def main():
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser._action_groups.pop()
required = parser.add_argument_group('required arguments')
optional = parser.add_argument_group('optional arguments')
required.add_argument('-tr', '--train_image', help='image used for training the algorithm', required=True)
required.add_argument('-te', '--test_image', help='image to evaluate', required=True)
optional.add_argument('-l', '--log', dest="logLevel", choices=['DEBUG', 'debug', 'INFO', 'info', 'ERROR', 'error'],
help='Argument use to set the logging level')
optional.add_argument('-knn', '--knn', help='flag to run knn', action='store_true')
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
logging.getLogger('regular.time').info('starting running handwritten-notes script')
digits, y_train = load_digits(args.train_image)
x_train = pixels_to_hog_20(digits)
num_pixels = x_train.shape[1]
num_classes = len(np.unique(y_train))
if args.knn:
logging.getLogger('regular.time').info('training knn model')
model = KNeighborsClassifier()
model.fit(x_train, y_train)
else:
logging.getLogger('regular.time').info('training NN model')
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))
model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
process_test_image(dataset=args.test_image, model=model, model_type=args.knn)
def main():
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input_file', help='dataset file that has not being processed')
parser.add_argument('-tr', '--train_file', help='processed training dataset file')
parser.add_argument('-te', '--test_file', help='processed testing dataset file')
parser.add_argument("-l", "--log", dest="logLevel", choices=['DEBUG', 'INFO', 'ERROR'], type=str.upper,
help="Set the logging level")
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
training_dir = args.train_file
testing_dir = args.test_file
# if those variables are not pass, then populate them
if not (training_dir or testing_dir):
training_dir = 'datasets/train_data_processed.csv'
testing_dir = 'datasets/test_data_processed.csv'
else:
training_dir = 'datasets/' + training_dir + '.csv'
testing_dir = 'datasets/' + testing_dir + '.csv'
load(train_directory=training_dir, test_directory=testing_dir)
logging.getLogger('regular.time').info('starting running pre-processing script')
# import data from file
dataset = load_data(args.input_file)
# calculate relevant variables' values
process_dataset(dataset=dataset, train_dir=training_dir, test_dir=testing_dir)
# save it
store_dataset()
logging.getLogger('regular.time').info('finished running pre-processing script')
def main():
"""
starts running the script
:return: None.
"""
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser.add_argument('-r',
'--retrieve',
help='arg use to pull data from PubMed',
action='store_true')
parser.add_argument(
'-p',
'--process',
help='arg use to process the info into paper, author, medical and '
'title_abstracts records',
action='store_true')
parser.add_argument('-a',
'--analyze',
help='run topic modeling on the file',
action='store_true')
parser.add_argument(
'-f',
'--file',
help=
'file to process. Depending on whether the retrieve, process or analysis '
'options were selected, there is a different default file')
parser.add_argument('-l',
'--log',
dest='logLevel',
choices=['DEBUG', 'INFO', 'ERROR'],
type=str.upper,
help='Set the logging level')
if sys.platform == "darwin" or sys.platform == "win32":
if sys.platform == "win32":
path = 'D:\dataset\scosy\dataset'
else:
path = '/Volumes/dataset/scosy/dataset'
# Respublica
else:
path = 'dataset/'
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
logging.getLogger('line.regular.time.line').info('Running SCOSY')
if args.retrieve:
logging.getLogger('regular').info('retrieving data from PudMed')
# databases such as PubMed, GenBank, GEO, and many others
# Use the mandatory email parameter so the NCBI can contact you if there is a proble
Entrez.email = "*****@*****.**" # Always tell NCBI who you are
logging.getLogger('regular').info(
'searching PubMed for CHOP and UPENN authors')
handle = Entrez.esearch(
db="pubmed",
retmax=100000000,
idtype="esearch",
mindate="2014/01/01",
maxdate="2020/08/21",
term=
"Perelman School of Medicine[Affiliation] OR Children's Hospital of "
"Philadelphia[Affiliation] OR University of Pennsylvania School of "
"Medicine[Affiliation] OR School of Medicine University of "
"Pennsylvania[Affiliation]",
usehistory="y")
search_results = Entrez.read(handle)
handle.close()
# obtaining the list of relevant PMIDs
id_list = search_results["IdList"]
# get all the record based on the PMIDs
# logging.getLogger('regular.time').info('getting relevant authors\' records based on PMIDs')
fetch_records_handle = Entrez.efetch(db="pubmed",
id=id_list,
retmode="text",
rettype="medline")
# need to read all the data from the handle and store in a file because if we just read line by line from the
# generator and the internet connection is not strong, then we run into http errors:
# http.client.IncompleteRead: IncompleteRead(0 bytes read)
result_path = Path(path, 'results.txt')
out_handle = result_path.open('w+')
out_handle.write(fetch_records_handle.read())
# the results are now in the results.xml file and the original handle has had all of its data extracted
# (so we close it)
out_handle.close()
msg = 'saved authors\' records on local file = {0}'.format(result_path)
logging.getLogger('regular.time').info(msg)
elif args.process:
# import data from file
logging.getLogger('regular').info('reading data from result file')
file_name = args.file
if not file_name:
file_name = 'results.txt'
result_path = Path(path, file_name)
records_handle = result_path.open()
fetch_records = parse(handle=records_handle)
# initializing variables
mesh_description_dict = obtain_descriptions()
# contains all the metadata elements on the author level: PubMed unique Identifier number(PMID), AuthorID (as a
# (CA) Ordinary Author (OA) or Principal Author (PA) and the author's affiliation
author_record_df = pd.DataFrame(columns=[
'PMID', 'Author', 'author_chop', 'author_penn', 'Role',
'AffiliationInfo'
])
# contains all the metadata elements on the paper level: PubMed unique Identifier number(PMID), Title, Abstract,
# Year, Month, AuthorList, SubjectList, date
paper_record_df = pd.DataFrame(columns=[
'PMID', 'Title', 'Abstract', 'Year', 'Month', 'author_list',
'subject_list', 'date'
])
# contains all the metadata of the medical information: PubMed unique Identifier number(PMID), Primary Medical
# Subject Header (MESH) and the description ID
medical_record_df = pd.DataFrame(
columns=['PMID', 'Desc', 'Primary_MeSH'])
title_list = list()
abstract_list = list()
# get the relevant information for each record
for record_index, record in enumerate(fetch_records):
logging.getLogger('regular').debug(
'record index = {0}'.format(record_index))
try:
pmid = record.get('PMID')
title = record.get('TI')
abstract = record.get('AB')
authors = record.get('FAU')
affiliations = record.get('AD')
publication_type = record.get('PT')
mesh_term = record.get('MH')
date_created = record.get('EDAT')
year, month = date_created.split('/')[:2]
date = year + '/' + month
logging.getLogger('regular').debug('pmid = {0}'.format(pmid))
logging.getLogger('regular').debug('title = {0}'.format(title))
logging.getLogger('regular').debug(
'abstract = {0}'.format(abstract))
logging.getLogger('regular').debug(
'authors = {0}'.format(authors))
logging.getLogger('regular').debug(
'affiliations = {0}'.format(affiliations))
logging.getLogger('regular').debug(
'publication type = {0}'.format(publication_type))
logging.getLogger('regular').debug(
'mesh term = {0}'.format(mesh_term))
logging.getLogger('regular').debug(
'data created = {0}'.format(date_created))
# assign the chief author, ordinary author or principal investigator role to each author
roles = assign_roles(authors)
# check and assign whether the authors belong to the CHOP or PENN organization
chop_organization, penn_organization = assign_organization(
affiliations)
mesh_description = ''
if mesh_term is None:
mesh_term = ''
else:
mesh_description, term = convert_mesh_description(
mesh_description_dict, mesh_term)
mesh_term = ';'.join(mesh_term)
# output information
if mesh_description:
row = pd.DataFrame(
[[pmid, term, mesh_description]],
columns=['PMID', 'Primary_MeSH', 'Desc'])
medical_record_df = medical_record_df.append(
row, ignore_index=True)
for author_index, organizations in enumerate(
zip(chop_organization, penn_organization)):
# check if the author belongs to either CHOP or PENN
if 1 in organizations:
row = pd.DataFrame([[
pmid, authors[author_index], organizations[0],
organizations[1], roles[author_index],
affiliations[author_index]
]],
columns=[
'PMID', 'Author', 'author_chop',
'author_penn', 'Role',
'AffiliationInfo'
])
author_record_df = author_record_df.append(
row, ignore_index=True)
authors = ';'.join(authors)
row = pd.DataFrame([[
pmid, title, abstract, year, month, authors, mesh_term,
date
]],
columns=[
'PMID', 'Title', 'Abstract', 'Year',
'Month', 'author_list', 'subject_list',
'date'
])
paper_record_df = paper_record_df.append(row)
title_list.append(title)
abstract_list.append(abstract)
except Exception as e:
msg = 'Error while processing PMID={0}'.format(pmid)
logging.getLogger('regular').debug(msg)
msg = 'Exception message = {0}'.format(e)
logging.getLogger('regular').debug(msg)
# contains all the metadata elements on the author level: Pubmed unique Identifier number(PMID), AuthorID (as a
# (CA) Ordinary Author (OA) or Principal Author (PA) and the author's affiliation
author_path = Path(path, 'author_record.csv')
author_record_df.to_csv(author_path, index=False)
# contains all the metadata elements on the paper level: Pubmed unique Identifier number(PMID), Title, Abstract,
# Year, Month, AuthorList, SubjectList, date
paper_path = Path(path, 'paper_record.csv')
paper_record_df.to_csv(paper_path, index=False)
# contains all the metadata of the medical information: Pubmed unique Identifier number(PMID), Primary Medical
# Subject Header (MESH) and the description ID
medical_path = Path(path, 'medical_record.csv')
medical_record_df.to_csv(medical_path, index=False)
# store the record in a file for processing
dataset = dict()
dataset['title'] = title_list
dataset['abstracts'] = abstract_list
dataset['mesh'] = mesh_term
dataset = pd.DataFrame(dataset)
titles_abstracts_mesh_path = Path(path, 'titles_abstracts_mesh.csv')
dataset.to_csv(path_or_buf=titles_abstracts_mesh_path, index=False)
logging.getLogger('line.regular.time.line').info(
'SCOSY finished running successfully.')
def main():
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser.add_argument("-l",
"--log",
dest="logLevel",
choices=['DEBUG', 'INFO', 'ERROR'],
type=str.upper,
help="Set the logging level")
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
logging.getLogger('line.regular.time.line').info(
'Running Recommendation System script')
# import data from file
logging.getLogger('regular').info('reading data from file')
# databases such as PubMed, GenBank, GEO, and many others
# Use the mandatory email parameter so the NCBI can contact you if there is a proble
Entrez.email = "*****@*****.**" # Always tell NCBI who you are
logging.getLogger('regular').info(
'searching pubmed for the CHOP and UPENN authors')
handle = Entrez.esearch(
db="pubmed",
retmax=50000,
idtype="esearch",
mindate="2014/01/01",
maxdate="2017/05/01",
term=
"Perelman School of Medicine[Affiliation] OR Children's Hospital of "
"Philadelphia[Affiliation] OR University of Pennsylvania School of "
"Medicine[Affiliation] OR School of Medicine University of Pennsylvania[Affiliation]",
usehistory="y")
search_results = Entrez.read(handle)
handle.close()
# obtaining the list of relevant PMIDs
id_list = search_results["IdList"]
# get all the record based on the PMIDs
logging.getLogger('regular').info(
'getting relevant authors\' records based on PMIDs')
fetch_records_handle = Entrez.efetch(db="pubmed",
id=id_list,
retmode="text",
rettype="medline")
# need to read all the data from the handle and store in a file because if we just read line by line from the
# generator and the internet connection is not strong, then we run into http errors:
# http.client.IncompleteRead: IncompleteRead(0 bytes read)
logging.getLogger('regular').info(
'storing authors\' records on local file')
with open("results.xml", "w") as out_handle:
out_handle.write(fetch_records_handle.read())
# the results are now in the results.xml file and the original handle has had all of its data extracted
# (so we close it)
fetch_records_handle.close()
logging.getLogger('regular').info('reading result files')
records_handle = open("results.xml")
fetch_records = parse(handle=records_handle)
# initializing variables
mesh_description_dict = obtain_descriptions()
# contains all the metadata elements on the author level: Pubmed unique Identifier number(PMID), AuthorID (as a
# (CA) Ordinary Author (OA) or Principal Author (PA) and the author's affiliation
author_record_df = pd.DataFrame(columns=[
'PMID', 'AuthorID', 'Author CHOP', 'Author PENN', 'ROLE', 'Affiliation'
])
# contains all the metadata elements on the paper level: Pubmed unique Identifier number(PMID), Title, Abstract,
# Year, Month, AuthorList, SubjectList, date
paper_record_df = pd.DataFrame(columns=[
'PMID', 'Title', 'Abstract', 'Year', 'Month', 'Author List',
'Subject List', 'Date'
])
# contains all the metadata of the medical information: Pubmed unique Identifier number(PMID), Primary Medical
# Subject Header (MESH) and the description ID
medical_record_df = pd.DataFrame(columns=['PMID', 'MESH', 'Description'])
title_list = list()
abstract_list = list()
# get the relevant information for each record
for record_index, record in enumerate(fetch_records):
logging.getLogger('regular').debug(
'record index = {0}'.format(record_index))
try:
pmid = record.get('PMID')
title = record.get('TI')
abstract = record.get('AB')
authors = record.get('FAU')
affiliations = record.get('AD')
publication_type = record.get('PT')
mesh_term = record.get('MH')
date_created = record.get('EDAT')
year, month = date_created.split('/')[:2]
date = year + '/' + month
logging.getLogger('regular').debug('pmid = {0}'.format(pmid))
logging.getLogger('regular').debug('title = {0}'.format(title))
logging.getLogger('regular').debug(
'abstract = {0}'.format(abstract))
logging.getLogger('regular').debug('authors = {0}'.format(authors))
logging.getLogger('regular').debug(
'affiliations = {0}'.format(affiliations))
logging.getLogger('regular').debug(
'publication type = {0}'.format(publication_type))
logging.getLogger('regular').debug(
'mesh term = {0}'.format(mesh_term))
logging.getLogger('regular').debug(
'data created = {0}'.format(date_created))
# assign the chief author, ordinary author or principal investigator role to each author
roles = assign_roles(authors)
# check and assign whether the authors belong to the CHOP or PENN organization
chop_organization, penn_organization = assign_organization(
affiliations)
mesh_description = ''
if mesh_term is None:
mesh_term = ''
else:
term, mesh_description = convert_mesh_description(
mesh_description_dict, mesh_term)
mesh_term = ';'.join(mesh_term)
# output information
if mesh_description:
row = pd.DataFrame([[pmid, term, mesh_description]],
columns=['PMID', 'Mesh', 'Description'])
medical_record_df = medical_record_df.append(row,
ignore_index=True)
for author_index, organizations in enumerate(
zip(chop_organization, penn_organization)):
if 1 in organizations:
row = pd.DataFrame([[
pmid, authors[author_index], organizations[0],
organizations[1], roles[author_index],
affiliations[author_index]
]],
columns=[
'PMID', 'AuthorID', 'Author CHOP',
'Author PENN', 'ROLE', 'Affiliation'
])
author_record_df = author_record_df.append(
row, ignore_index=True)
authors = ';'.join(authors)
row = pd.DataFrame([[
pmid, title, abstract, year, month, authors, mesh_term, date
]],
columns=[
'PMID', 'Title', 'Abstract', 'Year',
'Month', 'Author List', 'Subject List',
'Date'
])
paper_record_df = paper_record_df.append(row)
title_list.append(title)
abstract_list.append(abstract)
except Exception as e:
msg = 'Error while processing PMID={0}'.format(pmid)
logging.getLogger('regular').debug(msg)
msg = 'Exception message = {0}'.format(e)
logging.getLogger('regular').debug(msg)
# store the record in a file for processing
dataset = dict()
dataset['title'] = title_list
dataset['abstracts'] = abstract_list
dataset = pd.DataFrame(dataset)
dataset.to_csv(path_or_buf='record_results/titles_abstracts.csv',
index=False)
# read the records from the file
# dataset = pd.read_csv('record_results/titles_abstracts.csv')
# topic_modeling(dataset=dataset)
pandas.io.formats.excel.header_style = None
# contains all the metadata elements on the author level: Pubmed unique Identifier number(PMID), AuthorID (as a
# (CA) Ordinary Author (OA) or Principal Author (PA) and the author's affiliation
author_record_df.to_excel('record_results/author_record.xlsx',
sheet_name='author_record',
index=False)
# contains all the metadata elements on the paper level: Pubmed unique Identifier number(PMID), Title, Abstract,
# Year, Month, AuthorList, SubjectList, date
paper_record_df.to_excel('record_results/paper_record.xlsx',
sheet_name='paper_record',
index=False)
# contains all the metadata of the medical information: Pubmed unique Identifier number(PMID), Primary Medical
# Subject Header (MESH) and the description ID
medical_record_df.to_excel('record_results/medical_record.xlsx',
sheet_name='medical_record',
index=False)
logging.getLogger('line.regular.time.line').info(
'Recommendation System script finished running successfully.')
def main():
# ignore warning of compiling tensorflow from source
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser.add_argument('-i',
'--input_file',
help='dataset file that has not being processed')
parser.add_argument('-tr',
'--train_file',
help='processed training dataset file')
parser.add_argument('-te',
'--test_file',
help='processed testing dataset file')
parser.add_argument("-l",
"--log",
dest="logLevel",
choices=['DEBUG', 'INFO', 'ERROR'],
type=str.upper,
help="Set the logging level")
parser.add_argument('-cv', '--cross_validation', action='store_true')
parser.add_argument('-gs', '--grid_search', action='store_true')
parser.add_argument('-svm',
'--svm',
help='run support vector machine',
action='store_true')
parser.add_argument('-p',
'--processed_dataset',
action='store_true',
help='this flag is used when the training '
'and testing datasets are provided')
parser.add_argument('-s',
'--store_datasets',
action='store_true',
help='this flag is used to store the training'
'and testing dataset on local system')
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
logging.getLogger('line.regular.time.line').info('Running No_Show script')
# import data from file
logging.getLogger('regular').info('reading data from file')
tr_data = pd.read_csv(filepath_or_buffer=args.train_file, delimiter='|')
te_data = pd.read_csv(filepath_or_buffer=args.test_file, delimiter='|')
logging.getLogger('regular').debug('training dataset shape = {0}'.format(
tr_data.shape))
logging.getLogger('regular').debug('training dataset keys = {0}'.format(
tr_data.keys()))
logging.getLogger('regular').debug('testing dataset shape = {0}'.format(
te_data.shape))
logging.getLogger('regular').debug('testing dataset keys = {0}'.format(
te_data.keys()))
y_train_data = tr_data['NOSHOW'].values
y_test_data = te_data['NOSHOW'].values
x_train_data = tr_data.drop([
'PATIENT_KEY', 'ENCOUNTER_APPOINTMENT_DATETIME',
'ENCOUNTER_APPOINTMENT_STATUS', 'NOSHOW'
],
axis=1).values
x_test_data = te_data.drop([
'PATIENT_KEY', 'ENCOUNTER_APPOINTMENT_DATETIME',
'ENCOUNTER_APPOINTMENT_STATUS', 'NOSHOW'
],
axis=1).values
# check if cross validation flag is set
run_model(training_data=x_train_data,
testing_data=x_test_data,
training_y=y_train_data,
testing_y=y_test_data,
svm_flag=args.svm,
gs_flag=args.grid_search)
def main():
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser.add_argument("-l",
"--log",
dest="logLevel",
choices=['DEBUG', 'INFO', 'ERROR'],
type=str.upper,
help="Set the logging level")
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
logging.getLogger('line.regular.time.line').info(
'Running Recommendation System script')
# import data from file
logging.getLogger('regular').info('reading data from file')
# Entrez (http://www.ncbi.nlm.nih.gov/Entrez) is a data retrieval system that provides users access to NCBI’s
# databases such as PubMed, GenBank, GEO, and many others
# Use the mandatory email parameter so the NCBI can contact you if there is a proble
Entrez.email = "*****@*****.**" # Always tell NCBI who you are
# logging.getLogger('regular').info('searching pubmed for the CHOP and UPENN authors')
# handle = Entrez.esearch(db="pubmed", retmax=50000, idtype="esearch", mindate="2014/01/01", maxdate="2017/05/01",
# term="Perelman School of Medicine[Affiliation] OR Children's Hospital of "
# "Philadelphia[Affiliation] OR University of Pennsylvania School of "
# "Medicine[Affiliation] OR School of Medicine University of Pennsylvania[Affiliation]",
# usehistory="y")
# search_results = Entrez.read(handle)
# handle.close()
# # obtaining the list of relevant PMIDs
# id_list = search_results["IdList"]
#
# # get all the record based on the PMIDs
# logging.getLogger('regular').info('getting relevant authors\' records based on PMIDs')
# fetch_records_handle = Entrez.efetch(db="pubmed", id=id_list, retmode="text", rettype="medline")
# # need to read all the data from the handle and store in a file because if we just read line by line from the
# # generator and the internet connection is not strong, then we run into http errors:
# # http.client.IncompleteRead: IncompleteRead(0 bytes read)
# logging.getLogger('regular').info('storing authors\' records on local file')
# with open("results.xml", "w") as out_handle:
# out_handle.write(fetch_records_handle.read(validate=True))
# # the results are now in the results.xml file and the original handle has had all of its data extracted
# # (so we close it)
# fetch_records_handle.close()
logging.getLogger('regular').info('reading result files')
records_handle = open("results.xml")
fetch_records = parse(records_handle)
# initializing variables
mesh_description_dict = obtain_descriptions()
# PMID=PubMed Unique Identifier, TI=Title, AB=Abstract, AD=Affiliation, FAU=Full Author, MH=MeSH Terms,
# PT=Publication Type
# for more information, look at the abbreviations in the /template/abbreviations.txt file
author_information = {
'PMID': '',
'TI': '',
'AB': '',
'FAU': '',
'AU': '',
'MH': '',
'PT': '',
'AD': ''
}
author_list = list()
affiliation_list = list()
mesh_list = list()
first_record = True
# get the relevant information for each record
for record_index, line in enumerate(fetch_records):
logging.getLogger('regular').debug(
'line index = {0}'.format(record_index))
# remove new line delimiter
line = line.replace('\n', '')
# skip if empty string
if not line:
continue
# getting the key (PMID, TITLE, ABSTRACT, etc) and its value
key, value = line.split('- ')
# remove spaces
key.replace(' ', '')
# check if key is relevant to the information of interest
if key not in author_information.keys():
continue
if key == 'PMID':
# if it is not the first record, that means that it is a new record and therefore needs to reset all the
# variables
if not first_record:
author_information['AU'] = author_list
author_information['AD'] = affiliation_list
author_information['MH'] = mesh_list
logging.getLogger('regular').debug(
'authors\' information = {0}'.format(author_information))
# function to print's the author's information to the relevant files
# output_author_information(author_information)
author_information = dict['PMID':'', 'TI':'', 'AB':'',
'FAU':'', 'AU', 'ROLE':'', 'MH':'',
'PT':'', 'AD':'']
author_list = list()
affiliation_list = list()
# there might be multiple authors per PMID and therefore we need to add them to a list
if key == 'FAU':
author_list.append(value)
# each author might have one or more affiliations
elif key == 'AD':
affiliation_list.append(value)
# there might be multiple mesh terms
elif key == 'MH':
# some of the mesh terms might have an * that needs to be removed
mesh_list.append(value.replace('*', ''))
# add the authors' information
author_information[key] = value
# changing first record flag
first_record = False
logging.getLogger('line.regular.time.line').info(
'Recommendation System script finished running successfully.')
def main():
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser.add_argument("-l", "--log", dest="logLevel", choices=['DEBUG', 'INFO', 'ERROR'], type=str.upper,
help="Set the logging level")
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
logging.getLogger('line.regular.time.line').info('Running Recommendation System script')
# import data from file
logging.getLogger('regular').info('reading data from file')
# Entrez (http://www.ncbi.nlm.nih.gov/Entrez) is a data retrieval system that provides users access to NCBI’s
# databases such as PubMed, GenBank, GEO, and many others
# Use the mandatory email parameter so the NCBI can contact you if there is a proble
Entrez.email = "*****@*****.**" # Always tell NCBI who you are
# logging.getLogger('regular').info('searching pubmed for the CHOP and UPENN authors')
# handle = Entrez.esearch(db="pubmed", retmax=50000, idtype="esearch", mindate="2014/01/01", maxdate="2017/05/01",
# term="Perelman School of Medicine[Affiliation] OR Children's Hospital of "
# "Philadelphia[Affiliation] OR University of Pennsylvania School of "
# "Medicine[Affiliation] OR School of Medicine University of Pennsylvania[Affiliation]",
# usehistory="y")
# search_results = Entrez.read(handle)
# handle.close()
# # obtaining the list of relevant PMIDs
# id_list = search_results["IdList"]
#
# # get all the record based on the PMIDs
# logging.getLogger('regular').info('getting relevant authors\' records based on PMIDs')
# fetch_records_handle = Entrez.efetch(db="pubmed", id=id_list, retmode="text", rettype="medline")
# # need to read all the data from the handle and store in a file because if we just read line by line from the
# # generator and the internet connection is not strong, then we run into http errors:
# # http.client.IncompleteRead: IncompleteRead(0 bytes read)
# logging.getLogger('regular').info('storing authors\' records on local file')
# with open("results.xml", "w") as out_handle:
# out_handle.write(fetch_records_handle.read(validate=True))
# # the results are now in the results.xml file and the original handle has had all of its data extracted
# # (so we close it)
# fetch_records_handle.close()
logging.getLogger('regular').info('reading result files')
records_handle = open("results.xml")
fetch_records = parse(handle=records_handle)
# initializing variables
mesh_description_dict = obtain_descriptions()
# contains all the metadata of the medical information: Pubmed unique Identifier number(PMID), Primary Medical
# Subject Header (MESH) and the description ID
medical_record_file = open('record_results/medical_record.csv', 'w')
medical_record_file.write('PMID,Primary MeSH,Description\n')
# contains all the metadata elements on the author level: Pubmed unique Identifier number(PMID), AuthorID (as a
# combination of the author’s last name, first name, and initials), institution: chop=0, Penn=1, Role: Chief Author
# (CA) Ordinary Author (OA) or Principal Author (PA) and the author's affiliation
author_record_file = open('record_results/author_record.csv', 'w')
author_record_file.write('PMID,Author,Author_CHOP,Author_PENN,Role,Affiliation\n')
# contains all the metadata elements on the paper level: Pubmed unique Identifier number(PMID), Title, Abstract,
# Year, Month, AuthorList, SubjectList, date
paper_record_file = open('record_results/paper_record.csv', 'w')
paper_record_file.write('PMID,Title,Abstract,Year,Month,Author List,Subject List,Date\n')
# get the relevant information for each record
for record_index, record in enumerate(fetch_records):
logging.getLogger('regular').debug('record index = {0}'.format(record_index))
pmid = record.get('PMID')
title = record.get('TI')
abstract = record.get('AB')
authors = record.get('FAU')
affiliations = record.get('AD')
publication_type = record.get('PT')
mesh_term = record.get('MH')
date_created = record.get('EDAT')
year, month = date_created.split('/')[:2]
date = year + '/' + month
logging.getLogger('regular').debug('pmid = {0}'.format(pmid))
logging.getLogger('regular').debug('title = {0}'.format(title))
logging.getLogger('regular').debug('abstract = {0}'.format(abstract))
logging.getLogger('regular').debug('authors = {0}'.format(authors))
logging.getLogger('regular').debug('affiliations = {0}'.format(affiliations))
logging.getLogger('regular').debug('publication type = {0}'.format(publication_type))
logging.getLogger('regular').debug('mesh term = {0}'.format(mesh_term))
logging.getLogger('regular').debug('data created = {0}'.format(date_created))
# assign the chief author, ordinary author or principal investigator role to each author
roles = assign_roles(authors)
# check and assign whether the authors belong to the CHOP or PENN organization
chop_organization, penn_organization = assign_organization(affiliations)
mesh_description = ''
if mesh_term is None:
mesh_term = ''
else:
term, mesh_description = convert_mesh_description(mesh_description_dict, mesh_term)
mesh_term = ';'.join(mesh_term)
# output information
if mesh_description:
msg = print_str(pmid, term, mesh_description)
medical_record_file.write(msg)
for author_index, organizations in enumerate(zip(chop_organization, penn_organization)):
if 1 in organizations:
msg = print_str(pmid, authors[author_index], organizations[0], organizations[1], roles[author_index],
affiliations[author_index])
author_record_file.write(msg)
authors = ';'.join(authors)
msg = print_str(pmid, title, abstract, year, month, authors, mesh_term, date)
paper_record_file.write(msg)
if record_index == 10:
break
# closing all open files
medical_record_file.close()
author_record_file.close()
paper_record_file.close()
logging.getLogger('line.regular.time.line').info('Recommendation System script finished running successfully.')
def main():
# get the the path for the input file argument
parser = argparse.ArgumentParser()
parser.add_argument("-l",
"--log",
dest="logLevel",
choices=['DEBUG', 'INFO', 'ERROR'],
type=str.upper,
help="Set the logging level")
args = parser.parse_args()
logger_initialization(log_level=args.logLevel)
logging.getLogger('line.regular.time.line').info(
'Running Recommendation System script')
# import data from file
logging.getLogger('regular').info('reading data from file')
# Entrez (http://www.ncbi.nlm.nih.gov/Entrez) is a data retrieval system that provides users access to NCBI’s
# databases such as PubMed, GenBank, GEO, and many others
# Use the mandatory email parameter so the NCBI can contact you if there is a proble
Entrez.email = "*****@*****.**" # Always tell NCBI who you are
# logging.getLogger('regular').info('searching pubmed for the CHOP and UPENN authors')
# handle = Entrez.esearch(db="pubmed", retmax=50000, idtype="esearch", mindate="2014/01/01", maxdate="2017/05/01",
# term="Perelman School of Medicine[Affiliation] OR Children's Hospital of "
# "Philadelphia[Affiliation] OR University of Pennsylvania School of "
# "Medicine[Affiliation] OR School of Medicine University of Pennsylvania[Affiliation]",
# usehistory="y")
# search_results = Entrez.read(handle)
# handle.close()
# # obtaining the list of relevant PMIDs
# id_list = search_results["IdList"]
#
# # get all the record based on the PMIDs
# logging.getLogger('regular').info('getting relevant authors\' records based on PMIDs')
# fetch_records_handle = Entrez.efetch(db="pubmed", id=id_list, retmode="text", rettype="medline")
# # need to read all the data from the handle and store in a file because if we just read line by line from the
# # generator and the internet connection is not strong, then we run into http errors:
# # http.client.IncompleteRead: IncompleteRead(0 bytes read)
# logging.getLogger('regular').info('storing authors\' records on local file')
# with open("results.xml", "w") as out_handle:
# out_handle.write(fetch_records_handle.read(validate=True))
# # the results are now in the results.xml file and the original handle has had all of its data extracted
# # (so we close it)
# fetch_records_handle.close()
records_handle = open("results.xml")
logging.getLogger('regular').info('creating parser record handle')
# Use the Bio.Medline module to parse records
fetch_records = Medline.parse(records_handle)
# contains all the metadata elements on the author level: Pubmed unique Identifier number(PMID), AuthorID (as a
# combination of the author’s last name, first name, and initials), institution: chop=0, Penn=1, Role: Chief Author
# (CA) Ordinary Author (OA) or Principal Author (PA) and the author's affiliation
author_record_df = pd.DataFrame(
columns=['PMID', 'AuthorID', 'CHOP_PENN', 'ROLE', 'Affiliation'])
# contains all the metadata elements on the paper level: Pubmed unique Identifier number(PMID), Title, Abstract,
# Year, Month, AuthorList, SubjectList, date
paper_record_df = pd.DataFrame(columns=[
'PMID', 'Title', 'Abstract', 'Year', 'Month', 'AuthorList',
'SubjectList', 'Date'
])
# contains all the metadata of the medical information: Pubmed unique Identifier number(PMID), Primary Medical
# Subject Header (MESH) and the description ID
medical_record_df = pd.DataFrame(columns=['PMID', 'MESH', 'Description'])
# get the description, related to the MESH, in the 2017MeshTree.csv File
mesh_tree_file_object = open(
r'C:\Users\GUERRAMARJ\PycharmProjects\Pubmed\template\2017MeshTree.csv'
)
file_reader = csv.reader(mesh_tree_file_object, delimiter=',')
mesh_description_dict = dict()
logging.getLogger('regular').info(
'processing each record and obtaining relevant information')
for line in file_reader:
# split_line[0] = Number, split_line[1] = Description and split_line[2] = MESH
mesh_description_dict[line[2]] = line[1]
mesh_tree_file_object.close()
# get the relevant information for each record
for record_index, record in enumerate(fetch_records):
logging.getLogger('regular').debug(
'record index = {0}'.format(record_index))
# initialize all the variables
pmid = ''
title = ''
abstract = ''
affiliation = ''
author_id = ''
role = ''
mesh_term = ''
try:
pmid = record.get('PMID')
title = record.get('TI')
abstract = record.get('AB')
logging.getLogger('regular').debug('pmid = {0}'.format(pmid))
logging.getLogger('regular').debug('title = {0}'.format(title))
logging.getLogger('regular').debug(
'abstract = {0}'.format(abstract))
# only used for debugging
publication_type = record.get('PT')
logging.getLogger('regular').debug(
'publication type = {0}'.format(publication_type))
# Note: Currently the record.get("AD") method returns a string regardless of the number of authors i.e. if
# there are two author, it will return as a string both affiliations. As of result, this script has to
# manually get the author information and their respective affiliations
fetch_records_handle = Entrez.efetch(db="pubmed",
id=pmid,
retmode="xml")
manual_record = Entrez.read(fetch_records_handle)
try:
if 'Book Chapter' in publication_type:
authors_list = manual_record['PubmedBookArticle'][0][
'BookDocument']['AuthorList']
else:
# author_list for Pudmed Article
authors_list = manual_record['PubmedArticle'][0][
'MedlineCitation']['Article']['AuthorList']
except:
logging.getLogger('regular').debug(
'error while obtaining the authors\' list')
continue
for author_index, author in enumerate(authors_list):
try:
affiliation = author['AffiliationInfo'][0]['Affiliation']
author_id = author['LastName'] + ', ' + author[
'ForeName'] + ', ' + author['Initials']
logging.getLogger('regular').debug(
'affiliation = {0}'.format(affiliation))
logging.getLogger('regular').debug(
'author id = {0}'.format(author_id))
# Assign the author organization
# 1 = chop, 0 = penn
chop_penn = None
if 'children' in affiliation.lower():
chop_penn = 1
elif 'perelman' in affiliation.lower() or 'school of medicine' in affiliation.lower() or \
'pennsylvania' in affiliation.lower():
chop_penn = 0
logging.getLogger('regular').debug(
'chop_penn = {0}'.format(chop_penn))
# Assign the author's rle
# if less than 2 authors then they are considered "Chief Authors"
if author_index <= 1:
role = 'CA'
# If a person is after the first two authors and it'snt the last author its considered
# "Ordinary Author"
elif author_index > 1 and author_index != len(
authors_list):
role = 'OA'
# else "Principal Investigator)
elif author_index == len(authors_list):
role = 'PI'
else:
ValueError('Wrong author role specified')
logging.getLogger('regular').debug(
'role = {0}'.format(role))
if chop_penn is not None:
# insert the author information into the dataframe for later processing
author_record_df.loc[record_index] = [
pmid, author_id, chop_penn, role, affiliation
]
except (IndexError, KeyError):
# sometimes there wil be organizations on the authors list, in those cases, skip it
continue
# Medical Subject Headings (MESH)
# this can be a list
mesh_term = record.get("MH")
logging.getLogger('regular').debug(
'mesh term = {0}'.format(mesh_term))
if mesh_term is not None:
# fetch the description from the description obtain from the 2017MeshTree file
if len(mesh_term) > 1:
# because there are mesh_term that are not part of the 2017MeshTree, we have to loop through all
# of the mesh_term until one works i.e. the first one found in the 2017MeshTree
for mesh in mesh_term:
try:
term = mesh
print('term = {0}'.format(term))
# cleaning string
if '/' in term:
term = term.split('/')[0]
if '*' in term:
term = term.replace('*', '')
logging.getLogger('regular').debug(
'term = {0}'.format(term))
description = mesh_description_dict[term]
except KeyError:
logging.getLogger('regular').debug(
'not found term = {0}'.format(term))
continue
# insert the values in the dataframe
medical_record_df.append([pmid, mesh_term, description])
# insert the paper information in the paper record dataframe
# paper_record_df.append([pmid, title, abstract, year, month, authors_list, subject_list, date)
except ValueError as error_message:
msg = 'Problem while processing the following '
print(msg)
print('error message = {0}'.format(error_message))
logging.getLogger('line.regular.time.line').info(
'Recommendation System script finished running successfully.')
def main():
parser = argparse.ArgumentParser(description='Grasp Assertiveness Script')
parser.add_argument('-d',
'--directory',
help='dataset directory',
required=True)
parser.add_argument("-l",
"--log",
dest="logLevel",
choices=['DEBUG', 'INFO', 'ERROR'],
help="Set the logging level")
args = parser.parse_args()
# check directory exists
if not os.path.isdir(args.directory):
msg = 'Directory = {0} not found.'.format(args.directory)
raise IOError(msg)
else:
working_dir = args.directory
logger_initialization(logger_dir=working_dir, parser=parser)
logging.getLogger('time.info').info(
'Running the Grasp Assertiveness Script')
try:
dataset_dir = os.path.join(working_dir, 'dataset.csv')
dataset = pd.read_csv(dataset_dir)
except IOError:
msg = 'Could not find \'dataset.csv\' in directory {0}'.format(
working_dir)
logging.getLogger('info').error(msg)
raise IOError(msg)
# index the dataset based on the trial index
indexed_dataset = dataset.set_index(keys=['trial_index'])
random_state = 7
testing_size = 0.10
logging.getLogger('info').info('Obtaining training and testing datasets')
x_train, x_test, y_train, y_test, train_indx_list, test_indx_list, train_cl_indx, test_cl_indx = train_test_split(
indexed_dataset, test_size=testing_size, random_state=random_state)
msg = 'training and testing parameters:\n\t\ttesting size = {0}, random state = {1}'.format(
testing_size, random_state)
logging.getLogger('tab.info').info(msg)
# converting the index to int (to remove decimal) and the convert them to string to be able to print them all
x_train_index = [str(int(indx)) for indx in set(x_train.index)]
x_test_index = [str(int(indx)) for indx in set(x_test.index)]
training_indices = ','.join(x_train_index)
testing_indices = ','.join(x_test_index)
msg = 'training indices:\n\t\t' + training_indices + '\n\ttesting indices:\n\t\t' + testing_indices
logging.getLogger('tab.info.line').info(msg)
training_dataset = x_train.values[train_cl_indx, :]
training_labels = y_train.values[train_cl_indx]
testing_dataset = x_test.values[test_cl_indx, :]
testing_labels = y_test.values[test_cl_indx]
logging.getLogger('info').info('Running SVM')
# training and testing on time-independent dataset
clf = svm.SVC()
clf.fit(training_dataset, training_labels)
svm_score = clf.score(testing_dataset, testing_labels)
msg = 'SVM score = {0}'.format(svm_score)
logging.getLogger('tab.info').info(msg)
logging.getLogger('info').info('Running HMM')
n_pos_components = [2, 5, 7, 15]
cov_types = ['diag', 'tied', 'spherical']
n_iterations = [5, 10, 20, 50]
for nc in n_pos_components:
for cov in cov_types:
for _iter in n_iterations:
try:
msg = 'running HMM with the following parameters'
logging.getLogger('time.info').info(msg)
msg = 'number of states = {0}, type of covariance = {1}, number of iterations = {2}'.format(
nc, cov, _iter)
logging.getLogger('tab.info').info(msg)
# training and testing on time-dependent dataset
hmm_model = hmm.GaussianHMM(n_components=nc,
random_state=random_state,
covariance_type=cov,
n_iter=_iter)
hmm_model.fit(x_train, lengths=train_indx_list)
# training hmm and logistic regression
hmm_training_predictions = hmm_model.predict(
x_train, lengths=train_indx_list)
hmm_training_predictions_reshaped, labels_processed = reshape_predictions(
predictions=hmm_training_predictions, labels=y_train)
msg = 'running Logistic Regression'
logging.getLogger('tab.time.info').info(msg)
# mapping hmm labels to true labels
logistic_regression_model = LogisticRegression()
logistic_regression_model.fit(
X=hmm_training_predictions_reshaped,
y=labels_processed)
# predictions on testing dataset
hmm_testing_predictions = hmm_model.predict(
x_test, lengths=test_indx_list)
hmm_testing_prediction_reshaped, testing_labels_processed = reshape_predictions(
predictions=hmm_testing_predictions, labels=y_test)
time_score = logistic_regression_model.score(
X=hmm_testing_prediction_reshaped,
y=testing_labels_processed)
msg = 'HMM-Logistic Regression score = {0}'.format(
time_score)
logging.getLogger('tab.time.info').info(msg)
except ValueError as error_message:
msg = 'Error while processing the following parameters ' \
'\n\t\tnumber of states = {0}, type of covariance = {1}, number of iterations = {2}'.format(
nc, cov, _iter)
logging.getLogger('tab.info').error(msg)
msg = 'error message = {0}'.format(error_message)
logging.getLogger('tab.tab.info').error(msg)
pass
msg = 'finished running HMM'
logging.getLogger('time.info').info(msg)
logging.getLogger('time.info').info(
'Finished running the Grasp Assertiveness Script')
"""
end program
return : exit 0
"""
msg = 'Program terminated.\n'
logging.getLogger('').info(msg)
exit(0)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-l", "--log", dest="logLevel", choices=['DEBUG', 'INFO', 'ERROR'],
help="Set the logging level")
logger_initialization(parser=parser)
selected_option = -1
while selected_option != 9:
# options
print ''
print 'Program Menu:\n'
print '1: Gaussian-HMM program'
print '2: GMM-HMM program'
print '3: Check Matlab files'
print '4: Convert Matlab files to hdf5 format file'
print '5: Process matlab files with basic features'
print '6: Move Matlab files from Dropbox to Working Directory'
print '7: Perform Logistic Regression'
print '8: Perform LSTM'
