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Improve performance of data processing #155

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Improve performance of data processing #155

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24 changes: 15 additions & 9 deletions data_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1003,20 +1003,18 @@ def process_one_file(
y[i] = target
X_int[i] = np.array(line[1:14], dtype=np.int32)
if max_ind_range > 0:
X_cat[i] = np.array(
list(map(lambda x: int(x, 16) % max_ind_range, line[14:])),
X_cat[i] = np.fromiter(
map(lambda x: int(x, 16) % max_ind_range, line[14:]),
dtype=np.int32
)
else:
X_cat[i] = np.array(
list(map(lambda x: int(x, 16), line[14:])),
X_cat[i] = np.fromiter(
map(lambda x: int(x, 16), line[14:]),
dtype=np.int32
)

# count uniques
if dataset_multiprocessing:
for j in range(26):
convertDicts_day[j][X_cat[i][j]] = 1
# debug prints
if float(i)/num_data_in_split*100 > percent+1:
percent = int(float(i)/num_data_in_split*100)
Expand All @@ -1033,8 +1031,6 @@ def process_one_file(
end="\n",
)
else:
for j in range(26):
convertDicts[j][X_cat[i][j]] = 1
# debug prints
print(
"Load %d/%d Split: %d Label True: %d Stored: %d"
Expand All @@ -1049,6 +1045,16 @@ def process_one_file(
)
i += 1

if dataset_multiprocessing:
for j in range(26):
unique_cats = np.unique(X_cat[:, j])
for category in unique_cats:
convertDicts_day[j][category] = 1
else:
for j in range(26):
unique_cats = np.unique(X_cat[:, j])
for category in unique_cats:
convertDicts[j][category] = 1
# store num_data_in_split samples or extras at the end of file
# count uniques
# X_cat_t = np.transpose(X_cat)
Expand Down Expand Up @@ -1146,7 +1152,7 @@ def process_one_file(
if not path.exists(dict_file_j):
np.savez_compressed(
dict_file_j,
unique=np.array(list(convertDicts[j]), dtype=np.int32)
unique=np.fromiter(convertDicts[j].keys(), dtype=np.int32)
)
counts[j] = len(convertDicts[j])
# store (uniques and) counts
Expand Down