/
createDataset.py
executable file
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/
createDataset.py
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#!/usr/bin/env python
# coding: utf-8
import pickle
import os
import sys
import glob
import numpy as np
basePath = 'data/walkerExtracted'
# basePath = 'data/walkerSlow1000Extracted'
# basePath = 'data/walkerSlow1500Extracted'
# basePath = 'data/walkerSlowExtracted'
# basePath = 'data/walkerSimpleExtracted'
# basePath = 'data/walkerSingleMeasuresExtracted'
machinesGroups = [
['ibmq_athens', 'ibmq_santiago'],
# ['ibmq_casablanca', 'ibmq_santiago'],
# ['ibmq_casablanca', 'ibmq_casablanca-bis'],
# ['ibmq_casablanca', 'ibmq_casablanca'],
# ['ibmq_athens-splitA', 'ibmq_athens-splitB'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10B'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10C'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10D'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10E'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10F'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10G'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10H'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10I'],
# ['ibmq_athens-split10A', 'ibmq_athens-split10J'],
# ['ibmq_5_yorktown', 'ibmq_athens', 'ibmq_bogota', 'ibmq_casablanca', 'ibmq_santiago']
# ['ibmq_5_yorktown', 'ibmq_casablanca']
# ['ibmq_belem', 'ibmq_quito']
# walker: ['ibmq_athens', 'ibmq_bogota', 'ibmq_casablanca', 'ibmq_lima, 'ibmq_quito, 'ibmq_santiago', 'ibmq_5_yorktown']
# ['ibmq_athens', 'ibmq_bogota'],
# ['ibmq_athens', 'ibmq_casablanca'],
# ['ibmq_athens', 'ibmq_lima'],
# ['ibmq_athens', 'ibmq_quito'],
# ['ibmq_athens', 'ibmq_santiago'],
# ['ibmq_athens', 'ibmq_5_yorktown'],
# ['ibmq_bogota', 'ibmq_casablanca'],
# ['ibmq_bogota', 'ibmq_lima'],
# ['ibmq_bogota', 'ibmq_quito'],
# ['ibmq_bogota', 'ibmq_santiago'],
# ['ibmq_bogota', 'ibmq_5_yorktown'],
# ['ibmq_casablanca', 'ibmq_lima'],
# ['ibmq_casablanca', 'ibmq_quito'],
# ['ibmq_casablanca', 'ibmq_santiago'],
# ['ibmq_casablanca', 'ibmq_5_yorktown'],
# ['ibmq_lima', 'ibmq_quito'],
# ['ibmq_lima', 'ibmq_santiago'],
# ['ibmq_lima', 'ibmq_5_yorktown'],
# ['ibmq_quito', 'ibmq_santiago'],
# ['ibmq_quito', 'ibmq_5_yorktown'],
# ['ibmq_santiago', 'ibmq_5_yorktown'],
# multiclass complete
# ['ibmq_athens', 'ibmq_bogota', 'ibmq_casablanca', 'ibmq_lima', 'ibmq_quito', 'ibmq_santiago', 'ibmq_5_yorktown'],
# walkerSlow: ['ibmq_belem', 'ibmq_bogota', 'ibmq_quito', 'ibmq_rome]
# ['ibmq_belem', 'ibmq_quito']
# walkerSlow single temporal
# ['ibmq_belem']
]
outPath = 'data'
outPrefix = 'walker'
# outPrefix = 'walkerSlow1000'
# outPrefix = 'walkerSlow1500'
# outPrefix = 'walkerSlow'
# outPrefix = 'walkerSlowTemporal'
# outPrefix = 'walkerSimple'
# outPrefix = 'walkerSingleMeasures'
shuffle = True
# shuffle = False
testValidPercent = 20.
# testValidPercent = 25.
customSplits = {
'A': ((0,200), (400,600), (800,1000)),
'B': ((0,200), (200,400), (600,800)),
'C1a': ((0,200), (200,400), (400,600)),
'C2a': ((0,200), (600,800), (800,1000)),
'C3a': ((0,200), (1000,1200), (1200,1400)),
'C4a': ((0,200), (1400,1600), (1600,1800)),
'C5a': ((0,200), (1800,2000), (1800,2000)),
'C1b': ((200,400), (0,200), (400,600)),
'C2b': ((200,400), (600,800), (800,1000)),
'C3b': ((200,400), (1000,1200), (1200,1400)),
'C4b': ((200,400), (1400,1600), (1600,1800)),
'C5b': ((200,400), (1800,2000), (1800,2000)),
'C1c': ((400,600), (0,200), (200,400)),
'C2c': ((400,600), (600,800), (800,1000)),
'C3c': ((400,600), (1000,1200), (1200,1400)),
'C4c': ((400,600), (1400,1600), (1600,1800)),
'C5c': ((400,600), (1800,2000), (1800,2000)),
'C1d': ((600, 800), (0, 200), (200, 400)),
'C2d': ((600, 800), (400, 600), (800, 1000)),
'C3d': ((600, 800), (1000, 1200), (1200, 1400)),
'C4d': ((600, 800), (1400, 1600), (1600, 1800)),
'C5d': ((600, 800), (1800, 2000), (1800, 2000)),
'C1e': ((800, 1000), (0, 200), (200, 400)),
'C2e': ((800, 1000), (400, 600), (600, 800)),
'C3e': ((800, 1000), (1000, 1200), (1200, 1400)),
'C4e': ((800, 1000), (1400, 1600), (1600, 1800)),
'C5e': ((800, 1000), (1800, 2000), (1800, 2000)),
'C1f': ((1000, 1200), (0, 200), (200, 400)),
'C2f': ((1000, 1200), (400, 600), (600, 800)),
'C3f': ((1000, 1200), (800, 1000), (1200, 1400)),
'C4f': ((1000, 1200), (1400, 1600), (1600, 1800)),
'C5f': ((1000, 1200), (1800, 2000), (1800, 2000)),
'C1g': ((1200, 1400), (0, 200), (200, 400)),
'C2g': ((1200, 1400), (400, 600), (600, 800)),
'C3g': ((1200, 1400), (800, 1000), (1000, 1200)),
'C4g': ((1200, 1400), (1400, 1600), (1600, 1800)),
'C5g': ((1200, 1400), (1800, 2000), (1800, 2000)),
'C1h': ((1400, 1600), (0, 200), (200, 400)),
'C2h': ((1400, 1600), (400, 600), (600, 800)),
'C3h': ((1400, 1600), (800, 1000), (1000, 1200)),
'C4h': ((1400, 1600), (1200, 1400), (1600, 1800)),
'C5h': ((1400, 1600), (1800, 2000), (1800, 2000)),
'C1i': ((1600, 1800), (0, 200), (200, 400)),
'C2i': ((1600, 1800), (400, 600), (600, 800)),
'C3i': ((1600, 1800), (800, 1000), (1000, 1200)),
'C4i': ((1600, 1800), (1200, 1400), (1400, 1600)),
'C5i': ((1600, 1800), (1800, 2000), (1800, 2000)),
'C1j': ((1800, 2000), (0, 200), (200, 400)),
'C2j': ((1800, 2000), (400, 600), (600, 800)),
'C3j': ((1800, 2000), (800, 1000), (1000, 1200)),
'C4j': ((1800, 2000), (1200, 1400), (1400, 1600)),
'C5j': ((1800, 2000), (1600, 1800), (1800, 2000)),
}
customSplit = None
# customSplit = 'C5'
# customSplit = sys.argv[1]
shuffleAfter = False
# shuffleAfter = True
temporalDataset = None
# temporalDataset = 200
# temporalDataset = 400
customTemporalSplits = {
'A1': ((0, 200), (200, 400)),
'A2': ((0, 200), (400, 600)),
'A3': ((0, 200), (600, 800)),
'A4': ((0, 200), (800, 1000)),
'A5': ((0, 200), (1000, 1200)),
'A6': ((0, 200), (1200, 1400)),
'A7': ((0, 200), (1400, 1600)),
'A8': ((0, 200), (1600, 1800)),
'A9': ((0, 200), (1800, 2000)),
'B1': ((0, 200), (200, 400)),
'B2': ((0, 200), (210, 410)),
'B3': ((0, 200), (220, 420)),
'B4': ((0, 200), (230, 430)),
'B5': ((0, 200), (240, 440)),
'B6': ((0, 200), (250, 450)),
'B7': ((0, 200), (260, 460)),
'B8': ((0, 200), (270, 470)),
'B9': ((0, 200), (280, 480)),
'B10': ((0, 200), (290, 490)),
'B11': ((0, 200), (300, 500)),
'B12': ((0, 200), (310, 510)),
'B13': ((0, 200), (320, 520)),
'B14': ((0, 200), (330, 530)),
'B15': ((0, 200), (340, 540)),
'B16': ((0, 200), (340, 540)),
'B17': ((0, 200), (350, 550)),
'B18': ((0, 200), (360, 560)),
'B19': ((0, 200), (370, 570)),
'B20': ((0, 200), (380, 580)),
'B21': ((0, 200), (390, 590)),
'B22': ((0, 200), (400, 600)),
}
for i in range(100):
customTemporalSplits["C{}".format(i+1)] = ((0, 200), (200+i, 400+i))
for j,w in enumerate([(0, 200), (200, 400), (600, 800), (800, 1000), (1000, 1200)]):
for i in range(600):
customTemporalSplits["D-{}-{}".format(j+1, i+1)] = (w, (w[0]+200+i, w[1]+200+i))
for j,w in enumerate([(0, 400), (400, 800), (800, 1200)]):
for i in range(400):
customTemporalSplits["E-{}-{}".format(j+1, i+1)] = (w, (w[0]+400+i, w[1]+400+i))
for j,w in enumerate([(0, 400), (400, 800), (800, 1200)]):
for i in range(2000-(w[1]+400)):
customTemporalSplits["F-{}-{}".format(j+1, i+1)] = (w, (w[0]+400+i, w[1]+400+i))
customTemporalDataset = None
# customTemporalDataset = 'A1'
# customTemporalDataset = sys.argv[1]
# k = 200
k = 1000
for machines in machinesGroups:
print("========= Dataset with {} and k={}".format(machines,k))
outDataset = '{}-dataset-{}-{}{}{}{}.npz'.format(
outPrefix,
"-".join(machines),
k,
"" if shuffle else "-noShuffle",
"" if temporalDataset is None else "-temporal{}".format(temporalDataset),
"" if customTemporalDataset is None else "-customTemporal{}".format(customTemporalDataset)
)
outSplit = '{}-split-{}-{}{}{}{}{}{}{}.npz'.format(
outPrefix,
"-".join(machines),
k,
"" if shuffle else "-noShuffle",
"" if not shuffleAfter else "-shuffleAfter",
"" if not customSplit is None or testValidPercent == 20. else "-{}".format(int(testValidPercent)),
"" if customSplit is None else "-customSplit{}".format(customSplit),
"" if temporalDataset is None else "-temporal{}".format(temporalDataset),
"" if customTemporalDataset is None else "-customTemporal{}".format(customTemporalDataset)
)
os.makedirs(outPath, exist_ok=True)
np.random.seed(42)
probabilities = []
order = []
for m in machines:
fileProb = os.path.join(basePath, 'probabilities-{}-{}.npy'.format(k,m))
print("Load probabilities in {}".format(fileProb))
if not temporalDataset is None:
originalProbabilities = np.load(fileProb)
if originalProbabilities.shape[0] % temporalDataset != 0:
raise ValueError("TemporalDataset not compatible with shape")
for startIndex in range(0, originalProbabilities.shape[0], temporalDataset):
probabilities.append(originalProbabilities[startIndex:startIndex+temporalDataset])
if shuffle:
order.append(np.random.permutation(probabilities[-1].shape[0]))
else:
order.append(np.array(range(probabilities[-1].shape[0])))
elif not customTemporalDataset is None:
originalProbabilities = np.load(fileProb)
for (startIndex,endIndex) in customTemporalSplits[customTemporalDataset]:
probabilities.append(originalProbabilities[startIndex:endIndex])
if shuffle:
order.append(np.random.permutation(probabilities[-1].shape[0]))
else:
order.append(np.array(range(probabilities[-1].shape[0])))
else:
probabilities.append(np.load(fileProb))
if shuffle:
order.append(np.random.permutation(probabilities[-1].shape[0]))
else:
order.append(np.array(range(probabilities[-1].shape[0])))
x = []
y = []
for i in range(min(map(len, order))): #take min size if different
for p in range(len(probabilities)):
x.append(probabilities[p][order[p][i]])
y.append(p)
# currY = np.zeros((len(probabilities)), dtype='float32') #one hot encoding
# currY[p] = 1.
# y.append(currY)
x = np.array(x, dtype='float32')
y = np.array(y, dtype='int64')
np.savez_compressed(os.path.join(outPath, outDataset), x=x, y=y)
print("Saved dataset in {}".format(os.path.join(outPath, outDataset)))
dataLen = len(y)
index = np.array(range(dataLen), dtype='int32')
if customSplit is None:
testLen = int(dataLen / 100. * testValidPercent)
test = index[-testLen:]
valid = index[-2 * testLen: -testLen]
train = index[: -2 * testLen]
else:
cs = customSplits[customSplit]
train = index[cs[0][0]:cs[0][1]]
valid = index[cs[1][0]:cs[1][1]]
test = index[cs[2][0]:cs[2][1]]
if shuffleAfter:
np.random.shuffle(test)
np.random.shuffle(valid)
np.random.shuffle(train)
np.savez_compressed(os.path.join(outPath, outSplit), train=train, valid=valid, test=test)
print("Saved Split in {}".format(os.path.join(outPath, outSplit)))