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	449.054041	0.072177	1234	449.126217	448.981864	898.252434	897.963728
6	884.358091	-0.858172	1234	883.499919	885.216263	1766.999838	1770.432525
7	381.443598	-0.879676	1234	380.563923	382.323274	761.127846	764.646548
8	496.098734	-0.818988	1234	495.279746	496.917722	990.559492	993.835443
9	893.855142	-0.103630	1234	893.751512	893.958773	1787.503024	1787.917546
10	982.108468	-0.295690	1234	981.812778	982.404158	1963.625557	1964.808316
11	741.449439	-0.756023	1234	740.693416	742.205462	1481.386832	1484.410923
12	677.548089	-0.895149	1234	676.652939	678.443238	1353.305878	1356.886476
13	234.039916	-0.089959	1234	233.949957	234.129874	467.899913	468.259749
14	997.441646	0.528358	1234	997.970003	996.913288	1995.940007	1993.826576

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	-449.126217	448.981864
6	-883.499919	885.216263
7	-380.563923	382.323274
8	-495.279746	496.917722
9	-893.751512	893.958773
10	-981.812778	982.404158
11	-740.693416	742.205462
12	-676.652939	678.443238
13	-233.949957	234.129874
14	-997.970003	996.913288

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	-449.126217	448.981864
6	-883.499919	885.216263
7	-380.563923	382.323274
8	-495.279746	496.917722
9	-893.751512	893.958773
10	-981.812778	982.404158
11	-740.693416	742.205462
12	-676.652939	678.443238
13	-233.949957	234.129874
14	-997.970003	996.913288

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([[-449.12621722,  448.98186411],
       [-883.49991894,  885.21626252],
       [-380.56392278,  382.32327383],
       [-495.27974621,  496.91772154],
       [-893.75151215,  893.95877279],
       [-981.81277845,  982.40415818],
       [-740.69341602,  742.20546167],
       [-676.65293909,  678.44323805],
       [-233.9499567 ,  234.12987442],
       [-997.9700035 ,  996.91328789]])

In [14]:

vocs.constraint_data(data)

vocs.constraint_data(data)

Out[14]:

	constraint_e	constraint_f
5	896.252434	-897.963728
6	1764.999838	-1770.432525
7	759.127846	-764.646548
8	988.559492	-993.835443
9	1785.503024	-1787.917546
10	1961.625557	-1964.808316
11	1479.386832	-1484.410923
12	1351.305878	-1356.886476
13	465.899913	-468.259749
14	1993.940007	-1993.826576

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	0.449054	0.536088
6	0.884358	0.070914
7	0.381444	0.060162
8	0.496099	0.090506
9	0.893855	0.448185
10	0.982108	0.352155
11	0.741449	0.121989
12	0.677548	0.052425
13	0.234040	0.455021
14	0.997442	0.764179

In [17]:

# and denormalize
vocs.denormalize_inputs(normed_data)

# and denormalize vocs.denormalize_inputs(normed_data)

Out[17]:

	a	b
5	449.054041	0.072177
6	884.358091	-0.858172
7	381.443598	-0.879676
8	496.098734	-0.818988
9	893.855142	-0.103630
10	982.108468	-0.295690
11	741.449439	-0.756023
12	677.548089	-0.895149
13	234.039916	-0.089959
14	997.441646	0.528358

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