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mlp_aggregaton.train.yml
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mlp_aggregaton.train.yml
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name: mlp_aggregation
source:
name: mlpipe.dsl.sources.EmpaCsvSourceAdapter
pathToFile: data/meeting_room_sensors_201807_201907.csv
fields:
- 3200000 as TempAussen
- 40210012 as TempInnen
- 40210002 as Zuluft
- 40210005 as Abluft
- 40210013 as CO2
- 40210148 as Präsenz
pipelinePrimary:
- name: mlpipe.dsl.processors.SeedSetup
numpy: 2
tensorflow: 2
- name: mlpipe.dsl.processors.TimeExtractor
extract: hour
outputField: Stunden
- name: mlpipe.dsl.processors.TimeExtractor
extract: weekday
outputField: Wochentag
- name: mlpipe.dsl.aggregators.Max
sequence: 15
generate:
- inputField: CO2
outputField: CO2_Max15
- inputField: TempInnen
outputField: TempInnen_Max15
- name: mlpipe.dsl.aggregators.Mean
sequence: 15
generate:
- inputField: CO2
outputField: CO2_Mean15
- inputField: TempInnen
outputField: TempInnen_Mean15
- name: mlpipe.dsl.aggregators.Min
sequence: 15
generate:
- inputField: CO2
outputField: CO2_Min15
- inputField: TempInnen
outputField: TempInnen_Min15
- name: mlpipe.dsl.aggregators.Trend
sequence: 15
generate:
- inputField: CO2
outputField: CO2_Trend15
- inputField: TempInnen
outputField: TempInnen_Trend15
- name: mlpipe.dsl.processors.NanRemover
pipelineSecondary:
- name: mlpipe.dsl.processors.Shuffle
_condition: train
- name: mlpipe.dsl.processors.Scaler
scaler: sklearn.preprocessing.RobustScaler
fields:
- TempAussen
- Zuluft
- Abluft
- CO2
- CO2_Max15
- CO2_Min15
- CO2_Trend15
- CO2_Mean15
- TempInnen
- TempInnen_Max15
- TempInnen_Min15
- TempInnen_Trend15
- TempInnen_Mean15
- name: mlpipe.dsl.processors.RangeEncoder
value_from: 0
value_to: 24
generate:
- inputField: Stunden
outputField: StundenOneHot
- name: mlpipe.dsl.processors.RangeEncoder
value_from: 0
value_to: 7
generate:
- inputField: Wochentag
outputField: WochentagOneHot
model:
input:
- TempAussen
- TempInnen
- Zuluft
- Abluft
- CO2
- REGEX:StundenOneHot\$.*
- REGEX:WochentagOneHot\$.*
- CO2_Max15
- CO2_Min15
- CO2_Trend15
- TempInnen_Max15
- TempInnen_Min15
- TempInnen_Trend15
- CO2_Mean15
- TempInnen_Mean15
target: Präsenz
predictionType: binary
testRatio: .20
sequentialModel:
- name: keras.layers.Dense
units: 64
activation: relu
- name: keras.layers.Dense
units: 32
activation: relu
- name: keras.layers.Dense
units: 4
activation: relu
- name: keras.layers.Dense
units: 1
activation: sigmoid
compile:
loss: binary_crossentropy
optimizer: Adam
metrics:
- accuracy
fit:
epochs: 50
batch_size: 4000
class_weight: auto