hyperactive.optimizers._search.Search._search() - Code Metrics - Inspection of "V5 API rework - experimental" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#110)

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created 2025-05-03 22:34 UTC

hyperactive.optimizers._search.Search._search() B

↳ Parent: hyperactive.optimizers._search

Complexity

Conditions

Size

Total Lines	55
Code Lines	43

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	5
eloc	43
nop	2
dl	0
loc	55
rs	8.3813
c	0
b	0
f	0

How to fix Long Method

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

from ._optimizer_attributes import OptimizerAttributes
from ._constraint import Constraint

from ..optimization_backend.gradient_free_optimizers._objective_function import (
    ObjectiveFunction,
)
from ..optimization_backend.gradient_free_optimizers._hyper_gradient_conv import (
    HyperGradientConv,
)


class Search(OptimizerAttributes):
    max_time: float
    nth_process: int

    def __init__(self, optimizer_class, opt_params):
        super().__init__()
        self.optimizer_class = optimizer_class
        self.opt_params = opt_params

    def setup(
        self,
        experiment,
        s_space,
        n_iter,
        initialize,
        constraints,
        pass_through,
        max_score,
        early_stopping,
        random_state,
        memory,
        memory_warm_start,
    ):
        self.experiment = experiment
        self.s_space = s_space
        self.n_iter = n_iter

        self.initialize = initialize
        self.constraints = constraints
        self.pass_through = pass_through
        self.max_score = max_score
        self.early_stopping = early_stopping
        self.random_state = random_state
        self.memory = memory
        self.memory_warm_start = memory_warm_start

    def pass_args(self, max_time, nth_process, verbosity):
        self.max_time = max_time
        self.nth_process = nth_process

        if "progress_bar" in verbosity:
            self.verbosity = ["progress_bar"]
        else:
            self.verbosity = []

    def convert_results2hyper(self):
        self.eval_times = sum(self.gfo_optimizer.eval_times)
        self.iter_times = sum(self.gfo_optimizer.iter_times)

        if self.gfo_optimizer.best_para is not None:
            value = self.hg_conv.para2value(self.gfo_optimizer.best_para)
            position = self.hg_conv.position2value(value)
            best_para = self.hg_conv.value2para(position)
            self.best_para = best_para
        else:
            self.best_para = None

        self.best_score = self.gfo_optimizer.best_score
        self.positions = self.gfo_optimizer.search_data
        self.search_data = self.hg_conv.positions2results(self.positions)

        results_dd = self.gfo_optimizer.search_data.drop_duplicates(
            subset=self.s_space.dim_keys, keep="first"
        )
        self.memory_values_df = results_dd[
            self.s_space.dim_keys + ["score"]
        ].reset_index(drop=True)

    def _setup_process(self):
        self.hg_conv = HyperGradientConv(self.s_space)

        initialize = self.hg_conv.conv_initialize(self.initialize)
        search_space_positions = self.s_space.positions

        # conv warm start for smbo from values into positions
        if "warm_start_smbo" in self.opt_params:
            self.opt_params["warm_start_smbo"] = self.hg_conv.conv_memory_warm_start(
                self.opt_params["warm_start_smbo"]
            )

        gfo_constraints = [
            Constraint(constraint, self.s_space) for constraint in self.constraints
        ]

        self.gfo_optimizer = self.optimizer_class(
            search_space=search_space_positions,
            initialize=initialize,
            constraints=gfo_constraints,
            random_state=self.random_state,
            nth_process=self.nth_process,
            **self.opt_params,
        )

        self.conv = self.gfo_optimizer.conv

    def _search(self, p_bar):
        self._setup_process()

        memory_warm_start = self.hg_conv.conv_memory_warm_start(self.memory_warm_start)

        self.experiment.backend_adapter(ObjectiveFunction, self.s_space)

        self.gfo_optimizer.init_search(
            self.experiment.gfo_objective_function,
            self.n_iter,
            self.max_time,
            self.max_score,
            self.early_stopping,
            self.memory,
            memory_warm_start,
            False,
        )
        for nth_iter in range(self.n_iter):
            if p_bar:
                p_bar.set_description(
                    "["
                    + str(self.nth_process)
                    + "] "
                    + str(self.experiment.__class__.__name__)
                    + " ("
                    + self.optimizer_class.name
                    + ")",
                )

            self.gfo_optimizer.search_step(nth_iter)
            if self.gfo_optimizer.stop.check():
                break

            if p_bar:
                p_bar.set_postfix(
                    best_score=str(self.gfo_optimizer.score_best),
                    best_pos=str(self.gfo_optimizer.pos_best),
                    best_iter=str(self.gfo_optimizer.p_bar._best_since_iter),
                )

                p_bar.update(1)
                p_bar.refresh()

        self.gfo_optimizer.finish_search()

        self.convert_results2hyper()

        self._add_result_attributes(
            self.best_para,
            self.best_score,
            self.gfo_optimizer.p_bar._best_since_iter,
            self.eval_times,
            self.iter_times,
            self.search_data,
            self.gfo_optimizer.random_seed,
        )


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			from ._optimizer_attributes import OptimizerAttributes
6			from ._constraint import Constraint
7
8			from ..optimization_backend.gradient_free_optimizers._objective_function import (
9			ObjectiveFunction,
10			)
11			from ..optimization_backend.gradient_free_optimizers._hyper_gradient_conv import (
12			HyperGradientConv,
13			)
14
15
16			class Search(OptimizerAttributes):
17			max_time: float
18			nth_process: int
19
20			def __init__(self, optimizer_class, opt_params):
21			super().__init__()
22			self.optimizer_class = optimizer_class
23			self.opt_params = opt_params
24
25			def setup(
26			self,
27			experiment,
28			s_space,
29			n_iter,
30			initialize,
31			constraints,
32			pass_through,
33			max_score,
34			early_stopping,
35			random_state,
36			memory,
37			memory_warm_start,
38			):
39			self.experiment = experiment
40			self.s_space = s_space
41			self.n_iter = n_iter
42
43			self.initialize = initialize
44			self.constraints = constraints
45			self.pass_through = pass_through
46			self.max_score = max_score
47			self.early_stopping = early_stopping
48			self.random_state = random_state
49			self.memory = memory
50			self.memory_warm_start = memory_warm_start
51
52			def pass_args(self, max_time, nth_process, verbosity):
53			self.max_time = max_time
54			self.nth_process = nth_process
55
56			if "progress_bar" in verbosity:
57			self.verbosity = ["progress_bar"]
58			else:
59			self.verbosity = []
60
61			def convert_results2hyper(self):
62			self.eval_times = sum(self.gfo_optimizer.eval_times)
63			self.iter_times = sum(self.gfo_optimizer.iter_times)
64
65			if self.gfo_optimizer.best_para is not None:
66			value = self.hg_conv.para2value(self.gfo_optimizer.best_para)
67			position = self.hg_conv.position2value(value)
68			best_para = self.hg_conv.value2para(position)
69			self.best_para = best_para
70			else:
71			self.best_para = None
72
73			self.best_score = self.gfo_optimizer.best_score
74			self.positions = self.gfo_optimizer.search_data
75			self.search_data = self.hg_conv.positions2results(self.positions)
76
77			results_dd = self.gfo_optimizer.search_data.drop_duplicates(
78			subset=self.s_space.dim_keys, keep="first"
79			)
80			self.memory_values_df = results_dd[
81			self.s_space.dim_keys + ["score"]
82			].reset_index(drop=True)
83
84			def _setup_process(self):
85			self.hg_conv = HyperGradientConv(self.s_space)
86
87			initialize = self.hg_conv.conv_initialize(self.initialize)
88			search_space_positions = self.s_space.positions
89
90			# conv warm start for smbo from values into positions
91			if "warm_start_smbo" in self.opt_params:
92			self.opt_params["warm_start_smbo"] = self.hg_conv.conv_memory_warm_start(
93			self.opt_params["warm_start_smbo"]
94			)
95
96			gfo_constraints = [
97			Constraint(constraint, self.s_space) for constraint in self.constraints
98			]
99
100			self.gfo_optimizer = self.optimizer_class(
101			search_space=search_space_positions,
102			initialize=initialize,
103			constraints=gfo_constraints,
104			random_state=self.random_state,
105			nth_process=self.nth_process,
106			**self.opt_params,
107			)
108
109			self.conv = self.gfo_optimizer.conv
110
111			def _search(self, p_bar):
112			self._setup_process()
113
114			memory_warm_start = self.hg_conv.conv_memory_warm_start(self.memory_warm_start)
115
116			self.experiment.backend_adapter(ObjectiveFunction, self.s_space)
117
118			self.gfo_optimizer.init_search(
119			self.experiment.gfo_objective_function,
120			self.n_iter,
121			self.max_time,
122			self.max_score,
123			self.early_stopping,
124			self.memory,
125			memory_warm_start,
126			False,
127			)
128			for nth_iter in range(self.n_iter):
129			if p_bar:
130			p_bar.set_description(
131			"["
132			+ str(self.nth_process)
133			+ "] "
134			+ str(self.experiment.__class__.__name__)
135			+ " ("
136			+ self.optimizer_class.name
137			+ ")",
138			)
139
140			self.gfo_optimizer.search_step(nth_iter)
141			if self.gfo_optimizer.stop.check():
142			break
143
144			if p_bar:
145			p_bar.set_postfix(
146			best_score=str(self.gfo_optimizer.score_best),
147			best_pos=str(self.gfo_optimizer.pos_best),
148			best_iter=str(self.gfo_optimizer.p_bar._best_since_iter),
149			)
150
151			p_bar.update(1)
152			p_bar.refresh()
153
154			self.gfo_optimizer.finish_search()
155
156			self.convert_results2hyper()
157
158			self._add_result_attributes(
159			self.best_para,
160			self.best_score,
161			self.gfo_optimizer.p_bar._best_since_iter,
162			self.eval_times,
163			self.iter_times,
164			self.search_data,
165			self.gfo_optimizer.random_seed,
166			)
167

SimonBlanke / Hyperactive

Pull Request — master (#110)

hyperactive.optimizers._search.Search._search() B

Complexity

Size

Duplication

Importance

How to fix Long Method

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