VOCS data structure¶

Variables, Objectives, Constraints, and other Settings (VOCS) helps define our optimization problems.

In [1]:

from xopt.vocs import VOCS
from xopt.vocs import form_objective_data
import pandas as pd
import numpy as np

from xopt.vocs import VOCS from xopt.vocs import form_objective_data import pandas as pd import numpy as np

In [2]:

Y = """
variables:
  a: [0, 1e3] # Note that 1e3 usually parses as a str with YAML. 
  b: [-1, 1]
objectives:
  c: maximize
  d: minimize 
constraints:
  e: ['Less_than', 2]
  f: ['greater_than', 0]
constants:
  g: 1234

"""

vocs = VOCS.from_yaml(Y)
vocs

Y = """ variables: a: [0, 1e3] # Note that 1e3 usually parses as a str with YAML. b: [-1, 1] objectives: c: maximize d: minimize constraints: e: ['Less_than', 2] f: ['greater_than', 0] constants: g: 1234 """ vocs = VOCS.from_yaml(Y) vocs

Out[2]:

VOCS(variables={'a': [0.0, 1000.0], 'b': [-1.0, 1.0]}, constraints={'e': ['LESS_THAN', 2.0], 'f': ['GREATER_THAN', 0.0]}, objectives={'c': 'MAXIMIZE', 'd': 'MINIMIZE'}, constants={'g': 1234}, observables=[])

In [3]:

# as dict
dict(vocs)

# as dict dict(vocs)

Out[3]:

{'variables': {'a': [0.0, 1000.0], 'b': [-1.0, 1.0]},
 'constraints': {'e': ['LESS_THAN', 2.0], 'f': ['GREATER_THAN', 0.0]},
 'objectives': {'c': 'MAXIMIZE', 'd': 'MINIMIZE'},
 'constants': {'g': 1234},
 'observables': []}

In [4]:

#  re-parse dict
vocs2 = VOCS.from_dict(dict(vocs))

# re-parse dict vocs2 = VOCS.from_dict(dict(vocs))

In [5]:

# Check that these are the same
vocs2 == vocs

# Check that these are the same vocs2 == vocs

Out[5]:

True

In [6]:

# This replaces the old vocs["variables"]
getattr(vocs, "variables")

# This replaces the old vocs["variables"] getattr(vocs, "variables")

Out[6]:

{'a': [0.0, 1000.0], 'b': [-1.0, 1.0]}

In [7]:

vocs.objectives["c"] == "MAXIMIZE"

vocs.objectives["c"] == "MAXIMIZE"

Out[7]:

True

In [8]:

# json
vocs.to_json()

# json vocs.to_json()

Out[8]:

'{"variables":{"a":[0.0,1000.0],"b":[-1.0,1.0]},"constraints":{"e":["LESS_THAN",2.0],"f":["GREATER_THAN",0.0]},"objectives":{"c":"MAXIMIZE","d":"MINIMIZE"},"constants":{"g":1234},"observables":[]}'

Objective Evaluation¶

In [9]:

data = pd.DataFrame(vocs.random_inputs(10))
# Add some outputs
data["c"] = data["a"] + data["b"]
data["d"] = data["a"] - data["b"]
data["e"] = data["a"] * 2 + data["b"] * 2
data["f"] = data["a"] * 2 - data["b"] * 2
data.index = np.arange(len(data)) + 5  # custom index
data

data = pd.DataFrame(vocs.random_inputs(10)) # Add some outputs data["c"] = data["a"] + data["b"] data["d"] = data["a"] - data["b"] data["e"] = data["a"] * 2 + data["b"] * 2 data["f"] = data["a"] * 2 - data["b"] * 2 data.index = np.arange(len(data)) + 5 # custom index data

Out[9]:

	a	b	g	c	d	e	f
5	27.126897	-0.364478	1234	26.762420	27.491375	53.524839	54.982749
6	306.471146	-0.875397	1234	305.595749	307.346543	611.191498	614.693086
7	198.513291	0.552709	1234	199.066001	197.960582	398.132002	395.921164
8	251.735536	-0.809030	1234	250.926506	252.544566	501.853012	505.089132
9	611.565673	0.103141	1234	611.668814	611.462532	1223.337628	1222.925063
10	299.211735	0.779981	1234	299.991715	298.431754	599.983431	596.863508
11	837.114707	-0.883538	1234	836.231170	837.998245	1672.462339	1675.996490
12	527.008957	-0.898737	1234	526.110220	527.907694	1052.220439	1055.815387
13	822.891936	-0.151750	1234	822.740186	823.043685	1645.480372	1646.087371
14	580.734676	-0.739317	1234	579.995359	581.473993	1159.990719	1162.947985

In [10]:

vocs.objectives

vocs.objectives

Out[10]:

{'c': 'MAXIMIZE', 'd': 'MINIMIZE'}

In [11]:

# These are in standard form for minimization
form_objective_data(vocs.objectives, data)

# These are in standard form for minimization form_objective_data(vocs.objectives, data)

Out[11]:

	objective_c	objective_d
5	-26.762420	27.491375
6	-305.595749	307.346543
7	-199.066001	197.960582
8	-250.926506	252.544566
9	-611.668814	611.462532
10	-299.991715	298.431754
11	-836.231170	837.998245
12	-526.110220	527.907694
13	-822.740186	823.043685
14	-579.995359	581.473993

In [12]:

# This is also available as a method
vocs.objective_data(data)

# This is also available as a method vocs.objective_data(data)

Out[12]:

	objective_c	objective_d
5	-26.762420	27.491375
6	-305.595749	307.346543
7	-199.066001	197.960582
8	-250.926506	252.544566
9	-611.668814	611.462532
10	-299.991715	298.431754
11	-836.231170	837.998245
12	-526.110220	527.907694
13	-822.740186	823.043685
14	-579.995359	581.473993

In [13]:

# use the to_numpy() method to convert for low level use.
vocs.objective_data(data).to_numpy()

# use the to_numpy() method to convert for low level use. vocs.objective_data(data).to_numpy()

Out[13]:

array([[ -26.76241951,   27.49137473],
       [-305.59574911,  307.34654317],
       [-199.06600078,  197.96058205],
       [-250.926506  ,  252.54456589],
       [-611.66881389,  611.4625316 ],
       [-299.99171527,  298.43175395],
       [-836.23116963,  837.99824506],
       [-526.11021958,  527.90769353],
       [-822.74018604,  823.04368536],
       [-579.99535931,  581.47399263]])

In [14]:

vocs.constraint_data(data)

vocs.constraint_data(data)

Out[14]:

	constraint_e	constraint_f
5	51.524839	-54.982749
6	609.191498	-614.693086
7	396.132002	-395.921164
8	499.853012	-505.089132
9	1221.337628	-1222.925063
10	597.983431	-596.863508
11	1670.462339	-1675.996490
12	1050.220439	-1055.815387
13	1643.480372	-1646.087371
14	1157.990719	-1162.947985

In [15]:

vocs.feasibility_data(data)

vocs.feasibility_data(data)

Out[15]:

	feasible_e	feasible_f	feasible
5	False	True	False
6	False	True	False
7	False	True	False
8	False	True	False
9	False	True	False
10	False	True	False
11	False	True	False
12	False	True	False
13	False	True	False
14	False	True	False

In [16]:

# normalize inputs to unit domain [0,1]
normed_data = vocs.normalize_inputs(data)
normed_data

# normalize inputs to unit domain [0,1] normed_data = vocs.normalize_inputs(data) normed_data

Out[16]:

	a	b
5	0.027127	0.317761
6	0.306471	0.062301
7	0.198513	0.776355
8	0.251736	0.095485
9	0.611566	0.551571
10	0.299212	0.889990
11	0.837115	0.058231
12	0.527009	0.050632
13	0.822892	0.424125
14	0.580735	0.130342

In [17]:

# and denormalize
vocs.denormalize_inputs(normed_data)

# and denormalize vocs.denormalize_inputs(normed_data)

Out[17]:

	a	b
5	27.126897	-0.364478
6	306.471146	-0.875397
7	198.513291	0.552709
8	251.735536	-0.809030
9	611.565673	0.103141
10	299.211735	0.779981
11	837.114707	-0.883538
12	527.008957	-0.898737
13	822.891936	-0.151750
14	580.734676	-0.739317

Error handling¶

In [18]:

Y = """
variables:
  a: [0, 1e3] # Note that 1e3 usually parses as a str with YAML. 
  b: [-1, 1]
objectives:
  c: maximize
  d: minimize 
constraints:
  e: ['Less_than', 2]
  f: ['greater_than', 0]
constants:
  g: 1234

"""

vocs = VOCS.from_yaml(Y)

Y = """ variables: a: [0, 1e3] # Note that 1e3 usually parses as a str with YAML. b: [-1, 1] objectives: c: maximize d: minimize constraints: e: ['Less_than', 2] f: ['greater_than', 0] constants: g: 1234 """ vocs = VOCS.from_yaml(Y)

In [19]:

d = {"a": [1, 2, 3]}

df = pd.DataFrame(d)
df2 = pd.DataFrame(df).copy()

df2["b"] = np.nan
df2["b"] - 1

d = {"a": [1, 2, 3]} df = pd.DataFrame(d) df2 = pd.DataFrame(df).copy() df2["b"] = np.nan df2["b"] - 1

Out[19]:

0   NaN
1   NaN
2   NaN
Name: b, dtype: float64

In [20]:

data["a"] = np.nan

data["a"] = np.nan

In [21]:

a = 2


def f(x=a):
    return x


a = 99
f()

a = 2 def f(x=a): return x a = 99 f()

Out[21]:

2

In [22]:

pd.DataFrame(6e66, index=[1, 2, 3], columns=["A"])

pd.DataFrame(6e66, index=[1, 2, 3], columns=["A"])

Out[22]:

	A
1	6.000000e+66
2	6.000000e+66
3	6.000000e+66

In [23]:

# These are in standard form for minimization

data = pd.DataFrame({"c": [1, 2, 3, 4]}, index=[9, 3, 4, 5])

form_objective_data(vocs.objectives, data)

# These are in standard form for minimization data = pd.DataFrame({"c": [1, 2, 3, 4]}, index=[9, 3, 4, 5]) form_objective_data(vocs.objectives, data)

Out[23]:

	objective_c	objective_d
9	-1.0	inf
3	-2.0	inf
4	-3.0	inf
5	-4.0	inf