def createPredictionModel(dataFrame):
with open(MODEL_PARAMS_PATH, "r") as dataIn:
modelParams = json.loads(dataIn.read())
minInput, maxInput = getMinMax(dataFrame)
# RDSE - resolution calculation
valueEncoderParams = \
modelParams["modelParams"]["sensorParams"]["encoders"]["value"]
numBuckets = float(valueEncoderParams.pop("numBuckets"))
resolution = max(0.001, (maxInput - minInput) / numBuckets)
valueEncoderParams["resolution"] = resolution
model = ModelFactory.create(modelParams)
model.enableInference({"predictedField": "value"})
return model
def createPredictionModel(dataFrame):
with open(MODEL_PARAMS_PATH, "r") as dataIn:
modelParams = json.loads(dataIn.read())
minInput, maxInput = getMinMax(dataFrame)
# RDSE - resolution calculation
valueEncoderParams = \
modelParams["modelParams"]["sensorParams"]["encoders"]["value"]
numBuckets = float(valueEncoderParams.pop("numBuckets"))
resolution = max(0.001, (maxInput - minInput) / numBuckets)
valueEncoderParams["resolution"] = resolution
model = ModelFactory.create(modelParams)
model.enableInference({"predictedField": "value"})
return model
def createPredictionModel(dataFrame):
with open(MODEL_PARAMS_PATH, "r") as dataIn:
modelParams = json.loads(dataIn.read())
minInput, maxInput = getMinMax(dataFrame) # min: 8 max: 39197
# RDSE - resolution calculation
valueEncoderParams = \
modelParams["modelParams"]["sensorParams"]["encoders"]["value"]
# {'type': 'RandomDistributedScalarEncoder',
# 'seed': 42, 'fieldname': 'value', 'name': 'value', 'numBuckets': 130.0}
numBuckets = float(valueEncoderParams.pop("numBuckets"))
resolution = max(0.001, (maxInput - minInput) / numBuckets) # 301.45
valueEncoderParams["resolution"] = resolution
model = ModelFactory.create(modelParams)
model.enableInference({"predictedField": "value"})
return model