optimizers.MyOptimizer.get_test_params() - Code Metrics - Inspection of "V5 API rework - unified API for optimizers and exp..." - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#110)

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created 2025-05-17 08:13 UTC

optimizers.MyOptimizer.get_test_params() A

↳ Parent: optimizers

Complexity

Conditions

Size

Total Lines	41
Code Lines	14

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	14
dl	0
loc	41
rs	9.7
c	0
b	0
f	0
cc	1
nop	2

# copyright: hyperactive developers, MIT License (see LICENSE file)
"""Extension template for optimizers.

Purpose of this implementation template:
    quick implementation of new estimators following the template
    NOT a concrete class to import! This is NOT a base class or concrete class!
    This is to be used as a "fill-in" coding template.

How to use this implementation template to implement a new estimator:
- make a copy of the template in a suitable location, give it a descriptive name.
- work through all the "todo" comments below
- fill in code for mandatory methods, and optionally for optional methods
- do not write to reserved variables: _tags, _tags_dynamic
- you can add more private methods, but do not override BaseEstimator's private methods
    an easy way to be safe is to prefix your methods with "_custom"
- change docstrings for functions and the file
- ensure interface compatibility by hyperactive.utils.check_estimator
- once complete: use as a local library, or contribute to hyperactive via PR

Mandatory methods:
    scoring         - _score(self, params: dict) -> np.float64
    parameter names - _paramnames(self) -> list[str]

Testing - required for automated test framework and check_estimator usage:
    get default parameters for test instance(s) - get_test_params()
"""
# todo: write an informative docstring for the file or module, remove the above
# todo: add an appropriate copyright notice for your estimator
#       estimators contributed should have the copyright notice at the top
#       estimators of your own do not need to have permissive or MIT copyright

# todo: uncomment the following line, enter authors' GitHub IDs
# __author__ = [authorGitHubID, anotherAuthorGitHubID]

from hyperactive.base import BaseOptimizer

# todo: add any necessary imports here

# todo: for imports of soft dependencies:
# make sure to fill in the "python_dependencies" tag with the package import name
# import soft dependencies only inside methods of the class, not at the top of the file


class MyOptimizer(BaseOptimizer):
    """Custom optimizer. todo: write docstring.

    todo: describe your custom optimizer here

    Parameters
    ----------
    parama : int
        descriptive explanation of parama
    paramb : string, optional (default='default')
        descriptive explanation of paramb
    paramc : boolean, optional (default=MyOtherEstimator(foo=42))
        descriptive explanation of paramc
    and so on

    Examples
    --------
    >>> from somehwere import MyOptimizer
    >>> great_example(code)
    >>> multi_line_expressions(
    ...     require_dots_on_new_lines_so_that_expression_continues_properly
    ... )
    """

    # todo: fill in tags - most tags have sensible defaults below
    _tags = {
        # tags and full specifications are available in the tag API reference
        # TO BE ADDED
        #
        # --------------
        # packaging info
        # --------------
        #
        # ownership and contribution tags
        # -------------------------------
        #
        # author = author(s) of th estimator
        # an author is anyone with significant contribution to the code at some point
        "authors": ["author1", "author2"],
        # valid values: str or list of str, should be GitHub handles
        # this should follow best scientific contribution practices
        # scope is the code, not the methodology (method is per paper citation)
        # if interfacing a 3rd party estimator, ensure to give credit to the
        # authors of the interfaced estimator
        #
        # maintainer = current maintainer(s) of the estimator
        # per algorithm maintainer role, see governance document
        # this is an "owner" type role, with rights and maintenance duties
        # for 3rd party interfaces, the scope is the class only
        "maintainers": ["maintainer1", "maintainer2"],
        # valid values: str or list of str, should be GitHub handles
        # remove tag if maintained by package core team
        #
        # dependency tags: python version and soft dependencies
        # -----------------------------------------------------
        #
        # python version requirement
        "python_version": None,
        # valid values: str, PEP 440 valid python version specifiers
        # raises exception at construction if local python version is incompatible
        # delete tag if no python version requirement
        #
        # soft dependency requirement
        "python_dependencies": None,
        # valid values: str or list of str, PEP 440 valid package version specifiers
        # raises exception at construction if modules at strings cannot be imported
        # delete tag if no soft dependency requirement
    }

    # todo: add any hyper-parameters and components to constructor
    def __init__(self, parama, paramb="default", paramc=None, experiment=None):
        # todo: write any hyper-parameters to self
        self.parama = parama
        self.paramb = paramb
        self.paramc = paramc
        # IMPORTANT: the self.params should never be overwritten or mutated from now on
        # for handling defaults etc, write to other attributes, e.g., self._parama
        self.experiment = experiment
        # IMPORTANT: experiment must come last, and have default value None

        # leave this as is
        super().__init__()

        # todo: optional, parameter checking logic (if applicable) should happen here
        # if writes derived values to self, should *not* overwrite self.parama etc
        # instead, write to self._parama, self._newparam (starting with _)

    # todo: implement this, mandatory
    def _paramnames(self):
        """Return the parameter names of the search.

        Returns
        -------
        list of str
            The parameter names of the search parameters.
        """
        # for every instance, this should return the correct parameter names
        # i.e., the maximal set of keys of the dict expected by _score
        return ["score_param1", "score_param2"]

    # optional: implement this to prepare arguments for _run
    # the default is all parameters passed to __init__, except ex
    def get_search_config(self):
        """Get the search configuration.

        Returns
        -------
        dict with str keys
            The search configuration dictionary.
        """
        # the default
        search_config = super().get_search_config()
        search_config["one_more_param"] = 42
        return search_config

    # todo: implement this, mandatory
    def _run(self, experiment, **search_config):
        """Run the optimization search process.

        Parameters
        ----------
        experiment : BaseExperiment
            The experiment to optimize parameters for.
        search_config : dict with str keys
            identical to return of ``get_search_config``.

        Returns
        -------
        dict with str keys
            The best parameters found during the search.
            Must have keys a subset or identical to experiment.paramnames().
        """
        best_params = {"write_some_logic_to_get": "best_params"}
        return best_params

    # todo: implement this for testing purposes!
    #   required to run local automated unit and integration testing of estimator
    #   method should return default parameters, so that a test instance can be created
    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """Return testing parameter settings for the estimator.

        Parameters
        ----------
        parameter_set : str, default="default"
            Name of the set of test parameters to return, for use in tests. If no
            special parameters are defined for a value, will return `"default"` set.
            There are currently no reserved values for this type of estimator.

        Returns
        -------
        params : dict or list of dict, default = {}
            Parameters to create testing instances of the class
            Each dict are parameters to construct an "interesting" test instance, i.e.,
            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
            `create_test_instance` uses the first (or only) dictionary in `params`
        """
        # todo: set the testing parameters for the estimators
        # Testing parameters can be dictionary or list of dictionaries.
        # Testing parameter choice should cover internal cases well.
        #   for "simple" extension, ignore the parameter_set argument.
        #
        # IMPORTANT: all parameter sets must contain an experiment object
        # this must be passed here, even if experiment can be left None in __init__
        from somewhere import AnotherExperiment, MyExperiment

        paramset1 = {
            "parama": 0,
            "paramb": "default",
            "paramc": None,
            "experiment": MyExperiment("experiment_params"),
        }
        paramset2 = {
            "parama": 1,
            "paramb": "foo",
            "paramc": 42,
            "experiment": AnotherExperiment("another_experiment_params"),
        }
        return [paramset1, paramset2]

        # this method can, if required, use:
        #   class properties (e.g., inherited); parent class test case
        #   imported objects such as estimators from sklearn
        # important: all such imports should be *inside get_test_params*, not at the top
        #            since imports are used only at testing time
        #
        # A good parameter set should primarily satisfy two criteria,
        #   1. Chosen set of parameters should have a low testing time,
        #      ideally in the magnitude of few seconds for the entire test suite.
        #       This is vital for the cases where default values result in
        #       "big" models which not only increases test time but also
        #       run into the risk of test workers crashing.
        #   2. There should be a minimum two such parameter sets with different
        #      sets of values to ensure a wide range of code coverage is provided.
        #
        # example 1: specify params as dictionary
        # any number of params can be specified
        # params = {"est": value0, "parama": value1, "paramb": value2}
        #
        # example 2: specify params as list of dictionary
        # note: Only first dictionary will be used by create_test_instance
        # params = [{"est": value1, "parama": value2},
        #           {"est": value3, "parama": value4}]
        #
        # return params


1			# copyright: hyperactive developers, MIT License (see LICENSE file)
2			"""Extension template for optimizers.
3
4			Purpose of this implementation template:
5			quick implementation of new estimators following the template
6			NOT a concrete class to import! This is NOT a base class or concrete class!
7			This is to be used as a "fill-in" coding template.
8
9			How to use this implementation template to implement a new estimator:
10			- make a copy of the template in a suitable location, give it a descriptive name.
11			- work through all the "todo" comments below
12			- fill in code for mandatory methods, and optionally for optional methods
13			- do not write to reserved variables: _tags, _tags_dynamic
14			- you can add more private methods, but do not override BaseEstimator's private methods
15			an easy way to be safe is to prefix your methods with "_custom"
16			- change docstrings for functions and the file
17			- ensure interface compatibility by hyperactive.utils.check_estimator
18			- once complete: use as a local library, or contribute to hyperactive via PR
19
20			Mandatory methods:
21			scoring - _score(self, params: dict) -> np.float64
22			parameter names - _paramnames(self) -> list[str]
23
24			Testing - required for automated test framework and check_estimator usage:
25			get default parameters for test instance(s) - get_test_params()
26			"""
27			# todo: write an informative docstring for the file or module, remove the above
28			# todo: add an appropriate copyright notice for your estimator
29			# estimators contributed should have the copyright notice at the top
30			# estimators of your own do not need to have permissive or MIT copyright
31
32			# todo: uncomment the following line, enter authors' GitHub IDs
33			# __author__ = [authorGitHubID, anotherAuthorGitHubID]
34
35			from hyperactive.base import BaseOptimizer
36
37			# todo: add any necessary imports here
38
39			# todo: for imports of soft dependencies:
40			# make sure to fill in the "python_dependencies" tag with the package import name
41			# import soft dependencies only inside methods of the class, not at the top of the file
42
43
44			class MyOptimizer(BaseOptimizer):
45			"""Custom optimizer. todo: write docstring.
46
47			todo: describe your custom optimizer here
48
49			Parameters
50			----------
51			parama : int
52			descriptive explanation of parama
53			paramb : string, optional (default='default')
54			descriptive explanation of paramb
55			paramc : boolean, optional (default=MyOtherEstimator(foo=42))
56			descriptive explanation of paramc
57			and so on
58
59			Examples
60			--------
61			>>> from somehwere import MyOptimizer
62			>>> great_example(code)
63			>>> multi_line_expressions(
64			... require_dots_on_new_lines_so_that_expression_continues_properly
65			... )
66			"""
67
68			# todo: fill in tags - most tags have sensible defaults below
69			_tags = {
70			# tags and full specifications are available in the tag API reference
71			# TO BE ADDED
72			#
73			# --------------
74			# packaging info
75			# --------------
76			#
77			# ownership and contribution tags
78			# -------------------------------
79			#
80			# author = author(s) of th estimator
81			# an author is anyone with significant contribution to the code at some point
82			"authors": ["author1", "author2"],
83			# valid values: str or list of str, should be GitHub handles
84			# this should follow best scientific contribution practices
85			# scope is the code, not the methodology (method is per paper citation)
86			# if interfacing a 3rd party estimator, ensure to give credit to the
87			# authors of the interfaced estimator
88			#
89			# maintainer = current maintainer(s) of the estimator
90			# per algorithm maintainer role, see governance document
91			# this is an "owner" type role, with rights and maintenance duties
92			# for 3rd party interfaces, the scope is the class only
93			"maintainers": ["maintainer1", "maintainer2"],
94			# valid values: str or list of str, should be GitHub handles
95			# remove tag if maintained by package core team
96			#
97			# dependency tags: python version and soft dependencies
98			# -----------------------------------------------------
99			#
100			# python version requirement
101			"python_version": None,
102			# valid values: str, PEP 440 valid python version specifiers
103			# raises exception at construction if local python version is incompatible
104			# delete tag if no python version requirement
105			#
106			# soft dependency requirement
107			"python_dependencies": None,
108			# valid values: str or list of str, PEP 440 valid package version specifiers
109			# raises exception at construction if modules at strings cannot be imported
110			# delete tag if no soft dependency requirement
111			}
112
113			# todo: add any hyper-parameters and components to constructor
114			def __init__(self, parama, paramb="default", paramc=None, experiment=None):
115			# todo: write any hyper-parameters to self
116			self.parama = parama
117			self.paramb = paramb
118			self.paramc = paramc
119			# IMPORTANT: the self.params should never be overwritten or mutated from now on
120			# for handling defaults etc, write to other attributes, e.g., self._parama
121			self.experiment = experiment
122			# IMPORTANT: experiment must come last, and have default value None
123
124			# leave this as is
125			super().__init__()
126
127			# todo: optional, parameter checking logic (if applicable) should happen here
128			# if writes derived values to self, should not overwrite self.parama etc
129			# instead, write to self._parama, self._newparam (starting with _)
130
131			# todo: implement this, mandatory
132			def _paramnames(self):
133			"""Return the parameter names of the search.
134
135			Returns
136			-------
137			list of str
138			The parameter names of the search parameters.
139			"""
140			# for every instance, this should return the correct parameter names
141			# i.e., the maximal set of keys of the dict expected by _score
142			return ["score_param1", "score_param2"]
143
144			# optional: implement this to prepare arguments for _run
145			# the default is all parameters passed to __init__, except ex
146			def get_search_config(self):
147			"""Get the search configuration.
148
149			Returns
150			-------
151			dict with str keys
152			The search configuration dictionary.
153			"""
154			# the default
155			search_config = super().get_search_config()
156			search_config["one_more_param"] = 42
157			return search_config
158
159			# todo: implement this, mandatory
160			def _run(self, experiment, **search_config):
161			"""Run the optimization search process.
162
163			Parameters
164			----------
165			experiment : BaseExperiment
166			The experiment to optimize parameters for.
167			search_config : dict with str keys
168			identical to return of ``get_search_config``.
169
170			Returns
171			-------
172			dict with str keys
173			The best parameters found during the search.
174			Must have keys a subset or identical to experiment.paramnames().
175			"""
176			best_params = {"write_some_logic_to_get": "best_params"}
177			return best_params
178
179			# todo: implement this for testing purposes!
180			# required to run local automated unit and integration testing of estimator
181			# method should return default parameters, so that a test instance can be created
182			@classmethod
183			def get_test_params(cls, parameter_set="default"):
184			"""Return testing parameter settings for the estimator.
185
186			Parameters
187			----------
188			parameter_set : str, default="default"
189			Name of the set of test parameters to return, for use in tests. If no
190			special parameters are defined for a value, will return `"default"` set.
191			There are currently no reserved values for this type of estimator.
192
193			Returns
194			-------
195			params : dict or list of dict, default = {}
196			Parameters to create testing instances of the class
197			Each dict are parameters to construct an "interesting" test instance, i.e.,
198			`MyClass(params)` or `MyClass(params[i])` creates a valid test instance.
199			`create_test_instance` uses the first (or only) dictionary in `params`
200			"""
201			# todo: set the testing parameters for the estimators
202			# Testing parameters can be dictionary or list of dictionaries.
203			# Testing parameter choice should cover internal cases well.
204			# for "simple" extension, ignore the parameter_set argument.
205			#
206			# IMPORTANT: all parameter sets must contain an experiment object
207			# this must be passed here, even if experiment can be left None in __init__
208			from somewhere import AnotherExperiment, MyExperiment
209
210			paramset1 = {
211			"parama": 0,
212			"paramb": "default",
213			"paramc": None,
214			"experiment": MyExperiment("experiment_params"),
215			}
216			paramset2 = {
217			"parama": 1,
218			"paramb": "foo",
219			"paramc": 42,
220			"experiment": AnotherExperiment("another_experiment_params"),
221			}
222			return [paramset1, paramset2]
223
224			# this method can, if required, use:
225			# class properties (e.g., inherited); parent class test case
226			# imported objects such as estimators from sklearn
227			# important: all such imports should be inside get_test_params, not at the top
228			# since imports are used only at testing time
229			#
230			# A good parameter set should primarily satisfy two criteria,
231			# 1. Chosen set of parameters should have a low testing time,
232			# ideally in the magnitude of few seconds for the entire test suite.
233			# This is vital for the cases where default values result in
234			# "big" models which not only increases test time but also
235			# run into the risk of test workers crashing.
236			# 2. There should be a minimum two such parameter sets with different
237			# sets of values to ensure a wide range of code coverage is provided.
238			#
239			# example 1: specify params as dictionary
240			# any number of params can be specified
241			# params = {"est": value0, "parama": value1, "paramb": value2}
242			#
243			# example 2: specify params as list of dictionary
244			# note: Only first dictionary will be used by create_test_instance
245			# params = [{"est": value1, "parama": value2},
246			# {"est": value3, "parama": value4}]
247			#
248			# return params
249

SimonBlanke / Hyperactive

Pull Request — master (#110)

optimizers.MyOptimizer.get_test_params() A

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