before creating dataset

.gitignore

@@ -1 +1,2 @@
-/env
+/env
+credentials.json

.ipynb

@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "a = pd.read_parquet(\"data/test.parquet\")\n",
+    "b = pd.read_parquet(\"data/passages.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>question</th>\n",
+       "      <th>answer</th>\n",
+       "      <th>relevant_passage_ids</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Is Hirschsprung disease a mendelian or a multi...</td>\n",
+       "      <td>Coding sequence mutations in RET, GDNF, EDNRB,...</td>\n",
+       "      <td>[20598273, 6650562, 15829955, 15617541, 230011...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>List signaling molecules (ligands) that intera...</td>\n",
+       "      <td>The 7 known EGFR ligands  are: epidermal growt...</td>\n",
+       "      <td>[23821377, 24323361, 23382875, 22247333, 23787...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is the protein Papilin secreted?</td>\n",
+       "      <td>Yes,  papilin is a secreted protein</td>\n",
+       "      <td>[21784067, 19297413, 15094122, 7515725, 332004...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Are long non coding RNAs spliced?</td>\n",
+       "      <td>Long non coding RNAs appear to be spliced thro...</td>\n",
+       "      <td>[22955974, 21622663, 22707570, 22955988, 24285...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Is RANKL secreted from the cells?</td>\n",
+       "      <td>Receptor activator of nuclear factor κB ligand...</td>\n",
+       "      <td>[22867712, 23827649, 21618594, 23835909, 24265...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4714</th>\n",
+       "      <td>Is PPROM a condition that occurs in males or f...</td>\n",
+       "      <td>Preterm premature rupture of fetal membranes (...</td>\n",
+       "      <td>[23599878, 23573382, 24304137, 18301713, 23179...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4715</th>\n",
+       "      <td>What is EpiMethylTag?</td>\n",
+       "      <td>EpiMethylTag is a fast, low-input, low sequenc...</td>\n",
+       "      <td>[31752933]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4716</th>\n",
+       "      <td>What is the target of Sutimlimab?</td>\n",
+       "      <td>Sutimlimab is a novel humanized monoclonal ant...</td>\n",
+       "      <td>[30635392, 31229501, 33826820, 32176765, 31114...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4717</th>\n",
+       "      <td>Can parasite infections by Schistosoma japonic...</td>\n",
+       "      <td>A peptide named as SJMHE1 from Schistosoma jap...</td>\n",
+       "      <td>[26840774, 34703270, 28614408, 31496071, 18654...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4718</th>\n",
+       "      <td>Describe Multilocus Inherited Neoplasia Allele...</td>\n",
+       "      <td>Genetic testing of hereditary cancer using com...</td>\n",
+       "      <td>[30580288]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4719 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               question  \\\n",
+       "id                                                        \n",
+       "0     Is Hirschsprung disease a mendelian or a multi...   \n",
+       "1     List signaling molecules (ligands) that intera...   \n",
+       "2                      Is the protein Papilin secreted?   \n",
+       "3                     Are long non coding RNAs spliced?   \n",
+       "4                     Is RANKL secreted from the cells?   \n",
+       "...                                                 ...   \n",
+       "4714  Is PPROM a condition that occurs in males or f...   \n",
+       "4715                              What is EpiMethylTag?   \n",
+       "4716                  What is the target of Sutimlimab?   \n",
+       "4717  Can parasite infections by Schistosoma japonic...   \n",
+       "4718  Describe Multilocus Inherited Neoplasia Allele...   \n",
+       "\n",
+       "                                                 answer  \\\n",
+       "id                                                        \n",
+       "0     Coding sequence mutations in RET, GDNF, EDNRB,...   \n",
+       "1     The 7 known EGFR ligands  are: epidermal growt...   \n",
+       "2                   Yes,  papilin is a secreted protein   \n",
+       "3     Long non coding RNAs appear to be spliced thro...   \n",
+       "4     Receptor activator of nuclear factor κB ligand...   \n",
+       "...                                                 ...   \n",
+       "4714  Preterm premature rupture of fetal membranes (...   \n",
+       "4715  EpiMethylTag is a fast, low-input, low sequenc...   \n",
+       "4716  Sutimlimab is a novel humanized monoclonal ant...   \n",
+       "4717  A peptide named as SJMHE1 from Schistosoma jap...   \n",
+       "4718  Genetic testing of hereditary cancer using com...   \n",
+       "\n",
+       "                                   relevant_passage_ids  \n",
+       "id                                                       \n",
+       "0     [20598273, 6650562, 15829955, 15617541, 230011...  \n",
+       "1     [23821377, 24323361, 23382875, 22247333, 23787...  \n",
+       "2     [21784067, 19297413, 15094122, 7515725, 332004...  \n",
+       "3     [22955974, 21622663, 22707570, 22955988, 24285...  \n",
+       "4     [22867712, 23827649, 21618594, 23835909, 24265...  \n",
+       "...                                                 ...  \n",
+       "4714  [23599878, 23573382, 24304137, 18301713, 23179...  \n",
+       "4715                                         [31752933]  \n",
+       "4716  [30635392, 31229501, 33826820, 32176765, 31114...  \n",
+       "4717  [26840774, 34703270, 28614408, 31496071, 18654...  \n",
+       "4718                                         [30580288]  \n",
+       "\n",
+       "[4719 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>passage</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21495810</th>\n",
+       "      <td>OBJECT: Factors determining choice of an acade...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26869762</th>\n",
+       "      <td>Castleman disease (CD) is a rare, heterogeneou...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28049410</th>\n",
+       "      <td>BACKGROUND: Data extraction and integration me...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24510469</th>\n",
+       "      <td>Flecainide is recommended as a first-line anti...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8650761</th>\n",
+       "      <td>Primary intestinal lymphangiectasia (PIL), fir...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    passage\n",
+       "id                                                         \n",
+       "21495810  OBJECT: Factors determining choice of an acade...\n",
+       "26869762  Castleman disease (CD) is a rare, heterogeneou...\n",
+       "28049410  BACKGROUND: Data extraction and integration me...\n",
+       "24510469  Flecainide is recommended as a first-line anti...\n",
+       "8650761   Primary intestinal lymphangiectasia (PIL), fir..."
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

README.md

@@ -11,5 +11,14 @@ tags:
 - information-retrieval
 - question-answering
 - biomedical
+configs:
+- config_name: text-corpus
+  data_files: 
+  - split: passages
+    path: "data/passages.parquet/*"
+- config_name: question-answer
+  data_files: 
+  - split: test
+    path: "data/test.parquet/*"
 ---
-Derives from http://participants-area.bioasq.org/Tasks/11b/trainingDataset/ we generated our own subset using `generate.py`.
+Derives from http://participants-area.bioasq.org/Tasks/11b/trainingDataset/ we generated our own subset using `generate.py`.

data/passages.parquet/part.0.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c288905f142dde9c3c21207333380a81d3f34603584851be02ccf7e543041934
+size 12581

data/test.parquet/part.0.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12679e03615d16b423b5554f8b2a6eb334f4cad89e62d202da5cc43cb9aeafb0
+size 1290026

bioasq_ir_pubmed_corpus_subset.py → generate.py

@@ -4,19 +4,23 @@ import pandas as pd
 from Bio import Entrez
 from retry import retry
 from tqdm import tqdm
+import dask.dataframe as dd
 
 # provided your NIH credentials
-Entrez.email = "***"
-Entrez.api_key = "***"
+# read from .json file
+with open("credentials.json") as f:
+    credentials = json.load(f)
+    Entrez.email = credentials["email"]
+    Entrez.api_key = credentials["api_key"]
 
 
 # change output file names here if necessary
-RAW_EVALUATION_DATASET = "training11b.json"
-PATH_TO_PASSAGE_DATASET = "./passages.parquet"
-PATH_TO_EVALUATION_DATASET = "./eval.parquet"
+RAW_EVALUATION_DATASET = "./raw_data/training11b.json"
+PATH_TO_PASSAGE_DATASET = "./data/passages.parquet"
+PATH_TO_EVALUATION_DATASET = "./data/test.parquet"
 
 # only use questions that have at most MAX_PASSAGES passages to control the size of the dataset
-# set to None to use all passages
+# set to None to use all questions
 MAX_PASSAGES = None
 
 
@@ -42,43 +46,47 @@ if __name__ == "__main__":
     eval_df = eval_df.rename(
         columns={
             "body": "question",
-            "documents": "relevant_passages",
+            "documents": "relevant_passage_ids",
             "ideal_answer": "answer",
         }
     )
     eval_df.answer = eval_df.answer.apply(lambda x: x[0])
     # get abstract id from url
-    eval_df.relevant_passages = eval_df.relevant_passages.apply(
-        lambda x: [url.split("/")[-1] for url in x]
+    eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(
+        lambda x: [int(url.split("/")[-1]) for url in x]
     )
     if MAX_PASSAGES:
-        eval_df["passage_count"] = eval_df.relevant_passages.apply(lambda x: len(x))
+        eval_df["passage_count"] = eval_df.relevant_passage_ids.apply(lambda x: len(x))
         eval_df = eval_df.drop(columns=["passage_count"])
 
     # remove duplicate passage ids
-    eval_df.relevant_passages = eval_df.relevant_passages.apply(lambda x: set(x))
-    eval_df.relevant_passages = eval_df.relevant_passages.apply(lambda x: list(x))
+    eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(lambda x: set(x))
+    eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(lambda x: list(x))
 
     # get all passage ids that are relevant
-    passage_ids = set().union(*eval_df.relevant_passages)
+    passage_ids = set().union(*eval_df.relevant_passage_ids)
     passage_ids = list(passage_ids)
     passages = pd.DataFrame(index=passage_ids)
 
     for i, passage_id in enumerate(tqdm(passages.index)):
         passages.loc[passage_id, "passage"] = get_abstract(passage_id)
 
-        # intermidiate save
-        if i % 4000 == 0:
-            passages.to_parquet(PATH_TO_PASSAGE_DATASET)
+        # intermediate save
+        if i % 1000 == 0:
+            dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET)
+
 
     # filter out the passages whos pmids (pubmed ids) where not available
     unavailable_passages = passages[passages["passage"] == "1. "]
     passages = passages[passages["passage"] != "1. "]
-    passages.to_parquet(PATH_TO_PASSAGE_DATASET)
+    passages.index.name = "id"
+    dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET)
 
     # remove passages from evaluation dataset whose abstract could not be retrieved from pubmed website
     unavailable_ids = unavailable_passages.index.tolist()
-    eval_df["relevant_passages"] = eval_df["relevant_passages"].apply(
+    eval_df["relevant_passage_ids"] = eval_df["relevant_passage_ids"].apply(
         lambda x: [i for i in x if i not in unavailable_ids]
     )
-    eval_df.to_parquet(PATH_TO_EVALUATION_DATASET)
+    eval_df.index.name = "id"
+    eval_df = eval_df[["question", "answer", "relevant_passage_ids"]]
+    dd.from_pandas(eval_df, npartitions=1).to_parquet(PATH_TO_EVALUATION_DATASET)

requirements.txt

@@ -1,11 +1,50 @@
+asttokens==2.4.1
+backcall==0.2.0
 biopython==1.81
+click==8.1.7
+cloudpickle==3.0.0
+comm==0.1.4
+dask==2023.10.1
+debugpy==1.8.0
 decorator==5.1.1
+exceptiongroup==1.1.3
+executing==2.0.0
+fsspec==2023.10.0
+importlib-metadata==6.8.0
+ipykernel==6.26.0
+ipython==8.16.1
+jedi==0.19.1
+jupyter_client==8.5.0
+jupyter_core==5.4.0
+locket==1.0.0
+matplotlib-inline==0.1.6
+nest-asyncio==1.5.8
 numpy==1.26.1
+packaging==23.2
 pandas==2.1.2
+parso==0.8.3
+partd==1.4.1
+pexpect==4.8.0
+pickleshare==0.7.5
+platformdirs==3.11.0
+prompt-toolkit==3.0.39
+psutil==5.9.6
+ptyprocess==0.7.0
+pure-eval==0.2.2
 py==1.11.0
+pyarrow==13.0.0
+Pygments==2.16.1
 python-dateutil==2.8.2
 pytz==2023.3.post1
+PyYAML==6.0.1
+pyzmq==25.1.1
 retry==0.9.2
 six==1.16.0
+stack-data==0.6.3
+toolz==0.12.0
+tornado==6.3.3
 tqdm==4.66.1
+traitlets==5.12.0
 tzdata==2023.3
+wcwidth==0.2.8
+zipp==3.17.0