Update main.py
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creatingModel.py
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123
creatingModel.py
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import lzma
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import re
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months = {'01': 'January', '02': 'February', '03': 'March',
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'04': 'April', '05': 'May', '06': 'June',
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'07': 'July', '08': 'August', '09': 'September',
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'10': 'October', '11': 'November', '12': 'December'}
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def dayToWord(day):
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day = int(day)
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if day > 3:
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return str(day) + 'th'
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elif day == 3:
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return str(day) + 'rd'
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elif day == 2 :
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return str(day) + 'nd'
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else: return str(day) + 'st'
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def numToWord(number):
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number = int(number)
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d = {1 : 'one', 2 : 'two', 3 : 'three', 4 : 'four', 5 : 'five',
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6 : 'six', 7 : 'seven', 8 : 'eight', 9 : 'nine', 10 : 'ten',
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11 : 'eleven', 12 : 'twelve', 13 : 'thirteen', 14 : 'fourteen',
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15 : 'fifteen', 16 : 'sixteen', 17 : 'seventeen', 18 : 'eighteen',
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19 : 'nineteen', 20 : 'twenty',
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30 : 'thirty', 40 : 'forty', 50 : 'fifty', 60 : 'sixty',
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70 : 'seventy', 80 : 'eighty', 90 : 'ninety' }
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if number < 20:
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return d[number]
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else:
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if number % 10 == 0: return d[number]
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else: return d[number // 10 * 10] + '-' + d[number % 10]
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def labelJurisdiction(text, jurisdiction):
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jurisdictions = []
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for match in re.finditer(jurisdiction, text):
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tup = (match.start(), match.end(), 'JURISDICTION')
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jurisdictions.append(tup)
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return jurisdictions
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def labelEffectiveDate(text, date):
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dates = []
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year, month, day = date.split('-')
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dateFormats = [month + '/' + day + '/' + year,
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month + '/' + day + '/' + year[-2:],
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month[1] + '/' + day + '/' + year,
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month[1] + '/' + day[1] + '/' + year,
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month[1] + '/' + day + '/' + year[-2:],
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month[1] + '/' + day[1] + '/' + year[-2:],
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dayToWord(day) + ' of ' + months[month] + ', ' + year,
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dayToWord(day) + ' day of ' + months[month] + ', ' + year,
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months[month] + ' ' + day + ', ' + year ]
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for format in dateFormats:
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for match in re.finditer(format, text, flags=re.IGNORECASE):
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tup = (match.start(), match.end(), 'EFFECTIVE_DATE')
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dates.append(tup)
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return dates
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def labelParties(text, party):
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parties = []
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if 'Inc' in party:
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regular = ''
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for word in party.split('_'):
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regular += word + '(.*)'
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party = regular
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party = party.replace('_', ' ')
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for match in re.finditer(party, text, flags=re.IGNORECASE):
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tup = (match.start(), match.end(), 'PARTY')
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parties.append(tup)
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return parties
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def labelTerms(text, term):
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terms = []
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term = term.split('_')
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number = numToWord(term[0])
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units = term[1]
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for match in re.finditer(number + ' ' + units, text, flags=re.IGNORECASE):
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tup = (match.start(), match.end(), 'TERM')
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terms.append(tup)
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return terms
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if __name__ == '__main__':
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# Read NDAs with lzma
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NDAs = []
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with lzma.open('train/in.tsv.xz') as f:
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for line in f:
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NDAs.append(line.decode('utf-8'))
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# Read expected information
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expected = []
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with open('train/expected.tsv') as f:
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for line in f:
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expected.append(line.replace('\n', ''))
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# Expected to labeled entities
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expectEntities = []
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for expect in expected:
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entities = []
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for e in expect:
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label, entity = e.split('=')
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entities.append((label, entity))
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expectEntities.append(entities)
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# Training data for Spacy
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trainData =[]
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for i in range(len(expectEntities)):
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listOfEntities = []
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for entity in expectEntities[i]:
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if entity[0] == 'effective_date':
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listOfEntities.append(labelEffectiveDate(NDAs[i], entity[1]))
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elif entity[0] == 'jurisdiction':
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listOfEntities.append(labelJurisdiction(NDAs[i], entity[1]))
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elif entity[0] == 'party':
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listOfEntities.append(labelParties(NDAs[i], entity[1]))
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else: listOfEntities.append(labelTerms(NDAs[i], entity[1]))
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listOfEntities = [item for sublist in listOfEntities for item in sublist]
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trainData.append((NDAs[i], {'entities': listOfEntities}))
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168
heSaidEdgar.ipynb
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168
heSaidEdgar.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"import lzma\n",
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"\n",
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"NDAs = []\n",
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"with lzma.open('train/in.tsv.xz') as f:\n",
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" for line in f:\n",
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" NDAs.append(line.decode('utf-8'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"import spacy\n",
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"from spacy import displacy\n",
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"\n",
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"nlp = spacy.load('NER')\n",
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"\n",
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"text = NDAs[9]\n",
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"doc = nlp(text)\n",
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"\n",
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"effective_date = []\n",
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"jurisdiction = []\n",
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"party = []\n",
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"term = []\n",
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"\n",
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"for word in doc.ents:\n",
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" if word.label_ == 'effective_date':\n",
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" effective_date.append(word.text)\n",
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" elif word.label_ == 'jurisdiction':\n",
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" jurisdiction.append(word.text)\n",
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" elif word.label_ == 'party':\n",
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" party.append(word.text)\n",
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" else:\n",
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" term.append(word.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"12"
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]
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},
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"jurisdiction.count('New York')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"juris = { j : jurisdiction.count(j) for j in jurisdiction}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'New York': 12}"
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]
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},
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"execution_count": 47,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"juris"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CompuDyne Corporation --> party\n",
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"two years --> term\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n",
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"CompuDyne Corporation --> party\n",
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"two years --> term\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n",
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"CompuDyne Corporation --> party\n",
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"two years --> term\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n",
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"CompuDyne Corporation --> party\n",
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"two years --> term\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n",
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"New York --> jurisdiction\n"
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]
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}
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],
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"source": [
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"text = NDAs[9]\n",
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"doc = nlp(text)\n",
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"for word in doc.ents:\n",
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" print(word.text, '-->', word.label_)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "3a5b3979b9a2fc2c8e649de363a592bbf5a2c9da164843b1adb5b45661722ad0"
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},
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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20
main.ipynb
20
main.ipynb
File diff suppressed because one or more lines are too long
142
main.py
142
main.py
@ -1,123 +1,19 @@
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import lzma
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import re
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months = {'01': 'January', '02': 'February', '03': 'March',
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'04': 'April', '05': 'May', '06': 'June',
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'07': 'July', '08': 'August', '09': 'September',
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'10': 'October', '11': 'November', '12': 'December'}
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def dayToWord(day):
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day = int(day)
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if day > 3:
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return str(day) + 'th'
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elif day == 3:
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return str(day) + 'rd'
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elif day == 2 :
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return str(day) + 'nd'
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else: return str(day) + 'st'
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def numToWord(number):
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number = int(number)
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d = {1 : 'one', 2 : 'two', 3 : 'three', 4 : 'four', 5 : 'five',
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6 : 'six', 7 : 'seven', 8 : 'eight', 9 : 'nine', 10 : 'ten',
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11 : 'eleven', 12 : 'twelve', 13 : 'thirteen', 14 : 'fourteen',
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15 : 'fifteen', 16 : 'sixteen', 17 : 'seventeen', 18 : 'eighteen',
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19 : 'nineteen', 20 : 'twenty',
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30 : 'thirty', 40 : 'forty', 50 : 'fifty', 60 : 'sixty',
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70 : 'seventy', 80 : 'eighty', 90 : 'ninety' }
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if number < 20:
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return d[number]
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else:
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if number % 10 == 0: return d[number]
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else: return d[number // 10 * 10] + '-' + d[number % 10]
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def labelJurisdiction(text, jurisdiction):
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jurisdictions = []
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for match in re.finditer(jurisdiction, text):
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tup = (match.start(), match.end(), 'JURISDICTION')
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jurisdictions.append(tup)
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return jurisdictions
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def labelEffectiveDate(text, date):
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dates = []
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year, month, day = date.split('-')
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dateFormats = [month + '/' + day + '/' + year,
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month + '/' + day + '/' + year[-2:],
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month[1] + '/' + day + '/' + year,
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month[1] + '/' + day[1] + '/' + year,
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month[1] + '/' + day + '/' + year[-2:],
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month[1] + '/' + day[1] + '/' + year[-2:],
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dayToWord(day) + ' of ' + months[month] + ', ' + year,
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dayToWord(day) + ' day of ' + months[month] + ', ' + year,
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months[month] + ' ' + day + ', ' + year ]
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for format in dateFormats:
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for match in re.finditer(format, text, flags=re.IGNORECASE):
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tup = (match.start(), match.end(), 'EFFECTIVE_DATE')
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dates.append(tup)
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return dates
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def labelParties(text, party):
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parties = []
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if 'Inc' in party:
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regular = ''
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for word in party.split('_'):
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regular += word + '(.*)'
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party = regular
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party = party.replace('_', ' ')
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for match in re.finditer(party, text, flags=re.IGNORECASE):
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tup = (match.start(), match.end(), 'PARTY')
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parties.append(tup)
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return parties
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def labelTerms(text, term):
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terms = []
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term = term.split('_')
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number = numToWord(term[0])
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units = term[1]
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for match in re.finditer(number + ' ' + units, text, flags=re.IGNORECASE):
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tup = (match.start(), match.end(), 'TERM')
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terms.append(tup)
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return terms
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if __name__ == '__main__':
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# Read NDAs with lzma
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NDAs = []
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with lzma.open('train/in.tsv.xz') as f:
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for line in f:
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NDAs.append(line.decode('utf-8'))
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# Read expected information
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expected = []
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with open('train/expected.tsv') as f:
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for line in f:
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expected.append(line.replace('\n', ''))
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# Expected to labeled entities
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expectEntities = []
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for expect in expected:
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entities = []
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for e in expect:
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label, entity = e.split('=')
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entities.append((label, entity))
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expectEntities.append(entities)
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# Training data for Spacy
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trainData =[]
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for i in range(len(expectEntities)):
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listOfEntities = []
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for entity in expectEntities[i]:
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if entity[0] == 'effective_date':
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listOfEntities.append(labelEffectiveDate(NDAs[i], entity[1]))
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elif entity[0] == 'jurisdiction':
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listOfEntities.append(labelJurisdiction(NDAs[i], entity[1]))
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elif entity[0] == 'party':
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listOfEntities.append(labelParties(NDAs[i], entity[1]))
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else: listOfEntities.append(labelTerms(NDAs[i], entity[1]))
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listOfEntities = [item for sublist in listOfEntities for item in sublist]
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trainData.append((NDAs[i], {'entities': listOfEntities}))
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import lzma
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import spacy
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def readInput(dir):
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NDAs = []
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with lzma.open(dir) as f:
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for line in f:
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NDAs.append(line.decode('utf-8'))
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return NDAs
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if __name__ == '__main__':
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NDAs = readInput('train/in.tsv.xz')
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ner = spacy.load('NER')
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for nda in NDAs:
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print('pass')
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