12820278359

Committed 17 Jan 2025 12:39AM UTC coverage: 74.252%. Remained the same

Build # 12820278359

Build Type

push

github

Committed by

anugrahjo

Commit Message

Add API reference for merit functions.

Run Details

4790 of 6451 relevant lines covered (74.25%)

0.74 hits per line

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90.0

/modopt/core/merit_function.py

import numpy as np
from modopt.utils.options_dictionary import OptionsDictionary
from types import MethodType, FunctionType


# Note: Merit functions are only needed for constrained optimization
class MeritFunction(object):
    def __init__(self, **kwargs):
        self.options = OptionsDictionary()

        self.options.declare('f', types=(MethodType, FunctionType))
        self.options.declare('g', types=(MethodType, FunctionType))
        self.options.declare('c', types=(MethodType, FunctionType))
        self.options.declare('j', types=(MethodType, FunctionType))

        self.options.declare('nx',
                             types=(int, np.int32, np.int64, float, np.float64))
        self.options.declare('nc',
                             types=(int, np.int32, np.int64, float, np.float64))
        self.options.declare('nc_e', default=0,
                             types=(int, np.int32, np.int64, float, np.float64))

        self.cache = {}
        self.eval_count = {'f': 0, 'c': 0, 'g': 0, 'j': 0}

        self.initialize()

        self.options.update(kwargs)

        self.setup()

    def clear_cache(self):
        """
        Clear the cache of function evaluations.
        """
        self.cache = {}

    def initialize(self):
        pass

    def setup(self):
        pass

    def update_functions_in_cache(self, fnames, x):
        """
        Update the function values in the cache for the specified functions 
        in `fnames` with their values at the given point `x`.
        If the cache contains no values for a function in `fnames` 
        or the cached values correspond to a point other than `x`, 
        the function is evaluated at `x` and the cache is updated accordingly.

        Parameters
        ----------
        fnames : str or list of str
            Function names whose values need to be updated in the cache.
            `fnames` must be one or a subset of ['f', 'g', 'c', 'j'].
        x : np.ndarray
            Point at which to update the functions in `fnames`.
        """

        if not isinstance(fnames, (list, str)):
            raise ValueError("'fnames' must be a string or list of strings")
        
        fnames = [fnames] if isinstance(fnames, str) else fnames
        possible_fnames = ['f', 'g', 'c', 'j']
        if any([fname not in possible_fnames for fname in fnames]):
            raise ValueError("Invalid function name. Must be one of: ", possible_fnames)
        
        if not isinstance(x, np.ndarray):
            raise ValueError("'x' must be a numpy array")
        
        for fname in fnames:
            if fname not in self.cache:
                self.cache[fname] = (x, self.options[fname](x))
                self.eval_count[fname] += 1
            elif not np.array_equal(x, self.cache[fname][0]):
                self.cache[fname] = (x, self.options[fname](x))
                self.eval_count[fname] += 1
            # else:
            #     print("Cache hit for function: ", fname)

1	import numpy as np	1✔
2	from modopt.utils.options_dictionary import OptionsDictionary	1✔
3	from types import MethodType, FunctionType	1✔
4
5
6	# Note: Merit functions are only needed for constrained optimization
7	class MeritFunction(object):	1✔
8	def __init__(self, **kwargs):	1✔
9	self.options = OptionsDictionary()	1✔
10
11	self.options.declare('f', types=(MethodType, FunctionType))	1✔
12	self.options.declare('g', types=(MethodType, FunctionType))	1✔
13	self.options.declare('c', types=(MethodType, FunctionType))	1✔
14	self.options.declare('j', types=(MethodType, FunctionType))	1✔
15
16	self.options.declare('nx',	1✔
17	types=(int, np.int32, np.int64, float, np.float64))
18	self.options.declare('nc',	1✔
19	types=(int, np.int32, np.int64, float, np.float64))
20	self.options.declare('nc_e', default=0,	1✔
21	types=(int, np.int32, np.int64, float, np.float64))
22
23	self.cache = {}	1✔
24	self.eval_count = {'f': 0, 'c': 0, 'g': 0, 'j': 0}	1✔
25
26	self.initialize()	1✔
27
28	self.options.update(kwargs)	1✔
29
30	self.setup()	1✔
31
32	def clear_cache(self):	1✔
33	"""
34	Clear the cache of function evaluations.
35	"""
36	self.cache = {}	×
37
38	def initialize(self):	1✔
39	pass	1✔
40
41	def setup(self):	1✔
42	pass	1✔
43
44	def update_functions_in_cache(self, fnames, x):	1✔
45	"""
46	Update the function values in the cache for the specified functions
47	in `fnames` with their values at the given point `x`.
48	If the cache contains no values for a function in `fnames`
49	or the cached values correspond to a point other than `x`,
50	the function is evaluated at `x` and the cache is updated accordingly.
51
52	Parameters
53	----------
54	fnames : str or list of str
55	Function names whose values need to be updated in the cache.
56	`fnames` must be one or a subset of ['f', 'g', 'c', 'j'].
57	x : np.ndarray
58	Point at which to update the functions in `fnames`.
59	"""
60
61	if not isinstance(fnames, (list, str)):	1✔
62	raise ValueError("'fnames' must be a string or list of strings")	×
63
64	fnames = [fnames] if isinstance(fnames, str) else fnames	1✔
65	possible_fnames = ['f', 'g', 'c', 'j']	1✔
66	if any([fname not in possible_fnames for fname in fnames]):	1✔
67	raise ValueError("Invalid function name. Must be one of: ", possible_fnames)	×
68
69	if not isinstance(x, np.ndarray):	1✔
70	raise ValueError("'x' must be a numpy array")	×
71
72	for fname in fnames:	1✔
73	if fname not in self.cache:	1✔
74	self.cache[fname] = (x, self.options[fname](x))	1✔
75	self.eval_count[fname] += 1	1✔
76	elif not np.array_equal(x, self.cache[fname][0]):	1✔
77	self.cache[fname] = (x, self.options[fname](x))	1✔
78	self.eval_count[fname] += 1	1✔
79	# else:
80	# print("Cache hit for function: ", fname)

LSDOlab / modopt / 12820278359

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