Skip to content
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Go to file
Cannot retrieve contributors at this time
import numpy as np
import datetime
import re
from collections import Counter
from s2search.text import find_query_ngrams_in_text, fix_text, STOPWORDS
from s2search.text import extract_from_between_quotations, fix_author_text
from s2search.text import standardize_whitespace_length
now =
def nanwrapper(f, x):
"""numpy freaks out if you pass an empty arrays
to many of its functions (like min or max).
This wrapper just returns a nan in that case.
if len(x) == 0:
return np.nan
return f(x)
def remove_unigrams(s, st):
return ' '.join([i for i in s.split(' ') if i not in st])
def make_feature_names_and_constraints():
feats = [
# for lightgbm, 1 means positively monotonic, -1 means negatively monotonic and 0 means non-constraint
constraints = ['1', '1']
# features just for title, abstract, venue
for field in ['title', 'abstract', 'venue']:
f'{field}_frac_of_query_matched_in_text', # total fraction of the query that was matched in text
f'{field}_mean_of_log_probs', # statistics of the log-probs
# features for author field only
'sum_matched_authors_len_divided_by_query_len', # total amount of (fractional wrt query) matched authors
'max_matched_authors_len_divided_by_query_len', # largest (fractional wrt query) author match
'author_match_distance_from_ends', # how far the author matches are from the front/back of the author list
'paper_n_citations', # no need for log due to decision trees
# note: DO NOT change the paper_oldness constraint to -1
# if you do, then seminal papers will stop being on top.
constraints.extend(['0', '1', '1', '1'])
constraints.extend(['1', '1', '1', '1'])
return np.array(feats), ','.join(constraints)
def make_features(query, result_paper, lms, max_q_len=128, max_field_len=1024):
# the language model should have the beginning and end of sentences turned off
lm_tiab, lm_auth, lm_venu = lms
lm_dict = {
'title_abstract': lambda s: lm_tiab.score(s, eos=False, bos=False),
'author': lambda s: lm_auth.score(s, eos=False, bos=False),
'venue': lambda s: lm_venu.score(s, eos=False, bos=False)
# apply the language model in the field as necessary
def lm_score(s, which_lm='title'):
if 'title' in which_lm or 'abstract' in which_lm:
return lm_dict['title_abstract'](s)
elif 'venue' in which_lm:
return lm_dict['venue'](s)
elif 'author' in which_lm:
return lm_dict['author'](s)
elif 'max' in which_lm:
return np.max([lm_dict['title_abstract'](s), lm_dict['venue'](s), lm_dict['author'](s)])
year = int(result_paper['paper_year'])
year = np.minimum(now.year, year) # papers can't be from the future.
year = np.nan
if result_paper['author_name'] is None:
authors = []
authors = result_paper['author_name']
# fix the text and separate out quoted and unquoted
query = str(query)
q = fix_text(query)[:max_q_len]
q_quoted = [i for i in extract_from_between_quotations(q) if len(i) > 0]
q_split_on_quotes = [i.strip() for i in q.split('"') if len(i.strip()) > 0]
q_unquoted = [i.strip() for i in q_split_on_quotes if i not in q_quoted and len(i.strip()) > 0]
q_unquoted_split_set = set(' '.join(q_unquoted).split())
q_quoted_split_set = set(' '.join(q_quoted).split())
q_split_set = q_unquoted_split_set | q_quoted_split_set
q_split_set -= STOPWORDS
# we will find out how much of a match we have *across* fields
unquoted_matched_across_fields = []
quoted_matched_across_fields = []
# overall features for the paper and query
q_quoted_len = np.sum([len(i) for i in q_quoted]) # total length of quoted snippets
q_unquoted_len = np.sum([len(i) for i in q_unquoted]) # total length of non-quoted snippets
q_len = q_unquoted_len + q_quoted_len
# if there's no query left at this point, we return NaNs
# which the model natively supports
if q_len == 0:
return [np.nan] * len(FEATURE_NAMES)
# testing whether a year is somewhere in the query and making year-based features
if'\d{4}', q): # if year is in query, the feature is whether the paper year appears in the query
year_feat = (str(year) in q_split_set)
else: # if year isn't in the query, we don't care about matching
year_feat = np.nan
feats = [
result_paper['paper_abstract_cleaned'] is not None and len(result_paper['paper_abstract_cleaned']) > 1,
year_feat, # whether the year appears anywhere in the (split) query
# if year is matched, add it to the matched_across_all_fields but remove from query
# so it doesn't get matched in author/title/venue/abstract later
if np.any([str(year) in i for i in q_quoted]):
if np.any([str(year) in i for i in q_unquoted]):
# if year is matched, we don't need to match it again, so removing
if year_feat is True and len(q_split_set) > 1:
# later we will filter some features based on nonsensical unigrams in the query
# this is the log probability lower-bound for sensible unigrams
log_prob_nonsense = lm_score('qwertyuiop', 'max')
# features title, abstract, venue
title_and_venue_matches = set()
title_and_abstract_matches = set()
for field in ['paper_title_cleaned', 'paper_abstract_cleaned', 'paper_venue_cleaned']:
if result_paper[field] is not None:
text = result_paper[field][:max_field_len]
text = ''
text_len = len(text)
# unquoted matches
unquoted_match_spans, unquoted_match_text, unquoted_longest_starting_ngram = find_query_ngrams_in_text(q_unquoted, text, quotes=False)
unquoted_match_len = len(unquoted_match_spans)
# quoted matches
quoted_match_spans, quoted_match_text, quoted_longest_starting_ngram = find_query_ngrams_in_text(q_quoted, text, quotes=True)
quoted_match_len = len(quoted_match_text)
# now we (a) combine the quoted and unquoted results
match_spans = unquoted_match_spans + quoted_match_spans
match_text = unquoted_match_text + quoted_match_text
# and (b) take the set of the results
# while excluding sub-ngrams if longer ngrams are found
# e.g. if we already have 'sentiment analysis', then 'sentiment' is excluded
match_spans_set = []
match_text_set = []
for t, s in sorted(zip(match_text, match_spans), key=lambda s: len(s[0]))[::-1]:
if t not in match_text_set and ~np.any([t in i for i in match_text_set]):
# remove venue results if they already entirely appeared
if 'venue' in field:
text_unigram_len = len(text.split(' '))
match_spans_set_filtered = []
match_text_set_filtered = []
for sp, tx in zip(match_spans_set, match_text_set):
tx_unigrams = set(tx.split(' '))
# already matched all of these unigrams in title or abstract
condition_1 = (tx_unigrams.intersection(title_and_abstract_matches) == tx_unigrams)
# and matched too little of the venue text
condition_2 = len(tx_unigrams) / text_unigram_len <= 2/3
if not (condition_1 and condition_2):
match_spans_set = match_spans_set_filtered
match_text_set = match_text_set_filtered
# match_text_set but unigrams
matched_text_unigrams = set()
for i in match_text_set:
i_split = i.split()
if 'title' in field or 'venue' in field:
if 'title' in field or 'abstract' in field:
if len(match_text_set) > 0 and text_len > 0: # if any matches and the text has any length
# log probabilities of the scores
if 'venue' in field:
lm_probs = [lm_score(match, 'venue') for match in match_text_set]
lm_probs = [lm_score(match, 'max') for match in match_text_set]
# match character lengths
match_lens = [len(i) for i in match_text_set]
# match word lens
match_word_lens = [len(i.split()) for i in match_text_set]
# we have one feature that takes into account repetition of matches
match_text_counter = Counter(match_text)
match_spans_len_normed = np.log1p(list(match_text_counter.values())).sum()
# remove stopwords from unigrams
matched_text_unigrams -= STOPWORDS
len(q_split_set.intersection(matched_text_unigrams)) / np.maximum(len(q_split_set), 1), # total fraction of the query that was matched in text
np.nanmean(lm_probs), # average log-prob of the matches
np.nansum(np.array(lm_probs) * np.array(match_word_lens)), # sum of log-prob of matches times word-lengths
# if we have no matches, then the features are deterministically 0
feats.extend([0, 0, 0])
# features for author field only
# note: we aren't using citation info
# because we don't know which author we are matching
# in the case of multiple authors with the same name
q_auth = fix_author_text(query)[:max_q_len]
q_quoted_auth = extract_from_between_quotations(q_auth)
q_split_on_quotes = [i.strip() for i in q_auth.split('"') if len(i.strip()) > 0]
q_unquoted_auth = [i for i in q_split_on_quotes if i not in q_quoted_auth]
# remove any unigrams that we already matched in title or venue
# but not abstract since citations are included there
# note: not sure if this make sense for quotes, but keeping it for those now
q_quoted_auth = [remove_unigrams(i, title_and_venue_matches) for i in q_quoted_auth]
q_unquoted_auth = [remove_unigrams(i, title_and_venue_matches) for i in q_unquoted_auth]
unquoted_match_lens = [] # normalized author matches
quoted_match_lens = [] # quoted author matches
match_fracs = []
for paper_author in authors:
len_author = len(paper_author)
if len_author > 0:
# higher weight for the last name
paper_author_weights = np.ones(len_author)
len_last_name = len(paper_author.split(' ')[-1])
paper_author_weights[-len_last_name:] *= 10 # last name is ten times more important to match
paper_author_weights /= paper_author_weights.sum()
for quotes_flag, q_loop in zip([False, True], [q_unquoted_auth, q_quoted_auth]):
matched_spans, match_text, _ = find_query_ngrams_in_text(
remove_stopwords=True, # only removes entire matches that are stopwords. too bad for people named 'the' or 'less'
if len(matched_spans) > 0:
matched_text_joined = ' '.join(match_text)
# edge case: single character matches are not good
if len(matched_text_joined) == 1:
matched_text_joined = ''
weight = np.sum([paper_author_weights[i:j].sum() for i, j in matched_spans])
match_frac = np.minimum((len(matched_text_joined) / q_len), 1)
if quotes_flag:
quoted_match_lens.append(match_frac * weight)
unquoted_match_lens.append(match_frac * weight)
if quotes_flag:
# since we ran this separately (per author) for quoted and uquoted, we want to avoid potential double counting
match_lens_max = np.maximum(unquoted_match_lens, quoted_match_lens)
nonzero_inds = np.flatnonzero(match_lens_max)
# the closest index to the ends of author lists
if len(nonzero_inds) == 0:
author_ind_feature = np.nan
author_ind_feature = np.minimum(nonzero_inds[0], len(authors) - 1 - nonzero_inds[-1])
np.nansum(match_lens_max), # total amount of (weighted) matched authors
nanwrapper(np.nanmax, match_lens_max), # largest (weighted) author match
author_ind_feature, # penalizing matches that are far away from ends of author list
# oldness and citations
now.year - year, # oldness (could be nan if year is missing)
result_paper['n_citations'], # no need for log due to decision trees
np.nan if np.isnan(year) else result_paper['n_citations'] / (now.year - year + 1)
# special features for how much of the unquoted query was matched/unmatched across all fields
q_unquoted_split_set -= STOPWORDS
if len(q_unquoted_split_set) > 0:
matched_split_set = set()
for i in unquoted_matched_across_fields:
# making sure stopwords aren't an issue
matched_split_set -= STOPWORDS
# fraction of the unquery matched
numerator = len(q_unquoted_split_set.intersection(matched_split_set))
feats.append(numerator / np.maximum(len(q_unquoted_split_set), 1))
# the log-prob of the unmatched unquotes
unmatched_unquoted = q_unquoted_split_set - matched_split_set
log_probs_unmatched_unquoted = [lm_score(i, 'max') for i in unmatched_unquoted]
feats.append(np.nansum([i for i in log_probs_unmatched_unquoted if i > log_prob_nonsense]))
feats.extend([np.nan, np.nan])
# special features for how much of the quoted query was matched/unmatched across all fields
if len(q_quoted) > 0:
numerator = len(set(' '.join(quoted_matched_across_fields).split()))
feats.append(numerator / len(q_quoted_split_set))
# the log-prob of the unmatched quotes
unmatched_quoted = set(q_quoted) - set(quoted_matched_across_fields)
feats.append(np.nansum([lm_score(i, 'max') for i in unmatched_quoted]))
feats.extend([np.nan, np.nan])
return feats
# globals to use for posthoc_score_adjust
FEATURE_NAMES, FEATURE_CONSTRAINTS = make_feature_names_and_constraints()
feature_names = list(FEATURE_NAMES)
quotes_feat_ind = feature_names.index('fraction_of_quoted_query_matched_across_all_fields')
year_match_ind = feature_names.index('paper_year_is_in_query')
author_match_ind = feature_names.index('max_matched_authors_len_divided_by_query_len')
matched_all_ind = feature_names.index('fraction_of_unquoted_query_matched_across_all_fields')
title_match_ind = feature_names.index('title_frac_of_query_matched_in_text')
abstract_match_ind = feature_names.index('abstract_frac_of_query_matched_in_text')
venue_match_ind = feature_names.index('venue_frac_of_query_matched_in_text')
def posthoc_score_adjust(scores, X, query=None):
if query is None:
query_len = 100
query_len = len(str(query).split(' '))
# need to modify scores if there are any quote matches
# this ensures quoted-matching results are on top
quotes_frac_found = X[:, quotes_feat_ind]
has_quotes_to_match = ~np.isnan(quotes_frac_found)
scores[has_quotes_to_match] += 1000 * quotes_frac_found[has_quotes_to_match]
# if there is a year match, we want to boost that a lot
year_match = np.isclose(X[:, year_match_ind], 1.0)
scores += 100 * year_match
# full author matches if the query is long enough
if query_len > 1:
full_author_match = np.isclose(X[:, author_match_ind], 1.0)
scores += 100 * full_author_match
# then those with all ngrams matched anywhere
matched_all_flag = np.isclose(X[:, matched_all_ind], 1.0)
scores += 10 * matched_all_flag
# need to heavily penalize those with 0 percent ngram match
matched_none_flag = np.isclose(X[:, matched_all_ind], 0.0)
scores -= 10 * matched_none_flag
# find the most common match appearance pattern and upweight those
if query_len > 1:
if '"' in query:
qualifying_for_cutoff = np.isclose(X[:, quotes_feat_ind], 1.0) & matched_all_flag
qualifying_for_cutoff = matched_all_flag
scores_argsort = np.argsort(scores)[::-1]
where_zeros = np.where(qualifying_for_cutoff[scores_argsort] == 0)
if len(where_zeros[0]) > 0:
top_cutoff = where_zeros[0][0]
if top_cutoff > 1:
top_inds = scores_argsort[:top_cutoff]
pattern_of_matches = 1000 * ((X[top_inds, title_match_ind] > 0) | (X[top_inds, abstract_match_ind] > 0)) + \
100 * (X[top_inds, author_match_ind] > 0) + \
10 * (X[top_inds, venue_match_ind] > 0) + \
most_common_pattern = Counter(pattern_of_matches).most_common()[0][0]
# don't do this if title/abstract matches are the most common
# because usually the error is usually not irrelevant matches in author/venue
# but usually irrelevant matches in title + abstract
if most_common_pattern != 1000:
scores[top_inds[pattern_of_matches == most_common_pattern]] += 10000
return scores