def extract_univ(data, univ_dict, univ_normalize):
"""
This function will extract university name from resume text
Parameters:
-----------
data -- string
Resume text
univ_dict -- dict
University lookup dictionary
univ_normalize -- dict
University alias dictionary
Returns:
--------
university -- string
University name in string
"""
try:
data = stripxml(str(data))
data = data.lower()
data = data.replace('\xc2\xa0', ' ')
#print data
data = re.sub ('[^A-Za-z0-9 ]+',' ',str(data))
data = re.sub (' ',' ',str(data))
if 'education' in data:
parted = data.split('education')[1]
second = parted[:150]
else:
second = data
n = 10
while n > 1:
for ngram in ngrams(str(second).lower(), n):
if ngram.lower() in univ_normalize:
return univ_normalize[str(ngram.lower())]
elif ngram.lower() in univ_dict:
university = ngram.title()
return university
n -= 1
return ""
except UnicodeEncodeError:
return data
except UnicodeDecodeError:
return data
def extract_univ(data, univ_dict, univ_normalize):
"""
This function will extract university name from resume text
Parameters:
-----------
data -- string
Resume text
univ_dict -- dict
University lookup dictionary
univ_normalize -- dict
University alias dictionary
Returns:
--------
university -- string
University name in string
"""
try:
data = stripxml(str(data))
data = data.lower()
data = data.replace('\xc2\xa0', ' ')
#print data
data = re.sub('[^A-Za-z0-9 ]+', ' ', str(data))
data = re.sub(' ', ' ', str(data))
if 'education' in data:
parted = data.split('education')[1]
second = parted[:150]
else:
second = data
n = 10
while n > 1:
for ngram in ngrams(str(second).lower(), n):
if ngram.lower() in univ_normalize:
return univ_normalize[str(ngram.lower())]
elif ngram.lower() in univ_dict:
university = ngram.title()
return university
n -= 1
return ""
except UnicodeEncodeError:
return data
except UnicodeDecodeError:
return data
def extract_from_resume(data):
"""
This function will extract university name from resume text
Parameters:
-----------
data -- string
Resume text
Returns:
--------
out -- dict
Dictionary of university details
"""
out = {}
data = stripxml(str(data))
data = data.lower()
data = re.sub ('[^A-Za-z0-9 ]+',' ',str(data))
data = re.sub (' ',' ',str(data))
if 'education' in data:
parted = data.split('education')[1]
second = parted[:150]
out['split2'] = second
else:
second = data
with open("static/UniqueFBUnivNames.csv", 'rb') as f:
reader = csv.reader(f)
for row in reader:
row = re.sub('[^A-Za-z0-9 ]+', ' ', str(row))
row = re.sub(' ', ' ', str(row))
if str(row).lower() in second:
if len(str(row).split())>1:
out['univ'] = str(row)
return out
break
return out
def extract_from_resume(data):
"""
This function will extract university name from resume text
Parameters:
-----------
data -- string
Resume text
Returns:
--------
out -- dict
Dictionary of university details
"""
out = {}
data = stripxml(str(data))
data = data.lower()
data = re.sub('[^A-Za-z0-9 ]+', ' ', str(data))
data = re.sub(' ', ' ', str(data))
if 'education' in data:
parted = data.split('education')[1]
second = parted[:150]
out['split2'] = second
else:
second = data
with open("static/UniqueFBUnivNames.csv", 'rb') as f:
reader = csv.reader(f)
for row in reader:
row = re.sub('[^A-Za-z0-9 ]+', ' ', str(row))
row = re.sub(' ', ' ', str(row))
if str(row).lower() in second:
if len(str(row).split()) > 1:
out['univ'] = str(row)
return out
break
return out
for root, dirs, files in os.walk(xml_directory, topdown=False):
for f in files:
try:
xml = etree.parse(xml_directory + '/' + f)
name = xml.xpath('//givenname/text()')[0] + ' ' + xml.xpath('//surname/text()')[0]
if name not in names:
names.append(name)
education = xml.xpath('//education')[0]
schools = education.xpath('//school')
resume_id = f.split('.')[0]
temp_univ_major_list = []
for school in schools:
school_text = stripxml(etree.tostring(school))
#institution = school.xpath('institution/text()')[0]
institution = extract_univ( school_text, univ_dict, univ_normalize)
institution = re.sub ('[^A-Za-z0-9 ]+',' ',str(institution))
institution = re.sub (' ',' ',str(institution))
#print institution
if institution.lower() in univ_normalize:
#print "NORMALIZED"
institution = univ_normalize[institution]
degree_level = school.xpath('degree/@level')[0]
degree = school.xpath('degree/text()')[0]
major_code = str(school.xpath('major/@code')[0])
major = school.xpath('major/text()')[0]
temp_univ_major_list.append(str(institution + '_' + major_code).lower())
def clean_data_and_extract_job_titles(fname, paths, names, job_titles, labels_list):
"""
Function to clean data and extract job titles from resume.
Parameters:
-----------
fname - string.
Name of the resume file
paths - dict
Dict containing paths of source directories
names - string.
Extracted candidate names from resume. Used to remove duplicate resumes.
job_titles - list.
Titles extracted from resume
labels_list - list.
Titles that will be used as labels for classifier.
Returns:
--------
names - string.
Extracted candidate names from resume. Used to remove duplicate resumes.
job_titles - list.
Titles extracted from resume
labels_list - list.
Titles that will be used as labels for classifier.
"""
source_dir = paths['main_source_directory'] + '/' + paths['xml_data_directory']
try:
xml = etree.parse(source_dir + '/' + fname)
# Extract the current job title, and current job element from xml
current_job_title = xml.xpath('//job[@end = "present"]/title/text()')
current_job_title = normalize_job_titles(current_job_title)
current_job = xml.xpath('//job[@end = "present"]')
# Extract the contact information from xml.
contact = xml.xpath('//contact')
# Since there are many duplicate resumes in the data, filter out the resumes based on candidate name.
# Extract the candidate name from the resume
name = xml.xpath('//givenname/text()')[0] + ' ' + xml.xpath('//surname/text()')[0]
# Avoid duplicate resumes by only choosing resumes with unique names
if name not in names:
names.append(name)
else:
return names, job_titles, labels_list
# Remove the candidate contact information from the resume.
if contact:
contact[0].getparent().remove(contact[0])
# Remove the current job section from the resume as we will be using current job title as lable and
# use our algorithm to predict it.
if current_job:
if len(current_job) > 1:
i = 0
while i < len(current_job):
current_job[i].getparent().remove(current_job[i])
i += 1
else:
current_job[0].getparent().remove(current_job[0])
# Convert xml to string.
xml = etree.tostring(xml, pretty_print=True)
# Strip the xml tags from the resume.
text_data = stripxml(xml)
i = 0
flag = 0
# From the resume text remove all the words matching the current job title as we do not want any
# information about the current job in the resume text.
if current_job_title:
text_data = text_data.replace(current_job_title[0].strip(), '')
if current_job_title[0].strip() in top_jobs:
flag = 1
job_count[current_job_title[0].strip()] += 1
# Only save the resumes whose current job title is present in the top 100 jobs
if flag == 1 and job_count[current_job_title[0].strip()] < 300:
if current_job_title:
job_titles.append(current_job_title[0].strip())
directory = paths['main_source_directory'] + '/' + paths['plaintext_data_directory'] + '/'
f = open(directory + '%s' % fname[:-4] + '_plaintext.txt', 'w')
f.write(text_data)
f.close()
labels_list.append((fname[:-4] + '_plaintext.txt', current_job_title[0].strip().replace('\n', '')))
return names, job_titles, labels_list
except:
return names, job_titles, labels_list
def clean_data_and_extract_job_titles(fname, paths, names, job_titles, labels_list):
"""
Function to clean data and extract job titles from resume.
Parameters:
-----------
fname - string.
Name of the resume file
paths - dict
Dict containing paths of source directories
names - string.
Extracted candidate names from resume. Used to remove duplicate resumes.
job_titles - list.
Titles extracted from resume
labels_list - list.
Titles that will be used as labels for classifier.
Returns:
--------
names - string.
Extracted candidate names from resume. Used to remove duplicate resumes.
job_titles - list.
Titles extracted from resume
labels_list - list.
Titles that will be used as labels for classifier.
"""
source_dir = paths['main_source_directory'] + '/' + paths['xml_data_directory']
xml = etree.parse(source_dir + '/' + fname)
# Extract the current job title, and current job element from xml
current_job_title = xml.xpath('//job[@end = "present"]/title/text()')
current_job_title = normalize_job_titles(current_job_title)
current_job = xml.xpath('//job[@end = "present"]')
# Extract the contact information from xml.
contact = xml.xpath('//contact')
try:
# Since there are many duplicate resumes in the data, filter out the resumes based on candidate name.
# Extract the candidate name from the resume
name = xml.xpath('//givenname/text()')[0] + ' ' + xml.xpath('//surname/text()')[0]
# Avoid duplicate resumes by only choosing resumes with unique names
if name not in names:
names.append(name)
else:
return names, job_titles, labels_list
# Remove the candidate contact information from the resume.
if contact:
contact[0].getparent().remove(contact[0])
# Remove the current job section from the resume as we will be using current job title as lable and
# use our algorithm to predict it.
if current_job:
if len(current_job) > 1:
i = 0
while i < len(current_job):
current_job[i].getparent().remove(current_job[i])
i += 1
else:
current_job[0].getparent().remove(current_job[0])
# Convert xml to string.
xml = etree.tostring(xml, pretty_print=True)
# Strip the xml tags from the resume.
text_data = stripxml(xml)
i = 0
flag = 0
# From the resume text remove all the words matching the current job title as we do not want any
# information about the current job in the resume text.
if current_job_title:
text_data = text_data.replace(current_job_title[0].strip(), '')
job_titles.append(current_job_title[0].strip())
if current_job_title[0].strip() in top_jobs:
flag = 1
job_count[current_job_title[0].strip()] += 1
# Only save the resumes whose current job title is present in the top 100 jobs
if flag == 1 and job_count[current_job_title[0].strip()] < 250:
if current_job_title:
directory = paths['main_source_directory'] + '/' + paths['plaintext_data_directory'] + '/'
f = open(directory + '%s' % fname[:-4] + '_plaintext.txt', 'w')
f.write(text_data)
f.close()
labels_list.append((fname[:-4] + '_plaintext.txt', current_job_title[0].strip().replace('\n', '')))
return names, job_titles, labels_list
except:
return names, job_titles, labels_list
for root, dirs, files in os.walk(xml_directory, topdown=False):
for f in files:
try:
xml = etree.parse(xml_directory + '/' + f)
name = xml.xpath('//givenname/text()')[0] + ' ' + xml.xpath(
'//surname/text()')[0]
if name not in names:
names.append(name)
education = xml.xpath('//education')[0]
schools = education.xpath('//school')
resume_id = f.split('.')[0]
temp_univ_major_list = []
for school in schools:
school_text = stripxml(etree.tostring(school))
#institution = school.xpath('institution/text()')[0]
institution = extract_univ(school_text, univ_dict,
univ_normalize)
institution = re.sub('[^A-Za-z0-9 ]+', ' ',
str(institution))
institution = re.sub(' ', ' ', str(institution))
#print institution
if institution.lower() in univ_normalize:
#print "NORMALIZED"
institution = univ_normalize[institution]
degree_level = school.xpath('degree/@level')[0]
degree = school.xpath('degree/text()')[0]
major_code = str(school.xpath('major/@code')[0])
major = school.xpath('major/text()')[0]
