def sort_candidates(candidates_db_path, ranked_candidates_outdb_path, prod_reps_dbpath, quest_reps_dbpath):
MyUtils.init_logging("Rank_candidates_nn.log")
### Connecting to the databases: candidates, test products, test questions
candidates_nn_db = sqlite3.connect(candidates_db_path)
cands_db_c = candidates_nn_db.cursor()
testprods_rep_c = sqlite3.connect(prod_reps_dbpath).cursor()
testquests_rep_c = sqlite3.connect(quest_reps_dbpath).cursor()
f = open(ranked_candidates_outdb_path, "w"); f.close()
outdb = sqlite3.connect(ranked_candidates_outdb_path)
outdb_c = outdb.cursor()
outdb_c.execute('''CREATE TABLE candidates( p_id varchar(63),
q_id varchar(63),
distance int
)''')
###
test_products_ids = cands_db_c.execute("SELECT DISTINCT p_id FROM candidates").fetchall()
logging.info(test_products_ids[0])
#logging.debug(test_products_ids)
for tpl_pid in test_products_ids:
pid = tpl_pid[0]
product_representation = MyUtils_dbs.search_in_alltables_db(testprods_rep_c, "SELECT * FROM ",
"WHERE id = '" + str(pid) + "'")[0]
product_tuple = MyUtils.prodls_tonamedtuple(product_representation, offset=1)
quests_ids = list(map ( lambda results_tpl : results_tpl[0], cands_db_c.execute("SELECT q_id FROM candidates WHERE p_id = ?", tpl_pid).fetchall()))
logging.debug(quests_ids)
product_qs_sorted = sort_product_candidates(product_tuple, quests_ids, testprods_rep_c, testquests_rep_c)
outdb.executemany("INSERT INTO candidates VALUES (?,?,?)", product_qs_sorted)
outdb.commit()
def get_instance_encoded_dictionary(prod_id, question_id, ps_db_c, qs_db_c,
d2v_model):
product_row = MyUtils_dbs.search_in_alltables_db(
ps_db_c, "SELECT * FROM ", "WHERE id = '" + prod_id + "'")
question_row = MyUtils_dbs.search_in_alltables_db(
qs_db_c, "SELECT * FROM ", "WHERE id = '" + str(question_id) + "'")
prod_tuple = MyUtils.prodls_tonamedtuple(product_row[0])
q_tuple = MyUtils.quest_lstonamedtuple(question_row[0])
instance_x = {}
instance_x["p_descvec"] = MyUtils_strings.fromstring_toarray(
prod_tuple.descvec)
instance_x["p_titlevec"] = MyUtils_strings.fromstring_toarray(
prod_tuple.titlevec)
instance_x["p_kwsVectors"] = MyUtils_strings.fromlls_toarrays(
prod_tuple.kwsVectors)
#logging.debug("instance_x['p_kwsVectors'].shape : %s", np.array(instance_x["p_kwsVectors"]).shape)
instance_x["p_mdcategories"] = MyUtils_strings.categories_to_vecs_lls(
MyUtils_strings.fromlls_toarrays(prod_tuple.mdcategories), d2v_model)
if len(np.array(instance_x["p_mdcategories"]).shape) >= 3:
logging.debug("instance_x['p_mdcategories'].shape : %s",
np.array(instance_x["p_mdcategories"]).shape)
instance_x["p_mdcategories"] = instance_x["p_mdcategories"][0]
instance_x["q_questionVec"] = MyUtils_strings.fromstring_toarray(
q_tuple.questionVec)
instance_x["q_questionType"] = q_tuple.questionType
instance_x["q_kwsVectors"] = MyUtils_strings.fromlls_toarrays(
q_tuple.kwsVectors)
instance_y = 1 if q_tuple.id[0:10] in prod_id else 0
instance = namedtuple('instance', 'x y')
inst = instance(x=instance_x, y=instance_y)
return inst
def register_matches(product_featureflags, quest_featureflags, dataset_type,
use_existing_file):
allmatches_filepath = F.PRODSWITHQUESTS_IDS_ALL + dataset_type
if use_existing_file:
if os.path.exists(allmatches_filepath):
if os.path.getsize(allmatches_filepath) > 0:
logging.info(
"The P-Q matches for the requested dataset were already found. They are located in the file:%s",
allmatches_filepath)
last_prod_id = "x"
allmatches_file = open(file=allmatches_filepath,
mode="r",
newline='')
reader = csv.reader(allmatches_file,
delimiter='_',
quotechar='"')
reader.__next__() #skip header
count_ps_withmatches = 0
while True:
try:
p_ls = reader.__next__()
prod_id = p_ls[0]
if prod_id != last_prod_id:
count_ps_withmatches = count_ps_withmatches + 1
last_prod_id = prod_id
except StopIteration:
break
allmatches_file.close()
return count_ps_withmatches
if dataset_type == MyUtils_flags.FLAG_VALID:
ps_db_filepath = F.PRODS_NUMENCODING_DB_VALID
qs_db_filepath = F.QUESTS_NUMENCODING_DB_VALID
prods_filepath = F.PRODUCTS_FINAL_VALID
quests_filepath = F.QUESTIONS_FINAL_VALID
elif dataset_type == MyUtils_flags.FLAG_TEST:
ps_db_filepath = F.PRODS_NUMENCODING_DB_TEST
qs_db_filepath = F.QUESTS_NUMENCODING_DB_TEST
prods_filepath = F.PRODUCTS_FINAL_TEST
quests_filepath = F.QUESTIONS_FINAL_TEST
else: #"train"
ps_db_filepath = F.PRODS_NUMENCODING_DB_TRAIN
qs_db_filepath = F.QUESTS_NUMENCODING_DB_TRAIN
prods_filepath = F.PRODUCTS_FINAL_TRAIN
quests_filepath = F.QUESTIONS_FINAL_TRAIN
MyUtils.init_logging("RegisterMatches.log", logging.INFO)
start = time()
f = open(F.PRODSWITHQUESTS_IDS, "w")
f.close() #clean outfile between runs
ids_outfile = open(F.PRODSWITHQUESTS_IDS, "a")
ids_outfile.write("id_questionsAsked\n")
#connecting with the products, to filter them, based on the features we chose to include
ps_db_conn = sqlite3.connect(ps_db_filepath)
ps_db_cursor = ps_db_conn.cursor()
# connecting with the questions, to filter them, based on the features we chose to include
qs_db_conn = sqlite3.connect(qs_db_filepath)
qs_db_cursor = qs_db_conn.cursor()
prods_filehandler = open(prods_filepath, "r", newline='')
quests_filehandler = open(quests_filepath, "r", newline='')
reader_1 = csv.reader(prods_filehandler, delimiter='_', quotechar='"')
reader_2 = csv.reader(quests_filehandler, delimiter='_', quotechar='"')
num_prods_withmatches = 0
num_products_reviewed = 0
num_questions_reviewed = 0
last_prod_id = "x"
questionsasked_ids_ls = []
### init:
reader_1.__next__()
reader_2.__next__()
reader_1.__next__()
reader_2.__next__() #skip headers
p_ls = reader_1.__next__()
q_ls = reader_2.__next__()
prod_t = MyUtils.prodls_tonamedtuple(p_ls, offset=0)
quest_t = MyUtils.quest_lstonamedtuple(q_ls, offset=0)
q_prod = (quest_t.id)[0:10]
#loop:
while True:
try:
match = False
while not (match):
while q_prod > prod_t.id or (len(q_prod) > len(prod_t.id)):
logging.debug("%s < %s", prod_t.id, q_prod)
p_ls = reader_1.__next__() #advance product
num_products_reviewed = num_products_reviewed + 1
prod_t = MyUtils.prodls_tonamedtuple(p_ls, offset=0)
while q_prod < prod_t.id or (len(q_prod) < len(prod_t.id)):
logging.debug("%s > %s", prod_t.id, q_prod)
q_ls = reader_2.__next__() #advance question
num_questions_reviewed = num_questions_reviewed + 1
quest_t = MyUtils.quest_lstonamedtuple(q_ls, offset=0)
q_prod = (quest_t.id)[0:10]
if q_prod == prod_t.id:
match = True
#barrier: feature filtering on products and questions; DB lookup:
if featurefilter_prod(prod_t.id, product_featureflags, ps_db_cursor) == True and \
featurefilter_quest(quest_t.id, quest_featureflags, qs_db_cursor) == True:
logging.info("Match: product: %s , \t question: %s",
prod_t.id, quest_t.id)
#positive_qs_ids_file.write(str(quest_t.id) + "\n")#store the question id (positive example)
if len(prod_t.id) > 5:
if prod_t.id != last_prod_id:
if len(last_prod_id) > 5:
ids_outfile.write(
str(last_prod_id) + "_" +
str(questionsasked_ids_ls) +
"\n") #write the previous p and qs
questionsasked_ids_ls = [
] #reset, and then append
questionsasked_ids_ls.append(quest_t.id)
last_prod_id = prod_t.id
num_prods_withmatches = num_prods_withmatches + 1 #n: matches = number of products that have questions
else:
logging.info("***")
questionsasked_ids_ls.append(
quest_t.id
) #same product as previously; only append
else:
pass
#on to the next question:
q_ls = reader_2.__next__()
quest_t = MyUtils.quest_lstonamedtuple(q_ls, offset=0)
q_prod = (quest_t.id)[0:10]
except StopIteration:
exc_info = sys.exc_info()
logging.warning("Exception information: %s", exc_info)
break
logging.info("Total number products that have matching questions: %s",
num_prods_withmatches)
logging.info("Products reviewed: %s", num_products_reviewed)
logging.info("Questions reviewed: %s", num_questions_reviewed)
copy(src=F.PRODSWITHQUESTS_IDS,
dst=F.PRODSWITHQUESTS_IDS_ALL + dataset_type)
end = time()
logging.info("Time elapsed: %s", round(end - start, 4))
ids_outfile.close()
prods_filehandler.close()
quests_filehandler.close()
#positive_qs_ids_file.close()
return num_prods_withmatches
def define_negative_examples(doc2vec_model, dataset_typeflag):
MyUtils.init_logging("NN_Dataset_Instances-define_negative_examples.log",
logging.INFO)
f = open(F.PRODS_WITH_NOTASKEDQUESTS_IDS, "w")
f.close()
prodsnegativeqs_outfile = open(F.PRODS_WITH_NOTASKEDQUESTS_IDS, "a")
prodsnegativeqs_outfile.write("id_questionsNotAsked\n")
### Connect with the database to read from: candidate negative examples
db_conn = sqlite3.connect(F.CANDIDATE_NEGQS_DB)
c = db_conn.cursor()
### IF we are working to create the training dataset,
### then we before allowing a question Q asked for P2 to be a negative example for P1,
### we check the similarity between P1 and P2 (it must not be too high)
if dataset_typeflag == MyUtils_flags.FLAG_TRAIN:
### Determining the maximum allowed similarity between products. Creates the similarity db if it does not exist
if os.path.exists(F.SIMILARITY_PRODUCTS_DB) == True:
p_sim_breakpoint = ES.get_products_similarity_breakpoint(
fraction=0.97)
else:
p_sim_breakpoint = ES.explore_products_similarity(N=500,
fraction=0.97)
### Connect with the databases of product and questions representations, to be able to pick the products P1 and P2
product_reps_dbconn = sqlite3.connect(F.PRODUCTS_FINAL_TRAIN_DB)
product_reps_c = product_reps_dbconn.cursor()
segment_size = 10**4
for input_segment in pd.read_csv(F.PRODSWITHQUESTS_IDS,
sep="_",
chunksize=segment_size):
for id_askedqs_t in input_segment.itertuples():
prod_id = id_askedqs_t.id
#logging.debug("Reading from F.PRODSWITHQUESTS_IDS, the product.id is: %s", prod_id)
asked_qs = ast.literal_eval(id_askedqs_t.questionsAsked)
t = (prod_id, )
c.execute('SELECT * FROM prodnegatives WHERE prod_id=?', t)
row = c.fetchone()
if row is None: #i.e. if the product in the file PRODSWITHQUESTS_IDS was excluded from the previous random subsampling
continue
candidatenegativeqs_rawstring = row[1]
candidatenegativeqs_string = "[" + candidatenegativeqs_rawstring[:
-1] + "]"
candidatenegativeqs_ls = ast.literal_eval(
candidatenegativeqs_string)
candidatenegativeqs_ls1 = [
q_id for q_id in candidatenegativeqs_ls if q_id not in asked_qs
]
if dataset_typeflag == MyUtils_flags.FLAG_TRAIN:
p1_row = MyUtils_dbs.search_in_alltables_db(
dbcursor=product_reps_c,
query_pretext="SELECT * FROM",
query_aftertext=" WHERE id='" + str(prod_id) + "'")[0]
candidatenegativeqs_asins = list(
map(lambda q_id: q_id[0:10], candidatenegativeqs_ls1))
p2_rows = MyUtils_dbs.search_in_alltables_db(
dbcursor=product_reps_c,
query_pretext="SELECT * FROM",
query_aftertext="WHERE id IN " +
str(tuple(candidatenegativeqs_asins)))
qids_and_p2rows = list(zip(candidatenegativeqs_ls1, p2_rows))
for q_id, p2_row in qids_and_p2rows:
#logging.debug("p1_row : %s", p1_row)
if p2_row is not None and len(p2_row) > 0:
#there are questions without corresponding products, in which case no similarity check is to be done
p1_tuple = MyUtils.prodls_tonamedtuple(p1_row) #[1:]?
p2_tuple = MyUtils.prodls_tonamedtuple(p2_row)
p1_p2_sim, _simparts = PS.compute_2products_similarity_singleprocess(
prod1_tuple=p1_tuple,
prod2_tuple=p2_tuple,
d2v_model=doc2vec_model)
if p1_p2_sim > p_sim_breakpoint:
candidatenegativeqs_ls1.remove(q_id)
logging.info(
"Removing question from the candidate negative examples, "
+
"because the similarity between %s and %s is > %s",
prod_id, p2_tuple.id, p_sim_breakpoint)
logging.info(
"Choosing negative examples: P-to-p similarity checks done for product: %s",
prod_id)
random_indices = sorted(
np.random.choice(a=range(len(candidatenegativeqs_ls1)),
size=min(len(candidatenegativeqs_ls1),
len(asked_qs)),
replace=False,
p=None))
#logging.info(candidatenegativeqs_ls1)
negativeqs_ls = [
candidatenegativeqs_ls1[i] for i in random_indices
]
#logging.info(negativeqs_ls)
prodsnegativeqs_outfile.write(prod_id + "_" + str(negativeqs_ls) +
"\n")
prodsnegativeqs_outfile.close()
def register_matches():
prods_filepath = F.PRODUCTS_FINAL_TRAIN
quests_filepath = F.QUESTIONS_FINAL_TRAIN
MyUtils.init_logging("OnlineLearning_RegisterMatches.log", logging.INFO)
start = time()
f = open(F.ONLINE_PQMATCHES, "w"); f.close()#clean outfile between runs
ids_outfile = open(F.ONLINE_PQMATCHES, "a")
ids_outfile.write("id_questionsAsked\n")
prods_filehandler = open(prods_filepath, "r", newline='')
quests_filehandler = open(quests_filepath, "r", newline='')
reader_1 = csv.reader(prods_filehandler, delimiter='_', quotechar='"')
reader_2 = csv.reader(quests_filehandler, delimiter='_', quotechar='"')
num_prods_withmatches = 0
num_products_reviewed = 0
num_questions_reviewed = 0
last_prod_id = "x"
questionsasked_ids_ls = []
### init:
reader_1.__next__(); reader_2.__next__() ; reader_1.__next__(); reader_2.__next__() #skip headers
p_ls = reader_1.__next__()
q_ls = reader_2.__next__()
prod_t = MyUtils.prodls_tonamedtuple(p_ls, offset=0)
quest_t = utilities.MyUtils.quest_lstonamedtuple(q_ls, offset=0)
q_prod = (quest_t.id)[0:10]
#loop:
while True:
try:
match = False
while not(match):
while q_prod > prod_t.id or (len(q_prod) > len(prod_t.id)):
logging.debug("%s < %s", prod_t.id , q_prod)
p_ls = reader_1.__next__() #advance product
num_products_reviewed = num_products_reviewed + 1
prod_t = utilities.MyUtils.prodls_tonamedtuple(p_ls, offset=0)
while q_prod < prod_t.id or (len(q_prod) < len(prod_t.id)):
logging.debug("%s > %s", prod_t.id, q_prod)
q_ls = reader_2.__next__() #advance question
num_questions_reviewed = num_questions_reviewed + 1
quest_t = utilities.MyUtils.quest_lstonamedtuple(q_ls, offset=0)
q_prod = (quest_t.id)[0:10]
if q_prod == prod_t.id:
match = True
#barrier: feature filtering on products and questions; DB lookup:
logging.info("Match: product: %s , \t question: %s", prod_t.id, quest_t.id)
#positive_qs_ids_file.write(str(quest_t.id) + "\n")#store the question id (positive example)
if len(prod_t.id) > 5:
if prod_t.id != last_prod_id:
if len(last_prod_id) > 5:
ids_outfile.write(str(last_prod_id) + "_" + str(questionsasked_ids_ls) + "\n")#write the previous p and qs
questionsasked_ids_ls = [] #reset, and then append
questionsasked_ids_ls.append(quest_t.id)
last_prod_id = prod_t.id
num_prods_withmatches = num_prods_withmatches +1 #n: matches = number of products that have questions
else:
logging.info("***")
questionsasked_ids_ls.append(quest_t.id)#same product as previously; only append
#on to the next question:
q_ls = reader_2.__next__()
quest_t = utilities.MyUtils.quest_lstonamedtuple(q_ls, offset=0)
q_prod = (quest_t.id)[0:10]
except StopIteration:
logging.warning("Exception information: %s", exc_info())
break
logging.info("Total number products that have matching questions: %s", num_prods_withmatches)
logging.info("Products reviewed: %s", num_products_reviewed)
logging.info("Questions reviewed: %s", num_questions_reviewed)
end = time()
logging.info("Time elapsed: %s", round(end - start,4))
ids_outfile.close()
prods_filehandler.close()
quests_filehandler.close()
#positive_qs_ids_file.close()
return num_prods_withmatches
def attach_text_to_candidates(ranked_candidates_dbpath, prods_initial_dbpath, quests_initial_dbpath, prod_reps_dbpath, quest_reps_dbpath, final_outdb_path):
MyUtils.init_logging("Attach_text_to_candidates.log")
candidates_nn_db = sqlite3.connect(ranked_candidates_dbpath)
cands_db_c = candidates_nn_db.cursor()
f = open(F.RANKING_TEMP_DB, 'w'); f.close()
temp_db = sqlite3.connect(F.RANKING_TEMP_DB)
temp_db_c = temp_db.cursor()
testprods_initial_c = sqlite3.connect(prods_initial_dbpath).cursor()
testquests_initial_c = sqlite3.connect(quests_initial_dbpath).cursor()
testprods_rep_c = sqlite3.connect(prod_reps_dbpath).cursor()
testquests_rep_c = sqlite3.connect(quest_reps_dbpath).cursor()
temp_db_c.execute('''CREATE TABLE candidates( p_id varchar(63),
q_id varchar(63),
distance int,
p_titletext varchar(1023),
p_descriptiontext varchar(8191),
p_categorytext varchar (4095),
q_text varchar (8191)
)''')
num_of_candidates = MyUtils_dbs.get_tot_num_rows_db(cands_db_c)
logging.info(num_of_candidates)
counter_questionsameid = 0
last_prod_id = 'x'
for rowindex in range(1, num_of_candidates + 1):
row = cands_db_c.execute("SELECT * FROM candidates WHERE rowid = ?", (rowindex,)).fetchone()
#logging.info("info: %s", row)
prod_id = row[0]
quest_id = row[1]
distance = row[2]
if last_prod_id != prod_id:
product_titleinfo,product_descinfo, product_categinfo = \
MyUtils_dbs.search_in_alltables_db(testprods_initial_c, "SELECT title, description, categories FROM",
"WHERE asin = '" + str(prod_id) + "'")[0]
product_representation = MyUtils_dbs.search_in_alltables_db(testprods_rep_c, "SELECT * FROM ",
"WHERE id = '" + str(prod_id) + "'")[0]
prod_tpl = MyUtils.prodls_tonamedtuple(product_representation, offset=1)
counter_questionsameid = 0
###get question's unixTime
if len(quest_id)< 21: #format : @nan0
base_endpoint = 14
question_unixTime = str(quest_id[11:base_endpoint])
else:
base_endpoint = 23
question_unixTime = str(quest_id[11:base_endpoint])
logging.debug("Question unixTime: %s", question_unixTime)
if base_endpoint == 23: #if we have a valid unixTime specification
possible_questions_text = MyUtils_dbs.search_in_alltables_db(testquests_initial_c, "SELECT question FROM",
"WHERE asin = '" + str(quest_id[0:10]) + "'"
+ " AND unixTime LIKE '" + question_unixTime + "%'")
else: #if we have NULL in the unixTime field
possible_questions_text = MyUtils_dbs.search_in_alltables_db(testquests_initial_c, "SELECT question FROM",
"WHERE asin = '" + str(quest_id[0:10]) + "'"
+ " AND unixTime IS NULL")
base_q_id = str(quest_id[0:23])
possible_questions_reps = MyUtils_dbs.search_in_alltables_db(testquests_rep_c, "SELECT * FROM ",
"WHERE id LIKE '" + str(base_q_id) + "%'")
logging.debug("possible_questions_reps: %s", possible_questions_reps)
logging.debug("possible_questions_text:%s", possible_questions_text)
if len(possible_questions_text) > 1:
possible_questions_tuples = list(map ( lambda q_ls : MyUtils.quest_lstonamedtuple(q_ls, offset=1), possible_questions_reps))
possible_questions_distances = list(map (lambda q_tpl : CD.compute_dist_pq(prod_tpl, q_tpl) , possible_questions_tuples))
qs_dist_lts = list(zip(possible_questions_tuples, possible_questions_distances))
qs_dist_lts_sorted = sorted( qs_dist_lts, key=lambda tpl : tpl[1])
#logging.info("sorted question tuples: %s", qs_dist_lts_sorted)
question_textinfo = possible_questions_text[counter_questionsameid][0]
counter_questionsameid= counter_questionsameid+1
else:
question_textinfo = possible_questions_text[0][0]
logging.debug("question_textinfo: %s", question_textinfo)
temp_db_c.execute("INSERT INTO candidates VALUES (?,?,?,?,?,?,?)", (prod_id, quest_id, distance,
product_titleinfo, product_descinfo, product_categinfo, question_textinfo))
logging.debug("***")
temp_db.commit()
os.rename(F.RANKING_TEMP_DB , final_outdb_path)