def testPredictedFieldAndInferenceEnabledAreSaved(self):
m1 = ModelFactory.create(PY_MODEL_PARAMS)
m1.enableInference({'predictedField': 'consumption'})
self.assertTrue(m1.isInferenceEnabled())
self.assertEqual(m1.getInferenceArgs().get('predictedField'),
'consumption')
headers = ['timestamp', 'consumption']
record = [datetime.datetime(2013, 12, 12), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
m1.run(modelInput)
# Serialize
builderProto = HTMPredictionModelProto.new_message()
m1.write(builderProto)
# Construct HTMPredictionModelProto reader from populated builder
readerProto = HTMPredictionModelProto.from_bytes(
builderProto.to_bytes())
# Deserialize
m2 = HTMPredictionModel.read(readerProto)
self.assertTrue(m2.isInferenceEnabled())
self.assertEqual(m2.getInferenceArgs().get('predictedField'),
'consumption')
# Running the desrialized m2 without redundant enableInference call should
# work
record = [datetime.datetime(2013, 12, 14), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
m2.run(modelInput)
# Check that disabled inference is saved, too (since constructor defaults to
# enabled at time of this writing)
m1.disableInference()
self.assertFalse(m1.isInferenceEnabled())
builderProto = HTMPredictionModelProto.new_message()
m1.write(builderProto)
readerProto = HTMPredictionModelProto.from_bytes(
builderProto.to_bytes())
m3 = HTMPredictionModel.read(readerProto)
self.assertFalse(m3.isInferenceEnabled())
def testPredictedFieldAndInferenceEnabledAreSaved(self):
m1 = ModelFactory.create(PY_MODEL_PARAMS)
m1.enableInference({'predictedField': 'consumption'})
self.assertTrue(m1.isInferenceEnabled())
self.assertEqual(m1.getInferenceArgs().get('predictedField'), 'consumption')
headers = ['timestamp', 'consumption']
record = [datetime.datetime(2013, 12, 12), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
m1.run(modelInput)
# Serialize
builderProto = HTMPredictionModelProto.new_message()
m1.write(builderProto)
# Construct HTMPredictionModelProto reader from populated builder
readerProto = HTMPredictionModelProto.from_bytes(builderProto.to_bytes())
# Deserialize
m2 = HTMPredictionModel.read(readerProto)
self.assertTrue(m2.isInferenceEnabled())
self.assertEqual(m2.getInferenceArgs().get('predictedField'), 'consumption')
# Running the desrialized m2 without redundant enableInference call should
# work
record = [datetime.datetime(2013, 12, 14), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
m2.run(modelInput)
# Check that disabled inference is saved, too (since constructor defaults to
# enabled at time of this writing)
m1.disableInference()
self.assertFalse(m1.isInferenceEnabled())
builderProto = HTMPredictionModelProto.new_message()
m1.write(builderProto)
readerProto = HTMPredictionModelProto.from_bytes(builderProto.to_bytes())
m3 = HTMPredictionModel.read(readerProto)
self.assertFalse(m3.isInferenceEnabled())
def _runModelSerializationDeserializationChecks(self, modelParams):
m1 = ModelFactory.create(modelParams)
m1.enableInference({'predictedField': 'consumption'})
headers = ['timestamp', 'consumption']
record = [datetime.datetime(2013, 12, 12), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
m1.run(modelInput)
# Serialize
builderProto = HTMPredictionModelProto.new_message()
m1.write(builderProto)
# Construct HTMPredictionModelProto reader from populated builder
readerProto = HTMPredictionModelProto.from_bytes(
builderProto.to_bytes())
# Deserialize
m2 = HTMPredictionModel.read(readerProto)
self.assertEqual(m1.getInferenceType(),
modelParams['modelParams']['inferenceType'])
self.assertEqual(m1.getInferenceType(), m2.getInferenceType())
# TODO NUP-2463: remove this work-around.
# Work around a serialization bug that doesn't save the enabled predicted
# field
m2.enableInference({'predictedField': 'consumption'})
# Run computes on m1 & m2 and compare results
record = [datetime.datetime(2013, 12, 14), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
# Use deepcopy to guarantee no input side-effect between calls
r1 = m1.run(copy.deepcopy(modelInput))
r2 = m2.run(copy.deepcopy(modelInput))
# Compare results
self.assertEqual(r2.predictionNumber, r1.predictionNumber)
self.assertEqual(r2.rawInput, r1.rawInput)
self.assertEqual(r2.sensorInput.dataRow, r1.sensorInput.dataRow)
self.assertEqual(r2.sensorInput.dataDict, r1.sensorInput.dataDict)
numpy.testing.assert_array_equal(r2.sensorInput.dataEncodings,
r1.sensorInput.dataEncodings)
self.assertEqual(r2.sensorInput.sequenceReset,
r1.sensorInput.sequenceReset)
self.assertEqual(r2.sensorInput.category, r1.sensorInput.category)
self.assertEqual(r2.inferences, r1.inferences)
self.assertEqual(r2.metrics, r1.metrics)
self.assertEqual(r2.predictedFieldIdx, r1.predictedFieldIdx)
self.assertEqual(r2.predictedFieldName, r1.predictedFieldName)
numpy.testing.assert_array_equal(r2.classifierInput.dataRow,
r1.classifierInput.dataRow)
self.assertEqual(r2.classifierInput.bucketIndex,
r1.classifierInput.bucketIndex)
# Compre regions
self.assertIsNotNone(m2._getSensorRegion())
self.assertEqual(m2._getSensorRegion(), m1._getSensorRegion())
self.assertIsNotNone(m2._getClassifierRegion())
self.assertEqual(m2._getClassifierRegion(), m1._getClassifierRegion())
# TODO NUP-2356: Uncomment after issue is resolved.
#self.assertIsNotNone(m2._getTPRegion())
self.assertEqual(m2._getTPRegion(), m1._getTPRegion())
self.assertIsNotNone(m2._getSPRegion())
self.assertEqual(m2._getSPRegion(), m1._getSPRegion())
def _runModelSerializationDeserializationChecks(self, modelParams):
m1 = ModelFactory.create(modelParams)
m1.enableInference({'predictedField': 'consumption'})
headers = ['timestamp', 'consumption']
record = [datetime.datetime(2013, 12, 12), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
m1.run(modelInput)
# Serialize
builderProto = HTMPredictionModelProto.new_message()
m1.write(builderProto)
# Construct HTMPredictionModelProto reader from populated builder
readerProto = HTMPredictionModelProto.from_bytes(builderProto.to_bytes())
# Deserialize
m2 = HTMPredictionModel.read(readerProto)
self.assertEqual(m1.getInferenceType(),
modelParams['modelParams']['inferenceType'])
self.assertEqual(m1.getInferenceType(), m2.getInferenceType())
# Run computes on m1 & m2 and compare results
record = [datetime.datetime(2013, 12, 14), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
# Use deepcopy to guarantee no input side-effect between calls
r1 = m1.run(copy.deepcopy(modelInput))
r2 = m2.run(copy.deepcopy(modelInput))
# Compare results
self.assertEqual(r2.predictionNumber, r1.predictionNumber)
self.assertEqual(r2.rawInput, r1.rawInput)
self.assertEqual(r2.sensorInput.dataRow, r1.sensorInput.dataRow)
self.assertEqual(r2.sensorInput.dataDict, r1.sensorInput.dataDict)
numpy.testing.assert_array_equal(r2.sensorInput.dataEncodings,
r1.sensorInput.dataEncodings)
self.assertEqual(r2.sensorInput.sequenceReset, r1.sensorInput.sequenceReset)
self.assertEqual(r2.sensorInput.category, r1.sensorInput.category)
self.assertEqual(r2.inferences, r1.inferences)
self.assertEqual(r2.metrics, r1.metrics)
self.assertEqual(r2.predictedFieldIdx, r1.predictedFieldIdx)
self.assertEqual(r2.predictedFieldName, r1.predictedFieldName)
numpy.testing.assert_array_equal(r2.classifierInput.dataRow,
r1.classifierInput.dataRow)
self.assertEqual(r2.classifierInput.bucketIndex,
r1.classifierInput.bucketIndex)
# Compre regions
self.assertIsNotNone(m2._getSensorRegion())
self.assertEqual(m2._getSensorRegion(), m1._getSensorRegion())
self.assertIsNotNone(m2._getClassifierRegion())
self.assertEqual(m2._getClassifierRegion(), m1._getClassifierRegion())
self.assertIsNotNone(m2._getTPRegion())
self.assertEqual(m2._getTPRegion(), m1._getTPRegion())
self.assertIsNotNone(m2._getSPRegion())
self.assertEqual(m2._getSPRegion(), m1._getSPRegion())