RCDS

RCDS Optimization¶

In this example we demonstrate RCDS optimization.

In [1]:

# If you encounter the "Initializing libomp.dylib, but found libomp.dylib already initialized." error
# Please run this cell

import os

os.environ['KMP_DUPLICATE_LIB_OK']='True'

# If you encounter the "Initializing libomp.dylib, but found libomp.dylib already initialized." error # Please run this cell import os os.environ['KMP_DUPLICATE_LIB_OK']='True'

In [2]:

import time
import numpy as np
from xopt.generators.rcds.rcds import RCDSGenerator
from xopt.vocs import VOCS
from xopt.evaluator import Evaluator
from xopt import Xopt
import pandas as pd
from tqdm.auto import tqdm
import warnings
warnings.filterwarnings("ignore")

import time import numpy as np from xopt.generators.rcds.rcds import RCDSGenerator from xopt.vocs import VOCS from xopt.evaluator import Evaluator from xopt import Xopt import pandas as pd from tqdm.auto import tqdm import warnings warnings.filterwarnings("ignore")

RCDS test problem¶

This test problem is a 2-D quadratic function.

In [3]:

def f_RCDS_minimize(input_dict):
    p = []
    for i in range(2):
        p.append(input_dict[f'p{i}'])
    
    obj = np.linalg.norm(p)
    outcome_dict = {'f': obj}
    
    return outcome_dict

def f_RCDS_minimize(input_dict): p = [] for i in range(2): p.append(input_dict[f'p{i}']) obj = np.linalg.norm(p) outcome_dict = {'f': obj} return outcome_dict

Run RCDS on the test problem (YAML method)¶

In [4]:

YAML = """
max_evaluations: 100
generator:
    name: rcds
    x0: null
    init_mat: null
    noise: 0.00001
    step: 0.01
    tol: 0.00001
evaluator:
    function: __main__.f_RCDS_minimize
vocs:
    variables:
        p0: [0, 1]
        p1: [0, 1]
    objectives:
        f: MINIMIZE
"""

X = Xopt.from_yaml(YAML)
X

YAML = """ max_evaluations: 100 generator: name: rcds x0: null init_mat: null noise: 0.00001 step: 0.01 tol: 0.00001 evaluator: function: __main__.f_RCDS_minimize vocs: variables: p0: [0, 1] p1: [0, 1] objectives: f: MINIMIZE """ X = Xopt.from_yaml(YAML) X

Out[4]:

            Xopt
________________________________
Version: 0+untagged.1.ge872ea5
Data size: 0
Config as YAML:
dump_file: null
evaluator:
  function: __main__.f_RCDS_minimize
  function_kwargs: {}
  max_workers: 1
  vectorized: false
generator:
  init_mat: null
  name: rcds
  noise: 1.0e-05
  step: 0.01
  tol: 1.0e-05
  x0: null
max_evaluations: 100
serialize_inline: false
serialize_torch: false
strict: true
vocs:
  constants: {}
  constraints: {}
  objectives:
    f: MINIMIZE
  observables: []
  variables:
    p0:
    - 0.0
    - 1.0
    p1:
    - 0.0
    - 1.0

In [5]:

X.run()

X.run()

Now you can go directly to the Visualization section and check out the results.

Run RCDS on the test problem (API method)¶

VOCS¶

We'll set the bounds for all the variables pi to [0, 1].

In [6]:

n_var = 2

n_var = 2

In [7]:

variables = {}
for i in range(n_var):
    variables[f'p{i}'] = [0, 1]

vocs = VOCS(
    variables=variables,
    objectives={'f': 'MINIMIZE'},
)

variables = {} for i in range(n_var): variables[f'p{i}'] = [0, 1] vocs = VOCS( variables=variables, objectives={'f': 'MINIMIZE'}, )

In [8]:

vocs

vocs

Out[8]:

VOCS(variables={'p0': [0.0, 1.0], 'p1': [0.0, 1.0]}, constraints={}, objectives={'f': 'MINIMIZE'}, constants={}, observables=[])

Evaluator¶

In [9]:

evaluator = Evaluator(function=f_RCDS_minimize)

evaluator = Evaluator(function=f_RCDS_minimize)

Generator¶

In [10]:

generator = RCDSGenerator(vocs=vocs)

generator = RCDSGenerator(vocs=vocs)

In [11]:

generator.model_dump()

generator.model_dump()

Out[11]:

{'x0': None, 'init_mat': None, 'noise': 1e-05, 'step': 0.01, 'tol': 1e-05}

Run the optimization¶

In [12]:

X = Xopt.from_yaml(YAML)

X = Xopt.from_yaml(YAML)

In [13]:

for i in tqdm(range(X.max_evaluations)):
    X.step()

for i in tqdm(range(X.max_evaluations)): X.step()

Visualization¶

In [14]:

X.data.plot(y='f')

X.data.plot(y='f')

Out[14]:

<Axes: >

In [14]: