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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 = datetime.datetime.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
else:
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 = [
'abstract_is_available',
'paper_year_is_in_query'
]
# 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']:
feats.extend([
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
f'{field}_sum_of_log_probs*match_lens',
])
constraints.extend([
'1',
'-1',
'-1',
])
# features for author field only
feats.extend([
'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
])
constraints.extend([
'1',
'1',
'-1',
])
feats.extend([
'paper_oldness',
'paper_n_citations', # no need for log due to decision trees
'paper_n_key_citations',
'paper_n_citations_divided_by_oldness'
])
# 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'])
feats.extend([
'fraction_of_unquoted_query_matched_across_all_fields',
'sum_log_prob_of_unquoted_unmatched_unigrams',
'fraction_of_quoted_query_matched_across_all_fields',
'sum_log_prob_of_quoted_unmatched_unigrams',
])
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)])
try:
year = int(result_paper['paper_year'])
year = np.minimum(now.year, year) # papers can't be from the future.
except:
year = np.nan
if result_paper['author_name'] is None:
authors = []
else:
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 re.search('\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]):
quoted_matched_across_fields.append(str(year))
if np.any([str(year) in i for i in q_unquoted]):
unquoted_matched_across_fields.append(str(year))
# 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:
q_split_set.remove(str(year))
# 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]
else:
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_matched_across_fields.extend(unquoted_match_text)
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_matched_across_fields.extend(quoted_match_text)
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]):
match_spans_set.append(s)
match_text_set.append(t)
# 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_filtered.append(sp)
match_text_set_filtered.append(tx)
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()
matched_text_unigrams.update(i_split)
if 'title' in field or 'venue' in field:
title_and_venue_matches.update(i_split)
if 'title' in field or 'abstract' in field:
title_and_abstract_matches.update(i_split)
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]
else:
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
feats.extend([
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
])
else:
# 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(
q_loop,
paper_author,
quotes=quotes_flag,
len_filter=0,
remove_stopwords=True, # only removes entire matches that are stopwords. too bad for people named 'the' or 'less'
use_word_boundaries=False
)
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)
match_fracs.append(match_frac)
if quotes_flag:
quoted_match_lens.append(match_frac * weight)
quoted_matched_across_fields.append(matched_text_joined)
else:
unquoted_match_lens.append(match_frac * weight)
unquoted_matched_across_fields.append(matched_text_joined)
else:
if quotes_flag:
quoted_match_lens.append(0)
else:
unquoted_match_lens.append(0)
# 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
else:
author_ind_feature = np.minimum(nonzero_inds[0], len(authors) - 1 - nonzero_inds[-1])
feats.extend([
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
feats.extend([
now.year - year, # oldness (could be nan if year is missing)
result_paper['n_citations'], # no need for log due to decision trees
result_paper['n_key_citations'],
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:
matched_split_set.update(i.split())
# 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]))
else:
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]))
else:
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
else:
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
else:
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) + \
year_match[top_inds]
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