cjvt-valency/src_diploma/valency/val_struct.py

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2019-03-07 08:00:01 +00:00
from time import time
from copy import deepcopy as DC
from valency.frame import Frame
from valency.reduce_functions import *
from valency.lesk import *
from valency import mongo_tools
import random
import logging
from valency.evaluation import Evaluation
from valency.dictionary_interface import SloWnet, Sskj2
from valency.leskFour import LeskFour
from valency.k_kmeans import KmeansClass
from valency.ssj_struct import SsjDict, SsjEntry
from valency.seqparser.seqparser import Seqparser
import pickle
import sys
import hashlib
log = logging.getLogger(__name__)
def split_id(myid):
tmp = myid.split(".")
sid = ".".join(tmp[:-1])
tid = tmp[-1]
return (sid, tid)
class ValEntry():
def __init__(self, hw, frame):
self.hw = hw
self.raw_frames = [frame]
self.has_senses = False
class Vallex():
# Main class
def __init__(self):
# database
self.db, err_msg = mongo_tools.basic_connection("127.0.0.1", 26633)
if self.db is None:
log.error((
"Database not connected:"
"{}".format(err_msg)
))
exit(1)
mongo_tools.check_collections(self.db, [
"v2_users", "v2_senses", "v2_sense_map", "v2_user_tokens"
])
mongo_tools.prepare_user_tokens(self.db)
# these 3 might be obsolete for the web app (used for ML)
self.db_senses_map = self.db.senses_map3
self.slownet_interface = SloWnet(self)
self.sskj_interface = Sskj2(self)
# self.tokens["s0][t0"] = {word, lemma, msd, ...}
self.tokens = {}
# key = verb / adjective headword
self.entries = {}
# For alphabetical indexing in web app.
self.sorted_words = {}
# words = { first_letter: [hw1, hw2, ... sorted] }
self.functors_index = {}
self.has_se = [] # list of verbs with "se" ("bati se")
# Used for ML (deprecated).
self.leskFour = LeskFour(self)
self.kmeans = KmeansClass(self)
self.evaluation = Evaluation(self)
self.test_samples = []
# run self.process_after_read() after initiating Vallex
def read_ssj(self, ssj):
# ssj: object generated with ssj_strict.py.
BANNED_HW = ["biti"]
stats = {
"P_count": 0,
"skipped": 0,
}
log.info("Vallex.read_ssj({}).".format(
ssj
))
t_start = time()
for ssj_id, entry in ssj.entries.items():
# Read tokens
skip_entry = False
tmp_tokens = {}
for ssj_tid, token in entry.s.items():
sid, tid = split_id(ssj_tid)
# safety checks
if tid != "t" and not tid[1:].isdigit():
log.warning("dropping SID={} - corrupted keys".format(k))
skip_entry = True
break
if tid in tmp_tokens:
log.error(
"Vallex.read_ssj(): Duplicated ssj_tid:" + ssj_tid)
exit(1)
tmp_tokens[tid] = DC(token)
if skip_entry:
continue # skip corrupted keys
if sid in self.tokens:
log.error("sid duplicate: " + sid)
exit(1)
self.tokens[sid] = DC(tmp_tokens)
# Read frame data (each deep link gets its own raw frame).
link_map = {}
# hw_id: { hw_lemma: lemma, deep: [{functor: fnct, to: to}]}
for deep_link in entry.deep_links:
hw_id = deep_link["from"]
hw_token = self.get_token(hw_id)
hw_lemma = hw_token["lemma"]
hw_bv = hw_token["msd"][0]
if (hw_bv != "G" and hw_bv != "P"):
stats["skipped"] += 1
log.info("Skipping {}: not a verb or adjective.".format(
hw_lemma))
continue
if hw_bv == "P":
hw_lemma = hw_lemma + "_"
stats["P_count"] += 1
if hw_id in link_map:
link_map[hw_id]["deep"].append(deep_link)
else:
link_map[hw_id] = {
"hw_lemma": hw_lemma,
"deep": [deep_link]
}
for hw_id, data in link_map.items():
hw_lemma = data["hw_lemma"]
raw_frame = Frame(
hw=hw_lemma,
tids=[hw_id],
deep_links=data["deep"],
slots=None,
)
if hw_lemma not in self.entries:
self.entries[hw_lemma] = ValEntry(hw_lemma, raw_frame)
else:
self.entries[hw_lemma].raw_frames.append(raw_frame)
# cleanup banned
for hw in BANNED_HW:
if hw in self.entries:
del(self.entries[hw])
t_end = time()
log.info("Finished build_from_ssj() in {:.2}s.".format(
t_end - t_start
))
log.info("Vallex has a total of {} key entries.".format(
len(self.entries.keys())
))
log.info("Number of adjectives: {}".format(stats["P_count"]))
log.info("Number of skipped (not a verb or adjective): {}".format(
stats["skipped"]))
# Frames per hw
"""
for k, e in self.entries.items():
print(k + "," + str(len(e.raw_frames)))
"""
def get_token(self, myid):
# id = S123.t1
sid, tid = split_id(myid)
return self.tokens[sid][tid]
def get_sentence(self, myid):
sid, tid = split_id(myid)
tmp = []
sentence = ""
for k, token in self.tokens[sid].items():
if (k != "t") and (token["word"] is not None):
tmp.append((k, token))
for token in sorted(tmp, key=lambda x: int(x[0][1:])):
sentence += (token[1]["word"] + " ")
return sentence
def get_tokenized_sentence(self, myid):
sid, tid = split_id(myid)
tmp = []
sentence = []
for k, token in self.tokens[sid].items():
if k != "t":
tmp.append((k, token))
for token in sorted(tmp, key=lambda x: int(x[0][1:])):
sentence.append((".".join([sid, token[0]]), token[1]))
# return [(ssj_id, {word: _, lemma: _, msd: _}), ...]
return sentence
def process_after_read(
self, sskj_senses_pickle_path, se_list_pickle_path,
reload_sskj_senses
):
tstart = time()
# web app: index by hw
self.sorted_words = {}
self.gen_sorted_words()
# web app: index by functor
self.functors_index = {}
self.gen_functors_index()
# fill db.v2_senses
self.has_se = []
self.read_seqparser_pickles(
sskj_senses_pickle_path, se_list_pickle_path, reload_sskj_senses)
log.debug(
"vallex.process_after_read(): {:.2f}s".format(time() - tstart))
def gen_sorted_words(self):
res = {}
for hw, e in self.entries.items():
letter = hw[0].lower()
n_sent = len(e.raw_frames)
if letter not in res:
res[letter] = []
res[letter].append((hw, n_sent))
# sort and add to vallex object
self.sorted_words = {}
for letter, lst in res.items():
self.sorted_words[letter] = k_utils.slo_bucket_sort(
lst, key=lambda x: x[0])
def gen_functors_index(self):
for hw, e in self.entries.items():
for frame in e.raw_frames:
for slot in frame.slots:
if slot.functor not in self.functors_index:
self.functors_index[slot.functor] = []
self.functors_index[slot.functor].append(frame)
def read_seqparser_pickles(
self, sskj_senses_pickle_path, se_list_pickle_path,
reload_sskj_senses
):
log.info("read_seqparser_pickles()")
log.info((
"Reading list of has_se verbs from {}."
"Sskj senses into db.v2_senses from {}."
).format(se_list_pickle_path, sskj_senses_pickle_path))
AUTHOR_SSKJ = "SSKJ"
ERR_MSG = (
"Need to generate .pickle files first."
"Use: "
"$ python3 /script/valency/seqparser/seqparser.py"
"Input is /data/sskj_v2.html."
)
# has_se
with open(se_list_pickle_path, "rb") as f:
self.has_se = pickle.load(f)
if self.has_se is None:
log.error(ERR_MSG)
exit(1)
self.has_se = sorted(self.has_se)
log.info("Loaded self.has_se (len: {}) from {}.".format(
len(self.has_se), se_list_pickle_path))
# sskj senses
if reload_sskj_senses:
log.info("Reloading sskj_senses.")
reply = self.db.v2_senses.remove({"author": AUTHOR_SSKJ})
log.info(reply)
query = list(self.db.v2_senses.find({"author": AUTHOR_SSKJ}))
if len(query) > 0:
log.info("Sskj senses already in database.")
return
tstart = time()
data = None
with open(sskj_senses_pickle_path, "rb") as f:
data = pickle.load(f)
if data is None:
log.error(ERR_MSG)
exit(1)
for k, e in data.items():
for sense in e["senses"]:
db_entry = {
"hw": k,
"author": AUTHOR_SSKJ,
"desc": sense["sense_desc"],
# unique id for each sense
"sense_id": "{}-{}-{}-{}-{}".format(
AUTHOR_SSKJ,
sense["homonym_id"],
sense["sense_id"],
sense["sense_type"],
hashlib.sha256(
sense["sense_desc"].encode("utf-8")
).hexdigest()[:5]
)
}
self.db.v2_senses.insert(db_entry)
# print(db_entry)
log.info("db.v2_senses prepared in {:.2f}s".format(time() - tstart))
# Functions below can be used for interactively with flask_api.
def test_dev(self):
# self.prepare_sskj_senses()
hw = "dajati"
senses = self.sskj_interface.sense_glosses(hw)
return str(senses)
def calc_senses(self):
# self.calc_all_senses(self.leskFour.lesk_nltk)
# self.calc_all_senses(self.leskFour.lesk_sl)
# self.calc_all_senses(self.leskFour.lesk_al) # cca 8h!
# self.calc_all_senses(self.leskFour.lesk_ram)
self.calc_all_senses_kmeans(self.kmeans.bisection_kmeans)
self.calc_all_senses_kmeans(self.kmeans.normal_kmeans)
return "edit val_struct.py: calc_senses()"
# deprecated functions (used for machine learning experiments)
def prepare_sskj_senses(self):
# obsolete, using read_seqparser_pickles()
log.info("prepare_sskj_senses() (db.v2_senses)")
query = list(self.db.v2_senses.find({"author": "SSKJ2"}))
if len(query) > 0:
log.info("Sskj senses already in database.")
return
tstart = time()
log.info("Iterating over {} hw entries:".format(
len(self.entries.keys())))
for hw, e in self.entries.items():
senses = self.sskj_interface.sense_glosses(hw)
if len(senses) == 0:
continue
for sense in senses:
# create sense from each description
for i, de in enumerate(sense["def"]):
sense_def = sense["def"][i]
sense_def = sense_def[0].upper() + sense_def[1:]
if sense_def[-1] == ":" or sense_def[-1] == ";":
sense_def = sense_def[:-1] + "."
data = {
"hw": hw,
"author": "SSKJ2",
"desc": sense_def,
"sskj_id": sense["sskj_sense_id"],
"sskj_desc_id": i
}
self.db.v2_senses.insert(data)
log.info("sskj_senses prepared in {:.2f}s".format(time() - tstart))
def gen_sskj_sl(self):
# Takes about an hour.
tstart = time()
log.info("Generating new sskj_simple_lesk with Simple Lesk.")
for k, e in self.entries.items():
self.gen_sskj_sl_one(e.hw)
log.debug("gen_sskj_sl in {:.2f}s".format(time() - tstart))
def gen_sskj_sl_one(self, hw, update_db=True):
entry = None
ttstart = time()
e = self.entries.get(hw)
if e is None:
return
for frame in e.raw_frames:
tid = frame.tids[0]
sentence = self.get_sentence(tid)
res = self.lesk.simple_lesk_sskj(sentence, hw)
if res is None:
log.debug("headword {} not in sskj".format(hw))
continue
key = {"ssj_id": tid}
entry = {
"headword": hw,
"ssj_id": tid, # uniqe identifier
"sense_id": res[1],
# "sense_desc": k_utils.dict_safe_key(res[2], "ns0:def"),
"sense_desc": res[2]["def"]
}
# log.debug(str(res[2]))
# log.debug(entry["sense_id"])
# log.debug(entry["sense_desc"])
if update_db:
self.db.sskj_simple_lesk.update(key, entry, upsert=True)
log.debug("[*] sskj_ids for {} in {:.2f}s".format(
hw, time() - ttstart))
def get_context(self, myid, radius=None, min_lemma_size=None):
radius = radius or 5
min_lemma_size = min_lemma_size or 4
# gives you the token and 10 of its neighbors
sentence = self.get_sentence(myid)
sentlen = len(sentence.split(" "))
sid, tid = split_id(myid)
idx = int(tid[1:])
tokens_after = []
i = idx
while i < sentlen - 1 and len(tokens_after) < radius:
i += 1
token = self.get_token(sid + ".t" + str(i))
if (
token is not None and "lemma" in token and
len(token["lemma"]) >= min_lemma_size and
token["lemma"] != "biti"
):
tokens_after.append(token)
tokens_before = []
i = idx
while i > 1 and len(tokens_before) < radius:
i -= 1
token = self.get_token(sid + ".t" + str(i))
if (
token is not None and "lemma" in token and
len(token["lemma"]) >= min_lemma_size and
token["lemma"] != "biti"
):
tokens_before.append(token)
tokens = tokens_before + [self.get_token(myid)] + tokens_after
# find position of original token:
mid_idx = len(tokens_before)
return (mid_idx, tokens)
def get_sense_ids(self, collname, hw, sense_group=None):
query = {"headword": hw}
if sense_group is not None:
query["sense_group"] = sense_group
result = list(self.db[collname].find(query))
sense_ids = {}
for r in result:
sense_ids[r["ssj_id"]] = r["sense_id"]
return sense_ids
def t_get_context(self):
ii = 10
for k, e in self.entries.items():
for frame in e.raw_frames:
if random.randint(0, 100) > 20:
continue
ii -= 1
if ii <= 0:
return
mytid = frame.tids[0]
print()
print(mytid)
print(self.get_token(mytid))
sent = self.get_context(mytid, radius=3, min_lemma_size=4)
print("mid: {}".format(sent[0]))
for ii in range(len(sent[1])):
print("{} -> {}".format(
ii, sent[1][ii]))
def t_simple_lesk_sskj(self):
ii = 10
for k, e in self.entries.items():
if random.randint(0, 100) > 20:
continue
for frame in e.raw_frames:
if random.randint(0, 100) > 20:
continue
if ii == 0:
return
ii -= 1
print("\nTest frame: {}.".format(frame.tids))
hw_token = self.get_token(frame.tids[0])
print(hw_token)
context_sentence = self.get_sentence(frame.tids[0])
print(context_sentence)
self.lesk.simple_lesk_sskj(
context_sentence=context_sentence,
word_lemma=hw_token["lemma"]
)
def process_kmeans(self):
# Convert words to lemmas, cluseter using k-means.
# Number of clusters from sskj.
tstart = time()
log.info("Processing senses using kmeans.")
for k, e in self.entries.items():
# Frame start
ttstart = time()
lemma = e.hw
tokenized_sentences = []
for frame in e.raw_frames:
tid = frame.tids[0]
tokenized_sentences.append(self.get_tokenized_sentence(tid))
lemmatized_sentences = []
for sent in tokenized_sentences:
lemmatized = ""
for token in sent:
if "lemma" in token[1]:
lemmatized += (token[1]["lemma"] + " ")
lemmatized_sentences.append(lemmatized)
lls = len(lemmatized_sentences)
# We got the sentences
sskj_entry = self.db.sskj.find_one(
{"ns0:entry.ns0:form.ns0:orth": lemma})
if sskj_entry is None:
log.debug("headword {} has no <sense> in sskj".format(lemma))
continue
n_clusters = 1
if "ns0:sense" in sskj_entry["ns0:entry"]:
# Guess number of senses based on sskj senses.
n_clusters = len(sskj_entry["ns0:entry"]["ns0:sense"])
if lls >= n_clusters and n_clusters > 1:
labels = k_kmeans.k_means(
sentences=lemmatized_sentences,
n_clusters=n_clusters
)
kmeans_ids = [str(x) + "-" + str(lls) for x in labels]
elif n_clusters == 1:
kmeans_ids = ["1-1" for x in lemmatized_sentences]
elif lls < n_clusters:
# Each sentence gets its own sense.
kmeans_ids = []
for i in range(lls):
kmeans_ids.append(str(i + 1) + "lt" + str(n_clusters))
else:
log.error("Shouldn't be here (val_struct: process_kmeans()")
exit(1)
# Feed sense ides of whole frame to database.
for i in range(len(e.raw_frames)):
tid = e.raw_frames[i].tids[0]
key = {"ssj_id": tid}
entry = {
"headword": lemma,
"ssj_id": tid, # unique idenfitier
"sense_id": kmeans_ids[i],
}
self.db.kmeans.update(key, entry, upsert=True)
log.debug("[*] kemans_ids for {} in {:.2f}s".format(
lemma, time() - ttstart))
# Frame end
log.debug("process_kmeans in {:.2f}s".format(time() - tstart))
def get_context1(
self, mytid, collname, radius=None, min_token_len=3, get_glosses=None
):
# returns {
# "hw": headword lemma and its glosses
# "context": a list of lemmas and their glosses around the hw that
# have entries in collname dictionary (if get_glosses=True)
# }
# tstart = time()
if get_glosses is None:
get_glosses = False
if radius is None:
radius = 10000
if collname == "slownet":
dictionary_interface = self.slownet_interface
elif collname == "sskj":
dictionary_interface = self.sskj_interface
else:
log.error("argument error: get_context1(collname=<slownet/sskj>)")
return []
sentence = self.get_tokenized_sentence(mytid)
# return [(ssj_id, {word: _, lemma: _, msd: _}), ...]
hw_idx = -1
for i, e in enumerate(sentence):
if e[0] == mytid:
hw_idx = i
hw_lemma = e[1]["lemma"]
break
hw_glosses = dictionary_interface.sense_glosses(hw_lemma)
if len(hw_glosses) == 0:
log.info("hw: {} has 0 glosses".format(hw_lemma))
return {
"hw": None,
"err": "headword {} has no glosses in {}".format(
hw_lemma, collname)
}
tokens_before = []
ii = hw_idx - 1
while(ii >= 0 and len(tokens_before) < radius):
lemma = sentence[ii][1].get("lemma")
if (
lemma is not None and
len(lemma) >= min_token_len
):
if get_glosses:
glosses = dictionary_interface.sense_glosses(lemma)
else:
glosses = [{"def": "--none--", "gloss": "--none--"}]
if len(glosses) > 0:
tokens_before.insert(0, {
"lemma": lemma,
"glosses": glosses
})
ii -= 1
tokens_after = []
ii = hw_idx + 1
while(ii < len(sentence) and len(tokens_after) < radius):
lemma = sentence[ii][1].get("lemma")
if (
lemma is not None and
len(lemma) >= min_token_len
):
if get_glosses:
glosses = dictionary_interface.sense_glosses(lemma)
else:
glosses = [{"def": "--none--", "gloss": "--none--"}]
if len(glosses) > 0:
tokens_after.append({
"lemma": lemma,
"glosses": glosses
})
ii += 1
# log.debug("context1({}): {:.2f}".format(mytid, time() - tstart))
return {
"hw": {"lemma": hw_lemma, "glosses": hw_glosses},
"context": tokens_before + tokens_after
}
def test_context1(self, mytid, hw=""):
res = ""
context = self.get_context1(
mytid, collname="slownet", radius=2, get_glosses=True)
if context["hw"] is None:
return context["err"] + "<br><br>"
res = "hw: {}<br>sentence: {}<br>".format(
hw, self.get_sentence(mytid))
tfigf_input = []
glosses = [context["hw"]] + context["context"]
for e in glosses:
res += "--->lemma: {} ({} senses)<br>".format(
e["lemma"], len(e["glosses"]))
for g in e["glosses"]:
res += "{}<br>".format(str(g))
tfigf_input.append(" ".join(k_utils.tokenize_multiple(
g["gloss"],
min_token_len=3,
stem=k_utils.stem_eng
)))
res += "<br><br>"
return res
def calc_all_senses(self, lesk_algorithm):
allcount = 0
count = 0
for k, e in self.entries.items():
allcount += len(e.raw_frames)
for k, e in self.entries.items():
if k == "biti": # skip this huge bag of words
continue
for frame in e.raw_frames:
count += 1
if count % 10 == 0:
log.info("calc_all_senses: ({}/{})".format(
count, allcount))
lesk_algorithm(frame.tids[0])
return None
def calc_all_senses_kmeans(self, kmeans_algorithm):
tstart = time()
allcount = len(self.entries)
count = 0
avg_times = []
for key in self.entries:
count += 1
if key == "biti":
continue
# cluster frames of each entry
log.info("calc_all_senses_kmeans: ({}/{}) [{}]".format(
count, allcount, key))
kmeans_algorithm(key)
"""
try:
kmeans_algorithm(key)
except ValueError:
continue
"""
avg_times.append(1.0 * (time() - tstart) / count)
log.info("avg_time: {:.2f}s".format(avg_times[-1]))
log.info("calc_all_senses_kmeans in {:.2f}s.".format(time() - tstart))
return None
if __name__ == "__main__":
log.setLevel(logging.DEBUG)
ch = logging.StreamHandler(sys.stdout)
log.addHandler(ch)
# run ssj_struct to create a ssj_test.pickle file
with open("ssj_test.pickle", "rb") as file:
ssj = pickle.load(file)
vallex = Vallex()
vallex.read_ssj(ssj)
vallex.sorted_words = {}
vallex.gen_sorted_words()
vallex.functors_index = {}
vallex.gen_functors_index()