def predict(modelpath, UNTRAINED_MODEL=False):
if UNTRAINED_MODEL:
rnn = RNN(HIDDEN_NODES, LOOKBACK, WINDOW_SIZE, SAMPLERATE, 1)
else:
rnn = loadTrainedModel(modelpath)
trainingSet, validationSet, scaler = setup()
testSet = readDataset(TEST_SET)
if rnn.sampleRate < rnn.windowSize:
trainGen = DataGenerator(trainingSet,
scaler,
windowSize=rnn.windowSize,
lookback=rnn.lookBack,
sampleRate=rnn.windowSize)
validateGen = DataGenerator(validationSet,
scaler,
windowSize=rnn.windowSize,
lookback=rnn.lookBack,
sampleRate=rnn.windowSize)
testGen = DataGenerator(testSet,
scaler,
windowSize=rnn.windowSize,
lookback=rnn.lookBack,
sampleRate=rnn.windowSize)
batchLength = rnn.windowSize
else:
trainGen = DataGenerator(trainingSet,
scaler,
windowSize=rnn.windowSize,
lookback=rnn.lookBack,
sampleRate=rnn.sampleRate)
validateGen = DataGenerator(validationSet,
scaler,
windowSize=rnn.windowSize,
lookback=rnn.lookBack,
sampleRate=rnn.sampleRate)
testGen = DataGenerator(testSet,
scaler,
windowSize=rnn.windowSize,
lookback=rnn.lookBack,
sampleRate=rnn.sampleRate)
batchLength = rnn.sampleRate # or sampleRate * windowSize?
trainingSetTrueSize = TRAINING_DATASIZE - trainGen.maxStepIndex - trainGen.minIndex
validationSetTrueSize = VALIDATION_DATASIZE - validateGen.maxStepIndex - validateGen.minIndex
testSetTrueSize = TEST_DATASIZE - testGen.maxStepIndex - testGen.minIndex
trainStep = int(trainingSetTrueSize / batchLength)
validateStep = int(validationSetTrueSize / batchLength)
testStep = int(testSetTrueSize / batchLength)
if DEBUG:
print(
f"trainStep: {trainStep}, validationStep: {validateStep}, testStep: {testStep}"
)
# Model predictions
start = time.time()
trainPred = rnn.model.predict_generator(
trainGen.generator(returnLabel=False), trainStep)
end = time.time()
if DEBUG:
print(
f"Time to make {trainPred.shape} training predictions: {end - start:.3f}, training dataset shape {trainingSet.shape}"
)
start = time.time()
validatePred = rnn.model.predict_generator(
validateGen.generator(returnLabel=False), validateStep)
end = time.time()
if DEBUG:
print(
f"Time to make {validatePred.shape} validation predictions: {end - start:.3f}, validation dataset shape {validationSet.shape}"
)
start = time.time()
testPred = rnn.model.predict_generator(
testGen.generator(returnLabel=False), testStep)
end = time.time()
if DEBUG:
print(
f"Time to make {testPred.shape} test predictions: {end - start:.3f}, test dataset shape {testSet.shape}"
)
# Undo the standardization on the predictions
trainPred = scaler.inverse_transform(trainPred)
validatePred = scaler.inverse_transform(validatePred)
testPred = scaler.inverse_transform(testPred)
# Sampling like this
# | - minIndex - | | - maxStepIndex - |
# [ .......... { TRUE SIZE } .............. ]
trainingTruth = trainingSet[trainGen.
minIndex:-trainGen.maxStepIndex].ravel()
validationTruth = validationSet[validateGen.minIndex:-validateGen.
maxStepIndex].ravel()
testTruth = testSet[testGen.minIndex:-testGen.maxStepIndex].ravel()
if DEBUG:
print(
f"trainingTruth shape: {trainingTruth.shape}, validationTruth shape: {validationTruth.shape}, testTruth shape: {testTruth.shape}"
)
groundTruth = np.block([trainingTruth, validationTruth, testTruth])
return trainPred, validatePred, testPred, groundTruth
