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for i in range(days):
filename_i = d_path + npzfile + "_{0}_processed.npz".format(i)
with np.load(filename_i) as data:
X_cat_t = np.transpose(data["X_cat"])
X_int_t = np.transpose(data["X_int"])
y = data["y"]
size = len(y)
# sanity check
if total_per_file[i] != size:
sys.exit("ERROR: sanity check on number of samples failed")
# setup start and end ranges
start = offset_per_file[i]
end = offset_per_file[i + 1]
# print(filename_i)
# print("start=" + str(start) + " end=" + str(end)
# + " diff=" + str(end - start) + "=" + str(total_per_file[i]))
for j in range(tot_fea):
filename_j = trafile + "_{0}_reordered.npy".format(j)
fj = np.load(filename_j, mmap_mode='r+')
if j < tar_fea:
fj[indices[start:end]] = y
elif tar_fea <= j and j < tad_fea:
fj[indices[start:end]] = X_int_t[j - tar_fea, :]
else:
fj[indices[start:end]] = X_cat_t[j - tad_fea, :]
del fj
else:
print("Reordered fea files already exist, skipping ...")
# check if data already exists
recreate_flag = False
for i in range(days):
filename_i = d_path + npzfile + "_{0}_reordered.npz".format(i)
if path.exists(filename_i):
print("Using existing " + filename_i)
else:
recreate_flag = True
# split reordered data by files (memmap all reordered files per feature)
# on the day boundary del the file object and memmap again
if recreate_flag:
for i in range(days):
filename_i = d_path + npzfile + "_{0}_reordered.npz".format(i)
size = total_per_file[i]
X_int_t = np.zeros((den_fea, size))
X_cat_t = np.zeros((spa_fea, size))
# setup start and end ranges
start = offset_per_file[i]
end = offset_per_file[i + 1]
print("Creating " + filename_i)
# print("start=" + str(start) + " end=" + str(end)
# + " diff=" + str(end - start) + "=" + str(total_per_file[i]))
for j in range(tot_fea):
filename_j = trafile + "_{0}_reordered.npy".format(j)
fj = np.load(filename_j, mmap_mode='r')
if j < tar_fea:
y = fj[start:end]
elif tar_fea <= j and j < tad_fea:
X_int_t[j - tar_fea, :] = fj[start:end]
else:
X_cat_t[j - tad_fea, :] = fj[start:end]
del fj
np.savez_compressed(
filename_i,
X_cat=np.transpose(X_cat_t), # transpose of the data
X_int=np.transpose(X_int_t), # transpose of the data
y=y,
)
else:
print("Reordered day files already exist, skipping ...")
'''
'''
# Approach 2: group days
# check if data already exists
recreate_flag = False
for j in range(tot_fea):
filename_j = trafile + "_{0}_reordered.npy".format(j)
if path.exists(filename_j):
print("Using existing " + filename_j)
else:
recreate_flag = True
# load, reorder and concatenate data (memmap all reordered files per feature)
if recreate_flag:
# init reordered files (.npy appended automatically)
z = np.zeros((total_count))
for j in range(tot_fea):
filename_j = trafile + "_{0}_reordered".format(j)
np.save(filename_j, z)
print("Creating " + filename_j)
group_day = 3 # e.g. 8, 4 or 3
group_num = days // group_day
file_group = [i*group_day for i in range(group_num)] + [days]
for ii in range(group_num):
# for last may be group_size != group_num, therefore reset it below
group_size = file_group[ii + 1] - file_group[ii]
X_cat_t = [0]*group_size