]
t0 = time.time()
if nthreads == 1:
print('Extracting features with 1 thread ...')
for i in range(0, len(train.index)):
if i % 10000 == 0:
a.print_progress(i, t0, len(train.index))
ftrs.append(process_row(i))
else:
print('Extracting features multi-threaded ... ', end='', flush=True)
pool = Pool(nthreads)
ftrs = pool.map(process_row, range(0, len(train.index)))
pool.close()
a.print_elapsed(t0)
start = time.time()
print('Caching data to disk ... ', end='', flush=True)
ftrs = pd.DataFrame(ftrs)
ftrs.columns = [
'itemID_1', 'itemID_2', 'simtitle', 'mattitle1', 'mattitle2', 'nwords1',
'nwords2'
]
# Save updated dataset
if mode == 0:
feather.write_dataframe(ftrs, cache_loc + 'features_train_set3b.fthr')
if mode == 1:
feather.write_dataframe(ftrs, cache_loc + 'features_test_set3b.fthr')
allkey[x] = allkey[x] + 1
else:
allkey[x] = 1
except KeyboardInterrupt:
raise
except Exception as e:
pa += 1
t0 = time.time()
print('Transforming key dict ... ', end='', flush=True)
icount = 0
keydict = {}
for k, n in allkey.items():
keydict[k] = icount
icount += 1
a.print_elapsed(t0)
ftrs_train = []
print('Generating for train ... ')
t0 = time.time()
pa = 0
for i in range(0, len(df_train.index)):
if i % 10000 == 0:
a.print_progress(i, t0, len(df_train.index))
try:
jx = df_train.iloc[i]['cleanjson_1'].replace("'", "")
jy = df_train.iloc[i]['cleanjson_2'].replace("'", "")
resx = json.loads(jx)
resy = json.loads(jy)
except KeyboardInterrupt:
raise
