Deep Learning Projects/pubmed-200k-rct-nlp-classification-project.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/kelixirr/pubmed-200k-rct-on-10-data-nlp-classification?scriptVersionId=146015228\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"The goal of the dataset is to explore the ability for NLP models to classify sentences which appear in sequential order. First published in paper: PubMed 200k RCT: a Dataset for Sequenctial Sentence Classification in Medical Abstracts. Source: https://arxiv.org/abs/1710.06071","metadata":{}},{"cell_type":"markdown","source":"## Our Goal: \nThe model will take an abstract wall of text and predict the section label each sentence should have. Create an NLP model to classify abstract sentences into the role they play (e.g. objective, methods, results, etc) to enable researchers to skim through the literature\n\n### Model Input: \n\nNote: all numerical symbols replaced with \"@\"\n\nTo 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 ). A total of @ patients with primary knee OA were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks. Outcome measures included pain reduction and improvement in function scores and systemic inflammation markers. Pain was assessed using the visual analog pain scale ( @-@ mm ).\n\nSecondary 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 )., Serum levels of interleukin @ ( IL-@ ) , IL-@ , tumor necrosis factor ( TNF ) - , and high-sensitivity C-reactive protein ( hsCRP ) were measured. 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. The mean difference between treatment arms ( @ % CI ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively. 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. These differences remained significant at @ weeks. The Outcome Measures in Rheumatology Clinical Trials-Osteoarthritis Research Society International responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ). 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\n### Model output\n\n`['###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']`","metadata":{}},{"cell_type":"markdown","source":"## Getting Data\n\n* PubMed 20k is a subset of PubMed 200k. I.e., any abstract present in PubMed 20k is also present in PubMed 200k.\n* PubMed_200k_RCT is the same as PubMed_200k_RCT_numbers_replaced_with_at_sign, except that in the latter all numbers had been replaced by @. (same for PubMed_20k_RCT vs. PubMed_20k_RCT_numbers_replaced_with_at_sign).\n* Since Github file size limit is 100 MiB, we had to compress PubMed_200k_RCT\\train.7z and PubMed_200k_RCT_numbers_replaced_with_at_sign\\train.zip. To uncompress train.7z, you may use 7-Zip on Windows, Keka on Mac OS X, or p7zip on Linux.","metadata":{}},{"cell_type":"code","source":"!git clone https://github.com/Franck-Dernoncourt/pubmed-rct.git\n!ls pubmed-rct","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:30.159075Z","iopub.execute_input":"2023-10-10T15:35:30.1594Z","iopub.status.idle":"2023-10-10T15:35:46.691486Z","shell.execute_reply.started":"2023-10-10T15:35:30.159366Z","shell.execute_reply":"2023-10-10T15:35:46.690222Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Cloning into 'pubmed-rct'...\nremote: Enumerating objects: 33, done.\u001b[K\nremote: Counting objects: 100% (8/8), done.\u001b[K\nremote: Compressing objects: 100% (3/3), done.\u001b[K\nremote: Total 33 (delta 5), reused 5 (delta 5), pack-reused 25\u001b[K\nUnpacking objects: 100% (33/33), 177.08 MiB | 12.30 MiB/s, done.\nPubMed_200k_RCT\nPubMed_200k_RCT_numbers_replaced_with_at_sign\nPubMed_20k_RCT\nPubMed_20k_RCT_numbers_replaced_with_at_sign\nREADME.md\n","output_type":"stream"}]},{"cell_type":"code","source":"# let's work with (dev.text is validation set)\n!ls pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:46.696051Z","iopub.execute_input":"2023-10-10T15:35:46.697683Z","iopub.status.idle":"2023-10-10T15:35:46.949407Z","shell.execute_reply.started":"2023-10-10T15:35:46.697643Z","shell.execute_reply":"2023-10-10T15:35:46.947731Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"dev.txt  test.txt  train.txt\n","output_type":"stream"}]},{"cell_type":"code","source":"data_dir = \"pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/\"","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:46.950896Z","iopub.execute_input":"2023-10-10T15:35:46.952325Z","iopub.status.idle":"2023-10-10T15:35:46.959547Z","shell.execute_reply.started":"2023-10-10T15:35:46.952251Z","shell.execute_reply":"2023-10-10T15:35:46.958379Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"import os\nfilenames = [data_dir + filename for filename in os.listdir(data_dir)]\nfilenames","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:46.961865Z","iopub.execute_input":"2023-10-10T15:35:46.96277Z","iopub.status.idle":"2023-10-10T15:35:46.97867Z","shell.execute_reply.started":"2023-10-10T15:35:46.962555Z","shell.execute_reply":"2023-10-10T15:35:46.977496Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"['pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/dev.txt',\n 'pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/test.txt',\n 'pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/train.txt']"},"metadata":{}}]},{"cell_type":"code","source":"# function to read the lines of the document \ndef get_lines(file):\n    with open(file, \"r\") as f:\n        return f.readlines()    ","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:46.981531Z","iopub.execute_input":"2023-10-10T15:35:46.982112Z","iopub.status.idle":"2023-10-10T15:35:46.998973Z","shell.execute_reply.started":"2023-10-10T15:35:46.982084Z","shell.execute_reply":"2023-10-10T15:35:46.997654Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"train_lines = get_lines(data_dir + \"train.txt\")\ntrain_lines[:20]","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:47.000266Z","iopub.execute_input":"2023-10-10T15:35:47.000574Z","iopub.status.idle":"2023-10-10T15:35:47.057102Z","shell.execute_reply.started":"2023-10-10T15:35:47.000547Z","shell.execute_reply":"2023-10-10T15:35:47.056332Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","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']"},"metadata":{}}]},{"cell_type":"markdown","source":"## Preprocessing Our Data ","metadata":{}},{"cell_type":"code","source":"def preprocess_text_with_line_numbers(filename):\n    \n    input_lines = get_lines(filename)\n    abstract_lines = \"\"\n    abstract_samples = []\n    \n    for line in input_lines:\n        \n        if line.startswith(\"###\"):\n            abstract_id = line\n            abstract_lines = \"\"\n            \n        elif line.isspace():\n            \n            abstract_line_split = abstract_lines.splitlines()\n            \n            for abstract_line_number, abstract_line in enumerate(abstract_line_split):\n                line_data = {}\n                target_text_split = abstract_line.split(\"\\t\")\n                line_data[\"target\"] = target_text_split[0]\n                line_data[\"text\"] = target_text_split[1].lower()\n                line_data[\"line_number\"] = abstract_line_number\n                line_data[\"total_lines\"] = len(abstract_line_split) - 1\n                abstract_samples.append(line_data)\n                \n        else:\n            abstract_lines += line\n            \n    return abstract_samples","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:47.05805Z","iopub.execute_input":"2023-10-10T15:35:47.058462Z","iopub.status.idle":"2023-10-10T15:35:47.069798Z","shell.execute_reply.started":"2023-10-10T15:35:47.058438Z","shell.execute_reply":"2023-10-10T15:35:47.069177Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"train_samples = preprocess_text_with_line_numbers(data_dir + \"train.txt\")\nval_samples = preprocess_text_with_line_numbers(data_dir + \"dev.txt\")\ntest_samples = preprocess_text_with_line_numbers(data_dir + \"test.txt\")\nlen(train_samples), len(val_samples), len(test_samples)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:47.070948Z","iopub.execute_input":"2023-10-10T15:35:47.071939Z","iopub.status.idle":"2023-10-10T15:35:47.495099Z","shell.execute_reply.started":"2023-10-10T15:35:47.07191Z","shell.execute_reply":"2023-10-10T15:35:47.493781Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"(180040, 30212, 30135)"},"metadata":{}}]},{"cell_type":"code","source":"train_samples[:14]","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:47.49656Z","iopub.execute_input":"2023-10-10T15:35:47.497168Z","iopub.status.idle":"2023-10-10T15:35:47.503042Z","shell.execute_reply.started":"2023-10-10T15:35:47.497149Z","shell.execute_reply":"2023-10-10T15:35:47.50205Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","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}]"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\ntrain_df = pd.DataFrame(train_samples)\nval_df = pd.DataFrame(val_samples)\ntest_df = pd.DataFrame(test_samples)\ntrain_df.head(14)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:47.5039Z","iopub.execute_input":"2023-10-10T15:35:47.504359Z","iopub.status.idle":"2023-10-10T15:35:47.993648Z","shell.execute_reply.started":"2023-10-10T15:35:47.504332Z","shell.execute_reply":"2023-10-10T15:35:47.992756Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"         target                                               text  \\\n0     OBJECTIVE  to investigate the efficacy of @ weeks of dail...   \n1       METHODS  a total of @ patients with primary knee oa wer...   \n2       METHODS  outcome measures included pain reduction and i...   \n3       METHODS  pain was assessed using the visual analog pain...   \n4       METHODS  secondary outcome measures included the wester...   \n5       METHODS  serum levels of interleukin @ ( il-@ ) , il-@ ...   \n6       RESULTS  there was a clinically relevant reduction in t...   \n7       RESULTS  the mean difference between treatment arms ( @...   \n8       RESULTS  further , there was a clinically relevant redu...   \n9       RESULTS  these differences remained significant at @ we...   \n10      RESULTS  the outcome measures in rheumatology clinical ...   \n11  CONCLUSIONS  low-dose oral prednisolone had both a short-te...   \n12   BACKGROUND  emotional eating is associated with overeating...   \n13   BACKGROUND  yet , empirical evidence for individual ( trai...   \n\n    line_number  total_lines  \n0             0           11  \n1             1           11  \n2             2           11  \n3             3           11  \n4             4           11  \n5             5           11  \n6             6           11  \n7             7           11  \n8             8           11  \n9             9           11  \n10           10           11  \n11           11           11  \n12            0           10  \n13            1           10  ","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  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train_df.target.value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:47.994514Z","iopub.execute_input":"2023-10-10T15:35:47.995511Z","iopub.status.idle":"2023-10-10T15:35:48.021917Z","shell.execute_reply.started":"2023-10-10T15:35:47.995489Z","shell.execute_reply":"2023-10-10T15:35:48.021057Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"target\nMETHODS        59353\nRESULTS        57953\nCONCLUSIONS    27168\nBACKGROUND     21727\nOBJECTIVE      13839\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"train_df.total_lines.plot.hist();","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:48.0231Z","iopub.execute_input":"2023-10-10T15:35:48.023329Z","iopub.status.idle":"2023-10-10T15:35:48.277496Z","shell.execute_reply.started":"2023-10-10T15:35:48.023309Z","shell.execute_reply":"2023-10-10T15:35:48.27616Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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eN3Y1Wo8Ho8uXLigs2fPqqam5oo1R48e/dbeKysrVVlZaT72eDwNe/EAAKBZ8WloGj16tPnv/v37Kz4+Xt27d9fGjRsVGhrqw86ubcmSJXr66ad93QYAAPie+PzXc5fr0KGDbr75Zn388ceKjIxUVVWVysrKvGpKS0sVGRkpSYqMjKz3bbq6x9eqsdvtCg0NVadOnRQUFHTFmro5rmTBggUqLy83j5MnTzbqNQMAgObBr0LT+fPndfz4cXXp0kVxcXEKDg5WTk6OOV5YWKiioiI5nU5JktPp1KFDh7y+5bZ9+3bZ7XbFxsaaNZfPUVdTN4fNZlNcXJxXTW1trXJycsyaKwkJCZHdbvc6AABAy+XT0DRnzhzl5ubq008/1Z49e3TvvfcqKChI999/vxwOh5KTk5WamqqdO3cqPz9fU6ZMkdPp1LBhwyRJI0eOVGxsrB566CF98MEH2rZtmxYuXKiUlBSFhIRIkqZOnapPPvlEc+fO1dGjR7Vq1Spt3LhRs2fPNvtITU3V73//e61du1Yffvihpk2bpoqKCk2ZMsUn6wIAAPyPT+9pOnXqlO6//3598cUX6ty5s26//Xa9++676ty5syTp5ZdfVmBgoMaPH6/Kykq5XC6tWrXK/PmgoCBt2bJF06ZNk9PpVNu2bZWUlKRnnnnGrImJiVFWVpZmz56t5cuXq2vXrnrttdfkcrnMmgkTJujMmTNKS0uT2+3WwIEDlZ2dXe/mcAAA8MPl032aWhL2aWoZ2KcJAH5YmtU+TQAAAM0BoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFjQqNH3yySdN3QcAAIBfa1Ro6tmzp4YPH64///nPunjxYlP3BAAA4HcaFZoOHDig/v37KzU1VZGRkfrP//xP7du3r6l7AwAA8BuNCk0DBw7U8uXLVVxcrD/+8Y8qKSnR7bffrr59++qll17SmTNnmrpPAAAAn/pON4K3atVK48aNU2Zmpn7729/q448/1pw5cxQdHa3JkyerpKTE8lzPP/+8AgICNGvWLPPcxYsXlZKSoo4dO6pdu3YaP368SktLvX6uqKhIiYmJatOmjcLDw/XUU0/p0qVLXjW7du3SoEGDFBISop49eyojI6Pe869cuVI9evRQ69atFR8fz5UzAADg5TuFpv379+uJJ55Qly5d9NJLL2nOnDk6fvy4tm/fruLiYt1zzz2W5nnvvff0u9/9Tv379/c6P3v2bL311lvKzMxUbm6uiouLNW7cOHO8pqZGiYmJqqqq0p49e7R27VplZGQoLS3NrDlx4oQSExM1fPhwFRQUaNasWXr00Ue1bds2s2bDhg1KTU3VokWLdODAAQ0YMEAul0unT5/+LssDAABakADDMIyG/tBLL72kNWvWqLCwUGPGjNGjjz6qMWPGKDDw/zLYqVOn1KNHj3pXfb7p/PnzGjRokFatWqVnn31WAwcO1LJly1ReXq7OnTtr3bp1uu+++yRJR48e1S233KK8vDwNGzZMW7du1V133aXi4mJFRERIktLT0zVv3jydOXNGNptN8+bNU1ZWlg4fPmw+58SJE1VWVqbs7GxJUnx8vIYMGaIVK1ZIkmpraxUdHa0ZM2Zo/vz5ltbE4/HI4XCovLxcdrvd+mLCr/SYn+XrFn4QPn0+0dctAICkhn1+N+pK0+rVq/XAAw/os88+0+bNm3XXXXd5BSZJCg8P1x/+8IdrzpWSkqLExEQlJCR4nc/Pz1d1dbXX+T59+qhbt27Ky8uTJOXl5alfv35mYJIkl8slj8ejI0eOmDXfnNvlcplzVFVVKT8/36smMDBQCQkJZg0AAECrxvzQsWPHrlljs9mUlJR01Zr169frwIEDeu+99+qNud1u2Ww2dejQwet8RESE3G63WXN5YKobrxu7Wo3H49GFCxd09uxZ1dTUXLHm6NGj39p7ZWWlKisrzccej+eqrxUAADRvjbrStGbNGmVmZtY7n5mZqbVr11qa4+TJk5o5c6Zef/11tW7dujFt+NSSJUvkcDjMIzo62tctAQCA66hRoWnJkiXq1KlTvfPh4eF67rnnLM2Rn5+v06dPa9CgQWrVqpVatWql3NxcvfLKK2rVqpUiIiJUVVWlsrIyr58rLS1VZGSkJCkyMrLet+nqHl+rxm63KzQ0VJ06dVJQUNAVa+rmuJIFCxaovLzcPE6ePGnpdQMAgOapUaGpqKhIMTEx9c53795dRUVFluYYMWKEDh06pIKCAvMYPHiwJk2aZP47ODhYOTk55s8UFhaqqKhITqdTkuR0OnXo0CGvb7lt375ddrtdsbGxZs3lc9TV1M1hs9kUFxfnVVNbW6ucnByz5kpCQkJkt9u9DgAA0HI16p6m8PBwHTx4UD169PA6/8EHH6hjx46W5mjfvr369u3rda5t27bq2LGjeT45OVmpqakKCwuT3W7XjBkz5HQ6NWzYMEnSyJEjFRsbq4ceekhLly6V2+3WwoULlZKSopCQEEnS1KlTtWLFCs2dO1ePPPKIduzYoY0bNyor6/++JZWamqqkpCQNHjxYQ4cO1bJly1RRUaEpU6Y0ZnkAAEAL1KjQdP/99+uXv/yl2rdvrzvvvFOSlJubq5kzZ2rixIlN1tzLL7+swMBAjR8/XpWVlXK5XFq1apU5HhQUpC1btmjatGlyOp1q27atkpKS9Mwzz5g1MTExysrK0uzZs7V8+XJ17dpVr732mlwul1kzYcIEnTlzRmlpaXK73Ro4cKCys7Pr3RwOAAB+uBq1T1NVVZUeeughZWZmqlWrr3NXbW2tJk+erPT0dNlstiZv1N+xT1PLwD5N3w/2aQLgLxry+d2oK002m00bNmzQf/3Xf+mDDz5QaGio+vXrp+7duzeqYQAAAH/XqNBU5+abb9bNN9/cVL0AAAD4rUaFppqaGmVkZCgnJ0enT59WbW2t1/iOHTuapDkAAAB/0ajQNHPmTGVkZCgxMVF9+/ZVQEBAU/cFAADgVxoVmtavX6+NGzdqzJgxTd0PAACAX2rU5pY2m009e/Zs6l4AAAD8VqNC05NPPqnly5erEbsVAAAANEuN+vXcv/71L+3cuVNbt27VrbfequDgYK/xN954o0maAwAA8BeNCk0dOnTQvffe29S9AAAA+K1GhaY1a9Y0dR8AAAB+rVH3NEnSpUuX9I9//EO/+93vdO7cOUlScXGxzp8/32TNAQAA+ItGXWn67LPPNGrUKBUVFamyslI///nP1b59e/32t79VZWWl0tPTm7pPAAAAn2rUlaaZM2dq8ODBOnv2rEJDQ83z9957r3JycpqsOQAAAH/RqCtN//znP7Vnzx7ZbDav8z169NC///3vJmkMAADAnzTqSlNtba1qamrqnT916pTat2//nZsCAADwN40KTSNHjtSyZcvMxwEBATp//rwWLVrEn1YBAAAtUqN+Pffiiy/K5XIpNjZWFy9e1AMPPKBjx46pU6dO+stf/tLUPQIAAPhco0JT165d9cEHH2j9+vU6ePCgzp8/r+TkZE2aNMnrxnAAAICWolGhSZJatWqlBx98sCl7AQAA8FuNCk1/+tOfrjo+efLkRjUDAADgrxoVmmbOnOn1uLq6Wl999ZVsNpvatGlDaAIAAC1Oo749d/bsWa/j/PnzKiws1O23386N4AAAoEVq9N+e+6ZevXrp+eefr3cVCgAAoCVostAkfX1zeHFxcVNOCQAA4BcadU/Tm2++6fXYMAyVlJRoxYoVuu2225qkMQAAAH/SqNA0duxYr8cBAQHq3Lmzfvazn+nFF19sir4AAAD8SqNCU21tbVP3AQAA4Nea9J4mAACAlqpRV5pSU1Mt17700kuNeQoAAAC/0qjQ9P777+v9999XdXW1evfuLUn66KOPFBQUpEGDBpl1AQEBTdMlAACAjzUqNN19991q37691q5dqxtuuEHS1xteTpkyRXfccYeefPLJJm0SAADA1wIMwzAa+kM/+tGP9Pbbb+vWW2/1On/48GGNHDnyB7lXk8fjkcPhUHl5uex2u6/bQSP1mJ/l6xbgpz59PtHXLQC4Dhry+d2oG8E9Ho/OnDlT7/yZM2d07ty5xkwJAADg1xoVmu69915NmTJFb7zxhk6dOqVTp07pf/7nf5ScnKxx48Y1dY8AAAA+16h7mtLT0zVnzhw98MADqq6u/nqiVq2UnJysF154oUkbBAAA8AeNCk1t2rTRqlWr9MILL+j48eOSpJtuuklt27Zt0uYAAAD8xXfa3LKkpEQlJSXq1auX2rZtq0bcUw4AANAsNCo0ffHFFxoxYoRuvvlmjRkzRiUlJZKk5ORkthsAAAAtUqNC0+zZsxUcHKyioiK1adPGPD9hwgRlZ2c3WXMAAAD+olH3NL399tvatm2bunbt6nW+V69e+uyzz5qkMQAAAH/SqCtNFRUVXleY6nz55ZcKCQn5zk0BAAD4m0aFpjvuuEN/+tOfzMcBAQGqra3V0qVLNXz48CZrDgAAwF80KjQtXbpUr776qkaPHq2qqirNnTtXffv21e7du/Xb3/7W8jyrV69W//79ZbfbZbfb5XQ6tXXrVnP84sWLSklJUceOHdWuXTuNHz9epaWlXnMUFRUpMTFRbdq0UXh4uJ566ildunTJq2bXrl0aNGiQQkJC1LNnT2VkZNTrZeXKlerRo4dat26t+Ph47du3r2GLAgAAWrRGhaa+ffvqo48+0u2336577rlHFRUVGjdunN5//33ddNNNlufp2rWrnn/+eeXn52v//v362c9+pnvuuUdHjhyR9PUN52+99ZYyMzOVm5ur4uJirx3Ha2pqlJiYqKqqKu3Zs0dr165VRkaG0tLSzJoTJ04oMTFRw4cPV0FBgWbNmqVHH31U27ZtM2s2bNig1NRULVq0SAcOHNCAAQPkcrl0+vTpxiwPAABogRr8B3urq6s1atQopaenq1evXk3eUFhYmF544QXdd9996ty5s9atW6f77rtPknT06FHdcsstysvL07Bhw7R161bdddddKi4uVkREhKSvdyufN2+ezpw5I5vNpnnz5ikrK0uHDx82n2PixIkqKyszv+kXHx+vIUOGaMWKFZKk2tpaRUdHa8aMGZo/f76lvvmDvS0Df7AX34Y/2Au0TNf1D/YGBwfr4MGDjW7u29TU1Gj9+vWqqKiQ0+lUfn6+qqurlZCQYNb06dNH3bp1U15eniQpLy9P/fr1MwOTJLlcLnk8HvNqVV5entccdTV1c1RVVSk/P9+rJjAwUAkJCWbNlVRWVsrj8XgdAACg5WrUr+cefPBB/eEPf2iSBg4dOqR27dopJCREU6dO1aZNmxQbGyu32y2bzaYOHTp41UdERMjtdkuS3G63V2CqG68bu1qNx+PRhQsX9Pnnn6umpuaKNXVzXMmSJUvkcDjMIzo6ulGvHwAANA+N2qfp0qVL+uMf/6h//OMfiouLq/c351566SXLc/Xu3VsFBQUqLy/XX//6VyUlJSk3N7cxbX2vFixYoNTUVPOxx+MhOAEA0II1KDR98skn6tGjhw4fPqxBgwZJkj766COvmoCAgAY1YLPZ1LNnT0lSXFyc3nvvPS1fvlwTJkxQVVWVysrKvK42lZaWKjIyUpIUGRlZ71tudd+uu7zmm9+4Ky0tld1uV2hoqIKCghQUFHTFmro5riQkJIQ9qQAA+AFp0K/nevXqpc8//1w7d+7Uzp07FR4ervXr15uPd+7cqR07dnynhmpra1VZWam4uDgFBwcrJyfHHCssLFRRUZGcTqckyel06tChQ17fctu+fbvsdrtiY2PNmsvnqKupm8NmsykuLs6rpra2Vjk5OWYNAABAg640ffOLdlu3blVFRUWjn3zBggUaPXq0unXrpnPnzmndunXatWuXtm3bJofDoeTkZKWmpiosLEx2u10zZsyQ0+nUsGHDJEkjR45UbGysHnroIS1dulRut1sLFy5USkqKeRVo6tSpWrFihebOnatHHnlEO3bs0MaNG5WV9X/fkkpNTVVSUpIGDx6soUOHatmyZaqoqNCUKVMa/doAAEDL0qh7muo0cLeCek6fPq3JkyerpKREDodD/fv317Zt2/Tzn/9ckvTyyy8rMDBQ48ePV2VlpVwul1atWmX+fFBQkLZs2aJp06bJ6XSqbdu2SkpK0jPPPGPWxMTEKCsrS7Nnz9by5cvVtWtXvfbaa3K5XGbNhAkTdObMGaWlpcntdmvgwIHKzs6ud3M4AAD44WrQPk1BQUFyu93q3LmzJKl9+/Y6ePCgYmJirluDzQX7NLUM7NOEb8M+TUDL1JDP7wb/eu7hhx82f/V18eJFTZ06td635954440GtgwAAODfGhSakpKSvB4/+OCDTdoMAACAv2pQaFqzZs316gMAAMCvNWpHcAAAgB8aQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFrXzdAFquHvOzfN0CAABNhitNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFjg09C0ZMkSDRkyRO3bt1d4eLjGjh2rwsJCr5qLFy8qJSVFHTt2VLt27TR+/HiVlpZ61RQVFSkxMVFt2rRReHi4nnrqKV26dMmrZteuXRo0aJBCQkLUs2dPZWRk1Otn5cqV6tGjh1q3bq34+Hjt27evyV8zAABonnwamnJzc5WSkqJ3331X27dvV3V1tUaOHKmKigqzZvbs2XrrrbeUmZmp3NxcFRcXa9y4ceZ4TU2NEhMTVVVVpT179mjt2rXKyMhQWlqaWXPixAklJiZq+PDhKigo0KxZs/Too49q27ZtZs2GDRuUmpqqRYsW6cCBAxowYIBcLpdOnz79/SwGAADwawGGYRi+bqLOmTNnFB4ertzcXN15550qLy9X586dtW7dOt13332SpKNHj+qWW25RXl6ehg0bpq1bt+quu+5ScXGxIiIiJEnp6emaN2+ezpw5I5vNpnnz5ikrK0uHDx82n2vixIkqKytTdna2JCk+Pl5DhgzRihUrJEm1tbWKjo7WjBkzNH/+/Gv27vF45HA4VF5eLrvd3tRL0yz1mJ/l6xaAJvPp84m+bgHAddCQz2+/uqepvLxckhQWFiZJys/PV3V1tRISEsyaPn36qFu3bsrLy5Mk5eXlqV+/fmZgkiSXyyWPx6MjR46YNZfPUVdTN0dVVZXy8/O9agIDA5WQkGDWfFNlZaU8Ho/XAQAAWi6/CU21tbWaNWuWbrvtNvXt21eS5Ha7ZbPZ1KFDB6/aiIgIud1us+bywFQ3Xjd2tRqPx6MLFy7o888/V01NzRVr6ub4piVLlsjhcJhHdHR04144AABoFvwmNKWkpOjw4cNav369r1uxZMGCBSovLzePkydP+rolAABwHbXydQOSNH36dG3ZskW7d+9W165dzfORkZGqqqpSWVmZ19Wm0tJSRUZGmjXf/JZb3bfrLq/55jfuSktLZbfbFRoaqqCgIAUFBV2xpm6ObwoJCVFISEjjXjAAAGh2fHqlyTAMTZ8+XZs2bdKOHTsUExPjNR4XF6fg4GDl5OSY5woLC1VUVCSn0ylJcjqdOnTokNe33LZv3y673a7Y2Fiz5vI56mrq5rDZbIqLi/Oqqa2tVU5OjlkDAAB+2Hx6pSklJUXr1q3T3/72N7Vv3968f8jhcCg0NFQOh0PJyclKTU1VWFiY7Ha7ZsyYIafTqWHDhkmSRo4cqdjYWD300ENaunSp3G63Fi5cqJSUFPNK0NSpU7VixQrNnTtXjzzyiHbs2KGNGzcqK+v/vt2VmpqqpKQkDR48WEOHDtWyZctUUVGhKVOmfP8LAwAA/I5PQ9Pq1aslST/96U+9zq9Zs0YPP/ywJOnll19WYGCgxo8fr8rKSrlcLq1atcqsDQoK0pYtWzRt2jQ5nU61bdtWSUlJeuaZZ8yamJgYZWVlafbs2Vq+fLm6du2q1157TS6Xy6yZMGGCzpw5o7S0NLndbg0cOFDZ2dn1bg4HAAA/TH61T1Nzxj5N9bFPE1oS9mkCWqZmu08TAACAvyI0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWtPJ1AwDQHPSYn+XrFhrs0+cTfd0C0KL49ErT7t27dffddysqKkoBAQHavHmz17hhGEpLS1OXLl0UGhqqhIQEHTt2zKvmyy+/1KRJk2S329WhQwclJyfr/PnzXjUHDx7UHXfcodatWys6OlpLly6t10tmZqb69Omj1q1bq1+/fvr73//e5K8XAAA0Xz4NTRUVFRowYIBWrlx5xfGlS5fqlVdeUXp6uvbu3au2bdvK5XLp4sWLZs2kSZN05MgRbd++XVu2bNHu3bv1+OOPm+Mej0cjR45U9+7dlZ+frxdeeEGLFy/Wq6++atbs2bNH999/v5KTk/X+++9r7NixGjt2rA4fPnz9XjwAAGhWAgzDMHzdhCQFBARo06ZNGjt2rKSvrzJFRUXpySef1Jw5cyRJ5eXlioiIUEZGhiZOnKgPP/xQsbGxeu+99zR48GBJUnZ2tsaMGaNTp04pKipKq1ev1q9//Wu53W7ZbDZJ0vz587V582YdPXpUkjRhwgRVVFRoy5YtZj/Dhg3TwIEDlZ6ebql/j8cjh8Oh8vJy2e32plqWZq05/joDaEn49RxwbQ35/PbbG8FPnDght9uthIQE85zD4VB8fLzy8vIkSXl5eerQoYMZmCQpISFBgYGB2rt3r1lz5513moFJklwulwoLC3X27Fmz5vLnqaupe54rqayslMfj8ToAAEDL5behye12S5IiIiK8zkdERJhjbrdb4eHhXuOtWrVSWFiYV82V5rj8Ob6tpm78SpYsWSKHw2Ee0dHRDX2JAACgGfHb0OTvFixYoPLycvM4efKkr1sCAADXkd+GpsjISElSaWmp1/nS0lJzLDIyUqdPn/Yav3Tpkr788kuvmivNcflzfFtN3fiVhISEyG63ex0AAKDl8tvQFBMTo8jISOXk5JjnPB6P9u7dK6fTKUlyOp0qKytTfn6+WbNjxw7V1tYqPj7erNm9e7eqq6vNmu3bt6t379664YYbzJrLn6eupu55AAAAfBqazp8/r4KCAhUUFEj6+ubvgoICFRUVKSAgQLNmzdKzzz6rN998U4cOHdLkyZMVFRVlfsPulltu0ahRo/TYY49p3759eueddzR9+nRNnDhRUVFRkqQHHnhANptNycnJOnLkiDZs2KDly5crNTXV7GPmzJnKzs7Wiy++qKNHj2rx4sXav3+/pk+f/n0vCQAA8FM+3RF8//79Gj58uPm4LsgkJSUpIyNDc+fOVUVFhR5//HGVlZXp9ttvV3Z2tlq3bm3+zOuvv67p06drxIgRCgwM1Pjx4/XKK6+Y4w6HQ2+//bZSUlIUFxenTp06KS0tzWsvp5/85Cdat26dFi5cqF/96lfq1auXNm/erL59+34PqwAAAJoDv9mnqbljn6b62KcJ8C32aQKurUXs0wQAAOBPCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALGjl6wYAANdHj/lZvm6hwT59PtHXLQDfiitNAAAAFhCaAAAALODXc81Ec7zMDgBAS0JoAgD4jeb4H4jch/XDwa/nAAAALCA0fcPKlSvVo0cPtW7dWvHx8dq3b5+vWwIAAH6A0HSZDRs2KDU1VYsWLdKBAwc0YMAAuVwunT592tetAQAAHyM0Xeall17SY489pilTpig2Nlbp6elq06aN/vjHP/q6NQAA4GPcCP6/qqqqlJ+frwULFpjnAgMDlZCQoLy8vHr1lZWVqqysNB+Xl5dLkjwez3Xpr7byq+syLwDgu+k2O9PXLTTK4addvm7BL9R9bhuGcc1aQtP/+vzzz1VTU6OIiAiv8xERETp69Gi9+iVLlujpp5+udz46Ovq69QgAQFNxLPN1B/7l3LlzcjgcV60hNDXSggULlJqaaj6ura3Vl19+qY4dOyogIMCHnV0fHo9H0dHROnnypOx2u6/bafZYz6bDWjYt1rPpsJZN63qtp2EYOnfunKKioq5ZS2j6X506dVJQUJBKS0u9zpeWlioyMrJefUhIiEJCQrzOdejQ4Xq26Bfsdjv/529CrGfTYS2bFuvZdFjLpnU91vNaV5jqcCP4/7LZbIqLi1NOTo55rra2Vjk5OXI6nT7sDAAA+AOuNF0mNTVVSUlJGjx4sIYOHaply5apoqJCU6ZM8XVrAADAxwhNl5kwYYLOnDmjtLQ0ud1uDRw4UNnZ2fVuDv8hCgkJ0aJFi+r9ShKNw3o2HdayabGeTYe1bFr+sJ4BhpXv2AEAAPzAcU8TAACABYQmAAAACwhNAAAAFhCaAAAALCA04aoWL16sgIAAr6NPnz6+bqtZ2L17t+6++25FRUUpICBAmzdv9ho3DENpaWnq0qWLQkNDlZCQoGPHjvmm2WbgWuv58MMP13uvjho1yjfN+rklS5ZoyJAhat++vcLDwzV27FgVFhZ61Vy8eFEpKSnq2LGj2rVrp/Hjx9fb/BfW1vKnP/1pvffm1KlTfdSxf1u9erX69+9vbmDpdDq1detWc9zX70tCE67p1ltvVUlJiXn861//8nVLzUJFRYUGDBiglStXXnF86dKleuWVV5Senq69e/eqbdu2crlcunjx4vfcafNwrfWUpFGjRnm9V//yl798jx02H7m5uUpJSdG7776r7du3q7q6WiNHjlRFRYVZM3v2bL311lvKzMxUbm6uiouLNW7cOB927Z+srKUkPfbYY17vzaVLl/qoY//WtWtXPf/888rPz9f+/fv1s5/9TPfcc4+OHDkiyQ/elwZwFYsWLTIGDBjg6zaaPUnGpk2bzMe1tbVGZGSk8cILL5jnysrKjJCQEOMvf/mLDzpsXr65noZhGElJScY999zjk36au9OnTxuSjNzcXMMwvn4vBgcHG5mZmWbNhx9+aEgy8vLyfNVms/DNtTQMw/iP//gPY+bMmb5rqpm74YYbjNdee80v3pdcacI1HTt2TFFRUbrxxhs1adIkFRUV+bqlZu/EiRNyu91KSEgwzzkcDsXHxysvL8+HnTVvu3btUnh4uHr37q1p06bpiy++8HVLzUJ5ebkkKSwsTJKUn5+v6upqr/dnnz591K1bN96f1/DNtazz+uuvq1OnTurbt68WLFigr776yhftNSs1NTVav369Kioq5HQ6/eJ9yY7guKr4+HhlZGSod+/eKikp0dNPP6077rhDhw8fVvv27X3dXrPldrslqd5u8xEREeYYGmbUqFEaN26cYmJidPz4cf3qV7/S6NGjlZeXp6CgIF+357dqa2s1a9Ys3Xbbberbt6+kr9+fNput3h8h5/15dVdaS0l64IEH1L17d0VFRengwYOaN2+eCgsL9cYbb/iwW/916NAhOZ1OXbx4Ue3atdOmTZsUGxurgoICn78vCU24qtGjR5v/7t+/v+Lj49W9e3dt3LhRycnJPuwM8DZx4kTz3/369VP//v110003adeuXRoxYoQPO/NvKSkpOnz4MPcqNoFvW8vHH3/c/He/fv3UpUsXjRgxQsePH9dNN930fbfp93r37q2CggKVl5frr3/9q5KSkpSbm+vrtiRxIzgaqEOHDrr55pv18ccf+7qVZi0yMlKS6n3ro7S01BzDd3PjjTeqU6dOvFevYvr06dqyZYt27typrl27mucjIyNVVVWlsrIyr3ren9/u29bySuLj4yWJ9+a3sNls6tmzp+Li4rRkyRINGDBAy5cv94v3JaEJDXL+/HkdP35cXbp08XUrzVpMTIwiIyOVk5NjnvN4PNq7d6+cTqcPO2s5Tp06pS+++IL36hUYhqHp06dr06ZN2rFjh2JiYrzG4+LiFBwc7PX+LCwsVFFREe/Pb7jWWl5JQUGBJPHetKi2tlaVlZV+8b7k13O4qjlz5ujuu+9W9+7dVVxcrEWLFikoKEj333+/r1vze+fPn/f6L8kTJ06ooKBAYWFh6tatm2bNmqVnn31WvXr1UkxMjH7zm98oKipKY8eO9V3Tfuxq6xkWFqann35a48ePV2RkpI4fP665c+eqZ8+ecrlcPuzaP6WkpGjdunX629/+pvbt25v3gzgcDoWGhsrhcCg5OVmpqakKCwuT3W7XjBkz5HQ6NWzYMB9371+utZbHjx/XunXrNGbMGHXs2FEHDx7U7Nmzdeedd6p///4+7t7/LFiwQKNHj1a3bt107tw5rVu3Trt27dK2bdv84335vXxHD83WhAkTjC5duhg2m8340Y9+ZEyYMMH4+OOPfd1Ws7Bz505DUr0jKSnJMIyvtx34zW9+Y0RERBghISHGiBEjjMLCQt827ceutp5fffWVMXLkSKNz585GcHCw0b17d+Oxxx4z3G63r9v2S1daR0nGmjVrzJoLFy4YTzzxhHHDDTcYbdq0Me69916jpKTEd037qWutZVFRkXHnnXcaYWFhRkhIiNGzZ0/jqaeeMsrLy33buJ965JFHjO7duxs2m83o3LmzMWLECOPtt982x339vgwwDMP4fuIZAABA88U9TQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACw4P8DMVFzcjL+3EEAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"code","source":"# text lines to list\ntrain_sentences = train_df[\"text\"].tolist()\nval_sentences = val_df[\"text\"].tolist()\ntest_sentences = test_df[\"text\"].tolist()\ntrain_sentences[:10]","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:48.278698Z","iopub.execute_input":"2023-10-10T15:35:48.278942Z","iopub.status.idle":"2023-10-10T15:35:48.29659Z","shell.execute_reply.started":"2023-10-10T15:35:48.278923Z","shell.execute_reply":"2023-10-10T15:35:48.295375Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","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 .']"},"metadata":{}}]},{"cell_type":"code","source":"# one hot-encoded labels \nfrom sklearn.preprocessing import OneHotEncoder\none_hot_encoder = OneHotEncoder(sparse_output = False)\ntrain_labels_one_hot = one_hot_encoder.fit_transform(train_df[\"target\"].to_numpy().reshape(-1, 1))\nval_labels_one_hot = one_hot_encoder.transform(val_df[\"target\"].to_numpy().reshape(-1, 1))\ntest_labels_one_hot = one_hot_encoder.transform(test_df[\"target\"].to_numpy().reshape(-1, 1))\ntrain_labels_one_hot","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:48.300385Z","iopub.execute_input":"2023-10-10T15:35:48.300687Z","iopub.status.idle":"2023-10-10T15:35:48.709849Z","shell.execute_reply.started":"2023-10-10T15:35:48.300664Z","shell.execute_reply":"2023-10-10T15:35:48.70878Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","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.]])"},"metadata":{}}]},{"cell_type":"code","source":"# label encoded label\nfrom sklearn.preprocessing import LabelEncoder\nlabel_encoder = LabelEncoder()\ntrain_labels_encoded = label_encoder.fit_transform(train_df[\"target\"].to_numpy())\nval_labels_encoded = label_encoder.transform(val_df[\"target\"].to_numpy())\ntest_labels_encoded = label_encoder.transform(test_df[\"target\"].to_numpy())\ntrain_labels_encoded","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:48.711684Z","iopub.execute_input":"2023-10-10T15:35:48.712019Z","iopub.status.idle":"2023-10-10T15:35:48.753782Z","shell.execute_reply.started":"2023-10-10T15:35:48.711973Z","shell.execute_reply":"2023-10-10T15:35:48.752427Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"array([3, 2, 2, ..., 4, 1, 1])"},"metadata":{}}]},{"cell_type":"code","source":"num_classes = len(label_encoder.classes_)\nclass_names = label_encoder.classes_\nnum_classes, class_names","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:48.755806Z","iopub.execute_input":"2023-10-10T15:35:48.756199Z","iopub.status.idle":"2023-10-10T15:35:48.763259Z","shell.execute_reply.started":"2023-10-10T15:35:48.756168Z","shell.execute_reply":"2023-10-10T15:35:48.762055Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(5,\n array(['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'],\n       dtype=object))"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model 0 - Baseline","metadata":{}},{"cell_type":"code","source":"from sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.pipeline import Pipeline\n\nmodel_0 = Pipeline([\n    (\"tf-idf\", TfidfVectorizer()),\n    (\"clf\", MultinomialNB())\n])\n\nmodel_0.fit(X=train_sentences, \n            y=train_labels_encoded);","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:48.764688Z","iopub.execute_input":"2023-10-10T15:35:48.764969Z","iopub.status.idle":"2023-10-10T15:35:51.470943Z","shell.execute_reply.started":"2023-10-10T15:35:48.764944Z","shell.execute_reply":"2023-10-10T15:35:51.4691Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"# Evaluate baseline on validation data\nmodel_0.score(X=val_sentences,\n              y=val_labels_encoded)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:51.472726Z","iopub.execute_input":"2023-10-10T15:35:51.475527Z","iopub.status.idle":"2023-10-10T15:35:52.018963Z","shell.execute_reply.started":"2023-10-10T15:35:51.475472Z","shell.execute_reply":"2023-10-10T15:35:52.01752Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"0.7218323844829869"},"metadata":{}}]},{"cell_type":"code","source":"# Make predictions\nbaseline_preds = model_0.predict(val_sentences)\nbaseline_preds","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:52.020556Z","iopub.execute_input":"2023-10-10T15:35:52.020906Z","iopub.status.idle":"2023-10-10T15:35:52.48135Z","shell.execute_reply.started":"2023-10-10T15:35:52.020876Z","shell.execute_reply":"2023-10-10T15:35:52.480445Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"array([4, 1, 3, ..., 4, 4, 1])"},"metadata":{}}]},{"cell_type":"code","source":"# Download helper functions script\n!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:52.484079Z","iopub.execute_input":"2023-10-10T15:35:52.485453Z","iopub.status.idle":"2023-10-10T15:35:53.005591Z","shell.execute_reply.started":"2023-10-10T15:35:52.485429Z","shell.execute_reply":"2023-10-10T15:35:53.004732Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"--2023-10-10 15:35:52--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 10246 (10K) [text/plain]\nSaving to: ‘helper_functions.py’\n\nhelper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n\n2023-10-10 15:35:52 (58.3 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from helper_functions import calculate_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:35:53.006916Z","iopub.execute_input":"2023-10-10T15:35:53.007218Z","iopub.status.idle":"2023-10-10T15:36:03.004385Z","shell.execute_reply.started":"2023-10-10T15:35:53.007196Z","shell.execute_reply":"2023-10-10T15:36:03.002988Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"baseline_results = calculate_results(y_true=val_labels_encoded,\n                                     y_pred=baseline_preds)\nbaseline_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:03.005859Z","iopub.execute_input":"2023-10-10T15:36:03.007236Z","iopub.status.idle":"2023-10-10T15:36:03.024883Z","shell.execute_reply.started":"2023-10-10T15:36:03.007178Z","shell.execute_reply":"2023-10-10T15:36:03.024215Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"{'accuracy': 72.1832384482987,\n 'precision': 0.7186466952323352,\n 'recall': 0.7218323844829869,\n 'f1': 0.6989250353450294}"},"metadata":{}}]},{"cell_type":"markdown","source":"## Data Processing For Deep Sequence Models","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:03.025945Z","iopub.execute_input":"2023-10-10T15:36:03.026249Z","iopub.status.idle":"2023-10-10T15:36:03.076055Z","shell.execute_reply.started":"2023-10-10T15:36:03.026231Z","shell.execute_reply":"2023-10-10T15:36:03.074319Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"sent_lens = [len(sentence.split()) for sentence in train_sentences]\navg_sent_len = np.mean(sent_lens)\navg_sent_len","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:03.077825Z","iopub.execute_input":"2023-10-10T15:36:03.078414Z","iopub.status.idle":"2023-10-10T15:36:03.325281Z","shell.execute_reply.started":"2023-10-10T15:36:03.078387Z","shell.execute_reply":"2023-10-10T15:36:03.324362Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"26.338269273494777"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.hist(sent_lens, bins=7);","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:03.326466Z","iopub.execute_input":"2023-10-10T15:36:03.326782Z","iopub.status.idle":"2023-10-10T15:36:03.828596Z","shell.execute_reply.started":"2023-10-10T15:36:03.326758Z","shell.execute_reply":"2023-10-10T15:36:03.826878Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"output_seq_len = int(np.percentile(sent_lens, 95))\noutput_seq_len","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:03.829965Z","iopub.execute_input":"2023-10-10T15:36:03.830372Z","iopub.status.idle":"2023-10-10T15:36:03.850701Z","shell.execute_reply.started":"2023-10-10T15:36:03.830343Z","shell.execute_reply":"2023-10-10T15:36:03.849631Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"55"},"metadata":{}}]},{"cell_type":"markdown","source":"### Text Vectorizer","metadata":{}},{"cell_type":"code","source":"max_tokens = 68000 # based on the paper\nfrom tensorflow.keras.layers import TextVectorization\n\ntext_vectorizer = TextVectorization(max_tokens = max_tokens,\n                                 output_sequence_length = 55)\n\ntext_vectorizer.adapt(train_sentences)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:03.852532Z","iopub.execute_input":"2023-10-10T15:36:03.853282Z","iopub.status.idle":"2023-10-10T15:36:11.916669Z","shell.execute_reply.started":"2023-10-10T15:36:03.853255Z","shell.execute_reply":"2023-10-10T15:36:11.915474Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"import random\ntarget_sentence = random.choice(train_sentences)\nprint(f\"Text:\\n{target_sentence}\")\nprint(f\"\\nLength of text: {len(target_sentence.split())}\")\nprint(f\"\\nVectorized text:\\n{text_vectorizer([target_sentence])}\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:11.917989Z","iopub.execute_input":"2023-10-10T15:36:11.918932Z","iopub.status.idle":"2023-10-10T15:36:12.034551Z","shell.execute_reply.started":"2023-10-10T15:36:11.918876Z","shell.execute_reply":"2023-10-10T15:36:12.033566Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"Text:\nwhen switching from usual brand cigarettes , very low nicotine content ( vlnc ) cigarettes lead to a reduction in the number of cigarettes smoked , toxicant exposure , withdrawal symptoms and dependence .\n\nLength of text: 34\n\nVectorized text:\n[[  183  3799    27   370  9468  3609   849   220  1873  1014 16352  3609\n    944     6     8   169     5     2   154     4  3609  4564 21535   455\n   1474   144     3  1725     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","output_type":"stream"}]},{"cell_type":"code","source":"rct_20k_text_vocab = text_vectorizer.get_vocabulary()\nprint(f\"Number of words in vocabulary: {len(rct_20k_text_vocab)}\"), \nprint(f\"Most common words in the vocabulary: {rct_20k_text_vocab[:5]}\")\nprint(f\"Least common words in the vocabulary: {rct_20k_text_vocab[-5:]}\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:12.035592Z","iopub.execute_input":"2023-10-10T15:36:12.03585Z","iopub.status.idle":"2023-10-10T15:36:12.165708Z","shell.execute_reply.started":"2023-10-10T15:36:12.035829Z","shell.execute_reply":"2023-10-10T15:36:12.164873Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"Number of words in vocabulary: 64841\nMost common words in the vocabulary: ['', '[UNK]', 'the', 'and', 'of']\nLeast common words in the vocabulary: ['aainduced', 'aaigroup', 'aachener', 'aachen', 'aaacp']\n","output_type":"stream"}]},{"cell_type":"code","source":"text_vectorizer.get_config()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:12.167453Z","iopub.execute_input":"2023-10-10T15:36:12.167865Z","iopub.status.idle":"2023-10-10T15:36:12.17693Z","shell.execute_reply.started":"2023-10-10T15:36:12.167836Z","shell.execute_reply":"2023-10-10T15:36:12.175473Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","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}"},"metadata":{}}]},{"cell_type":"markdown","source":"embedding captures the meanigful relationship between vectorized texts","metadata":{}},{"cell_type":"code","source":"# create a token embedding layer\ntoken_embed = layers.Embedding(input_dim = len(rct_20k_text_vocab),\n                               output_dim=128,\n                               mask_zero=True,\n                               name=\"token_embedding\")\n\nprint(f\"Sentence before vectorization:\\n{target_sentence}\\n\")\nvectorized_sentence = text_vectorizer([target_sentence])\nprint(f\"Sentence after vectorization (before embedding):\\n{vectorized_sentence}\\n\")\nembedded_sentence = token_embed(vectorized_sentence)\nprint(f\"Sentence after embedding:\\n{embedded_sentence}\\n\")\nprint(f\"Embedded sentence shape: {embedded_sentence.shape}\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:12.17933Z","iopub.execute_input":"2023-10-10T15:36:12.180305Z","iopub.status.idle":"2023-10-10T15:36:12.275128Z","shell.execute_reply.started":"2023-10-10T15:36:12.180261Z","shell.execute_reply":"2023-10-10T15:36:12.273691Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Sentence before vectorization:\nwhen switching from usual brand cigarettes , very low nicotine content ( vlnc ) cigarettes lead to a reduction in the number of cigarettes smoked , toxicant exposure , withdrawal symptoms and dependence .\n\nSentence after vectorization (before embedding):\n[[  183  3799    27   370  9468  3609   849   220  1873  1014 16352  3609\n    944     6     8   169     5     2   154     4  3609  4564 21535   455\n   1474   144     3  1725     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\nSentence after embedding:\n[[[ 0.02310754 -0.01235237  0.0468341  ...  0.02182071 -0.01566359\n    0.03183058]\n  [-0.04171778 -0.03777422 -0.04566292 ... -0.04773817  0.01937953\n    0.0393295 ]\n  [-0.00456557  0.01242639  0.04072413 ... -0.00905876  0.0239012\n    0.03510454]\n  ...\n  [ 0.04755283  0.01254699  0.03754903 ... -0.04134838  0.02641114\n    0.02962815]\n  [ 0.04755283  0.01254699  0.03754903 ... -0.04134838  0.02641114\n    0.02962815]\n  [ 0.04755283  0.01254699  0.03754903 ... -0.04134838  0.02641114\n    0.02962815]]]\n\nEmbedded sentence shape: (1, 55, 128)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Turn our data into TensorFlow Datasets\ntrain_dataset = tf.data.Dataset.from_tensor_slices((train_sentences, train_labels_one_hot))\nvalid_dataset = tf.data.Dataset.from_tensor_slices((val_sentences, val_labels_one_hot))\ntest_dataset = tf.data.Dataset.from_tensor_slices((test_sentences, test_labels_one_hot))\n\ntrain_dataset","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:12.277098Z","iopub.execute_input":"2023-10-10T15:36:12.277461Z","iopub.status.idle":"2023-10-10T15:36:12.770839Z","shell.execute_reply.started":"2023-10-10T15:36:12.277433Z","shell.execute_reply":"2023-10-10T15:36:12.769929Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"<_TensorSliceDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(5,), dtype=tf.float64, name=None))>"},"metadata":{}}]},{"cell_type":"code","source":"# Take the TensorSliceDataset's and turn them into prefetched batches\ntrain_dataset = train_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\nvalid_dataset = valid_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\ntest_dataset = test_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n\ntrain_dataset","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:36:12.772124Z","iopub.execute_input":"2023-10-10T15:36:12.772414Z","iopub.status.idle":"2023-10-10T15:36:12.786631Z","shell.execute_reply.started":"2023-10-10T15:36:12.772389Z","shell.execute_reply":"2023-10-10T15:36:12.784939Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model 1 - Conv1D","metadata":{}},{"cell_type":"code","source":"inputs = layers.Input(shape=(1,), dtype=tf.string)\ntext_vectors = text_vectorizer(inputs) \ntoken_embeddings = token_embed(text_vectors) \nx = layers.Conv1D(64, kernel_size=5, padding=\"same\", activation=\"relu\")(token_embeddings)\nx = layers.GlobalAveragePooling1D()(x) \noutputs = layers.Dense(num_classes, activation=\"softmax\")(x)\nmodel_1 = tf.keras.Model(inputs, outputs)\n\n# Compile\nmodel_1.compile(loss=\"categorical_crossentropy\",\n                optimizer=tf.keras.optimizers.Adam(),\n                metrics=[\"accuracy\"])\nmodel_1.summary()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:43:15.216975Z","iopub.execute_input":"2023-10-10T15:43:15.217372Z","iopub.status.idle":"2023-10-10T15:43:15.302926Z","shell.execute_reply.started":"2023-10-10T15:43:15.217345Z","shell.execute_reply":"2023-10-10T15:43:15.301943Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"Model: \"model_1\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n input_2 (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_1 (Conv1D)           (None, 55, 64)            41024     \n                                                                 \n global_average_pooling1d_1   (None, 64)               0         \n (GlobalAveragePooling1D)                                        \n                                                                 \n dense_1 (Dense)             (None, 5)                 325       \n                                                                 \n=================================================================\nTotal params: 8,340,997\nTrainable params: 8,340,997\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model_1_history = model_1.fit(train_dataset,\n                              steps_per_epoch=int(0.1 * len(train_dataset)), # only fit on 10% of batches for faster training time\n                              epochs=3,\n                              validation_data=valid_dataset,\n                              validation_steps=int(0.1 * len(valid_dataset))) # only validate on 10% of batches\n     ","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:44:23.48227Z","iopub.execute_input":"2023-10-10T15:44:23.482588Z","iopub.status.idle":"2023-10-10T15:45:41.727663Z","shell.execute_reply.started":"2023-10-10T15:44:23.482558Z","shell.execute_reply":"2023-10-10T15:45:41.725982Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"Epoch 1/3\n562/562 [==============================] - 27s 47ms/step - loss: 0.9117 - accuracy: 0.6385 - val_loss: 0.6850 - val_accuracy: 0.7414\nEpoch 2/3\n562/562 [==============================] - 26s 45ms/step - loss: 0.6562 - accuracy: 0.7562 - val_loss: 0.6307 - val_accuracy: 0.7716\nEpoch 3/3\n562/562 [==============================] - 26s 46ms/step - loss: 0.6179 - accuracy: 0.7741 - val_loss: 0.5983 - val_accuracy: 0.7846\n","output_type":"stream"}]},{"cell_type":"code","source":"model_1.evaluate(valid_dataset)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:45:41.730082Z","iopub.execute_input":"2023-10-10T15:45:41.730449Z","iopub.status.idle":"2023-10-10T15:45:46.85168Z","shell.execute_reply.started":"2023-10-10T15:45:41.730422Z","shell.execute_reply":"2023-10-10T15:45:46.850199Z"},"trusted":true},"execution_count":37,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 4s 4ms/step - loss: 0.5984 - accuracy: 0.7854\n","output_type":"stream"},{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"[0.5983666777610779, 0.7853502035140991]"},"metadata":{}}]},{"cell_type":"code","source":"model_1_pred_probs = model_1.predict(valid_dataset)\nmodel_1_pred_probs","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:45:46.853229Z","iopub.execute_input":"2023-10-10T15:45:46.853505Z","iopub.status.idle":"2023-10-10T15:45:51.165814Z","shell.execute_reply.started":"2023-10-10T15:45:46.853483Z","shell.execute_reply":"2023-10-10T15:45:51.163987Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 4s 4ms/step\n","output_type":"stream"},{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"array([[4.1030014e-01, 1.9668576e-01, 6.4212389e-02, 3.0434537e-01,\n        2.4456304e-02],\n       [4.7017571e-01, 2.5132504e-01, 1.4320798e-02, 2.5588328e-01,\n        8.2952250e-03],\n       [1.5025365e-01, 4.8785089e-03, 9.7807182e-04, 8.4386384e-01,\n        2.5868752e-05],\n       ...,\n       [4.2514225e-06, 7.5660320e-04, 7.0575683e-04, 6.0930652e-06,\n        9.9852729e-01],\n       [4.4538788e-02, 4.5068818e-01, 1.0097654e-01, 5.8229961e-02,\n        3.4556651e-01],\n       [1.7082489e-01, 7.0689136e-01, 4.3036051e-02, 3.1841759e-02,\n        4.7406022e-02]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"model_1_preds = tf.argmax(model_1_pred_probs, axis=1)\nmodel_1_preds","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:45:51.168099Z","iopub.execute_input":"2023-10-10T15:45:51.168452Z","iopub.status.idle":"2023-10-10T15:45:51.180561Z","shell.execute_reply.started":"2023-10-10T15:45:51.168424Z","shell.execute_reply":"2023-10-10T15:45:51.178636Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 0, 3, ..., 4, 1, 1])>"},"metadata":{}}]},{"cell_type":"code","source":"model_1_results = calculate_results(y_true=val_labels_encoded,\n                                    y_pred=model_1_preds)\nmodel_1_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:45:51.182492Z","iopub.execute_input":"2023-10-10T15:45:51.182901Z","iopub.status.idle":"2023-10-10T15:45:51.203081Z","shell.execute_reply.started":"2023-10-10T15:45:51.182874Z","shell.execute_reply":"2023-10-10T15:45:51.201673Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"{'accuracy': 78.5350191976698,\n 'precision': 0.7820005785624711,\n 'recall': 0.785350191976698,\n 'f1': 0.7828263800287568}"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model 2: Feature extraction with pretrained token embeddings","metadata":{}},{"cell_type":"code","source":"import tensorflow_hub as hub\ntf_hub_embedding_layer = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder/4\",\n                                        trainable=False,\n                                        name=\"universal_sentence_encoder\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:47:09.280696Z","iopub.execute_input":"2023-10-10T15:47:09.281045Z","iopub.status.idle":"2023-10-10T15:47:38.818679Z","shell.execute_reply.started":"2023-10-10T15:47:09.281016Z","shell.execute_reply":"2023-10-10T15:47:38.817654Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"random_training_sentence = random.choice(train_sentences)\nprint(f\"Random training sentence:\\n{random_training_sentence}\\n\")\nuse_embedded_sentence = tf_hub_embedding_layer([random_training_sentence])\nprint(f\"Sentence after embedding:\\n{use_embedded_sentence[0][:30]} (truncated output)...\\n\")\nprint(f\"Length of sentence embedding:\\n{len(use_embedded_sentence[0])}\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:47:38.822244Z","iopub.execute_input":"2023-10-10T15:47:38.823501Z","iopub.status.idle":"2023-10-10T15:47:39.085373Z","shell.execute_reply.started":"2023-10-10T15:47:38.823454Z","shell.execute_reply":"2023-10-10T15:47:39.083683Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"Random training sentence:\na randomized double-blind controlled trial was performed .\n\nSentence after embedding:\n[ 0.0179584  -0.02013148  0.00652586 -0.03171596  0.00165641 -0.02811588\n  0.05728803 -0.02235735  0.06219016 -0.02963094  0.06844708  0.03899976\n  0.07874133  0.03700233  0.00831219 -0.01107951 -0.01287032  0.06910878\n  0.02573623 -0.00271561 -0.06214035  0.02388146 -0.0759071  -0.02794813\n  0.01408617  0.00486093  0.06312976  0.08788984  0.0033254   0.04016846] (truncated output)...\n\nLength of sentence embedding:\n512\n","output_type":"stream"}]},{"cell_type":"code","source":"inputs = layers.Input(shape=[], dtype=tf.string)\npretrained_embedding = tf_hub_embedding_layer(inputs) # tokenize text and create embedding\nx = layers.Dense(128, activation=\"relu\")(pretrained_embedding)\noutputs = layers.Dense(5, activation=\"softmax\")(x) # create the output layer\nmodel_2 = tf.keras.Model(inputs=inputs,\n                        outputs=outputs)\n\n# Compile the model\nmodel_2.compile(loss=\"categorical_crossentropy\",\n                optimizer=tf.keras.optimizers.Adam(),\n                metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:48:30.768981Z","iopub.execute_input":"2023-10-10T15:48:30.769334Z","iopub.status.idle":"2023-10-10T15:48:31.123585Z","shell.execute_reply.started":"2023-10-10T15:48:30.769312Z","shell.execute_reply":"2023-10-10T15:48:31.122328Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"model_2.summary()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:48:40.448331Z","iopub.execute_input":"2023-10-10T15:48:40.448689Z","iopub.status.idle":"2023-10-10T15:48:40.468165Z","shell.execute_reply.started":"2023-10-10T15:48:40.448661Z","shell.execute_reply":"2023-10-10T15:48:40.466734Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"Model: \"model_2\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n input_3 (InputLayer)        [(None,)]                 0         \n                                                                 \n universal_sentence_encoder   (None, 512)              256797824 \n (KerasLayer)                                                    \n                                                                 \n dense_2 (Dense)             (None, 128)               65664     \n                                                                 \n dense_3 (Dense)             (None, 5)                 645       \n                                                                 \n=================================================================\nTotal params: 256,864,133\nTrainable params: 66,309\nNon-trainable params: 256,797,824\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model_2.fit(train_dataset,\n            steps_per_epoch=int(0.1 * len(train_dataset)), # training on 10% data\n            epochs=3,\n            validation_data=valid_dataset,\n            validation_steps=int(0.1 * len(valid_dataset)))","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:48:51.705978Z","iopub.execute_input":"2023-10-10T15:48:51.706368Z","iopub.status.idle":"2023-10-10T15:49:14.422425Z","shell.execute_reply.started":"2023-10-10T15:48:51.706349Z","shell.execute_reply":"2023-10-10T15:49:14.420592Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"Epoch 1/3\n562/562 [==============================] - 9s 12ms/step - loss: 0.9192 - accuracy: 0.6511 - val_loss: 0.7952 - val_accuracy: 0.6905\nEpoch 2/3\n562/562 [==============================] - 5s 10ms/step - loss: 0.7686 - accuracy: 0.7016 - val_loss: 0.7556 - val_accuracy: 0.7035\nEpoch 3/3\n562/562 [==============================] - 6s 11ms/step - loss: 0.7539 - accuracy: 0.7117 - val_loss: 0.7405 - val_accuracy: 0.7151\n","output_type":"stream"},{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x79a63aee0a90>"},"metadata":{}}]},{"cell_type":"code","source":"model_2.evaluate(valid_dataset)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:49:20.988816Z","iopub.execute_input":"2023-10-10T15:49:20.989625Z","iopub.status.idle":"2023-10-10T15:49:28.603317Z","shell.execute_reply.started":"2023-10-10T15:49:20.989567Z","shell.execute_reply":"2023-10-10T15:49:28.601935Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 8s 8ms/step - loss: 0.7423 - accuracy: 0.7140\n","output_type":"stream"},{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"[0.7422997951507568, 0.7139878273010254]"},"metadata":{}}]},{"cell_type":"code","source":"model_2_pred_probs = model_2.predict(valid_dataset)\nmodel_2_pred_probs","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:49:28.604766Z","iopub.execute_input":"2023-10-10T15:49:28.605583Z","iopub.status.idle":"2023-10-10T15:49:39.087374Z","shell.execute_reply.started":"2023-10-10T15:49:28.605554Z","shell.execute_reply":"2023-10-10T15:49:39.086308Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 8s 8ms/step\n","output_type":"stream"},{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"array([[4.2955175e-01, 3.5869110e-01, 1.9683912e-03, 2.0130153e-01,\n        8.4871631e-03],\n       [3.2669827e-01, 4.9416229e-01, 2.7419508e-03, 1.7321561e-01,\n        3.1819004e-03],\n       [2.2766730e-01, 1.5462337e-01, 1.6304597e-02, 5.6045139e-01,\n        4.0953301e-02],\n       ...,\n       [1.7971299e-03, 6.6022049e-03, 5.7278000e-02, 9.0424938e-04,\n        9.3341839e-01],\n       [4.4947648e-03, 4.8233472e-02, 1.9985205e-01, 1.7892654e-03,\n        7.4563044e-01],\n       [2.0938455e-01, 2.4712086e-01, 4.7612190e-01, 7.1764025e-03,\n        6.0196299e-02]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"model_2_preds = tf.argmax(model_2_pred_probs, axis=1)\nmodel_2_preds","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:49:39.089127Z","iopub.execute_input":"2023-10-10T15:49:39.089747Z","iopub.status.idle":"2023-10-10T15:49:39.09744Z","shell.execute_reply.started":"2023-10-10T15:49:39.089703Z","shell.execute_reply":"2023-10-10T15:49:39.096137Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 1, 3, ..., 4, 4, 2])>"},"metadata":{}}]},{"cell_type":"code","source":"model_2_results = calculate_results(y_true=val_labels_encoded,\n                                    y_pred=model_2_preds)\nmodel_2_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:49:46.063746Z","iopub.execute_input":"2023-10-10T15:49:46.065044Z","iopub.status.idle":"2023-10-10T15:49:46.083125Z","shell.execute_reply.started":"2023-10-10T15:49:46.064973Z","shell.execute_reply":"2023-10-10T15:49:46.082078Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"{'accuracy': 71.39878194095061,\n 'precision': 0.7143419193653655,\n 'recall': 0.7139878194095062,\n 'f1': 0.7109826936118474}"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model 3: Conv1D with character embeddings\nThe difference between a character and token embedding is that the character embedding is created using sequences split into characters (e.g. hello -> [h, e, l, l, o]) where as a token embedding is created on sequences split into tokens.","metadata":{}},{"cell_type":"code","source":"def split_chars(text):\n    return \" \".join(list(text))","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:53:16.825241Z","iopub.execute_input":"2023-10-10T15:53:16.825661Z","iopub.status.idle":"2023-10-10T15:53:16.831824Z","shell.execute_reply.started":"2023-10-10T15:53:16.825617Z","shell.execute_reply":"2023-10-10T15:53:16.830071Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"split_chars(random_training_sentence)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:53:28.477946Z","iopub.execute_input":"2023-10-10T15:53:28.478314Z","iopub.status.idle":"2023-10-10T15:53:28.487299Z","shell.execute_reply.started":"2023-10-10T15:53:28.478288Z","shell.execute_reply":"2023-10-10T15:53:28.485707Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"'a   r a n d o m i z e d   d o u b l e - b l i n d   c o n t r o l l e d   t r i a l   w a s   p e r f o r m e d   .'"},"metadata":{}}]},{"cell_type":"code","source":"train_chars = [split_chars(sentence) for sentence in train_sentences]\nval_chars = [split_chars(sentence) for sentence in val_sentences]\ntest_chars = [split_chars(sentence) for sentence in test_sentences]\nprint(train_chars[0])","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:55:30.772919Z","iopub.execute_input":"2023-10-10T15:55:30.773342Z","iopub.status.idle":"2023-10-10T15:55:31.485959Z","shell.execute_reply.started":"2023-10-10T15:55:30.773296Z","shell.execute_reply":"2023-10-10T15:55:31.48514Z"},"trusted":true},"execution_count":59,"outputs":[{"name":"stdout","text":"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","output_type":"stream"}]},{"cell_type":"code","source":"char_lens = [len(sentence) for sentence in train_sentences]\nmean_char_len = np.mean(char_lens)\nmean_char_len","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:55:41.913174Z","iopub.execute_input":"2023-10-10T15:55:41.913511Z","iopub.status.idle":"2023-10-10T15:55:41.962368Z","shell.execute_reply.started":"2023-10-10T15:55:41.913489Z","shell.execute_reply":"2023-10-10T15:55:41.961488Z"},"trusted":true},"execution_count":60,"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"149.3662574983337"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.hist(char_lens, bins=7);","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:55:51.297985Z","iopub.execute_input":"2023-10-10T15:55:51.298345Z","iopub.status.idle":"2023-10-10T15:55:51.786341Z","shell.execute_reply.started":"2023-10-10T15:55:51.29832Z","shell.execute_reply":"2023-10-10T15:55:51.785471Z"},"trusted":true},"execution_count":61,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Find what character length covers 95% of sequences\noutput_seq_char_len = int(np.percentile(char_lens, 95))\noutput_seq_char_len","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:56:05.270745Z","iopub.execute_input":"2023-10-10T15:56:05.271108Z","iopub.status.idle":"2023-10-10T15:56:05.291299Z","shell.execute_reply.started":"2023-10-10T15:56:05.271084Z","shell.execute_reply":"2023-10-10T15:56:05.290321Z"},"trusted":true},"execution_count":62,"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"290"},"metadata":{}}]},{"cell_type":"code","source":"import string\nalphabet = string.ascii_lowercase + string.digits + string.punctuation\nalphabet","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:56:59.997629Z","iopub.execute_input":"2023-10-10T15:56:59.998024Z","iopub.status.idle":"2023-10-10T15:57:00.005925Z","shell.execute_reply.started":"2023-10-10T15:56:59.997957Z","shell.execute_reply":"2023-10-10T15:57:00.005042Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"'abcdefghijklmnopqrstuvwxyz0123456789!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"},"metadata":{}}]},{"cell_type":"code","source":"# Create char-level token vectorizer instance\nNUM_CHAR_TOKENS = len(alphabet) + 2 # num characters in alphabet + space + OOV token\nchar_vectorizer = TextVectorization(max_tokens=NUM_CHAR_TOKENS,  \n                                    output_sequence_length=output_seq_char_len,\n                                    standardize=\"lower_and_strip_punctuation\",\n                                    name=\"char_vectorizer\")\n\n# Adapt character vectorizer to training characters\nchar_vectorizer.adapt(train_chars)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:58:02.50687Z","iopub.execute_input":"2023-10-10T15:58:02.507213Z","iopub.status.idle":"2023-10-10T15:58:11.412218Z","shell.execute_reply.started":"2023-10-10T15:58:02.507194Z","shell.execute_reply":"2023-10-10T15:58:11.409871Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"code","source":"char_vocab = char_vectorizer.get_vocabulary()\nprint(f\"Number of different characters in character vocab: {len(char_vocab)}\")\nprint(f\"5 most common characters: {char_vocab[:5]}\")\nprint(f\"5 least common characters: {char_vocab[-5:]}\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:58:20.641958Z","iopub.execute_input":"2023-10-10T15:58:20.642445Z","iopub.status.idle":"2023-10-10T15:58:20.650448Z","shell.execute_reply.started":"2023-10-10T15:58:20.64241Z","shell.execute_reply":"2023-10-10T15:58:20.648929Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"Number of different characters in character vocab: 28\n5 most common characters: ['', '[UNK]', 'e', 't', 'i']\n5 least common characters: ['k', 'x', 'z', 'q', 'j']\n","output_type":"stream"}]},{"cell_type":"code","source":"# Test out character vectorizer\nrandom_train_chars = random.choice(train_chars)\nprint(f\"Charified text:\\n{random_train_chars}\")\nprint(f\"\\nLength of chars: {len(random_train_chars.split())}\")\nvectorized_chars = char_vectorizer([random_train_chars])\nprint(f\"\\nVectorized chars:\\n{vectorized_chars}\")\nprint(f\"\\nLength of vectorized chars: {len(vectorized_chars[0])}\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T15:58:41.461343Z","iopub.execute_input":"2023-10-10T15:58:41.463277Z","iopub.status.idle":"2023-10-10T15:58:41.486794Z","shell.execute_reply.started":"2023-10-10T15:58:41.463224Z","shell.execute_reply":"2023-10-10T15:58:41.484871Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"Charified text:\nu n i q u e   i d e n t i f i e r   :   n c t @   .\n\nLength of chars: 22\n\nVectorized chars:\n[[16  6  4 26 16  2  4 10  2  6  3  4 17  4  2  8  6 11  3  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  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\nLength of vectorized chars: 290\n","output_type":"stream"}]},{"cell_type":"code","source":"char_embed = layers.Embedding(input_dim=NUM_CHAR_TOKENS, # number of different characters\n                              output_dim=25, # embedding dimension of each character (same as Figure 1 in https://arxiv.org/pdf/1612.05251.pdf)\n                              mask_zero=False, # don't use masks (this messes up model_5 if set to True)\n                              name=\"char_embed\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:00:20.564606Z","iopub.execute_input":"2023-10-10T16:00:20.564922Z","iopub.status.idle":"2023-10-10T16:00:20.572085Z","shell.execute_reply.started":"2023-10-10T16:00:20.5649Z","shell.execute_reply":"2023-10-10T16:00:20.57084Z"},"trusted":true},"execution_count":67,"outputs":[]},{"cell_type":"code","source":"# Test out character embedding layer\nprint(f\"Charified text (before vectorization and embedding):\\n{random_train_chars}\\n\")\nchar_embed_example = char_embed(char_vectorizer([random_train_chars]))\nprint(f\"Embedded chars (after vectorization and embedding):\\n{char_embed_example}\\n\")\nprint(f\"Character embedding shape: {char_embed_example.shape}\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:00:28.315595Z","iopub.execute_input":"2023-10-10T16:00:28.316087Z","iopub.status.idle":"2023-10-10T16:00:28.343591Z","shell.execute_reply.started":"2023-10-10T16:00:28.316056Z","shell.execute_reply":"2023-10-10T16:00:28.342605Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"Charified text (before vectorization and embedding):\nu n i q u e   i d e n t i f i e r   :   n c t @   .\n\nEmbedded chars (after vectorization and embedding):\n[[[ 4.2603049e-02 -2.4572564e-02  3.7870441e-02 ... -1.2478579e-02\n    3.2640994e-05 -1.5304469e-02]\n  [-3.2623187e-02 -2.4366988e-02  2.0346526e-02 ... -2.6138937e-02\n   -3.5295546e-02  4.5115996e-02]\n  [-6.0677528e-05 -9.2828758e-03 -3.3649109e-02 ... -2.1486295e-02\n   -5.0367936e-03 -1.0015689e-02]\n  ...\n  [-6.6512823e-04  2.4346422e-02 -8.1239343e-03 ...  1.0848045e-04\n    2.2559490e-02 -4.4047870e-02]\n  [-6.6512823e-04  2.4346422e-02 -8.1239343e-03 ...  1.0848045e-04\n    2.2559490e-02 -4.4047870e-02]\n  [-6.6512823e-04  2.4346422e-02 -8.1239343e-03 ...  1.0848045e-04\n    2.2559490e-02 -4.4047870e-02]]]\n\nCharacter embedding shape: (1, 290, 25)\n","output_type":"stream"}]},{"cell_type":"code","source":"inputs = layers.Input(shape=(1,), dtype=\"string\")\nchar_vectors = char_vectorizer(inputs)\nchar_embeddings = char_embed(char_vectors)\nx = layers.Conv1D(64, kernel_size=5, padding=\"same\", activation=\"relu\")(char_embeddings)\nx = layers.GlobalMaxPool1D()(x)\noutputs = layers.Dense(num_classes, activation=\"softmax\")(x)\nmodel_3 = tf.keras.Model(inputs=inputs,\n                         outputs=outputs,\n                         name=\"model_3_conv1D_char_embedding\")\n\n# Compile model\nmodel_3.compile(loss=\"categorical_crossentropy\",\n                optimizer=tf.keras.optimizers.Adam(),\n                metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:01:01.048225Z","iopub.execute_input":"2023-10-10T16:01:01.048636Z","iopub.status.idle":"2023-10-10T16:01:01.11913Z","shell.execute_reply.started":"2023-10-10T16:01:01.048604Z","shell.execute_reply":"2023-10-10T16:01:01.118229Z"},"trusted":true},"execution_count":69,"outputs":[]},{"cell_type":"code","source":"model_3.summary()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:01:09.6538Z","iopub.execute_input":"2023-10-10T16:01:09.654202Z","iopub.status.idle":"2023-10-10T16:01:09.681351Z","shell.execute_reply.started":"2023-10-10T16:01:09.654172Z","shell.execute_reply":"2023-10-10T16:01:09.680779Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"Model: \"model_3_conv1D_char_embedding\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n input_4 (InputLayer)        [(None, 1)]               0         \n                                                                 \n char_vectorizer (TextVector  (None, 290)              0         \n ization)                                                        \n                                                                 \n char_embed (Embedding)      (None, 290, 25)           1750      \n                                                                 \n conv1d_2 (Conv1D)           (None, 290, 64)           8064      \n                                                                 \n global_max_pooling1d (Globa  (None, 64)               0         \n lMaxPooling1D)                                                  \n                                                                 \n dense_4 (Dense)             (None, 5)                 325       \n                                                                 \n=================================================================\nTotal params: 10,139\nTrainable params: 10,139\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"train_char_dataset = tf.data.Dataset.from_tensor_slices((train_chars, train_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\nval_char_dataset = tf.data.Dataset.from_tensor_slices((val_chars, val_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n\ntrain_char_dataset","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:01:26.312208Z","iopub.execute_input":"2023-10-10T16:01:26.312601Z","iopub.status.idle":"2023-10-10T16:01:26.999909Z","shell.execute_reply.started":"2023-10-10T16:01:26.312576Z","shell.execute_reply":"2023-10-10T16:01:26.998724Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"},"metadata":{}}]},{"cell_type":"code","source":"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)))","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:01:40.888502Z","iopub.execute_input":"2023-10-10T16:01:40.888879Z","iopub.status.idle":"2023-10-10T16:02:04.572825Z","shell.execute_reply.started":"2023-10-10T16:01:40.888853Z","shell.execute_reply":"2023-10-10T16:02:04.572087Z"},"trusted":true},"execution_count":72,"outputs":[{"name":"stdout","text":"Epoch 1/3\n562/562 [==============================] - 8s 14ms/step - loss: 1.2618 - accuracy: 0.4976 - val_loss: 1.0358 - val_accuracy: 0.5914\nEpoch 2/3\n562/562 [==============================] - 8s 14ms/step - loss: 0.9860 - accuracy: 0.6087 - val_loss: 0.9289 - val_accuracy: 0.6433\nEpoch 3/3\n562/562 [==============================] - 8s 14ms/step - loss: 0.9081 - accuracy: 0.6452 - val_loss: 0.8565 - val_accuracy: 0.6725\n","output_type":"stream"}]},{"cell_type":"code","source":"model_3.evaluate(val_char_dataset)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:02:04.574637Z","iopub.execute_input":"2023-10-10T16:02:04.574958Z","iopub.status.idle":"2023-10-10T16:02:09.693861Z","shell.execute_reply.started":"2023-10-10T16:02:04.574933Z","shell.execute_reply":"2023-10-10T16:02:09.692141Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 5s 5ms/step - loss: 0.8660 - accuracy: 0.6639\n","output_type":"stream"},{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"[0.8659974932670593, 0.6638752818107605]"},"metadata":{}}]},{"cell_type":"code","source":"model_3_pred_probs = model_3.predict(val_char_dataset)\nmodel_3_pred_probs","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:02:09.695716Z","iopub.execute_input":"2023-10-10T16:02:09.696108Z","iopub.status.idle":"2023-10-10T16:02:14.79957Z","shell.execute_reply.started":"2023-10-10T16:02:09.69608Z","shell.execute_reply":"2023-10-10T16:02:14.798229Z"},"trusted":true},"execution_count":74,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 5s 5ms/step\n","output_type":"stream"},{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"array([[0.10711311, 0.35768878, 0.22793321, 0.18430112, 0.12296375],\n       [0.2312324 , 0.547134  , 0.00878778, 0.17577845, 0.03706737],\n       [0.11775717, 0.22094047, 0.24447295, 0.37552595, 0.04130348],\n       ...,\n       [0.03089536, 0.04507967, 0.23583093, 0.03508129, 0.65311277],\n       [0.03132134, 0.09420309, 0.10729191, 0.01761378, 0.74956995],\n       [0.33495277, 0.42993733, 0.15596691, 0.05024277, 0.02890026]],\n      dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"model_3_preds = tf.argmax(model_3_pred_probs, axis=1)\nmodel_3_preds","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:02:39.416758Z","iopub.execute_input":"2023-10-10T16:02:39.417195Z","iopub.status.idle":"2023-10-10T16:02:39.428396Z","shell.execute_reply.started":"2023-10-10T16:02:39.417167Z","shell.execute_reply":"2023-10-10T16:02:39.426777Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([1, 1, 3, ..., 4, 4, 1])>"},"metadata":{}}]},{"cell_type":"code","source":"model_3_results = calculate_results(y_true=val_labels_encoded,\n                                        y_pred=model_3_preds)\nmodel_3_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:02:42.091684Z","iopub.execute_input":"2023-10-10T16:02:42.092099Z","iopub.status.idle":"2023-10-10T16:02:42.114165Z","shell.execute_reply.started":"2023-10-10T16:02:42.092068Z","shell.execute_reply":"2023-10-10T16:02:42.112366Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"{'accuracy': 66.38752813451609,\n 'precision': 0.6565844794012639,\n 'recall': 0.6638752813451608,\n 'f1': 0.654338917164117}"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model 4: Combining pretrained token embeddings + character embeddings (hybrid embedding layer)","metadata":{}},{"cell_type":"code","source":"# 1. Setup token inputs/model\ntoken_inputs = layers.Input(shape=[], dtype=tf.string, name=\"token_input\")\ntoken_embeddings = tf_hub_embedding_layer(token_inputs)\ntoken_output = layers.Dense(128, activation=\"relu\")(token_embeddings)\ntoken_model = tf.keras.Model(inputs=token_inputs,\n                             outputs=token_output)\n\n# 2. Setup char inputs/model\nchar_inputs = layers.Input(shape=(1,), dtype=tf.string, name=\"char_input\")\nchar_vectors = char_vectorizer(char_inputs)\nchar_embeddings = char_embed(char_vectors)\nchar_bi_lstm = layers.Bidirectional(layers.LSTM(25))(char_embeddings) # bi-LSTM shown in Figure 1 of https://arxiv.org/pdf/1612.05251.pdf\nchar_model = tf.keras.Model(inputs=char_inputs,\n                            outputs=char_bi_lstm)\n\n# 3. Concatenate token and char inputs (create hybrid token embedding)\ntoken_char_concat = layers.Concatenate(name=\"token_char_hybrid\")([token_model.output, \n                                                                  char_model.output])\n\n# 4. Create output layers - addition of dropout discussed in 4.2 of https://arxiv.org/pdf/1612.05251.pdf\ncombined_dropout = layers.Dropout(0.5)(token_char_concat)\ncombined_dense = layers.Dense(200, activation=\"relu\")(combined_dropout) \nfinal_dropout = layers.Dropout(0.5)(combined_dense)\noutput_layer = layers.Dense(num_classes, activation=\"softmax\")(final_dropout)\n\n# 5. Construct model with char and token inputs\nmodel_4 = tf.keras.Model(inputs=[token_model.input, char_model.input],\n                         outputs=output_layer,\n                         name=\"model_4_token_and_char_embeddings\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:07:15.956433Z","iopub.execute_input":"2023-10-10T16:07:15.956874Z","iopub.status.idle":"2023-10-10T16:07:16.548788Z","shell.execute_reply.started":"2023-10-10T16:07:15.956844Z","shell.execute_reply":"2023-10-10T16:07:16.547782Z"},"trusted":true},"execution_count":78,"outputs":[]},{"cell_type":"code","source":"model_4.summary()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:07:29.056878Z","iopub.execute_input":"2023-10-10T16:07:29.057237Z","iopub.status.idle":"2023-10-10T16:07:29.105708Z","shell.execute_reply.started":"2023-10-10T16:07:29.057216Z","shell.execute_reply":"2023-10-10T16:07:29.104506Z"},"trusted":true},"execution_count":79,"outputs":[{"name":"stdout","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)      1750        ['char_vectorizer[1][0]']        \n                                                                                                  \n dense_5 (Dense)                (None, 128)          65664       ['universal_sentence_encoder[1][0\n                                                                 ]']                              \n                                                                                                  \n bidirectional (Bidirectional)  (None, 50)           10200       ['char_embed[1][0]']             \n                                                                                                  \n token_char_hybrid (Concatenate  (None, 178)         0           ['dense_5[0][0]',                \n )                                                                'bidirectional[0][0]']          \n                                                                                                  \n dropout (Dropout)              (None, 178)          0           ['token_char_hybrid[0][0]']      \n                                                                                                  \n dense_6 (Dense)                (None, 200)          35800       ['dropout[0][0]']                \n                                                                                                  \n dropout_1 (Dropout)            (None, 200)          0           ['dense_6[0][0]']                \n                                                                                                  \n dense_7 (Dense)                (None, 5)            1005        ['dropout_1[0][0]']              \n                                                                                                  \n==================================================================================================\nTotal params: 256,912,243\nTrainable params: 114,419\nNon-trainable params: 256,797,824\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"from tensorflow.keras.utils import plot_model\nplot_model(model_4)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:08:12.81208Z","iopub.execute_input":"2023-10-10T16:08:12.812425Z","iopub.status.idle":"2023-10-10T16:08:13.196207Z","shell.execute_reply.started":"2023-10-10T16:08:12.812406Z","shell.execute_reply":"2023-10-10T16:08:13.194539Z"},"trusted":true},"execution_count":80,"outputs":[{"execution_count":80,"output_type":"execute_result","data":{"image/png":"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","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"cell_type":"code","source":"# Compile token char model\nmodel_4.compile(loss=\"categorical_crossentropy\",\n                optimizer=tf.keras.optimizers.Adam(), # section 4.2 of https://arxiv.org/pdf/1612.05251.pdf mentions using SGD but we'll stick with Adam\n                metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:08:56.889913Z","iopub.execute_input":"2023-10-10T16:08:56.890343Z","iopub.status.idle":"2023-10-10T16:08:56.903488Z","shell.execute_reply.started":"2023-10-10T16:08:56.890312Z","shell.execute_reply":"2023-10-10T16:08:56.901825Z"},"trusted":true},"execution_count":81,"outputs":[]},{"cell_type":"code","source":"# Combine chars and tokens into a dataset\ntrain_char_token_data = tf.data.Dataset.from_tensor_slices((train_sentences, train_chars)) # make data\ntrain_char_token_labels = tf.data.Dataset.from_tensor_slices(train_labels_one_hot) # make labels\ntrain_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\ntrain_char_token_dataset = train_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE) \n\n# Repeat same steps validation data\nval_char_token_data = tf.data.Dataset.from_tensor_slices((val_sentences, val_chars))\nval_char_token_labels = tf.data.Dataset.from_tensor_slices(val_labels_one_hot)\nval_char_token_dataset = tf.data.Dataset.zip((val_char_token_data, val_char_token_labels))\nval_char_token_dataset = val_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:10:55.268481Z","iopub.execute_input":"2023-10-10T16:10:55.268877Z","iopub.status.idle":"2023-10-10T16:10:56.390419Z","shell.execute_reply.started":"2023-10-10T16:10:55.268848Z","shell.execute_reply":"2023-10-10T16:10:56.388505Z"},"trusted":true},"execution_count":82,"outputs":[]},{"cell_type":"code","source":"train_char_token_dataset, val_char_token_dataset","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:11:06.837723Z","iopub.execute_input":"2023-10-10T16:11:06.83815Z","iopub.status.idle":"2023-10-10T16:11:06.846857Z","shell.execute_reply.started":"2023-10-10T16:11:06.838122Z","shell.execute_reply":"2023-10-10T16:11:06.845221Z"},"trusted":true},"execution_count":83,"outputs":[{"execution_count":83,"output_type":"execute_result","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))>)"},"metadata":{}}]},{"cell_type":"code","source":"model_4_history = model_4.fit(train_char_token_dataset, # train on dataset of token and characters\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)))","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:12:53.198016Z","iopub.execute_input":"2023-10-10T16:12:53.19832Z","iopub.status.idle":"2023-10-10T16:16:01.783919Z","shell.execute_reply.started":"2023-10-10T16:12:53.198302Z","shell.execute_reply":"2023-10-10T16:16:01.782914Z"},"trusted":true},"execution_count":85,"outputs":[{"name":"stdout","text":"Epoch 1/3\n562/562 [==============================] - 66s 111ms/step - loss: 0.9755 - accuracy: 0.6078 - val_loss: 0.7824 - val_accuracy: 0.6988\nEpoch 2/3\n562/562 [==============================] - 61s 108ms/step - loss: 0.7870 - accuracy: 0.6954 - val_loss: 0.7094 - val_accuracy: 0.7344\nEpoch 3/3\n562/562 [==============================] - 62s 110ms/step - loss: 0.7627 - accuracy: 0.7068 - val_loss: 0.6863 - val_accuracy: 0.7410\n","output_type":"stream"}]},{"cell_type":"code","source":"model_4.evaluate(val_char_token_dataset)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:16:01.786021Z","iopub.execute_input":"2023-10-10T16:16:01.787221Z","iopub.status.idle":"2023-10-10T16:16:42.745274Z","shell.execute_reply.started":"2023-10-10T16:16:01.787161Z","shell.execute_reply":"2023-10-10T16:16:42.743691Z"},"trusted":true},"execution_count":86,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 26s 28ms/step - loss: 0.6904 - accuracy: 0.7353\n","output_type":"stream"},{"execution_count":86,"output_type":"execute_result","data":{"text/plain":"[0.6903828978538513, 0.7353038787841797]"},"metadata":{}}]},{"cell_type":"code","source":"model_4_pred_probs = model_4.predict(val_char_token_dataset)\nmodel_4_pred_probs","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:16:42.746757Z","iopub.execute_input":"2023-10-10T16:16:42.747074Z","iopub.status.idle":"2023-10-10T16:17:10.025711Z","shell.execute_reply.started":"2023-10-10T16:16:42.747047Z","shell.execute_reply":"2023-10-10T16:17:10.024376Z"},"trusted":true},"execution_count":87,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 27s 28ms/step\n","output_type":"stream"},{"execution_count":87,"output_type":"execute_result","data":{"text/plain":"array([[4.9074841e-01, 2.8624538e-01, 3.4025677e-03, 2.1160859e-01,\n        7.9949219e-03],\n       [3.7981799e-01, 4.6891466e-01, 2.0781644e-03, 1.4785625e-01,\n        1.3328135e-03],\n       [2.8846300e-01, 1.4769225e-01, 5.1968075e-02, 4.6931297e-01,\n        4.2563722e-02],\n       ...,\n       [7.4366905e-04, 8.1341704e-03, 4.3143135e-02, 2.3679607e-04,\n        9.4774228e-01],\n       [5.2064601e-03, 4.8742224e-02, 1.9442108e-01, 2.1887042e-03,\n        7.4944150e-01],\n       [3.4032664e-01, 4.0891439e-01, 1.7728886e-01, 4.3119691e-02,\n        3.0350400e-02]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"model_4_preds = tf.argmax(model_4_pred_probs, axis=1)\nmodel_4_preds","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:17:10.028561Z","iopub.execute_input":"2023-10-10T16:17:10.028948Z","iopub.status.idle":"2023-10-10T16:17:10.038824Z","shell.execute_reply.started":"2023-10-10T16:17:10.02892Z","shell.execute_reply":"2023-10-10T16:17:10.037119Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 1, 3, ..., 4, 4, 1])>"},"metadata":{}}]},{"cell_type":"code","source":"model_4_results = calculate_results(y_true=val_labels_encoded,\n                                    y_pred=model_4_preds)\nmodel_4_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:17:10.040904Z","iopub.execute_input":"2023-10-10T16:17:10.042964Z","iopub.status.idle":"2023-10-10T16:17:10.074149Z","shell.execute_reply.started":"2023-10-10T16:17:10.0429Z","shell.execute_reply":"2023-10-10T16:17:10.073252Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"{'accuracy': 73.53038527737323,\n 'precision': 0.7370675131443982,\n 'recall': 0.7353038527737323,\n 'f1': 0.7329172180186582}"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model 5: Transfer Learning with pretrained token embeddings + character embeddings + positional embeddings","metadata":{}},{"cell_type":"markdown","source":"the positional embedding is where the sentence appears in an abstract.","metadata":{}},{"cell_type":"code","source":"train_df[\"line_number\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:17:10.076794Z","iopub.execute_input":"2023-10-10T16:17:10.077739Z","iopub.status.idle":"2023-10-10T16:17:10.095129Z","shell.execute_reply.started":"2023-10-10T16:17:10.07769Z","shell.execute_reply":"2023-10-10T16:17:10.093518Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"line_number\n0     15000\n1     15000\n2     15000\n3     15000\n4     14992\n5     14949\n6     14758\n7     14279\n8     13346\n9     11981\n10    10041\n11     7892\n12     5853\n13     4152\n14     2835\n15     1861\n16     1188\n17      751\n18      462\n19      286\n20      162\n21      101\n22       66\n23       33\n24       22\n25       14\n26        7\n27        4\n28        3\n29        1\n30        1\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"train_df.line_number.plot.hist()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:17:10.097501Z","iopub.execute_input":"2023-10-10T16:17:10.097944Z","iopub.status.idle":"2023-10-10T16:17:10.277041Z","shell.execute_reply.started":"2023-10-10T16:17:10.097908Z","shell.execute_reply":"2023-10-10T16:17:10.276174Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":"<Axes: ylabel='Frequency'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"train_line_numbers_one_hot = tf.one_hot(train_df[\"line_number\"].to_numpy(), depth=15)\nval_line_numbers_one_hot = tf.one_hot(val_df[\"line_number\"].to_numpy(), depth=15)\ntest_line_numbers_one_hot = tf.one_hot(test_df[\"line_number\"].to_numpy(), depth=15)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:17:35.334019Z","iopub.execute_input":"2023-10-10T16:17:35.334332Z","iopub.status.idle":"2023-10-10T16:17:35.355758Z","shell.execute_reply.started":"2023-10-10T16:17:35.334313Z","shell.execute_reply":"2023-10-10T16:17:35.355058Z"},"trusted":true},"execution_count":92,"outputs":[]},{"cell_type":"code","source":"# Check one-hot encoded \"line_number\" feature samples\ntrain_line_numbers_one_hot.shape, train_line_numbers_one_hot[:20]","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:18:51.692163Z","iopub.execute_input":"2023-10-10T16:18:51.692519Z","iopub.status.idle":"2023-10-10T16:18:51.704292Z","shell.execute_reply.started":"2023-10-10T16:18:51.692495Z","shell.execute_reply":"2023-10-10T16:18:51.703073Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"(TensorShape([180040, 15]),\n <tf.Tensor: shape=(20, 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        [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)>)"},"metadata":{}}]},{"cell_type":"code","source":"train_df[\"total_lines\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:19:19.632159Z","iopub.execute_input":"2023-10-10T16:19:19.632552Z","iopub.status.idle":"2023-10-10T16:19:19.644538Z","shell.execute_reply.started":"2023-10-10T16:19:19.632525Z","shell.execute_reply":"2023-10-10T16:19:19.643156Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"total_lines\n11    24468\n10    23639\n12    22113\n9     19400\n13    18438\n14    14610\n8     12285\n15    10768\n7      7464\n16     7429\n17     5202\n6      3353\n18     3344\n19     2480\n20     1281\n5      1146\n21      770\n22      759\n23      264\n4       215\n24      200\n25      182\n26       81\n28       58\n3        32\n30       31\n27       28\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"train_df.total_lines.plot.hist();","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:19:33.24579Z","iopub.execute_input":"2023-10-10T16:19:33.246212Z","iopub.status.idle":"2023-10-10T16:19:33.399245Z","shell.execute_reply.started":"2023-10-10T16:19:33.246184Z","shell.execute_reply":"2023-10-10T16:19:33.398272Z"},"trusted":true},"execution_count":95,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"np.percentile(train_df.total_lines, 98)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:19:51.444354Z","iopub.execute_input":"2023-10-10T16:19:51.444734Z","iopub.status.idle":"2023-10-10T16:19:51.457852Z","shell.execute_reply.started":"2023-10-10T16:19:51.444708Z","shell.execute_reply":"2023-10-10T16:19:51.455415Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"20.0"},"metadata":{}}]},{"cell_type":"code","source":"# Use TensorFlow to create one-hot-encoded tensors of our \"total_lines\" column \ntrain_total_lines_one_hot = tf.one_hot(train_df[\"total_lines\"].to_numpy(), depth=20)\nval_total_lines_one_hot = tf.one_hot(val_df[\"total_lines\"].to_numpy(), depth=20)\ntest_total_lines_one_hot = tf.one_hot(test_df[\"total_lines\"].to_numpy(), depth=20)\n\n# Check shape and samples of total lines one-hot tensor\ntrain_total_lines_one_hot.shape, train_total_lines_one_hot[:10]","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:20:00.770033Z","iopub.execute_input":"2023-10-10T16:20:00.770401Z","iopub.status.idle":"2023-10-10T16:20:00.791078Z","shell.execute_reply.started":"2023-10-10T16:20:00.770377Z","shell.execute_reply":"2023-10-10T16:20:00.789932Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","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)>)"},"metadata":{}}]},{"cell_type":"code","source":"# 1. Token inputs\ntoken_inputs = layers.Input(shape=[], dtype=\"string\", name=\"token_inputs\")\ntoken_embeddings = tf_hub_embedding_layer(token_inputs)\ntoken_outputs = layers.Dense(128, activation=\"relu\")(token_embeddings)\ntoken_model = tf.keras.Model(inputs=token_inputs,\n                             outputs=token_outputs)\n\n# 2. Char inputs\nchar_inputs = layers.Input(shape=(1,), dtype=\"string\", name=\"char_inputs\")\nchar_vectors = char_vectorizer(char_inputs)\nchar_embeddings = char_embed(char_vectors)\nchar_bi_lstm = layers.Bidirectional(layers.LSTM(32))(char_embeddings)\nchar_model = tf.keras.Model(inputs=char_inputs,\n                            outputs=char_bi_lstm)\n\n# 3. Line numbers inputs\nline_number_inputs = layers.Input(shape=(15,), dtype=tf.int32, name=\"line_number_input\")\nx = layers.Dense(32, activation=\"relu\")(line_number_inputs)\nline_number_model = tf.keras.Model(inputs=line_number_inputs,\n                                   outputs=x)\n\n# 4. Total lines inputs\ntotal_lines_inputs = layers.Input(shape=(20,), dtype=tf.int32, name=\"total_lines_input\")\ny = layers.Dense(32, activation=\"relu\")(total_lines_inputs)\ntotal_line_model = tf.keras.Model(inputs=total_lines_inputs,\n                                  outputs=y)\n\n# 5. Combine token and char embeddings into a hybrid embedding\ncombined_embeddings = layers.Concatenate(name=\"token_char_hybrid_embedding\")([token_model.output, \n                                                                              char_model.output])\nz = layers.Dense(256, activation=\"relu\")(combined_embeddings)\nz = layers.Dropout(0.5)(z)\n\n# 6. Combine positional embeddings with combined token and char embeddings into a tribrid embedding\nz = layers.Concatenate(name=\"token_char_positional_embedding\")([line_number_model.output,\n                                                                total_line_model.output,\n                                                                z])\n\n# 7. Create output layer\noutput_layer = layers.Dense(5, activation=\"softmax\", name=\"output_layer\")(z)\n\n# 8. Put together model\nmodel_5 = tf.keras.Model(inputs=[line_number_model.input,\n                                 total_line_model.input,\n                                 token_model.input, \n                                 char_model.input],\n                         outputs=output_layer)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:22:07.626738Z","iopub.execute_input":"2023-10-10T16:22:07.627146Z","iopub.status.idle":"2023-10-10T16:22:08.227154Z","shell.execute_reply.started":"2023-10-10T16:22:07.627124Z","shell.execute_reply":"2023-10-10T16:22:08.225502Z"},"trusted":true},"execution_count":98,"outputs":[]},{"cell_type":"code","source":"model_5.summary()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:22:31.842731Z","iopub.execute_input":"2023-10-10T16:22:31.843123Z","iopub.status.idle":"2023-10-10T16:22:31.88602Z","shell.execute_reply.started":"2023-10-10T16:22:31.843098Z","shell.execute_reply":"2023-10-10T16:22:31.884345Z"},"trusted":true},"execution_count":99,"outputs":[{"name":"stdout","text":"Model: \"model_9\"\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)      1750        ['char_vectorizer[2][0]']        \n                                                                                                  \n dense_8 (Dense)                (None, 128)          65664       ['universal_sentence_encoder[2][0\n                                                                 ]']                              \n                                                                                                  \n bidirectional_1 (Bidirectional  (None, 64)          14848       ['char_embed[2][0]']             \n )                                                                                                \n                                                                                                  \n token_char_hybrid_embedding (C  (None, 192)         0           ['dense_8[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_11 (Dense)               (None, 256)          49408       ['token_char_hybrid_embedding[0][\n                                                                 0]']                             \n                                                                                                  \n dense_9 (Dense)                (None, 32)           512         ['line_number_input[0][0]']      \n                                                                                                  \n dense_10 (Dense)               (None, 32)           672         ['total_lines_input[0][0]']      \n                                                                                                  \n dropout_2 (Dropout)            (None, 256)          0           ['dense_11[0][0]']               \n                                                                                                  \n token_char_positional_embeddin  (None, 320)         0           ['dense_9[0][0]',                \n g (Concatenate)                                                  'dense_10[0][0]',               \n                                                                  'dropout_2[0][0]']              \n                                                                                                  \n output_layer (Dense)           (None, 5)            1605        ['token_char_positional_embedding\n                                                                 [0][0]']                         \n                                                                                                  \n==================================================================================================\nTotal params: 256,932,283\nTrainable params: 134,459\nNon-trainable params: 256,797,824\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"from tensorflow.keras.utils import plot_model\nplot_model(model_5)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:22:45.005289Z","iopub.execute_input":"2023-10-10T16:22:45.0057Z","iopub.status.idle":"2023-10-10T16:22:45.119024Z","shell.execute_reply.started":"2023-10-10T16:22:45.005673Z","shell.execute_reply":"2023-10-10T16:22:45.117612Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"image/png":"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object>"},"metadata":{}}]},{"cell_type":"code","source":"# Check which layers of our model are trainable or not\nfor layer in model_5.layers:\n    print(layer.name, layer.trainable)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:26:31.858027Z","iopub.execute_input":"2023-10-10T16:26:31.858407Z","iopub.status.idle":"2023-10-10T16:26:31.863488Z","shell.execute_reply.started":"2023-10-10T16:26:31.858379Z","shell.execute_reply":"2023-10-10T16:26:31.862373Z"},"trusted":true},"execution_count":102,"outputs":[{"name":"stdout","text":"char_inputs True\ntoken_inputs True\nchar_vectorizer True\nuniversal_sentence_encoder False\nchar_embed True\ndense_8 True\nbidirectional_1 True\ntoken_char_hybrid_embedding True\nline_number_input True\ntotal_lines_input True\ndense_11 True\ndense_9 True\ndense_10 True\ndropout_2 True\ntoken_char_positional_embedding True\noutput_layer True\n","output_type":"stream"}]},{"cell_type":"code","source":"model_5.compile(loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.2), # add label smoothing (examples which are really confident get smoothed a little)\n                optimizer=tf.keras.optimizers.Adam(),\n                metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:27:32.637116Z","iopub.execute_input":"2023-10-10T16:27:32.63748Z","iopub.status.idle":"2023-10-10T16:27:32.650742Z","shell.execute_reply.started":"2023-10-10T16:27:32.63746Z","shell.execute_reply":"2023-10-10T16:27:32.649452Z"},"trusted":true},"execution_count":103,"outputs":[]},{"cell_type":"markdown","source":"Label smoothing helps to regularize our model (prevent overfitting) by making sure it doesn't get too focused on applying one particular label to a sample.","metadata":{}},{"cell_type":"code","source":"# Create training and validation datasets (all four kinds of inputs)\ntrain_pos_char_token_data = tf.data.Dataset.from_tensor_slices((train_line_numbers_one_hot, # line numbers\n                                                                train_total_lines_one_hot, # total lines\n                                                                train_sentences, # train tokens\n                                                                train_chars)) # train chars\ntrain_pos_char_token_labels = tf.data.Dataset.from_tensor_slices(train_labels_one_hot) # train labels\ntrain_pos_char_token_dataset = tf.data.Dataset.zip((train_pos_char_token_data, train_pos_char_token_labels)) # combine data and labels\ntrain_pos_char_token_dataset = train_pos_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE) # turn into batches and prefetch appropriately\n\n# Validation dataset\nval_pos_char_token_data = tf.data.Dataset.from_tensor_slices((val_line_numbers_one_hot,\n                                                              val_total_lines_one_hot,\n                                                              val_sentences,\n                                                              val_chars))\nval_pos_char_token_labels = tf.data.Dataset.from_tensor_slices(val_labels_one_hot)\nval_pos_char_token_dataset = tf.data.Dataset.zip((val_pos_char_token_data, val_pos_char_token_labels))\nval_pos_char_token_dataset = val_pos_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE) # turn into batches and prefetch appropriately\n\n# Check input shapes\ntrain_pos_char_token_dataset, val_pos_char_token_dataset","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:29:04.184411Z","iopub.execute_input":"2023-10-10T16:29:04.184805Z","iopub.status.idle":"2023-10-10T16:29:05.323656Z","shell.execute_reply.started":"2023-10-10T16:29:04.184776Z","shell.execute_reply":"2023-10-10T16:29:05.322218Z"},"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","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))>)"},"metadata":{}}]},{"cell_type":"code","source":"history_model_5 = model_5.fit(train_pos_char_token_dataset,\n                              steps_per_epoch=int(0.1 * len(train_pos_char_token_dataset)),\n                              epochs=3,\n                              validation_data=val_pos_char_token_dataset,\n                              validation_steps=int(0.1 * len(val_pos_char_token_dataset)))\n     ","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:29:11.054578Z","iopub.execute_input":"2023-10-10T16:29:11.055028Z","iopub.status.idle":"2023-10-10T16:32:38.316262Z","shell.execute_reply.started":"2023-10-10T16:29:11.054967Z","shell.execute_reply":"2023-10-10T16:32:38.314609Z"},"trusted":true},"execution_count":105,"outputs":[{"name":"stdout","text":"Epoch 1/3\n562/562 [==============================] - 71s 120ms/step - loss: 1.0990 - accuracy: 0.7239 - val_loss: 0.9864 - val_accuracy: 0.8032\nEpoch 2/3\n562/562 [==============================] - 68s 122ms/step - loss: 0.9666 - accuracy: 0.8152 - val_loss: 0.9498 - val_accuracy: 0.8265\nEpoch 3/3\n562/562 [==============================] - 67s 120ms/step - loss: 0.9510 - accuracy: 0.8214 - val_loss: 0.9400 - val_accuracy: 0.8288\n","output_type":"stream"}]},{"cell_type":"code","source":"# Make predictions with token-char-positional hybrid model\nmodel_5_pred_probs = model_5.predict(val_pos_char_token_dataset, verbose=1)\nmodel_5_pred_probs","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:32:38.319018Z","iopub.execute_input":"2023-10-10T16:32:38.319438Z","iopub.status.idle":"2023-10-10T16:33:20.107038Z","shell.execute_reply.started":"2023-10-10T16:32:38.31941Z","shell.execute_reply":"2023-10-10T16:33:20.105252Z"},"trusted":true},"execution_count":106,"outputs":[{"name":"stdout","text":"945/945 [==============================] - 30s 31ms/step\n","output_type":"stream"},{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"array([[0.46766073, 0.11095748, 0.0111226 , 0.39387867, 0.01638038],\n       [0.56164825, 0.08420362, 0.0404687 , 0.3062571 , 0.00742226],\n       [0.26734492, 0.09133486, 0.13029154, 0.44651687, 0.06451177],\n       ...,\n       [0.03379436, 0.11622185, 0.03776274, 0.03462305, 0.77759796],\n       [0.02744728, 0.30335197, 0.0681956 , 0.02465135, 0.57635385],\n       [0.2206073 , 0.5820783 , 0.11185648, 0.04159917, 0.04385879]],\n      dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"model_5_preds = tf.argmax(model_5_pred_probs, axis=1)\nmodel_5_preds","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:33:20.109094Z","iopub.execute_input":"2023-10-10T16:33:20.109557Z","iopub.status.idle":"2023-10-10T16:33:20.121265Z","shell.execute_reply.started":"2023-10-10T16:33:20.10952Z","shell.execute_reply":"2023-10-10T16:33:20.119501Z"},"trusted":true},"execution_count":107,"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 0, 3, ..., 4, 4, 1])>"},"metadata":{}}]},{"cell_type":"code","source":"model_5_results = calculate_results(y_true=val_labels_encoded,\n                                    y_pred=model_5_preds)\nmodel_5_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:33:20.124581Z","iopub.execute_input":"2023-10-10T16:33:20.125298Z","iopub.status.idle":"2023-10-10T16:33:20.153189Z","shell.execute_reply.started":"2023-10-10T16:33:20.125215Z","shell.execute_reply":"2023-10-10T16:33:20.150924Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"{'accuracy': 83.22189858334437,\n 'precision': 0.8310388721572383,\n 'recall': 0.8322189858334437,\n 'f1': 0.8313576894022233}"},"metadata":{}}]},{"cell_type":"code","source":"# Combine model results into a DataFrame\nall_model_results = pd.DataFrame({\"baseline\": baseline_results,\n                                  \"custom_token_embed_conv1d\": model_1_results,\n                                  \"pretrained_token_embed\": model_2_results,\n                                  \"custom_char_embed_conv1d\": model_3_results,\n                                  \"hybrid_char_token_embed\": model_4_results,\n                                  \"tribrid_pos_char_token_embed\": model_5_results})\nall_model_results = all_model_results.transpose()\nall_model_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:33:20.15483Z","iopub.execute_input":"2023-10-10T16:33:20.155262Z","iopub.status.idle":"2023-10-10T16:33:20.169109Z","shell.execute_reply.started":"2023-10-10T16:33:20.155236Z","shell.execute_reply":"2023-10-10T16:33:20.167186Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"                               accuracy  precision    recall        f1\nbaseline                      72.183238   0.718647  0.721832  0.698925\ncustom_token_embed_conv1d     78.535019   0.782001  0.785350  0.782826\npretrained_token_embed        71.398782   0.714342  0.713988  0.710983\ncustom_char_embed_conv1d      66.387528   0.656584  0.663875  0.654339\nhybrid_char_token_embed       73.530385   0.737068  0.735304  0.732917\ntribrid_pos_char_token_embed  83.221899   0.831039  0.832219  0.831358","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>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>custom_token_embed_conv1d</th>\n      <td>78.535019</td>\n      <td>0.782001</td>\n      <td>0.785350</td>\n      <td>0.782826</td>\n    </tr>\n    <tr>\n      <th>pretrained_token_embed</th>\n      <td>71.398782</td>\n      <td>0.714342</td>\n      <td>0.713988</td>\n      <td>0.710983</td>\n    </tr>\n    <tr>\n      <th>custom_char_embed_conv1d</th>\n      <td>66.387528</td>\n      <td>0.656584</td>\n      <td>0.663875</td>\n      <td>0.654339</td>\n    </tr>\n    <tr>\n      <th>hybrid_char_token_embed</th>\n      <td>73.530385</td>\n      <td>0.737068</td>\n      <td>0.735304</td>\n      <td>0.732917</td>\n    </tr>\n    <tr>\n      <th>tribrid_pos_char_token_embed</th>\n      <td>83.221899</td>\n      <td>0.831039</td>\n      <td>0.832219</td>\n      <td>0.831358</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"all_model_results[\"accuracy\"] = all_model_results[\"accuracy\"]/100","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:33:20.170394Z","iopub.execute_input":"2023-10-10T16:33:20.170663Z","iopub.status.idle":"2023-10-10T16:33:20.186848Z","shell.execute_reply.started":"2023-10-10T16:33:20.170636Z","shell.execute_reply":"2023-10-10T16:33:20.185234Z"},"trusted":true},"execution_count":110,"outputs":[]},{"cell_type":"code","source":"all_model_results.plot(kind=\"bar\", figsize=(10, 7)).legend(bbox_to_anchor=(1.0, 1.0));","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:33:20.188073Z","iopub.execute_input":"2023-10-10T16:33:20.188404Z","iopub.status.idle":"2023-10-10T16:33:20.449885Z","shell.execute_reply.started":"2023-10-10T16:33:20.18838Z","shell.execute_reply":"2023-10-10T16:33:20.449246Z"},"trusted":true},"execution_count":111,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x700 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"# Sort model results by f1-score\nall_model_results.sort_values(\"f1\", ascending=False)[\"f1\"].plot(kind=\"bar\", figsize=(10, 7));     ","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:33:20.656117Z","iopub.execute_input":"2023-10-10T16:33:20.656534Z","iopub.status.idle":"2023-10-10T16:33:20.865288Z","shell.execute_reply.started":"2023-10-10T16:33:20.656502Z","shell.execute_reply":"2023-10-10T16:33:20.863782Z"},"trusted":true},"execution_count":113,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x700 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"# Save best performing model to SavedModel format (default)\nmodel_5.save(\"skimlit_tribrid_model\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:34:22.415431Z","iopub.execute_input":"2023-10-10T16:34:22.415816Z","iopub.status.idle":"2023-10-10T16:34:39.353499Z","shell.execute_reply.started":"2023-10-10T16:34:22.415794Z","shell.execute_reply":"2023-10-10T16:34:39.352784Z"},"trusted":true},"execution_count":114,"outputs":[]},{"cell_type":"code","source":"# Create test dataset batch and prefetched\ntest_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))\ntest_pos_char_token_labels = tf.data.Dataset.from_tensor_slices(test_labels_one_hot)\ntest_pos_char_token_dataset = tf.data.Dataset.zip((test_pos_char_token_data, test_pos_char_token_labels))\ntest_pos_char_token_dataset = test_pos_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n\n# Check shapes\ntest_pos_char_token_dataset","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:37:32.05522Z","iopub.execute_input":"2023-10-10T16:37:32.055644Z","iopub.status.idle":"2023-10-10T16:37:32.222866Z","shell.execute_reply.started":"2023-10-10T16:37:32.055615Z","shell.execute_reply":"2023-10-10T16:37:32.220514Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","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))>"},"metadata":{}}]},{"cell_type":"code","source":"test_pred_probs = model_5.predict(test_pos_char_token_dataset,\n                                       verbose=1)\ntest_preds = tf.argmax(test_pred_probs, axis=1)\ntest_preds[:10]","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:37:54.347579Z","iopub.execute_input":"2023-10-10T16:37:54.347945Z","iopub.status.idle":"2023-10-10T16:38:24.116305Z","shell.execute_reply.started":"2023-10-10T16:37:54.347921Z","shell.execute_reply":"2023-10-10T16:38:24.114282Z"},"trusted":true},"execution_count":120,"outputs":[{"name":"stdout","text":"942/942 [==============================] - 29s 31ms/step\n","output_type":"stream"},{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(10,), dtype=int64, numpy=array([3, 0, 2, 2, 4, 4, 4, 1, 4, 0])>"},"metadata":{}}]},{"cell_type":"code","source":"model_test_results = calculate_results(y_true=test_labels_encoded,\n                                              y_pred=test_preds)\nmodel_test_results","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:38:24.117977Z","iopub.execute_input":"2023-10-10T16:38:24.118269Z","iopub.status.idle":"2023-10-10T16:38:24.141914Z","shell.execute_reply.started":"2023-10-10T16:38:24.118249Z","shell.execute_reply":"2023-10-10T16:38:24.140643Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"{'accuracy': 82.90360046457607,\n 'precision': 0.8277066668745837,\n 'recall': 0.8290360046457608,\n 'f1': 0.8281055513276673}"},"metadata":{}}]},{"cell_type":"markdown","source":"Our model is not trained on entire dataset. We can improve it further by implementing what is in the paper. ","metadata":{}},{"cell_type":"code","source":"# Create prediction-enriched test dataframe\ntest_df[\"prediction\"] = [label_encoder.classes_[pred] for pred in test_preds] # create column with test prediction class names\ntest_df[\"pred_prob\"] = tf.reduce_max(test_pred_probs, axis=1).numpy() # get the maximum prediction probability\ntest_df[\"correct\"] = test_df[\"prediction\"] == test_df[\"target\"] # create binary column for whether the prediction is right or not\ntest_df.head(20)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:40:29.868475Z","iopub.execute_input":"2023-10-10T16:40:29.868861Z","iopub.status.idle":"2023-10-10T16:40:35.942392Z","shell.execute_reply.started":"2023-10-10T16:40:29.868834Z","shell.execute_reply":"2023-10-10T16:40:35.940711Z"},"trusted":true},"execution_count":123,"outputs":[{"execution_count":123,"output_type":"execute_result","data":{"text/plain":"         target                                               text  \\\n0    BACKGROUND  this study analyzed liver function abnormaliti...   \n1       RESULTS  a post hoc analysis was conducted with the use...   \n2       RESULTS  liver function tests ( lfts ) were measured at...   \n3       RESULTS  survival analyses were used to assess the asso...   \n4       RESULTS  the percentage of patients with abnormal lfts ...   \n5       RESULTS  when mean hemodynamic profiles were compared i...   \n6       RESULTS  multivariable analyses revealed that patients ...   \n7   CONCLUSIONS  abnormal lfts are common in the adhf populatio...   \n8   CONCLUSIONS  elevated meld-xi scores are associated with po...   \n9    BACKGROUND  minimally invasive endovascular aneurysm repai...   \n10   BACKGROUND  the aim of this study was to analyse the cost-...   \n11      METHODS  resource use was determined from the amsterdam...   \n12      METHODS  the analysis was performed from a provider per...   \n13      METHODS  all costs were calculated as if all patients h...   \n14      RESULTS            a total of @ patients were randomized .   \n15      RESULTS  the @-day mortality rate was @ per cent after ...   \n16      RESULTS  at @months , the total mortality rate for evar...   \n17      RESULTS  the mean cost difference between evar and or w...   \n18      RESULTS  the incremental cost-effectiveness ratio per p...   \n19      RESULTS  there was no significant difference in quality...   \n\n    line_number  total_lines   prediction  pred_prob  correct  \n0             0            8    OBJECTIVE   0.481721    False  \n1             1            8   BACKGROUND   0.349853    False  \n2             2            8      METHODS   0.778349    False  \n3             3            8      METHODS   0.585959    False  \n4             4            8      RESULTS   0.731534     True  \n5             5            8      RESULTS   0.887475     True  \n6             6            8      RESULTS   0.561133     True  \n7             7            8  CONCLUSIONS   0.504918     True  \n8             8            8      RESULTS   0.460189    False  \n9             0           12   BACKGROUND   0.541956     True  \n10            1           12    OBJECTIVE   0.533715    False  \n11            2           12      METHODS   0.613455     True  \n12            3           12      METHODS   0.857983     True  \n13            4           12      METHODS   0.554229     True  \n14            5           12      RESULTS   0.738051     True  \n15            6           12      RESULTS   0.624403     True  \n16            7           12      RESULTS   0.871847     True  \n17            8           12      RESULTS   0.852108     True  \n18            9           12      RESULTS   0.799605     True  \n19           10           12      RESULTS   0.753628     True  ","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>0</th>\n      <td>BACKGROUND</td>\n      <td>this study analyzed liver function abnormaliti...</td>\n      <td>0</td>\n      <td>8</td>\n      <td>OBJECTIVE</td>\n      <td>0.481721</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>RESULTS</td>\n      <td>a post hoc analysis was conducted with the use...</td>\n      <td>1</td>\n      <td>8</td>\n      <td>BACKGROUND</td>\n      <td>0.349853</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RESULTS</td>\n      <td>liver function tests ( lfts ) were measured at...</td>\n      <td>2</td>\n      <td>8</td>\n      <td>METHODS</td>\n      <td>0.778349</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>RESULTS</td>\n      <td>survival analyses were used to assess the asso...</td>\n      <td>3</td>\n      <td>8</td>\n      <td>METHODS</td>\n      <td>0.585959</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>RESULTS</td>\n      <td>the percentage of patients with abnormal lfts ...</td>\n      <td>4</td>\n      <td>8</td>\n      <td>RESULTS</td>\n      <td>0.731534</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>RESULTS</td>\n      <td>when mean hemodynamic profiles were compared i...</td>\n      <td>5</td>\n      <td>8</td>\n      <td>RESULTS</td>\n      <td>0.887475</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>RESULTS</td>\n      <td>multivariable analyses revealed that patients ...</td>\n      <td>6</td>\n      <td>8</td>\n      <td>RESULTS</td>\n      <td>0.561133</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CONCLUSIONS</td>\n      <td>abnormal lfts are common in the adhf populatio...</td>\n      <td>7</td>\n      <td>8</td>\n      <td>CONCLUSIONS</td>\n      <td>0.504918</td>\n      <td>True</td>\n    </tr>\n    <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>RESULTS</td>\n      <td>0.460189</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>BACKGROUND</td>\n      <td>minimally invasive endovascular aneurysm repai...</td>\n      <td>0</td>\n      <td>12</td>\n      <td>BACKGROUND</td>\n      <td>0.541956</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>BACKGROUND</td>\n      <td>the aim of this study was to analyse the cost-...</td>\n      <td>1</td>\n      <td>12</td>\n      <td>OBJECTIVE</td>\n      <td>0.533715</td>\n      <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>0.613455</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>0.857983</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>0.554229</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>0.738051</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>0.624403</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>0.871847</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>0.852108</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>0.799605</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>0.753628</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"top_100_wrong = test_df[test_df[\"correct\"] == False].sort_values(\"pred_prob\", ascending=False)[:100]\ntop_100_wrong","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:42:10.445224Z","iopub.execute_input":"2023-10-10T16:42:10.445637Z","iopub.status.idle":"2023-10-10T16:42:10.465449Z","shell.execute_reply.started":"2023-10-10T16:42:10.445607Z","shell.execute_reply":"2023-10-10T16:42:10.464428Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"            target                                               text  \\\n16347   BACKGROUND  to evaluate the effects of the lactic acid bac...   \n13874  CONCLUSIONS  symptom outcomes will be assessed and estimate...   \n835     BACKGROUND  to assess the temporal patterns of late gastro...   \n1827   CONCLUSIONS                      nct@ ( clinicaltrials.gov ) .   \n23630      RESULTS  the primary outcome was to evaluate changes in...   \n...            ...                                                ...   \n8573       RESULTS  primary outcome was breastfeeding at @ days , ...   \n4860       RESULTS  we randomly assigned @ patients with drug-resi...   \n12136      RESULTS  for each individual enrolled as a case , we id...   \n18789   BACKGROUND  these programs were evaluated in a head-to-hea...   \n24794      RESULTS  we judged that informed consent would undermin...   \n\n       line_number  total_lines   prediction  pred_prob  correct  \n16347            0           12    OBJECTIVE   0.934501    False  \n13874            4            6      METHODS   0.925790    False  \n835              0           11    OBJECTIVE   0.919944    False  \n1827            18           18   BACKGROUND   0.917880    False  \n23630            6           14      METHODS   0.915177    False  \n...            ...          ...          ...        ...      ...  \n8573             5           16      METHODS   0.835235    False  \n4860             2           10      METHODS   0.835228    False  \n12136            4           16      METHODS   0.834765    False  \n18789            3           12      METHODS   0.834002    False  \n24794           11           13  CONCLUSIONS   0.833093    False  \n\n[100 rows x 7 columns]","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>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>0.934501</td>\n      <td>False</td>\n    </tr>\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>0.925790</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>835</th>\n      <td>BACKGROUND</td>\n      <td>to assess the temporal patterns of late gastro...</td>\n      <td>0</td>\n      <td>11</td>\n      <td>OBJECTIVE</td>\n      <td>0.919944</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>0.917880</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>23630</th>\n      <td>RESULTS</td>\n      <td>the primary outcome was to evaluate changes in...</td>\n      <td>6</td>\n      <td>14</td>\n      <td>METHODS</td>\n      <td>0.915177</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>8573</th>\n      <td>RESULTS</td>\n      <td>primary outcome was breastfeeding at @ days , ...</td>\n      <td>5</td>\n      <td>16</td>\n      <td>METHODS</td>\n      <td>0.835235</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4860</th>\n      <td>RESULTS</td>\n      <td>we randomly assigned @ patients with drug-resi...</td>\n      <td>2</td>\n      <td>10</td>\n      <td>METHODS</td>\n      <td>0.835228</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>12136</th>\n      <td>RESULTS</td>\n      <td>for each individual enrolled as a case , we id...</td>\n      <td>4</td>\n      <td>16</td>\n      <td>METHODS</td>\n      <td>0.834765</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>18789</th>\n      <td>BACKGROUND</td>\n      <td>these programs were evaluated in a head-to-hea...</td>\n      <td>3</td>\n      <td>12</td>\n      <td>METHODS</td>\n      <td>0.834002</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>24794</th>\n      <td>RESULTS</td>\n      <td>we judged that informed consent would undermin...</td>\n      <td>11</td>\n      <td>13</td>\n      <td>CONCLUSIONS</td>\n      <td>0.833093</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 7 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#top wrong preds\nfor row in top_100_wrong[0:10].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\")","metadata":{"execution":{"iopub.status.busy":"2023-10-10T16:42:16.51256Z","iopub.execute_input":"2023-10-10T16:42:16.512886Z","iopub.status.idle":"2023-10-10T16:42:16.522314Z","shell.execute_reply.started":"2023-10-10T16:42:16.512866Z","shell.execute_reply":"2023-10-10T16:42:16.520963Z"},"trusted":true},"execution_count":127,"outputs":[{"name":"stdout","text":"Target: BACKGROUND, Pred: OBJECTIVE, Prob: 0.9345008134841919, Line number: 0, Total lines: 12\n\nText:\nto evaluate the effects of the lactic acid bacterium lactobacillus salivarius on caries risk factors .\n\n-----\n\nTarget: CONCLUSIONS, Pred: METHODS, Prob: 0.9257904291152954, Line number: 4, Total lines: 6\n\nText:\nsymptom outcomes will be assessed and estimates of cost-effectiveness made .\n\n-----\n\nTarget: BACKGROUND, Pred: OBJECTIVE, Prob: 0.919944167137146, Line number: 0, Total lines: 11\n\nText:\nto assess the temporal patterns of late gastrointestinal ( gi ) and genitourinary ( gu ) radiotherapy toxicity and resolution rates in a randomised controlled trial ( all-ireland cooperative oncology research group @-@ ) assessing duration of neo-adjuvant ( na ) hormone therapy for localised prostate cancer .\n\n-----\n\nTarget: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9178802371025085, Line number: 18, Total lines: 18\n\nText:\nnct@ ( clinicaltrials.gov ) .\n\n-----\n\nTarget: RESULTS, Pred: METHODS, Prob: 0.915177047252655, Line number: 6, Total lines: 14\n\nText:\nthe primary outcome was to evaluate changes in abdominal and shoulder-tip pain via a @-mm visual analog scale at @ , @ , and @hours postoperatively .\n\n-----\n\nTarget: METHODS, Pred: OBJECTIVE, Prob: 0.9102720022201538, Line number: 0, Total lines: 7\n\nText:\nto determine whether the insulin resistance that exists in metabolic syndrome ( mets ) patients is modulated by dietary fat composition .\n\n-----\n\nTarget: BACKGROUND, Pred: OBJECTIVE, Prob: 0.9098522067070007, Line number: 0, Total lines: 10\n\nText:\nto validate the association between accommodation and visual asthenopia by measuring objective accommodative amplitude with the optical quality analysis system ( oqas , visiometrics , terrassa , spain ) , and to investigate associations among accommodation , ocular surface instability , and visual asthenopia while viewing @d displays .\n\n-----\n\nTarget: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9097451567649841, Line number: 19, Total lines: 19\n\nText:\nclinicaltrials.gov identifier : nct@ .\n\n-----\n\nTarget: BACKGROUND, Pred: OBJECTIVE, Prob: 0.9075023531913757, Line number: 0, Total lines: 9\n\nText:\nto compare the efficacy of the newcastle infant dialysis and ultrafiltration system ( nidus ) with peritoneal dialysis ( pd ) and conventional haemodialysis ( hd ) in infants weighing < @ kg .\n\n-----\n\nTarget: RESULTS, Pred: METHODS, Prob: 0.9041044116020203, Line number: 3, Total lines: 16\n\nText:\na cluster randomised trial was implemented with @,@ children in @ government primary schools on the south coast of kenya in @-@ .\n\n-----\n\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}