def define_search_sources(self, verbose=False):
"""Define .search_sources.
Parameters
----------
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
"""
# Get list of source IDs of scientist's own sources
own_source_ids, _ = zip(*self.sources)
# Select sources in scientist's main field
df = self.field_source
same_field = df["asjc"] == self.main_field[0]
# Select sources of same type as those in scientist's main field
same_sources = same_field & df["source_id"].isin(own_source_ids)
main_types = set(df[same_sources]["type"])
same_type = same_field & df["type"].isin(main_types)
source_ids = df[same_type]["source_id"].unique()
selected = df[df["source_id"].isin(source_ids)].copy()
selected["asjc"] = selected["asjc"].astype(str) + " "
grouped = selected.groupby("source_id").sum()["asjc"].to_frame()
# Deselect sources with alien fields
mask = grouped["asjc"].apply(
lambda s: any(x for x in s.split() if int(x) not in self.fields))
grouped = grouped[~mask]
sources = set((s, self.source_names.get(s)) for s in grouped.index)
# Add own sources
sources.update(self.sources)
# Finalize
self._search_sources = sorted(list(sources))
types = "; ".join(list(main_types))
text = "Found {} sources matching main field {} and type(s) {}".format(
len(self._search_sources), self.main_field[0], types)
custom_print(text, verbose)
return self
def define_search_group(self,
stacked=False,
verbose=False,
refresh=False,
ignore_first_id=False):
"""Define search_group.
Parameters
----------
stacked : bool (optional, default=False)
Whether to combine searches in few queries or not. Cached
files with most likely not be resuable. Set to True if you
query in distinct fields or you want to minimize API key usage.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached search files.
ignore_first_id: boolean (optional, default=False)
If True, the authors in the first year of publication of the
scientist are not selected based on their Author ID but based on
their surname and first name.
"""
# Checks
if not self.search_sources:
text = "No search sources defined. Please run "\
".define_search_sources() first."
raise Exception(text)
self._ignore_first_id = ignore_first_id
if ignore_first_id and not self.period:
self._ignore_first_id = False
warn("ignore_first_id set back to False: period is None or "
"the first year of the period is before the first year "
"of publication of the scientist.")
# Query journals
params = {
"self": self,
"stacked": stacked,
"refresh": refresh,
"verbose": verbose
}
today, then, negative = search_group_from_sources(**params)
# Finalize and select
group = today
if not self._ignore_first_id:
group = today.intersection(then)
negative.update({str(i) for i in self.identifier})
negative.update({str(i) for i in self.coauthors})
self._search_group = sorted(list(group - negative))
text = "Found {:,} authors for search_group".format(
len(self._search_group))
custom_print(text, verbose)
return self
def inform_matches(self, fields=None, verbose=False, refresh=False):
"""Add information to matches to aid in selection process.
Parameters
----------
fields : iterable (optional, default=None)
Which information to provide. Allowed values are "first_name",
"surname", "first_year", "num_coauthors", "num_publications",
"num_citations", "num_coauthors_period", "num_publications_period",
"num_citations_period", "subjects", "affiliation_country",
"affiliation_id", "affiliation_name", "affiliation_type",
"language", "num_cited_refs". If None, will use all
available fields.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached results (if they exist) or not. If int
is passed and stacked=False, results will be refreshed if they are
older than that value in number of days.
Notes
-----
Matches including corresponding information are available through
property `.matches`.
Raises
------
ValueError
If `fields` contains invalid keywords.
"""
# Checks
if not self._matches:
text = "No matches defined. Please run .find_matches() first."
raise Exception(text)
allowed_fields = ["first_name", "surname", "first_year",
"num_coauthors", "num_publications", "num_citations",
"num_coauthors_period", "num_publications_period",
"num_citations_period", "subjects",
"affiliation_country", "affiliation_id",
"affiliation_name", "affiliation_type", "language",
"num_cited_refs"]
if fields:
invalid = [x for x in fields if x not in allowed_fields]
if invalid:
text = "Parameter fields contains invalid keywords: " +\
", ".join(invalid)
raise ValueError(text)
else:
fields = allowed_fields
text = f"Providing information for {len(self._matches):,} matches..."
custom_print(text, verbose)
matches = inform_matches(self, fields, verbose, refresh)
self._matches = matches
def define_search_sources(self, verbose=False):
"""Define .search_sources.
Within the list of search sources sosia will search for matching
scientists. A search source is of the same main field as
the original scientist, the same types (journal, conference
proceeding, etc.), and must not be related to fields alien to the
original scientist.
Parameters
----------
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
"""
df = self.field_source
# Sources in scientist's main field
same_field = df["asjc"] == self.main_field[0]
# Types of Scientist's sources
own_source_ids, _ = zip(*self.sources)
same_sources = df["source_id"].isin(own_source_ids)
main_types = df[same_sources]["type"].unique()
same_type = df["type"].isin(main_types)
# Select source IDs
selected_ids = df[same_field & same_type]["source_id"].unique()
selected = df[df["source_id"].isin(selected_ids)].copy()
selected["asjc"] = selected["asjc"].astype(int).astype(str) + " "
grouped = (selected.groupby("source_id")
.sum()["asjc"]
.to_frame())
# Deselect sources with alien fields
grouped["asjc"] = grouped["asjc"].astype(str).str.split().apply(set)
fields = set(str(f) for f in self.fields)
no_alien_field = grouped["asjc"].apply(lambda s: len(s - fields) == 0)
grouped = grouped[no_alien_field]
# Add source names
sources = set((s, self.source_names.get(s)) for s in grouped.index)
# Add own sources
sources.update(self.sources)
# Finalize
self._search_sources = sorted(sources)
text = f"Found {len(sources):,} sources matching main field "\
f"{self.main_field[0]} and source type(s) {'; '.join(main_types)}"
custom_print(text, verbose)
return self
def find_matches(self, stacked=False, verbose=False, refresh=False):
"""Find matches within search_group based on four criteria:
1. Started publishing in about the same year
2. Has about the same number of publications in the treatment year
3. Has about the same number of coauthors in the treatment year
4. Has about the same number of citations in the treatment year
5. Works in the same field as the scientist's main field
Parameters
----------
stacked : bool (optional, default=False)
Whether to combine searches in few queries or not. Cached
files will most likely not be reusable. Set to True if you
query in distinct fields or you want to minimize API key usage.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached results (if they exist) or not. If int
is passed and stacked=False, results will be refreshed if they are
older than that value in number of days.
Notes
-----
Matches are available through property `.matches`.
"""
# Checks
if not self.search_group:
text = "No search group defined. Please run "\
".define_search_group() first."
raise Exception(text)
if not isinstance(refresh, bool) and stacked:
refresh = False
warn("refresh parameter must be boolean when stacked=True. "
"Continuing with refresh=False.")
# Find matches
matches = find_matches(self, stacked, verbose, refresh)
text = f"Found {len(matches):,} author(s) matching all criteria"
custom_print(text, verbose)
self._matches = sorted([auth_id for auth_id in matches])
def define_search_group(self, stacked=False, verbose=False, refresh=False):
"""Define search_group.
Parameters
----------
stacked : bool (optional, default=False)
Whether to combine searches in few queries or not. Cached
files with most likely not be reusable. Set to True if you
query in distinct fields or you want to minimize API key usage.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached results (if they exist) or not.
"""
# Checks
if not self.search_sources:
text = "No search sources defined. Please run "\
".define_search_sources() first."
raise Exception(text)
# Query journals
params = {
"original": self,
"stacked": stacked,
"refresh": refresh,
"verbose": verbose
}
search_group = search_group_from_sources(**params)
# Remove own IDs and coauthors
search_group -= set(self.identifier)
search_group -= {str(i) for i in self.coauthors}
# Finalize
self._search_group = sorted(search_group)
text = f"Found {len(search_group):,} authors for search_group"
custom_print(text, verbose)
return self
def search_group_from_sources(self, stacked, verbose, refresh=False):
"""Define groups of authors based on publications from a set of sources.
Parameters
----------
self : sosia.Original
The object of the Scientist to search information for.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached search files.
Returns
-------
today, then, negative : set
Set of authors publishing in three periods: During the year of
treatment, during years to match on, and during years before the
first publication.
"""
# Filtering variables
min_year = self.first_year - self.year_margin
max_year = self.first_year + self.year_margin
if self.period:
_margin_setter = self.publications_period
else:
_margin_setter = self.publications
max_pubs = max(margin_range(len(_margin_setter), self.pub_margin))
years = list(range(min_year, max_year + 1))
search_years = [min_year - 1]
if not self._ignore_first_id:
search_years.extend(range(min_year, max_year + 1))
search_sources, _ = zip(*self.search_sources)
# Verbose variables
n = len(search_sources)
text = "Searching authors for search_group in {} sources...".format(n)
custom_print(text, verbose)
today = set()
then = set()
negative = set()
if stacked: # Make use of SQL cache
# Year provided (select also based on location)
# Get already cached sources from cache
sources_ay = DataFrame(list(product(search_sources,
[self.active_year])),
columns=["source_id", "year"])
_, _search = sources_in_cache(sources_ay, refresh=refresh, afid=True)
res = query_year(self.active_year,
_search.source_id.tolist(),
refresh,
verbose,
afid=True)
cache_insert(res, table="sources_afids")
sources_ay, _ = sources_in_cache(sources_ay,
refresh=refresh,
afid=True)
# Authors publishing in provided year and locations
mask = None
if self.search_affiliations:
mask = sources_ay.afid.isin(self.search_affiliations)
today = flat_set_from_df(sources_ay, "auids", mask)
# Years before active year
# Get already cached sources from cache
sources_ys = DataFrame(list(product(search_sources, search_years)),
columns=["source_id", "year"])
_, sources_ys_search = sources_in_cache(sources_ys, refresh=refresh)
missing_years = set(sources_ys_search.year.tolist())
# Eventually add information for missing years to cache
for y in missing_years:
mask = sources_ys_search.year == y
_sources_search = sources_ys_search[mask].source_id.tolist()
res = query_year(y, _sources_search, refresh, verbose)
cache_insert(res, table="sources")
# Get full cache
sources_ys, _ = sources_in_cache(sources_ys, refresh=False)
# Authors publishing in year(s) of first publication
if not self._ignore_first_id:
mask = sources_ys.year.between(min_year, max_year, inclusive=True)
then = flat_set_from_df(sources_ys, "auids", mask)
# Authors with publications before
mask = sources_ys.year < min_year
negative = flat_set_from_df(sources_ys, "auids", mask)
else:
auth_count = []
print_progress(0, n, verbose)
for i, source_id in enumerate(search_sources):
info = query_journal(source_id, [self.active_year] + years,
refresh)
today.update(info[str(self.active_year)])
if not self._ignore_first_id:
for y in years:
then.update(info[str(y)])
for y in range(int(min(info.keys())), min_year):
negative.update(info[str(y)])
for y in info:
if int(y) <= self.active_year:
auth_count.extend(info[str(y)])
print_progress(i + 1, n, verbose)
c = Counter(auth_count)
negative.update({a for a, npub in c.items() if npub > max_pubs})
return today, then, negative
def filter_pub_counts(group,
ybefore,
yupto,
npapers,
yfrom=None,
verbose=False):
"""Filter authors based on restrictions in the number of
publications in different periods, searched by query_size.
Parameters
----------
group : list of str
Scopus IDs of authors to be filtered.
ybefore : int
Year to be used as first year. Publications on this year and before
need to be 0.
yupto : int
Year up to which to count publications.
npapers : list
List of count of publications, minimum and maximum.
yfrom : int (optional, default=None)
If provided, publications are counted only after this year.
Publications are still set to 0 before ybefore.
Returns
-------
group : list of str
Scopus IDs filtered.
pubs_counts : list of int
List of count of publications within the period provided for authors
in group.
older_authors : list of str
Scopus IDs filtered out because have publications before ybefore.
Notes
-----
It uses cached values first, and searches for more data if needed.
"""
group = [int(x) for x in group]
years_check = [ybefore, yupto]
if yfrom:
years_check.extend([yfrom - 1])
authors = DataFrame(list(product(group, years_check)),
columns=["auth_id", "year"],
dtype="int64")
authors_size = author_size_in_cache(authors)
au_skip = []
group_tocheck = [x for x in group]
older_authors = []
pubs_counts = []
# use information in cache
if not authors_size.empty:
# authors that can be already removed because older
mask = ((authors_size.year <= ybefore) & (authors_size.n_pubs > 0))
remove = (authors_size[mask]["auth_id"].drop_duplicates().tolist())
older_authors.extend(remove)
au_remove = [x for x in remove]
# remove if number of pubs in year is in any case too small
mask = ((authors_size.year >= yupto) &
(authors_size.n_pubs < min(npapers)))
remove = (authors_size[mask]["auth_id"].drop_duplicates().tolist())
au_remove.extend(remove)
# authors with no pubs before min year
mask = (((authors_size.year == ybefore) & (authors_size.n_pubs == 0)))
au_ok_miny = (authors_size[mask]["auth_id"].drop_duplicates().tolist())
# check publications in range
if yfrom:
# adjust count by substracting the count before period; keep
# only authors for which it is possible
mask = (authors_size.year == yfrom - 1)
authors_size_bef = authors_size[mask]
authors_size_bef["year"] = yupto
authors_size_bef.columns = ["auth_id", "year", "n_pubs_bef"]
bef_auth = set(authors_size_bef["auth_id"])
mask = ((authors_size["auth_id"].isin(bef_auth)) &
(authors_size["year"] == yupto))
authors_size = authors_size[mask]
authors_size = authors_size.merge(authors_size_bef,
"left",
on=["auth_id", "year"])
authors_size = authors_size.fillna(0)
authors_size["n_pubs"] -= authors_size["n_pubs_bef"]
# authors that can be already removed because of pubs count
mask = (((authors_size.year >= yupto) &
(authors_size.n_pubs < min(npapers))) |
((authors_size.year <= yupto) &
(authors_size.n_pubs > max(npapers))))
remove = (authors_size[mask]["auth_id"].drop_duplicates().tolist())
au_remove.extend(remove)
# authors with pubs count within the range before the given year
mask = (((authors_size.year == yupto) &
(authors_size.n_pubs >= min(npapers))) &
(authors_size.n_pubs <= max(npapers)))
au_ok_year = authors_size[mask][["auth_id",
"n_pubs"]].drop_duplicates()
# authors ok (match both conditions)
au_ok = list(set(au_ok_miny).intersection(set(au_ok_year["auth_id"])))
mask = au_ok_year["auth_id"].isin(au_ok)
pubs_counts = au_ok_year[mask]["n_pubs"].tolist()
# authors that match only the first condition, but the second is
# not known, can skip the first cindition check.
au_skip = [x for x in au_ok_miny if x not in au_remove + au_ok]
group = [x for x in group if x not in au_remove]
group_tocheck = [x for x in group if x not in au_skip + au_ok]
text = "Left with {} authors based on size information already in "\
"cache.\n{} to check\n".format(len(group), len(group_tocheck))
custom_print(text, verbose)
# Verify that publications before minimum year are 0
if group_tocheck:
text = "Searching through characteristics of {:,} authors...".format(
len(group_tocheck))
custom_print(text, verbose)
print_progress(0, len(group_tocheck), verbose)
to_loop = [x for x in group_tocheck] # Temporary copy
for i, au in enumerate(to_loop):
q = "AU-ID({}) AND PUBYEAR BEF {}".format(au, ybefore + 1)
size = base_query("docs", q, size_only=True)
tp = (au, ybefore, size)
cache_insert(tp, table="author_size")
print_progress(i + 1, len(to_loop), verbose)
if not size == 0:
group.remove(au)
group_tocheck.remove(au)
older_authors.append(au)
text = "Left with {} authors based on size information before "\
"minium year\n Filtering based on size query before "\
"provided year\n".format(len(group))
custom_print(text, verbose)
# Verify that publications before the given year falle in range
group_tocheck.extend(au_skip)
n = len(group_tocheck)
if group_tocheck:
text = "Searching through characteristics of {:,} authors".format(n)
custom_print(text, verbose)
print_progress(0, n, verbose)
for i, au in enumerate(group_tocheck):
q = "AU-ID({}) AND PUBYEAR BEF {}".format(au, yupto + 1)
n_pubs_yupto = base_query("docs", q, size_only=True)
tp = (au, yupto, n_pubs_yupto)
cache_insert(tp, table="author_size")
# Eventually decrease publication count
if yfrom and n_pubs_yupto >= min(npapers):
q = "AU-ID({}) AND PUBYEAR BEF {}".format(au, yfrom)
n_pubs_yfrom = base_query("docs", q, size_only=True)
tp = (au, yfrom - 1, n_pubs_yfrom)
cache_insert(tp, table="author_size")
n_pubs_yupto -= n_pubs_yfrom
if n_pubs_yupto < min(npapers) or n_pubs_yupto > max(npapers):
group.remove(au)
else:
pubs_counts.append(n_pubs_yupto)
print_progress(i + 1, n, verbose)
return group, pubs_counts, older_authors
def search_group_from_sources(original,
stacked=False,
verbose=False,
refresh=False):
"""Define groups of authors based on publications from a set of sources.
Parameters
----------
original : sosia.Original
The object of the Scientist to search information for.
stacked : bool (optional, default=False)
Whether to use fewer queries that are not reusable, or to use modular
queries of the form "SOURCE-ID(<SID>) AND PUBYEAR IS <YYYY>".
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached search files.
Returns
-------
group : set
Set of authors publishing in year of treatment, in years around
first publication, and not before the latter period.
"""
# Define variables
search_sources, _ = zip(*original.search_sources)
text = f"Defining 'search_group' using up to {len(search_sources):,} sources..."
custom_print(text, verbose)
# Retrieve author list for today
sources_today = pd.DataFrame(product(search_sources,
[original.active_year]),
columns=["source_id", "year"])
auth_today = get_authors_from_sourceyear(sources_today,
original.sql_conn,
refresh=refresh,
stacked=stacked,
verbose=verbose)
mask = None
if original.search_affiliations:
mask = auth_today["afid"].isin(original.search_affiliations)
today = flat_set_from_df(auth_today, "auids", condition=mask)
# Authors active around year of first publication
min_year = original.first_year - original.first_year_margin
max_year = original.first_year + original.first_year_margin
then_years = [min_year - 1]
if not original.first_year_name_search:
then_years.extend(range(min_year, max_year + 1))
sources_then = pd.DataFrame(product(search_sources, then_years),
columns=["source_id", "year"])
auth_then = get_authors_from_sourceyear(sources_then,
original.sql_conn,
refresh=refresh,
stacked=stacked,
verbose=verbose)
mask = auth_then["year"].between(min_year, max_year)
then = flat_set_from_df(auth_then, "auids", condition=mask)
# Remove authors active before
mask = auth_then["year"] < min_year
before = flat_set_from_df(auth_then, "auids", condition=mask)
today -= before
# Compile group
group = today
if not original.first_year_name_search:
group = today.intersection(then)
return {int(a) for a in group}
def find_matches(original, stacked, verbose, refresh):
"""Find matches within the search group.
Parameters
----------
original : sosia.Original()
The object containing information for the original scientist to
search for. Attribute search_group needs to exist.
stacked : bool (optional, default=False)
Whether to combine searches in few queries or not. Cached
files will most likely not be reusable. Set to True if you
query in distinct fields or you want to minimize API key usage.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached results (if they exist) or not. If int
is passed and stacked=False, results will be refreshed if they are
older than that value in number of days.
"""
# Variables
_years = range(original.first_year - original.first_year_margin,
original.first_year + original.first_year_margin + 1)
_npapers = margin_range(len(original.publications), original.pub_margin)
_max_papers = max(_npapers)
_ncits = margin_range(original.citations, original.cits_margin)
_max_cits = max(_ncits)
_ncoauth = margin_range(len(original.coauthors), original.coauth_margin)
_max_coauth = max(_ncoauth)
if original.period:
_npapers = margin_range(len(original.publications_period),
original.pub_margin)
_ncits = margin_range(original.citations_period, original.cits_margin)
_ncoauth = margin_range(len(original.coauthors_period),
original.coauth_margin)
text = "Searching through characteristics of "\
f"{len(original.search_group):,} authors..."
custom_print(text, verbose)
conn = original.sql_conn
# First round of filtering: minimum publications and main field
# create df of authors
authors = get_authors(original.search_group,
original.sql_conn,
verbose=verbose)
same_field = authors['areas'].str.startswith(original.main_field[1])
enough_pubs = authors['documents'].astype(int) >= int(min(_npapers))
group = sorted(authors[same_field & enough_pubs]["auth_id"].tolist())
text = f"Left with {len(group):,} authors with sufficient "\
"number of publications and same main field"
custom_print(text, verbose)
# Second round of filtering:
# Check having no publications before minimum year, and if 0, the
# number of publications in the relevant period.
params = {
"group": group,
"ybefore": min(_years) - 1,
"yupto": original.year,
"npapers": _npapers,
"yfrom": original._period_year,
"verbose": verbose,
"conn": conn
}
group, _, _ = filter_pub_counts(**params)
# Screen out profiles with too many publications over the full period
if original.period:
params.update({
"npapers": [1, _max_papers],
"yfrom": None,
"group": group
})
group, _, _ = filter_pub_counts(**params)
text = f"Left with {len(group):,} researchers"
custom_print(text, verbose)
# Third round of filtering: citations (in the FULL period)
authors = pd.DataFrame({"auth_id": group, "year": original.year})
auth_cits, missing = retrieve_author_info(authors, conn, "author_ncits")
if not missing.empty:
total = missing.shape[0]
text = f"Counting citations of {total:,} authors..."
custom_print(text, verbose)
missing['n_cits'] = 0
print_progress(0, total, verbose)
start = 0
for i, au in missing.iterrows():
n_cits = count_citations([str(au['auth_id'])], original.year + 1)
missing.at[i, 'n_cits'] = n_cits
print_progress(i + 1, total, verbose)
if i % 100 == 0 or i == len(missing) - 1:
insert_data(missing.iloc[start:i + 1],
conn,
table="author_ncits")
start = i
auth_cits = pd.concat([auth_cits, missing])
auth_cits['auth_id'] = auth_cits['auth_id'].astype("uint64")
# Keep if citations are in range
custom_print("Filtering based on count of citations...", verbose)
mask = auth_cits["n_cits"].between(min(_ncits), _max_cits)
group = auth_cits[mask]['auth_id'].tolist()
# Fourth round of filtering: Download publications, verify coauthors
# (in the FULL period) and first year
text = f"Left with {len(group):,} authors\nFiltering based on "\
"coauthor count..."
custom_print(text, verbose)
authors = pd.DataFrame({
"auth_id": group,
"year": original.year
},
dtype="uint64")
_, author_year_search = retrieve_author_info(authors, conn, "author_year")
matches = []
if not author_year_search.empty:
q = Template(f"AU-ID($fill) AND PUBYEAR BEF {original.year + 1}")
auth_year_group = author_year_search["auth_id"].tolist()
params = {
"group": auth_year_group,
"template": q,
"refresh": refresh,
"joiner": ") OR AU-ID(",
"q_type": "docs",
"verbose": verbose,
"stacked": stacked
}
res = stacked_query(**params)
res = build_dict(res, auth_year_group)
if res:
# res can become empty after build_dict if a au_id is old
res = pd.DataFrame.from_dict(res, orient="index")
res["year"] = original.year
res = res[["year", "first_year", "n_pubs", "n_coauth"]]
res.index.name = "auth_id"
res = res.reset_index()
insert_data(res, original.sql_conn, table="author_year")
authors_year, _ = retrieve_author_info(authors, conn, "author_year")
# Check for number of coauthors within margin
mask = authors_year["n_coauth"].between(min(_ncoauth), _max_coauth)
# Check for year of first publication within range
if not original.first_year_name_search:
same_start = authors_year["first_year"].between(
min(_years), max(_years))
mask = mask & same_start
# Filter
matches = sorted(authors_year[mask]["auth_id"].tolist())
text = f"Left with {len(matches)} authors"
custom_print(text, verbose)
if original.period:
text = "Filtering based on citations and coauthor count during period..."
custom_print(text, verbose)
# Further screen matches based on period cits and coauths
to_loop = [m for m in matches] # temporary copy
for m in to_loop:
res = base_query("docs",
f"AU-ID({m})",
refresh=refresh,
fields=["eid", "author_ids", "coverDate"])
pubs = [
p for p in res if
original._period_year <= int(p.coverDate[:4]) <= original.year
]
coauths = set(extract_authors(pubs)) - {str(m)}
if not (min(_ncoauth) <= len(coauths) <= max(_ncoauth)):
matches.remove(m)
continue
eids_period = [p.eid for p in pubs]
n_cits = count_citations(eids_period, original.year + 1, [str(m)])
if not (min(_ncits) <= n_cits <= max(_ncits)):
matches.remove(m)
# Eventually filter on affiliations
if original.search_affiliations:
text = "Filtering based on affiliations..."
custom_print(text, verbose)
matches[:] = [
m for m in matches if same_affiliation(original, m, refresh)
]
return matches
def inform_matches(self,
fields=None,
verbose=False,
refresh=False,
stop_words=None,
**tfidf_kwds):
"""Add information to matches to aid in selection process.
Parameters
----------
fields : iterable (optional, default=None)
Which information to provide. Allowed values are "first_year",
"num_coauthors", "num_publications", "num_citations", "country",
"language", "reference_sim", "abstract_sim". If None, will
use all available fields.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
refresh : bool (optional, default=False)
Whether to refresh cached results (if they exist) or not. If int
is passed and stacked=False, results will be refreshed if they are
older than that value in number of days.
stop_words : list (optional, default=None)
A list of words that should be filtered in the analysis of
abstracts. If None uses the list of English stopwords
by nltk, augmented with numbers and interpunctuation.
tfidf_kwds : keywords
Parameters to pass to TfidfVectorizer from the sklearn package
for abstract vectorization. Not used when `information=False` or
or when "abstract_sim" is not in `information`. See
https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
for possible values.
Notes
-----
Matches including corresponding information are available through
property `.matches`.
Raises
------
fields
If fields contains invalid keywords.
"""
# Checks
if not self._matches:
text = "No matches defined. Please run .find_matches() first."
raise Exception(text)
allowed_fields = [
"first_name", "surname", "first_year", "num_coauthors",
"num_publications", "num_citations", "num_coauthors_period",
"num_publications_period", "num_citations_period", "subjects",
"country", "affiliation_id", "affiliation", "language",
"reference_sim", "abstract_sim"
]
if fields:
invalid = [x for x in fields if x not in allowed_fields]
if invalid:
text = "Parameter fields contains invalid keywords: " +\
", ".join(invalid)
raise ValueError(text)
else:
fields = allowed_fields
custom_print("Providing additional information...", verbose)
matches = inform_matches(self, fields, stop_words, verbose, refresh,
**tfidf_kwds)
self._matches = matches
def query_pubs_by_sourceyear(source_ids,
year,
stacked=False,
refresh=False,
verbose=False):
"""Get authors lists for each source in a particular year.
Parameters
----------
source_ids : list
List of Scopus IDs of sources to search.
year : str or int
The year of the search.
stacked : bool (optional, default=False)
Whether to use fewer queries that are not reusable, or to use modular
queries of the form "SOURCE-ID(<SID>) AND PUBYEAR IS <YYYY>".
refresh : bool (optional, default=False)
Whether to refresh cached files if they exist, or not.
verbose : bool (optional, default=False)
Whether to print information on the search progress.
"""
# Dummy return value
columns = ["source_id", "year", "auids", "afid"]
dummy = pd.DataFrame(columns=columns)
# Search authors
msg = f"... parsing Scopus information for {year}..."
custom_print(msg, verbose)
q = Template(f"SOURCE-ID($fill) AND PUBYEAR IS {year}")
params = {
"group": [str(x) for x in sorted(source_ids)],
"joiner": " OR ",
"refresh": refresh,
"q_type": "docs",
"template": q,
"verbose": verbose,
"stacked": stacked
}
res = stacked_query(**params)
# Verify data is not empty
if res:
res = pd.DataFrame(res).dropna(subset=["author_ids"])
if res.empty:
return dummy
else:
return dummy
# Group data
res = expand_affiliation(res)
if res.empty:
return dummy
res["year"] = year
res["author_ids"] = res["author_ids"] + ";"
grouping_cols = ["source_id", "year", "afid"]
res = (res.groupby(grouping_cols)[[
"author_ids"
]].apply(sum).reset_index().rename(columns={"author_ids": "auids"}))
res["auids"] = res["auids"].str.strip(";")
return res
def find_matches(self,
stacked=False,
verbose=False,
stop_words=STOPWORDS,
information=True,
refresh=False,
**tfidf_kwds):
"""Find matches within search_group based on four criteria:
1. Started publishing in about the same year
2. Has about the same number of publications in the year of treatment
3. Has about the same number of coauthors in the year of treatment
4. Has about the same number of citations in the year of treatment
5. Works in the same field as the scientist's main field
Parameters
----------
stacked : bool (optional, default=False)
Whether to combine searches in few queries or not. Cached
files will most likely not be resuable. Set to True if you
query in distinct fields or you want to minimize API key usage.
verbose : bool (optional, default=False)
Whether to report on the progress of the process.
stop_words : list (optional, default=STOPWORDS)
A list of words that should be filtered in the analysis of
abstracts. Default list is the list of english stopwords
by nltk, augmented with numbers and interpunctuation.
information : bool or iterable (optional, default=True)
Whether to return additional information on the matches that may
help in the selection process. If an iterable of keywords is
provied, only return information for these keywords. Allowed
values are "first_year", "num_coauthors", "num_publications",
"num_citations", "country", "language",
"reference_sim", "abstract_sim".
refresh : bool (optional, default=False)
Whether to refresh cached search files.
tfidf_kwds : keywords
Parameters to pass to TfidfVectorizer from the sklearn package
for abstract vectorization. Not used when `information=False` or
or when "abstract_sim" is not in `information`. See
https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
for possible values.
Returns
-------
matches : list
A list of Scopus IDs of scientists matching all the criteria (if
information is False) or a list of namedtuples with the Scopus ID
and additional information (if information is True).
Raises
------
ValueError
If information is not bool and contains invalid keywords.
"""
# Checks
info_keys = [
"first_name", "surname", "first_year", "num_coauthors",
"num_publications", "num_citations", "num_coauthors_period",
"num_publications_period", "num_citations_period", "subjects",
"country", "affiliation_id", "affiliation", "language",
"reference_sim", "abstract_sim"
]
if isinstance(information, bool):
if information:
keywords = info_keys
elif self.search_affiliations:
information = True
keywords = ["affiliation_id"]
else:
keywords = None
else:
keywords = information
invalid = [x for x in keywords if x not in info_keys]
if invalid:
text = ("Parameter information contains invalid keywords: ",
", ".join(invalid))
raise ValueError(text)
if self.search_affiliations and "affiliation_id" not in keywords:
keywords.append("affiliation_id")
# Variables
_years = range(self.first_year - self.year_margin,
self.first_year + self.year_margin + 1)
if self.period:
_npapers = margin_range(len(self.publications_period),
self.pub_margin)
_ncits = margin_range(self.citations_period, self.cits_margin)
_ncoauth = margin_range(len(self.coauthors_period),
self.coauth_margin)
_npapers_full = margin_range(len(self.publications),
self.pub_margin)
_ncits_full = margin_range(self.citations, self.cits_margin)
_ncoauth_full = margin_range(len(self.coauthors),
self.coauth_margin)
else:
_npapers = margin_range(len(self.publications), self.pub_margin)
_ncits = margin_range(self.citations, self.cits_margin)
_ncoauth = margin_range(len(self.coauthors), self.coauth_margin)
n = len(self.search_group)
text = "Searching through characteristics of {:,} authors".format(n)
custom_print(text, verbose)
# First round of filtering: minimum publications and main field
# create df of authors
authors = query_author_data(self.search_group, verbose=verbose)
same_field = (authors.areas.str.startswith(self.main_field[1]))
enough_pubs = (authors.documents.astype(int) >= int(min(_npapers)))
group = authors[same_field & enough_pubs]["auth_id"].tolist()
group.sort()
n = len(group)
text = "Left with {} authors\nFiltering based on provided "\
"conditions...".format(n)
custom_print(text, verbose)
# Second round of filtering:
# Check having no publications before minimum year, and if 0, the
# number of publications in the relevant period.
params = {
"group": group,
"ybefore": min(_years) - 1,
"yupto": self.year,
"npapers": _npapers,
"yfrom": self.year_period,
"verbose": verbose
}
group, _, _ = filter_pub_counts(**params)
# Also screen out ids with too many publications over the full period
if self.period:
params.update({
"npapers": [1, max(_npapers_full)],
"yfrom": None,
"group": group
})
group, _, _ = filter_pub_counts(**params)
# Third round of filtering: citations (in the FULL period).
authors = pd.DataFrame({"auth_id": group, "year": self.year})
_, authors_cits_search = author_cits_in_cache(authors)
text = "Search and filter based on count of citations\n{} to search "\
"out of {}\n".format(len(authors_cits_search), len(group))
custom_print(text, verbose)
if not authors_cits_search.empty:
authors_cits_search['n_cits'] = 0
print_progress(0, len(authors_cits_search), verbose)
for i, au in authors_cits_search.iterrows():
q = "REF({}) AND PUBYEAR BEF {} AND NOT AU-ID({})".format(
au['auth_id'], self.year + 1, au['auth_id'])
n = base_query("docs", q, size_only=True)
authors_cits_search.at[i, 'n_cits'] = n
print_progress(i + 1, len(authors_cits_search), verbose)
cache_insert(authors_cits_search, table="author_cits_size")
auth_cits_incache, _ = author_cits_in_cache(
authors[["auth_id", "year"]])
# keep if citations are in range
mask = ((auth_cits_incache.n_cits <= max(_ncits)) &
(auth_cits_incache.n_cits >= min(_ncits)))
if self.period:
mask = ((auth_cits_incache.n_cits >= min(_ncits)) &
(auth_cits_incache.n_cits <= max(_ncits_full)))
group = (auth_cits_incache[mask]['auth_id'].tolist())
# Fourth round of filtering: Download publications, verify coauthors
# (in the FULL period) and first year.
n = len(group)
text = "Left with {} authors\nFiltering based on coauthors "\
"number...".format(n)
custom_print(text, verbose)
authors = pd.DataFrame({
"auth_id": group,
"year": self.year
},
dtype="uint64")
_, author_year_search = author_year_in_cache(authors)
matches = []
if stacked: # Combine searches
if not author_year_search.empty:
q = Template(
"AU-ID($fill) AND PUBYEAR BEF {}".format(self.year + 1))
auth_year_group = author_year_search.auth_id.tolist()
params = {
"group": auth_year_group,
"res": [],
"template": q,
"refresh": refresh,
"joiner": ") OR AU-ID(",
"q_type": "docs"
}
if verbose:
params.update({"total": len(auth_year_group)})
res, _ = stacked_query(**params)
res = build_dict(res, auth_year_group)
if res:
# res can become empty after build_dict if a au_id is old
res = pd.DataFrame.from_dict(res, orient="index")
res["year"] = self.year
res = res[["year", "first_year", "n_pubs", "n_coauth"]]
res.index.name = "auth_id"
res = res.reset_index()
cache_insert(res, table="author_year")
author_year_cache, _ = author_year_in_cache(authors)
if self._ignore_first_id:
# only number of coauthors should be big enough
enough = (author_year_cache.n_coauth >= min(_ncoauth))
notoomany = (author_year_cache.n_coauth <= max(_ncoauth_full))
mask = enough & notoomany
elif self.period:
# number of coauthors should be "big enough" and first year in
# window
same_start = (author_year_cache.first_year.between(
min(_years), max(_years)))
enough = (author_year_cache.n_coauth >= min(_ncoauth))
notoomany = (author_year_cache.n_coauth <= max(_ncoauth_full))
mask = same_start & enough & notoomany
else:
# all restrictions apply
same_start = (author_year_cache.first_year.between(
min(_years), max(_years)))
same_coauths = (author_year_cache.n_coauth.between(
min(_ncoauth), max(_ncoauth)))
mask = same_start & same_coauths
matches = author_year_cache[mask]["auth_id"].tolist()
else: # Query each author individually
for i, au in enumerate(group):
print_progress(i + 1, len(group), verbose)
res = base_query("docs",
"AU-ID({})".format(au),
refresh=refresh)
res = [
p for p in res
if p.coverDate and int(p.coverDate[:4]) <= self.year
]
# Filter
min_year = int(min([p.coverDate[:4] for p in res]))
authids = [p.author_ids for p in res if p.author_ids]
authors = set([a for p in authids for a in p.split(";")])
n_coauth = len(authors) - 1 # Subtract 1 for focal author
if self._ignore_first_id and (n_coauth < max(_ncoauth)):
# only number of coauthors should be big enough
continue
elif (self.period and ((n_coauth < max(_ncoauth)) or
(min_year not in _years))):
# number of coauthors should be "big enough" and first year
# in window
continue
elif ((len(res) not in _npapers) or (min_year not in _years)
or (n_coauth not in _ncoauth)):
continue
matches.append(au)
if self.period:
text = "Left with {} authors\nFiltering based on exact period "\
"citations and coauthors...".format(len(matches))
custom_print(text, verbose)
# Further screen matches based on period cits and coauths
to_loop = [m for m in matches] # temporary copy
for m in to_loop:
q = "AU-ID({})".format(m)
res = base_query("docs",
"AU-ID({})".format(m),
refresh=refresh,
fields=["eid", "author_ids", "coverDate"])
pubs = [
p for p in res if int(p.coverDate[:4]) <= self.year
and int(p.coverDate[:4]) >= self.year_period
]
coauths = set(get_authors(pubs)) - {str(m)}
if not (min(_ncoauth) <= len(coauths) <= max(_ncoauth)):
matches.remove(m)
continue
eids_period = [p.eid for p in pubs]
cits = count_citations(search_ids=eids_period,
pubyear=self.year + 1,
exclusion_key="AU-ID",
exclusion_ids=[str(m)])
if not (min(_ncits) <= cits <= max(_ncits)):
matches.remove(m)
text = "Found {:,} author(s) matching all criteria".format(
len(matches))
custom_print(text, verbose)
# Possibly add information to matches
if keywords and len(matches) > 0:
custom_print("Providing additional information...", verbose)
profiles = [
Scientist([str(a)],
self.year,
period=self.period,
refresh=refresh) for a in matches
]
matches = inform_matches(profiles, self, keywords, stop_words,
verbose, refresh, **tfidf_kwds)
if self.search_affiliations:
matches = [
m for m in matches if len(
set(m.affiliation_id.replace(" ", "").split(";")).
intersection([str(a) for a in self.search_affiliations]))
]
return matches
def filter_pub_counts(group,
conn,
ybefore,
yupto,
npapers,
yfrom=None,
verbose=False):
"""Filter authors based on restrictions in the number of
publications in different periods, searched by query_size.
Parameters
----------
conn : sqlite3 connection
Standing connection to a SQLite3 database.
group : list of str
Scopus IDs of authors to be filtered.
ybefore : int
Year to be used as first year. Publications on this year and before
need to be 0.
yupto : int
Year up to which to count publications.
npapers : list
List of count of publications, minimum and maximum.
yfrom : int (optional, default=None)
If provided, publications are counted only after this year.
Publications are still set to 0 before ybefore.
verbose : bool (optional, default=False)
Whether to print information on the search progress.
Returns
-------
group : list of str
Scopus IDs filtered.
pubs_counts : list of int
List of count of publications within the period provided for authors
in group.
older_authors : list of str
Scopus IDs filtered out because have publications before ybefore.
"""
from itertools import product
from pandas import DataFrame
group = [int(x) for x in group]
years_check = [ybefore, yupto]
if yfrom:
years_check.extend([yfrom - 1])
authors = DataFrame(product(group, years_check),
dtype="uint64",
columns=["auth_id", "year"])
auth_npubs, _ = retrieve_author_info(authors, conn, "author_pubs")
au_skip = []
group_tocheck = set(group)
older_authors = []
pubs_counts = []
# Use information in database
if not auth_npubs.empty:
# Remove authors based on age
mask = ((auth_npubs["year"] <= ybefore) & (auth_npubs["n_pubs"] > 0))
au_remove = set(auth_npubs[mask]["auth_id"].unique())
older_authors.extend(au_remove)
# Remove if number of pubs in year is in any case too small
mask = ((auth_npubs["year"] >= yupto) &
(auth_npubs["n_pubs"] < min(npapers)))
au_remove.update(auth_npubs[mask]["auth_id"])
# Authors with no pubs before min year
mask = ((auth_npubs["year"] == ybefore) & (auth_npubs["n_pubs"] == 0))
au_ok_miny = set(auth_npubs[mask]["auth_id"].unique())
# Check publications in range
if yfrom:
# Keep authors where subtracting publications from before period
# from publication count is possible
mask = auth_npubs["year"] == yfrom - 1
rename = {"n_pubs": "n_pubs_bef"}
auth_npubs_bef = auth_npubs[mask].copy().rename(columns=rename)
auth_npubs_bef["year"] = yupto
auth_npubs = (auth_npubs.merge(auth_npubs_bef,
"inner",
on=["auth_id", "year"]).fillna(0))
auth_npubs["n_pubs"] -= auth_npubs["n_pubs_bef"]
# Remove authors because of their publication count
mask = (((auth_npubs["year"] >= yupto) &
(auth_npubs["n_pubs"] < min(npapers))) |
((auth_npubs["year"] <= yupto) &
(auth_npubs["n_pubs"] > max(npapers))))
remove = auth_npubs[mask]["auth_id"]
au_remove.update(remove)
# Authors with pubs count within the range before the given year
mask = (((auth_npubs["year"] == yupto) &
(auth_npubs["n_pubs"] >= min(npapers))) &
(auth_npubs["n_pubs"] <= max(npapers)))
au_ok_year = auth_npubs[mask][["auth_id", "n_pubs"]].drop_duplicates()
# Keep authors that match both conditions
au_ok = au_ok_miny.intersection(au_ok_year["auth_id"].unique())
mask = au_ok_year["auth_id"].isin(au_ok)
pubs_counts = au_ok_year[mask]["n_pubs"].tolist()
# Skip citation check for authors that match only the first condition,
# with the second being unknown
au_skip = set([x for x in au_ok_miny if x not in au_remove | au_ok])
group = [x for x in group if x not in au_remove]
group_tocheck = set([x for x in group if x not in au_skip | au_ok])
# Verify that publications before minimum year are 0
if group_tocheck:
n = len(group_tocheck)
text = f"Obtaining information for {n:,} authors without sufficient "\
"information in database..."
custom_print(text, verbose)
print_progress(0, n, verbose)
to_loop = [x for x in group_tocheck] # Temporary copy
for i, auth_id in enumerate(to_loop):
npubs_ybefore = auth_npubs_retrieve_insert(auth_id, ybefore, conn)
if npubs_ybefore:
group.remove(auth_id)
group_tocheck.remove(auth_id)
older_authors.append(auth_id)
print_progress(i + 1, n, verbose)
text = f"Left with {len(group):,} authors based on publication "\
f"information before {ybefore}"
custom_print(text, verbose)
# Verify that publications before the given year fall in range
group_tocheck.update(au_skip)
if group_tocheck:
n = len(group_tocheck)
text = f"Counting publications of {n:,} authors before {yupto+1}..."
custom_print(text, verbose)
print_progress(0, n, verbose)
for i, au in enumerate(group_tocheck):
n_pubs_yupto = auth_npubs_retrieve_insert(au, yupto, conn)
# Eventually decrease publication count
if yfrom and n_pubs_yupto >= min(npapers):
n_pubs_yfrom = auth_npubs_retrieve_insert(au, yfrom - 1, conn)
n_pubs_yupto -= n_pubs_yfrom
if n_pubs_yupto < min(npapers) or n_pubs_yupto > max(npapers):
group.remove(au)
else:
pubs_counts.append(n_pubs_yupto)
print_progress(i + 1, n, verbose)
return group, pubs_counts, older_authors
