From 10a9d068ddeda3454fb0e9abf0ca8e7ebed00fa5 Mon Sep 17 00:00:00 2001 From: Ofer Katz Date: Mon, 23 Dec 2024 19:57:15 +0200 Subject: [PATCH] WIP --- nbs/benchmark/001_covid.ipynb | 1065 ----------------- nbs/benchmark/covid_bench.py | 213 ++++ nbs/benchmark/covid_bench_logic.pl | 79 ++ nbs/benchmark/covid_data/covid_logic.pl | 2 - .../{temp => sample_inputs}/sample100.txt | 0 .../{temp => sample_inputs}/sample1000.txt | 0 .../{temp => sample_inputs}/sample10000.txt | 0 .../{temp => sample_inputs}/sample10001.txt | 0 .../{temp => sample_inputs}/sample10002.txt | 0 .../{temp => sample_inputs}/sample10003.txt | 0 .../{temp => sample_inputs}/sample10004.txt | 0 .../{temp => sample_inputs}/sample10005.txt | 0 .../{temp => sample_inputs}/sample10006.txt | 0 .../{temp => sample_inputs}/sample10007.txt | 0 .../{temp => sample_inputs}/sample10008.txt | 0 .../{temp => sample_inputs}/sample10009.txt | 0 .../{temp => sample_inputs}/sample1001.txt | 0 .../{temp => sample_inputs}/sample10010.txt | 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a/nbs/benchmark/001_covid.ipynb b/nbs/benchmark/001_covid.ipynb deleted file mode 100644 index 5f662c33..00000000 --- a/nbs/benchmark/001_covid.ipynb +++ /dev/null @@ -1,1065 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "skip_showdoc: true\n", - "skip_exec: true\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp banchmark.covid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - "\n", - "\n", - "
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#| hide\n", - "from nbdev.showdoc import show_doc\n", - "from IPython.display import display, HTML\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "from itables import init_notebook_mode,show\n", - "init_notebook_mode(all_interactive=False,connected=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| exports\n", - "# importing dependencies\n", - "import re\n", - "import csv\n", - "import time\n", - "import pandas as pd\n", - "from pandas import DataFrame\n", - "from pathlib import Path\n", - "from spannerlib import get_magic_session,Session\n", - "from spannerlib.ie_func.basic import rgx, rgx_is_match, rgx_split, span_arity, span_contained\n", - "\n", - "VERSION = \"OLD\"\n", - "# VERSION = \"SPANNERFLOW\"\n", - "# VERSION = \"SPANNERFLOW_PYTHON_IE\"\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " from spannerflow.span import Span\n", - "else:\n", - " from spannerlib import Span\n", - "sess = get_magic_session()\n", - "\n", - "if VERSION == \"SPANNERFLOW_PYTHON_IE\":\n", - " sess.register('py_rgx', rgx, [str, Span], span_arity)\n", - " sess.register('py_rgx_split', rgx_split, [str, Span], [Span,Span])\n", - " sess.register('py_rgx_is_match', rgx_is_match, [str, Span], [bool])\n", - " sess.register('py_span_contained', span_contained, [Span, Span], [bool]) \n", - "\n", - "import spacy\n", - "nlp = spacy.load(\"en_core_web_sm\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "start_time = time.time()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#|exports\n", - "# configurations\n", - "input_dir = Path('covid_data/sample_inputs')\n", - "data_dir = Path('covid_data/rules_data')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| exports\n", - "def split_sentence(text):\n", - " \"\"\"\n", - " Splits a text into individual sentences. using spacy's sentence detection.\n", - " \n", - " Returns:\n", - " str: Individual sentences extracted from the input text.\n", - " \"\"\"\n", - "\n", - " doc = nlp(str(text))\n", - " start = 0\n", - " for sentence in doc.sents:\n", - " end = start+len(sentence.text)\n", - " # note that we yield a Span object, so we can keep track of the locations of the sentences\n", - " yield Span(text,start,end)\n", - " start = end + 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| exports\n", - "class LemmaFromList():\n", - " def __init__(self,lemma_list):\n", - " self.lemma_list = lemma_list\n", - "\n", - " def __call__(self,text):\n", - " doc = nlp(str(text))\n", - " for word in doc:\n", - " start = word.idx\n", - " end = start + len(word.text)\n", - " if word.lemma_ in self.lemma_list:\n", - " yield (Span(text,start,end),word.lemma_)\n", - " elif word.like_num:\n", - " yield (Span(text,start,end),'like_num')\n", - " else:\n", - " pass\n", - "\n", - "lemma_list = (data_dir/'lemma_words.txt').read_text().split()\n", - "lemmatizer = LemmaFromList(lemma_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| exports\n", - "class PosFromList():\n", - " def __init__(self,pos_list):\n", - " self.pos_list = pos_list\n", - " def __call__(self,text):\n", - " doc = nlp(str(text))\n", - " for word in doc:\n", - " start = word.idx\n", - " end = start + len(word.text)\n", - " if word.pos_ in self.pos_list:\n", - " yield (Span(text,start,end),word.pos_)\n", - "\n", - "pos_annotator = PosFromList([\"NOUN\", \"PROPN\", \"PRON\", \"ADJ\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| exports\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " sess.register('split_sentence',split_sentence,[Span],[Span])\n", - " sess.register('pos',pos_annotator,[Span],[Span,str])\n", - " sess.register('lemma',lemmatizer,[Span],[Span,str])\n", - "else:\n", - " sess.register('split_sentence',split_sentence,[str],[Span])\n", - " sess.register('pos',pos_annotator,[str],[Span,str])\n", - " sess.register('lemma',lemmatizer,[str],[Span,str])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| exports\n", - "def rewrite(text,span_label_pairs):\n", - " \"\"\"rewrites a string given a dataframe with spans and the string to rewrite them to\n", - " assumes that the spans belong to the text\n", - "\n", - " Args:\n", - " text (str like): string to rewrite\n", - " span_label_pairs (pd.Dataframe) dataframe with two columns, first is spans in the doc to rewrite\n", - " second is what to rewrite to\n", - " Returns:\n", - " The rewritten string\n", - " \"\"\" \n", - " if isinstance(text,Span):\n", - " text = text.as_str()\n", - " span_label_pairs = sorted(list(span_label_pairs.itertuples(index=False,name=None)), key=lambda x: x[0].start)\n", - "\n", - " rewritten_text = ''\n", - " current_pos = 0\n", - " for span,label in span_label_pairs:\n", - " rewritten_text += text[current_pos:span.start] + label \n", - " current_pos = span.end\n", - "\n", - " rewritten_text += text[current_pos:]\n", - "\n", - " return rewritten_text\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def rewrite_docs(docs,span_label,new_version):\n", - " \"\"\"Given a dataframe of documents of the form (path,doc,version) and a dataframe of spans to rewrite\n", - " of the form (path,word,from_span,to_tag), rewrites the documents and returns a new dataframe of the form\n", - " (path,doc,new_version)\n", - "\n", - " \"\"\"\n", - " new_tuples =[]\n", - " span_label.columns = ['P','D','W','L']\n", - " for path,doc,_ in docs.itertuples(index=False,name=None):\n", - " span_label_per_doc = span_label[span_label['P'] == path][['W','L']]\n", - " new_text = rewrite(doc,span_label_per_doc)\n", - " new_tuples.append((path,new_text,new_version))\n", - " return pd.DataFrame(new_tuples,columns=['P','D','V'])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "sess.import_rel(\"ConceptTagRules\",data_dir/\"concept_tags_rules.csv\" , delim=\",\")\n", - "sess.import_rel(\"TargetTagRules\",data_dir/\"target_rules.csv\",delim=\",\")\n", - "sess.import_rel(\"SectionTags\",data_dir/\"section_tags.csv\",delim=\",\")\n", - "sess.import_rel(\"PositiveSectionTags\",data_dir/\"positive_section_tags.csv\",delim=\",\")\n", - "sess.import_rel(\"SentenceContextRules\",data_dir/'sentence_context_rules.csv',delim=\"#\")\n", - "sess.import_rel(\"PostprocessPatternRules\",data_dir/'postprocess_pattern_rules.csv',delim=\"#\")\n", - "sess.import_rel(\"PostprocessRulesWithAttributes\",data_dir/'postprocess_attributes_rules.csv',delim=\"#\")\n", - "sess.import_rel(\"NextSentencePostprocessPatternRules\",data_dir/'postprocess_pattern_next_sentence_rules.csv',delim=',')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PathDocVersion
0sample10.txtpatient was screened for cov-19. results came ...raw_text
1sample8.txtPatient was sent for a covid test. Someone was...raw_text
2sample9.txtPatient had contact patient with coronavirus. ...raw_text
3sample7.txtElevated cholesterol levels require further as...raw_text
4sample6.txtThe patient have reported novel coronavirus.raw_text
5sample4.txtneg covid education.raw_text
6sample5.txtpositive covid precaution.raw_text
7sample1.txtPatient presents to be tested for COVID-19. Hi...raw_text
8sample2.txtThe patient was tested for Coronavirus 2019. R...raw_text
9sample3.txtProblem List: 1. Pneumonia 2. Novel Coronaviru...raw_text
\n", - "
" - ], - "text/plain": [ - " Path Doc Version\n", - "0 sample10.txt patient was screened for cov-19. results came ... raw_text\n", - "1 sample8.txt Patient was sent for a covid test. Someone was... raw_text\n", - "2 sample9.txt Patient had contact patient with coronavirus. ... raw_text\n", - "3 sample7.txt Elevated cholesterol levels require further as... raw_text\n", - "4 sample6.txt The patient have reported novel coronavirus. raw_text\n", - "5 sample4.txt neg covid education. raw_text\n", - "6 sample5.txt positive covid precaution. raw_text\n", - "7 sample1.txt Patient presents to be tested for COVID-19. Hi... raw_text\n", - "8 sample2.txt The patient was tested for Coronavirus 2019. R... raw_text\n", - "9 sample3.txt Problem List: 1. Pneumonia 2. Novel Coronaviru... raw_text" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#| export\n", - "from glob import glob\n", - "file_paths = [Path(p) for p in glob(str(input_dir/'*.txt'))]\n", - "raw_docs = pd.DataFrame([\n", - " [p.name,p.read_text(),'raw_text'] for p in file_paths\n", - "],columns=['Path','Doc','Version']\n", - ")\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " sess.import_rel('Docs',raw_docs, scheme=[str, Span, str])\n", - "else:\n", - " sess.import_rel('Docs',raw_docs)\n", - "raw_docs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "Lemmas(P,D,Word,Lem)<-Docs(P,D,\"raw_text\"),lemma(D)->(Word,Lem)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "lemma_tags = sess.export('?Lemmas(P,D,W,L)')\n", - "lemma_docs = rewrite_docs(raw_docs,lemma_tags,'lemma')\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " sess.import_rel('Docs',lemma_docs, scheme=[str, Span, str])\n", - "else:\n", - " sess.import_rel('Docs',lemma_docs)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "%%spannerlog\n", - "LemmaConceptMatches(Path,Doc,Span,Label) <- \n", - " Docs(Path,Doc,\"lemma\"),\n", - " ConceptTagRules(Pattern, Label, \"lemma\"),\n", - " # TODO CHANGE: on different version\n", - " rgx(Pattern,Doc) -> (Span)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "lemma_concept_matches = sess.export('?LemmaConceptMatches(Path,Doc,Span,Label)')\n", - "lemma_concepts = rewrite_docs(lemma_docs,lemma_concept_matches,'lemma_concept')\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " sess.import_rel('Docs',lemma_concepts, scheme=[str, Span, str])\n", - "else:\n", - " sess.import_rel('Docs',lemma_concepts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "# here we get the spans of all POS\n", - "Pos(P,D,Word,Lem)<-Docs(P,D,\"lemma_concept\"),pos(D)->(Word,Lem)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "# here we look for concept rule matches where the matched word is also tagged via POS\n", - "PosConceptMatches(Path,Doc,Span,Label) <- \n", - " Docs(Path,Doc,\"lemma_concept\"),\n", - " ConceptTagRules(Pattern, Label, \"pos\"),\n", - " # TODO CHANGE: on different version\n", - " rgx(Pattern,Doc) -> (Span),\n", - " Pos(Path,Doc,Span,POSLabel).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "pos_concept_matches = sess.export('?PosConceptMatches(P,D,W,L)')\n", - "pos_concept_docs = rewrite_docs(lemma_concepts,pos_concept_matches,'pos_concept')\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " sess.import_rel('Docs',pos_concept_docs, scheme=[str, Span, str])\n", - "else:\n", - " sess.import_rel('Docs',pos_concept_docs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "TargetMatches(Path,Doc, Span, Label) <- \n", - " Docs(Path,Doc,\"pos_concept\"),\n", - " # TODO CHANGE: on different version\n", - " TargetTagRules(Pattern, Label), rgx(Pattern,Doc) -> (Span)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "target_matches = sess.export('?TargetMatches(P,D,W,L)')\n", - "target_rule_docs = rewrite_docs(pos_concept_docs,target_matches,'target_concept')\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " sess.import_rel('Docs',target_rule_docs, scheme=[str, Span, str])\n", - "else:\n", - " sess.import_rel('Docs',target_rule_docs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "section_tags = pd.read_csv(data_dir/'section_tags.csv',names=['literal','tag'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Lab results:|ADDENDUM:|Addendum:|ALLERGIC REACTIONS:|ALLERGIES:|CC:|CHIEF COMPLAINT:|Chief Complaint:|COMMENTS:|ADMISSION DIAGNOSES:|DIAGNOSES:|Diagnosis:|Primary Diagnosis:|Primary:|SECONDARY DIAGNOSES:|Secondary Diagnoses:|Secondary Diagnosis:|Secondary:|Family History:|Brief Hospital Course:|CONCISE SUMMARY OF HOSPITAL COURSE BY ISSUE/SYSTEM:|HOSPITAL COURSE:|SUMMARY OF HOSPITAL COURSE:|IMAGING:|INTERPRETATION:|Imaging:|MRI:|Radiology:|ADMISSION LABS:|Admission Labs:|Discharge Labs:|ECHO:|FINDINGS:|Findings:|INDICATION:|LABS:|Labs:|MICRO:|Micro:|Microbiology:|Pertinent Results:|STUDIES:|Studies:|ACTIVE MEDICATIONS LIST:|ACTIVE MEDICATIONS:|ADMISSION MEDICATIONS:|CURRENT MEDICATIONS:|DISCHARGE MEDICATIONS:|Discharge Medications:|HOME MEDICATIONS:|MEDICATIONS AT HOME:|MEDICATIONS LIST:|MEDICATIONS ON ADMISSION:|MEDICATIONS ON DISCHARGE:|MEDICATIONS ON TRANSFER:|MEDICATIONS PRIOR TO ADMISSION:|MEDICATIONS:|MEDICATIONS:|Neuro:|A/P:|ASSESSMENT/PLAN:|ASSESSMENT:|Assessment/Plan:|Clinical Impression:|DISCHARGE DIAGNOSES:|DISCHARGE DIAGNOSIS:|Discharge Condition:|Discharge Diagnoses:|Discharge Diagnosis:|Discharge Disposition:|FINAL DIAGNOSES:|FINAL DIAGNOSIS:|IMPRESSION:|Impression and Plan:|Impression and Recommendation:|Facility:|Service:|Current Medical Problems:|History of Chronic Illness:|MHx:|PAST HISTORY:|PAST MEDICAL HISTORY:|PAST MEDICAL Hx:|PAST SURGICAL HISTORY:|PMH:|PMHx:|Past Medical History:|UNDERLYING MEDICAL CONDITION:|Education:|Patient Education:|DISCHARGE INSTRUCTIONS/FOLLOWUP:|DISCHARGE INSTRUCTIONS:|Discharge Instructions:|Followup Instructions:|PE:|PHYSICAL EXAM:|PHYSICAL EXAMINATION:|Physical Exam:|Active Problem List:|Current Problems:|Medical Problems:|PROBLEM LIST:|Problem List:|REASON FOR THIS EXAMINATION:|Electronic Signature:|Signed electronically by:|PMHSx:|PSH:|SH:|Sexual History:|Social History:'" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#| export\n", - "# we will programatically build a regex that matches all the section patterns\n", - "section_delimeter_pattern = section_tags['literal'].str.cat(sep='|')\n", - "sess.import_var('section_delimeter_pattern',section_delimeter_pattern)\n", - "section_delimeter_pattern" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "# we get section spans and their content using our regex pattern and the rgx_split ie function\n", - "Sections(P,D,Sec,Content)<-Docs(P,D,\"target_concept\"),\n", - " rgx_split($section_delimeter_pattern,D)->(SecSpan,Content),\n", - " as_str(SecSpan)->(Sec).\n", - "\n", - "PositiveSections(P,D,Sec,Content)<-Sections(P,D,Sec,Content),SectionTags(Sec,Tag),PositiveSectionTags(Tag)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "Sents(P,S)<-Docs(P,D,\"target_concept\"),split_sentence(D)->(S)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from itertools import pairwise\n", - "\n", - "def sentence_pairs(text):\n", - " yield from pairwise(split_sentence(text))\n", - "\n", - "if VERSION in [\"SPANNERFLOW\", \"SPANNERFLOW_PYTHON_IE\"]:\n", - " sess.register('sentence_pairs',sentence_pairs,[Span],[Span,Span])\n", - "else:\n", - " sess.register('sentence_pairs',sentence_pairs,[str],[Span,Span])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def is_adjacent(span1,span2):\n", - " yield span1.name==span2.name and span1.end +1 == span2.start\n", - "\n", - "sess.register('is_adjacent',is_adjacent,[Span,Span],[bool])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "SentPairs(P,S1,S2)<-Sents(P,S1),Sents(P,S2),is_adjacent(S1,S2)->(True).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "# first we get the covid mentions and their surrounding sentences, using the span_contained ie function\n", - "# TODO CHANGE: on different version\n", - "CovidMentions(Path, Span) <- Docs(Path,D,\"target_concept\"), rgx(\"COVID-19\",D) -> (Span).\n", - "\n", - "# TODO CHANGE: on different version\n", - "CovidMentionSents(P,Mention,Sent)<-CovidMentions(P,Mention),Sents(P,Sent),span_contained(Mention,Sent)->(True)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "\n", - "# note that for ease of debugging, we extended our head to track which rule a fact was derived from\n", - "\n", - "# a tag is positive if it is contained in a positive section\n", - "CovidTags(Path,Mention,'positive','section')<-\n", - " PositiveSections(Path,D,Title,Section),\n", - " CovidMentions(Path,Mention),\n", - " # TODO CHANGE: on different version\n", - " span_contained(Mention,Section)->(True).\n", - "\n", - "# Context rules tags\n", - "CovidTags(Path,Mention,Tag,'sentence context')<-\n", - " CovidMentionSents(Path,Mention,Sent),\n", - " SentenceContextRules(Pattern,Tag,DisambiguationPattern),\n", - " # TODO CHANGE: on different version\n", - " rgx(Pattern,Sent)->(ContextSpan),\n", - " # TODO CHANGE: on different version\n", - " span_contained(Mention,ContextSpan)->(True),\n", - " # TODO CHANGE: on different version\n", - " rgx_is_match(DisambiguationPattern,Sent)->(False).\n", - "\n", - "# post processing based on pattern\n", - "CovidTags(Path,Mention,Tag,'post pattern')<-\n", - " CovidMentionSents(Path,Mention,Sent),\n", - " PostprocessPatternRules(Pattern,Tag),\n", - " # TODO CHANGE: on different version\n", - " rgx(Pattern,Sent)->(ContextSpan),\n", - " # TODO CHANGE: on different version\n", - " span_contained(Mention,ContextSpan)->(True).\n", - "\n", - "# post processing based on pattern and existing attributes\n", - "# notice the recursive call to CovidTags\n", - "CovidTags(Path,Mention,Tag,\"post attribute change\")<-\n", - " CovidTags(Path,Mention,OldTag,Derivation),\n", - " PostprocessRulesWithAttributes(Pattern,OldTag,Tag),\n", - " CovidMentionSents(Path,Mention,Sent),\n", - " # TODO CHANGE: on different version\n", - " rgx(Pattern,Sent)->(ContextSpan),\n", - " # TODO CHANGE: on different version\n", - " span_contained(Mention,ContextSpan)->(True).\n", - "\n", - "# post processing based on pattern in the next sentence\n", - "CovidTags(Path,Mention,Tag,\"next sentence\")<-\n", - " CovidMentionSents(Path,Mention,Sent),\n", - " SentPairs(Path,Sent,NextSent),\n", - " PostprocessPatternRules(Pattern,Tag),\n", - " # TODO CHANGE: on different version\n", - " rgx(Pattern,NextSent)->(ContextSpan).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "def agg_mention(group):\n", - " \"\"\"\n", - " aggregates attribute groups of covid spans\n", - " \"\"\"\n", - " if VERSION == \"OLD\":\n", - " group = group.values\n", - " if 'IGNORE' in group:\n", - " return 'IGNORE'\n", - " elif 'negated' in group and not 'no_negated' in group:\n", - " return 'negated'\n", - " elif 'future' in group and not 'no_future' in group:\n", - " return 'negated'\n", - " elif 'other experiencer' in group or 'not relevant' in group:\n", - " return 'negated'\n", - " elif 'positive' in group and not 'uncertain' in group and not 'no_positive' in group:\n", - " return 'positive'\n", - " else:\n", - " return 'uncertain'\n", - "\n", - "#| export\n", - "def AggDocumentTags(group):\n", - " \"\"\"\n", - " Classifies a document as 'POS', 'UNK', or 'NEG' based on COVID-19 attributes.\n", - " \"\"\"\n", - " if VERSION == \"OLD\":\n", - " group = group.values\n", - " if 'positive' in group:\n", - " return 'POS'\n", - " elif 'uncertain' in group:\n", - " return 'UNK'\n", - " elif 'negated' in group:\n", - " return 'NEG'\n", - " else:\n", - " return 'UNK'\n", - "\n", - "\n", - "sess.register_agg('agg_mention',agg_mention,[str],[str])\n", - "sess.register_agg('agg_doc_tags',AggDocumentTags,[str],[str])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%spannerlog\n", - "AggregatedCovidTags(Path,Mention,agg_mention(Tag))<-\n", - " CovidTags(Path,Mention,Tag,Derivation).\n", - "\n", - "DocumentTags(Path,agg_doc_tags(Tag))<-\n", - " AggregatedCovidTags(Path,Mention,Tag).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "doc_tags = sess.export('?DocumentTags(P,T)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " P T\n", - "0 sample1.txt UNK\n", - "1 sample10.txt UNK\n", - "2 sample2.txt UNK\n", - "3 sample3.txt UNK\n", - "4 sample4.txt UNK\n", - "5 sample5.txt UNK\n", - "6 sample6.txt UNK\n", - "7 sample7.txt UNK\n", - "8 sample8.txt UNK\n", - "9 sample9.txt UNK" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#| export\n", - "paths = pd.DataFrame([p.name for p in file_paths],columns=['P'])\n", - "classification = paths.merge(doc_tags,on='P',how='outer')\n", - "classification['T']=classification['T'].fillna('UNK')\n", - "classification" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of Documents: 10\n", - "Time taken: 5.86 seconds\n" - ] - } - ], - "source": [ - "end_time = time.time()\n", - "print(f\"Number of Documents: {len(file_paths)}\")\n", - "print(f\"Time taken: {end_time-start_time:.2f} seconds\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":11: SyntaxWarning: invalid escape sequence '\\s'\n", - ":11: SyntaxWarning: invalid escape sequence '\\s'\n", - ":11: SyntaxWarning: invalid escape sequence '\\s'\n", - ":11: SyntaxWarning: invalid escape sequence '\\s'\n" - ] - } - ], - "source": [ - "#|hide\n", - "import nbdev; nbdev.nbdev_export()\n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/nbs/benchmark/covid_bench.py b/nbs/benchmark/covid_bench.py new file mode 100644 index 00000000..5be0b737 --- /dev/null +++ b/nbs/benchmark/covid_bench.py @@ -0,0 +1,213 @@ +import time +from glob import glob +import pandas as pd +from pandas import DataFrame +from pathlib import Path +from spannerlib import get_magic_session,Session,Span +sess = get_magic_session() + +import spacy +nlp = spacy.load("en_core_web_sm") + +# configurations +slog_file = Path('covid_bench_logic.pl') +input_dir = Path('covid_data/sample_inputs') +data_dir = Path('covid_data/rules_data') + +start_time = time.time() + +def split_sentence(text): + """ + Splits a text into individual sentences. using spacy's sentence detection. + + Returns: + str: Individual sentences extracted from the input text. + """ + + doc = nlp(str(text)) + start = 0 + for sentence in doc.sents: + end = start+len(sentence.text) + # note that we yield a Span object, so we can keep track of the locations of the sentences + yield Span(text,start,end) + start = end + 1 + +class LemmaFromList(): + def __init__(self,lemma_list): + self.lemma_list = lemma_list + + def __call__(self,text): + doc = nlp(str(text)) + for word in doc: + start = word.idx + end = start + len(word.text) + if word.lemma_ in self.lemma_list: + yield (Span(text,start,end),word.lemma_) + elif word.like_num: + yield (Span(text,start,end),'like_num') + else: + pass + +lemma_list = (data_dir/'lemma_words.txt').read_text().split() +lemmatizer = LemmaFromList(lemma_list) + +class PosFromList(): + def __init__(self,pos_list): + self.pos_list = pos_list + def __call__(self,text): + doc = nlp(str(text)) + for word in doc: + start = word.idx + end = start + len(word.text) + if word.pos_ in self.pos_list: + yield (Span(text,start,end),word.pos_) + +pos_annotator = PosFromList(["NOUN", "PROPN", "PRON", "ADJ"]) + +def agg_mention(group): + """ + aggregates attribute groups of covid spans + """ + if 'IGNORE' in group.values: + return 'IGNORE' + elif 'negated' in group.values and not 'no_negated' in group.values: + return 'negated' + elif 'future' in group.values and not 'no_future' in group.values: + return 'negated' + elif 'other experiencer' in group.values or 'not relevant' in group.values: + return 'negated' + elif 'positive' in group.values and not 'uncertain' in group.values and not 'no_positive' in group.values: + return 'positive' + else: + return 'uncertain' + +def AggDocumentTags(group): + """ + Classifies a document as 'POS', 'UNK', or 'NEG' based on COVID-19 attributes. + """ + if 'positive' in group.values: + return 'POS' + elif 'uncertain' in group.values: + return 'UNK' + elif 'negated' in group.values: + return 'NEG' + else: + return 'UNK' + +def rewrite(text,span_label_pairs): + """rewrites a string given a dataframe with spans and the string to rewrite them to + assumes that the spans belong to the text + + Args: + text (str like): string to rewrite + span_label_pairs (pd.Dataframe) dataframe with two columns, first is spans in the doc to rewrite + second is what to rewrite to + Returns: + The rewritten string + """ + if isinstance(text,Span): + text = text.as_str() + span_label_pairs = sorted(list(span_label_pairs.itertuples(index=False,name=None)), key=lambda x: x[0].start) + + rewritten_text = '' + current_pos = 0 + for span,label in span_label_pairs: + rewritten_text += text[current_pos:span.start] + label + current_pos = span.end + + rewritten_text += text[current_pos:] + + return rewritten_text + + +def rewrite_docs(docs,span_label,new_version): + """Given a dataframe of documents of the form (path,doc,version) and a dataframe of spans to rewrite + of the form (path,word,from_span,to_tag), rewrites the documents and returns a new dataframe of the form + (path,doc,new_version) + + """ + new_tuples =[] + span_label.columns = ['P','D','W','L'] + for path,doc,_ in docs.itertuples(index=False,name=None): + span_label_per_doc = span_label[span_label['P'] == path][['W','L']] + new_text = rewrite(doc,span_label_per_doc) + new_tuples.append((path,new_text,new_version)) + return pd.DataFrame(new_tuples,columns=['P','D','V']) + +file_paths = [] +def main(input_dir,data_dir,logic_file, start=0, end=10): + global file_paths + sess = Session() + # define callback functions + sess.register('split_sentence',split_sentence,[(str,Span)],[Span]) + sess.register('pos',pos_annotator,[(Span,str)],[Span,str]) + sess.register('lemma',lemmatizer,[(Span,str)],[Span,str]) + sess.register_agg('agg_mention',agg_mention,[str],[str]) + sess.register_agg('agg_doc_tags',AggDocumentTags,[str],[str]) + + # bring in code as data + sess.import_rel("ConceptTagRules",data_dir/"concept_tags_rules.csv" , delim=",") + sess.import_rel("TargetTagRules",data_dir/"target_rules.csv",delim=",") + sess.import_rel("SectionTags",data_dir/"section_tags.csv",delim=",") + sess.import_rel("PositiveSectionTags",data_dir/"positive_section_tags.csv",delim=",") + sess.import_rel("SentenceContextRules",data_dir/'sentence_context_rules.csv',delim="#") + sess.import_rel("PostprocessPatternRules",data_dir/'postprocess_pattern_rules.csv',delim="#") + sess.import_rel("PostprocessRulesWithAttributes",data_dir/'postprocess_attributes_rules.csv',delim="#") + sess.import_rel("NextSentencePostprocessPatternRules",data_dir/'postprocess_pattern_next_sentence_rules.csv',delim=',') + + + # we will programatically build a regex that matches all the section patterns + section_tags = pd.read_csv(data_dir/'section_tags.csv',names=['literal','tag']) + section_delimeter_pattern = section_tags['literal'].str.cat(sep='|') + sess.import_var('section_delimeter_pattern',section_delimeter_pattern) + + # bring in data + file_paths = [Path(p) for p in glob(str(input_dir/'*.txt'))] + file_paths.sort() + file_paths = file_paths[start:end] + raw_docs = pd.DataFrame([ + [p.name,p.read_text(),'raw_text'] for p in file_paths + ],columns=['Path','Doc','Version'] + ) + sess.import_rel('Docs',raw_docs) + + # load logic, note that since we did not define the data relations in the logic file, + # we need to load the logic after the data has been loaded + sess.export(logic_file.read_text()) + + ## Rewritting the documents + lemma_tags = sess.export('?Lemmas(P,D,W,L)') + lemma_docs = rewrite_docs(raw_docs,lemma_tags,'lemma') + sess.import_rel('Docs',lemma_docs) + + lemma_concept_matches = sess.export('?LemmaConceptMatches(Path,Doc,Span,Label)') + lemma_concepts = rewrite_docs(lemma_docs,lemma_concept_matches,'lemma_concept') + sess.import_rel('Docs',lemma_concepts) + + pos_concept_matches = sess.export('?PosConceptMatches(P,D,W,L)') + pos_concept_docs = rewrite_docs(lemma_concepts,pos_concept_matches,'pos_concept') + sess.import_rel('Docs',pos_concept_docs) + + target_matches = sess.export('?TargetMatches(P,D,W,L)') + target_rule_docs = rewrite_docs(pos_concept_docs,target_matches,'target_concept') + sess.import_rel('Docs',target_rule_docs) + + ## computing the tags based on the target concept documents + doc_tags = sess.export('?DocumentTags(P,T)') + + # handling files with no mentions + paths = pd.DataFrame([p.name for p in file_paths],columns=['P']) + classification = paths.merge(doc_tags,on='P',how='outer') + classification['T']=classification['T'].fillna('UNK') + classification + + return classification + + +for i in range(0, 1000, 20): + res = main(input_dir,data_dir,slog_file, start=i, end=i+20) + print(res) + +end_time = time.time() +print(f"Number of Documents: {len(file_paths)}") +print(f"Time taken: {end_time-start_time:.2f} seconds") \ No newline at end of file diff --git a/nbs/benchmark/covid_bench_logic.pl b/nbs/benchmark/covid_bench_logic.pl new file mode 100644 index 00000000..eb90bd08 --- /dev/null +++ b/nbs/benchmark/covid_bench_logic.pl @@ -0,0 +1,79 @@ +Lemmas(P,D,Word,Lem)<-Docs(P,D,"raw_text"),lemma(D)->(Word,Lem). + +LemmaConceptMatches(Path,Doc,Span,Label) <- \ + Docs(Path,Doc,"lemma"),\ + ConceptTagRules(Pattern, Label, "lemma"),\ + rgx(Pattern,Doc) -> (Span). + +# here we get the spans of all POS +Pos(P,D,Word,Lem)<-Docs(P,D,"lemma_concept"),pos(D)->(Word,Lem). + +# here we look for concept rule matches where the matched word is also tagged via POS +PosConceptMatches(Path,Doc,Span,Label) <- \ + Docs(Path,Doc,"lemma_concept"),\ + ConceptTagRules(Pattern, Label, "pos"),\ + rgx(Pattern,Doc) -> (Span),\ + Pos(Path,Doc,Span,POSLabel). + +TargetMatches(Path,Doc, Span, Label) <- \ + Docs(Path,Doc,"pos_concept"),\ + TargetTagRules(Pattern, Label), rgx(Pattern,Doc) -> (Span). + +# we get section spans and their content using our regex pattern and the rgx_split ie function +Sections(P,D,Sec,Content)<-Docs(P,D,"target_concept"),\ + rgx_split($section_delimeter_pattern,D)->(SecSpan,Content),\ + as_str(SecSpan)->(Sec). + +PositiveSections(P,D,Sec,Content)<-Sections(P,D,Sec,Content),SectionTags(Sec,Tag),PositiveSectionTags(Tag). + +Sents(P,S)<-Docs(P,D,"target_concept"),split_sentence(D)->(S). + +SentPairs(P,S1,S2)<-Sents(P,S1),Sents(P,S2),expr_eval("{0}.end +1 == {1}.start",S1,S2)->(True). + +# first we get the covid mentions and their surrounding sentences, using the span_contained ie function +CovidMentions(Path, Span) <- Docs(Path,D,"target_concept"), rgx("COVID-19",D) -> (Span). +CovidMentionSents(P,Mention,Sent)<-CovidMentions(P,Mention),Sents(P,Sent),span_contained(Mention,Sent)->(True). + +# note that for ease of debugging, we extended our head to track which rule a fact was derived from +# a tag is positive if it is contained in a positive section +CovidTags(Path,Mention,'positive','section')<-\ + PositiveSections(Path,D,Title,Section),\ + CovidMentions(Path,Mention),\ + span_contained(Mention,Section)->(True). + +# Context rules tags +CovidTags(Path,Mention,Tag,'sentence context')<-\ + CovidMentionSents(Path,Mention,Sent),\ + SentenceContextRules(Pattern,Tag,DisambiguationPattern),\ + rgx(Pattern,Sent)->(ContextSpan),\ + span_contained(Mention,ContextSpan)->(True),\ + rgx_is_match(DisambiguationPattern,Sent)->(False). + +# post processing based on pattern +CovidTags(Path,Mention,Tag,'post pattern')<-\ + CovidMentionSents(Path,Mention,Sent),\ + PostprocessPatternRules(Pattern,Tag),\ + rgx(Pattern,Sent)->(ContextSpan),\ + span_contained(Mention,ContextSpan)->(True). + +# post processing based on pattern and existing attributes +# notice the recursive call to CovidTags +CovidTags(Path,Mention,Tag,"post attribute change")<-\ + CovidTags(Path,Mention,OldTag,Derivation),\ + PostprocessRulesWithAttributes(Pattern,OldTag,Tag),\ + CovidMentionSents(Path,Mention,Sent),\ + rgx(Pattern,Sent)->(ContextSpan),\ + span_contained(Mention,ContextSpan)->(True). + +# post processing based on pattern in the next sentence +CovidTags(Path,Mention,Tag,"next sentence")<-\ + CovidMentionSents(Path,Mention,Sent),\ + SentPairs(Path,Sent,NextSent),\ + PostprocessPatternRules(Pattern,Tag),\ + rgx(Pattern,NextSent)->(ContextSpan). + +AggregatedCovidTags(Path,Mention,agg_mention(Tag))<-\ + CovidTags(Path,Mention,Tag,Derivation). + +DocumentTags(Path,agg_doc_tags(Tag))<-\ + AggregatedCovidTags(Path,Mention,Tag). \ No newline at end of file diff --git a/nbs/benchmark/covid_data/covid_logic.pl b/nbs/benchmark/covid_data/covid_logic.pl deleted file mode 100644 index c1bb4f7b..00000000 --- a/nbs/benchmark/covid_data/covid_logic.pl +++ /dev/null @@ -1,2 +0,0 @@ -Lemmas(P,D,Word,Lem)<-Docs(P,D,"raw_text"),lemma(D)->(Word,Lem). - diff --git a/nbs/benchmark/covid_data/temp/sample100.txt b/nbs/benchmark/covid_data/sample_inputs/sample100.txt similarity index 100% rename from nbs/benchmark/covid_data/temp/sample100.txt rename to nbs/benchmark/covid_data/sample_inputs/sample100.txt diff --git a/nbs/benchmark/covid_data/temp/sample1000.txt b/nbs/benchmark/covid_data/sample_inputs/sample1000.txt similarity index 100% rename from nbs/benchmark/covid_data/temp/sample1000.txt rename to nbs/benchmark/covid_data/sample_inputs/sample1000.txt diff --git a/nbs/benchmark/covid_data/temp/sample10000.txt b/nbs/benchmark/covid_data/sample_inputs/sample10000.txt similarity index 100% rename from nbs/benchmark/covid_data/temp/sample10000.txt rename to 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b/nbs/benchmark/covid_data/sample_inputs/sample9999.txt similarity index 100% rename from nbs/benchmark/covid_data/temp/sample9999.txt rename to nbs/benchmark/covid_data/sample_inputs/sample9999.txt diff --git a/spannerlib/_modidx.py b/spannerlib/_modidx.py index dd308de0..b7d31c42 100644 --- a/spannerlib/_modidx.py +++ b/spannerlib/_modidx.py @@ -13,28 +13,6 @@ 'spannerlib/adding_inference_rules_to_term_graph.py'), 'spannerlib.adding_inference_rules_to_term_graph.AddRulesToTermGraph.run_pass': ( 'adding_inference_rules_to_term_graph.html#addrulestotermgraph.run_pass', 'spannerlib/adding_inference_rules_to_term_graph.py')}, - 'spannerlib.banchmark.covid': { 'spannerlib.banchmark.covid.AggDocumentTags': ( 'benchmark/covid.html#aggdocumenttags', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.LemmaFromList': ( 'benchmark/covid.html#lemmafromlist', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.LemmaFromList.__call__': ( 'benchmark/covid.html#lemmafromlist.__call__', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.LemmaFromList.__init__': ( 'benchmark/covid.html#lemmafromlist.__init__', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.PosFromList': ( 'benchmark/covid.html#posfromlist', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.PosFromList.__call__': ( 'benchmark/covid.html#posfromlist.__call__', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.PosFromList.__init__': ( 'benchmark/covid.html#posfromlist.__init__', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.agg_mention': ( 'benchmark/covid.html#agg_mention', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.rewrite': ( 'benchmark/covid.html#rewrite', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.rewrite_docs': ( 'benchmark/covid.html#rewrite_docs', - 'spannerlib/banchmark/covid.py'), - 'spannerlib.banchmark.covid.split_sentence': ( 'benchmark/covid.html#split_sentence', - 'spannerlib/banchmark/covid.py')}, 'spannerlib.data_types': { 'spannerlib.data_types.AGGFunction': ( 'primitive_data_types.html#aggfunction', 'spannerlib/data_types.py'), 'spannerlib.data_types.FreeVar': ('primitive_data_types.html#freevar', 'spannerlib/data_types.py'), diff --git a/spannerlib/banchmark/__init__.py b/spannerlib/banchmark/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/spannerlib/banchmark/covid.py b/spannerlib/banchmark/covid.py deleted file mode 100644 index b57c7c45..00000000 --- a/spannerlib/banchmark/covid.py +++ /dev/null @@ -1,262 +0,0 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/benchmark/001_covid.ipynb. - -# %% auto 0 -__all__ = ['VERSION', 'sess', 'nlp', 'input_dir', 'data_dir', 'lemma_list', 'lemmatizer', 'pos_annotator', 'file_paths', - 'raw_docs', 'lemma_tags', 'lemma_docs', 'lemma_concept_matches', 'lemma_concepts', 'pos_concept_matches', - 'pos_concept_docs', 'target_matches', 'target_rule_docs', 'section_tags', 'section_delimeter_pattern', - 'doc_tags', 'paths', 'classification', 'split_sentence', 'LemmaFromList', 'PosFromList', 'rewrite', - 'rewrite_docs', 'agg_mention', 'AggDocumentTags'] - -# %% ../../nbs/benchmark/001_covid.ipynb 3 -# importing dependencies -import re -import csv -import time -import pandas as pd -from pandas import DataFrame -from pathlib import Path -from .. import get_magic_session,Session -from ..ie_func.basic import rgx, rgx_is_match, rgx_split, span_arity, span_contained - -VERSION = "OLD" -# VERSION = "SPANNERFLOW" -# VERSION = "SPANNERFLOW_PYTHON_IE" -if VERSION in ["SPANNERFLOW", "SPANNERFLOW_PYTHON_IE"]: - from spannerflow.span import Span -else: - from spannerlib import Span -sess = get_magic_session() - -if VERSION == "SPANNERFLOW_PYTHON_IE": - sess.register('py_rgx', rgx, [str, Span], span_arity) - sess.register('py_rgx_split', rgx_split, [str, Span], [Span,Span]) - sess.register('py_rgx_is_match', rgx_is_match, [str, Span], [bool]) - sess.register('py_span_contained', span_contained, [Span, Span], [bool]) - -import spacy -nlp = spacy.load("en_core_web_sm") - - -# %% ../../nbs/benchmark/001_covid.ipynb 5 -# configurations -input_dir = Path('covid_data/sample_inputs') -data_dir = Path('covid_data/rules_data') - -# %% ../../nbs/benchmark/001_covid.ipynb 6 -def split_sentence(text): - """ - Splits a text into individual sentences. using spacy's sentence detection. - - Returns: - str: Individual sentences extracted from the input text. - """ - - doc = nlp(str(text)) - start = 0 - for sentence in doc.sents: - end = start+len(sentence.text) - # note that we yield a Span object, so we can keep track of the locations of the sentences - yield Span(text,start,end) - start = end + 1 - -# %% ../../nbs/benchmark/001_covid.ipynb 7 -class LemmaFromList(): - def __init__(self,lemma_list): - self.lemma_list = lemma_list - - def __call__(self,text): - doc = nlp(str(text)) - for word in doc: - start = word.idx - end = start + len(word.text) - if word.lemma_ in self.lemma_list: - yield (Span(text,start,end),word.lemma_) - elif word.like_num: - yield (Span(text,start,end),'like_num') - else: - pass - -lemma_list = (data_dir/'lemma_words.txt').read_text().split() -lemmatizer = LemmaFromList(lemma_list) - -# %% ../../nbs/benchmark/001_covid.ipynb 8 -class PosFromList(): - def __init__(self,pos_list): - self.pos_list = pos_list - def __call__(self,text): - doc = nlp(str(text)) - for word in doc: - start = word.idx - end = start + len(word.text) - if word.pos_ in self.pos_list: - yield (Span(text,start,end),word.pos_) - -pos_annotator = PosFromList(["NOUN", "PROPN", "PRON", "ADJ"]) - -# %% ../../nbs/benchmark/001_covid.ipynb 9 -if VERSION in ["SPANNERFLOW", "SPANNERFLOW_PYTHON_IE"]: - sess.register('split_sentence',split_sentence,[Span],[Span]) - sess.register('pos',pos_annotator,[Span],[Span,str]) - sess.register('lemma',lemmatizer,[Span],[Span,str]) -else: - sess.register('split_sentence',split_sentence,[str],[Span]) - sess.register('pos',pos_annotator,[str],[Span,str]) - sess.register('lemma',lemmatizer,[str],[Span,str]) - -# %% ../../nbs/benchmark/001_covid.ipynb 10 -def rewrite(text,span_label_pairs): - """rewrites a string given a dataframe with spans and the string to rewrite them to - assumes that the spans belong to the text - - Args: - text (str like): string to rewrite - span_label_pairs (pd.Dataframe) dataframe with two columns, first is spans in the doc to rewrite - second is what to rewrite to - Returns: - The rewritten string - """ - if isinstance(text,Span): - text = text.as_str() - span_label_pairs = sorted(list(span_label_pairs.itertuples(index=False,name=None)), key=lambda x: x[0].start) - - rewritten_text = '' - current_pos = 0 - for span,label in span_label_pairs: - rewritten_text += text[current_pos:span.start] + label - current_pos = span.end - - rewritten_text += text[current_pos:] - - return rewritten_text - - -# %% ../../nbs/benchmark/001_covid.ipynb 11 -def rewrite_docs(docs,span_label,new_version): - """Given a dataframe of documents of the form (path,doc,version) and a dataframe of spans to rewrite - of the form (path,word,from_span,to_tag), rewrites the documents and returns a new dataframe of the form - (path,doc,new_version) - - """ - new_tuples =[] - span_label.columns = ['P','D','W','L'] - for path,doc,_ in docs.itertuples(index=False,name=None): - span_label_per_doc = span_label[span_label['P'] == path][['W','L']] - new_text = rewrite(doc,span_label_per_doc) - new_tuples.append((path,new_text,new_version)) - return pd.DataFrame(new_tuples,columns=['P','D','V']) - - -# %% ../../nbs/benchmark/001_covid.ipynb 12 -sess.import_rel("ConceptTagRules",data_dir/"concept_tags_rules.csv" , delim=",") -sess.import_rel("TargetTagRules",data_dir/"target_rules.csv",delim=",") -sess.import_rel("SectionTags",data_dir/"section_tags.csv",delim=",") -sess.import_rel("PositiveSectionTags",data_dir/"positive_section_tags.csv",delim=",") -sess.import_rel("SentenceContextRules",data_dir/'sentence_context_rules.csv',delim="#") -sess.import_rel("PostprocessPatternRules",data_dir/'postprocess_pattern_rules.csv',delim="#") -sess.import_rel("PostprocessRulesWithAttributes",data_dir/'postprocess_attributes_rules.csv',delim="#") -sess.import_rel("NextSentencePostprocessPatternRules",data_dir/'postprocess_pattern_next_sentence_rules.csv',delim=',') - - -# %% ../../nbs/benchmark/001_covid.ipynb 13 -from glob import glob -file_paths = [Path(p) for p in glob(str(input_dir/'*.txt'))] -raw_docs = pd.DataFrame([ - [p.name,p.read_text(),'raw_text'] for p in file_paths -],columns=['Path','Doc','Version'] -) -if VERSION in ["SPANNERFLOW", "SPANNERFLOW_PYTHON_IE"]: - sess.import_rel('Docs',raw_docs, scheme=[str, Span, str]) -else: - sess.import_rel('Docs',raw_docs) -raw_docs - -# %% ../../nbs/benchmark/001_covid.ipynb 15 -lemma_tags = sess.export('?Lemmas(P,D,W,L)') -lemma_docs = rewrite_docs(raw_docs,lemma_tags,'lemma') -if VERSION in ["SPANNERFLOW", "SPANNERFLOW_PYTHON_IE"]: - sess.import_rel('Docs',lemma_docs, scheme=[str, Span, str]) -else: - sess.import_rel('Docs',lemma_docs) - - -# %% ../../nbs/benchmark/001_covid.ipynb 17 -lemma_concept_matches = sess.export('?LemmaConceptMatches(Path,Doc,Span,Label)') -lemma_concepts = rewrite_docs(lemma_docs,lemma_concept_matches,'lemma_concept') -if VERSION in ["SPANNERFLOW", "SPANNERFLOW_PYTHON_IE"]: - sess.import_rel('Docs',lemma_concepts, scheme=[str, Span, str]) -else: - sess.import_rel('Docs',lemma_concepts) - -# %% ../../nbs/benchmark/001_covid.ipynb 20 -pos_concept_matches = sess.export('?PosConceptMatches(P,D,W,L)') -pos_concept_docs = rewrite_docs(lemma_concepts,pos_concept_matches,'pos_concept') -if VERSION in ["SPANNERFLOW", "SPANNERFLOW_PYTHON_IE"]: - sess.import_rel('Docs',pos_concept_docs, scheme=[str, Span, str]) -else: - sess.import_rel('Docs',pos_concept_docs) - -# %% ../../nbs/benchmark/001_covid.ipynb 22 -target_matches = sess.export('?TargetMatches(P,D,W,L)') -target_rule_docs = rewrite_docs(pos_concept_docs,target_matches,'target_concept') -if VERSION in ["SPANNERFLOW", "SPANNERFLOW_PYTHON_IE"]: - sess.import_rel('Docs',target_rule_docs, scheme=[str, Span, str]) -else: - sess.import_rel('Docs',target_rule_docs) - -# %% ../../nbs/benchmark/001_covid.ipynb 23 -section_tags = pd.read_csv(data_dir/'section_tags.csv',names=['literal','tag']) - -# %% ../../nbs/benchmark/001_covid.ipynb 24 -# we will programatically build a regex that matches all the section patterns -section_delimeter_pattern = section_tags['literal'].str.cat(sep='|') -sess.import_var('section_delimeter_pattern',section_delimeter_pattern) -section_delimeter_pattern - -# %% ../../nbs/benchmark/001_covid.ipynb 32 -def agg_mention(group): - """ - aggregates attribute groups of covid spans - """ - if VERSION == "OLD": - group = group.values - if 'IGNORE' in group: - return 'IGNORE' - elif 'negated' in group and not 'no_negated' in group: - return 'negated' - elif 'future' in group and not 'no_future' in group: - return 'negated' - elif 'other experiencer' in group or 'not relevant' in group: - return 'negated' - elif 'positive' in group and not 'uncertain' in group and not 'no_positive' in group: - return 'positive' - else: - return 'uncertain' - -#| export -def AggDocumentTags(group): - """ - Classifies a document as 'POS', 'UNK', or 'NEG' based on COVID-19 attributes. - """ - if VERSION == "OLD": - group = group.values - if 'positive' in group: - return 'POS' - elif 'uncertain' in group: - return 'UNK' - elif 'negated' in group: - return 'NEG' - else: - return 'UNK' - - -sess.register_agg('agg_mention',agg_mention,[str],[str]) -sess.register_agg('agg_doc_tags',AggDocumentTags,[str],[str]) - -# %% ../../nbs/benchmark/001_covid.ipynb 34 -doc_tags = sess.export('?DocumentTags(P,T)') - -# %% ../../nbs/benchmark/001_covid.ipynb 35 -paths = pd.DataFrame([p.name for p in file_paths],columns=['P']) -classification = paths.merge(doc_tags,on='P',how='outer') -classification['T']=classification['T'].fillna('UNK') -classification