text stringlengths 1 93.6k |
|---|
X_int_val,
|
y_val,
|
X_cat_test,
|
X_int_test,
|
y_test,
|
)
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else:
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# randomize data
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if randomize == "total":
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indices = np.random.permutation(indices)
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print("Randomized indices...")
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X_cat = X_cat[indices].astype(np.long)
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X_int = np.log(X_int[indices].astype(np.float32) + 1)
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y = y[indices].astype(np.float32)
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print("Converted to tensors...done!")
|
return (X_cat, X_int, y, [], [], [], [], [], [])
|
def getCriteoAdData(
|
datafile,
|
o_filename,
|
max_ind_range=-1,
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sub_sample_rate=0.0,
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days=7,
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data_split='train',
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randomize='total',
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criteo_kaggle=True,
|
memory_map=False
|
):
|
# Passes through entire dataset and defines dictionaries for categorical
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# features and determines the number of total categories.
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#
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# Inputs:
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# datafile : path to downloaded raw data file
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# o_filename (str): saves results under o_filename if filename is not ""
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#
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# Output:
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# o_file (str): output file path
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#split the datafile into path and filename
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lstr = datafile.split("/")
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d_path = "/".join(lstr[0:-1]) + "/"
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d_file = lstr[-1].split(".")[0] if criteo_kaggle else lstr[-1]
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npzfile = d_path + ((d_file + "_day") if criteo_kaggle else d_file)
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trafile = d_path + ((d_file + "_fea") if criteo_kaggle else "fea")
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# count number of datapoints in training set
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total_file = d_path + d_file + "_day_count.npz"
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if path.exists(total_file):
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with np.load(total_file) as data:
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total_per_file = list(data["total_per_file"])
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total_count = np.sum(total_per_file)
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print("Skipping counts per file (already exist)")
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else:
|
total_count = 0
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total_per_file = []
|
if criteo_kaggle:
|
# WARNING: The raw data consists of a single train.txt file
|
# Each line in the file is a sample, consisting of 13 continuous and
|
# 26 categorical features (an extra space indicates that feature is
|
# missing and will be interpreted as 0).
|
if path.exists(datafile):
|
print("Reading data from path=%s" % (datafile))
|
with open(str(datafile)) as f:
|
for _ in f:
|
total_count += 1
|
total_per_file.append(total_count)
|
# reset total per file due to split
|
num_data_per_split, extras = divmod(total_count, days)
|
total_per_file = [num_data_per_split] * days
|
for j in range(extras):
|
total_per_file[j] += 1
|
# split into days (simplifies code later on)
|
file_id = 0
|
boundary = total_per_file[file_id]
|
nf = open(npzfile + "_" + str(file_id), "w")
|
with open(str(datafile)) as f:
|
for j, line in enumerate(f):
|
if j == boundary:
|
nf.close()
|
file_id += 1
|
nf = open(npzfile + "_" + str(file_id), "w")
|
boundary += total_per_file[file_id]
|
nf.write(line)
|
nf.close()
|
else:
|
sys.exit("ERROR: Criteo Kaggle Display Ad Challenge Dataset path is invalid; please download from https://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset")
|
else:
|
# WARNING: The raw data consist of day_0.gz,... ,day_23.gz text files
|
# Each line in the file is a sample, consisting of 13 continuous and
|
# 26 categorical features (an extra space indicates that feature is
|
# missing and will be interpreted as 0).
|
# for i in range(days):
|
for i in range(days):
|
datafile_i = datafile +"_" + str(i) # + ".gz"
|
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