text stringlengths 1 93.6k |
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np.savez_compressed(
|
filename_i,
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# X_cat = X_cat,
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X_cat=np.transpose(X_cat_t), # transpose of the data
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X_int=X_int,
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y=y,
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)
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print("Processed " + filename_i, end="\r")
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print("")
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# sanity check (applicable only if counts have been pre-computed & are re-computed)
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# for j in range(26):
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# if pre_comp_counts[j] != counts[j]:
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# sys.exit("ERROR: Sanity check on counts has failed")
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# print("\nSanity check on counts passed")
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return
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def 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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# Concatenates different days and saves the result.
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#
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# Inputs:
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# days (int): total number of days in the dataset (typically 7 or 24)
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# d_path (str): path for {kaggle|terabyte}_day_i.npz files
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# o_filename (str): output file name
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#
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# Output:
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# o_file (str): output file path
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if memory_map:
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# dataset break up per fea
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# tar_fea = 1 # single target
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den_fea = 13 # 13 dense features
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spa_fea = 26 # 26 sparse features
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# tad_fea = tar_fea + den_fea
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# tot_fea = tad_fea + spa_fea
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# create offset per file
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offset_per_file = np.array([0] + [x for x in total_per_file])
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for i in range(days):
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offset_per_file[i + 1] += offset_per_file[i]
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'''
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# Approach 1, 2 and 3 use indices, while Approach 4 does not use them
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# create indices
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indices = np.arange(total_count)
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if data_split == "none":
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if randomize == "total":
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indices = np.random.permutation(indices)
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else:
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indices = np.array_split(indices, offset_per_file[1:-1])
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# randomize train data (per day)
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if randomize == "day": # or randomize == "total":
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for i in range(len(indices) - 1):
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indices[i] = np.random.permutation(indices[i])
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print("Randomized indices per day ...")
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train_indices = np.concatenate(indices[:-1])
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test_indices = indices[-1]
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# randomize train data (across days)
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if randomize == "total":
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train_indices = np.random.permutation(train_indices)
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print("Randomized indices across days ...")
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indices = np.concatenate((train_indices, test_indices))
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# no reordering
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# indices = np.arange(total_count)
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'''
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'''
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# Approach 1: simple and slow (no grouping is used)
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# check if data already exists
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recreate_flag = False
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for j in range(tot_fea):
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filename_j = trafile + "_{0}_reordered.npy".format(j)
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if path.exists(filename_j):
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print("Using existing " + filename_j)
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else:
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recreate_flag = True
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# load, reorder and concatenate data (memmap all reordered files per feature)
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if recreate_flag:
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# init reordered files (.npy appended automatically)
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z = np.zeros((total_count))
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for j in range(tot_fea):
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filename_j = trafile + "_{0}_reordered".format(j)
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np.save(filename_j, z)
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print("Creating " + filename_j)
|
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