_model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce the accuracy to same scale as other metrics\n",
    "all_model_results[\"accuracy\"] = all_model_results[\"accuracy\"]/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot and compare all model results\n",
    "all_model_results.plot(kind=\"bar\", figsize=(10, 7)).legend(bbox_to_anchor=(1.0, 1.0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort models results by f1-score\n",
    "all_model_results.sort_values(\"f1\", ascending=True)[\"f1\"].plot(kind=\"bar\", figsize=(10, 7));"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save and Load best performing model (Model_5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_4_layer_call_fn, lstm_cell_4_layer_call_and_return_conditional_losses, lstm_cell_5_layer_call_fn, lstm_cell_5_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: paper_peek_model_5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: paper_peek_model_5\\assets\n"
     ]
    }
   ],
   "source": [
    "# Save the best performing model to SavedModel format (default)\n",
    "model_5.save(\"paper_peek_model_5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in best performing model\n",
    "loaded_model = tf.keras.models.load_model(\"paper_peek_model_5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 51s 49ms/step - loss: 0.9369 - accuracy: 0.8312\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9368525147438049, 0.8311929106712341]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the loaded model\n",
    "loaded_model.evaluate(val_char_token_pos_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945/945 [==============================] - 46s 46ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=int64, numpy=array([0, 0, 3, 2, 2, 4, 4, 4, 4, 1], dtype=int64)>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with the loaded model on the validation set\n",
    "loaded_pred_probs = loaded_model.predict(val_char_token_pos_dataset)\n",
    "loaded_preds = tf.argmax(loaded_pred_probs, axis=1)\n",
    "loaded_preds[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 83.11929034820601,\n",
       " 'precision': 0.8300064360312509,\n",
       " 'recall': 0.8311929034820601,\n",
       " 'f1': 0.8300789995917315}"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the results of our loaded model\n",
    "loaded_model_results = calculate_results(y_true=val_labels_encoded,\n",
    "                                         y_pred=loaded_preds)\n",
    "loaded_model_results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare our loaded model's predictions with the prediction results we obtained before saving our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare loaded model results with original trained model results\n",
    "np.isclose(list(model_5_results.values()), list(loaded_model_results.values()), rtol=1e-02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_5_tribrid_embedding_model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " char_inputs (InputLayer)       [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " token_inputs (InputLayer)      [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " char_vectorizer (TextVectoriza  (None, 290)         0           ['char_inputs[0][0]']            \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " universal_sentence_encoder (Ke  (None, 512)         256797824   ['token_inputs[0][0]']           \n",
      " rasLayer)                                                                                        \n",
      "                                                                                                  \n",
      " char_embed (Embedding)         (None, 290, 25)      700         ['char_vectorizer[0][0]']        \n",
      "                                                                                                  \n",
      " dense_7 (Dense)                (None, 128)          65664       ['universal_sentence_encoder[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " bidirectional_1 (Bidirectional  (None, 48)          9600        ['char_embed[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " char_token_hybrid_embedding (C  (None, 176)         0           ['dense_7[0][0]',                \n",
      " oncatenate)                                                      'bidirectional_1[0][0]']        \n",
      "                                                                                                  \n",
      " line_number_input (InputLayer)  [(None, 15)]        0           []                               \n",
      "                                                                                                  \n",
      " total_lines_input (InputLayer)  [(None, 20)]        0           []                               \n",
      "                                                                                                  \n",
      " dense_10 (Dense)               (None, 256)          45312       ['char_token_hybrid_embedding[0][\n",
      "                                                                 0]']                             \n",
      "                                                                                                  \n",
      " dense_8 (Dense)                (None, 32)           512         ['line_number_input[0][0]']      \n",
      "                                                                                                  \n",
      " dense_9 (Dense)                (None, 32)           672         ['total_lines_input[0][0]']      \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 256)          0           ['dense_10[0][0]']               \n",
      "                                                                                                  \n",
      " char_token_positional_embeddin  (None, 320)         0           ['dense_8[0][0]',                \n",
      " g (Concatenate)                                                  'dense_9[0][0]',                \n",
      "                                                                  'dropout_2[0][0]']              \n",
      "                                                                                                  \n",
      " output_layer (Dense)           (None, 5)            1605        ['char_token_positional_embedding\n",
      "                                                                 [0][0]']                         \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 256,921,889\n",
      "Trainable params: 124,065\n",
      "Non-trainable params: 256,797,824\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Check the loaded model summary\n",
    "loaded_model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model on test dataset\n",
    "\n",
    "To make our model's performance more comparable with the results reported in Table 3 of the [*PubMed 200k RCT:\n",
    "a Dataset for Sequential Sentence Classification in Medical Abstracts*](https://arxiv.org/pdf/1710.06071.pdf) paper, Making predictions on the test dataset and evaluate them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_PrefetchDataset element_spec=((TensorSpec(shape=(None, 15), dtype=tf.float32, name=None), TensorSpec(shape=(None, 20), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create test dataset batch and prefetched\n",
    "test_pos_char_token_data = tf.data.Dataset.from_tensor_slices((test_line_numbers_one_hot,\n",
    "                                                               test_total_lines_one_hot,\n",
    "                                                               test_sentences,\n",
    "                                                               test_chars))\n",
    "test_pos_char_token_labels = tf.data.Dataset.from_tensor_slices(test_labels_one_hot)\n",
    "test_pos_char_token_dataset = tf.data.Dataset.zip((test_pos_char_token_data, test_pos_char_token_labels))\n",
    "test_pos_char_token_dataset = test_pos_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
    "\n",
    "# Check shapes\n",
    "test_pos_char_token_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "942/942 [==============================] - 40s 42ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=int64, numpy=array([3, 3, 2, 2, 4, 4, 4, 1, 1, 0], dtype=int64)>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions on the test dataset\n",
    "test_pred_probs = loaded_model.predict(test_pos_char_token_dataset,\n",
    "                                       verbose=1)\n",
    "test_preds = tf.argmax(test_pred_probs, axis=1)\n",
    "test_preds[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 82.67131242740999,\n",
       " 'precision': 0.8251533963820284,\n",
       " 'recall': 0.8267131242740999,\n",
       " 'f1': 0.8255842258854734}"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate loaded model test predictions\n",
    "loaded_model_test_results = calculate_results(y_true=test_label_encoded,\n",
    "                                              y_pred=test_preds)\n",
    "loaded_model_test_results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it seems our best model still has not been able to match the performance of the results in the paper (their model gets 90.0 F1-score on the test dataset, while our Model_5 gets ~82.67 F1-score).\n",
    "\n",
    "But our model has only been trained on 20,000 out of the total ~180,000 sequences in the RCT 20k dataset. We also haven't fine-tuned our pretrained embeddings (the paper fine-tunes GloVe embeddings). So we can still improve the model but changes few things which might help us improve our results."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find most wrong\n",
    "\n",
    "One of the best ways to see whether the model is going wrong (or potentially where your data is wrong) is to visualize the \"most wrong\" predictions.\n",
    "\n",
    "**The most wrong predictions are samples where the model has made a prediction with a high probability but has gotten it wrong (the model's prediction disagree with the ground truth label).**\n",
    "\n",
    "Looking at the most wrong predictions can give us valuable information on how to improve further models or fix the labels in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 5.61 s\n",
      "Wall time: 5.74 s\n"
     ]
    },
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     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "%%time\n",
    "# Get list of class names of test predictions\n",
    "test_pred_classes = [label_encoder.classes_[pred] for pred in test_preds]\n",
    "test_pred_classes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll improve our test DataFame with a few values:\n",
    "* A `\"prediction\"` (string) column containing our model's prediction for a given sample.\n",
    "* A `\"pred_prob\"` (float) column containing the model's maximum prediction probabiliy for a given sample.\n",
    "* A `\"correct\"` (bool) column to indicate whether or not the model's prediction matches the sample's target label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>line_number</th>\n",
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       "      <th>0</th>\n",
       "      <td>BACKGROUND</td>\n",
       "      <td>this study analyzed liver function abnormaliti...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>a post hoc analysis was conducted with the use...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>liver function tests ( lfts ) were measured at...</td>\n",
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       "      <td>METHODS</td>\n",
       "      <td>0.748487</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>survival analyses were used to assess the asso...</td>\n",
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       "      <td>8</td>\n",
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       "      <td>RESULTS</td>\n",
       "      <td>the percentage of patients with abnormal lfts ...</td>\n",
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       "      <td>True</td>\n",
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       "      <td>RESULTS</td>\n",
       "      <td>when mean hemodynamic profiles were compared i...</td>\n",
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       "      <td>8</td>\n",
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       "      <td>0.891503</td>\n",
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       "      <td>multivariable analyses revealed that patients ...</td>\n",
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       "      <th>7</th>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>abnormal lfts are common in the adhf populatio...</td>\n",
       "      <td>7</td>\n",
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       "      <td>CONCLUSIONS</td>\n",
       "      <td>0.499744</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>elevated meld-xi scores are associated with po...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>0.455520</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>minimally invasive endovascular aneurysm repai...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.552207</td>\n",
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       "      <td>the aim of this study was to analyse the cost-...</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>resource use was determined from the amsterdam...</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.703079</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>the analysis was performed from a provider per...</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.866637</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>all costs were calculated as if all patients h...</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.504455</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>a total of @ patients were randomized .</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.687126</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>the @-day mortality rate was @ per cent after ...</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.672464</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>at @months , the total mortality rate for evar...</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.869061</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>the mean cost difference between evar and or w...</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.832845</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>the incremental cost-effectiveness ratio per p...</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.793725</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>there was no significant difference in quality...</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.760565</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         target                                               text  \\\n",
       "0    BACKGROUND  this study analyzed liver function abnormaliti...   \n",
       "1       RESULTS  a post hoc analysis was conducted with the use...   \n",
       "2       RESULTS  liver function tests ( lfts ) were measured at...   \n",
       "3       RESULTS  survival analyses were used to assess the asso...   \n",
       "4       RESULTS  the percentage of patients with abnormal lfts ...   \n",
       "5       RESULTS  when mean hemodynamic profiles were compared i...   \n",
       "6       RESULTS  multivariable analyses revealed that patients ...   \n",
       "7   CONCLUSIONS  abnormal lfts are common in the adhf populatio...   \n",
       "8   CONCLUSIONS  elevated meld-xi scores are associated with po...   \n",
       "9    BACKGROUND  minimally invasive endovascular aneurysm repai...   \n",
       "10   BACKGROUND  the aim of this study was to analyse the cost-...   \n",
       "11      METHODS  resource use was determined from the amsterdam...   \n",
       "12      METHODS  the analysis was performed from a provider per...   \n",
       "13      METHODS  all costs were calculated as if all patients h...   \n",
       "14      RESULTS            a total of @ patients were randomized .   \n",
       "15      RESULTS  the @-day mortality rate was @ per cent after ...   \n",
       "16      RESULTS  at @months , the total mortality rate for evar...   \n",
       "17      RESULTS  the mean cost difference between evar and or w...   \n",
       "18      RESULTS  the incremental cost-effectiveness ratio per p...   \n",
       "19      RESULTS  there was no significant difference in quality...   \n",
       "\n",
       "    line_number  total_lines   prediction  pred_prob  correct  \n",
       "0             0            8    OBJECTIVE   0.448799    False  \n",
       "1             1            8    OBJECTIVE   0.331142    False  \n",
       "2             2            8      METHODS   0.748487    False  \n",
       "3             3            8      METHODS   0.605703    False  \n",
       "4             4            8      RESULTS   0.744526     True  \n",
       "5             5            8      RESULTS   0.891503     True  \n",
       "6             6            8      RESULTS   0.571470     True  \n",
       "7             7            8  CONCLUSIONS   0.499744     True  \n",
       "8             8            8  CONCLUSIONS   0.455520     True  \n",
       "9             0           12   BACKGROUND   0.552207     True  \n",
       "10            1           12    OBJECTIVE   0.421101    False  \n",
       "11            2           12      METHODS   0.703079     True  \n",
       "12            3           12      METHODS   0.866637     True  \n",
       "13            4           12      METHODS   0.504455     True  \n",
       "14            5           12      RESULTS   0.687126     True  \n",
       "15            6           12      RESULTS   0.672464     True  \n",
       "16            7           12      RESULTS   0.869061     True  \n",
       "17            8           12      RESULTS   0.832845     True  \n",
       "18            9           12      RESULTS   0.793725     True  \n",
       "19           10           12      RESULTS   0.760565     True  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create prediction-improved test dataframe\n",
    "test_df[\"prediction\"] = test_pred_classes # create column with test prediction class names\n",
    "test_df[\"pred_prob\"] = tf.reduce_max(test_pred_probs, axis=1).numpy() # get the maximum prediction probability\n",
    "test_df[\"correct\"] = test_df[\"prediction\"] == test_df[\"target\"] # create binary column for whether the prediction is right or not\n",
    "test_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>line_number</th>\n",
       "      <th>total_lines</th>\n",
       "      <th>prediction</th>\n",
       "      <th>pred_prob</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13874</th>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>symptom outcomes will be assessed and estimate...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.948799</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16347</th>\n",
       "      <td>BACKGROUND</td>\n",
       "      <td>to evaluate the effects of the lactic acid bac...</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>OBJECTIVE</td>\n",
       "      <td>0.934343</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16633</th>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>clinicaltrials.gov identifier : nct@ .</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>BACKGROUND</td>\n",
       "      <td>0.933929</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>the primary endpoint is the cumulative three-y...</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.933726</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>nct@ ( clinicaltrials.gov ) .</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>BACKGROUND</td>\n",
       "      <td>0.930123</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8638</th>\n",
       "      <td>METHODS</td>\n",
       "      <td>this study is registered with clinicaltrials.g...</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.844621</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25345</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>participants were randomized to an hpd or an s...</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.843534</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23305</th>\n",
       "      <td>CONCLUSIONS</td>\n",
       "      <td>the c/s rate was higher in groups c and d ; ho...</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>RESULTS</td>\n",
       "      <td>0.842113</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29007</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>control group patients received a wallet card ...</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.842109</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12458</th>\n",
       "      <td>RESULTS</td>\n",
       "      <td>home monitoring patients were assessed remotel...</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>METHODS</td>\n",
       "      <td>0.841679</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target                                               text  \\\n",
       "13874  CONCLUSIONS  symptom outcomes will be assessed and estimate...   \n",
       "16347   BACKGROUND  to evaluate the effects of the lactic acid bac...   \n",
       "16633  CONCLUSIONS             clinicaltrials.gov identifier : nct@ .   \n",
       "2388       RESULTS  the primary endpoint is the cumulative three-y...   \n",
       "1827   CONCLUSIONS                      nct@ ( clinicaltrials.gov ) .   \n",
       "...            ...                                                ...   \n",
       "8638       METHODS  this study is registered with clinicaltrials.g...   \n",
       "25345      RESULTS  participants were randomized to an hpd or an s...   \n",
       "23305  CONCLUSIONS  the c/s rate was higher in groups c and d ; ho...   \n",
       "29007      RESULTS  control group patients received a wallet card ...   \n",
       "12458      RESULTS  home monitoring patients were assessed remotel...   \n",
       "\n",
       "       line_number  total_lines  prediction  pred_prob  correct  \n",
       "13874            4            6     METHODS   0.948799    False  \n",
       "16347            0           12   OBJECTIVE   0.934343    False  \n",
       "16633           19           19  BACKGROUND   0.933929    False  \n",
       "2388             4           13     METHODS   0.933726    False  \n",
       "1827            18           18  BACKGROUND   0.930123    False  \n",
       "...            ...          ...         ...        ...      ...  \n",
       "8638             5            9     RESULTS   0.844621    False  \n",
       "25345            3           12     METHODS   0.843534    False  \n",
       "23305           10           11     RESULTS   0.842113    False  \n",
       "29007            5           14     METHODS   0.842109    False  \n",
       "12458            3           13     METHODS   0.841679    False  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 most wrong samples\n",
    "top_10_wrong = test_df[test_df[\"correct\"] == False].sort_values(\"pred_prob\", ascending=False)[:10]\n",
    "top_10_wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: CONCLUSIONS, Pred: METHODS, Prob: 0.9487990736961365, Line number: 4, Total lines: 6\n",
      "\n",
      "Text:\n",
      "symptom outcomes will be assessed and estimates of cost-effectiveness made .\n",
      "\n",
      "-----\n",
      "\n",
      "Target: BACKGROUND, Pred: OBJECTIVE, Prob: 0.9343425631523132, Line number: 0, Total lines: 12\n",
      "\n",
      "Text:\n",
      "to evaluate the effects of the lactic acid bacterium lactobacillus salivarius on caries risk factors .\n",
      "\n",
      "-----\n",
      "\n",
      "Target: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9339290857315063, Line number: 19, Total lines: 19\n",
      "\n",
      "Text:\n",
      "clinicaltrials.gov identifier : nct@ .\n",
      "\n",
      "-----\n",
      "\n",
      "Target: RESULTS, Pred: METHODS, Prob: 0.9337255358695984, Line number: 4, Total lines: 13\n",
      "\n",
      "Text:\n",
      "the primary endpoint is the cumulative three-year hiv incidence .\n",
      "\n",
      "-----\n",
      "\n",
      "Target: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9301228523254395, Line number: 18, Total lines: 18\n",
      "\n",
      "Text:\n",
      "nct@ ( clinicaltrials.gov ) .\n",
      "\n",
      "-----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Visualize top wrong predictions\n",
    "for row in top_10_wrong[0:5].itertuples(): # adjust indexes to view different samples\n",
    "  _, target, text, line_number, total_lines, prediction, pred_prob, _ = row\n",
    "  print(f\"Target: {target}, Pred: {prediction}, Prob: {pred_prob}, Line number: {line_number}, Total lines: {total_lines}\\n\")\n",
    "  print(f\"Text:\\n{text}\\n\")\n",
    "  print(\"-----\\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There might be a possibility that the dataset might been labelled incorrectly.To improve, we could go through your training dataset, update the labels and then retrain a model. \n",
    "> **NOTE:** The process of using a model to help improve/investigate your dataset's labels is often referred to as **active learning**."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions on example abstracts\n",
    " \n",
    "We have made some predictions on the test dataset, So now we will test our model on random abstract other than the test dataset.\n",
    "\n",
    "In other words, were going to find an RCT abstract from PubMed, preprocess the text so it works with our model, then pass each sequence in the wild abstract through our model to see what label it predicts.\n",
    "\n",
    "For an appropriate sample, we'll need to search PubMed for RCT's (randomized controlled trials) without abstracts which have been split up (on exploring PubMed you'll notice many of the abstracts are already preformatted into separate sections, this helps dramatically with readability).\n",
    "\n",
    "I have created a JSON file with multiple abstracts on which we will apply our best model so far (`model_5`) and makes predictions on it.\n",
    "\n",
    "The above abstract hasn't been formatted in the same structure as the data our model has been trained on. Therefore, before we can make a prediction on it, we need to preprocess it just as we have our other sequences.\n",
    "\n",
    "More specifically, for each abstract, we'll need to:\n",
    "\n",
    "1. Split it into sentences (lines).\n",
    "2. Split it into characters.\n",
    "3. Find the number of each line.\n",
    "4. Find the total number of lines.\n",
    "\n",
    "**Resources:** Another more advanced option would be to leverage [spaCy's](https://spacy.io/) (a very powerful NLP library) [`sentencizer`](https://spacy.io/usage/linguistic-features#sbd) class. Which is an easy to use sentence splitter based on spaCy's English language model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'abstract': 'This RCT examined the efficacy of a manualized social intervention for children with HFASDs. Participants were randomly assigned to treatment or wait-list conditions. Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language. A response-cost program was applied to reduce problem behaviors and foster skills acquisition. Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures). Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents. High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity. Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.',\n",
       "  'source': 'https://pubmed.ncbi.nlm.nih.gov/20232240/',\n",
       "  'details': 'RCT of a manualized social treatment for high-functioning autism spectrum disorders'},\n",
       " {'abstract': \"Postpartum depression (PPD) is the most prevalent mood disorder associated with childbirth. No single cause of PPD has been identified, however the increased risk of nutritional deficiencies incurred through the high nutritional requirements of pregnancy may play a role in the pathology of depressive symptoms. Three nutritional interventions have drawn particular interest as possible non-invasive and cost-effective prevention and/or treatment strategies for PPD; omega-3 (n-3) long chain polyunsaturated fatty acids (LCPUFA), vitamin D and overall diet. We searched for meta-analyses of randomised controlled trials (RCT's) of nutritional interventions during the perinatal period with PPD as an outcome, and checked for any trials published subsequently to the meta-analyses. Fish oil: Eleven RCT's of prenatal fish oil supplementation RCT's show null and positive effects on PPD symptoms. Vitamin D: no relevant RCT's were identified, however seven observational studies of maternal vitamin D levels with PPD outcomes showed inconsistent associations. Diet: Two Australian RCT's with dietary advice interventions in pregnancy had a positive and null result on PPD. With the exception of fish oil, few RCT's with nutritional interventions during pregnancy assess PPD. Further research is needed to determine whether nutritional intervention strategies during pregnancy can protect against symptoms of PPD. Given the prevalence of PPD and ease of administering PPD measures, we recommend future prenatal nutritional RCT's include PPD as an outcome.\",\n",
       "  'source': 'https://pubmed.ncbi.nlm.nih.gov/28012571/',\n",
       "  'details': 'Formatting removed (can be used to compare model to actual example)'},\n",
       " {'abstract': 'Mental illness, including depression, anxiety and bipolar disorder, accounts for a significant proportion of global disability and poses a substantial social, economic and heath burden. Treatment is presently dominated by pharmacotherapy, such as antidepressants, and psychotherapy, such as cognitive behavioural therapy; however, such treatments avert less than half of the disease burden, suggesting that additional strategies are needed to prevent and treat mental disorders. There are now consistent mechanistic, observational and interventional data to suggest diet quality may be a modifiable risk factor for mental illness. This review provides an overview of the nutritional psychiatry field. It includes a discussion of the neurobiological mechanisms likely modulated by diet, the use of dietary and nutraceutical interventions in mental disorders, and recommendations for further research. Potential biological pathways related to mental disorders include inflammation, oxidative stress, the gut microbiome, epigenetic modifications and neuroplasticity. Consistent epidemiological evidence, particularly for depression, suggests an association between measures of diet quality and mental health, across multiple populations and age groups; these do not appear to be explained by other demographic, lifestyle factors or reverse causality. Our recently published intervention trial provides preliminary clinical evidence that dietary interventions in clinically diagnosed populations are feasible and can provide significant clinical benefit. Furthermore, nutraceuticals including n-3 fatty acids, folate, S-adenosylmethionine, N-acetyl cysteine and probiotics, among others, are promising avenues for future research. Continued research is now required to investigate the efficacy of intervention studies in large cohorts and within clinically relevant populations, particularly in patients with schizophrenia, bipolar and anxiety disorders.',\n",
       "  'source': 'https://pubmed.ncbi.nlm.nih.gov/28942748/',\n",
       "  'details': 'Effect of nutrition on mental health'},\n",
       " {'abstract': \"Hepatitis C virus (HCV) and alcoholic liver disease (ALD), either alone or in combination, count for more than two thirds of all liver diseases in the Western world. There is no safe level of drinking in HCV-infected patients and the most effective goal for these patients is total abstinence. Baclofen, a GABA(B) receptor agonist, represents a promising pharmacotherapy for alcohol dependence (AD). Previously, we performed a randomized clinical trial (RCT), which demonstrated the safety and efficacy of baclofen in patients affected by AD and cirrhosis. The goal of this post-hoc analysis was to explore baclofen's effect in a subgroup of alcohol-dependent HCV-infected cirrhotic patients. Any patient with HCV infection was selected for this analysis. Among the 84 subjects randomized in the main trial, 24 alcohol-dependent cirrhotic patients had a HCV infection; 12 received baclofen 10mg t.i.d. and 12 received placebo for 12-weeks. With respect to the placebo group (3/12, 25.0%), a significantly higher number of patients who achieved and maintained total alcohol abstinence was found in the baclofen group (10/12, 83.3%; p=0.0123). Furthermore, in the baclofen group, compared to placebo, there was a significantly higher increase in albumin values from baseline (p=0.0132) and a trend toward a significant reduction in INR levels from baseline (p=0.0716). In conclusion, baclofen was safe and significantly more effective than placebo in promoting alcohol abstinence, and improving some Liver Function Tests (LFTs) (i.e. albumin, INR) in alcohol-dependent HCV-infected cirrhotic patients. Baclofen may represent a clinically relevant alcohol pharmacotherapy for these patients.\",\n",
       "  'source': 'https://pubmed.ncbi.nlm.nih.gov/22244707/',\n",
       "  'details': 'Baclofen promotes alcohol abstinence in alcohol dependent cirrhotic patients with hepatitis C virus (HCV) infection'}]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open(\"example_abstracts.json\", \"r\") as f:\n",
    "  example_abstracts = json.load(f)\n",
    "\n",
    "example_abstracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "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>abstract</th>\n",
       "      <th>source</th>\n",
       "      <th>details</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This RCT examined the efficacy of a manualized...</td>\n",
       "      <td>https://pubmed.ncbi.nlm.nih.gov/20232240/</td>\n",
       "      <td>RCT of a manualized social treatment for high-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Postpartum depression (PPD) is the most preval...</td>\n",
       "      <td>https://pubmed.ncbi.nlm.nih.gov/28012571/</td>\n",
       "      <td>Formatting removed (can be used to compare mod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mental illness, including depression, anxiety ...</td>\n",
       "      <td>https://pubmed.ncbi.nlm.nih.gov/28942748/</td>\n",
       "      <td>Effect of nutrition on mental health</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hepatitis C virus (HCV) and alcoholic liver di...</td>\n",
       "      <td>https://pubmed.ncbi.nlm.nih.gov/22244707/</td>\n",
       "      <td>Baclofen promotes alcohol abstinence in alcoho...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            abstract  \\\n",
       "0  This RCT examined the efficacy of a manualized...   \n",
       "1  Postpartum depression (PPD) is the most preval...   \n",
       "2  Mental illness, including depression, anxiety ...   \n",
       "3  Hepatitis C virus (HCV) and alcoholic liver di...   \n",
       "\n",
       "                                      source  \\\n",
       "0  https://pubmed.ncbi.nlm.nih.gov/20232240/   \n",
       "1  https://pubmed.ncbi.nlm.nih.gov/28012571/   \n",
       "2  https://pubmed.ncbi.nlm.nih.gov/28942748/   \n",
       "3  https://pubmed.ncbi.nlm.nih.gov/22244707/   \n",
       "\n",
       "                                             details  \n",
       "0  RCT of a manualized social treatment for high-...  \n",
       "1  Formatting removed (can be used to compare mod...  \n",
       "2               Effect of nutrition on mental health  \n",
       "3  Baclofen promotes alcohol abstinence in alcoho...  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize our example abstracts in dataframe format\n",
    "abstracts = pd.DataFrame(example_abstracts)\n",
    "abstracts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We will need to parse (split) it using spaCy to turn it from a large paragraphs of text into sentences as per the format on which our model is trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This RCT examined the efficacy of a manualized social intervention for children with HFASDs.',\n",
       " 'Participants were randomly assigned to treatment or wait-list conditions.',\n",
       " 'Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language.',\n",
       " 'A response-cost program was applied to reduce problem behaviors and foster skills acquisition.',\n",
       " 'Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures).',\n",
       " 'Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents.',\n",
       " 'High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity.',\n",
       " 'Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.']"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create sentencizer - Source: https://spacy.io/usage/linguistic-features#sbd \n",
    "from spacy.lang.en import English\n",
    "nlp = English() # setup English sentence parser\n",
    "\n",
    "sentencizer = nlp.add_pipe(\"sentencizer\") # create sentence splitting pipeline object\n",
    "\n",
    "# Create \"doc\" of parsed sequences, we will consider the first abstract (change index for a different abstract)\n",
    "doc = nlp(example_abstracts[0][\"abstract\"]) \n",
    "abstract_lines = [str(sent) for sent in list(doc.sents)] # return detected sentences from doc in string type (not spaCy token type)\n",
    "abstract_lines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the abstract has been split into sentences, We will count line numbers as well as total lines.\n",
    "\n",
    "We will use some of the functionality of our `preprocess_text_with_line_numbers()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': 'This RCT examined the efficacy of a manualized social intervention for children with HFASDs.',\n",
       "  'line_number': 0,\n",
       "  'total_lines': 7},\n",
       " {'text': 'Participants were randomly assigned to treatment or wait-list conditions.',\n",
       "  'line_number': 1,\n",
       "  'total_lines': 7},\n",
       " {'text': 'Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language.',\n",
       "  'line_number': 2,\n",
       "  'total_lines': 7},\n",
       " {'text': 'A response-cost program was applied to reduce problem behaviors and foster skills acquisition.',\n",
       "  'line_number': 3,\n",
       "  'total_lines': 7},\n",
       " {'text': 'Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures).',\n",
       "  'line_number': 4,\n",
       "  'total_lines': 7},\n",
       " {'text': 'Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents.',\n",
       "  'line_number': 5,\n",
       "  'total_lines': 7},\n",
       " {'text': 'High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity.',\n",
       "  'line_number': 6,\n",
       "  'total_lines': 7},\n",
       " {'text': 'Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.',\n",
       "  'line_number': 7,\n",
       "  'total_lines': 7}]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total number of lines\n",
    "total_lines_in_sample = len(abstract_lines)\n",
    "\n",
    "# Go through each line in abstract and create a list of dictionaries containing features for each line\n",
    "sample_lines = []\n",
    "for i, line in enumerate(abstract_lines):\n",
    "  sample_dict = {}\n",
    "  sample_dict[\"text\"] = str(line)\n",
    "  sample_dict[\"line_number\"] = i\n",
    "  sample_dict[\"total_lines\"] = total_lines_in_sample - 1\n",
    "  sample_lines.append(sample_dict)\n",
    "sample_lines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can one-hot encode with `tf.one_hot` same as we did with our training dataset (using the same values for the depth parameter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(8, 15), dtype=float32, numpy=\n",
       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all line_number values from sample abstract\n",
    "test_abstract_line_numbers = [line[\"line_number\"] for line in sample_lines]\n",
    "# One-hot encode to same depth as training data, so model accepts right input shape\n",
    "test_abstract_line_numbers_one_hot = tf.one_hot(test_abstract_line_numbers, depth=15) \n",
    "test_abstract_line_numbers_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(8, 20), dtype=float32, numpy=\n",
       "array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all total_lines values from sample abstract\n",
    "test_abstract_total_lines = [line[\"total_lines\"] for line in sample_lines]\n",
    "# One-hot encode to same depth as training data, so model accepts right input shape\n",
    "test_abstract_total_lines_one_hot = tf.one_hot(test_abstract_total_lines, depth=20)\n",
    "test_abstract_total_lines_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T h i s   R C T   e x a m i n e d   t h e   e f f i c a c y   o f   a   m a n u a l i z e d   s o c i a l   i n t e r v e n t i o n   f o r   c h i l d r e n   w i t h   H F A S D s .',\n",
       " 'P a r t i c i p a n t s   w e r e   r a n d o m l y   a s s i g n e d   t o   t r e a t m e n t   o r   w a i t - l i s t   c o n d i t i o n s .',\n",
       " 'T r e a t m e n t   i n c l u d e d   i n s t r u c t i o n   a n d   t h e r a p e u t i c   a c t i v i t i e s   t a r g e t i n g   s o c i a l   s k i l l s ,   f a c e - e m o t i o n   r e c o g n i t i o n ,   i n t e r e s t   e x p a n s i o n ,   a n d   i n t e r p r e t a t i o n   o f   n o n - l i t e r a l   l a n g u a g e .',\n",
       " 'A   r e s p o n s e - c o s t   p r o g r a m   w a s   a p p l i e d   t o   r e d u c e   p r o b l e m   b e h a v i o r s   a n d   f o s t e r   s k i l l s   a c q u i s i t i o n .',\n",
       " 'S i g n i f i c a n t   t r e a t m e n t   e f f e c t s   w e r e   f o u n d   f o r   f i v e   o f   s e v e n   p r i m a r y   o u t c o m e   m e a s u r e s   ( p a r e n t   r a t i n g s   a n d   d i r e c t   c h i l d   m e a s u r e s ) .',\n",
       " 'S e c o n d a r y   m e a s u r e s   b a s e d   o n   s t a f f   r a t i n g s   ( t r e a t m e n t   g r o u p   o n l y )   c o r r o b o r a t e d   g a i n s   r e p o r t e d   b y   p a r e n t s .',\n",
       " 'H i g h   l e v e l s   o f   p a r e n t ,   c h i l d   a n d   s t a f f   s a t i s f a c t i o n   w e r e   r e p o r t e d ,   a l o n g   w i t h   h i g h   l e v e l s   o f   t r e a t m e n t   f i d e l i t y .',\n",
       " 'S t a n d a r d i z e d   e f f e c t   s i z e   e s t i m a t e s   w e r e   p r i m a r i l y   i n   t h e   m e d i u m   a n d   l a r g e   r a n g e s   a n d   f a v o r e d   t h e   t r e a t m e n t   g r o u p .']"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split abstract lines into characters\n",
    "abstract_chars = [split_chars(sentence) for sentence in abstract_lines]\n",
    "abstract_chars"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have preprocessed example RCT abstract into all of the same features our model was trained on, we can pass these features to our model and make sequence label predictions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 6s 6s/step\n",
      "CPU times: total: 4.48 s\n",
      "Wall time: 6.49 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.296537  , 0.10425055, 0.01677961, 0.5576071 , 0.02482566],\n",
       "       [0.08008897, 0.0290936 , 0.7028516 , 0.10828149, 0.07968445],\n",
       "       [0.12665947, 0.03691499, 0.6247381 , 0.15265559, 0.05903186],\n",
       "       [0.10658211, 0.15903422, 0.55198437, 0.08295646, 0.09944281],\n",
       "       [0.05294678, 0.09239621, 0.39482096, 0.05319859, 0.4066375 ],\n",
       "       [0.03691096, 0.10566743, 0.5814235 , 0.04697098, 0.22902718],\n",
       "       [0.032461  , 0.15832543, 0.07545675, 0.03118329, 0.7025735 ],\n",
       "       [0.02029894, 0.08563837, 0.1469813 , 0.03574361, 0.7113378 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Make predictions on sample abstract features\n",
    "test_abstract_pred_probs = loaded_model.predict(x=(test_abstract_line_numbers_one_hot,\n",
    "                                                   test_abstract_total_lines_one_hot,\n",
    "                                                   tf.constant(abstract_lines),\n",
    "                                                   tf.constant(abstract_chars)))\n",
    "test_abstract_pred_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(8,), dtype=int64, numpy=array([3, 2, 2, 2, 4, 2, 4, 4], dtype=int64)>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn prediction probabilities into prediction classes\n",
    "test_abstract_preds = tf.argmax(test_abstract_pred_probs, axis=1)\n",
    "test_abstract_preds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have got the predicted sequence label for each line in our sample abstract. We will visualize each sentence with its predicted label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OBJECTIVE',\n",
       " 'METHODS',\n",
       " 'METHODS',\n",
       " 'METHODS',\n",
       " 'RESULTS',\n",
       " 'METHODS',\n",
       " 'RESULTS',\n",
       " 'RESULTS']"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn prediction class integers into string class names\n",
    "test_abstract_pred_classes = [label_encoder.classes_[i] for i in test_abstract_preds]\n",
    "test_abstract_pred_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OBJECTIVE: This RCT examined the efficacy of a manualized social intervention for children with HFASDs.\n",
      "METHODS: Participants were randomly assigned to treatment or wait-list conditions.\n",
      "METHODS: Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language.\n",
      "METHODS: A response-cost program was applied to reduce problem behaviors and foster skills acquisition.\n",
      "RESULTS: Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures).\n",
      "METHODS: Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents.\n",
      "RESULTS: High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity.\n",
      "RESULTS: Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.\n"
     ]
    }
   ],
   "source": [
    "# Visualize abstract lines and predicted sequence labels\n",
    "for i, line in enumerate(abstract_lines):\n",
    "  print(f\"{test_abstract_pred_classes[i]}: {line}\")"
   ]
  }
 ],
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