text stringlengths 1 93.6k |
|---|
print("test",str(datafile_i))
|
# if path.exists(str(datafile_i)):
|
if True:
|
print("Reading data from path=%s" % (str(datafile_i)))
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# file day_<number>
|
total_per_file_count = 0
|
with open(str(datafile_i)) as f:
|
for _ in f:
|
total_per_file_count += 1
|
total_per_file.append(total_per_file_count)
|
total_count += total_per_file_count
|
else:
|
sys.exit("ERROR: Criteo Terabyte Dataset path is invalid; please download from https://labs.criteo.com/2013/12/download-terabyte-click-logs")
|
# process a file worth of data and reinitialize data
|
# note that a file main contain a single or multiple splits
|
def process_one_file(
|
datfile,
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npzfile,
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split,
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num_data_in_split,
|
):
|
with open(str(datfile)) as f:
|
y = np.zeros(num_data_in_split, dtype="i4") # 4 byte int
|
X_int = np.zeros((num_data_in_split, 13), dtype="i4") # 4 byte int
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X_cat = np.zeros((num_data_in_split, 26), dtype="i4") # 4 byte int
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if sub_sample_rate == 0.0:
|
rand_u = 1.0
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else:
|
rand_u = np.random.uniform(low=0.0, high=1.0, size=num_data_in_split)
|
i = 0
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for k, line in enumerate(f):
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# process a line (data point)
|
line = line.split('\t')
|
# set missing values to zero
|
for j in range(len(line)):
|
if (line[j] == '') or (line[j] == '\n'):
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line[j] = '0'
|
# sub-sample data by dropping zero targets, if needed
|
target = np.int32(line[0])
|
if target == 0 and \
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(rand_u if sub_sample_rate == 0.0 else rand_u[k]) < sub_sample_rate:
|
continue
|
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:])),
|
dtype=np.int32
|
)
|
else:
|
X_cat[i] = np.array(
|
list(map(lambda x: int(x, 16), line[14:])),
|
dtype=np.int32
|
)
|
# count uniques
|
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"
|
% (
|
i,
|
num_data_in_split,
|
split,
|
target,
|
y[i],
|
),
|
end="\r",
|
)
|
i += 1
|
# store num_data_in_split samples or extras at the end of file
|
# count uniques
|
# X_cat_t = np.transpose(X_cat)
|
# for j in range(26):
|
# for x in X_cat_t[j,:]:
|
# convertDicts[j][x] = 1
|
# store parsed
|
filename_s = npzfile + "_{0}.npz".format(split)
|
if path.exists(filename_s):
|
print("\nSkip existing " + filename_s)
|
else:
|
np.savez_compressed(
|
filename_s,
|
X_int=X_int[0:i, :],
|
# X_cat=X_cat[0:i, :],
|
X_cat_t=np.transpose(X_cat[0:i, :]), # transpose of the data
|
y=y[0:i],
|
)
|
print("\nSaved " + npzfile + "_{0}.npz!".format(split))
|
return i
|
# create all splits (reuse existing files if possible)
|
recreate_flag = False
|
convertDicts = [{} for _ in range(26)]
|
# WARNING: to get reproducable sub-sampling results you must reset the seed below
|
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