def frequency_threshold(self, table, threshold):
messages.msg("Filtering ngrams...")
delete = sql.DELETE.format(table=table)
self.execute(delete, threshold)
self.commit()
messages.done()
self.upd_info(table)
def lex_fixedness(ngram_db, variant_dict: dict, table: str, field: str,
m_field: str):
in_fld = ['rowid', field, m_field]
output_db, n_max = initialize(ngram_db, table, in_fld)
new_fld = [('lex_fixedness_' + m_field, 'float')]
output_db.new_fields(table, new_fld)
for n in range(2, n_max+1):
messages.msg("Collecting statistics from {}grams...".format(n))
lemma_pmis = ngram_db.to_dict('lemma_counts', ['lemma'], ['pmi'], n)
ngram_freqs = ngram_db[n]
lex_fixedness = list()
for i, (ngram, pmi, rowid) in enumerate(messages.pbar(ngram_freqs,
total=num_rows[str(n)])):
V = sim(ngram, neighbours_dict)
V_pmis = list()
for v in V:
try:
V_pmis.append(lemma_pmis[v])
except KeyError:
pass
if len(V_pmis) > 1:
fixedness = (pmi - np.mean(V_pmis)) / np.var(V_pmis)
else:
fixedness = 0
lex_fixedness.append((rowid, fixedness))
if (i+1) % save_every == 0:
output_db.update_data(lex_fixedness, in_table, 'lex_fixedness')
lex_fixedness = list()
output_db.update_data(lex_fixedness, in_table, 'lex_fixedness')
ngram_db.disconnect()
output_db.disconnect()
del_and_rename(ngram_db, output_db)
messages.done()
def copy_db(ngram_db):
messages.msg('Copying database...')
outroot = ngram_db.fileroot + '_'
if not os.path.exists(outroot + '.info.json'):
copyfile(ngram_db.db, outroot + '.db')
copyfile(ngram_db.info_file, outroot + '.info.json')
messages.done()
return NgramDb.load(outroot)
def gen_skipos(ngram_db, display=True):
if display:
messages.new_display()
messages.msg('Generating skipos statistics...')
ngram_db.connect()
ngram_db.aggregate_by('skipos_counts', 'skipgram_counts', ['freq'],
['length'], ['skipos'])
ngram_db.upd_info('skipos_counts')
ngram_db.disconnect()
messages.done()
def finalize(self, calculator, sum_fld=None):
self.ngram_db.disconnect()
if self.new:
if self.sv_every > 0 and calculator.has_dict:
self._aggregate_tmp(sum_fld, calculator.n_keys)
self.output_db.upd_info(self.new_tb)
self.output_db.disconnect()
self.del_and_rename(self.ngram_db, self.output_db)
self.ngram_db._load_info()
messages.done()
def _gen_info(self, dim: int):
messages.msg("Generating info file...")
num_rows = dict()
table = self.get_tables()[0]
n_max = self.query(sql.MAX_LEN.format(table=table))[0][0]
for n in range(1, n_max + 1):
n_rows = self.query(sql.ROW_COUNT.format(table=table), n)
num_rows.setdefault(table, dict())[n] = n_rows[0][0]
info = {'n_max': n_max, 'dim': dim, 'num_rows': num_rows}
utils.save_json(info, self.info_file)
messages.done()
def upd_info(self, table: str):
"""Updates the .info.json file with regards to a table.
Parameters
----------
table: str
The name of the table of which to update the info
"""
for n in range(1, self.n_max + 1):
n_rows = self.query(sql.ROW_COUNT.format(table=table), n)
self.num_rows.setdefault(table, dict())[n] = n_rows[0][0]
info = {'n_max': self.n_max, 'num_rows': self.num_rows}
utils.save_json(info, self.info_file)
messages.done()
def _gen_info(self):
messages.msg("Generating info file...")
num_rows = dict()
tables = self.get_tables()
if tables:
n_max = self.query(sql.MAX_LEN.format(table=tables[0]))[0][0]
for t in tables:
for n in range(1, n_max + 1):
n_rows = self.query(sql.ROW_COUNT.format(table=t), n)
num_rows.setdefault(t, dict())[n] = n_rows[0][0]
else:
n_max, num_rows = 0, None
info = {'n_max': n_max, 'num_rows': num_rows}
utils.save_json(info, self.info_file)
messages.done()
def count_ngrams(self,
sentences,
n,
use_bounter=True,
sep='\t',
**bounterargs):
"""Counts n-gram occurrences in a corpus.
Counts n-gram occurrences in a corpus and inserts the output in an
SQLite database.
Parameters
----------
sentences: Iterable
Iterable of sentences. Each sentence must be a list of strings
representing word features separated with the character that
is passed to the 'sep' argument of this function.
n: int or list of int
length of the n-grams
use_bounter: bool, default=True
If True, the counts are performed via bounter, a probabilistic and
memory efficient counter. If false, they are performed via regular
Counter. The use of bounter is strongly recommended when working
with a large corpus.
sep: str, default '\t'
The character that separates the features of each word in the
input.
**bounterargs
keyword arguments passed to the bounter constructor if used.
"""
messages.msg("Counting ngrams of length {}...".format(n))
if use_bounter:
bounterargs.setdefault('size_mb', 1024)
counter = bounter(**bounterargs)
else:
counter = Counter()
for sent in sentences:
if type(n) == list:
ngrams = list()
for i in n:
ngrams += NgramCounter._gen_ngrams(sent, i)
else:
ngrams = NgramCounter._gen_ngrams(sent, n)
counter.update(ngrams)
messages.done()
self._counts_to_db(counter, sep)
def aggregate_by(self, key: str):
"""Sum frequency counts over a given field.
Parameters
----------
key: str
the field over which to compute the sum.
"""
messages.msg("Aggregating values...")
new_tb = '{key}_counts'.format(key=key)
self.connect()
super().aggregate_by(new_tb,
'ngram_counts', ['freq'],
other_f=['length'],
grp_f=[key])
self.disconnect()
messages.done()