import os
import requests
from classes.KafkaPC import KafkaPC
env_vars = {'config_path': os.getenv('config_path'),
'config_section': os.getenv('config_section')}
new_pc = KafkaPC(**env_vars)
API_URL = new_pc.config['API_URL']
ENDPOINT = "/production_parameter/x"
URL = API_URL + ENDPOINT
for msg in new_pc.consumer:
"""
"name": "New X",
"fields": [
{"name": "new_x", "type": ["float"]}
]
"""
new_message = new_pc.decode_avro_msg(msg)
# defining a params dict for the parameters to be sent to the API
params = {"value": new_message['new_x']}
# sending get request and saving the response as response object
print(f"Send x={round(new_message['new_x'], 3)} to the CPPS Controller")
r = requests.put(url=URL, params=params)
"""
new_pc = KafkaPC(**env_vars)
for msg in new_pc.consumer:
"""
"name": "Data",
"fields": [
{"name": "phase", "type": ["string"]},
{"name": "id_x", "type": ["int"]},
{"name": "x", "type": ["float"]},
{"name": "y", "type": ["float"]}
]
"""
new_data = new_pc.decode_avro_msg(msg)
"""
"name": "Data",
"fields": [
{"name": "phase", "type": ["string"]},
{"name": "id_x", "type": ["int"]},
{"name": "x", "type": ["float"]},
{"name": "y", "type": ["float"]}
]
"""
new_data_point = {'phase': new_data['phase'],
'id': new_data['id'],
'x': new_data['x'],
'y': new_data['y']}
"fields": [
{"name": "phase", "type": ["enum"], "symbols": ["init", "observation"]},
{"name": "model_name", "type": ["string"]},
{"name": "n_data_points", "type": ["int"]},
{"name": "id_start_x", "type": ["int"]},
{"name": "model", "type": ["bytes"]},
{"name": "model_size", "type": ["int"]},
{"name": "rmse", "type": ["null", "float"]},
{"name": "mae", "type": ["null", "float"]},
{"name": "rsquared", "type": ["null", "float"]},
{"name": "CPU_ms", "type": ["int"]},
{"name": "RAM", "type": ["int"]}
]
"""
new_model = new_pc.decode_avro_msg(msg)
model = pickle.loads(new_model['model'])
result = differential_evolution(evaluate_diff_evo,
bounds,
maxiter=N_MAX_ITER,
popsize=N_POP_SIZE)
surrogate_x = result.x[0]
surrogate_y = result.fun
print(f"The {new_model['model_name']} optimization suggests "
f"x={round(surrogate_x, 3)}, y={round(surrogate_y, 3)}")
"""
"name": "Model_Application",
"fields": [