def get_valid_venues_papers_ids_sframe_from_mag(min_ref_number,
min_journal_papers_num):
dataset_dir = pathlib.Path(STORAGE_PATH)
mag_path = dataset_dir / "MAG"
mag = MicrosoftAcademicGraph(mag_path)
sf = mag.extended_papers['Journal ID mapped to venue name',
'Original venue name', 'Paper ID',
'Ref Number']
sf = sf[sf['Ref Number'] >= min_ref_number]
sf.materialize()
sf['Journal name'] = sf['Original venue name'].apply(
lambda n: n.lower().strip())
sf.materialize()
g = sf.groupby(
['Journal ID mapped to venue name'], {
'Count': agg.COUNT(),
'Paper IDs List': agg.CONCAT("Paper ID"),
'Journals names': agg.CONCAT('Journal name')
})
g['Journals names'] = g['Journals names'].apply(lambda l: list(set(l)))
g = g[g['Count'] >= min_journal_papers_num]
g = g[g['Journals names'].apply(lambda l: len(l) == 1)]
g['Journals names'] = g['Journals names'].apply(lambda l: l[0])
g = g.rename({'Journals names': 'Journal name'})
g.materialize()
return g
def get_valid_venues_papers_ids_sframe(min_ref_number,
min_journal_papers_num):
# Criteria I: we use only journals that have paper with valid DOI that appears in both AMiner and MAG datasets
sf = tc.load_sframe(str(AMINER_MAG_JOIN_SFRAME))
sf['Original venue name'] = sf['Original venue name'].apply(
lambda n: n.lower())
g = sf.groupby(
'Journal ID mapped to venue name', {
'venue name': agg.CONCAT('Original venue name'),
'issn': agg.CONCAT('issn')
})
g['issn'] = g['issn'].apply(lambda l: list(set(l)))
g['venue name'] = g['venue name'].apply(lambda l: list(set(l)))
# Criteria II: the journal as only signle name
g = g[g['venue name'].apply(lambda l: len(l) == 1)]
g.materialize()
g['venue name'] = g['venue name'].apply(lambda l: l[0].strip())
# Criteria III: the journal's name appears in SJR
sjr_dict = VenueFetcher.get_sjr_journals_dict()
g = g[g['venue name'].apply(lambda v: v in sjr_dict)]
venues_ids = set(g['Journal ID mapped to venue name'])
# Criteria IV: Each venue need to have at least min_journal_papers_num papers with at
# least min_ref_number refs in each paper
dataset_dir = pathlib.Path(STORAGE_PATH)
mag_path = dataset_dir / "MAG"
mag = MicrosoftAcademicGraph(mag_path)
sf = mag.extended_papers['Journal ID mapped to venue name',
'Original venue name', 'Paper ID',
'Ref Number']
sf = sf[sf['Ref Number'] >= min_ref_number]
sf.materialize()
sf = sf[sf['Journal ID mapped to venue name'].apply(
lambda i: i in venues_ids)]
sf['Journal name'] = sf['Original venue name'].apply(
lambda n: n.lower().strip())
sf.materialize()
# Notice that with the full Papers SFrmae journal can have several names
g = sf.groupby(
['Journal ID mapped to venue name'], {
'Count': agg.COUNT(),
'Paper IDs List': agg.CONCAT("Paper ID"),
'Journals names': agg.CONCAT('Journal name')
})
g['Journals names'] = g['Journals names'].apply(lambda l: list(set(l)))
g = g[g['Count'] >= min_journal_papers_num]
g = g[g['Journals names'].apply(lambda l: len(l) == 1)]
g['Journals names'] = g['Journals names'].apply(lambda l: l[0])
g = g.rename({'Journals names': 'Journal name'})
g.materialize()
return g
def _papers_citations_number_by_year(self, without_self_citation=True):
"""
Get papers total number of citation in each year
:param without_self_citation: if True calculate only non-self citations, other calculate with self-citations
:return: SFrame with a column that contains citations_dict by year
"""
ref_sf = self.extended_references
if without_self_citation:
ref_sf = ref_sf[ref_sf['self citation'] == 0]
sf = self.papers["PaperId", "Year"]
sf = ref_sf.join(sf, on="PaperId")
g = sf.groupby(["PaperReferenceId", "Year"],
{"Citation Number": agg.COUNT()})
g = g.rename({"Year": "Year", "PaperReferenceId": "PaperId"})
g['Citation by Year'] = g.apply(lambda r:
(r["Year"], r["Citation Number"]))
h = g.groupby('PaperId',
{'Citation by Years': agg.CONCAT('Citation by Year')})
if without_self_citation:
h['Total Citations by Year without Self Citations'] = h[
'Citation by Years'].apply(
lambda l: self._get_total_citation_by_year(l))
else:
h['Total Citations by Year'] = h['Citation by Years'].apply(
lambda l: self._get_total_citation_by_year(l))
return h.remove_column("Citation by Years")
def get_venue_median_number_of_authors_by_year(self):
sf = self._all_papers_sf.groupby(
"Paper publish year",
{'Authors Number List': agg.CONCAT("Authors Number")})
return {
r["Paper publish year"]: np.median(r['Authors Number List'])
for r in sf
}
def get_venues_authors_ids(self, end_year):
p_sf = get_papers_sframe(min_ref_num=self._min_ref_num,
end_year=end_year)
a_sf = get_authors_sframe(min_ref_num=self._min_ref_num,
end_year=end_year)
sf = a_sf.join(p_sf, on="Paper ID")
return sf.groupby(self._venue_col_name,
{'authors_list': agg.CONCAT('Author ID')})
def urls(self):
"""
Creating URLs SFrame from.txt.gz files
"""
cols = ["PaperId", "SourceType", "SourceUrl", "LanguageCode"]
urls = SFrame(
pd.read_csv(self._dataset_dir / "PaperUrls.txt.gz",
sep="\t",
names=cols).replace({pd.NA: None}))
return urls.groupby("PaperId", {"Urls": agg.CONCAT("SourceUrl")})
def create_paper_keywords_list_sframe():
"""
Creating Paper Keywords List SFrame
"""
logger.info("Creating Papers' Keywords List SFrame")
if os.path.isdir(PAPER_KEYWORDS_LIST_SFRAME):
return
sf = tc.load_sframe(PAPER_KEYWORDS_SFRAME)
g = sf.groupby("Paper ID", {"Keywords List": agg.CONCAT("Keyword name")})
g.save(PAPER_KEYWORDS_LIST_SFRAME)
def create_urls_sframe():
"""
Creating URLs SFrame from txt files
"""
logger.info("Creating urls SFrame")
if os.path.isdir(PAPER_URLS_SFRAME):
return
sf = tc.SFrame.read_csv(PAPER_URLS_TXT, header=False, delimiter="\t")
sf = sf.rename({"X1": "Paper ID", "X2": "Url"})
g = sf.groupby("Paper ID", {"Urls": agg.CONCAT("Url")})
g.save(PAPER_URLS_SFRAME)
def get_co_authors_dict_sframe(self):
"""
Create SFrame with each author's coauthors by year
:return: SFrame with AuthorId and Coauthors by Years Dict
:note: the function can take considerable amount of time to execute
"""
logger.info("Calcualting authors' coauthors by year")
sf = self.paper_authors_years
sf = sf.join(sf, on='PaperId')
sf = sf[sf['AuthorId'] != sf['AuthorId.1']]
sf = sf.remove_column('Year.1')
sf = sf.groupby(['AuthorId', 'Year'],
{'Coauthors List': agg.CONCAT('AuthorId.1')})
sf['Coauthors Year'] = sf.apply(lambda r:
(r['Year'], r['Coauthors List']))
sf = sf.groupby("AuthorId",
{'Coauthors list': agg.CONCAT('Coauthors Year')})
sf['Coauthors by Years Dict'] = sf['Coauthors list'].apply(
lambda l: {y: coa_list
for y, coa_list in l})
sf = sf.remove_column('Coauthors list')
return sf
def get_authors_papers_dict_sframe(self):
"""
Create SFrame in which each row contains an AuthorId and a dict with the author's publication by year dict
:return: SFrame with Authors ID and Papers by Years Dict columns
:rtype: tc.SFrame
"""
logger.info("Calcualting authors' papers by year")
a_sf = self.paper_authors_years
a_sf['Paper Year'] = a_sf.apply(lambda r: (r["Year"], r["PaperId"]))
g = a_sf.groupby("AuthorId", {"Papers List": agg.CONCAT("Paper Year")})
g['Papers by Years Dict'] = g["Papers List"].apply(
lambda l: _entities_years_list_to_dict(l))
g = g.remove_column("Papers List")
return g
def create_ground_truth_names(baby_names_path, wikitree_users_path, ratio=0.9):
"""
Createing SFrame with statistics on first name gender probability using data from WikiTree and SSA
:param baby_names_path: the file to SSA baby names files
:param wikitree_users_path: link to file with WikiTree names
:param ratio: the ratio that above it the name gender is considered male
:return: SFrame with data regarding first name gender
:rtype: tc.SFrame
:note: first names data files can be downloaded from http://www.ssa.gov/oact/babynames/names.zip and
https://www.wikitree.com/wiki/Help:Database_Dumps
"""
sf = tc.SFrame.read_csv("%s/*.txt" % baby_names_path, header=False)
sf = sf.rename({'X1': 'First Name', 'X2': 'Gender', 'X3': 'Count'})
w_sf = tc.SFrame.read_csv(wikitree_users_path, delimiter="\t", header=True)
w_sf = w_sf[['Preferred Name', 'Gender']]
w_sf = w_sf.rename({'Preferred Name': 'First Name'})
w_sf = w_sf[w_sf['Gender'] != 0]
w_sf['First Name'] = w_sf['First Name'].apply(lambda n: n.split()[0]
if len(n) > 0 else '')
w_sf = w_sf[w_sf['First Name'] != '']
w_sf['Gender'] = w_sf['Gender'].apply(lambda g: 'M' if g == 1 else 'F')
w_sf = w_sf.groupby(['First Name', 'Gender'], {'Count': agg.COUNT()})
sf = sf.append(w_sf)
sf['First Name'] = sf['First Name'].apply(lambda n: n.lower())
g = sf.groupby(['First Name', 'Gender'], agg.SUM('Count'))
g['stat'] = g.apply(lambda r: (r['Gender'], r['Sum of Count']))
sf = g.groupby('First Name', {'Stats': agg.CONCAT('stat')})
sf['Total Births'] = sf['Stats'].apply(lambda l: sum([i[1] for i in l]))
sf['Total Males'] = sf['Stats'].apply(
lambda l: sum([i[1] for i in l if i[0] == 'M']))
sf['Percentage Males'] = sf.apply(
lambda r: float(r['Total Males']) / r['Total Births'])
sf = sf[sf['Total Births'] >= 5]
def get_name_gender(p):
if p >= ratio:
return 'Male'
if p <= (1 - ratio):
return 'Female'
return 'Unisex'
sf['Gender'] = sf['Percentage Males'].apply(lambda p: get_name_gender(p))
sf = sf.remove_column('Stats')
return sf
def papers_authors_lists(self):
"""
Create SFrame in which each row contains PaperId and a sorted list of the paper's authors
"""
authors_sf = self.paper_author_affiliations["PaperId", "AuthorId",
"AuthorSequenceNumber"]
authors_sf['Author_Seq'] = authors_sf.apply(
lambda r: [r["AuthorId"], r["AuthorSequenceNumber"]])
g = authors_sf.groupby("PaperId",
{"Authors List": agg.CONCAT('Author_Seq')})
g['Authors List Sorted'] = g["Authors List"].apply(
lambda l: sorted(l, key=lambda i: i[1]))
g['Authors List Sorted'] = g['Authors List Sorted'].apply(
lambda l: [i[0] for i in l])
g = g.remove_column("Authors List")
g = g["PaperId", 'Authors List Sorted']
g['Authors Number'] = g['Authors List Sorted'].apply(lambda l: len(l))
return g
def create_papers_fields_of_study(flevels=(0, 1, 2, 3)):
"""
Create SFrame with each paper fields of study by hierarchical levels
:param flevels: list of levels, for each level add the papers fields of study in this level
"""
logger.info("Creating Papers Fields of Study SFrame")
if os.path.isdir(PAPERS_FIELDS_OF_STUDY_SFRAME):
return
k_sf = tc.load_sframe(KEYWORDS_SFRAME)
g = k_sf.groupby('Paper ID', {
'Field of study list':
agg.CONCAT("Field of study ID mapped to keyword")
})
fh = FieldsHierarchyAnalyzer()
# add fileds of study names from ID
names = []
for l in g['Field of study list']:
names.append([fh.get_field_name(i) for i in l])
g['Field of study list names'] = names
for flevel in flevels:
logger.info("Adding papers fields of study level %s" % flevel)
parent_list = []
for paper_field_of_study_list in g['Field of study list']:
parent_list.append(
list(
set.union(*[
fh.get_parents_field_of_study(field, flevel)
for field in paper_field_of_study_list
])))
g['Fields of study parent list (L%s)' % flevel] = parent_list
names = []
for paper_field_of_study_parents_list in g[
'Fields of study parent list (L%s)' % flevel]:
names.append([
fh.get_field_name(field_of_study)
for field_of_study in paper_field_of_study_parents_list
])
g['Fields of study parent list names (L%s)' % flevel] = names
g.save(PAPERS_FIELDS_OF_STUDY_SFRAME)
def _create_field_of_study_paper_ids(self, level):
"""
Create SFrame in which each row contains a field of study and it's matching list of PaperIds
:param level: field of study level
:return: SFrame with the fields of study in the input level papers ids
:rtype: SFrame
"""
col = 'Fields of study parent list (L%s)' % level
sf = self.extended_papers
new_col_name = "Field ID"
sf = sf[sf[col] != None]
sf = sf.stack(col, new_column_name=new_col_name)
g = sf.groupby(new_col_name, {'PaperIds': agg.CONCAT("PaperId")})
g[new_col_name] = g[new_col_name].astype(int)
f_sf = self.fields_of_study
g = g.join(f_sf, on={new_col_name: "FieldOfStudyId"})
g['Number of Paper'] = g['PaperIds'].apply(lambda l: len(l))
g['Level'] = level
return g.rename({new_col_name: "Field of study ID"})
def create_papers_authors_lists_sframe():
"""
Create SFrame in which each row contains paper id and a sorted list of the paper's authors
"""
logger.info("Creating Authors Lists SFrame")
if os.path.isdir(PAPERS_ORDERED_AUTHORS_LIST_SFRAME):
return
authors_sf = tc.load_sframe(PAPER_AUTHOR_AFFILIATIONS_SFRAME)
authors_sf = authors_sf["Paper ID", "Author ID", "Author sequence number"]
authors_sf['Author_Seq'] = authors_sf.apply(
lambda r: [r["Author ID"], r["Author sequence number"]])
g = authors_sf.groupby("Paper ID",
{"Authors List": agg.CONCAT('Author_Seq')})
g['Authors List Sorted'] = g["Authors List"].apply(
lambda l: sorted(l, key=lambda i: i[1]))
g['Authors List Sorted'] = g['Authors List Sorted'].apply(
lambda l: [i[0] for i in l])
g = g.remove_column("Authors List")
g = g["Paper ID", 'Authors List Sorted']
g['Authors Number'] = g['Authors List Sorted'].apply(lambda l: len(l))
g.save(PAPERS_ORDERED_AUTHORS_LIST_SFRAME)
def _get_author_feature_by_year_sframe(self, feature_name,
feature_col_name):
"""
Create a SFrame with AuthorId and a dict with the author's input feature (feature_name) over the years values
:param feature_name: input feature name
:param feature_col_name: the Sframe column name which contains dict with the author feature_name values over the years
:return: SFrame with AuthorId and feature_col_name columns
:rtype: tc.SFrame
"""
logger.info("Calcualting authors feature %s by year" % feature_name)
a_sf = self.paper_author_affiliation_sframe['AuthorId', 'Year',
feature_name]
a_sf['Feature Year'] = a_sf.apply(lambda r:
(int(r["Year"]), r[feature_name]))
g = a_sf.groupby("AuthorId",
{"Feature List": agg.CONCAT("Feature Year")})
g[feature_col_name] = g["Feature List"].apply(
lambda l: _entities_years_list_to_dict(l))
g = g.remove_column("Feature List")
return g
def _create_field_of_study_paper_ids_sframe(level):
"""
Create SFrame in which each row contains a field of study and it's matching list of paper ids
:param level: field of study level
:return: SFrame with the fields of stuyd in the input level papers ids
:rtype: tc.SFrame
"""
logger.info("Creating fields os study paper ids SFrame level - %s " %
level)
col = 'Fields of study parent list (L%s)' % level
sf = tc.load_sframe(EXTENDED_PAPERS_SFRAME)
new_col_name = "Field ID"
sf = sf.stack(col, new_column_name=new_col_name)
sf = sf[sf[col] != None]
g = sf.groupby(new_col_name, {'Paper IDs': agg.CONCAT("Paper ID")})
f_sf = tc.load_sframe(FIELDS_OF_STUDY_SFRAME)
g = g.join(f_sf, on={new_col_name: "Field of study ID"})
g['Number of Paper'] = g['Paper IDs'].apply(lambda l: len(l))
g['Level'] = level
g = g.rename({new_col_name: "Field of study ID"})
return g
def papers_fields_of_study_level(self, flevels=(0, 1, 2, 3)):
"""
Create SFrame with each paper fields of study by hierarchical levels
:param flevels: list of levels, for each level add the papers fields of study in this level
"""
k_sf = self.paper_fields_of_study
# FieldOfStudyId
g = k_sf.groupby('PaperId',
{'Field of study list': agg.CONCAT("FieldOfStudyId")})
fh = FieldsHierarchyAnalyzer(self)
# add fields of study names from ID
names = []
for l in tqdm(g['Field of study list']):
names.append([fh.get_field_name(i) for i in l])
g['Field of study list names'] = names
for flevel in flevels:
parent_list = []
for paper_field_of_study_list in tqdm(g['Field of study list']):
parent_list.append(
list(
set.union(*[
fh.get_parents_field_of_study(field, flevel)
for field in paper_field_of_study_list
])))
g[f'Fields of study parent list (L{flevel})'] = parent_list
names = []
for paper_field_of_study_parents_list in g[
f'Fields of study parent list (L{flevel})']:
names.append([
fh.get_field_name(field_of_study)
for field_of_study in paper_field_of_study_parents_list
])
g[f'Fields of study parent list names (L{flevel})'] = names
return g
def get_venues_papers_ids(self, end_year):
p_sf = get_papers_sframe(min_ref_num=self._min_ref_num,
end_year=end_year)
return p_sf.groupby(self._venue_col_name,
{'papers_list': agg.CONCAT('Paper ID')})
p_sf = gl.load_sframe("./Papers.sframe/") # 126,903,970 rows
p_sf = r_sf.join(p_sf) # 22,082,741
p_sf.save('./PapersMin5Ref.sframe')
p_sf = gl.load_sframe('./PapersMin5Ref.sframe')
a_sf = gl.load_sframe('./PaperAuthorAffiliations.sframe/') # 337000127
sf = p_sf[['Paper ID']].join(a_sf) # 86,561,861 rows
sf = sf.join(p_sf, on="Paper ID")
sf.groupby(
"Author ID", {
'Papers Count': agg.COUNT_DISTINCT('Paper ID'),
'start_year': agg.MIN('Paper publish year'),
'last_year': agg.MAX('Paper publish year'),
'mean_ref_count': agg.AVG('Ref Count'),
'papers_list': agg.CONCAT('Paper ID'),
'journals_list': agg.CONCAT('Journal ID mapped to venue name'),
'conference_list': agg.CONCAT('Conference ID mapped to venue name'),
'affilation_list': agg.CONCAT('Affiliation ID')
})
sf = gl.SFrame()
r = re.compile(r"\d{4}")
for i in l:
try:
y = r.findall(i)[0]
x = gl.SFrame.read_csv("%s/%s" % (p, i))
x['Year'] = y
x['Total Docs'] = x['Total Docs. (%s)' % y]
x = x['Title', 'H index', 'SJR Best Quartile', 'SJR', 'Type', 'Rank',
'Year', 'Total Docs']
