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	927.031624	0.645742	1234	927.677366	926.385882	1855.354731	1852.771765
6	773.282104	-0.163225	1234	773.118879	773.445329	1546.237758	1546.890659
7	920.579358	0.717932	1234	921.297289	919.861426	1842.594579	1839.722852
8	268.308512	0.234685	1234	268.543197	268.073827	537.086395	536.147654
9	122.172565	0.595717	1234	122.768282	121.576849	245.536564	243.153697
10	381.696110	0.794627	1234	382.490738	380.901483	764.981475	761.802965
11	366.802322	-0.406137	1234	366.396184	367.208459	732.792368	734.416918
12	142.342224	-0.943329	1234	141.398895	143.285554	282.797790	286.571108
13	807.685927	-0.150885	1234	807.535042	807.836812	1615.070085	1615.673625
14	646.629452	-0.145523	1234	646.483929	646.774975	1292.967857	1293.549949

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	-927.677366	926.385882
6	-773.118879	773.445329
7	-921.297289	919.861426
8	-268.543197	268.073827
9	-122.768282	121.576849
10	-382.490738	380.901483
11	-366.396184	367.208459
12	-141.398895	143.285554
13	-807.535042	807.836812
14	-646.483929	646.774975

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	-927.677366	926.385882
6	-773.118879	773.445329
7	-921.297289	919.861426
8	-268.543197	268.073827
9	-122.768282	121.576849
10	-382.490738	380.901483
11	-366.396184	367.208459
12	-141.398895	143.285554
13	-807.535042	807.836812
14	-646.483929	646.774975

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([[-927.67736571,  926.38588235],
       [-773.11887905,  773.44532948],
       [-921.29728941,  919.86142599],
       [-268.54319738,  268.07382697],
       [-122.76828176,  121.57684861],
       [-382.49073755,  380.90148266],
       [-366.3961842 ,  367.20845905],
       [-141.39889479,  143.28555375],
       [-807.53504241,  807.83681234],
       [-646.48392857,  646.77497452]])

In [14]:

vocs.constraint_data(data)

vocs.constraint_data(data)

Out[14]:

	constraint_e	constraint_f
5	1853.354731	-1852.771765
6	1544.237758	-1546.890659
7	1840.594579	-1839.722852
8	535.086395	-536.147654
9	243.536564	-243.153697
10	762.981475	-761.802965
11	730.792368	-734.416918
12	280.797790	-286.571108
13	1613.070085	-1615.673625
14	1290.967857	-1293.549949

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	0.927032	0.822871
6	0.773282	0.418387
7	0.920579	0.858966
8	0.268309	0.617343
9	0.122173	0.797858
10	0.381696	0.897314
11	0.366802	0.296931
12	0.142342	0.028335
13	0.807686	0.424558
14	0.646629	0.427239

In [17]:

# and denormalize
vocs.denormalize_inputs(normed_data)

# and denormalize vocs.denormalize_inputs(normed_data)

Out[17]:

	a	b
5	927.031624	0.645742
6	773.282104	-0.163225
7	920.579358	0.717932
8	268.308512	0.234685
9	122.172565	0.595717
10	381.696110	0.794627
11	366.802322	-0.406137
12	142.342224	-0.943329
13	807.685927	-0.150885
14	646.629452	-0.145523

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