text stringlengths 1 93.6k |
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'''
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# Approach 4: Fisher-Yates-Rao (FYR) shuffle algorithm
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# 1st pass of FYR shuffle
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# check if data already exists
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recreate_flag = False
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for j in range(days):
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filename_j_y = npzfile + "_{0}_intermediate_y.npy".format(j)
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filename_j_d = npzfile + "_{0}_intermediate_d.npy".format(j)
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filename_j_s = npzfile + "_{0}_intermediate_s.npy".format(j)
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if (
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path.exists(filename_j_y)
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and path.exists(filename_j_d)
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and path.exists(filename_j_s)
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):
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print(
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"Using existing\n"
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+ filename_j_y + "\n"
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+ filename_j_d + "\n"
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+ filename_j_s
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)
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else:
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recreate_flag = True
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# reorder across buckets using sampling
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if recreate_flag:
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# init intermediate files (.npy appended automatically)
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for j in range(days):
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filename_j_y = npzfile + "_{0}_intermediate_y".format(j)
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filename_j_d = npzfile + "_{0}_intermediate_d".format(j)
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filename_j_s = npzfile + "_{0}_intermediate_s".format(j)
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np.save(filename_j_y, np.zeros((total_per_file[j])))
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np.save(filename_j_d, np.zeros((total_per_file[j], den_fea)))
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np.save(filename_j_s, np.zeros((total_per_file[j], spa_fea)))
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# start processing files
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total_counter = [0] * days
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for i in range(days):
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filename_i = npzfile + "_{0}_processed.npz".format(i)
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with np.load(filename_i) as data:
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X_cat = data["X_cat"]
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X_int = data["X_int"]
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y = data["y"]
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size = len(y)
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# sanity check
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if total_per_file[i] != size:
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sys.exit("ERROR: sanity check on number of samples failed")
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# debug prints
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print("Reordering (1st pass) " + filename_i)
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# create buckets using sampling of random ints
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# from (discrete) uniform distribution
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buckets = []
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for _j in range(days):
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buckets.append([])
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counter = [0] * days
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days_to_sample = days if data_split == "none" else days - 1
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if randomize == "total":
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rand_u = np.random.randint(low=0, high=days_to_sample, size=size)
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for k in range(size):
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# sample and make sure elements per buckets do not overflow
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if data_split == "none" or i < days - 1:
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# choose bucket
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p = rand_u[k]
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# retry of the bucket is full
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while total_counter[p] + counter[p] >= total_per_file[p]:
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p = np.random.randint(low=0, high=days_to_sample)
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else: # preserve the last day/bucket if needed
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p = i
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buckets[p].append(k)
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counter[p] += 1
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else: # randomize is day or none
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for k in range(size):
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# do not sample, preserve the data in this bucket
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p = i
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buckets[p].append(k)
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counter[p] += 1
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# sanity check
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if np.sum(counter) != size:
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sys.exit("ERROR: sanity check on number of samples failed")
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# debug prints
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# print(counter)
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# print(str(np.sum(counter)) + " = " + str(size))
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# print([len(x) for x in buckets])
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# print(total_counter)
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# partially feel the buckets
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for j in range(days):
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filename_j_y = npzfile + "_{0}_intermediate_y.npy".format(j)
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filename_j_d = npzfile + "_{0}_intermediate_d.npy".format(j)
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filename_j_s = npzfile + "_{0}_intermediate_s.npy".format(j)
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start = total_counter[j]
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end = total_counter[j] + counter[j]
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# target buckets
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fj_y = np.load(filename_j_y, mmap_mode='r+')
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# print("start=" + str(start) + " end=" + str(end)
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# + " end - start=" + str(end - start) + " "
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# + str(fj_y[start:end].shape) + " "
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# + str(len(buckets[j])))
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fj_y[start:end] = y[buckets[j]]
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del fj_y
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