def test_breakTrainSeq(targetSeries, exogenousSeries, seqLength,
forecastHorizon):
""" Tests Utility.breakTrainSeq """
n = targetSeries.shape[0]
trainSequences = Utility.breakTrainSeq(targetSeries, exogenousSeries,
seqLength, forecastHorizon)
# If exogenousSeries is none, breakTrainSeq behaves differently,
# here we test that behaviour
if exogenousSeries is None:
# On concatenating the trainSequences, we should get back data
assert np.array_equal(np.concatenate(trainSequences, axis=0),
targetSeries)
# length of each seq except the last should be exactly seqLength
for seq in trainSequences[:-1]:
assert seq.shape[0] == seqLength
return
# Forecast horizon cannot be None
assert forecastHorizon is not None
# Target and Exogenous series must have same number of elements
assert targetSeries.shape[0] == exogenousSeries.shape[0]
# Check if the train sequences are correct
startIdx = 0
for (targetSeriesSeq, exogenousSeriesSeq) in trainSequences:
lenTargetSeries = targetSeriesSeq.shape[0]
lenExogenousSeries = exogenousSeriesSeq.shape[0]
assert lenTargetSeries == lenExogenousSeries + forecastHorizon
exoEndIdx = startIdx + lenExogenousSeries
targetEndIdx = exoEndIdx + forecastHorizon
# Check if the broken sequence matches the correct part of the
# original sequence
assert np.array_equal(targetSeriesSeq,
targetSeries[startIdx:targetEndIdx])
assert np.array_equal(exogenousSeriesSeq,
exogenousSeries[startIdx:exoEndIdx])
startIdx = exoEndIdx
assert (startIdx + forecastHorizon
== n) or (n - startIdx <= forecastHorizon)
def main():
n = 20200
trainN = 20000
seqLength = 500
data = np.expand_dims(StandardGenerator('long_term').generate(n), axis=1)
trainData, testData = Utility.trainTestSplit(data, trainN)
trainSequences = Utility.breakTrainSeq(trainData, None, seqLength)
forecastHorizon = 1
lag = 30
model = DeepNN(
forecastHorizon=forecastHorizon,
lag=lag,
numUnitsPerLayer=10,
numLayers=2,
numTargetVariables=1,
numExoVariables=0
)
loss = model.train(
trainSequences=trainSequences,
numIterations=20,
optimizer=tf.keras.optimizers.Adam(
learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(
0.1,
25,
0.97
)
),
verboseLevel=2,
returnLosses=True
)
Plot.plotLoss(loss)
evalLoss, Ypred = model.evaluate(
testData,
returnPred=True
)
Ytrue = testData[lag + forecastHorizon:, :]
print(f'Eval Loss: {evalLoss}')
Plot.plotPredTrue(Ypred, Ytrue)