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8_ede_tpot.yaml
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Connector:
PREndpoint: hal720m.sage.ieat.ro
Dask:
SchedulerEndpoint: local # if not local add DASK schedueler endpoint
Scale: 3 # Number of workers if local othervise ignored
SchedulerPort: 8787 # This is the default point
EnforceCheck: False # Irrelevant for local
MPort: 9200 # Moitoring port
KafkaEndpoint: 10.9.8.136
KafkaPort: 9092
KafkaTopic: edetopic
Query: {"query": '{__name__=~"node.+"}[1m]'}
MetricsInterval: "1m" # Metrics datapoint interval definition
QSize: 0
Index: time
QDelay: "10s" # Polling period for metrics fetching
Local: /Users/Gabriel/Documents/workspaces/Event-Detection-Engine/data/demo_data.csv # Define the path to the local file for training
Mode:
Training: True
Validate: False
Detect: False
# TPOT Optimizer
Training:
Type: tpot
TPOTParam:
generations: 2
population_size: 2
offspring_size: 2
mutation_rate: 0.9
crossover_rate: 0.1
scoring: balanced_accuracy # Scoring different from HPO check TPOT documentation
max_time_mins: 1
max_eval_time_mins: 5
random_state: 42
n_jobs: -1
verbosity: 2
config_dict: TPOT light # "TPOT light", "TPOT MDR", "TPOT sparse" or None
use_dask: True
Target: target
Export: tpotopt
CV:
Type: StratifiedKFold # user defined all from sklearn
Params:
n_splits: 5
shuffle: True
random_state: 5
Detect:
Method: Tpot
Type: classification
Load: tpotopt
# Scaler: StandardScaler # Same as for training
Point:
Memory:
cached:
gd: 231313
ld: 312334
buffered:
gd: 231313
ld: 312334
used:
gd: 231313
ld: 312334
Load:
shortterm:
gd: 231313
ld: 312334
midterm:
gd: 231313
ld: 312334
Network:
tx:
gd: 231313
ld: 312334
rx:
gd: 231313
ld: 312334
# Not yet Implemented
#Validation:
# DataSource: /path/to/data # if datasource is not defined use default from data connector, last column is ground truth named "Target"
# Treashold: 0.2 # improvement percent
# Models:
# - m1
# - m2
Misc:
heap: 512m
checkpoint: True
delay: 15s
interval: 30m
resetindex: False
point: False