text stringlengths 1 93.6k |
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# np.random.seed(123)
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# in this case there is a single split in each day
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print("days", days)
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for i in range(days):
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print("i",i)
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datfile_i = npzfile + "_{0}".format(i) # + ".gz"
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npzfile_i = npzfile + "_{0}.npz".format(i)
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npzfile_p = npzfile + "_{0}_processed.npz".format(i)
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if path.exists(npzfile_i):
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print("Skip existing " + npzfile_i)
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elif path.exists(npzfile_p):
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print("Skip existing " + npzfile_p)
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else:
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recreate_flag = True
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total_per_file[i] = process_one_file(
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datfile_i,
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npzfile,
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i,
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total_per_file[i],
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)
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# report and save total into a file
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total_count = np.sum(total_per_file)
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if not path.exists(total_file):
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np.savez_compressed(total_file, total_per_file=total_per_file)
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print("Total number of samples:", total_count)
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print("Divided into days/splits:\n", total_per_file)
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# dictionary files
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counts = np.zeros(26, dtype=np.int32)
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if recreate_flag:
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# create dictionaries
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for j in range(26):
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for i, x in enumerate(convertDicts[j]):
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convertDicts[j][x] = i
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dict_file_j = d_path + d_file + "_fea_dict_{0}.npz".format(j)
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if not path.exists(dict_file_j):
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np.savez_compressed(
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dict_file_j,
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unique=np.array(list(convertDicts[j]), dtype=np.int32)
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)
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counts[j] = len(convertDicts[j])
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# store (uniques and) counts
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count_file = d_path + d_file + "_fea_count.npz"
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if not path.exists(count_file):
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np.savez_compressed(count_file, counts=counts)
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else:
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# create dictionaries (from existing files)
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for j in range(26):
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with np.load(d_path + d_file + "_fea_dict_{0}.npz".format(j)) as data:
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unique = data["unique"]
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for i, x in enumerate(unique):
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convertDicts[j][x] = i
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# load (uniques and) counts
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with np.load(d_path + d_file + "_fea_count.npz") as data:
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counts = data["counts"]
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# process all splits
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processCriteoAdData(d_path, d_file, npzfile, days, convertDicts, counts)
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o_file = concatCriteoAdData(
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d_path,
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d_file,
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npzfile,
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trafile,
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days,
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data_split,
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randomize,
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total_per_file,
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total_count,
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memory_map,
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o_filename
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)
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return o_file
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def loadDataset(
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dataset,
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max_ind_range,
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sub_sample_rate,
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randomize,
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data_split,
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raw_path="",
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pro_data="",
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memory_map=False
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):
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# dataset
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if dataset == "kaggle":
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days = 7
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o_filename = "kaggleAdDisplayChallenge_processed"
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elif dataset == "terabyte":
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days = 24
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o_filename = "terabyte_processed"
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else:
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raise(ValueError("Data set option is not supported"))
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# split the datafile into path and filename
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lstr = raw_path.split("/")
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d_path = "/".join(lstr[0:-1]) + "/"
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d_file = lstr[-1].split(".")[0] if dataset == "kaggle" else lstr[-1]
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