Camelback example

A sample camelback example is shown below:

"""
Licensed Materials - Property of IBM
“Restricted Materials of IBM”

5765-R17

© Copyright IBM Corp. 2020  All Rights Reserved.
US Government Users Restricted Rights - Use, duplication or
disclosure restricted by GSA ADP Schedule Contract with IBM Corp
"""

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import os, sys
from common.boa_examples_utils import BoaExamplesUtils
from boaas_sdk import BOaaSClient
import signal

example_description="""
        This example demonstrates basic BOA usage for a simple optimization problem.

        The BOA SDK has been designed to be simple to use, but flexible in the range of
        configurations available for tailoring the optimization. This is achieved using
        the BOaaSClient object, which facilitates all communication with the BOA server.
        The optimization configuration is handled via a Python dictionary (or
        equivalently a JSON file).

        The camelback example is a two dimensional optimization problem that optimizes
        the six humped camelback function. This function has global minima
        f(x) = -1.0316 at x = (0.0898, -0.7126) and (-0.0898, 0.7126))
        """

"""
Perform parsing of common commandline arguments shared by all BOA examples.
"""
args = BoaExamplesUtils.parse_commandline_args(example_description,default_epochs=40)
hostname = BoaExamplesUtils.get_connection_url(args)
print("Connecting to host: {}".format(hostname))
boaas = BOaaSClient(host=hostname,ca_cert_path=args.ca_cert_path)

def camelback6(x):
    # Six-hump camelback function
    x1 = x[0]
    x2 = x[1]
    f = (4 - 2.1 * (x1 * x1) + (x1 * x1 * x1 * x1) / 3.0) * (x1 * x1) + x1 * x2 + (-4 + 4 * (x2 * x2)) * (x2 * x2)
    return f


experiment_config = {
    "name": "Reg camelback function",
    "domain": [
        {
            "name": "x1",
            "min": -2,
            "max": 2,
            "step": 0.01
        }, {
            "name": "x2",
            "min": -1,
            "max": 1,
            "step": 0.01
        }
    ],
    "model": {"gaussian_process": {
        "kernel_func": "Matern52",
        "scale_y": True,
        "scale_x": False,
        "noise_kernel": True,
        "use_scikit": True
    }},
    "optimization_type": "min",
    "initialization": {
        "type": "random",
        "random": {
            "no_samples": 3,
            "seed": None
        }
    },
    "sampling_function": {
        "type": "expected_improvement",
        "epsilon": 0.03,
        "optimize_acq": False,
        "outlier": False,
        "bounds": None
    }

}

user = {"_id": "boa_test@test.com", "password": "password"}
user_login = boaas.login(user)

if user_login == None:
    user = {"_id": "boa_test@test.com", "name": "BOA Test",
            "password": "password", "confirm_password": "password"}
    boaas.register(user)
    user_login = boaas.login(user)

print(user_login)
user_token = user_login["logged_in"]["token"]
print("user token")
print(user_token)
create_exp_user_object = {"_id": user["_id"], "token": user_token}
experiment_res = boaas.create_experiment(create_exp_user_object, experiment_config)
print(experiment_res)
experiment_id = experiment_res["experiment"]["_id"]

def signal_handler(signal, frame):
    """
    Function to handle Ctrl+C signal
    """

    print("Aborting...")
    boaas.abort(experiment_id, user_token)
    sys.exit()


# Catch ctrl+c signal to invoke its signal handler
signal.signal(signal.SIGINT, signal_handler)

boaas.run(experiment_id=experiment_id, user_token=user_token, func=camelback6, no_epochs=args.epochs, explain=False)
best_observation = boaas.best_observation(experiment_id, user_token)
print("best observation:")
print(best_observation)
boaas.stop_experiment(experiment_id=experiment_id, user_token=user_token)

The login call maps to the REST API call /users/login user_login = boaas.login(user). A sample of the authenticate a registered user object is displayed in the following example.

if user_login == None:
    user = {"_id": "boa_test@test.com", "name": "BOA Test",
            "password": "password", "confirm_password": "password"}
			
    # Register a new user. The Register call maps to REST API call /users/register
    boaas.register(user)
	
    user_login = boaas.login(user)

print(user_login)
user_token = user_login["logged_in"]["token"]
print("user token")
print(user_token)
create_exp_user_object = {"_id": user["_id"], "token": user_token}

The login call maps to the REST API call /users/login user_login = boaas.login(user). A sample of the authenticate a registered user object is displayed in the following example.

if user_login == None:     user = {"_id": "boa_test@test.com", "name": "BOA Test",
            "password": "password", "confirm_password": "password"}
   
    # Register a new user. The Register call maps to REST API call /users/register
    boaas.register(user)
 
    user_login = boaas.login(user)
print(user_login)
user_token = user_login["logged_in"]["token"]
print("user token") print(user_token)
create_exp_user_object = {"_id": user["_id"], "token": user_token}

The create_experiment call maps to REST API call /experiments/create. A sample of the create an experiment for a registered user with the specified configuration object is displayed in the following example.

experiment_res = boaas.create_experiment(create_exp_user_object, experiment_config)

print(experiment_res)
experiment_id = experiment_res["experiment"]["_id"]

Get the next parameter set to try, and evaluate y by using the specified evaluation function. Then, insert the new observation into BOA until the wanted number of iterations has been reached. A sample of the run BOA object is displayed in the following example.

boaas.run(experiment_id=experiment_id, user_token=user_token, func=camelback6, no_epochs=4, explain=False)

This API call maps to the REST API call /experiments/best_pbservation. A sample of the print the best minimum or maximum observation of all the observations set object is displayed in the following example.

best_observation = boaas.best_observation(experiment_id, user_token)
print("best observation:")
print(best_observation)

The stop_experiment call maps to the REST API call /experiments/stop. A sample of the stop an experiment for a registered user object is displayed in the following example.

boaas.stop_experiment(experiment_id=experiment_id, user_token=user_token)

The following screen displays a sample output of running a BOA experiment:

{'_id': 'boa_test@test.com', 'logged_in': {'status': True, 'token': '5c806a36-2b01-11eb-ab4c-a9d04ca0275f'}, 'user': {'_id': 'boa_test@test.com', 'logged_in': {'status': True, 'token': '5c806a36-2b01-11eb-ab4c-a9d04ca0275f'}, 'name': 'boa_test', 'role': 'expuser', 'whitelist': 'true'}}
user token
5c806a36-2b01-11eb-ab4c-a9d04ca0275f
{'experiment': {'_id': '92c7a0be-2b01-11eb-a3b0-f9d9ecbd5771', 'created_at': 1605857132245, 'domain_id': '92c7a3b6-2b01-11eb-a3b0-f9d9ecbd5771', 'initialization': {'random': {'no_samples': 3, 'seed': None}, 'type': 'random'}, 'model': {'gaussian_process': {'kernel_func': 'Matern52', 'noise_kernel': True, 'scale_x': False, 'scale_y': True, 'use_scikit': True}}, 'n_sample': 1, 'name': 'Reg camelback function', 'next_parameter_set': {'X': [[]]}, 'observations_id': '92c7a348-2b01-11eb-a3b0-f9d9ecbd5771', 'optimization_type': 'min', 'sampler_config': {'generating_prior': False, 'mqtt': {'error_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/error', 'host': '172.20.181.37', 'lsf_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/lsf', 'new_explanation_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/new_explanation', 'new_observation_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/new_observation', 'new_y_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/new_y', 'next_constraint_X_function_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/next_constraint_X_function', 'next_constraint_X_function_notifications_topic_ui': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/next_constraint_X_function_ui', 'next_constraint_X_model_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/next_constraint_X_model', 'next_parameter_set_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/next_parameter_set', 'port': '80', 'sampler_status_notifications_topic': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771/sampler_status', 'subscription_topic_base': '92c7a1f4-2b01-11eb-a3b0-f9d9ecbd5771', 'url': 'mqtt://172:80', 'ws_https_url': 'wss://172:443/mqttws/', 'ws_port': '80', 'ws_url': 'ws://172:80/mqttws/'}, 'status': 'initializing'}, 'sampling_function': {'batch_sampling': {'use': False}, 'bounds': None, 'epsilon': 0.03, 'explain': None, 'optimize_acq': False, 'outlier': False, 'type': 'expected_improvement'}, 'type': 'default', 'user_id': 'boa_test@test.com', 'y_best': None}, 'status': 'Experiment created'}
Attempting to connect to message broker at URL ws://172:80/mqttws/...
Connected to message broker with result code 0
Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Calculating the next y value for suggested parameters...
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Completed 40 iterations of Bayesian Optimisation.
40 iterations of Bayesian Optimisation have been completed in total for this experiment.
best observation:
{'best': {'X': [-0.12999999999999834, 0.7100000000000015], 'y': -1.0252309320636674}}